Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Remote Sens., Volume 6, Issue 6 (June 2014), Pages 4647-5884

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-55
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Open AccessEditorial Calibration and Verification of Remote Sensing Instruments and Observations
Remote Sens. 2014, 6(6), 5692-5695; doi:10.3390/rs6065692
Received: 10 June 2014 / Accepted: 11 June 2014 / Published: 17 June 2014
PDF Full-text (88 KB) | HTML Full-text | XML Full-text
Abstract
Satellite instruments are nowadays a very important source of information. The physical quantities (essential variables) derived from satellites are utilized in a wide field of applications, in particular in atmospheric physics and geoscience. In contrast to ground measurements the physical quantities are not
[...] Read more.
Satellite instruments are nowadays a very important source of information. The physical quantities (essential variables) derived from satellites are utilized in a wide field of applications, in particular in atmospheric physics and geoscience. In contrast to ground measurements the physical quantities are not directly measured, but have to be retrieved from satellite observations. Satellites observe hereby the reflection or emission of radiation by the Earth's surface or atmosphere, which enables the retrieval of respective physical quantities (essential variables). The physical basis for the retrieval is the interaction of the radiation with the Earth’s atmosphere and surface. This interaction is defined by radiative transfer, which favors the use of radiances and their respective units within retrieval methods. [...] Full article

Research

Jump to: Editorial, Review

Open AccessArticle Improving the Geolocation Algorithm for Sensors Onboard the ISS: Effect of Drift Angle
Remote Sens. 2014, 6(6), 4647-4659; doi:10.3390/rs6064647
Received: 13 December 2013 / Revised: 4 May 2014 / Accepted: 15 May 2014 / Published: 26 May 2014
Cited by 2 | PDF Full-text (965 KB) | HTML Full-text | XML Full-text
Abstract
The drift angle caused by the Earth’s self-rotation may introduce rotational displacement artifact on the geolocation results of imagery acquired by an Earth observing sensor onboard the International Space Station (ISS). If uncorrected, it would cause a gradual degradation of positional accuracy from
[...] Read more.
The drift angle caused by the Earth’s self-rotation may introduce rotational displacement artifact on the geolocation results of imagery acquired by an Earth observing sensor onboard the International Space Station (ISS). If uncorrected, it would cause a gradual degradation of positional accuracy from the center towards the edges of an image. One correction method to account for the drift angle effect was developed. The drift angle was calculated from the ISS state vectors and positional information of the ground nadir point of the imagery. Tests with images acquired by the International Space Station Agriculture Camera (ISSAC) using Google EarthTM as a reference indicated that applying the drift angle correction can reduce the residual geolocation error for the corner points of the ISSAC images from over 1000 to less than 500 m. The improved geolocation accuracy is well within the inherent geolocation uncertainty of up to 800 m, mainly due to imprecise knowledge of the ISS attitude and state parameters required to perform the geolocation algorithm. Full article
Figures

Open AccessArticle Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model
Remote Sens. 2014, 6(6), 4660-4686; doi:10.3390/rs6064660
Received: 28 March 2014 / Revised: 16 May 2014 / Accepted: 19 May 2014 / Published: 26 May 2014
Cited by 5 | PDF Full-text (461 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance
[...] Read more.
Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance of phenological models embedded in DEMs. As ground-based phenological observations are limited, phenology derived from remote sensing can be used as an alternative to parameterize phenological models. It is important to evaluate to what extent remotely sensed phenological metrics are capturing the phenology observed on the ground. We evaluated six methods based on two vegetation indices (VIs) (i.e., Normalized Difference Vegetation Index and Enhanced Vegetation Index) for retrieving the phenology of temperate forest in the Agro-IBIS model. First, we compared the remotely sensed phenological metrics with observations at Harvard Forest and found that most of the methods have large biases regardless of the VI used. Only two methods for the leaf onset and one method for the leaf offset showed a moderate performance. When remotely sensed phenological metrics were used to parameterize phenological models, the bias is maintained, and errors propagate to predictions of gross primary productivity and net ecosystem production. Our results show that Agro-IBIS has different sensitivities to leaf onset and offset in terms of carbon assimilation, suggesting it might be better to examine the respective impact of leaf onset and offset rather than the overall impact of the growing season length. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
Figures

Open AccessArticle GPR Raw-Data Order Statistic Filtering and Split-Spectrum Processing to Detect Moisture
Remote Sens. 2014, 6(6), 4687-4704; doi:10.3390/rs6064687
Received: 31 January 2014 / Revised: 5 May 2014 / Accepted: 7 May 2014 / Published: 26 May 2014
Cited by 1 | PDF Full-text (1416 KB) | HTML Full-text | XML Full-text
Abstract
Considerable research into the area of bridge health monitoring has been undertaken; however, information is still lacking on the effects of certain defects, such as moisture ingress, on the results of ground penetrating radar (GPR) surveying. In this paper, this issue will be
[...] Read more.
Considerable research into the area of bridge health monitoring has been undertaken; however, information is still lacking on the effects of certain defects, such as moisture ingress, on the results of ground penetrating radar (GPR) surveying. In this paper, this issue will be addressed by examining the results of a GPR bridge survey, specifically the effect of moisture in the predicted position of the rebars. It was found that moisture ingress alters the radargram to indicate distortion or skewing of the steel reinforcements, when in fact destructive testing was able to confirm that no such distortion or skewing had occurred. Additionally, split-spectrum processing with order statistic filters was utilized to detect moisture ingress from the GPR raw data. Full article
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
Open AccessArticle Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China
Remote Sens. 2014, 6(6), 4705-4722; doi:10.3390/rs6064705
Received: 27 February 2014 / Revised: 9 May 2014 / Accepted: 14 May 2014 / Published: 26 May 2014
Cited by 8 | PDF Full-text (985 KB) | HTML Full-text | XML Full-text
Abstract
Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation
[...] Read more.
Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VIsoil and VIveg, with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly. Full article
Open AccessArticle Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina)
Remote Sens. 2014, 6(6), 4723-4740; doi:10.3390/rs6064723
Received: 24 February 2014 / Revised: 23 April 2014 / Accepted: 4 May 2014 / Published: 26 May 2014
Cited by 8 | PDF Full-text (1155 KB) | HTML Full-text | XML Full-text
Abstract
Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the
[...] Read more.
Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat’s infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94. Full article
Open AccessArticle Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR
Remote Sens. 2014, 6(6), 4741-4763; doi:10.3390/rs6064741
Received: 7 March 2014 / Revised: 6 May 2014 / Accepted: 7 May 2014 / Published: 26 May 2014
Cited by 9 | PDF Full-text (1093 KB) | HTML Full-text | XML Full-text
Abstract
The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high
[...] Read more.
The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure. Full article
Open AccessArticle Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale
Remote Sens. 2014, 6(6), 4764-4779; doi:10.3390/rs6064764
Received: 7 January 2014 / Revised: 18 May 2014 / Accepted: 19 May 2014 / Published: 26 May 2014
Cited by 5 | PDF Full-text (679 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is among the most important variables for monitoring crop growth and estimating grain yield. Previous reports have shown that LAI derived from remote sensing data can be effectively applied in crop growth simulation models for improving the accuracy of
[...] Read more.
Leaf area index (LAI) is among the most important variables for monitoring crop growth and estimating grain yield. Previous reports have shown that LAI derived from remote sensing data can be effectively applied in crop growth simulation models for improving the accuracy of grain yield estimation. Therefore, precise estimation of LAI from remote sensing data is expected to be useful for global monitoring of crop growth. In this study, as a preliminary step toward application at the regional and global scale, the suitability of several vegetation indices for estimating multi-year LAI were validated against field survey data. In particular, the performance of a vegetation index known as time-series index of plant structure (TIPS), which was developed by the authors, was evaluated by comparison with other well-known vegetation indices. The estimated equation derived from the relationship between TIPS and LAI was more accurate at estimating LAI than were equations derived from other vegetation indices. Although further research is required to demonstrate the effectiveness of TIPS, this study indicates that TIPS has the potential to provide accurate estimates for multi-year LAI at the field scale. Full article
Open AccessArticle Modeling In-Use Steel Stock in China’s Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP/OLS Nighttime Lights
Remote Sens. 2014, 6(6), 4780-4800; doi:10.3390/rs6064780
Received: 19 March 2014 / Revised: 16 May 2014 / Accepted: 19 May 2014 / Published: 27 May 2014
Cited by 9 | PDF Full-text (1441 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
China’s rapid urbanization has led to increasing steel consumption for buildings and civil engineering infrastructure. The in-use steel stock in the same is considered to be closely related to social welfare and urban metabolism. Traditional approaches for determining the in-use steel stock are
[...] Read more.
China’s rapid urbanization has led to increasing steel consumption for buildings and civil engineering infrastructure. The in-use steel stock in the same is considered to be closely related to social welfare and urban metabolism. Traditional approaches for determining the in-use steel stock are labor-intensive and time-consuming processes and always hindered by the availability of statistical data. To address this issue, this study proposed the use of long-term nighttime lights as a proxy to effectively estimate in-use steel stock for buildings (IUSSB) and civil engineering infrastructure (IUSSCE) at the provincial level in China. Significant relationships between nighttime lights versus IUSSB and IUSSCE were observed for provincial variables in a single year, as well as for time series variables of a single province. However, these relationships were found to differ among provinces (referred to as “inter-individual differences”) and with time (referred to as “temporal differences”). Panel regression models were therefore proposed to estimate IUSSB and IUSSCE in consideration of the temporal and inter-individual differences based on a dataset covering 1992–2007. These models were validated using data for 2008, and the results showed good estimation for both IUSSB and IUSSCE. The proposed approach can be used to easily monitor the dynamic of IUSSB and IUSSCE in China. This should be critical in providing valuable information for policy making regarding regional development of buildings and infrastructure, sustainable urban resource management, and cross-boundary material recycling. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
Open AccessArticle Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification
Remote Sens. 2014, 6(6), 4801-4830; doi:10.3390/rs6064801
Received: 2 December 2013 / Revised: 28 April 2014 / Accepted: 29 April 2014 / Published: 27 May 2014
Cited by 3 | PDF Full-text (7119 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution
[...] Read more.
In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution data; yet on the other hand, the process of obtaining the ground truth data may be cumbersome for such large repositories. In this paper, we investigate the application of semi-supervised learning approaches and particularly focus on the small sample size problem. To that extend, we consider two basic unsupervised approaches by enlarging the initial labeled training set as well as an ensemble-based self-training method. We propose different strategies within self-training on how to select more reliable candidates from the pool of unlabeled samples to speed-up the learning process and to improve the classification performance of the underlying classifier ensemble. We evaluate the effectiveness of the proposed semi-supervised learning approach over polarimetric SAR data. Results show that the proposed self-training approach using an ensemble-based classifier that is initially trained over a small training set can achieve a similar performance level of a fully supervised learning approach where the training is performed over significantly larger labeled data. Considering the difficulties of the manual data labeling in such massive volumes of SAR repositories, this is indeed a promising accomplishment for semi-supervised SAR classification. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Investigating the Performance of Four Empirical Cross-Calibration Methods for the Proposed SWOT Mission
Remote Sens. 2014, 6(6), 4831-4869; doi:10.3390/rs6064831
Received: 21 March 2014 / Revised: 12 May 2014 / Accepted: 19 May 2014 / Published: 27 May 2014
PDF Full-text (4816 KB) | HTML Full-text | XML Full-text
Abstract
The proposed surface water and ocean topography (SWOT) mission aims at observing short scale ocean topography with an unprecedented resolution and accuracy. Its main proposed sensor is a radar interferometer, so a major source of topography error is the roll angle: the relative
[...] Read more.
The proposed surface water and ocean topography (SWOT) mission aims at observing short scale ocean topography with an unprecedented resolution and accuracy. Its main proposed sensor is a radar interferometer, so a major source of topography error is the roll angle: the relative positions of SWOT’s antennas must be known within a few micrometers. Because reaching SWOT’s stringent requirements with onboard roll values is challenging, we carried out simulations as a contingency strategy (i.e., to be ready if roll is larger than anticipated) that could be used with ground-based data. We revisit the empirical calibration algorithms with additional solving methods (e.g., based on orbit sub-cycle) and more sophisticated performance assessments with spectral decompositions. We also explore the link between the performance of four calibration methods and the attributes of their respective calibration zones: size and geometry (e.g., crossover diamonds), temporal variability (e.g., how many days between overlapping SWOT images). In general, the so-called direct method (using a single SWOT image) yields better coverage and smaller calibrated roll residuals because the full extent of the swath can be used for calibration, but this method makes an extensive use of the external nadir constellation to separate roll from oceanic variability, and it is more prone to leakages from oceanic variability on roll (i.e., true topography signal is more likely to be corrupted if it is misinterpreted as roll) and inaccurate modeling of the true topography spectrum. For SWOT’s baseline orbit (21 days repeat and 10.9 days sub-cycle), three other methods are found to be complementary with the direct method: swath crossovers, external nadir crossovers, and sub-cycle overlaps are shown to provide an additional calibration capability, albeit with complex latitude-varying coverage and performance. The main asset of using three or four methods concurrently is to minimize systematic leakages from oceanic variability or measurement errors, by maximizing overlap zones and by minimizing the temporal variability with one-day to three-day image differences. To that extent, SWOT’s proposed “contingency orbit” is an attractive risk reduction asset: the one-day sub-cycle overlaps of adjoining swaths would provide a good, continuous, and self-sufficient (no need for external nadirs) calibration scheme. The benefit is however essentially located at mid to high-latitudes and it is substantial only for wavelengths longer than 100 km. Full article
Open AccessArticle Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing
Remote Sens. 2014, 6(6), 4907-4926; doi:10.3390/rs6064907
Received: 22 February 2014 / Revised: 15 May 2014 / Accepted: 19 May 2014 / Published: 28 May 2014
Cited by 8 | PDF Full-text (3112 KB) | HTML Full-text | XML Full-text
Abstract
The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal
[...] Read more.
The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow. The method uses a neural network approach to determine cloud, cloud shadow, water, snow/ice and clear sky classification memberships of each pixel in a Landsat scene. It then applies a series of spatial procedures to resolve pixels with ambiguous membership by using information, such as the membership values of neighboring pixels and an estimate of cloud shadow locations from cloud and solar geometry. In a comparison with FMask, a high-quality cloud and cloud shadow classification algorithm currently available, SPARCS performs favorably, with substantially lower omission errors for cloud shadow (8.0% and 3.2%), only slightly higher omission errors for clouds (0.9% and 1.3%, respectively) and fewer errors of commission (2.6% and 0.3%). Additionally, SPARCS provides a measure of uncertainty in its classification that can be exploited by other algorithms that require clear sky pixels. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of a method for vegetation change detection. Full article
Open AccessArticle On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization
Remote Sens. 2014, 6(6), 4927-4951; doi:10.3390/rs6064927
Received: 27 February 2014 / Revised: 16 May 2014 / Accepted: 16 May 2014 / Published: 28 May 2014
Cited by 12 | PDF Full-text (12986 KB) | HTML Full-text | XML Full-text
Abstract
Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one,
[...] Read more.
Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) have been assessed. HyMap imaging spectrometer (430–2490 nm) data obtained during the SPARC-2003 campaign in Barrax, Spain, have been used to extract leaf area index (LAI) and leaf chlorophyll content (LCC) estimates. For both SR and ND formulations the most sensitive regions have been identified for two-band combinations of green (539–570 nm) with longwave SWIR (2421–2453 nm) for LAI (r2: 0.83) and far-red (692 nm) with NIR (1340 nm) or shortwave SWIR (1661–1686 nm) for LCC (r2: 0.93). Polynomial, logarithmic and linear fitting functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval. Full article
Open AccessArticle Atmospheric Corrections for Altimetry Studies over Inland Water
Remote Sens. 2014, 6(6), 4952-4997; doi:10.3390/rs6064952
Received: 7 January 2014 / Revised: 30 April 2014 / Accepted: 4 May 2014 / Published: 30 May 2014
Cited by 12 | PDF Full-text (3059 KB) | HTML Full-text | XML Full-text
Abstract
Originally designed for applications over the ocean, satellite altimetry has been proven to be a useful tool for hydrologic studies. Altimeter products, mainly conceived for oceanographic studies, often fail to provide atmospheric corrections suitable for inland water studies. The focus of this paper
[...] Read more.
Originally designed for applications over the ocean, satellite altimetry has been proven to be a useful tool for hydrologic studies. Altimeter products, mainly conceived for oceanographic studies, often fail to provide atmospheric corrections suitable for inland water studies. The focus of this paper is the analysis of the main issues related with the atmospheric corrections that need to be applied to the altimeter range to get precise water level heights. Using the corrections provided on the Radar Altimeter Database System, the main errors present in the dry and wet tropospheric corrections and in the ionospheric correction of the various satellites are reported. It has been shown that the model-based tropospheric corrections are not modeled properly and in a consistent way in the various altimetric products. While over the ocean, the dry tropospheric correction (DTC) is one of the most precise range corrections, in some of the present altimeter products, it is the correction with the largest errors over continental water regions, causing large biases of several decimeters, and along-track interpolation errors up to several centimeters, both with small temporal variations. The wet tropospheric correction (WTC) from the on-board microwave radiometers is hampered by the contamination on the radiometer measurements of the surrounding lands, making it usable only in the central parts of large lakes. In addition, the WTC from atmospheric models may also have large errors when it is provided at sea level instead of surface height. These errors cannot be corrected by the user, since no accurate expression exists for the height variation of the WTC. Alternative and accurate corrections can be computed from in situ data, e.g., DTC from surface pressure at barometric stations and WTC from Global Navigation Satellite System permanent stations. The latter approach is particularly favorable for small lakes and reservoirs, where GNSS-derived WTC at a single location can be representative of the whole lake. For non-timely critical studies, for consistency and stability, model-derived tropospheric corrections from European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis ERA Interim, properly computed at surface height, are recommended. The instrument-based dual-frequency ionospheric correction may have errors related with the land contamination in the Ku and C/S bands, making it more suitable to use a model-based correction. The most suitable model-based ionospheric correction is the Jet Propulsion Laboratory (JPL) global ionosphere map (GIM) model, available after 1998, properly scaled to the altimeter height. Most altimeter products provide the GIM correction unreduced for the total electron content extending above the altitude of these satellites, thus overestimating the ionospheric correction by about 8%. Prior to 1998, the NIC09 (NOAA Ionosphere Climatology 2009) climatology provides the best accuracy. Full article
Open AccessArticle Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China
Remote Sens. 2014, 6(6), 4998-5018; doi:10.3390/rs6064998
Received: 18 February 2014 / Revised: 19 May 2014 / Accepted: 19 May 2014 / Published: 30 May 2014
Cited by 12 | PDF Full-text (695 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study assesses the applicability of remote sensing data for retrieval of key drought indicators including the degree of moisture deficiency, drought duration and areal extent of drought within different land cover types across the landscape. A Normalized Vegetation Supply Water Index (NVSWI)
[...] Read more.
This study assesses the applicability of remote sensing data for retrieval of key drought indicators including the degree of moisture deficiency, drought duration and areal extent of drought within different land cover types across the landscape. A Normalized Vegetation Supply Water Index (NVSWI) is devised, combining remotely sensed climate data to retrieve key drought indicators over different vegetation cover types and a lag-time relationship is established based on preceding rainfall. The results indicate that during the major drought event of spring 2010, Evergreen Forest (EF) experienced severe dry conditions for 48 days fewer than Cropland (CL) and Shrubland (SL). Testing of vegetation response to drought conditions with different lag-time periods since the last rainfall indicated a highest correlation for CL and SL with the 4th lag period (i.e., 64 days) whereas EF exhibited maximum correlation with the 5th lag period (i.e., 80 days). Evergreen Forest, which includes tree crops, appears to act as a green reservoir of water, and is more resistant than CL and SL to drought due to its water retention capacity with deeper roots to tap sub-surface water. Identifying differences in rainfall lag-time relationships among land cover types using a remote sensing-based integrated drought index enables more accurate drought prediction, and can thus assist in the development of more specific drought adaptation strategies. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
Open AccessArticle Object-Based Image Classification of Summer Crops with Machine Learning Methods
Remote Sens. 2014, 6(6), 5019-5041; doi:10.3390/rs6065019
Received: 24 January 2014 / Revised: 16 May 2014 / Accepted: 19 May 2014 / Published: 30 May 2014
Cited by 13 | PDF Full-text (1714 KB) | HTML Full-text | XML Full-text
Abstract
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining
[...] Read more.
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks. Full article
Open AccessArticle Physical, Bio-Optical State and Correlations in North–Western European Shelf Seas
Remote Sens. 2014, 6(6), 5042-5066; doi:10.3390/rs6065042
Received: 14 January 2014 / Revised: 21 May 2014 / Accepted: 23 May 2014 / Published: 30 May 2014
Cited by 5 | PDF Full-text (2947 KB) | HTML Full-text | XML Full-text
Abstract
Color of seawater has become an integral tool in understanding surface marine ecosystems and processes. In this paper we seek to assess the correlations and consequently the potential of using shipborne remote sensing products to infer marine environmental parameters. Typical seawater parameters are
[...] Read more.
Color of seawater has become an integral tool in understanding surface marine ecosystems and processes. In this paper we seek to assess the correlations and consequently the potential of using shipborne remote sensing products to infer marine environmental parameters. Typical seawater parameters are chlorophyll–a (chl–a), colored dissolved organic material (CDOM), suspended particulate material (SPM), Secchi–disk depth (SDD), temperature, and salinity. These parameters and radiometric quantities were observed from a total of 60 stations covering German Bight, North Sea, Inner Seas, Irish Sea, and Celtic Sea. Bio-optical models developed in this study were used to predict the in situ measured parameters, with low mean unbiased percent differences and absolute percent difference less than 35%. Our investigations show that the use of ocean color products namely the Forel–Ule Index to infer seawater parameters is encouraging. The constrained spatial and temporal span of measured in situ parameters does limit the accuracy of our models. Absorption coefficients of the main color producing agents CDOM, chl–a, and inorganic fraction of SPM (iSPM) were determined to estimate absorption budgets. During the field campaign, iSPM was the primary light absorber over the spectral range (400–700 nm) although variabilities were observed in the regional seas. Full article
Figures

Open AccessArticle An Automated Method for Extracting Rivers and Lakes from Landsat Imagery
Remote Sens. 2014, 6(6), 5067-5089; doi:10.3390/rs6065067
Received: 14 February 2014 / Revised: 19 May 2014 / Accepted: 20 May 2014 / Published: 30 May 2014
Cited by 23 | PDF Full-text (1882 KB) | HTML Full-text | XML Full-text
Abstract
The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of
[...] Read more.
The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and commission errors, edge position accuracy and completeness, were calculated to assess the method’s performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed. Full article
Figures

Open AccessArticle A Spectral Decomposition Algorithm for Estimating Chlorophyll-a Concentrations in Lake Taihu, China
Remote Sens. 2014, 6(6), 5090-5106; doi:10.3390/rs6065090
Received: 28 March 2014 / Revised: 31 March 2014 / Accepted: 23 May 2014 / Published: 5 June 2014
Cited by 2 | PDF Full-text (1106 KB) | HTML Full-text | XML Full-text
Abstract
The complex interactions among optically active substances in Case II waters make it difficult to associate the variability in spectral radiance (or reflectance) to any single component. In the present study, we developed a four end-member spectral decomposition model to estimate chlorophyll-a
[...] Read more.
The complex interactions among optically active substances in Case II waters make it difficult to associate the variability in spectral radiance (or reflectance) to any single component. In the present study, we developed a four end-member spectral decomposition model to estimate chlorophyll-a concentrations in a eutrophic shallow lake—Lake Taihu. The new model was constructed by simulated spectral data from Hydrolight and was successfully validated using both of simulated reflectance and in situ reflectance data. Using MEdium Resolution Imaging Spectrometer (MERIS) images, the accuracy of the new model was estimated and compared with other published models. According to the MERIS retrieved results, the spatial distribution of chlorophyll-a concentrations and its relationship with environment factors were analyzed. The application of the new model and its limits to estimate water surface chlorophyll-a concentrations in turbid lakes is also discussed. Full article
Figures

Open AccessArticle Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements
Remote Sens. 2014, 6(6), 5107-5123; doi:10.3390/rs6065107
Received: 30 November 2013 / Revised: 1 February 2014 / Accepted: 11 February 2014 / Published: 5 June 2014
Cited by 14 | PDF Full-text (974 KB) | HTML Full-text | XML Full-text
Abstract
Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of
[...] Read more.
Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease. Full article
Open AccessArticle Daytime Low Stratiform Cloud Detection on AVHRR Imagery
Remote Sens. 2014, 6(6), 5124-5150; doi:10.3390/rs6065124
Received: 25 November 2013 / Revised: 26 May 2014 / Accepted: 27 May 2014 / Published: 5 June 2014
Cited by 3 | PDF Full-text (3578 KB) | HTML Full-text | XML Full-text
Abstract
The near-real time retrieval of low stratiform cloud (LSC) coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite
[...] Read more.
The near-real time retrieval of low stratiform cloud (LSC) coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR) data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV). Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS) cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe. Full article
Open AccessArticle A Multi-Scale Weighted Back Projection Imaging Technique for Ground Penetrating Radar Applications
Remote Sens. 2014, 6(6), 5151-5163; doi:10.3390/rs6065151
Received: 9 October 2013 / Revised: 22 May 2014 / Accepted: 26 May 2014 / Published: 5 June 2014
Cited by 1 | PDF Full-text (491 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we propose a new ground penetrating radar (GPR) imaging technique based on multi-scale weighted back projection (BP) processing. Firstly, the whole imaging region is discretized by large scale and low-resolution imaging result is obtained by using traditional BP imaging technique.
[...] Read more.
In this paper, we propose a new ground penetrating radar (GPR) imaging technique based on multi-scale weighted back projection (BP) processing. Firstly, the whole imaging region is discretized by large scale and low-resolution imaging result is obtained by using traditional BP imaging technique. Secondly, the potential targets regions (PTR) are delineated from low-resolution imaging result by using intensity detection method. In the PTR, small scale discretization is implemented and higher resolution imaging result is obtained by using weighted BP imaging technique. A weight factor is designed by analyzing the statistical characteristics of scattering data on the time-delay curve. The above “discretization-imaging-PTR delineation” processing continues until the imaging resolution reaches the specified requirement. In the multi-scale imaging result, the resolution in other regions is not as high as that in PTR. This algorithm can get higher resolution imaging results with much lower computation compared with traditional BP imaging algorithm. The simulation of this algorithm is processed and experimental results validate the feasibility of this method. Full article
Open AccessArticle Statistical Characteristics of Mesoscale Eddies in the North Pacific Derived from Satellite Altimetry
Remote Sens. 2014, 6(6), 5164-5183; doi:10.3390/rs6065164
Received: 16 March 2014 / Revised: 25 May 2014 / Accepted: 27 May 2014 / Published: 5 June 2014
Cited by 4 | PDF Full-text (2724 KB) | HTML Full-text | XML Full-text
Abstract
The sea level anomaly data derived from satellite altimetry are analyzed to investigate statistical characteristics of mesoscale eddies in the North Pacific. Eddies are detected by a free-threshold eddy identification algorithm. The results show that the distributions of size, amplitude, propagation speed, and
[...] Read more.
The sea level anomaly data derived from satellite altimetry are analyzed to investigate statistical characteristics of mesoscale eddies in the North Pacific. Eddies are detected by a free-threshold eddy identification algorithm. The results show that the distributions of size, amplitude, propagation speed, and eddy kinetic energy of eddy follow the Rayleigh distribution. The most active regions of eddies are the Kuroshio Extension region, the Subtropical Counter Current zone, and the Northeastern Tropical Pacific region. By contrast, eddies are seldom observed around the center of the eastern part of the North Pacific Subarctic Gyre. The propagation speed and kinetic energy of cyclonic and anticyclonic eddies are almost the same, but anticyclonic eddies possess greater lifespans, sizes, and amplitudes than those of cyclonic eddies. Most eddies in the North Pacific propagate westward except in the Oyashio region. Around the northeastern tropical Pacific and the California currents, cyclonic and anticyclonic eddies propagate westward with slightly equatorward (197° average azimuth relative to east) and poleward (165°) deflection, respectively. This implies that the background current may play an important role in formation of the eddy pathway patterns. Full article
Open AccessArticle An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques
Remote Sens. 2014, 6(6), 5184-5237; doi:10.3390/rs6065184
Received: 16 October 2013 / Revised: 12 May 2014 / Accepted: 13 May 2014 / Published: 5 June 2014
Cited by 5 | PDF Full-text (7735 KB) | HTML Full-text | XML Full-text
Abstract
Thermal inertia and albedo provide information on the distribution of surface materials on Mars. These parameters have been mapped globally on Mars by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor. Two-dimensional clusters of thermal inertia and albedo reflect the thermophysical
[...] Read more.
Thermal inertia and albedo provide information on the distribution of surface materials on Mars. These parameters have been mapped globally on Mars by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor. Two-dimensional clusters of thermal inertia and albedo reflect the thermophysical attributes of the dominant materials on the surface. In this paper three automated, non-deterministic, algorithmic classification methods are employed for defining thermophysical units: Expectation Maximisation of a Gaussian Mixture Model; Iterative Self-Organizing Data Analysis Technique (ISODATA); and Maximum Likelihood. We analyse the behaviour of the thermophysical classes resulting from the three classifiers, operating on the 2007 TES thermal inertia and albedo datasets. Producing a rigorous mapping of thermophysical classes at ~3 km/pixel resolution remains important for constraining the geologic processes that have shaped the Martian surface on a regional scale, and for choosing appropriate landing sites. The results from applying these algorithms are compared to geologic maps, surface data from lander missions, features derived from imaging, and previous classifications of thermophysical units which utilized manual (and potentially more time consuming) classification methods. These comparisons comprise data suitable for validation of our classifications. Our work shows that a combination of the algorithms—ISODATA and Maximum Likelihood—optimises the sensitivity to the underlying dataspace, and that new information on Martian surface materials can be obtained by using these methods. We demonstrate that the algorithms used here can be applied to define a finer partitioning of albedo and thermal inertia for a more detailed mapping of surface materials, grain sizes and thermal behaviour of the Martian surface and shallow subsurface, at the ~3 km scale. Full article
Open AccessArticle Comparison of Medium Spatial Resolution ENVISAT-MERIS and Terra-MODIS Time Series for Vegetation Decline Analysis: A Case Study in Central Asia
Remote Sens. 2014, 6(6), 5238-5256; doi:10.3390/rs6065238
Received: 2 October 2013 / Revised: 30 May 2014 / Accepted: 30 May 2014 / Published: 6 June 2014
Cited by 11 | PDF Full-text (2383 KB) | HTML Full-text | XML Full-text
Abstract
Accurate monitoring of land surface dynamics using remote sensing is essential for the synoptic assessment of environmental change. We assessed a Medium Resolution Imaging Spectrometer (MERIS) full resolution dataset for vegetation monitoring as an alternative to the more commonly used Moderate-Resolution Imaging Spectroradiometer
[...] Read more.
Accurate monitoring of land surface dynamics using remote sensing is essential for the synoptic assessment of environmental change. We assessed a Medium Resolution Imaging Spectrometer (MERIS) full resolution dataset for vegetation monitoring as an alternative to the more commonly used Moderate-Resolution Imaging Spectroradiometer (MODIS) data. Time series of vegetation indices calculated from 300 m resolution MERIS and 250 m resolution MODIS datasets were analyzed to monitor vegetation productivity trends in the irrigated lowlands in Northern Uzbekistan for the period 2003–2011. Mann-Kendall trend analysis was conducted using the time series of Normalized Differenced Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and MERIS-based Terrestrial Chlorophyll Index (MTCI) to detect trends and examine the capabilities of each sensor and index. The methodology consisted of (1) preprocessing of the original imagery; (2) processing and statistical analysis of the corresponding time series datasets; and (3) comparison of the resulting trends. Results confirmed the occurrence of widespread vegetation productivity decline, ranging from 5.5% (MERIS-MTCI) to 21% (MODIS-NDVI) of the total irrigated cropland in the study area. All indices identified the same spatial patterns of decreasing vegetation. Average vegetation index values of NDVI and SAVI were slightly higher when measured by MERIS than by MODIS. These differences merit further investigation to allow a fusion of these datasets for consistent monitoring of cropland productivity decline at scales suitable for guiding operational land management practices. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Figures

Open AccessArticle An Airborne Multispectral Imaging System Based on Two Consumer-Grade Cameras for Agricultural Remote Sensing
Remote Sens. 2014, 6(6), 5257-5278; doi:10.3390/rs6065257
Received: 23 April 2014 / Revised: 29 May 2014 / Accepted: 29 May 2014 / Published: 6 June 2014
Cited by 7 | PDF Full-text (1425 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One camera
[...] Read more.
This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One camera captures normal color images, while the other is modified to obtain near-infrared (NIR) images. The color camera is also equipped with a GPS receiver to allow geotagged images. A remote control is used to trigger both cameras simultaneously. Images are stored in 14-bit RAW and 8-bit JPEG files in CompactFlash cards. The second-order transformation was used to align the color and NIR images to achieve subpixel alignment in four-band images. The imaging system was tested under various flight and land cover conditions and optimal camera settings were determined for airborne image acquisition. Images were captured at altitudes of 305–3050 m (1000–10,000 ft) and pixel sizes of 0.1–1.0 m were achieved. Four practical application examples are presented to illustrate how the imaging system was used to estimate cotton canopy cover, detect cotton root rot, and map henbit and giant reed infestations. Preliminary analysis of example images has shown that this system has potential for crop condition assessment, pest detection, and other agricultural applications. Full article
Open AccessArticle Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data
Remote Sens. 2014, 6(6), 5279-5305; doi:10.3390/rs6065279
Received: 28 March 2014 / Revised: 27 May 2014 / Accepted: 3 June 2014 / Published: 6 June 2014
Cited by 7 | PDF Full-text (3018 KB) | HTML Full-text | XML Full-text
Abstract
The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because
[...] Read more.
The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors. Full article
Open AccessArticle Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa
Remote Sens. 2014, 6(6), 5306-5324; doi:10.3390/rs6065306
Received: 7 February 2014 / Revised: 23 May 2014 / Accepted: 26 May 2014 / Published: 6 June 2014
Cited by 2 | PDF Full-text (11149 KB) | HTML Full-text | XML Full-text
Abstract
The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive
[...] Read more.
The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive microwave data that contain cloud information is very limited over land because of strong heterogeneous land surface emissions. The Coupled Atmosphere and Land Data Assimilation System (CALDAS) was developed by merging soil moisture information estimated from low-frequency data with corresponding high-frequency data to estimate cloud information and, thus, improve weather forecasting over Niger, West Africa. The results showed that the assimilated soil moisture and cloud distributions were reasonably comparable to satellite retrievals of soil moisture and cloud observations. However, assimilating soil moisture alone within a mesoscale model produced only marginal improvements in the forecast, whereas the assimilation of both soil moisture and cloud distributions improved the simulation of temperature and humidity profiles. Rainfall forecasts from CALDAS also correlated well with satellite retrievals. This indicates the potential use of CALDAS as a reliable forecasting tool for developing regions. Further developments of CALDAS and the inclusion of data from several other sensors will be researched in future studies. Full article
Open AccessArticle A Circa 2010 Thirty Meter Resolution Forest Map for China
Remote Sens. 2014, 6(6), 5325-5343; doi:10.3390/rs6065325
Received: 14 February 2014 / Revised: 22 May 2014 / Accepted: 23 May 2014 / Published: 10 June 2014
Cited by 4 | PDF Full-text (2156 KB) | HTML Full-text | XML Full-text
Abstract
This study examines the suitability of 30 m Landsat Thematic Mapper (TM), 250 m time-series Moderate Resolution Imaging Spectrometer (MODIS) Enhanced Vegetation Index (EVI) and other auxiliary datasets for mapping forest extent in China at 30 m resolution circa 2010. We calculated numerous
[...] Read more.
This study examines the suitability of 30 m Landsat Thematic Mapper (TM), 250 m time-series Moderate Resolution Imaging Spectrometer (MODIS) Enhanced Vegetation Index (EVI) and other auxiliary datasets for mapping forest extent in China at 30 m resolution circa 2010. We calculated numerous spectral features, EVI time series, and topographical features that are helpful for forest/non-forest distinction. In this research, extensive efforts have been made in developing training samples over difficult to map or complex regions. Scene by scene quality checking was done on the initial forest extent results and low quality results were refined until satisfactory. Based on the forest extent mask, we classified the forested area into 6 types (evergreen/deciduous broadleaf, evergreen/deciduous needleleaf, mixed forests, and bamboos). Accuracy assessment of our forest/non-forest classification using 2195 test sample units independent of the training sample indicates that the producer’s accuracy (PA) and user’s accuracy (UA) are 92.0% and 95.7%, respectively. According to this map, the total forested area in China was 164.90 million ha (Mha) circa 2010. It is close to the forest area of 7th National Forest Resource Inventory with the same definition of forest. The overall accuracy for the more detailed forest type classification is 72.7%. Full article
Figures

Open AccessArticle Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region
Remote Sens. 2014, 6(6), 5344-5367; doi:10.3390/rs6065344
Received: 26 March 2014 / Revised: 20 May 2014 / Accepted: 3 June 2014 / Published: 10 June 2014
Cited by 6 | PDF Full-text (944 KB) | HTML Full-text | XML Full-text
Abstract
The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can
[...] Read more.
The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content). In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS) is selected as a test case. The Global Data Assimilation System (GDAS) atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from in situ measured radiosonde profiles and GDAS atmospheric profiles, the in situ measured LSTs, and a pair of daytime and nighttime MOD11A1 products in the study area. The results demonstrate that RM-GA has a good ability to estimate the LSTs directly from the MODIS data without any auxiliary atmospheric parameters. Although this research is for local application in the Heihe River basin, the findings and proposed method can easily be extended to other satellite sensors and regions with arid climates and high elevations. Full article
Figures

Open AccessArticle Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China
Remote Sens. 2014, 6(6), 5368-5386; doi:10.3390/rs6065368
Received: 15 April 2014 / Revised: 28 May 2014 / Accepted: 3 June 2014 / Published: 10 June 2014
Cited by 6 | PDF Full-text (1077 KB) | HTML Full-text | XML Full-text
Abstract
The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated”
[...] Read more.
The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and “accumulated” MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005–2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m2; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005–2012, ranging from 10.56–17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert. Full article
Open AccessArticle FAO-56 Dual Model Combined with Multi-Sensor Remote Sensing for Regional Evapotranspiration Estimations
Remote Sens. 2014, 6(6), 5387-5406; doi:10.3390/rs6065387
Received: 27 January 2014 / Revised: 3 June 2014 / Accepted: 4 June 2014 / Published: 11 June 2014
Cited by 1 | PDF Full-text (1644 KB) | HTML Full-text | XML Full-text
Abstract
The main goal of this study is to evaluate the potential of the FAO-56 dual technique for the estimation of regional evapotranspiration (ET) and its constituent components (crop transpiration and soil evaporation), for two classes of vegetation (olives trees and cereals) in the
[...] Read more.
The main goal of this study is to evaluate the potential of the FAO-56 dual technique for the estimation of regional evapotranspiration (ET) and its constituent components (crop transpiration and soil evaporation), for two classes of vegetation (olives trees and cereals) in the semi-arid region of the Kairouan plain in central Tunisia. The proposed approach combines the FAO-56 technique with remote sensing (optical and microwave), not only for vegetation characterization, as proposed in other studies but also for the estimation of soil evaporation, through the use of satellite moisture products. Since it is difficult to use ground flux measurements to validate remotely sensed data at regional scales, comparisons were made with the land surface model ISBA-A-gs which is a physical SVAT (Soil–Vegetation–Atmosphere Transfer) model, an operational tool developed by Météo-France. It is thus shown that good results can be obtained with this relatively simple approach, based on the FAO-56 technique combined with remote sensing, to retrieve temporal variations of ET. The approach proposed for the daily mapping of evapotranspiration at 1 km resolution is approved in two steps, for the period between 1991 and 2007. In an initial step, the ISBA-A-gs soil moisture outputs are compared with ERS/WSC products. Then, the output of the FAO-56 technique is compared with the output generated by the SVAT ISBA-A-gs model. Full article
Open AccessArticle Comparing Two Photo-Reconstruction Methods to Produce High Density Point Clouds and DEMs in the Corral del Veleta Rock Glacier (Sierra Nevada, Spain)
Remote Sens. 2014, 6(6), 5407-5427; doi:10.3390/rs6065407
Received: 28 February 2014 / Revised: 30 May 2014 / Accepted: 3 June 2014 / Published: 11 June 2014
Cited by 6 | PDF Full-text (1958 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, two methods based on computer vision are presented in order to produce dense point clouds and high resolution DEMs (digital elevation models) of the Corral del Veleta rock glacier in Sierra Nevada (Spain). The first one is a semi-automatic 3D
[...] Read more.
In this paper, two methods based on computer vision are presented in order to produce dense point clouds and high resolution DEMs (digital elevation models) of the Corral del Veleta rock glacier in Sierra Nevada (Spain). The first one is a semi-automatic 3D photo-reconstruction method (SA-3D-PR) based on the Scale-Invariant Feature Transform algorithm and the epipolar geometry theory that uses oblique photographs and camera calibration parameters as input. The second method is fully automatic (FA-3D-PR) and is based on the recently released software 123D-Catch that uses the Structure from Motion and MultiView Stereo algorithms and needs as input oblique photographs and some measurements in order to scale and geo-reference the resulting model. The accuracy of the models was tested using as benchmark a 3D model registered by means of a Terrestrial Laser Scanner (TLS). The results indicate that both methods can be applied to micro-scale study of rock glacier morphologies and processes with average distances to the TLS point cloud of 0.28 m and 0.21 m, for the SA-3D-PR and the FA-3D-PR methods, respectively. The performance of the models was also tested by means of the dimensionless relative precision ratio parameter resulting in figures of 1:1071 and 1:1429 for the SA-3D-PR and the FA-3D-PR methods, respectively. Finally, Digital Elevation Models (DEMs) of the study area were produced and compared with the TLS-derived DEM. The results showed average absolute differences with the TLS-derived DEM of 0.52 m and 0.51 m for the SA-3D-PR and the FA-3D-PR methods, respectively. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
Open AccessArticle Assessment of MODIS, MERIS, GEOV1 FPAR Products over Northern China with Ground Measured Data and by Analyzing Residential Effect in Mixed Pixel
Remote Sens. 2014, 6(6), 5428-5451; doi:10.3390/rs6065428
Received: 11 February 2014 / Revised: 22 May 2014 / Accepted: 23 May 2014 / Published: 12 June 2014
Cited by 1 | PDF Full-text (3105 KB) | HTML Full-text | XML Full-text
Abstract
Fraction of Photosynthetically Active Radiation (FPAR) is a critical parameter in land surface energy balance and climate modeling. Several global FPAR products are available, but these still require considerable assessment and validation due to low spatial resolution. Three major FPAR products that have
[...] Read more.
Fraction of Photosynthetically Active Radiation (FPAR) is a critical parameter in land surface energy balance and climate modeling. Several global FPAR products are available, but these still require considerable assessment and validation due to low spatial resolution. Three major FPAR products that have covered China and provided continuous time series data—MODIS, MERIS and GEOV1—were assessed from 2006–2010. Based on the ground measurement data, the accuracies of these three FPAR products were directly validated for maize and winter wheat over northern China. This investigation also assessed the consistencies among the three FPAR products, and analyzed the residential area in mixed pixels effect on the FPAR products accuracy, at each of the main growth stages of maize and winter wheat. The GEOV1 FPAR product was found to be the most accurate with regression R2 values of 0.818 and 0.655 for ground measured maize and winter wheat FPAR. The maize FPAR data were generally more accurate than the winter wheat FPAR data. The MODIS, MERIS and GEOV1 products all indicated that FPAR variations among the growth stages differed from year to year. The scattered residential areas in mixed pixels were found to significantly affect the FPAR data uncertainties, and these were also analyzed in detail. The effect of residential area percentage in mixed pixels on FPAR values differed for different crops, and this was not necessarily in accordance with the FPAR product accuracy. For the mixed pixels, a quadratic polynomial was able to fit the residential area and FPAR data reasonably well with R2 values higher than 0.9 for most relationships. Quadratic polynomial fitting may provide a simple and convenient method to assess and reduce the residential area effect on FPAR in the mixed pixels. Full article
Open AccessArticle Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR
Remote Sens. 2014, 6(6), 5452-5479; doi:10.3390/rs6065452
Received: 29 January 2014 / Revised: 29 May 2014 / Accepted: 29 May 2014 / Published: 12 June 2014
Cited by 5 | PDF Full-text (1400 KB) | HTML Full-text | XML Full-text
Abstract
Savannakhet Province, Lao People’s Democratic Republic (PDR), is a small area that is connected to Thailand, other areas of Lao PDR, and Vietnam via road No. 9. This province has been increasingly affected by carbon dioxide (CO2) emitted from the transport
[...] Read more.
Savannakhet Province, Lao People’s Democratic Republic (PDR), is a small area that is connected to Thailand, other areas of Lao PDR, and Vietnam via road No. 9. This province has been increasingly affected by carbon dioxide (CO2) emitted from the transport corridors that have been developed across the region. To determine the effect of the CO2 increases caused by deforestation and emissions, the total above-ground biomass (AGB) and carbon stocks for different land-cover types were assessed. This study estimated the AGB and carbon stocks (t/ha) of vegetation and soil using standard sampling techniques and allometric equations. Overall, 81 plots, each measuring 1600 m2, were established to represent samples from dry evergreen forest (DEF), mixed deciduous forest (MDF), dry dipterocarp forest (DDF), disturbed forest (DF), and paddy fields (PFi). In each plot, the diameter at breast height (DBH) and height (H) of the overstory trees were measured. Soil samples (composite n = 2) were collected at depths of 0–30 cm. Soil carbon was assessed using the soil depth, soil bulk density, and carbon content. Remote sensing (RS; Landsat Thematic Mapper (TM) image) was used for land-cover classification and development of the AGB estimation model. The relationships between the AGB and RS data (e.g., single TM band, various vegetation indices (VIs), and elevation) were investigated using a multiple linear regression analysis. The results of the total carbon stock assessments from the ground data showed that the MDF site had the highest value, followed by the DEF, DDF, DF, and PFi sites. The RS data showed that the MDF site had the highest area coverage, followed by the DDF, PFi, DF, and DEF sites. The results indicated significant relationships between the AGB and RS data. The strongest correlation was found for the PFi site, followed by the MDF, DDF, DEF, and DF sites. Full article
Open AccessArticle An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors
Remote Sens. 2014, 6(6), 5480-5496; doi:10.3390/rs6065480
Received: 28 March 2014 / Revised: 9 May 2014 / Accepted: 3 June 2014 / Published: 12 June 2014
PDF Full-text (978 KB) | HTML Full-text | XML Full-text
Abstract
In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral
[...] Read more.
In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall) were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that “spatial misplacement” was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year) fire history in the Mediterranean, based on extended time series of Landsat or similar data. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
Figures

Open AccessArticle Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters
Remote Sens. 2014, 6(6), 5497-5519; doi:10.3390/rs6065497
Received: 27 February 2014 / Revised: 29 May 2014 / Accepted: 30 May 2014 / Published: 16 June 2014
Cited by 2 | PDF Full-text (1294 KB) | HTML Full-text | XML Full-text
Abstract
Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is
[...] Read more.
Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is based on curvelet subband Gaussian distribution parameter estimation and cascading these estimated values. The implemented method is compared against original data, polarimetric decomposition features and speckle noise reduced data with use of k-means, fuzzy c-means, spatial fuzzy c-means and self-organizing maps clustering methods. Experimental results show that the curvelet subband Gaussian distribution parameter estimation method with use of self-organizing maps has the best results among other feature extraction-clustering performances, with up to 94.94% overall clustering accuracies. The results also suggest that the implemented method is robust against speckle noise. Full article
Figures

Open AccessArticle Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012
Remote Sens. 2014, 6(6), 5520-5540; doi:10.3390/rs6065520
Received: 24 February 2014 / Revised: 5 June 2014 / Accepted: 6 June 2014 / Published: 16 June 2014
Cited by 1 | PDF Full-text (1369 KB) | HTML Full-text | XML Full-text
Abstract
Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain
[...] Read more.
Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM). Full article
Open AccessArticle Monitoring Trends in Light Pollution in China Based on Nighttime Satellite Imagery
Remote Sens. 2014, 6(6), 5541-5558; doi:10.3390/rs6065541
Received: 4 March 2014 / Revised: 19 May 2014 / Accepted: 20 May 2014 / Published: 16 June 2014
Cited by 8 | PDF Full-text (5333 KB) | HTML Full-text | XML Full-text
Abstract
China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study
[...] Read more.
China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study is to model the light pollution spatial pattern, and monitor changes in trends of spatial distribution from 1992 to 2012 in China using nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System. Based on the intercalibration of nighttime light imageries of the study area from 1992 to 2012, this study obtained the change trends map. This result shows an increase in light pollution of the study area; light pollution in the spatial scale increased from 2.08% in the period from 1992–1996 to 2000–2004, to 5.64% in the period from 2000–2004 to 2008–2012. However, light pollution change trends presented varying styles in different regions and times. In the 1990s, the increasing trend in light pollution regions mostly occurred in larger urban cities, which are mainly located in eastern and coastal areas, whereas the decreasing trend areas were chiefly industrial and mining cities rich in mineral resources, in addition to the central parts of large cities. Similarly, the increasing trend regions dominated urban cities of the study area, and the expanded direction changed from larger cities to small and middle-sized cities and towns in the 2000s. The percentages of regions where light pollution transformed to severe and slight were 5.64% and 0.39%, respectively. The results can inform and help identify how local economic and environmental decisions influence our global nighttime environment, and assist government agencies in creating environmental protection measures. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
Open AccessArticle A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico
Remote Sens. 2014, 6(6), 5559-5588; doi:10.3390/rs6065559
Received: 4 April 2014 / Revised: 10 June 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
Cited by 16 | PDF Full-text (2050 KB) | HTML Full-text | XML Full-text
Abstract
A spatially explicit map of aboveground carbon stored in Mexico’s forests was generated from empirical modeling on forest inventory and spaceborne optical and radar data. Between 2004 and 2007, the Mexican National Forestry Commission (CONAFOR) established a network of ~26,000 permanent inventory plots
[...] Read more.
A spatially explicit map of aboveground carbon stored in Mexico’s forests was generated from empirical modeling on forest inventory and spaceborne optical and radar data. Between 2004 and 2007, the Mexican National Forestry Commission (CONAFOR) established a network of ~26,000 permanent inventory plots in the frame of their national inventory program, the Inventario Nacional Forestal y de Suelos (INFyS). INFyS data served as model response for spatially extending the field-based estimates of carbon stored in the aboveground live dry biomass to a wall-to-wall map, with 30 × 30 m2 pixel posting using canopy density estimates derived from Landsat, L-Band radar data from ALOS PALSAR, as well as elevation information derived from the Shuttle Radar Topography Mission (SRTM) data set. Validation against an independent set of INFyS plots resulted in a coefficient of determination (R2) of 0.5 with a root mean square error (RMSE) of 14 t∙C/ha in the case of flat terrain. The validation for different forest types showed a consistently low estimation bias (<3 t∙C/ha) and R2s in the range of 0.5 except for mangroves (R2 = 0.2). Lower accuracies were achieved for forests located on steep slopes (>15°) with an R2 of 0.34. A comparison of the average carbon stocks computed from: (a) the map; and (b) statistical estimates from INFyS, at the scale of ~650 km2 large hexagons (R2 of 0.78, RMSE of 5 t∙C/ha) and Mexican states (R2 of 0.98, RMSE of 1.4 t∙C/ha), showed strong agreement. Full article
Figures

Open AccessArticle A Bayesian Based Method to Generate a Synergetic Land-Cover Map from Existing Land-Cover Products
Remote Sens. 2014, 6(6), 5589-5613; doi:10.3390/rs6065589
Received: 27 February 2014 / Revised: 4 June 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
Cited by 3 | PDF Full-text (2003 KB) | HTML Full-text | XML Full-text
Abstract
Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully
[...] Read more.
Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully integrate existing land cover information. In this paper, we developed a method to generate a synergetic global land cover map (synGLC) based on Bayes theorem. A state probability vector was defined to precisely and quantitatively describe the land cover classification of every pixel and reduce the errors caused by legends harmonization and spatial resampling. Simple axiomatic approaches were used to generate the prior land cover map, in which pixels with high consistency were regarded to be correct and then used as benchmark to obtain posterior land cover map. Validation results show that our hybrid land cover map (synGLC, the dataset is available on request) has the best overall performance compared with the existing global land cover products. Closed shrub-lands and permanent wetlands have the highest uncertainty in our fused land cover map. This novel method can be extensively applied to fusion of land cover maps with different legends, spatial resolutions or geographic ranges. Full article
Figures

Open AccessArticle Estimation of Mass Balance of the Grosser Aletschgletscher, Swiss Alps, from ICESat Laser Altimetry Data and Digital Elevation Models
Remote Sens. 2014, 6(6), 5614-5632; doi:10.3390/rs6065614
Received: 19 November 2013 / Revised: 30 May 2014 / Accepted: 30 May 2014 / Published: 17 June 2014
Cited by 7 | PDF Full-text (936 KB) | HTML Full-text | XML Full-text
Abstract
Traditional glaciological mass balance measurements of mountain glaciers are a demanding and cost intensive task. In this study, we combine data from the Ice Cloud and Elevation Satellite (ICESat) acquired between 2003 and 2009 with air and space borne Digital Elevation Models (DEMs)
[...] Read more.
Traditional glaciological mass balance measurements of mountain glaciers are a demanding and cost intensive task. In this study, we combine data from the Ice Cloud and Elevation Satellite (ICESat) acquired between 2003 and 2009 with air and space borne Digital Elevation Models (DEMs) in order to derive surface elevation changes of the Grosser Aletschgletscher in the Swiss Alps. Three different areas of the glacier are covered by one nominal ICESat track, allowing us to investigate the performance of the approach under different conditions in terms of ICESat data coverage, and surface characteristics. In order to test the sensitivity of the derived trend in surface lowering, several variables were tested. Employing correction for perennial snow accumulation, footprint selection and adequate reference DEM, we estimated a mean mass balance of −0.92 ± 0.18 m w.e. a−1. for the whole glacier in the studied time period. The resulting mass balance was validated by a comparison with another geodetic approach based on the subtraction of two DEMs for the years 1999 and 2009. It appears that the processing parameters need to be selected depending on the amount of available ICESat measurements, quality of the elevation reference and character of the glacier surface. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing)
Open AccessArticle Remote Sensing Assessment of Forest Disturbance across Complex Mountainous Terrain: The Pattern and Severity of Impacts of Tropical Cyclone Yasi on Australian Rainforests
Remote Sens. 2014, 6(6), 5633-5649; doi:10.3390/rs6065633
Received: 29 January 2014 / Revised: 3 June 2014 / Accepted: 4 June 2014 / Published: 17 June 2014
Cited by 1 | PDF Full-text (1548 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves
[...] Read more.
Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves attention given the multi-scale spatial coverage of these data and the projected increase in intensity of the strongest cyclones. Here, multispectral satellite data, topographic maps and cyclone surface wind data were used to study the patterns of disturbance in an Australian rainforest with complex mountainous terrain produced by tropical cyclone Yasi (2011). The cyclone surface wind data (H*wind) was produced by the Hurricane Research Division of the National Oceanic and Atmospheric Administration (HRD/NOAA), and this was the first time that this data was produced for a cyclone outside of United States territory. A disturbance map was obtained by applying spectral mixture analyses on satellite data and presented a significant correlation with field-measured tree mortality. Our results showed that, consistent with cyclones in the southern hemisphere, multispectral data revealed that forest disturbance was higher on the left side of the cyclone track. The highest level of forest disturbance occurred in forests along the path of the cyclone track (±30°). Levels of forest disturbance decreased with decreasing slope and with an aspect facing off the track of the cyclone or away from the dominant surface winds. An increase in disturbance with surface elevation was also observed. However, areas affected by the same wind intensity presented increased levels of disturbance with increasing elevation suggesting that complex terrain interactions act to speed up wind at higher elevations. Yasi produced an important offset to Australia’s forest carbon sink in 2010. We concluded that multispectral data was sensitive to the main effects of complex topography on disturbance patterns. High resolution cyclone wind surface data are needed in order to quantify the effects of topographic accelerations on cyclone related forest disturbances. Full article
Open AccessArticle A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data
Remote Sens. 2014, 6(6), 5650-5670; doi:10.3390/rs6065650
Received: 6 March 2014 / Revised: 6 June 2014 / Accepted: 9 June 2014 / Published: 17 June 2014
Cited by 6 | PDF Full-text (987 KB) | HTML Full-text | XML Full-text
Abstract
Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a
[...] Read more.
Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively. Full article
Figures

Open AccessArticle A Photogrammetric and Computer Vision-Based Approach for Automated 3D Architectural Modeling and Its Typological Analysis
Remote Sens. 2014, 6(6), 5671-5691; doi:10.3390/rs6065671
Received: 13 March 2014 / Revised: 12 June 2014 / Accepted: 12 June 2014 / Published: 17 June 2014
Cited by 7 | PDF Full-text (1372 KB) | HTML Full-text | XML Full-text
Abstract
Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a
[...] Read more.
Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a new method through the development of PW (Photogrammetry Workbench) (and how this could be useful for architectural modeling). This tool enables the user to turn images into scale 3D point cloud models, which have a better quality than those of laser systems. Moreover, the point clouds may include the respective orthophotos with photographic texture. The method allows the study of the typology of architecture and has been successfully tested on a sample of ten religious buildings located in the region of Aliste, Zamora (Spain). Full article
Open AccessArticle Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data
Remote Sens. 2014, 6(6), 5696-5716; doi:10.3390/rs6065696
Received: 14 March 2014 / Revised: 9 June 2014 / Accepted: 9 June 2014 / Published: 18 June 2014
Cited by 10 | PDF Full-text (2234 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data
[...] Read more.
Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions. Full article
Open AccessArticle Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity
Remote Sens. 2014, 6(6), 5717-5731; doi:10.3390/rs6065717
Received: 31 December 2013 / Revised: 4 May 2014 / Accepted: 13 May 2014 / Published: 18 June 2014
Cited by 10 | PDF Full-text (2731 KB) | HTML Full-text | XML Full-text
Abstract
Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of
[...] Read more.
Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth’s human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery
Remote Sens. 2014, 6(6), 5732-5753; doi:10.3390/rs6065732
Received: 21 January 2014 / Revised: 30 May 2014 / Accepted: 4 June 2014 / Published: 18 June 2014
Cited by 4 | PDF Full-text (1845 KB) | HTML Full-text | XML Full-text
Abstract
In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this
[...] Read more.
In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this way, the relationship between neighboring pixels, which was hidden in the original data, can be extracted more effectively. Specifically, in the proposed algorithm, a two-step process is adopted to make use of the clustering-based information. A clustering approach is first used to produce the initial clustering map, and, subsequently, a multiscale cluster histogram (MCH) is proposed to represent the spatial information around each pixel. In order to evaluate the robustness of the proposed MCH, four clustering techniques are employed to analyze the influence of the clustering methods. Meanwhile, the performance of the MCH is compared to three other widely used spatial features: the gray-level co-occurrence matrix (GLCM), the 3D wavelet texture, and differential morphological profiles (DMPs). The experiments conducted on four well-known hyperspectral datasets verify that the proposed MCH can significantly improve the classification accuracy, and it outperforms other commonly used spatial features. Full article
Open AccessArticle 3D Ground Penetrating Radar to Detect Tree Roots and Estimate Root Biomass in the Field
Remote Sens. 2014, 6(6), 5754-5773; doi:10.3390/rs6065754
Received: 25 April 2014 / Revised: 11 June 2014 / Accepted: 12 June 2014 / Published: 18 June 2014
Cited by 8 | PDF Full-text (1457 KB) | HTML Full-text | XML Full-text
Abstract
The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500
[...] Read more.
The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500 MHz and 800 MHz bow-tie antennas, respectively. The measurement site included two larch trees, and one of them was excavated after GPR measurements. In this paper, a searching algorithm, based on the continuity of pixel intensity along the root in 3D space, is proposed, and two coarse roots whose diameters are more than 5 cm were detected and delineated correctly. Based on the detection results and the measured root biomass, a linear regression model is proposed to estimate the total root biomass in different depth ranges, and the total error was less than 10%. Additionally, based on the detected root samples, a new index named “magnitude width” is proposed to estimate the root diameter that has good correlation with root diameter compared with other common GPR indexes. This index also provides direct measurement of the root diameter with 13%–16% error, providing reasonable and practical root diameter estimation especially in the field. Full article
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
Open AccessArticle Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio
Remote Sens. 2014, 6(6), 5774-5794; doi:10.3390/rs6065774
Received: 19 January 2014 / Revised: 31 May 2014 / Accepted: 3 June 2014 / Published: 19 June 2014
Cited by 6 | PDF Full-text (5230 KB) | HTML Full-text | XML Full-text
Abstract
Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics
[...] Read more.
Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map’s data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method’s application in other regions and agricultural systems. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
Open AccessArticle Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine
Remote Sens. 2014, 6(6), 5795-5814; doi:10.3390/rs6065795
Received: 31 March 2014 / Revised: 26 May 2014 / Accepted: 27 May 2014 / Published: 19 June 2014
Cited by 28 | PDF Full-text (1833 KB) | HTML Full-text | XML Full-text
Abstract
Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral
[...] Read more.
Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM) classifier. Specifically, Gabor filtering and multihypothesis (MH) prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM) and MH-prediction-based SVM in challenging small training sample size conditions. Full article
Open AccessArticle Monitoring of Irrigation Schemes by Remote Sensing: Phenology versus Retrieval of Biophysical Variables
Remote Sens. 2014, 6(6), 5815-5851; doi:10.3390/rs6065815
Received: 18 February 2014 / Revised: 7 May 2014 / Accepted: 30 May 2014 / Published: 20 June 2014
Cited by 5 | PDF Full-text (1956 KB) | HTML Full-text | XML Full-text
Abstract
The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been
[...] Read more.
The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been applied to estimate CWR: the first one is called Kc-NDVI method, based on the correlation between the Normalized Difference Vegetation Index (NDVI) and the crop coefficient (Kc); the second one is the analytical approach based on the direct application of the Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (hc). A time series of high spatial resolution RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season has been used to assess the spatial and temporal variability of crop evapotranspiration ETc and biophysical variables. The validation using the dual crop coefficient approach (Kcb) showed that the satellite-based estimates of daily ETc were in good agreement with ground-based ETc, i.e., R2 = 0.75 and RMSE = 0.79 versus R2 = 0.73 and RMSE = 0.89 for the Kc-NDVI, respective of the analytical approach. The assessment of irrigation performance in terms of adequacy between water requirements and allocations showed that CWR were much larger than allocated surface water for the entire area, with this difference being small at the beginning of the growing season. Even smaller differences were observed between surface water allocations and Irrigation Water Requirements (IWR) throughout the irrigation season. Finally, surface water allocations were rather close to Net Irrigation Water Requirements (NIWR). Full article
Figures

Open AccessArticle Assessment of Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface Temperature Data Retrieved from COMS
Remote Sens. 2014, 6(6), 5852-5867; doi:10.3390/rs6065852
Received: 10 March 2014 / Revised: 3 June 2014 / Accepted: 3 June 2014 / Published: 20 June 2014
Cited by 4 | PDF Full-text (1242 KB) | HTML Full-text | XML Full-text
Abstract
Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using
[...] Read more.
Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011–March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10–13 °C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4–7 °C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime. Full article
Open AccessArticle Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel
Remote Sens. 2014, 6(6), 5868-5884; doi:10.3390/rs6065868
Received: 9 April 2014 / Revised: 5 June 2014 / Accepted: 6 June 2014 / Published: 20 June 2014
Cited by 12 | PDF Full-text (888 KB) | HTML Full-text | XML Full-text
Abstract
In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation
[...] Read more.
In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
Figures

Review

Jump to: Editorial, Research

Open AccessReview Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1
Remote Sens. 2014, 6(6), 4870-4906; doi:10.3390/rs6064870
Received: 29 March 2014 / Revised: 20 May 2014 / Accepted: 20 May 2014 / Published: 28 May 2014
Cited by 10 | PDF Full-text (790 KB) | HTML Full-text | XML Full-text
Abstract
Fast crisis response after natural disasters, such as earthquakes and tropical storms, is necessary to support, for instance, rescue, humanitarian, and reconstruction operations in the crisis area. Therefore, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area,
[...] Read more.
Fast crisis response after natural disasters, such as earthquakes and tropical storms, is necessary to support, for instance, rescue, humanitarian, and reconstruction operations in the crisis area. Therefore, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area, including grade and type of damage. Thereby, satellite remote sensing plays a key role due to its fast response, wide field of view, and low cost. With the increasing availability of remote sensing data, numerous methods have been developed for damage assessment. This article gives a comprehensive review of these techniques focusing on multi-temporal SAR procedures for rapid damage assessment: interferometric coherence and intensity correlation. The review is divided into six parts: First, methods based on coherence; second, the ones using intensity correlation; and third, techniques using both methodologies combined to increase the accuracy of the damage assessment are reviewed. Next, studies using additional data (e.g., GIS and optical imagery) to support the damage assessment and increase its accuracy are reported. Moreover, selected studies on post-event SAR damage assessment techniques and examples of other applications of the interferometric coherence are presented. Then, the preconditions for a successful worldwide application of multi-temporal SAR methods for damage assessment and the limitations of current SAR satellite missions are reported. Finally, an outlook to the Sentinel-1 SAR mission shows possible solutions of these limitations, enabling a worldwide applicability of the presented damage assessment methods. Full article

Journal Contact

MDPI AG
Remote Sensing Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
remotesensing@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Remote Sensing
Back to Top