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Remote Sens., Volume 5, Issue 10 (October 2013), Pages 4735-5423

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Research

Open AccessArticle Evaluation of Clear-Sky Incoming Radiation Estimating Equations Typically Used in Remote Sensing Evapotranspiration Algorithms
Remote Sens. 2013, 5(10), 4735-4752; doi:10.3390/rs5104735
Received: 23 July 2013 / Revised: 17 September 2013 / Accepted: 18 September 2013 / Published: 25 September 2013
Cited by 2 | PDF Full-text (3909 KB) | HTML Full-text | XML Full-text
Abstract
Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave components of net radiation are obtained from [...] Read more.
Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave components of net radiation are obtained from remote sensing data, while the incoming shortwave (RS) and longwave (RL) components are typically estimated from weather data using empirical equations. This study evaluates the accuracy of empirical equations commonly used in remote sensing ET algorithms for estimating RS and RL radiation. Evaluation is carried out through comparison of estimates and observations at five sites that represent different climatic regions from humid to arid. Results reveal (1) both RS and RL estimates from all evaluated equations well correlate with observations (R2 ≥ 0.92), (2) RS estimating equations tend to overestimate, especially at higher values, (3) RL estimating equations tend to give more biased values in arid and semi-arid regions, (4) a model that parameterizes the diffuse component of radiation using two clearness indices and a simple model that assumes a linear increase of atmospheric transmissivity with elevation give better RS estimates, and (5) mean relative absolute errors in the net radiation (Rn) estimates caused by the use of RS and RL estimating equations varies from 10% to 22%. This study suggests that Rn estimates using recommended incoming radiation estimating equations could improve ET estimates. Full article
Open AccessArticle Damage to Buildings in Large Slope Rock Instabilities Monitored with the PSInSAR™ Technique
Remote Sens. 2013, 5(10), 4753-4773; doi:10.3390/rs5104753
Received: 30 July 2013 / Revised: 17 September 2013 / Accepted: 17 September 2013 / Published: 25 September 2013
Cited by 12 | PDF Full-text (5476 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The slow movement of active deep-seated slope gravitational deformations (DSGSDs) and deep-seated rockslides can cause damage to structures and infrastructures. We use Permanent Scatterers Synthetic Aperture Radar Interferometry (PSInSAR™) displacement rate data for the analysis of DSGSD/rockslide activity and kinematics and for [...] Read more.
The slow movement of active deep-seated slope gravitational deformations (DSGSDs) and deep-seated rockslides can cause damage to structures and infrastructures. We use Permanent Scatterers Synthetic Aperture Radar Interferometry (PSInSAR™) displacement rate data for the analysis of DSGSD/rockslide activity and kinematics and for the analysis of damage to buildings. We surveyed the degree of damage to buildings directly in the field, and we tried to correlate it with the superficial displacement rate obtained by the PSInSAR™ technique at seven sites. Overall, we observe that the degree of damage increases with increasing displacement rate, but this trend shows a large dispersion that can be due to different causes, including: the uncertainty in the attribution of the degree of damage for buildings presenting wall coatings; the complexity of the deformation for large phenomena with different materials and subjected to differential behavior within the displaced mass; the absence of differential superficial movements in buildings, due to the large size of the investigated phenomena; and the different types of buildings and their position along the slope or relative to landslide portions. Full article
Open AccessArticle A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters
Remote Sens. 2013, 5(10), 4774-4798; doi:10.3390/rs5104774
Received: 26 July 2013 / Revised: 23 September 2013 / Accepted: 23 September 2013 / Published: 26 September 2013
Cited by 9 | PDF Full-text (1663 KB) | HTML Full-text | XML Full-text
Abstract
We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the [...] Read more.
We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Phytoplankton)
Open AccessArticle Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011
Remote Sens. 2013, 5(10), 4799-4818; doi:10.3390/rs5104799
Received: 8 July 2013 / Revised: 20 September 2013 / Accepted: 23 September 2013 / Published: 30 September 2013
Cited by 35 | PDF Full-text (1446 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over [...] Read more.
A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs
Remote Sens. 2013, 5(10), 4819-4838; doi:10.3390/rs5104819
Received: 15 August 2013 / Revised: 9 September 2013 / Accepted: 17 September 2013 / Published: 8 October 2013
Cited by 12 | PDF Full-text (2133 KB) | HTML Full-text | XML Full-text
Abstract
Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering [...] Read more.
Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Enhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner
Remote Sens. 2013, 5(10), 4839-4856; doi:10.3390/rs5104839
Received: 8 July 2013 / Revised: 9 September 2013 / Accepted: 25 September 2013 / Published: 8 October 2013
Cited by 7 | PDF Full-text (549 KB) | HTML Full-text | XML Full-text
Abstract
Accurate vehicle localization in forest environments is still an unresolved problem. Global navigation satellite systems (GNSS) have well known limitations in dense forest, and have to be combined with for instance laser based SLAM algorithms to provide satisfying accuracy. Such algorithms typically [...] Read more.
Accurate vehicle localization in forest environments is still an unresolved problem. Global navigation satellite systems (GNSS) have well known limitations in dense forest, and have to be combined with for instance laser based SLAM algorithms to provide satisfying accuracy. Such algorithms typically require accurate detection of trees, and estimation of tree center locations in laser data. Both these operations depend on accurate estimations of tree trunk diameter. Diameter estimations are important also for several other forestry automation and remote sensing applications. This paper evaluates several existing algorithms for diameter estimation using 2D laser scanner data. Enhanced algorithms, compensating for beam width and using multiple scans, were also developed and evaluated. The best existing algorithms overestimated tree trunk diameter by ca. 40%. Our enhanced algorithms, compensating for laser beam width, reduced this error to less than 12%. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Figures

Open AccessArticle Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts
Remote Sens. 2013, 5(10), 4857-4876; doi:10.3390/rs5104857
Received: 4 August 2013 / Revised: 7 September 2013 / Accepted: 23 September 2013 / Published: 8 October 2013
Cited by 6 | PDF Full-text (960 KB) | HTML Full-text | XML Full-text
Abstract
Basalt outcrops are significant features in the Western United States and consistently present challenges to Natural Resources Conservation Service (NRCS) soil mapping efforts. Current soil survey methods to estimate basalt outcrops involve field transects and are impractical for mapping regionally extensive areas. [...] Read more.
Basalt outcrops are significant features in the Western United States and consistently present challenges to Natural Resources Conservation Service (NRCS) soil mapping efforts. Current soil survey methods to estimate basalt outcrops involve field transects and are impractical for mapping regionally extensive areas. The purpose of this research was to investigate remote sensing methods to effectively determine the presence of basalt rock outcrops. Five Landsat 5 TM scenes (path 39, row 29) over the year 2007 growing season were processed and analyzed to detect and quantify basalt outcrops across the Clark Area Soil Survey, ID, USA (4,570 km2). The Robust Classification Method (RCM) using the Spectral Angle Mapper (SAM) method and Random Forest (RF) classifications was applied to individual scenes and to a multitemporal stack of the five images. The highest performing RCM basalt classification was obtained using the 18 July scene, which yielded an overall accuracy of 60.45%. The RF classifications applied to the same datasets yielded slightly better overall classification rates when using the multitemporal stack (72.35%) than when using the 18 July scene (71.13%) and the same rate of successfully predicting basalt (61.76%) using out-of-bag sampling. For optimal RCM and RF classifications, uncertainty tended to be lowest in irrigated areas; however, the RCM uncertainty map included more extensive areas of low uncertainty that also encompassed forested hillslopes and riparian areas. RCM uncertainty was sensitive to the influence of bright soil reflectance, while RF uncertainty was sensitive to the influence of shadows. Quantification of basalt requires continued investigation to reduce the influence of vegetation, lichen and loess on basalt detection. With further development, remote sensing tools have the potential to support soil survey mapping of lava fields covering expansive areas in the Western United States and other regions of the world with similar soilscapes. Full article
(This article belongs to the Special Issue Geological Remote Sensing)
Open AccessArticle Mapping Spatial Patterns of Posidonia oceanica Meadows by Means of Daedalus ATM Airborne Sensor in the Coastal Area of Civitavecchia (Central Tyrrhenian Sea, Italy)
Remote Sens. 2013, 5(10), 4877-4899; doi:10.3390/rs5104877
Received: 2 August 2013 / Revised: 20 September 2013 / Accepted: 23 September 2013 / Published: 8 October 2013
Cited by 1 | PDF Full-text (2781 KB) | HTML Full-text | XML Full-text
Abstract
The spatial distribution of sea bed covers and seagrass in coastal waters is of key importance in monitoring and managing Mediterranean shallow water environments often subject to both increasing anthropogenic impacts and climate change effects. In this context we present a methodology [...] Read more.
The spatial distribution of sea bed covers and seagrass in coastal waters is of key importance in monitoring and managing Mediterranean shallow water environments often subject to both increasing anthropogenic impacts and climate change effects. In this context we present a methodology for effective monitoring and mapping of Posidonia oceanica (PO) meadows in turbid waters using remote sensing techniques tested by means of LAI (Leaf Area Index) point sea truth measurements. Preliminary results using Daedalus airborne sensor are reported referring to the PO meadows at Civitavecchia site (central Tyrrhenian sea) where vessel traffic due to presence of important harbors and huge power plant represent strong impact factors. This coastal area, 100 km far from Rome (Central Italy), is characterized also by significant hydrodynamic variations and other anthropogenic factors that affect the health of seagrass meadows with frequent turbidity and suspended sediments in the water column. During 2011–2012 years point measurements of several parameters related to PO meadows phenology were acquired on various stations distributed along 20 km of coast between the Civitavecchia and S. Marinella sites. The Daedalus airborne sensor multispectral data were preprocessed with the support of satellite (MERIS) derived water quality parameters to obtain here improved thematic maps of the local PO distribution. Their thematic accuracy was then evaluated as agreement (R2) with the point sea truth measurements and regressive modeling using an on purpose developd method. Full article
Open AccessArticle Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling
Remote Sens. 2013, 5(10), 4900-4918; doi:10.3390/rs5104900
Received: 3 May 2013 / Revised: 19 September 2013 / Accepted: 23 September 2013 / Published: 9 October 2013
Cited by 5 | PDF Full-text (3155 KB) | HTML Full-text | XML Full-text
Abstract
Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, [...] Read more.
Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1) a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview) as the dependent variable, and (2) ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s) tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems. Full article
Open AccessArticle Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data
Remote Sens. 2013, 5(10), 4919-4941; doi:10.3390/rs5104919
Received: 5 August 2013 / Revised: 22 September 2013 / Accepted: 23 September 2013 / Published: 9 October 2013
Cited by 3 | PDF Full-text (2594 KB) | HTML Full-text | XML Full-text
Abstract
Understanding the hydrological dynamics of boreal wetland ecosystems (peatlands) is essential in order to better manage hydropower inter-annual productivity at the La Grande basin (Northern Quebec, QC, Canada). Given the remoteness and the huge dimension of the La Grande basin, it is [...] Read more.
Understanding the hydrological dynamics of boreal wetland ecosystems (peatlands) is essential in order to better manage hydropower inter-annual productivity at the La Grande basin (Northern Quebec, QC, Canada). Given the remoteness and the huge dimension of the La Grande basin, it is imperative to develop remote sensing monitoring techniques to retrieve hydrological parameters. The main objective of this study is to find out if multi-date and multi-polarization Radar Satellite 2 (RADARSAT-2) (C-band) image analysis could detect seasonal variations of surface soil moisture conditions of the acrotelm. A change detection approach through the use of multi temporal indexes was chosen based on the assumption that the temporal variability of surface roughness and natural vegetation biomass is generally at a much longer time scale than that of surface soil moisture (Δ-Index is based on a reference image that represents dry soil, in order to maximize the sensitivity of σ° to changes in soil moisture with respect to the same location when soil is wet). The Δ-Index approach was tested with each polarization: σ° for fully polarimetric mode (HH, HV, VV) and the cross-polarization coefficient (HV/HH). Results show that the best regression adjustment with regard to surface soil moisture content in boreal wetlands was obtained with the cross-polarization coefficient. The cross-polarization multi-temporal index enables precise volumetric surface soil moisture estimation and monitoring on boreal wetlands, regardless of the influence of vegetation cover and surface roughness conditions (bias was under 1%, standard deviation and RMSE were under 10% for almost all estimation errors). Surface soil moisture estimation was more precise over permanently flooded areas than seasonally flooded ones (standard deviation is systematically greater for the seasonally flooded areas, at all analyzed scales), although the overall quality of the estimation is still precise. Cross-polarization ratio image analysis appears to be a useful mean to exploit radar data spatially, as we were able to relate changes in wetland eco-hydrological dynamics to variations in the intensity of the ratio. Full article
Open AccessArticle Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography
Remote Sens. 2013, 5(10), 4942-4960; doi:10.3390/rs5104942
Received: 10 July 2013 / Revised: 14 September 2013 / Accepted: 25 September 2013 / Published: 10 October 2013
Cited by 7 | PDF Full-text (2397 KB) | HTML Full-text | XML Full-text
Abstract
This paper evaluates accuracies of selected image classification strategies, as applied to Landsat imagery to assess urban impervious surfaces by comparing them to reference data manually delineated from high-resolution aerial photos. Our goal is to identify the most effective methods for delineating [...] Read more.
This paper evaluates accuracies of selected image classification strategies, as applied to Landsat imagery to assess urban impervious surfaces by comparing them to reference data manually delineated from high-resolution aerial photos. Our goal is to identify the most effective methods for delineating urban impervious surfaces using Landsat imagery, thereby guiding applications for selecting cost-effective delineation techniques. A high-resolution aerial photo was used to delineate impervious surfaces for selected census tracts for the City of Roanoke, Virginia. National Land Cover Database Impervious Surface data provided an overall accuracy benchmark at the city scale which was used to assess the Landsat classifications. Three different classification methods using three different band combinations provided overall accuracies in excess of 70% for the entire city. However, there were substantial variations in accuracy when the results were subdivided by census tract. No single classification method was found most effective across all census tracts; the best method for a specific tract depended on method, band combination, and physical characteristics of the area. These results highlight impacts of inherent local variability upon attempts to characterize physical structures of urban regions using a single metric, and the value of analysis at finer spatial scales. Full article
Open AccessArticle Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site
Remote Sens. 2013, 5(10), 4961-4976; doi:10.3390/rs5104961
Received: 11 August 2013 / Revised: 11 September 2013 / Accepted: 24 September 2013 / Published: 11 October 2013
Cited by 10 | PDF Full-text (1718 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test [...] Read more.
In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0.04 m3/m3 and very low bias (i.e., <0.01 m3/m3), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m3/m3 (≤0.058 m3/m3). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m3/m3. In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m3/m3 to 0.051 m3/m3 for comparison with ground measurements and SAR estimates, respectively. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle Analysis and Inter-Calibration of Wet Path Delay Datasets to Compute the Wet Tropospheric Correction for CryoSat-2 over Ocean
Remote Sens. 2013, 5(10), 4977-5005; doi:10.3390/rs5104977
Received: 19 April 2013 / Revised: 22 September 2013 / Accepted: 24 September 2013 / Published: 14 October 2013
Cited by 6 | PDF Full-text (4573 KB) | HTML Full-text | XML Full-text
Abstract
Unlike most altimetric missions, CryoSat-2 is not equipped with an onboard microwave radiometer (MWR) to provide wet tropospheric correction (WTC) to radar altimeter measurements, thus, relying on a model-based one provided by the European Center for Medium-range Weather Forecasts (ECMWF). In the [...] Read more.
Unlike most altimetric missions, CryoSat-2 is not equipped with an onboard microwave radiometer (MWR) to provide wet tropospheric correction (WTC) to radar altimeter measurements, thus, relying on a model-based one provided by the European Center for Medium-range Weather Forecasts (ECMWF). In the ambit of ESA funded project CP4O, an improved WTC for CryoSat-2 data over ocean is under development, based on a data combination algorithm (DComb) through objective analysis of WTC values derived from all existing global-scale data types. The scope of this study is the analysis and inter-calibration of the large dataset of total column water vapor (TCWV) products from scanning MWR aboard Remote Sensing (RS) missions for use in the WTC computation for CryoSat-2. The main issues regarding the computation of the WTC from all TCWV products are discussed. The analysis of the orbital parameters of CryoSat-2 and all other considered RS missions, their sensor characteristics and inter-calibration is presented, providing an insight into the expected impact of these datasets on the WTC estimation. The most suitable approach for calculating the WTC from TCWV is investigated. For this type of application, after calibration with respect to an appropriate reference, two approaches were found to give very similar results, with root mean square differences of 2 mm. Full article
Open AccessArticle Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture
Remote Sens. 2013, 5(10), 5006-5039; doi:10.3390/rs5105006
Received: 16 August 2013 / Revised: 30 September 2013 / Accepted: 6 October 2013 / Published: 14 October 2013
Cited by 29 | PDF Full-text (2887 KB) | HTML Full-text | XML Full-text
Abstract
Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with [...] Read more.
Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with stereoscopic overlaps using light-weight UAV platforms. This new technology is highly relevant, because it opens up new possibilities for measuring and monitoring the environment, which is becoming increasingly important for many environmental challenges. Our objectives were to investigate the processing and use of this new type of image data in precision agriculture. We developed the entire processing chain from raw images up to georeferenced reflectance images, digital surface models and biomass estimates. The processing integrates photogrammetric and quantitative remote sensing approaches. We carried out an empirical assessment using FPI spectral imagery collected at an agricultural wheat test site in the summer of 2012. Poor weather conditions during the campaign complicated the data processing, but this is one of the challenges that are faced in operational applications. The results indicated that the camera performed consistently and that the data processing was consistent, as well. During the agricultural experiments, promising results were obtained for biomass estimation when the spectral data was used and when an appropriate radiometric correction was applied to the data. Our results showed that the new FPI technology has a great potential in precision agriculture and indicated many possible future research topics. Full article
Open AccessArticle Leaf Area Index (LAI) Estimation in Boreal Mixedwood Forest of Ontario, Canada Using Light Detection and Ranging (LiDAR) and WorldView-2 Imagery
Remote Sens. 2013, 5(10), 5040-5063; doi:10.3390/rs5105040
Received: 18 July 2013 / Revised: 7 October 2013 / Accepted: 9 October 2013 / Published: 14 October 2013
Cited by 7 | PDF Full-text (1577 KB) | HTML Full-text | XML Full-text
Abstract
Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very [...] Read more.
Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers for tactical planning. This paper focuses on estimating LAI from: (i) height and density metrics derived from Light Detection and Ranging (LiDAR); (ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and (iii) a combination of these methods. For the Hearst Forest of Northern Ontario, in situ measurements of LAI were derived from digital hemispherical photographs (DHPs) while remote sensing variables were derived from low density LiDAR (i.e., 1 m−2) and high spatial resolution WorldView-2 data (2 m). Multiple Linear Regression (MLR) models were generated using these variables. Results from these analyses demonstrate: (i) moderate explanatory power (i.e., R2 = 0.53) for LiDAR height and density metrics that have proven to be related to canopy structure; (ii) no relationship when using SVIs; and (iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with narrow ranges of LAI for model calibration. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at high spatial resolution for decision makers in the forestry community. This method can be easily incorporated into simultaneous modeling efforts for forest inventory variables using LiDAR. Full article
Open AccessArticle Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing
Remote Sens. 2013, 5(10), 5064-5088; doi:10.3390/rs5105064
Received: 24 July 2013 / Revised: 12 September 2013 / Accepted: 9 October 2013 / Published: 15 October 2013
Cited by 8 | PDF Full-text (5472 KB) | HTML Full-text | XML Full-text
Abstract
The hyper-arid Atacama Desert is one of the most extreme environments for life and only few species have evolved to survive its aridness. One such species is the tree Prosopis tamarugo Phil. Because Tamarugo completely depends on groundwater, it is being threatened [...] Read more.
The hyper-arid Atacama Desert is one of the most extreme environments for life and only few species have evolved to survive its aridness. One such species is the tree Prosopis tamarugo Phil. Because Tamarugo completely depends on groundwater, it is being threatened by the high water demand from the Chilean mining industry and the human consumption. In this paper, we identified the most important biophysical variables to assess the water status of Tamarugo trees and tested the potential of WorldView2 satellite images to retrieve these variables. We propose green canopy fraction (GCF) and green drip line leaf area index (DLLAIgreen) as best variables and a value of 0.25 GCF as a critical threshold for Tamarugo survival. Using the WorldView2 spectral bands and an object-based image analysis, we showed that the NDVI and the Red-edge Chlorophyll Index (CIRed-edge) have good potential to retrieve GCF and DLLAIgreen. The NDVI performed best for DLLAIgreen (RMSE = 0.4) while the CIRed-edge was best for GCF (RMSE = 0.1). However, both indices were affected by Tamarugo leaf movements (leaves avoid facing direct solar radiation at the hottest time of the day). Thus, monitoring systems based on these indices should consider the time of the day and the season of the year at which the satellite images are acquired. Full article
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Open AccessArticle A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model
Remote Sens. 2013, 5(10), 5089-5121; doi:10.3390/rs5105089
Received: 25 August 2013 / Revised: 19 September 2013 / Accepted: 23 September 2013 / Published: 15 October 2013
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Abstract
Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. [...] Read more.
Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced. Full article
Open AccessArticle Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province
Remote Sens. 2013, 5(10), 5122-5142; doi:10.3390/rs5105122
Received: 2 August 2013 / Revised: 6 October 2013 / Accepted: 10 October 2013 / Published: 17 October 2013
Cited by 11 | PDF Full-text (2920 KB) | HTML Full-text | XML Full-text
Abstract
Earth Observation is a powerful tool for the detection of floods. Microwave sensors are typically favored as they deliver data enabling water detection independent of solar illumination or cloud cover conditions. However, scale issues play an important role in radar based flood [...] Read more.
Earth Observation is a powerful tool for the detection of floods. Microwave sensors are typically favored as they deliver data enabling water detection independent of solar illumination or cloud cover conditions. However, scale issues play an important role in radar based flood mapping. Depending on the flood related phenomenon under investigation, some sensors might be more suitable than others. In this study, we elucidate flood mapping at different spatial scale investigating the capability of Envisat ASAR Wide Swath Mode data at 150 m spatial resolution, as well as TerraSAR-X Scansar and Stripmap data at 8.25 m and 2.5 m resolution to especially assess urban flooding. For this purpose, we evaluate the results of automated multi-temporal water extraction from data sources of different scale against other parameters, such as settlement density, also taking a highly accurate building layer digitized from Quickbird data into consideration. Results reveal that while Envisat ASAR WSM derived flood maps are suitable to support the understanding of general flood patterns in a larger region, high resolution data of sensors such as TerraSAR-X is needed to truly assess urban flooding. However, even radar data of high spatial resolution still shows limitations; mainly in regions with a dense accumulation of corner reflectors leading to effects of layover, foreshortening, and shadowing, and hence the “over radiation” of flood affected areas. Full article
Open AccessArticle A Deviation-Time-Space-Thermal (DTS-T) Method for Global Earth Observation System of Systems (GEOSS)-Based Earthquake Anomaly Recognition: Criterions and Quantify Indices
Remote Sens. 2013, 5(10), 5143-5151; doi:10.3390/rs5105143
Received: 20 August 2013 / Revised: 11 October 2013 / Accepted: 14 October 2013 / Published: 17 October 2013
Cited by 5 | PDF Full-text (1093 KB) | HTML Full-text | XML Full-text
Abstract
The particular process of LCA (Lithosphere-Coversphere-Atmosphere) coupling referring to local tectonic structures and coversphere conditions is very important for understanding seismic anomaly from GEOSS (Global Earth Observation System of Systems). The LCA coupling based multiple-parameters analysis should be the foundation for earthquake [...] Read more.
The particular process of LCA (Lithosphere-Coversphere-Atmosphere) coupling referring to local tectonic structures and coversphere conditions is very important for understanding seismic anomaly from GEOSS (Global Earth Observation System of Systems). The LCA coupling based multiple-parameters analysis should be the foundation for earthquake prewarning. Three improved criterions: deviation notable enough, time quasi-synchronism, and space geo-adjacency, plus their quantify indices are defined for earthquake anomaly recognition, and applied to thermal parameters as a DTS-T (Deviation-Time-Space-Thermal) method. A normalized reliability index is preliminarily defined considering three quantify indices for deviation-time-space criterions. As an example, the DTS-T method is applied to the Ms 7.1 Yushu earthquake of 14 April 2010 in China. Furthermore, combining with the previous analysis of six recent significant earthquakes in the world, the statistical results regarding effective parameters, the occurrence of anomaly before the main shocks and a reliability index for each earthquake are introduced. It shows that the DTS-T method is reasonable and can be applied for routine monitoring and prewarning in the tectonic seismicity region. Full article
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Open AccessArticle Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices
Remote Sens. 2013, 5(10), 5152-5172; doi:10.3390/rs5105152
Received: 29 August 2013 / Revised: 25 September 2013 / Accepted: 9 October 2013 / Published: 17 October 2013
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Abstract
Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently necessary to provide some [...] Read more.
Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently necessary to provide some distinct analysis methods to analyze them across a range of remote sensing imagery resolutions. The objective of our study is to quantify urban land use patterns from various perspectives using multidimensional fractal methods. Three commonly used fractal dimensions, i.e., the boundary dimension, the radius dimension, and the information entropy dimension, are introduced as the typical indices to examine the complexity, centrality and balance of land use spatial patterns, respectively. Moreover, a new lacunarity dimension for describing the degree of self-organization of urban land use at the macro level is presented. A cloud-free Landsat ETM+ image acquired on 17 September 2010 was used to extract land use information in Wuhan, China. The results show that there are significant linear relationships represented by good statistical fitness related to these four indices. The results indicate that rapid urbanization has substantially affected the urban landscape pattern, and different land use types show different spatial patterns in response. This analysis reveals that multiple fractal/nonfractal indices provides a more comprehensive understanding of the spatial heterogeneity of urban land use spatial patterns than any single fractal dimension index. These findings can help us to gain deeper insight into the complex spatial patterns of urban land use. Full article
Open AccessArticle FUEGO — Fire Urgency Estimator in Geosynchronous Orbit — A Proposed Early-Warning Fire Detection System
Remote Sens. 2013, 5(10), 5173-5192; doi:10.3390/rs5105173
Received: 12 July 2013 / Revised: 27 September 2013 / Accepted: 9 October 2013 / Published: 17 October 2013
Cited by 4 | PDF Full-text (3148 KB) | HTML Full-text | XML Full-text
Abstract
Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m2) fires on the surface of the [...] Read more.
Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m2) fires on the surface of the earth, cover most of the western United States (under conditions of moderately clear skies) every few minutes or so, and attain very good signal-to-noise ratio against Poisson fluctuations in a second. Hence, these favorable statistical significances have initiated a study of how such a satellite could operate and reject the large number of expected systematic false alarms from a number of sources. Here we present both studies of the backgrounds in Geostationary Operational Environmental Satellites (GOES) 15 data and studies that probe the sensitivity of a fire detection satellite in geosynchronous orbit. We suggest a number of algorithms that can help reduce false alarms, and show efficacy on a few. Early detection and response would be of true value in the United States and other nations, as wildland fires continue to severely stress resource managers, policy makers, and the public, particularly in the western US. Here, we propose the framework for a geosynchronous satellite with modern imaging detectors, software, and algorithms able to detect heat from early and small fires, and yield minute-scale detection times. Full article
Open AccessArticle Analysis of the Phenology in the Mongolian Plateau by Inter-Comparison of Global Vegetation Datasets
Remote Sens. 2013, 5(10), 5193-5208; doi:10.3390/rs5105193
Received: 26 August 2013 / Revised: 8 October 2013 / Accepted: 14 October 2013 / Published: 18 October 2013
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Abstract
This study evaluates the performances of three global satellite datasets (Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l’ observation de la Terre (SPOT) of the Mongolian Plateau, where in situ observation is insufficient to assess [...] Read more.
This study evaluates the performances of three global satellite datasets (Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l’ observation de la Terre (SPOT) of the Mongolian Plateau, where in situ observation is insufficient to assess vegetation dynamics on terrestrial systems. We give a comprehensive assessment of the historical changes in vegetation dynamics by using comparative and correlation methods on the three archives using two indices: the growing season’s Normalized Difference Vegetation Index (NDVI) and the Start of the Season Index (SOS). The main findings are: (1) MODIS and SPOT have generally better comparability and consistency in the spatial-temporal trends of NDVI and SOS than AVHRR in this area; (2) all the three archives exhibit better consistency in Inner Mongolia than in Mongolia; (3) integration data analysis of AVHRR (1982–1997) and SPOT (1998–2012) shows that the dynamics of vegetation growth has three distinct phases: enhanced before 1994; a flatter/slightly decreasing trend before 2001; and, then, a rapid recovery between 2001 and 2012 with remarkable spatial heterogeneity, with Inner Mongolia experiencing a significant greening in vegetation NDVI compared with no obvious changes in Mongolia; (4) the temporal average SOS showed no significant “earlier spring” onset during the past 31 years, on the middle and northern Mongolian Plateau. Full article
Open AccessArticle Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results
Remote Sens. 2013, 5(10), 5209-5264; doi:10.3390/rs5105209
Received: 1 July 2013 / Revised: 17 September 2013 / Accepted: 9 October 2013 / Published: 18 October 2013
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Abstract
This paper complies with the Quality Assurance Framework for Earth Observation (QA4EO) international guidelines to provide a metrological/statistically-based quality assessment of the Spectral Classification of surface reflectance signatures (SPECL) secondary product, implemented within the popular Atmospheric/Topographic Correction (ATCOR™) commercial software suite, and [...] Read more.
This paper complies with the Quality Assurance Framework for Earth Observation (QA4EO) international guidelines to provide a metrological/statistically-based quality assessment of the Spectral Classification of surface reflectance signatures (SPECL) secondary product, implemented within the popular Atmospheric/Topographic Correction (ATCOR™) commercial software suite, and of the Satellite Image Automatic Mapper™ (SIAM™) software product, proposed to the remote sensing (RS) community in recent years. The ATCOR™-SPECL and SIAM™ physical model-based expert systems are considered of potential interest to a wide RS audience: in operating mode, they require neither user-defined parameters nor training data samples to map, in near real-time, a spaceborne/airborne multi-spectral (MS) image into a discrete and finite set of (pre-attentional first-stage) spectral-based semi-concepts (e.g., “vegetation”), whose informative content is always equal or inferior to that of target (attentional second-stage) land cover (LC) concepts (e.g., “deciduous forest”). For the sake of simplicity, this paper is split into two: Part 1—Theory and Part 2—Experimental results. The Part 1 provides the present Part 2 with an interdisciplinary terminology and a theoretical background. To comply with the principle of statistics and the QA4EO guidelines discussed in the Part 1, the present Part 2 applies an original adaptation of a novel probability sampling protocol for thematic map quality assessment to the ATCOR™-SPECL and SIAM™ pre-classification maps, generated from three spaceborne/airborne MS test images. Collected metrological/ statistically-based quality indicators (QIs) comprise: (i) an original Categorical Variable Pair Similarity Index (CVPSI), capable of estimating the degree of match between a test pre-classification map’s legend and a reference LC map’s legend that do not coincide and must be harmonized (reconciled); (ii) pixel-based Thematic (symbolic, semantic) QIs (TQIs) and (iii) polygon-based sub-symbolic (non-semantic) Spatial QIs (SQIs), where all TQIs and SQIs are provided with a degree of uncertainty in measurement. Main experimental conclusions of the present Part 2 are the following. (I) Across the three test images, the CVPSI values of the SIAM™ pre-classification maps at the intermediate and fine semantic granularities are superior to those of the ATCOR™-SPECL single-granule maps. (II) TQIs of both the ATCOR™-SPECL and the SIAM™ tend to exceed community-agreed reference standards of accuracy. (III) Across the three test images and the SIAM™’s three semantic granularities, TQIs of the SIAM™ tend to be significantly higher (in statistical terms) than the ATCOR™-SPECL’s. Stemming from the proposed experimental evidence in support to theoretical considerations, the final conclusion of this paper is that, in compliance with the QA4EO objectives, the SIAM™ software product can be considered eligible for injecting prior spectral knowledge into the pre-attentive vision first stage of a novel generation of hybrid (combined deductive and inductive) RS image understanding systems, capable of transforming large-scale multi-source multi-resolution EO image databases into operational, comprehensive and timely knowledge/information products. Full article
Open AccessArticle Empirical and Physical Estimation of Canopy Water Content from CHRIS/PROBA Data
Remote Sens. 2013, 5(10), 5265-5284; doi:10.3390/rs5105265
Received: 31 July 2013 / Revised: 11 October 2013 / Accepted: 14 October 2013 / Published: 21 October 2013
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Abstract
Efficient monitoring of Canopy Water Content (CWC) is a central feature in vegetation studies. The potential of hyperspectral high spatial resolution CHRIS/PROBA satellite data for the retrieval of CWC was here investigated using empirical and physical based approaches. Special attention was paid [...] Read more.
Efficient monitoring of Canopy Water Content (CWC) is a central feature in vegetation studies. The potential of hyperspectral high spatial resolution CHRIS/PROBA satellite data for the retrieval of CWC was here investigated using empirical and physical based approaches. Special attention was paid to the spectral band selection, inversion technique and training process. Performances were evaluated with ground measurements from the SEN3EXP field campaign over a range of crops. Results showed that the optimal band selection includes four spectral bands: one centered about 970 nm absorption feature which is sensible to Cw, and three bands in green, red and near infrared to estimate LAI and compensate from leaf- and canopy-level effects. A simple neural network with a single hidden layer of five tangent sigmoid transfer functions trained over PROSAIL radiative transfer simulations showed benefits in the retrieval performances compared with a look up table inversion approach (root mean square error of 0.16 kg/m2 vs. 0.22 kg/m2). The neural network inversion approach showed a good agreement and performances similar to an empirical up-scaling approach based on a multivariate iteratively re-weighted least squares algorithm, demonstrating the applicability of radiative transfer model inversion methods to CHRIS/PROBA for high spatial resolution monitoring of CWC. Full article
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Open AccessArticle Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning
Remote Sens. 2013, 5(10), 5285-5303; doi:10.3390/rs5105285
Received: 6 September 2013 / Revised: 16 October 2013 / Accepted: 16 October 2013 / Published: 22 October 2013
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Abstract
Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an [...] Read more.
Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Open AccessArticle Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation
Remote Sens. 2013, 5(10), 5304-5329; doi:10.3390/rs5105304
Received: 22 August 2013 / Revised: 9 October 2013 / Accepted: 14 October 2013 / Published: 22 October 2013
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Abstract
The airborne downward looking sparse linear array three dimensional synthetic aperture radar (DLSLA 3-D SAR) operates nadir observation with the along-track synthetic aperture formulated by platform movement and the cross-track synthetic aperture formulated by physical sparse linear array. Considering the lack of [...] Read more.
The airborne downward looking sparse linear array three dimensional synthetic aperture radar (DLSLA 3-D SAR) operates nadir observation with the along-track synthetic aperture formulated by platform movement and the cross-track synthetic aperture formulated by physical sparse linear array. Considering the lack of DLSLA 3-D SAR data in the current preliminary study stage, it is very important and essential to develop DLSLA 3-D SAR simulation (echo generation simulation and image reconstruction simulation, including point targets simulation and 3-D distributed scene simulation). In this paper, DLSLA 3-D SAR imaging geometry, the echo signal model and the heterogeneous parallel technique are discussed first. Then, heterogeneous parallel echo generation simulation with time domain correlation and the frequency domain correlation method is described. In the following, heterogeneous parallel image reconstruction simulation with two imaging algorithms, e.g., 3-D polar format algorithm, polar formatting and L1 regularization algorithm is discussed. Finally, the point targets and the 3-D distributed scene simulation are demonstrated to validate the effectiveness and performance of our proposed heterogeneous parallel simulation technique. The 3-D distributed scene employs airborne X-band DEM and P-band Circular SAR image of the same area as simulation scene input. Full article
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Open AccessArticle Response of Spectral Reflectances and Vegetation Indices on Varying Juniper Cone Densities
Remote Sens. 2013, 5(10), 5330-5345; doi:10.3390/rs5105330
Received: 24 August 2013 / Revised: 29 September 2013 / Accepted: 8 October 2013 / Published: 22 October 2013
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Abstract
Juniper trees are widely distributed throughout the world and are common sources of allergies when microscopic pollen grains are transported by wind and inhaled. In this study, we investigated the spectral influences of pollen-discharging male juniper cones within a juniper canopy. This [...] Read more.
Juniper trees are widely distributed throughout the world and are common sources of allergies when microscopic pollen grains are transported by wind and inhaled. In this study, we investigated the spectral influences of pollen-discharging male juniper cones within a juniper canopy. This was done through a controlled outdoor experiment involving ASD FieldSpec Pro Spectroradiometer measurements over juniper canopies of varying cone densities. Broadband and narrowband spectral reflectance and vegetation index (VI) patterns were evaluated as to their sensitivity and their ability to discriminate the presence of cones. The overall aim of this research was to assess remotely sensed phenological capabilities to detect pollen-bearing juniper trees for public health applications. A general decrease in reflectance values with increasing juniper cone density was found, particularly in the Green (545–565 nm) and NIR (750–1,350 nm) regions. In contrast, reflectances in the shortwave-infrared (SWIR, 2,000 nm to 2,350 nm) region decreased from no cone presence to intermediate amounts (90 g/m2) and then increased from intermediate levels to the highest cone densities (200 g/m2). Reflectance patterns in the Red (620–700 nm) were more complex due to shifting contrast patterns in absorptance between cones and juniper foliage, where juniper foliage is more absorbing than cones only within the intense narrowband region of maximum chlorophyll absorption near 680 nm. Overall, narrowband reflectances were more sensitive to cone density changes than the equivalent MODIS broadbands. In all VIs analyzed, there were significant relationships with cone density levels, particularly with the narrowband versions and the two-band vegetation index (TBVI) based on Green and Red bands, a promising outcome for the use of phenocams in juniper phenology trait studies. These results indicate that spectral indices are sensitive to certain juniper phenologic traits that can potentially be used for juniper cone detection in support of public health applications. Full article
Open AccessArticle An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data
Remote Sens. 2013, 5(10), 5346-5368; doi:10.3390/rs5105346
Received: 31 August 2013 / Revised: 23 September 2013 / Accepted: 24 September 2013 / Published: 22 October 2013
Cited by 14 | PDF Full-text (7707 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring [...] Read more.
Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales. Full article
Open AccessArticle Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation
Remote Sens. 2013, 5(10), 5369-5396; doi:10.3390/rs5105369
Received: 30 August 2013 / Revised: 25 September 2013 / Accepted: 9 October 2013 / Published: 22 October 2013
Cited by 10 | PDF Full-text (3812 KB) | HTML Full-text | XML Full-text
Abstract
Based on surface energy balance and the assumption of fairly invariant evaporative fraction (EF) during daytime, this study proposes a new parameterization scheme of directly estimating daily EF. Daily EF is parameterized as a function of temporal variations in surface temperature, air [...] Read more.
Based on surface energy balance and the assumption of fairly invariant evaporative fraction (EF) during daytime, this study proposes a new parameterization scheme of directly estimating daily EF. Daily EF is parameterized as a function of temporal variations in surface temperature, air temperature, and net radiation. The proposed EF parameterization scheme can well reproduce daily EF estimates from a soil-vegetation-atmosphere transfer (SVAT) model with a root mean square error (RMSE) of 0.13 and a coefficient of determination (R2) of 0.719. When input variables from in situ measurements at the Yucheng station in North China are used, daily EF estimated by the proposed method is in good agreement with measurements from the eddy covariance system corrected by the residual energy method with an R2 of 0.857 and an RMSE of 0.119. MODIS/Aqua remotely sensed data were also applied to estimate daily EF. Though there are some inconsistencies between the remotely sensed daily EF estimates and in situ measurements due to errors in input variables and measurements, the result from the proposed parameterization scheme shows a slight improvement to SEBS-estimated EF with remotely sensed instantaneous inputs. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle Estimation of Actual Evapotranspiration along the Middle Rio Grande of New Mexico Using MODIS and Landsat Imagery with the METRIC Model
Remote Sens. 2013, 5(10), 5397-5423; doi:10.3390/rs5105397
Received: 22 July 2013 / Revised: 16 October 2013 / Accepted: 17 October 2013 / Published: 23 October 2013
Cited by 6 | PDF Full-text (3872 KB) | HTML Full-text | XML Full-text
Abstract
Estimation of actual evapotranspiration (ET) for the Middle Rio Grande valley in central New Mexico via the METRIC surface energy balance model using MODIS and Landsat imagery is described. MODIS images are a useful resource for estimating ET at large scales when [...] Read more.
Estimation of actual evapotranspiration (ET) for the Middle Rio Grande valley in central New Mexico via the METRIC surface energy balance model using MODIS and Landsat imagery is described. MODIS images are a useful resource for estimating ET at large scales when high spatial resolution is not required. One advantage of MODIS satellites is that images having a view angle < ~15° are potentially available about every four to five days. The main challenge of applying METRIC using MODIS is the selection of the two calibration conditions due to the low spatial resolution of MODIS. A calibration procedure specific to MODIS is described that utilizes the higher vegetation index areas of the image along with a consistently low ET location to develop the estimation function for sensible heat flux. This paper compares ET images for the Rio Grande region as produced by both MODIS and by Landsat. Application of METRIC energy balance processes along the Middle Rio Grande using MODIS imagery indicates that one can successfully produce monthly and annual ET estimates that are similar in value to those obtained using Landsat imagery if a cross-calibration scheme is considered. However, spatial fidelity is degraded. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)

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