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Remote Sens., Volume 7, Issue 6 (June 2015) – 75 articles , Pages 6510-8249

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4883 KiB  
Article
Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities
by Per Skougaard Kaspersen, Rasmus Fensholt and Martin Drews
Remote Sens. 2015, 7(6), 8224-8249; https://doi.org/10.3390/rs70608224 - 19 Jun 2015
Cited by 60 | Viewed by 11044
Abstract
Impervious surfaces (IS) are a key indicator of environmental quality, and mapping of urban IS is important for a wide range of applications including hydrological modelling, water management, urban and environmental planning and urban climate studies. This paper addresses the accuracy and applicability [...] Read more.
Impervious surfaces (IS) are a key indicator of environmental quality, and mapping of urban IS is important for a wide range of applications including hydrological modelling, water management, urban and environmental planning and urban climate studies. This paper addresses the accuracy and applicability of vegetation indices (VI), from Landsat imagery, to estimate IS fractions for European cities. The accuracy of three different measures of vegetation cover is examined for eight urban areas at different locations in Europe. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are converted to IS fractions using a regression modelling approach. Also, NDVI is used to estimate fractional vegetation cover (FR), and consequently IS fractions. All three indices provide fairly accurate estimates (MAEs ≈ 10%, MBE’s < 2%) of sub-pixel imperviousness, and are found to be applicable for cities with dissimilar climatic and vegetative conditions. The VI/IS relationship across cities is examined by quantifying the MAEs and MBEs between all combinations of models and urban areas. Also, regional regression models are developed by compiling data from multiple cities to examine the potential for developing and applying a single regression model to estimate IS fractions for numerous urban areas without reducing the accuracy considerably. Our findings indicate that the models can be applied broadly for multiple urban areas, and that the accuracy is reduced only marginally by applying the regional models. SAVI is identified as a superior index for the development of regional quantification models. The findings of this study highlight that IS fractions, and spatiotemporal changes herein, can be mapped by use of simple regression models based on VIs from remote sensors, and that the method presented enables simple, accurate and resource efficient quantification of IS. Full article
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Article
Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan
by Wei-Chen Hsu, Hung-Cheng Chang, Kuan-Tsung Chang, En-Kai Lin, Jin-King Liu and Yuei-An Liou
Remote Sens. 2015, 7(6), 8202-8223; https://doi.org/10.3390/rs70608202 - 19 Jun 2015
Cited by 30 | Viewed by 8807
Abstract
Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system [...] Read more.
Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system comprising GPS stations, leveling surveys, monitoring wells, and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) was employed to monitor land subsidence in Western Taiwan. The results indicate that land subsidence in Yunlin County was mainly affected by the compaction of subsurface soils and over-pumping of groundwater from deep soils. The study area comprised western foothills, characterized by sediments containing predominantly gravel, and coastal areas, where clay was predominant. The subsidence in coastal areas was more severe than that in the western foothills, as a result of groundwater removal. An additional factor affecting subsidence was the compaction of deep layers caused by deep groundwater removal and the deep-layer compaction was difficult to recover. Based on multisensor monitoring results, severe subsidence is mainly affected by compaction of subsurface soils, over-pumping of groundwater from deep soils, and deep soil compaction. Full article
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
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9505 KiB  
Article
Assessing Metrics for Estimating Fire Induced Change in the Forest Understorey Structure Using Terrestrial Laser Scanning
by Vaibhav Gupta, Karin J. Reinke, Simon D. Jones, Luke Wallace and Lucas Holden
Remote Sens. 2015, 7(6), 8180-8201; https://doi.org/10.3390/rs70608180 - 19 Jun 2015
Cited by 22 | Viewed by 7124
Abstract
Quantifying post-fire effects in a forested landscape is important to ascertain burn severity, ecosystem recovery and post-fire hazard assessments and mitigation planning. Reporting of such post-fire effects assumes significance in fire-prone countries such as USA, Australia, Spain, Greece and Portugal where prescribed burns [...] Read more.
Quantifying post-fire effects in a forested landscape is important to ascertain burn severity, ecosystem recovery and post-fire hazard assessments and mitigation planning. Reporting of such post-fire effects assumes significance in fire-prone countries such as USA, Australia, Spain, Greece and Portugal where prescribed burns are routinely carried out. This paper describes the use of Terrestrial Laser Scanning (TLS) to estimate and map change in the forest understorey following a prescribed burn. Eighteen descriptive metrics are derived from bi-temporal TLS which are used to analyse and visualise change in a control and fire-altered plot. Metrics derived are Above Ground Height-based (AGH) percentiles and heights, point count and mean intensity. Metrics such as AGH50change, mean AGHchange and point countchange are sensitive enough to detect subtle fire-induced change (28%–52%) whilst observing little or no change in the control plot (0–4%). A qualitative examination with field measurements of the spatial distribution of burnt areas and percentage area burnt also show similar patterns. This study is novel in that it examines the behaviour of TLS metrics for estimating and mapping fire induced change in understorey structure in a single-scan mode with a minimal fixed reference system. Further, the TLS-derived metrics can be used to produce high resolution maps of change in the understorey landscape. Full article
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10121 KiB  
Article
Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions
by Maria Lanfredi, Rosa Coppola, Tiziana Simoniello, Rosa Coluzzi, Mariagrazia D'Emilio, Vito Imbrenda and Maria Macchiato
Remote Sens. 2015, 7(6), 8154-8179; https://doi.org/10.3390/rs70608154 - 19 Jun 2015
Cited by 54 | Viewed by 8764
Abstract
The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current [...] Read more.
The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002–2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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10207 KiB  
Article
Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region
by Sat Kumar Tomer, Ahmad Al Bitar, Muddu Sekhar, Mehrez Zribi, S. Bandyopadhyay, K. Sreelash, A.K. Sharma, Samuel Corgne and Yann Kerr
Remote Sens. 2015, 7(6), 8128-8153; https://doi.org/10.3390/rs70608128 - 18 Jun 2015
Cited by 74 | Viewed by 9167
Abstract
The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and [...] Read more.
The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m3/m3 for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m3/m3 and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available. Full article
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Article
Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data
by Abderrazak Bannari, Karl Staenz, Catherine Champagne and K. Shahid Khurshid
Remote Sens. 2015, 7(6), 8107-8127; https://doi.org/10.3390/rs70608107 - 18 Jun 2015
Cited by 35 | Viewed by 8305
Abstract
Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good [...] Read more.
Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features). Full article
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Editorial
Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing
by Duccio Rocchini
Remote Sens. 2015, 7(6), 8102-8106; https://doi.org/10.3390/rs70608102 - 18 Jun 2015
Cited by 2 | Viewed by 6416
Abstract
This Editorial introduces the papers published in the special issue “Earth Observation for Ecosystems Monitoring in Space and Time” which includes the most important researchers in the field and the most challenging aspects of the application of remote sensing to study ecosystems. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Article
Digging the METEOSAT Treasure—3 Decades of Solar Surface Radiation
by Richard Müller, Uwe Pfeifroth, Christine Träger-Chatterjee, Jörg Trentmann and Roswitha Cremer
Remote Sens. 2015, 7(6), 8067-8101; https://doi.org/10.3390/rs70608067 - 18 Jun 2015
Cited by 124 | Viewed by 10130
Abstract
Solar surface radiation data of high quality is essential for the appropriate monitoring and analysis of the Earth's radiation budget and the climate system. Further, they are crucial for the efficient planning and operation of solar energy systems. However, well maintained surface measurements [...] Read more.
Solar surface radiation data of high quality is essential for the appropriate monitoring and analysis of the Earth's radiation budget and the climate system. Further, they are crucial for the efficient planning and operation of solar energy systems. However, well maintained surface measurements are rare in many regions of the world and over the oceans. There, satellite derived information is the exclusive observational source. This emphasizes the important role of satellite based surface radiation data. Within this scope, the new satellite based CM-SAF SARAH (Solar surfAce RAdiation Heliosat) data record is discussed as well as the retrieval method used. The SARAH data are retrieved with the sophisticated SPECMAGIC method, which is based on radiative transfer modeling. The resulting climate data of solar surface irradiance, direct irradiance (horizontal and direct normal) and clear sky irradiance are covering 3 decades. The SARAH data set is validated with surface measurements of the Baseline Surface Radiation Network (BSRN) and of the Global Energy and Balance Archive (GEBA). Comparison with BSRN data is performed in order to estimate the accuracy and precision of the monthly and daily means of solar surface irradiance. The SARAH solar surface irradiance shows a bias of 1.3 \(W/m^2\) and a mean absolute bias (MAB) of 5.5 \(W/m^2\) for monthly means. For direct irradiance the bias and MAB is 1 \(W/m^2\) and 8.2 \(W/m^2\) respectively. Thus, the uncertainty of the SARAH data is in the range of the uncertainty of ground based measurements. In order to evaluate the uncertainty of SARAH based trend analysis the time series of SARAH monthly means are compared to GEBA. It has been found that SARAH enables the analysis of trends with an uncertainty of 1 \(W/m^2/dec\); a remarkable good result for a satellite based climate data record. SARAH has been also compared to its legacy version, the satellite based CM-SAF MVIRI climate data record. Overall, SARAH shows a significant higher accuracy and homogeneity than its legacy version. With its high accuracy and temporal and spatial resolution SARAH is well suited for regional climate monitoring and analysis as well as for solar energy applications. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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Article
Estimating Pasture Quality of Fresh Vegetation Based on Spectral Slope of Mixed Data of Dry and Fresh Vegetation—Method Development
by Rachel Lugassi, Alexandra Chudnovsky, Eli Zaady, Levana Dvash and Naftaly Goldshleger
Remote Sens. 2015, 7(6), 8045-8066; https://doi.org/10.3390/rs70608045 - 18 Jun 2015
Cited by 22 | Viewed by 7422
Abstract
The main objective of the present study was to apply a slope-based spectral method to both dry and fresh pasture vegetation. Differences in eight spectral ranges were identified across the near infrared-shortwave infrared (NIR-SWIR) that were indicative of changes in chemical properties. Slopes [...] Read more.
The main objective of the present study was to apply a slope-based spectral method to both dry and fresh pasture vegetation. Differences in eight spectral ranges were identified across the near infrared-shortwave infrared (NIR-SWIR) that were indicative of changes in chemical properties. Slopes across these ranges were calculated and a partial least squares (PLS) analytical model was constructed for the slopes vs. crude protein (CP) and neutral detergent fiber (NDF) contents. Different datasets with different numbers of fresh/dry samples were constructed to predict CP and NDF contents. When using a mixed-sample dataset with dry-to-fresh ratios of 85%:15% and 75%:25%, the correlations of CP (R2 = 0.95, in both) and NDF (R2 = 0.84 and 0.82, respectively) were almost as high as when using only dry samples (0.97 and 0.85, respectively). Furthermore, satisfactory correlations were obtained with a dry-to-fresh ratio of 50%:50% for CP (R2 = 0.92). The results of our study are especially encouraging because CP and NDF contents could be predicted even though some of the selected spectral regions were directly affected by atmospheric water vapor or water in the plants. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Article
Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain
by Alijafar Mousivand, Wout Verhoef, Massimo Menenti and Ben Gorte
Remote Sens. 2015, 7(6), 8019-8044; https://doi.org/10.3390/rs70608019 - 18 Jun 2015
Cited by 73 | Viewed by 12932
Abstract
Topography affects the fraction of direct and diffuse radiation received on a pixel and changes the sun–target–sensor geometry, resulting in variations in the observed radiance. Retrieval of surface–atmosphere properties from top of atmosphere radiance may need to account for topographic effects. This study [...] Read more.
Topography affects the fraction of direct and diffuse radiation received on a pixel and changes the sun–target–sensor geometry, resulting in variations in the observed radiance. Retrieval of surface–atmosphere properties from top of atmosphere radiance may need to account for topographic effects. This study investigates how such effects can be taken into account for top of atmosphere radiance modeling. In this paper, a system for top of atmosphere radiance modeling over heterogeneous non-Lambertian rugged terrain through radiative transfer modeling is presented. The paper proposes an extension of “the four-stream radiative transfer theory” (Verhoef and Bach 2003, 2007 and 2012) mainly aimed at representing topography-induced contributions to the top of atmosphere radiance modeling. A detailed account for BRDF effects, adjacency effects and topography effects on the radiance modeling is given, in which sky-view factor and non-Lambertian reflected radiance from adjacent slopes are modeled precisely. The paper also provides a new formulation to derive the atmospheric coefficients from MODTRAN with only two model runs, to make it more computationally efficient and also avoiding the use of zero surface albedo as used in the four-stream radiative transfer theory. The modeling begins with four surface reflectance factors calculated by the Soil–Leaf–Canopy radiative transfer model SLC at the top of canopy and propagates them through the effects of the atmosphere, which is explained by six atmospheric coefficients, derived from MODTRAN radiative transfer code. The top of the atmosphere radiance is then convolved with the sensor characteristics to generate sensor-like radiance. Using a composite dataset, it has been shown that neglecting sky view factor and/or terrain reflected radiance can cause uncertainty in the forward TOA radiance modeling up to 5 (mW/m2·sr·nm). It has also been shown that this level of uncertainty can be translated into an over/underestimation of more than 0.5 in LAI (or 0.07 in fCover) in variable retrieval. Full article
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4487 KiB  
Article
Estimating Leaf Bulk Density Distribution in a Tree Canopy Using Terrestrial LiDAR and a Straightforward Calibration Procedure
by François Pimont, Jean-Luc Dupuy, Eric Rigolot, Vincent Prat and Alexandre Piboule
Remote Sens. 2015, 7(6), 7995-8018; https://doi.org/10.3390/rs70607995 - 18 Jun 2015
Cited by 23 | Viewed by 6864 | Correction
Abstract
Leaf biomass distribution is a key factor for modeling energy and carbon fluxes in forest canopies and for assessing fire behavior. We propose a new method to estimate 3D leaf bulk density distribution, based on a calibration of indices derived from T-LiDAR. We [...] Read more.
Leaf biomass distribution is a key factor for modeling energy and carbon fluxes in forest canopies and for assessing fire behavior. We propose a new method to estimate 3D leaf bulk density distribution, based on a calibration of indices derived from T-LiDAR. We applied the method to four contrasted plots in a mature Quercus pubescens forest. Leaf bulk densities were measured inside 0.7 m-diameter spheres, referred to as Calibration Volumes. Indices were derived from LiDAR point clouds and calibrated over the Calibration Volume bulk densities. Several indices were proposed and tested to account for noise resulting from mixed pixels and other theoretical considerations. The best index and its calibration parameter were then used to estimate leaf bulk densities at the grid nodes of each plot. These LiDAR-derived bulk density distributions were used to estimate bulk density vertical profiles and loads and above four meters compared well with those assessed by the classical inventory-based approach. Below four meters, the LiDAR-based approach overestimated bulk densities since no distinction was made between wood and leaf returns. The results of our method are promising since they demonstrate the possibility to assess bulk density on small plots at a reasonable operational cost. Full article
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649 KiB  
Editorial
Remote Sensing and GIS for Habitat Quality Monitoring: New Approaches and Future Research
by András Zlinszky, Hermann Heilmeier, Heiko Balzter, Bálint Czúcz and Norbert Pfeifer
Remote Sens. 2015, 7(6), 7987-7994; https://doi.org/10.3390/rs70607987 - 17 Jun 2015
Cited by 51 | Viewed by 12891
Abstract
Habitat quality is the ability of the environment to provide conditions appropriate for individual and species persistence. Measuring or monitoring habitat quality requires complex integration of many properties of the ecosystem, where traditional terrestrial data collection methods have proven extremely time-demanding. Remote sensing [...] Read more.
Habitat quality is the ability of the environment to provide conditions appropriate for individual and species persistence. Measuring or monitoring habitat quality requires complex integration of many properties of the ecosystem, where traditional terrestrial data collection methods have proven extremely time-demanding. Remote sensing has known potential to map various ecosystem properties, also allowing rigorous checking of accuracy and supporting standardized processing. Our Special Issue presents examples where remote sensing has been successfully used for habitat mapping, quantification of habitat quality parameters, or multi-parameter modelling of habitat quality itself. New frontiers such as bathymetric scanning, grassland vegetation classification and operational use were explored, various new ecological verification methods were introduced and integration with ongoing habitat conservation schemes was demonstrated. These studies show that remote sensing and Geoinformation Science for habitat quality analysis have evolved from isolated experimental studies to an active field of research with a dedicated community. It is expected that these new methods will substantially contribute to biodiversity conservation worldwide. Full article
(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)
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Article
Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps
by François Waldner, Steffen Fritz, Antonio Di Gregorio and Pierre Defourny
Remote Sens. 2015, 7(6), 7959-7986; https://doi.org/10.3390/rs70607959 - 17 Jun 2015
Cited by 93 | Viewed by 12183
Abstract
Timely and accurate information on the global cropland extent is critical for applications in the fields of food security, agricultural monitoring, water management, land-use change modeling and Earth system modeling. On the one hand, it gives detailed location information on where to analyze [...] Read more.
Timely and accurate information on the global cropland extent is critical for applications in the fields of food security, agricultural monitoring, water management, land-use change modeling and Earth system modeling. On the one hand, it gives detailed location information on where to analyze satellite image time series to assess crop condition. On the other hand, it isolates the agriculture component to focus food security monitoring on agriculture and to assess the potential impacts of climate change on agricultural lands. The cropland class is often poorly captured in global land cover products due to its dynamic nature and the large variety of agro-systems. The overall objective was to evaluate the current availability of cropland datasets in order to propose a strategic planning and effort distribution for future cropland mapping activities and, therefore, to maximize their impact. Following a very comprehensive identification and collection of national to global land cover maps, a multi-criteria analysis was designed at the country level to identify the priority areas for cropland mapping. As a result, the analysis highlighted priority regions, such as Western Africa, Ethiopia, Madagascar and Southeast Asia, for the remote sensing community to focus its efforts. A Unified Cropland Layer at 250 m for the year 2014 was produced combining the fittest products. It was assessed using global validation datasets and yields an overall accuracy ranging from 82%–94%. Masking cropland areas with a global forest map reduced the commission errors from 46% down to 26%. Compared to the GLC-Share and the International Institute for Applied Systems Analysis-International Food Policy Research Institute (IIASA-IFPRI) cropland maps, significant spatial disagreements were found, which might be attributed to discrepancies in the cropland definition. This advocates for a shared definition of cropland, as well as global validation datasets relevant for the agriculture class in order to systematically assess existing and future cropland maps. Full article
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Article
Sensitivity of a Floodplain Hydrodynamic Model to Satellite-Based DEM Scale and Accuracy: Case Study—The Atchafalaya Basin
by Hahn Chul Jung and Michael F. Jasinski
Remote Sens. 2015, 7(6), 7938-7958; https://doi.org/10.3390/rs70607938 - 17 Jun 2015
Cited by 15 | Viewed by 6095
Abstract
The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions imposes more stringent requirements on the input Digital Elevation [...] Read more.
The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions imposes more stringent requirements on the input Digital Elevation Model (DEM) compared to upland regions with comparatively high slopes. This current study provides a systematic approach to evaluate the required relative vertical accuracy and spatial resolution of current and future satellite-based altimeters within the context of DEM requirements for 2-D floodplain hydrodynamic models. A case study is presented for the Atchafalaya Basin with a model domain of 1190 km2. The approach analyzes the sensitivity of modeled floodplain water elevation and velocity to typical satellite-based DEM grid-box scale and vertical error, using a previously calibrated version of the physically-based flood inundation model (LISFLOOD-ACC). Results indicate a trade-off relationship between DEM relative vertical error and grid-box size. Higher resolution models are the most sensitive to vertical accuracy, but the impact diminishes at coarser resolutions because of spatial averaging. The results provide guidance to engineers and scientists when defining the observation scales of future altimetry missions such as the Surface Water and Ocean Topography (SWOT) mission from the perspective of numerical modeling requirements for large floodplains of O[103] km2 and greater. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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Article
Retrieval of High-Resolution Atmospheric Particulate Matter Concentrations from Satellite-Based Aerosol Optical Thickness over the Pearl River Delta Area, China
by Lili Li, Jingxue Yang and Yunpeng Wang
Remote Sens. 2015, 7(6), 7914-7937; https://doi.org/10.3390/rs70607914 - 17 Jun 2015
Cited by 11 | Viewed by 7285
Abstract
Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD) in China. [...] Read more.
Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD) in China. This paper starts with examining ground observations of particulate matter (PM) and the relationship between PM10 (particles smaller than 10 μm) and aerosol optical thickness (AOT) by analyzing observations on the sampling sites in the PRD. A linear regression (R2 = 0.51) is carried out using MODIS-derived 500 m-resolution AOT and PM10 concentration from monitoring stations. Data of atmospheric boundary layer (ABL) height and relative humidity are used to make vertical and humidity corrections on AOT. Results after correction show higher correlations (R2 = 0.55) between extinction coefficient and PM10. However, coarse spatial resolution of meteorological data affects the smoothness of retrieved maps, which suggests high-resolution and accurate meteorological data are critical to increase retrieval accuracy of PM. Finally, the model provides the spatial distribution maps of instantaneous and yearly average PM10 over the PRD. It is proved that observed PM10 is more relevant to yearly mean AOT than instantaneous values. Full article
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6716 KiB  
Article
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory
by Caiyun Zhang, Yuhong Zhou and Fang Qiu
Remote Sens. 2015, 7(6), 7892-7913; https://doi.org/10.3390/rs70607892 - 16 Jun 2015
Cited by 125 | Viewed by 13723
Abstract
The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, [...] Read more.
The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Article
An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images
by Yuhan Rao, Xiaolin Zhu, Jin Chen and Jianmin Wang
Remote Sens. 2015, 7(6), 7865-7891; https://doi.org/10.3390/rs70607865 - 16 Jun 2015
Cited by 124 | Viewed by 12158
Abstract
Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation [...] Read more.
Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Linear Mixing Growth Model (NDVI-LMGM), is proposed to achieve the goal of accurately and efficiently blending MODIS NDVI time-series data and multi-temporal Landsat TM/ETM+ images. This method firstly unmixes the NDVI temporal changes in MODIS time-series to different land cover types and then uses unmixed NDVI temporal changes to predict Landsat-like NDVI dataset. The test over a forest site shows high accuracy (average difference: −0.0070; average absolute difference: 0.0228; and average absolute relative difference: 4.02%) and computation efficiency of NDVI-LMGM (31 seconds using a personal computer). Experiments over more complex landscape and long-term time-series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations. Comparisons between NDVI-LMGM and current methods (i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM) and Weighted Linear Model (WLM)) show that NDVI-LMGM is more accurate and efficient than current methods. The proposed method will benefit land surface process research, which requires a dense NDVI time-series dataset with high spatial resolution. Full article
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2887 KiB  
Article
Validation of Land Cover Products Using Reliability Evaluation Methods
by Wenzhong Shi, Xiaokang Zhang, Ming Hao, Pan Shao, Liping Cai and Xuzhe Lyu
Remote Sens. 2015, 7(6), 7846-7864; https://doi.org/10.3390/rs70607846 - 16 Jun 2015
Cited by 11 | Viewed by 5904
Abstract
Validation of land cover products is a fundamental task prior to data applications. Current validation schemes and methods are, however, suited only for assessing classification accuracy and disregard the reliability of land cover products. The reliability evaluation of land cover products should be [...] Read more.
Validation of land cover products is a fundamental task prior to data applications. Current validation schemes and methods are, however, suited only for assessing classification accuracy and disregard the reliability of land cover products. The reliability evaluation of land cover products should be undertaken to provide reliable land cover information. In addition, the lack of high-quality reference data often constrains validation and affects the reliability results of land cover products. This study proposes a validation schema to evaluate the reliability of land cover products, including two methods, namely, result reliability evaluation and process reliability evaluation. Result reliability evaluation computes the reliability of land cover products using seven reliability indicators. Process reliability evaluation analyzes the reliability propagation in the data production process to obtain the reliability of land cover products. Fuzzy fault tree analysis is introduced and improved in the reliability analysis of a data production process. Research results show that the proposed reliability evaluation scheme is reasonable and can be applied to validate land cover products. Through the analysis of the seven indicators of result reliability evaluation, more information on land cover can be obtained for strategic decision-making and planning, compared with traditional accuracy assessment methods. Process reliability evaluation without the need for reference data can facilitate the validation and reflect the change trends of reliabilities to some extent. Full article
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11436 KiB  
Article
Evaluation of BRDF Archetypes for Representing Surface Reflectance Anisotropy Using MODIS BRDF Data
by Hu Zhang, Ziti Jiao, Yadong Dong and Xiaowen Li
Remote Sens. 2015, 7(6), 7826-7845; https://doi.org/10.3390/rs70607826 - 15 Jun 2015
Cited by 23 | Viewed by 7935
Abstract
Bidirectional reflectance distribution function (BRDF) archetypes extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product over the global Earth Observing System Land Validation Core Sites can be used to simplify BRDF models. The present study attempts to evaluate the representativeness of BRDF [...] Read more.
Bidirectional reflectance distribution function (BRDF) archetypes extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product over the global Earth Observing System Land Validation Core Sites can be used to simplify BRDF models. The present study attempts to evaluate the representativeness of BRDF archetypes for surface reflectance anisotropy. Five-year forward-modeled MODIS multi-angular reflectance (MCD-ref) and aditional actual MODIS multi-angular observations (MCD-obs) in four growing periods in 2008 over three tiles were taken as validation data. First, BRDF archetypes in the principal plane were qualitatively compared with the time-series MODIS BRDF product of randomly sampled pixels. Secondly, BRDF archetypes were used to fit MCD-ref, and the average root-mean-squared errors (RMSEs) over each tile were examined for these five years. Finally, both BRDF archetypes and the MODIS BRDF were used to fit MCD-obs, and the histograms of the fit-RMSEs were compared. The consistency of the directional reflectance between the BRDF archetypes and MODIS BRDFs in nadir-view, hotspot and entire viewing hemisphere at 30° and 50° solar geometries were also examined. The results confirm that BRDF archetypes are representative of surface reflectance anisotropy for available snow-free MODIS data. Full article
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Technical Note
Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan
by Riccardo Tortini, Audrey L. Mayer and Pieralberto Maianti
Remote Sens. 2015, 7(6), 7809-7825; https://doi.org/10.3390/rs70607809 - 15 Jun 2015
Cited by 5 | Viewed by 6468
Abstract
Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the [...] Read more.
Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition. Full article
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
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2415 KiB  
Article
Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization
by Yi Long, Heng-Chao Li, Turgay Celik, Nathan Longbotham and William J. Emery
Remote Sens. 2015, 7(6), 7785-7808; https://doi.org/10.3390/rs70607785 - 12 Jun 2015
Cited by 6 | Viewed by 7553
Abstract
This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of [...] Read more.
This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of distorting a few pairwise distances. Specifically, the principle of equal variance is introduced to divide all hyperspectral bands into three subgroups and to ensure the energy is distributed uniformly between them, as in natural color images. Then, after detecting both normal and outlier pixels, these three subgroups are mapped into three color components of the output visualization using two different mapping (i.e., dimensionality reduction) schemes for the two types of pixels. The widely-used multidimensional scaling (MDS) is used for normal pixels and a new objective function, taking into account the weighting of pairwise distances, is presented for the outlier pixels. The pairwise distance weighting is designed such that small pairwise distances between the outliers and their respective neighbors are emphasized and large deviations are suppressed. This produces an image with high contrast and good interpretability while retaining the detailed information content. The proposed algorithm is compared with several state-of-the-art visualization techniques and evaluated on the well-known AVIRIS hyperspectral images. The effectiveness of the proposed strategy is substantiated both visually and quantitatively. Full article
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1228 KiB  
Article
A 10-Year Cloud Fraction Climatology of Liquid Water Clouds over Bern Observed by a Ground-Based Microwave Radiometer
by Federico Cossu, Klemens Hocke and Christian Mätzler
Remote Sens. 2015, 7(6), 7768-7784; https://doi.org/10.3390/rs70607768 - 11 Jun 2015
Cited by 14 | Viewed by 5813
Abstract
Cloud fraction (CF) is known as the dominant modulator of Earth’s radiative fluxes. Ground-based CF observations are useful to characterize the cloudiness of a specific site and are valuable for comparison with satellite observations and numerical models. We present for the first time [...] Read more.
Cloud fraction (CF) is known as the dominant modulator of Earth’s radiative fluxes. Ground-based CF observations are useful to characterize the cloudiness of a specific site and are valuable for comparison with satellite observations and numerical models. We present for the first time CF statistics (relative to liquid clouds only) for Bern, Switzerland, derived from the observations of a ground-based microwave radiometer. CF is derived with a new method involving the analysis of the integrated liquid water distribution measured by the radiometer. The 10-year analyzed period (2004–2013) allowed us to compute a CF climatology for Bern, showing a maximum CF of 60.9% in winter and a minimum CF of 42.0% in summer. The CF monthly anomalies are identified with respect to the climatological mean values, and they are confirmed through MeteoSwiss yearly climatological bulletins. The CF monthly mean variations are similar to the observations taken at another Swiss location, Payerne, suggesting a large-scale correlation between different sites on the Swiss Plateau. A CF diurnal cycle is also computed, and large intraseasonal variations are found. The overall mean CF diurnal cycle, however, shows a typical sinusoidal cycle, with higher values in the morning and lower values in the afternoon. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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789 KiB  
Article
An Algorithm for Surface Current Retrieval from X-band Marine Radar Images
by Chengxi Shen, Weimin Huang, Eric W. Gill, Ruben Carrasco and Jochen Horstmann
Remote Sens. 2015, 7(6), 7753-7767; https://doi.org/10.3390/rs70607753 - 11 Jun 2015
Cited by 38 | Viewed by 6685
Abstract
In this paper, a novel current inversion algorithm from X-band marine radar images is proposed. The routine, for which deep water is assumed, begins with 3-D FFT of the radar image sequence, followed by the extraction of the dispersion shell from the 3-D [...] Read more.
In this paper, a novel current inversion algorithm from X-band marine radar images is proposed. The routine, for which deep water is assumed, begins with 3-D FFT of the radar image sequence, followed by the extraction of the dispersion shell from the 3-D image spectrum. Next, the dispersion shell is converted to a polar current shell (PCS) using a polar coordinate transformation. After removing outliers along each radial direction of the PCS, a robust sinusoidal curve fitting is applied to the data points along each circumferential direction of the PCS. The angle corresponding to the maximum of the estimated sinusoid function is determined to be the current direction, and the amplitude of this sinusoidal function is the current speed. For validation, the algorithm is tested against both simulated radar images and field data collected by a vertically-polarized X-band system and ground-truthed with measurements from an acoustic Doppler current profiler (ADCP). From the field data, it is observed that when the current speed is less than 0.5 m/s, the root mean square differences between the radar-derived and the ADCP-measured current speed and direction are 7.3 cm/s and 32.7°, respectively. The results indicate that the proposed procedure, unlike most existing current inversion schemes, is not susceptible to high current speeds and circumvents the need to consider aliasing. Meanwhile, the relatively low computational cost makes it an excellent choice in practical marine applications. Full article
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7399 KiB  
Article
Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany
by Sandro Martinis and Christoph Rieke
Remote Sens. 2015, 7(6), 7732-7752; https://doi.org/10.3390/rs70607732 - 11 Jun 2015
Cited by 122 | Viewed by 13519
Abstract
In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as [...] Read more.
In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is covered by a time series of 39 TerraSAR-X data acquired within the time interval December 2009 to June 2013. The data set is supplemented by ALOS PALSAR L-band and RADARSAT-2 C-band data. The time series covers two inundations in January 2011 and June 2013 which allows evaluating backscatter variations between flood periods and normal water level conditions using different radar wavelengths. According to the results, there is potential in detecting flooding beneath vegetation in all microwave wavelengths, even in X-band for sparse vegetation or leaf-off forests. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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1235 KiB  
Article
A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece
by Foula Nioti, Fotios Xystrakis, Nikos Koutsias and Panayotis Dimopoulos
Remote Sens. 2015, 7(6), 7712-7731; https://doi.org/10.3390/rs70607712 - 10 Jun 2015
Cited by 13 | Viewed by 8741
Abstract
Management strategies and silvicultural treatments of fire-prone ecosystems often rely on knowledge of the regeneration potential and long-term recovery ability of vegetation types. Remote sensing and GIS applications are valuable tools providing cost-efficient information on vegetation recovery patterns and their associated environmental factors. [...] Read more.
Management strategies and silvicultural treatments of fire-prone ecosystems often rely on knowledge of the regeneration potential and long-term recovery ability of vegetation types. Remote sensing and GIS applications are valuable tools providing cost-efficient information on vegetation recovery patterns and their associated environmental factors. In this study we used an ordinal classification scheme to describe the land cover changes induced by a wildfire that occurred in 1983 in Pinus brutia woodlands on Karpathos Aegean Island, south-eastern Greece. As a proxy variable that indicates ecosystem recovery, we also estimated the difference between the NDVI and NBR indices a few months (1984) and almost 30 years after the fire (2012). Environmental explanatory variables were selected using a digital elevation model and various thematic maps. To identify the most influential environmental factors contributing to woodland recovery, binary logistic regression and linear regression techniques were applied. The analyses showed that although a large proportion of the P. brutia woodland has recovered 26 years after the fire event, a considerable amount of woodland had turned into scrub vegetation. Altitude, slope inclination, solar radiation, and pre-fire woodland physiognomy were identified as dominant factors influencing the vegetation’s recovery probability. Additionally, altitude and inclination are the variables that explain changes in the satellite remote sensing vegetation indices reflecting the recovery potential. Pinus brutia showed a good post-fire recovery potential, especially in parts of the study area with increased moisture availability. Full article
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4294 KiB  
Article
Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision
by Xiaojing Huang, Wen Yang, Haijian Zhang and Gui-Song Xia
Remote Sens. 2015, 7(6), 7695-7711; https://doi.org/10.3390/rs70607695 - 10 Jun 2015
Cited by 37 | Viewed by 6780
Abstract
The robust detection of ships is one of the key techniques in coastal and marine applications of synthetic aperture radar (SAR). Conventional SAR ship detectors involved multiple parameters, which need to be estimated or determined very carefully. In this paper, we propose a [...] Read more.
The robust detection of ships is one of the key techniques in coastal and marine applications of synthetic aperture radar (SAR). Conventional SAR ship detectors involved multiple parameters, which need to be estimated or determined very carefully. In this paper, we propose a new ship detection approach based on multi-scale heterogeneities under the a contrario decision framework, with a few parameters that can be easily determined. First, multi-scale heterogeneity features are extracted and fused to build a heterogeneity map, in which ships are well highlighted from backgrounds. Second, a set of reference objects are automatically selected by analyzing the saliency of local regions in the heterogeneity map and then are used to construct a null hypothesis model for the final decision. Finally, the detection results are obtained by using an a contrario decision. Experimental results on real SAR images demonstrate that the proposed method not only works more stably for ships with different sizes, but also has better performance than conventional ship detectors. Full article
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7010 KiB  
Technical Note
Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification
by Xiya Zhang, Peijun Li and Cai Cai
Remote Sens. 2015, 7(6), 7671-7694; https://doi.org/10.3390/rs70607671 - 9 Jun 2015
Cited by 20 | Viewed by 5963
Abstract
Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) [...] Read more.
Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) data, MODIS normalized difference vegetation index (NDVI) data, and land surface temperature (LST) data. We first analyzed how well MODIS NDVI and LST data quantify the properties of urban areas. Considering that urban area is the only class of interest, we applied the one-class support vector machine (OCSVM) to classify different combinations of the three datasets. We evaluated the effectiveness of the proposed method and compared with the locally optimized threshold method in regional urban extent mapping in China. The experimental results demonstrate that DMSP/OLS NTL data, MODIS NDVI and LST data provide different but complementary information sources to quantify the urban extent at a regional scale. The results also indicate that the OCSVM classification of the combination of all three datasets generally outperformed the locally optimized threshold method. The proposed method effectively and efficiently extracted the urban extent at a regional scale, and is applicable to other study areas. Full article
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5506 KiB  
Article
Provenance Information Representation and Tracking for Remote Sensing Observations in a Sensor Web Enabled Environment
by Zeqiang Chen and Nengcheng Chen
Remote Sens. 2015, 7(6), 7646-7670; https://doi.org/10.3390/rs70607646 - 9 Jun 2015
Cited by 3 | Viewed by 7463
Abstract
The provenance of observations from a Sensor Web enabled remote sensing application represents a great challenge. There are currently no representations or tracking methods. We propose a provenance method that represents and tracks remote sensing observations in the Sensor Web enabled environment. The [...] Read more.
The provenance of observations from a Sensor Web enabled remote sensing application represents a great challenge. There are currently no representations or tracking methods. We propose a provenance method that represents and tracks remote sensing observations in the Sensor Web enabled environment. The representation can be divided into the description model, encoding method, and service implementation. The description model uses a tuple to define four objects (sensor, data, processing, and service) and their relationships at a time point or interval. The encoding method incorporates the description into the Observations & Measurements specification of the Sensor Web. The service implementation addresses the effects of the encoding method on the implementation of Sensor Web services. The tracking method abstracts a common provenance algorithm and four algorithms that track the four objects (sensor, data, processing, and service) in a remote sensing observation application based on the representation. We conducted an experiment on the representation and tracking of provenance information for vegetation condition products, such as the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI). Our experiments used raw Moderate Resolution Imaging Spectroradiometer (MODIS) data to produce daily NDVI, weekly NDVI, and weekly VCI for the 48 contiguous states of the United States, for May from 2000 to 2012. We also implemented inverse tracking. We evaluated the time and space requirements of the proposed method in this scenario. Our results show that this technique provides a solution for determining provenance information in remote sensing observations. Full article
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Review
A Collection of SAR Methodologies for Monitoring Wetlands
by Lori White, Brian Brisco, Mohammed Dabboor, Andreas Schmitt and Andrew Pratt
Remote Sens. 2015, 7(6), 7615-7645; https://doi.org/10.3390/rs70607615 - 9 Jun 2015
Cited by 179 | Viewed by 16211
Abstract
Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way [...] Read more.
Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way for wetland resources because their data can be used to map and monitor changes in surface water extent, saturated soils, flooded vegetation, and changes in wetland vegetation cover. We review a few techniques to demonstrate SAR capabilities for wetland monitoring, including the commonly used method of grey-level thresholding for mapping surface water and highlighting changes in extent, and approaches for polarimetric decompositions to map flooded vegetation and changes from one class of land cover to another. We use the Curvelet-based change detection and the Wishart-Chernoff Distance approaches to show how they substantially improve mapping of flooded vegetation and flagging areas of change, respectively. We recommend that the increasing availability SAR data and the proven ability of these data to map various components of wetlands mean SAR should be considered as a critical component of a wetland monitoring system. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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6800 KiB  
Article
Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring
by Linlin Lu, Claudia Kuenzer, Cuizhen Wang, Huadong Guo and Qingting Li
Remote Sens. 2015, 7(6), 7597-7614; https://doi.org/10.3390/rs70607597 - 9 Jun 2015
Cited by 72 | Viewed by 11183
Abstract
Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution [...] Read more.
Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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