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Remote Sens., Volume 5, Issue 8 (August 2013), Pages 3637-4144

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Open AccessArticle Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models
Remote Sens. 2013, 5(8), 3637-3661; doi:10.3390/rs5083637
Received: 4 June 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 25 July 2013
Cited by 18 | PDF Full-text (4209 KB) | HTML Full-text | XML Full-text
Abstract
Leaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for
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Leaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades’ worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report. We focus on the average LAI and the length of the growing season on Northern Hemisphere over the period 1986–2005. Additionally we compare the results with previous analyses (Part I) of uncoupled land surface models (LSMs) to assess the relative contribution of vegetation and climatic drivers on the correct representation of LAI. Our results show that models tend to overestimate the average values of LAI and have a longer growing season due to the later dormancy. The similarities with the uncoupled models suggest that representing the correct vegetation fraction with the associated parameterizations; is more important in controlling the distribution and value of LAI than the climatic variables. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Hybrid Map-Based Navigation Method for Unmanned Ground Vehicle in Urban Scenario
Remote Sens. 2013, 5(8), 3662-3680; doi:10.3390/rs5083662
Received: 31 May 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 25 July 2013
Cited by 8 | PDF Full-text (2126 KB) | HTML Full-text | XML Full-text
Abstract
To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are
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To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Slope Stability Assessment of the Sarcheshmeh Landslide, Northeast Iran, Investigated Using InSAR and GPS Observations
Remote Sens. 2013, 5(8), 3681-3700; doi:10.3390/rs5083681
Received: 29 May 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 25 July 2013
Cited by 18 | PDF Full-text (1920 KB) | HTML Full-text | XML Full-text
Abstract
The detection and monitoring of mass movement of susceptible slopes plays a key role in mitigating hazards and potential damage associated with creeping slopes and landslides. In this paper, we use observations from both Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System
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The detection and monitoring of mass movement of susceptible slopes plays a key role in mitigating hazards and potential damage associated with creeping slopes and landslides. In this paper, we use observations from both Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) to assess the slope stability of the Sarcheshmeh ancient landslide in the North Khorasan province of northeast Iran. InSAR observations were obtained by the time-series analysis of Envisat SAR images covering 2004–2006, whereas repeated GPS observations were conducted by campaign measurements during 2010–2012. Surface displacement maps of the Sarcheshmeh landslide obtained from InSAR and GPS are both indicative of slope stability. Hydrogeological analysis suggests that the multi-year drought and lower than average precipitation levels over the last decade might have contributed to the current dormancy of the Sarcheshmeh landslide. Full article
Open AccessArticle Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating
Remote Sens. 2013, 5(8), 3701-3728; doi:10.3390/rs5083701
Received: 27 May 2013 / Revised: 12 July 2013 / Accepted: 16 July 2013 / Published: 30 July 2013
Cited by 5 | PDF Full-text (5928 KB) | HTML Full-text | XML Full-text
Abstract
In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified
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In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified into three main object classes: permanently static, temporarily static and mobile, using a new point matching technique. The temporarily static and mobile objects are then removed from the 3D point clouds, leaving behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from successive passages (in the same place) on different days and at different times are then matched together to complete the 3D urban landscape. The changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity, and the resulting 3D cartography is progressively modified accordingly. The specialized functions introduced help to remove the different imperfections, due to occlusions, misclassifications and different changes occurring in the environment over time, thus ncreasing the robustness of the method. The results, evaluated on real data, demonstrate that not only is the resulting 3D cartography accurate, containing only the exact permanent features free from imperfections, but the method is also suitable for handling large urban scenes. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data
Remote Sens. 2013, 5(8), 3729-3748; doi:10.3390/rs5083729
Received: 21 May 2013 / Revised: 8 July 2013 / Accepted: 19 July 2013 / Published: 30 July 2013
Cited by 6 | PDF Full-text (1357 KB) | HTML Full-text | XML Full-text
Abstract
Thermal remote sensing of soil moisture in vineyards is a challenge. The grass-covered soil, in addition to a standing grape canopy, create complex patterns of heating and cooling and increase the surface temperature variability between vine rows. In this study, we evaluate the
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Thermal remote sensing of soil moisture in vineyards is a challenge. The grass-covered soil, in addition to a standing grape canopy, create complex patterns of heating and cooling and increase the surface temperature variability between vine rows. In this study, we evaluate the strength of relationships between soil moisture, mechanical resistance and thermal inertia calculated from the drop of surface temperature during a clear sky night over a vineyard in the Niagara region. We utilized data from two sensors, an airborne thermal camera (height ≈ 500 m a.g.l.) and a handheld thermal gun (height ≈ 1 m a.g.l.), to explore the effects of different field of views and the high inter-row temperature variability. Spatial patterns of soil moisture correlated more with estimated thermal inertia than with surface temperature recorded at sunrise or sunset. Despite the coarse resolution of airborne thermal inertia images, it performed better than estimates from the handheld thermal gun. Between-row variation was further analyzed using a linear mixed-effects model. Despite the limited spatial variability of soil properties within a single vineyard, the magnitudes of the model coefficients for soil moisture and mechanical resistance are encouraging indicators of the utility of thermal inertia in vineyard management. Full article
Open AccessArticle SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas
Remote Sens. 2013, 5(8), 3749-3775; doi:10.3390/rs5083749
Received: 4 May 2013 / Revised: 17 July 2013 / Accepted: 18 July 2013 / Published: 31 July 2013
Cited by 17 | PDF Full-text (2370 KB) | HTML Full-text | XML Full-text
Abstract
Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed
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Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Application of Landsat to Evaluate Effects of Irrigation Forbearance
Remote Sens. 2013, 5(8), 3776-3802; doi:10.3390/rs5083776
Received: 15 June 2013 / Revised: 26 July 2013 / Accepted: 30 July 2013 / Published: 2 August 2013
Cited by 12 | PDF Full-text (4624 KB) | HTML Full-text | XML Full-text
Abstract
Thirty-meter resolution Landsat data were used to evaluate the effects of irrigation management in the Wood River Valley, Upper Klamath Basin, Oregon. In an effort to reduce water use and leave more of the water resource in-stream, 4,674 ha of previously flood irrigated
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Thirty-meter resolution Landsat data were used to evaluate the effects of irrigation management in the Wood River Valley, Upper Klamath Basin, Oregon. In an effort to reduce water use and leave more of the water resource in-stream, 4,674 ha of previously flood irrigated pasture was managed as dryland pasture. Ground-based measurements over one irrigated and one unirrigated pasture site were used to monitor the difference in evapotranspiration (ET) using the Bowen ratio-energy balance method. These data sets represent point measurements of the response to irrigation, but do not allow for the spatial integration of effects of irrigated versus unirrigated land treatment. Four Landsat scenes of the Wood River Valley during the 2004 growing season were evaluated using reconstructed METRIC algorithms. Comparisons of ET algorithm output with ground-based data for all components of the energy balance, including net radiation, soil heat flux, sensible heat flux and evapotranspiration, were made for the four scenes. The excellent net radiation estimates, along with reasonable estimates of the other components, are demonstrated along with the capability to integrate results to the basin scale. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle Disentangling the Relationships between Net Primary Production and Precipitation in Southern Africa Savannas Using Satellite Observations from 1982 to 2010
Remote Sens. 2013, 5(8), 3803-3825; doi:10.3390/rs5083803
Received: 18 June 2013 / Revised: 29 July 2013 / Accepted: 29 July 2013 / Published: 2 August 2013
Cited by 16 | PDF Full-text (1391 KB) | HTML Full-text | XML Full-text
Abstract
To obtain a better understanding of the variability in net primary production (NPP) in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in
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To obtain a better understanding of the variability in net primary production (NPP) in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in NPP in southern African savannas from 1982 to 2010, and disentangled the relationships between NPP and precipitation by land cover classes and mean annual precipitation (MAP) gradients. Specifically, we evaluate the utility of the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) dataset, in comparison with Moderate-resolution Imaging Spectroradiometer (MODIS) derived NPP products, and find strong relationships between the overlapping data periods (2000–2010), such that we can apply our model to derive NPP estimates to the full 29-year NDVI time-series. Generally, the northern portion of the study area is characterized by high NPP and low variability, whereas the southern portion is characteristic of low NPP and high variability. During the period 1982 through 2010, NPP has reduced at a rate of −2.13 g∙C∙m−2∙yr−1 (p < 0.1), corresponding to a decrease of 6.7% over 29 years, and about half of bush and grassland savanna has experienced a decrease in NPP. There is a significant positive relationship between mean annual NPP and MAP in bush and grassland savannas, but no significant relationship is observed in tree savannas. The relationship between mean annual NPP and MAP varies with increases in MAP, characterized as a linear relationship that breaks down when MAP exceeding around 850–900 mm. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Dynamic Assessment of Soil Erosion Risk Using Landsat TM and HJ Satellite Data in Danjiangkou Reservoir Area, China
Remote Sens. 2013, 5(8), 3826-3848; doi:10.3390/rs5083826
Received: 15 June 2013 / Revised: 25 July 2013 / Accepted: 26 July 2013 / Published: 2 August 2013
Cited by 7 | PDF Full-text (1485 KB) | HTML Full-text | XML Full-text
Abstract
Danjiangkou reservoir area is the main water source and the submerged area of the Middle Route South-to-North Water Transfer Project of China. Soil erosion is a factor that significantly influences the quality and transfer of water from the Danjiangkou reservoir. The objective of
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Danjiangkou reservoir area is the main water source and the submerged area of the Middle Route South-to-North Water Transfer Project of China. Soil erosion is a factor that significantly influences the quality and transfer of water from the Danjiangkou reservoir. The objective of this study is to assess the water erosion (rill and sheet erosion) risk and dynamic change trend of spatial distribution in erosion status and intensity between 2004 and 2010 in the Danjiangkou reservoir area using a multicriteria evaluation method.The multicriteria evaluation method synthesizes the vegetation fraction cover, slope gradient, and land use. Based on the rules and erosion risk assessment results of the study area in 2004 and 2010, the research obtained the conservation priority map. This study result shows an improvement in erosion status of the study area, the eroded area decreased from 32.1% in 2004 to 25.43% in 2010. The unchanged regions dominated the study area and that the total area of improvement grade erosion was larger than that of deterioration grade erosion. The severe, more severe, and extremely severe areas decreased by 4.71%, 2.28%, and 0.61% of the total study area, respectively. The percentages of regions where erosion grade transformed from extremely severe to slight, light and moderate were 0.18%, 0.02%, and 0.30%, respectively. However, a deteriorated region with a 2,897.60 km2 area was still observed. This area cannot be ignored in the determination of a general governance scheme. The top two conservation priority levels cover almost all regions with severe erosion and prominent increase in erosion risk, accounting for 7.31% of the study area. The study results can assist government agencies in decision making for determining erosion control areas, starting regulation projects, and making soil conservation measures. Full article
Open AccessArticle Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index
Remote Sens. 2013, 5(8), 3849-3871; doi:10.3390/rs5083849
Received: 26 June 2013 / Revised: 22 July 2013 / Accepted: 22 July 2013 / Published: 5 August 2013
Cited by 13 | PDF Full-text (2035 KB) | HTML Full-text | XML Full-text
Abstract
Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on
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Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle Spectrometer for Sky-Scanning Sun-Tracking Atmospheric Research (4STAR): Instrument Technology
Remote Sens. 2013, 5(8), 3872-3895; doi:10.3390/rs5083872
Received: 14 June 2013 / Revised: 22 July 2013 / Accepted: 23 July 2013 / Published: 6 August 2013
Cited by 10 | PDF Full-text (1612 KB) | HTML Full-text | XML Full-text
Abstract
The Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) combines airborne sun tracking and sky scanning with diffraction spectroscopy to improve knowledge of atmospheric constituents and their links to air-pollution/climate. Direct beam hyper-spectral measurement of optical depth improves retrievals of gas constituents and determination
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The Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) combines airborne sun tracking and sky scanning with diffraction spectroscopy to improve knowledge of atmospheric constituents and their links to air-pollution/climate. Direct beam hyper-spectral measurement of optical depth improves retrievals of gas constituents and determination of aerosol properties. Sky scanning enhances retrievals of aerosol type and size distribution. 4STAR measurements will tighten the closure between satellite and ground-based measurements. 4STAR incorporates a modular sun-tracking/ sky-scanning optical head with fiber optic signal transmission to rack mounted spectrometers, permitting miniaturization of the external optical head, and future detector evolution. Technical challenges include compact optical collector design, radiometric dynamic range and stability, and broad spectral coverage. Test results establishing the performance of the instrument against the full range of operational requirements are presented, along with calibration, engineering flight test, and scientific field campaign data and results. Full article
(This article belongs to the Special Issue Optical Remote Sensing of the Atmosphere)
Open AccessArticle Exploitation of Large Archives of ERS and ENVISAT C-Band SAR Data to Characterize Ground Deformations
Remote Sens. 2013, 5(8), 3896-3917; doi:10.3390/rs5083896
Received: 8 June 2013 / Revised: 31 July 2013 / Accepted: 1 August 2013 / Published: 8 August 2013
Cited by 28 | PDF Full-text (2920 KB) | HTML Full-text | XML Full-text
Abstract
In the last few years, several advances have been made in the use of radar images to detect, map and monitor ground deformations. DInSAR (Differential Synthetic Aperture Radar Interferometry) and A-DInSAR/PSI (Advanced DInSAR/Persistent Scatterers Interferometry) technologies have been successfully applied in the study
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In the last few years, several advances have been made in the use of radar images to detect, map and monitor ground deformations. DInSAR (Differential Synthetic Aperture Radar Interferometry) and A-DInSAR/PSI (Advanced DInSAR/Persistent Scatterers Interferometry) technologies have been successfully applied in the study of deformation phenomena induced by, for example, active tectonics, volcanic activity, ground water exploitation, mining, and landslides, both at local and regional scales. In this paper, the existing European Space Agency (ESA) archives (acquired as part of the FP7-DORIS project), which were collected by the ERS-1/2 and ENVISAT satellites operating in the microwave C-band, were analyzed and exploited to understand the dynamics of landslide and subsidence phenomena. In particular, this paper presents the results obtained as part of the FP7-DORIS project to demonstrate that the full exploitation of very long deformation time series (more than 15 years) can play a key role in understanding the dynamics of natural and human-induced hazards. Full article
Open AccessArticle Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales
Remote Sens. 2013, 5(8), 3918-3950; doi:10.3390/rs5083918
Received: 24 May 2013 / Revised: 23 July 2013 / Accepted: 23 July 2013 / Published: 8 August 2013
Cited by 34 | PDF Full-text (4564 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models.
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Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability in the new 30.5-year long global satellite-derived surface reflectance index data, Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). Pearson’s correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation anomalies. Temperature correlations were strongest in northern mid- to high-latitudes in the spring and early summer where up to 70% of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America, winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wet season precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data
Remote Sens. 2013, 5(8), 3951-3970; doi:10.3390/rs5083951
Received: 21 June 2013 / Revised: 30 July 2013 / Accepted: 1 August 2013 / Published: 9 August 2013
Cited by 11 | PDF Full-text (11435 KB) | HTML Full-text | XML Full-text
Abstract
We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface
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We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99); however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively. Full article
Open AccessArticle Ground-Based Hyperspectral Characterization of Alaska Tundra Vegetation along Environmental Gradients
Remote Sens. 2013, 5(8), 3971-4005; doi:10.3390/rs5083971
Received: 20 June 2013 / Revised: 31 July 2013 / Accepted: 5 August 2013 / Published: 9 August 2013
Cited by 4 | PDF Full-text (1888 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing has become a valuable tool in monitoring arctic environments. The aim of this paper is ground-based hyperspectral characterization of Low Arctic Alaskan tundra communities along four environmental gradients (regional climate, soil pH, toposequence, and soil moisture) that all vary in ground
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Remote sensing has become a valuable tool in monitoring arctic environments. The aim of this paper is ground-based hyperspectral characterization of Low Arctic Alaskan tundra communities along four environmental gradients (regional climate, soil pH, toposequence, and soil moisture) that all vary in ground cover, biomass, and dominating plant communities. Field spectroscopy in connection with vegetation analysis was carried out in summer 2012, along the North American Arctic Transect (NAAT). Spectral metrics were extracted, including the averaged reflectance and absorption-related metrics such as absorption depths and area of continuum removal. The spectral metrics were investigated with respect to “greenness”, biomass, vegetation height, and soil moisture regimes. The results show that the surface reflectances of all sites are similar in shape with a reduced near-infrared (NIR) reflectance that is specific for low-growing biomes. The main spectro-radiometric findings are: (i) Southern sites along the climate gradient have taller shrubs and greater overall vegetation biomass, which leads to higher reflectance in the NIR. (ii) Vegetation height and surface wetness are two antagonists that balance each other out with respect to the NIR reflectance along the toposequence and soil moisture gradients. (iii) Moist acidic tundra (MAT) sites have “greener” species, more leaf biomass, and green-colored moss species that lead to higher pigment absorption compared to moist non-acidic tundra (MNT) sites. (iv) MAT and MNT plant community separation via narrowband Normalized Difference Vegetation Index (NDVI) shows the potential of hyperspectral remote sensing applications in the tundra. Full article
Open AccessArticle Physical Reflectivity and Polarization Characteristics for Snow and Ice-Covered Surfaces Interacting with GPS Signals
Remote Sens. 2013, 5(8), 4006-4030; doi:10.3390/rs5084006
Received: 31 May 2013 / Revised: 22 July 2013 / Accepted: 23 July 2013 / Published: 9 August 2013
Cited by 17 | PDF Full-text (899 KB) | HTML Full-text | XML Full-text
Abstract
The Global Positioning System (GPS) reflected signal has been demonstrated to remotely sense the oceans, land surfaces and the cryosphere, including measuring snow depth, soil moisture, vegetation growth and wind direction. Since the Earth surface’s characteristics are very complex, the surface reflectivity process
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The Global Positioning System (GPS) reflected signal has been demonstrated to remotely sense the oceans, land surfaces and the cryosphere, including measuring snow depth, soil moisture, vegetation growth and wind direction. Since the Earth surface’s characteristics are very complex, the surface reflectivity process and interaction with GPS signals is not well understood. In this study, we investigate the surface’s reflectivity and variability of snow and ice surfaces interacting with GPS L1 and L2 signals in order to retrieve multipath signals and infer surface characteristics by using the direct and reflected polarizations of each signal. Firstly, the effects of both GPS satellite elevation angle and GPS receiver’s antenna height variations on the multipath signal variability have been investigated by numerical formulations. Secondly, the specular reflection coefficients’ features and the total surface polarization for liquid and solid surfaces are discussed. Moreover, the linear polarization and circular polarizations (co-polarized and cross-polarized) as well as their corresponding convolution functions are developed horizontally and vertically. The results show that the multipath signals are more sensitive to the satellite elevation angle variations than to changes in the GPS receiver’s antenna height. The convolution function demonstrates that the snowy surface has a minimum reflectance in circular polarization but maximum reflectance in linear polarization. GPS signals reflecting from an ice-covered surface show a maximum value in circular polarization reflectance and a minimum for linear polarization reflectance. Moreover, the values for reflection from soils are between those for snow and ice in all polarization types. The placement of soil surface reflectance values between snowy and icy surface ones may be noteworthy in new remote sensing applications. Full article
Open AccessArticle A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982–2008
Remote Sens. 2013, 5(8), 4031-4044; doi:10.3390/rs5084031
Received: 9 June 2013 / Revised: 18 July 2013 / Accepted: 6 August 2013 / Published: 12 August 2013
Cited by 20 | PDF Full-text (1074 KB) | HTML Full-text | XML Full-text
Abstract
The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies
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The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager
Remote Sens. 2013, 5(8), 4045-4066; doi:10.3390/rs5084045
Received: 20 June 2013 / Revised: 6 August 2013 / Accepted: 8 August 2013 / Published: 13 August 2013
Cited by 43 | PDF Full-text (9332 KB) | HTML Full-text | XML Full-text
Abstract
The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a
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The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy. Full article
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Open AccessArticle Unravelling Eastern Pacific and Central Pacific ENSO Contributions in South Pacific Chlorophyll-a Variability through Remote Sensing
Remote Sens. 2013, 5(8), 4067-4087; doi:10.3390/rs5084067
Received: 18 June 2013 / Revised: 22 July 2013 / Accepted: 5 August 2013 / Published: 13 August 2013
Cited by 4 | PDF Full-text (1836 KB) | HTML Full-text | XML Full-text
Abstract
El Niño—Southern Oscillation (ENSO) is regarded as the main driver of phytoplankton inter-annual variability. Remotely sensed surface chlorophyll-a (Chl-a), has made it possible to examine phytoplankton variability at a resolution and scale that allows for the investigation of climate signals
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El Niño—Southern Oscillation (ENSO) is regarded as the main driver of phytoplankton inter-annual variability. Remotely sensed surface chlorophyll-a (Chl-a), has made it possible to examine phytoplankton variability at a resolution and scale that allows for the investigation of climate signals such as ENSO. We provide empirical evidence of an immediate and lagged influence of ENSO on SeaWiFS and MODIS-Aqua derived global Chl-a concentrations. We use 13 years of Chl-a remotely sensed observations along with sea surface temperature (SST) observations across the Tropical and South Pacific to isolate and examine the spatial development of Chl-a anomalies during ENSO: its canonical or eastern Pacific (EP) mode, and El Niño Modoki or central Pacific (CP) mode, using the extended empirical orthogonal function (EEOF) technique. We describe how an EP ENSO phase transition affects Chl-a, and identify an interannual CP mode of variability induced spatial pattern. We argue that when ENSO is analysed as a propagating signal by the EEOF, CP ENSO is found to be more influential on Chl-a interannual to decadal variability than the canonical EP ENSO. Our results cannot confirm the independence of the two ENSO modes but clearly demonstrate that both ENSO flavors manifest a distinct biological response. Full article
(This article belongs to the Special Issue Observing the Ocean’s Interior from Satellite Remote Sensing)
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Open AccessArticle Correlation between Synthetic Aperture Radar Surface Winds and Deep Water Velocity in the Amundsen Sea, Antarctica
Remote Sens. 2013, 5(8), 4088-4106; doi:10.3390/rs5084088
Received: 7 June 2013 / Revised: 10 July 2013 / Accepted: 6 August 2013 / Published: 16 August 2013
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Abstract
The recent observed thinning of the glacier ice shelves in the Amundsen Sea (Antarctica) has been attributed to warm deep currents, possibly induced by along-coast winds in the vicinity of the glacial ice sheet. Here, high resolution maps of wind fields derived from
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The recent observed thinning of the glacier ice shelves in the Amundsen Sea (Antarctica) has been attributed to warm deep currents, possibly induced by along-coast winds in the vicinity of the glacial ice sheet. Here, high resolution maps of wind fields derived from Synthetic Aperture Radar (SAR) data have been studied and correlated with subsurface measurements of the deep water velocities in the Amundsen Sea area. Focus is on periods with low ice coverage in 2010 and 2011. In 2010, which had comparatively low ice coverage, the results indicate a more rapid response to wind forcing in the deep currents than in 2011. The SAR wind speed maps have better spatial resolution than available reanalysis data, and higher maximum correlation was obtained with SAR data than with reanalysis data despite the lower temporal resolution. The maximum correlation was R = 0.71, in a direction that is consistent with wind-driven Ekman theory. This is significantly larger than in previous studies. The larger correlation could be due to the better spatial resolution or the restriction to months with minimum ice coverage. The results indicate that SAR is a useful complement to infer the subsurface variability of the ocean circulation in remote areas in polar oceans. Full article
(This article belongs to the Special Issue Observing the Ocean’s Interior from Satellite Remote Sensing)
Open AccessArticle Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal
Remote Sens. 2013, 5(8), 4107-4123; doi:10.3390/rs5084107
Received: 26 June 2013 / Revised: 12 August 2013 / Accepted: 13 August 2013 / Published: 16 August 2013
Cited by 19 | PDF Full-text (443 KB) | HTML Full-text | XML Full-text
Abstract
Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that
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Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM) 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP), the Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)) were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle Assessment Impacts of Weather and Land Use/Land Cover (LULC) Change on Urban Vegetation Net Primary Productivity (NPP): A Case Study in Guangzhou, China
Remote Sens. 2013, 5(8), 4125-4144; doi:10.3390/rs5084125
Received: 22 June 2013 / Revised: 13 August 2013 / Accepted: 13 August 2013 / Published: 20 August 2013
Cited by 4 | PDF Full-text (1328 KB) | HTML Full-text | XML Full-text
Abstract
Net primary productivity (NPP) can indicate vegetation ecosystem services ability and reflect variation response to climate change and human activities. This study applied MODIS-1 km NPP products to investigate the NPP variation from 2001 to 2006, a fast urban expansion and adjustment period
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Net primary productivity (NPP) can indicate vegetation ecosystem services ability and reflect variation response to climate change and human activities. This study applied MODIS-1 km NPP products to investigate the NPP variation from 2001 to 2006, a fast urban expansion and adjustment period in Guangzhou, China, and quantify the impacts of weather and land use/land cover (LULC) changes, respectively. The results showed that the NPP mean value increased at a rate of 11.6 g∙C∙m−2∙yr−1 during the initial three years and decreased at an accelerated rate of 31.0 g∙C∙m−2∙yr−1 during the final three years, resulting in a total NPP loss of approximately 167 × 106 g∙C. The spatiotemporal of NPP varied obviously in the central area, suburb and exurb of Guangzhou driven by three patterns of weather and LULC changes. By the interactive effects and the weather variation dominated effects, NPP of most areas changed slightly with dynamic index less than 5% of NPP mean value in the central area and the suburb. The LULC change dominated effects caused obvious NPP reduction, by more than 15% of the NPP mean value, which occurred in some areas of the suburb and extended to the exurb with the outward urban sprawl. Importantly, conversion from wood grassland, shrublands and even forests to croplands occupied by urban landscapes proved to be a main process in the conversion from high-NPP coverage to low-NPP coverage, thereby leading to the rapid degradation of urban carbon stock capacity in urban fringe areas. It is helpful for government to monitor urban ecological health and safety and make relevant policies. Full article
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Open AccessCorrection Correction: Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sens. 2013, 5, 949–981
Remote Sens. 2013, 5(8), 4124; doi:10.3390/rs5084124
Received: 26 July 2013 / Revised: 2 August 2013 / Accepted: 9 August 2013 / Published: 16 August 2013
Cited by 2 | PDF Full-text (110 KB) | HTML Full-text | XML Full-text
Abstract The author mistakenly spelt Nadine Brisson as Nadine Gobron in the Acknowledgements of [1]. [...] Full article

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