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Remote Sens., Volume 8, Issue 11 (November 2016)

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Cover Story (view full-size image) The first operational Sentinel-2 data service platform for obtaining atmospherically-corrected [...] Read more.
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Open AccessArticle
Climate-Induced Extreme Hydrologic Events in the Arctic
Remote Sens. 2016, 8(11), 971; https://doi.org/10.3390/rs8110971
Received: 30 August 2016 / Revised: 17 November 2016 / Accepted: 21 November 2016 / Published: 23 November 2016
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Abstract
The objectives were (i) to evaluate the relationship between recent climate change and extreme hydrological events and (ii) to characterize the behavior of hydrological events along the Alazeya River. The warming rate of air temperature observed at the meteorological station in Chersky was [...] Read more.
The objectives were (i) to evaluate the relationship between recent climate change and extreme hydrological events and (ii) to characterize the behavior of hydrological events along the Alazeya River. The warming rate of air temperature observed at the meteorological station in Chersky was 0.0472 °C·year−1, and an extraordinary increase in air temperatures was observed in 2007. However, data from meteorological stations are somewhat limited in sparsely populated regions. Therefore, this study employed historical remote sensing data for supplementary information. The time-series analysis of the area-averaged Global Precipitation Climatology Project (GPCP) precipitation showed a positive trend because warming leads to an increase in the water vapor content in the atmosphere. In particular, heavy precipitation of 459 ± 113 mm was observed in 2006. On the other hand, the second-highest summer National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution radiometer (AVHRR) brightness temperature (BT) was observed in 2007 when the highest air temperature was observed in Chersky, and the anomaly from normal revealed that the summer AVHRR BTs showed mostly positive values. Conversely, riverbank, lakeshore and seashore areas were much cooler due to the formation, expansion and drainage of lakes and/or the increase in water level by heavy precipitation and melting of frozen ground. The large lake drainage resulted in a flood. Although the flooding was triggered by the thermal erosion along the riverbanks and lakeshores—itself induced by the heat wave in 2007—the increase in soil water content due to the heavy precipitation in 2006 appeared to contribute the magnitude of flood. The flood was characterized by the low streamflow velocity because the Kolyma Lowlands had a very gentle gradient. Therefore, the flood continued for a long time over large areas. Information based on remote sensing data gave basic insights for understanding the mechanism and behavior of climate-induced extreme hydrologic events. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle
Modeling and Reconstruction of Time Series of Passive Microwave Data by Discrete Fourier Transform Guided Filtering and Harmonic Analysis
Remote Sens. 2016, 8(11), 970; https://doi.org/10.3390/rs8110970
Received: 26 July 2016 / Revised: 7 November 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
Cited by 2 | Viewed by 1997 | PDF Full-text (8015 KB) | HTML Full-text | XML Full-text
Abstract
Daily time series of microwave radiometer data obtained in one-orbit direction are full of observation gaps due to satellite configuration and errors from spatial sampling. Such time series carry information about the surface signal including surface emittance and vegetation attenuation, and the atmospheric [...] Read more.
Daily time series of microwave radiometer data obtained in one-orbit direction are full of observation gaps due to satellite configuration and errors from spatial sampling. Such time series carry information about the surface signal including surface emittance and vegetation attenuation, and the atmospheric signal including atmosphere emittance and atmospheric attenuation. To extract the surface signal from this noisy time series, the Time Series Analysis Procedure (TSAP) was developed, based on the properties of the Discrete Fourier Transform (DFT). TSAP includes two stages: (1) identify the spectral features of observation gaps and errors and remove them with a modified boxcar filter; and (2) identify the spectral features of the surface signal and reconstruct it with the Harmonic Analysis of Time Series (HANTS) algorithm. Polarization Difference Brightness Temperature (PDBT) at 37 GHz data were used to illustrate the problems, to explain the implementation of TSAP and to validate this method, due to the PDBT sensitivity to the water content both at the land surface and in the atmosphere. We carried out a case study on a limited heterogeneous crop land and lake area, where the power spectrum of the PDBT time series showed that the harmonic components associated with observation gaps and errors have periods ≤8 days. After applying the modified boxcar filter with a length of 10 days, the RMSD between raw and filtered time series was above 11 K, mainly related to the power reduction in the frequency range associated with observation gaps and errors. Noise reduction is beneficial when applying PDBT observations to monitor wet areas and open water, since the PDBT range between dryland and open water is about 20 K. The spectral features of the atmospheric signal can be revealed by time series analysis of rain-gauge data, since the PDBT at 37 GHz is mainly attenuated by hydrometeors that yield precipitation. Thus, the spectral features of the surface signal were identified in the PDBT time series with the help of the rain-gauge data. HANTS reconstructed the upper envelope of the signal, i.e., correcting for atmospheric influence, while retaining the spectral features of the surface signal. To evaluate the impact of TSAP on retrieval accuracy, the fraction of Water Saturated Surface (WSS) in the region of Poyang Lake was retrieved with 37 GHz observations. The retrievals were evaluated against estimations of the lake area obtained with MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Synthetic Aperture Radar (ASAR) data. The Relative RMSE on WSS was 39.5% with unfiltered data and 23% after applying TSAP, i.e., using the estimated surface signal only. Full article
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Open AccessArticle
Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data
Remote Sens. 2016, 8(11), 969; https://doi.org/10.3390/rs8110969
Received: 25 July 2016 / Revised: 7 November 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
Cited by 5 | Viewed by 2049 | PDF Full-text (10174 KB) | HTML Full-text | XML Full-text
Abstract
Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and [...] Read more.
Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B14) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B14, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B14, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features. Full article
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Open AccessArticle
Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland
Remote Sens. 2016, 8(11), 968; https://doi.org/10.3390/rs8110968
Received: 27 August 2016 / Revised: 14 November 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
Cited by 25 | Viewed by 3007 | PDF Full-text (3301 KB) | HTML Full-text | XML Full-text
Abstract
Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on [...] Read more.
Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on the availability of reliable digital terrain models (DTMs). The main objective of this study was to evaluate application of 3D data derived from UAV imagery in biomass estimation and to compare impacts of DTMs generated based on different methods and parameter settings. Biomass was modeled using data acquired from 107 sample plots in a forest reserve in miombo woodlands of Malawi. The results indicated that there are no significant differences (p = 0.985) between tested DTMs except for that based on shuttle radar topography mission (SRTM). A model developed using unsupervised ground filtering based on a grid search approach, had the smallest root mean square error (RMSE) of 46.7% of a mean biomass value of 38.99 Mg·ha−1. Amongst the independent variables, maximum canopy height (Hmax) was the most frequently selected. In addition, all models included spectral variables incorporating the three color bands red, green and blue. The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle
Interpolation of GPS and Geological Data Using InSAR Deformation Maps: Method and Application to Land Subsidence in the Alto Guadalentín Aquifer (SE Spain)
Remote Sens. 2016, 8(11), 965; https://doi.org/10.3390/rs8110965
Received: 30 August 2016 / Revised: 28 October 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
Cited by 10 | Viewed by 2405 | PDF Full-text (7903 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Land subsidence resulting from groundwater extractions is a global phenomenon adversely affecting many regions worldwide. Understanding the governing processes and mitigating associated hazards require knowing the spatial distribution of the implicated factors (piezometric levels, lithology, ground deformation), usually only known at discrete locations. [...] Read more.
Land subsidence resulting from groundwater extractions is a global phenomenon adversely affecting many regions worldwide. Understanding the governing processes and mitigating associated hazards require knowing the spatial distribution of the implicated factors (piezometric levels, lithology, ground deformation), usually only known at discrete locations. Here, we propose a methodology based on the Kriging with External Drift (KED) approach to interpolate sparse point measurements of variables influencing land subsidence using high density InSAR measurements. In our study, located in the Alto Guadalentín basin, SE Spain, these variables are GPS vertical velocities and the thickness of compressible soils. First, we estimate InSAR and GPS rates of subsidence covering the periods 2003–2010 and 2004–2013, respectively. Then, we apply the KED method to the discrete variables. The resulting continuous GPS velocity map shows maximum subsidence rates of 13 cm/year in the center of the basin, in agreement with previous studies. The compressible deposits thickness map is significantly improved. We also test the coherence of Sentinel-1 data in the study region and evaluate the applicability of this methodology with the new satellite, which will improve the monitoring of aquifer-related subsidence and the mapping of variables governing this phenomenon. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle
Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data
Remote Sens. 2016, 8(11), 967; https://doi.org/10.3390/rs8110967
Received: 26 September 2016 / Revised: 7 November 2016 / Accepted: 9 November 2016 / Published: 22 November 2016
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Abstract
The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve [...] Read more.
The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC) in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme—the Incremental Recursive Segmentation (IRIS), that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on three datasets acquired from a handheld Velodyne HDL-32E LiDAR device. The experimental results verify the efficiency of IRIS for any segmentation method and the advantages of SLCC for processing mobile LiDAR data. Full article
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Open AccessFeature PaperArticle
Multi-Sensor Geomagnetic Prospection: A Case Study from Neolithic Thessaly, Greece
Remote Sens. 2016, 8(11), 966; https://doi.org/10.3390/rs8110966
Received: 3 October 2016 / Revised: 7 November 2016 / Accepted: 16 November 2016 / Published: 22 November 2016
Cited by 2 | Viewed by 1591 | PDF Full-text (36024 KB) | HTML Full-text | XML Full-text
Abstract
Multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensor arrays drastically increase efficiency in data collection. In particular, geomagnetic prospecting benefits from this development. Despite these advancements, data processing still lacks a best-practice approach. Conventional processing [...] Read more.
Multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensor arrays drastically increase efficiency in data collection. In particular, geomagnetic prospecting benefits from this development. Despite these advancements, data processing still lacks a best-practice approach. Conventional processing methods developed for gridded data has been challenged by sensor arrays “roaming” in the landscape. In realization of the issue, the Innovative Geophysical Approaches for the Study of Early Agricultural Villages of Neolithic Thessaly (IGEAN) Project explored various innovative techniques for the betterment of the multi-sensor geomagnetic data processing. As a result, a modular pipeline is produced with minimal user intervention. In addition to standard steps, such as data clipping, various other algorithms have been introduced. This pipeline is tested over 20 Neolithic settlements in Thessaly, Greece, three of which are presented here in detail. The proposed workflow provides drastic improvements over raw data. As a result of these improvements, the IGEAN project revealed astonishing details on architectural elements, settlement enclosures, and paleolandscapes, changing completely the existing perspective of the Neolithic habitation in Thessaly. Full article
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
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Open AccessArticle
Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning
Remote Sens. 2016, 8(11), 963; https://doi.org/10.3390/rs8110963
Received: 12 August 2016 / Revised: 11 November 2016 / Accepted: 15 November 2016 / Published: 22 November 2016
Cited by 12 | Viewed by 3048 | PDF Full-text (17184 KB) | HTML Full-text | XML Full-text
Abstract
Automatic cloud extraction from satellite imagery is a vital process for many applications in optical remote sensing since clouds can locally obscure the surface features and alter the reflectance. Clouds can be easily distinguished by the human eyes in satellite imagery via remarkable [...] Read more.
Automatic cloud extraction from satellite imagery is a vital process for many applications in optical remote sensing since clouds can locally obscure the surface features and alter the reflectance. Clouds can be easily distinguished by the human eyes in satellite imagery via remarkable regional characteristics, but finding a way to automatically detect various kinds of clouds by computer programs to speed up the processing efficiency remains a challenge. This paper introduces a new cloud detection method based on probabilistic latent semantic analysis (PLSA) and object-based machine learning. The method begins by segmenting satellite images into superpixels by Simple Linear Iterative Clustering (SLIC) algorithm while also extracting the spectral, texture, frequency and line segment features. Then, the implicit information in each superpixel is extracted from the feature histogram through the PLSA model by which the descriptor of each superpixel can be computed to form a feature vector for classification. Thereafter, the cloud mask is extracted by optimal thresholding and applying the Support Vector Machine (SVM) algorithm at the superpixel level. The GrabCut algorithm is then applied to extract more accurate cloud regions at the pixel level by assuming the cloud mask as the prior knowledge. When compared to different cloud detection methods in the literature, the overall accuracy of the proposed cloud detection method was up to 90 percent for ZY-3 and GF-1 images, which is about a 6.8 percent improvement over the traditional spectral-based methods. The experimental results show that the proposed method can automatically and accurately detect clouds using the multispectral information of the available four bands. Full article
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Open AccessArticle
Spatial Distribution of Diffuse Attenuation of Photosynthetic Active Radiation and Its Main Regulating Factors in Inland Waters of Northeast China
Remote Sens. 2016, 8(11), 964; https://doi.org/10.3390/rs8110964
Received: 15 August 2016 / Revised: 9 November 2016 / Accepted: 16 November 2016 / Published: 21 November 2016
Cited by 9 | Viewed by 1939 | PDF Full-text (3877 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Light availability in lakes or reservoirs is affected by optically active components (OACs) in the water. Light plays a key role in the distribution of phytoplankton and hydrophytes, thus, is a good indicator of the trophic state of an aquatic system. Diffuse attenuation [...] Read more.
Light availability in lakes or reservoirs is affected by optically active components (OACs) in the water. Light plays a key role in the distribution of phytoplankton and hydrophytes, thus, is a good indicator of the trophic state of an aquatic system. Diffuse attenuation of photosynthetic active radiation (PAR) (Kd(PAR)) is commonly used to quantitatively assess the light availability. The PAR and the concentration of OACs were measured at 206 sites, which covered 26 lakes and reservoirs in Northeast China. The spatial distribution of Kd(PAR) was depicted and its association with the OACs was assessed by grey incidences(GIs) and linear regression analysis. Kd(PAR) varied from 0.45 to 15.04 m−1. This investigation revealed that reservoirs in the east part of Northeast China were clear with small Kd(PAR) values, while lakes located in plain areas, where the source of total suspended matter (TSM) varied, displayed high Kd(PAR) values. The GIs and linear regression analysis indicated that the TSM was the dominant factor in determining Kd(PAR) values and best correlated with Kd(PAR) (R2 = 0.906, RMSE = 0.709). Most importantly, we have demonstrated that the TSM concentration is a reliable measurement for the estimation of the Kd(PAR) as 74% of the data produced a relative error (RE) of less than 0.4 in a leave-one-out cross validation (LOO-CV) analysis. Spatial transferability assessment of the model also revealed that TSM performed well as a determining factor of the Kd(PAR) for the majority of the lakes. However, a few exceptions were identified where the optically regulating dominant factors were chlorophyll-a (Chl-a) and/or the chromophroic dissolved organic matter (CDOM). These extreme cases represent lakes with exceptionally clear waters. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing) Printed Edition available
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Open AccessArticle
Global Daily High-Resolution Satellite-Based Foundation Sea Surface Temperature Dataset: Development and Validation against Two Definitions of Foundation SST
Remote Sens. 2016, 8(11), 962; https://doi.org/10.3390/rs8110962
Received: 26 August 2016 / Revised: 28 October 2016 / Accepted: 6 November 2016 / Published: 21 November 2016
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Abstract
This paper describes a global, daily sea surface temperature (SST) analysis based on satellite microwave and infrared measurements. The SST analysis includes a diurnal correction method to estimate foundation SST (SST free from diurnal variability) using satellite sea surface wind and solar radiation [...] Read more.
This paper describes a global, daily sea surface temperature (SST) analysis based on satellite microwave and infrared measurements. The SST analysis includes a diurnal correction method to estimate foundation SST (SST free from diurnal variability) using satellite sea surface wind and solar radiation data, frequency splitting to reproduce intra-seasonal variability and a quality control procedure repeated twice to avoid operation errors. An optimal interpolation method designed for foundation SST is applied to blend the microwave and infrared satellite measurements. Although in situ SST measurements are not used for bias correction adjustments in the analysis, the output product, with a spatial grid size of 0.1°, has an accuracy of 0.48 C and 0.46 C compared to the in situ foundation SST measurements derived by drifting buoys and Argo floats, respectively. The same quality against the two types of in situ foundation SST (drifters and Argo) suggests that the two definitions of foundation SST proposed by past studies can provide same-quality information about the sea surface state underlying the diurnal thermocline. Full article
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Open AccessCorrection
Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113–17134
Remote Sens. 2016, 8(11), 960; https://doi.org/10.3390/rs8110960
Received: 9 October 2015 / Accepted: 7 December 2015 / Published: 21 November 2016
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Abstract
The authors wish to make the following corrections to their paper [1].[...] Full article
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Open AccessArticle
Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data
Remote Sens. 2016, 8(11), 961; https://doi.org/10.3390/rs8110961
Received: 27 June 2016 / Revised: 4 November 2016 / Accepted: 7 November 2016 / Published: 19 November 2016
Cited by 3 | Viewed by 1939 | PDF Full-text (21134 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), [...] Read more.
Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%–12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%–33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle
Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data
Remote Sens. 2016, 8(11), 959; https://doi.org/10.3390/rs8110959
Received: 19 July 2016 / Revised: 4 November 2016 / Accepted: 9 November 2016 / Published: 18 November 2016
Cited by 17 | Viewed by 1875 | PDF Full-text (16495 KB) | HTML Full-text | XML Full-text
Abstract
A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with [...] Read more.
A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region. Full article
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Open AccessFeature PaperArticle
Scanning, Multibeam, Single Photon Lidars for Rapid, Large Scale, High Resolution, Topographic and Bathymetric Mapping
Remote Sens. 2016, 8(11), 958; https://doi.org/10.3390/rs8110958
Received: 29 July 2016 / Revised: 19 October 2016 / Accepted: 10 November 2016 / Published: 18 November 2016
Cited by 13 | Viewed by 2705 | PDF Full-text (31180 KB) | HTML Full-text | XML Full-text
Abstract
Several scanning, single photon sensitive, 3D imaging lidars are herein described that operate at aircraft above ground levels (AGLs) between 1 and 11 km, and speeds in excess of 200 knots. With 100 beamlets and laser fire rates up to 60 kHz, we, [...] Read more.
Several scanning, single photon sensitive, 3D imaging lidars are herein described that operate at aircraft above ground levels (AGLs) between 1 and 11 km, and speeds in excess of 200 knots. With 100 beamlets and laser fire rates up to 60 kHz, we, at the Sigma Space Corporation (Lanham, MD, USA), have interrogated up to 6 million ground pixels per second, all of which can record multiple returns from volumetric scatterers such as tree canopies. High range resolution has been achieved through the use of subnanosecond laser pulsewidths, detectors and timing receivers. The systems are presently being deployed on a variety of aircraft to demonstrate their utility in multiple applications including large scale surveying, bathymetry, forestry, etc. Efficient noise filters, suitable for near realtime imaging, have been shown to effectively eliminate the solar background during daytime operations. Geolocation elevation errors measured to date are at the subdecimeter level. Key differences between our Single Photon Lidars, and competing Geiger Mode lidars are also discussed. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle
Study of Subsidence and Earthquake Swarms in the Western Pakistan
Remote Sens. 2016, 8(11), 956; https://doi.org/10.3390/rs8110956
Received: 15 June 2016 / Revised: 27 October 2016 / Accepted: 14 November 2016 / Published: 18 November 2016
Cited by 2 | Viewed by 2141 | PDF Full-text (27221 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In recent years, the Quetta Valley and surrounding areas have experienced unprecedented levels of subsidence, which has been attributed mainly to groundwater withdrawal. However, this region is also tectonically active and is home to several regional strike-slip faults, including the north–south striking left-lateral [...] Read more.
In recent years, the Quetta Valley and surrounding areas have experienced unprecedented levels of subsidence, which has been attributed mainly to groundwater withdrawal. However, this region is also tectonically active and is home to several regional strike-slip faults, including the north–south striking left-lateral Chaman Fault System. Several large earthquakes have occurred recently in this area, including one deadly Mw 6.4 earthquake that struck on 28 October 2008. This study integrated Interferometric Synthetic Aperture Radar (InSAR) results with GPS, gravity, seismic reflection profiles, and earthquake centroid-moment-tensor (CMT) data to identify the impact of tectonic and anthropogenic processes on subsidence and earthquake patterns in this region. To detect and map the spatial-temporal features of the processes that led to the surface deformation, this study used two Synthetic Aperture Radar (SAR) time series, i.e., 15 Phased Array L-band Synthetic Aperture Radar (PALSAR) images acquired by an Advanced Land Observing Satellite (ALOS) from 2006–2011 and 40 Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) images spanning 2003–2010. A Small Baseline Subset (SBAS) technique was used to investigate surface deformation. Five seismic lines totaling ~60 km, acquired in 2003, were used to map the blind thrust faults beneath a Quaternary alluvium layer. The median filtered SBAS-InSAR average velocity profile supports groundwater withdrawal as the dominant source of subsidence, with some contribution from tectonic subsidence in the Quetta Valley. Results of SBAS-InSAR multi-temporal analysis provide a better explanation for the pre-, co-, and post-seismic displacement pattern caused by the 2008 earthquake swarms across two strike-slip faults. Full article
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Open AccessEditorial
Preface: The Environmental Mapping and Analysis Program (EnMAP) Mission: Preparing for Its Scientific Exploitation
Remote Sens. 2016, 8(11), 957; https://doi.org/10.3390/rs8110957
Received: 15 November 2016 / Accepted: 15 November 2016 / Published: 17 November 2016
Cited by 8 | Viewed by 1791 | PDF Full-text (169 KB) | HTML Full-text | XML Full-text
Abstract
The imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrial and aquatic ecosystems, examine the multifaceted impacts of human activities, and support a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide [...] Read more.
The imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrial and aquatic ecosystems, examine the multifaceted impacts of human activities, and support a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation. Full article
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Open AccessArticle
Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013
Remote Sens. 2016, 8(11), 955; https://doi.org/10.3390/rs8110955
Received: 8 September 2016 / Revised: 3 November 2016 / Accepted: 10 November 2016 / Published: 17 November 2016
Cited by 11 | Viewed by 2231 | PDF Full-text (20037 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g [...] Read more.
Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate–NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe. Full article
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Open AccessArticle
Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China
Remote Sens. 2016, 8(11), 954; https://doi.org/10.3390/rs8110954
Received: 17 June 2016 / Revised: 7 November 2016 / Accepted: 8 November 2016 / Published: 16 November 2016
Cited by 21 | Viewed by 2486 | PDF Full-text (4190 KB) | HTML Full-text | XML Full-text
Abstract
The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B [...] Read more.
The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%–10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas. Full article
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Open AccessArticle
Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead
Remote Sens. 2016, 8(11), 953; https://doi.org/10.3390/rs8110953
Received: 22 August 2016 / Revised: 20 October 2016 / Accepted: 8 November 2016 / Published: 16 November 2016
Cited by 3 | Viewed by 2053 | PDF Full-text (5264 KB) | HTML Full-text | XML Full-text
Abstract
The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent [...] Read more.
The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/semi-arid regions, GLDAS based storage estimations perform better than WGHM. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle
Using Landsat, MODIS, and a Biophysical Model to Evaluate LST in Urban Centers
Remote Sens. 2016, 8(11), 952; https://doi.org/10.3390/rs8110952
Received: 13 August 2016 / Revised: 23 September 2016 / Accepted: 19 October 2016 / Published: 16 November 2016
Cited by 3 | Viewed by 2151 | PDF Full-text (8288 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model’s LST to different land cover types, fractions (percentages), and emissivities [...] Read more.
In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model’s LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle
Investigation on Mining Subsidence Based on Multi-Temporal InSAR and Time-Series Analysis of the Small Baseline Subset—Case Study of Working Faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield, China
Remote Sens. 2016, 8(11), 951; https://doi.org/10.3390/rs8110951
Received: 9 June 2016 / Revised: 6 November 2016 / Accepted: 8 November 2016 / Published: 16 November 2016
Cited by 12 | Viewed by 2207 | PDF Full-text (26592 KB) | HTML Full-text | XML Full-text
Abstract
High-intensity coal mining (large mining height, shallow mining depth, and rapid advancing) frequently causes large-scale ground damage within a short period of time. Understanding mining subsidence under high-intensity mining can provide a basis for mining-induced damage assessment, land remediation in a subsidence area, [...] Read more.
High-intensity coal mining (large mining height, shallow mining depth, and rapid advancing) frequently causes large-scale ground damage within a short period of time. Understanding mining subsidence under high-intensity mining can provide a basis for mining-induced damage assessment, land remediation in a subsidence area, and ecological reconstruction in vulnerable ecological regions in Western China. In this study, the mining subsidence status of Shendong Coalfield was investigated and analyzed using two-pass differential interferometric synthetic aperture radar (DInSAR) technology based on high-resolution synthetic aperture radar data (RADARSAT-2 precise orbit, multilook fine, 5 m) collected from 20 January 2012 to June 2013. Surface damages in Shendong Coalfield over a period of 504 days under open-pit mining and underground mining were observed. Ground deformation of the high-intensity mining working faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield was monitored using small baseline subset (SBAS) InSAR technology. (1) DInSAR detected and located 85 ground deformation areas (including ground deformations associated with past-mining activity). The extent of subsidence in Shendong Coalfield presented a progressive increase at an average monthly rate of 13.09 km2 from the initial 54.98 km2 to 225.20 km2, approximately, which accounted for 7% of the total area of Shendong Coalfield; (2) SBAS-InSAR reported that the maximum cumulative subsidence area reached 5.58 km2 above the working faces 22201-1/2. The advance speed of ground destruction (7.9 m/day) was nearly equal to that of underground mining (8.1 m/day). Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle
An Alternative Quality Control Technique for Mineral Chemistry Analysis of Portland Cement-Grade Limestone Using Shortwave Infrared Spectroscopy
Remote Sens. 2016, 8(11), 950; https://doi.org/10.3390/rs8110950
Received: 30 June 2016 / Revised: 26 October 2016 / Accepted: 3 November 2016 / Published: 15 November 2016
Cited by 6 | Viewed by 2279 | PDF Full-text (3038 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Shortwave infrared (SWIR) spectroscopy can be applied directly to analyze the mineral chemistry of raw or geologic materials. It provides diagnostic spectral characteristics of the chemical composition of minerals, information that is invaluable for the identification and quality control of such materials. The [...] Read more.
Shortwave infrared (SWIR) spectroscopy can be applied directly to analyze the mineral chemistry of raw or geologic materials. It provides diagnostic spectral characteristics of the chemical composition of minerals, information that is invaluable for the identification and quality control of such materials. The present study aims to investigate the potential of SWIR spectroscopy as an alternative quality control technique for the mineral chemistry analysis of Portland cement-grade limestone. We used the spectroscopic (wavelength position and depth of absorption feature) and geochemical characteristics of limestone samples to estimate the abundance and composition of carbonate and clay minerals on rock surfaces. The depth of the carbonate (CO3) and Al-OH absorption features are linearly correlated with the contents of CaO and Al2O3 in the samples, respectively, as determined by portable X-ray fluorescence (PXRF) measurements. Variations in the wavelength position of CO3 and Al-OH absorption features are related to changes in the chemical compositions of the samples. The results showed that the dark gray and light gray limestone samples are better suited for manufacturing Portland cement clinker than the dolomitic limestone samples. This finding is based on the CaO, MgO, Al2O3, and SiO2 concentrations and compositions. The results indicate that SWIR spectroscopy is an appropriate approach for the chemical quality control of cement raw materials. Full article
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Open AccessArticle
Variability of Particle Size Distributions in the Bohai Sea and the Yellow Sea
Remote Sens. 2016, 8(11), 949; https://doi.org/10.3390/rs8110949
Received: 28 July 2016 / Revised: 4 November 2016 / Accepted: 9 November 2016 / Published: 15 November 2016
Cited by 2 | Viewed by 1566 | PDF Full-text (4744 KB) | HTML Full-text | XML Full-text
Abstract
Particle size distribution (PSD) is an important parameter that is relevant to many aspects of marine ecosystems, such as phytoplankton functional types, optical absorption and scattering from particulates, sediment fluxes, and carbon export. However, only a handful of studies have documented the PSD [...] Read more.
Particle size distribution (PSD) is an important parameter that is relevant to many aspects of marine ecosystems, such as phytoplankton functional types, optical absorption and scattering from particulates, sediment fluxes, and carbon export. However, only a handful of studies have documented the PSD variability in different regions. Here, we investigate the PSD properties and variability in two shallow and semi-enclosed seas (the Bohai Sea (BS) and Yellow Sea (YS)), using in situ laser diffraction measurements (LISST-100X Type C) and other measurements at 79 stations in November 2013. The results show large variability in particle concentrations (in both volume and number concentrations), with volume concentrations varying by 57-fold. The median particle diameter (Dv50) from each of the water samples also covers a large range (22.4–307.0 μm) and has an irregular statistical distribution, indicating complexity in the PSD. The PSD slopes (2.7–4.5), estimated from a power-law model, cover nearly the entire range reported previously for natural waters. Small mineral particles (with large PSD slopes) are characteristic of near-shore waters prone to sediment resuspension by winds and tides, while large biological particles (with small PSD slopes) dominate the total suspended particulates for waters away from the coast. For the BS and YS, this study provides the first report on the properties and spatial variability of the PSD, which may influence the optical properties of the ocean surface and remote sensing algorithms that are based on estimations of particle concentrations and sizes. Full article
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Open AccessArticle
Environmental and Anthropogenic Degradation of Vegetation in the Sahel from 1982 to 2006
Remote Sens. 2016, 8(11), 948; https://doi.org/10.3390/rs8110948
Received: 14 June 2016 / Accepted: 10 November 2016 / Published: 13 November 2016
Cited by 5 | Viewed by 1901 | PDF Full-text (4921 KB) | HTML Full-text | XML Full-text
Abstract
There is a great deal of debate on the extent, causes, and even the reality of land degradation in the Sahel. Investigations carried out before approximately 2000 using remote sensing data suggest widespread reductions in biological productivity, while studies extending beyond 2000 consistently [...] Read more.
There is a great deal of debate on the extent, causes, and even the reality of land degradation in the Sahel. Investigations carried out before approximately 2000 using remote sensing data suggest widespread reductions in biological productivity, while studies extending beyond 2000 consistently reveal a net increase in vegetation production, strongly related to the recovery of rainfall following the extreme droughts of the 1970s and 1980s, and thus challenging the notion of widespread, long-term, subcontinental-scale degradation. Yet, the spatial variations in the rates of vegetation recovery are not fully explained by rainfall trends. It is hypothesized that, in addition to rainfall, other meteorological variables and human land use have contributed to vegetation dynamics. Throughout most of the Sahel, the interannual variability in growing season ΣNDVIgs (measured from satellites, used as a proxy of vegetation productivity) was strongly related to rainfall, humidity, and temperature (mean r2 = 0.67), but with rainfall alone was weaker (mean r2 = 0.41). The mean and upper 95th quantile (UQ) rates of change in ΣNDVIgs in response to climate were used to predict potential ΣNDVIgs—that is, the ΣNDVIgs expected in response to climate variability alone, excluding any anthropogenic effects. The differences between predicted and observed ΣNDVIgs were regressed against time to detect any long-term (positive or negative) trends in vegetation productivity. Over most of the Sahel, the trends did not significantly depart from what is expected from the trends in meteorological variables. However, substantial and spatially contiguous areas (~8% of the total area of the Sahel) were characterized by negative, and, in some areas, positive trends. To explore whether the negative trends were human-induced, they were compared with the available data of population density, land use, and land biophysical properties that are known to affect the susceptibility of land to degradation. The spatial variations in the trends of the residuals were partly related to soils and tree cover, but also to several anthropogenic pressures. Full article
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Open AccessArticle
Development of a New BRDF-Resistant Vegetation Index for Improving the Estimation of Leaf Area Index
Remote Sens. 2016, 8(11), 947; https://doi.org/10.3390/rs8110947
Received: 12 August 2016 / Revised: 1 November 2016 / Accepted: 3 November 2016 / Published: 12 November 2016
Cited by 2 | Viewed by 1638 | PDF Full-text (2359 KB) | HTML Full-text | XML Full-text
Abstract
The leaf area index (LAI) is one of the most important Earth surface parameters used in the modeling of ecosystems and their interaction with climate. Numerous vegetation indices have been developed to estimate the LAI. However, because of the effects of the bi-directional [...] Read more.
The leaf area index (LAI) is one of the most important Earth surface parameters used in the modeling of ecosystems and their interaction with climate. Numerous vegetation indices have been developed to estimate the LAI. However, because of the effects of the bi-directional reflectance distribution function (BRDF), most of these vegetation indices are also sensitive to the effect of BRDF. In this study, we aim to present a new BRDF-resistant vegetation index (BRVI), which is sensitive to the LAI but insensitive to the effect of BRDF. Firstly, the BRDF effects of different bands were investigated using both simulated data and in-situ measurements of winter wheat made at different growth stages. We found bi-directional shape similarity in the solar principal plane between the green and the near-infrared (NIR) bands and between the blue and red bands for farmland soil conditions and with medium chlorophyll content level. Secondly, the consistency of the shape of the BRDF across different bands was employed to develop a new BRDF-resistant vegetation index for estimating the LAI. The reflectance ratios of the NIR band to the green band and the blue band to the red band were reasonably assumed to be resistant to the BRDF effects. Nevertheless, the variation amplitude of the bi-directional reflectance in the solar principal plane was different for different bands. The divisors in the two reflectance ratios were improved by combining the reflectances at the red and green bands. The new BRVI was defined as a normalized combination of the two improved reflectance ratios. Finally, the potential of the proposed BRVI for estimation of the LAI was evaluated using both simulated data and in-situ measurements and also compared to other popular vegetation indices. The results showed that the influence of the BRDF on the BRVI was the weakest and that the BRVI retrieved LAI values well, with a coefficient of determination (R2) of 0.84 and an RMSE of 0.83 for the field data and with an R2 of 0.97 and an RMSE of 0.25 for the simulated data. It was concluded, therefore, that the new BRVI is resistant to BRDF effect and is also promising for use in estimating the LAI. Full article
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Open AccessArticle
Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level
Remote Sens. 2016, 8(11), 945; https://doi.org/10.3390/rs8110945
Received: 31 July 2016 / Revised: 30 October 2016 / Accepted: 7 November 2016 / Published: 12 November 2016
Cited by 11 | Viewed by 2367 | PDF Full-text (23653 KB) | HTML Full-text | XML Full-text
Abstract
The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological [...] Read more.
The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical and synthetic aperture radar (SAR) data has shown advantages in accurately characterizing impervious surface. However, the fusion mainly occurs at the pixel and feature levels which are subject to influences of data noises and feature selections, respectively. In this paper, an innovative and effective method was developed to extract urban impervious surface by synergistically utilizing optical and SAR images at the decision level. The objective of this paper was to obtain an accurate urban impervious surface map based on the random forest classifier and the evidence theory and to provide a detailed uncertainty analysis accompanying the fused impervious surface maps. In this study, both the GaoFen (GF-1) and Sentinel-1A imagery were first used as independent data sources for mapping urban impervious surfaces. Then additional spectral features and texture features were extracted and integrated with the original GF-1 and Sentinel-1A images in generating impervious surfaces. Finally, based on the Dempster-Shafer (D-S) theory, impervious surfaces were produced by fusing the previously estimated impervious surfaces from different datasets at the decision level. Results showed that impervious surfaces estimated from the combined use of original images and features yielded a higher accuracy than those from the original optical or SAR data. Further validations suggested that optical data was better than SAR data in separating impervious surfaces from non-impervious surfaces. The fused impervious surfaces at the decision level had a higher overall accuracy than those produced independently by optical or SAR data. It was also highlighted that the fusion of GF-1 and Sentinel-1A images reduced the amount of confusions among the low reflectance of impervious surface and water, as well as for low reflectance of bare land. An overall accuracy of 95.33% was achieved for extracting urban impervious surfaces by fused datasets. The spatial distributions of uncertainties provided by the evidence theory displayed a confidence level of at least 75% for the impervious surfaces derived from the fused datasets. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle
Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis
Remote Sens. 2016, 8(11), 944; https://doi.org/10.3390/rs8110944
Received: 24 August 2016 / Revised: 31 October 2016 / Accepted: 8 November 2016 / Published: 11 November 2016
Cited by 6 | Viewed by 2340 | PDF Full-text (14204 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained [...] Read more.
This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000–2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m−2·year−1) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type. Full article
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Open AccessArticle
An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data
Remote Sens. 2016, 8(11), 943; https://doi.org/10.3390/rs8110943
Received: 11 August 2016 / Revised: 13 October 2016 / Accepted: 7 November 2016 / Published: 11 November 2016
Cited by 8 | Viewed by 1783 | PDF Full-text (3174 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling [...] Read more.
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling. Full article
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Open AccessArticle
A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR
Remote Sens. 2016, 8(11), 942; https://doi.org/10.3390/rs8110942
Received: 14 July 2016 / Revised: 3 November 2016 / Accepted: 7 November 2016 / Published: 11 November 2016
Cited by 16 | Viewed by 2518 | PDF Full-text (7939 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this [...] Read more.
Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle
Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake
Remote Sens. 2016, 8(11), 941; https://doi.org/10.3390/rs8110941
Received: 30 August 2016 / Revised: 17 October 2016 / Accepted: 3 November 2016 / Published: 11 November 2016
Cited by 37 | Viewed by 3142 | PDF Full-text (8981 KB) | HTML Full-text | XML Full-text
Abstract
Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at [...] Read more.
Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr−1). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (aCDOM(440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and aCDOM(440) were modelled in optically shallow water. In deep water, SPM and aCDOM(440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m−1) showed an underestimation of S2-A derived aCDOM(440) (mean: 0.14 m−1); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m−1 vs. 0.019 m−1). Chlorophyll-a concentrations (~1 mg·m−3) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A. Full article
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Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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