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Remote Sens., Volume 6, Issue 7 (July 2014), Pages 5885-6726

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Open AccessArticle Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests
Remote Sens. 2014, 6(7), 6709-6726; https://doi.org/10.3390/rs6076709
Received: 28 April 2014 / Revised: 4 July 2014 / Accepted: 9 July 2014 / Published: 22 July 2014
Cited by 14 | PDF Full-text (701 KB) | HTML Full-text | XML Full-text
Abstract
In many arid mountains, dwarf shrubs represent the most important fodder and firewood resources; therefore, they are intensely used. For the Eastern Pamirs (Tajikistan), they are assumed to be overused. However, empirical evidence on this issue is lacking. We aim to provide a
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In many arid mountains, dwarf shrubs represent the most important fodder and firewood resources; therefore, they are intensely used. For the Eastern Pamirs (Tajikistan), they are assumed to be overused. However, empirical evidence on this issue is lacking. We aim to provide a method capable of mapping vegetation in this mountain desert. We used random forest models based on remote sensing data (RapidEye, ASTER GDEM) and 359 plots to predictively map total vegetative cover and the distribution of the most important firewood plants, K. ceratoides and A. leucotricha. These species were mapped as present in 33.8% of the study area (accuracy 90.6%). The total cover of the dwarf shrub communities ranged from 0.5% to 51% (per pixel). Areas with very low cover were limited to the vicinity of roads and settlements. The model could explain 80.2% of the total variance. The most important predictor across the models was MSAVI2 (a spectral vegetation index particularly invented for low-cover areas). We conclude that the combination of statistical models and remote sensing data worked well to map vegetation in an arid mountainous environment. With this approach, we were able to provide tangible data on dwarf shrub resources in the Eastern Pamirs and to relativize previous reports about their extensive depletion. Full article
Open AccessArticle Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile
Remote Sens. 2014, 6(7), 6688-6708; https://doi.org/10.3390/rs6076688
Received: 28 March 2014 / Revised: 24 June 2014 / Accepted: 25 June 2014 / Published: 22 July 2014
Cited by 18 | PDF Full-text (621 KB) | HTML Full-text | XML Full-text
Abstract
Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the
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Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume. Full article
Open AccessLetter Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data
Remote Sens. 2014, 6(7), 6680-6687; https://doi.org/10.3390/rs6076680
Received: 14 November 2013 / Revised: 30 June 2014 / Accepted: 3 July 2014 / Published: 22 July 2014
Cited by 9 | PDF Full-text (773 KB) | HTML Full-text | XML Full-text
Abstract
The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction
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The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and −0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream. Full article
Open AccessArticle An Approach to Persistent Scatterer Interferometry
Remote Sens. 2014, 6(7), 6662-6679; https://doi.org/10.3390/rs6076662
Received: 27 March 2014 / Revised: 11 July 2014 / Accepted: 14 July 2014 / Published: 22 July 2014
Cited by 21 | PDF Full-text (1164 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes a new approach to Persistent Scatterer Interferometry (PSI) data processing and analysis, which is implemented in the PSI chain of the Geomatics (PSIG) Division of CTTC. This approach includes three main processing blocks. In the first one, a set of
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This paper describes a new approach to Persistent Scatterer Interferometry (PSI) data processing and analysis, which is implemented in the PSI chain of the Geomatics (PSIG) Division of CTTC. This approach includes three main processing blocks. In the first one, a set of correctly unwrapped and temporally ordered phases are derived, which are computed on Persistent Scatterers (PSs) that cover homogeneously the area of interest. The key element of this block is given by the so-called Cousin PSs (CPSs), which are PSs characterized by a moderate spatial phase variation that ensures a correct phase unwrapping. This block makes use of flexible tools to check the consistency of phase unwrapping and guarantee a uniform CPS coverage. In the second block, the above phases are used to estimate the atmospheric phase screen. The third block is used to derive the PS deformation velocity and time series. Its key tool is a new 2+1D phase unwrapping algorithm. The procedure offers different tools to globally control the quality of the processing steps. The PSIG procedure has been successfully tested over urban, rural and vegetated areas using X-band PSI data. Its performance is illustrated using 28 TerraSAR-X StripMap images over the metropolitan area of Barcelona. Full article
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Open AccessArticle Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo
Remote Sens. 2014, 6(7), 6636-6661; https://doi.org/10.3390/rs6076636
Received: 28 March 2014 / Revised: 24 June 2014 / Accepted: 27 June 2014 / Published: 21 July 2014
Cited by 5 | PDF Full-text (11793 KB) | HTML Full-text | XML Full-text
Abstract
Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to
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Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to identify hot spots of mining activities and provide reliable information about the situation in and around two selected mining sites, Mumba-Bibatama and Bisie. The first is the test case for the approach and the detection of unknown mining sites, whereas the second acts as reference case since it is the largest and most well-known location for cassiterite extraction in eastern Congo. Thus it plays a key-role within the context of the conflicts in this region. Detailed multi-temporal analyses of very high-resolution (VHR) satellite data demonstrates the capabilities of Geographic Object-Based Image Analysis (GEOBIA) techniques for providing information about the situation during a mining ban announced by the Congolese President between September 2010 and March 2011. Although the opening of new surface patches can serve as an indication for activities in the area, the pure change between the two satellite images does not in itself produce confirming evidence. However, in combination with observations on the ground, it becomes evident that mining activities continued in Bisie during the ban, even though the production volume went down considerably. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island
Remote Sens. 2014, 6(7), 6620-6635; https://doi.org/10.3390/rs6076620
Received: 5 May 2014 / Revised: 4 July 2014 / Accepted: 9 July 2014 / Published: 18 July 2014
Cited by 13 | PDF Full-text (2053 KB) | HTML Full-text | XML Full-text
Abstract
Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based
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Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1) an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2) the Kumar-Monteith efficiency model, and (3) a forced-coupling method with a sugarcane crop model (MOSICAS) and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1). Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency. Full article
Open AccessArticle Modelling the Spatial Distribution of Culicoides imicola: Climatic versus Remote Sensing Data
Remote Sens. 2014, 6(7), 6604-6619; https://doi.org/10.3390/rs6076604
Received: 17 February 2014 / Revised: 24 June 2014 / Accepted: 14 July 2014 / Published: 18 July 2014
Cited by 2 | PDF Full-text (1606 KB) | HTML Full-text | XML Full-text
Abstract
Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of
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Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of C. imicola distribution models based on climatic versus remote sensing data, even though remotely sensed datasets may offer advantages over climatic datasets with respect to spatial and temporal resolution. This study performs such an analysis for datasets over the peninsula of Calabria, Italy. Spatial distribution modelling based on climatic data using the random forests machine learning technique resulted in a percentage of correctly classified C. imicola trapping sites of nearly 88%, thereby outperforming the linear discriminant analysis and logistic regression modelling techniques. When replacing climatic data by remote sensing data, random forests modelling accuracies decreased only slightly. Assessment of the different variables’ importance showed that precipitation during late spring was the most important amongst 48 climatic variables. The dominant remotely sensed variables could be linked to climatic variables. Notwithstanding the slight decrease in predictive performance in this study, remotely sensed datasets could be preferred over climatic datasets for the modelling of C. imicola. Unlike climatic observations, remote sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species’ presence and changing environment. Full article
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Open AccessArticle The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots
Remote Sens. 2014, 6(7), 6587-6603; https://doi.org/10.3390/rs6076587
Received: 5 May 2014 / Revised: 2 July 2014 / Accepted: 3 July 2014 / Published: 18 July 2014
Cited by 25 | PDF Full-text (412 KB) | HTML Full-text | XML Full-text
Abstract
This paper evaluated the feasibility of a terrestrial point cloud generated utilizing an uncalibrated hand-held consumer camera at a plot level and measuring the plot at an individual-tree level. Individual tree stems in the plot were detected and modeled from the image-based point
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This paper evaluated the feasibility of a terrestrial point cloud generated utilizing an uncalibrated hand-held consumer camera at a plot level and measuring the plot at an individual-tree level. Individual tree stems in the plot were detected and modeled from the image-based point cloud, and the diameter-at-breast-height (DBH) of each tree was estimated. The detected-results were compared with field measurements and with those derived from the single-scan terrestrial laser scanning (TLS) data. The experiment showed that the mapping accuracy was 88% and the root mean squared error of DBH estimates of individual trees was 2.39 cm, which is acceptable for practical applications and was similar to the results achieved using TLS. The main advantages of the image-based point cloud data lie in the low cost of the equipment required for the data collection, the simple and fast field measurements and the automated data processing, which may be interesting and important for certain applications, such as field inventories by landowners who do not have supports from external experts. The disadvantages of the image-based point cloud data include the limited capability of mapping small trees and complex forest stands. Full article
Open AccessArticle Application of Physically-Based Slope Correction for Maximum Forest Canopy Height Estimation Using Waveform Lidar across Different Footprint Sizes and Locations: Tests on LVIS and GLAS
Remote Sens. 2014, 6(7), 6566-6586; https://doi.org/10.3390/rs6076566
Received: 28 January 2014 / Revised: 8 July 2014 / Accepted: 14 July 2014 / Published: 18 July 2014
Cited by 9 | PDF Full-text (2314 KB) | HTML Full-text | XML Full-text
Abstract
Forest canopy height is an important biophysical variable for quantifying carbon storage in terrestrial ecosystems. Active light detection and ranging (lidar) sensors with discrete-return or waveform lidar have produced reliable measures of forest canopy height. However, rigorous procedures are required for an accurate
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Forest canopy height is an important biophysical variable for quantifying carbon storage in terrestrial ecosystems. Active light detection and ranging (lidar) sensors with discrete-return or waveform lidar have produced reliable measures of forest canopy height. However, rigorous procedures are required for an accurate estimation, especially when using waveform lidar, since backscattered signals are likely distorted by topographic conditions within the footprint. Based on extracted waveform parameters, we explore how well a physical slope correction approach performs across different footprint sizes and study sites. The data are derived from airborne (Laser Vegetation Imaging Sensor; LVIS) and spaceborne (Geoscience Laser Altimeter System; GLAS) lidar campaigns. Comparisons against field measurements show that LVIS data can satisfactorily provide a proxy for maximum forest canopy heights (n = 705, RMSE = 4.99 m, and R2 = 0.78), and the simple slope correction grants slight accuracy advancement in the LVIS canopy height retrieval (RMSE of 0.39 m improved). In the same vein of the LVIS with relatively smaller footprint size (~20 m), substantial progress resulted from the physically-based correction for the GLAS (footprint size = ~50 m). When compared against reference LVIS data, RMSE and R2 for the GLAS metrics (n = 527) are improved from 12.74–7.83 m and from 0.54–0.63, respectively. RMSE of 5.32 m and R2 of 0.80 are finally achieved without 38 outliers (n = 489). From this study, we found that both LVIS and GLAS lidar campaigns could be benefited from the physical correction approach, and the magnitude of accuracy improvement was determined by footprint size and terrain slope. Full article
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Open AccessArticle Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery
Remote Sens. 2014, 6(7), 6549-6565; https://doi.org/10.3390/rs6076549
Received: 19 March 2014 / Revised: 9 July 2014 / Accepted: 10 July 2014 / Published: 18 July 2014
Cited by 21 | PDF Full-text (1362 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.)
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This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha−1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI), defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e., %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index/Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image. Full article
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Open AccessArticle Algorithm for Extracting Digital Terrain Models under Forest Canopy from Airborne LiDAR Data
Remote Sens. 2014, 6(7), 6524-6548; https://doi.org/10.3390/rs6076524
Received: 3 April 2014 / Revised: 18 June 2014 / Accepted: 26 June 2014 / Published: 16 July 2014
Cited by 13 | PDF Full-text (1521 KB) | HTML Full-text | XML Full-text
Abstract
Extracting digital elevationmodels (DTMs) from LiDAR data under forest canopy is a challenging task. This is because the forest canopy tends to block a portion of the LiDAR pulses from reaching the ground, hence introducing gaps in the data. This paper presents an
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Extracting digital elevationmodels (DTMs) from LiDAR data under forest canopy is a challenging task. This is because the forest canopy tends to block a portion of the LiDAR pulses from reaching the ground, hence introducing gaps in the data. This paper presents an algorithm for DTM extraction from LiDAR data under forest canopy. The algorithm copes with the challenge of low data density by generating a series of coarse DTMs by using the few ground points available and using trend surfaces to interpolate missing elevation values in the vicinity of the available points. This process generates a cloud of ground points from which the final DTM is generated. The algorithm has been compared to two other algorithms proposed in the literature in three different test sites with varying degrees of difficulty. Results show that the algorithm presented in this paper is more tolerant to low data density compared to the other two algorithms. The results further show that with decreasing point density, the differences between the three algorithms dramatically increased from about 0.5m to over 10m. Full article
Open AccessArticle Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis
Remote Sens. 2014, 6(7), 6500-6523; https://doi.org/10.3390/rs6076500
Received: 22 April 2014 / Revised: 24 June 2014 / Accepted: 25 June 2014 / Published: 16 July 2014
Cited by 7 | PDF Full-text (2244 KB) | HTML Full-text | XML Full-text
Abstract
Due to the fact that WorldView-2 (WV2) has a small time lag while acquiring images from panchromatic (PAN) and two multispectral (MS1 and MS2) sensors, a moving vehicle is located at different positions in three image bands. Consequently, such displacement can be utilized
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Due to the fact that WorldView-2 (WV2) has a small time lag while acquiring images from panchromatic (PAN) and two multispectral (MS1 and MS2) sensors, a moving vehicle is located at different positions in three image bands. Consequently, such displacement can be utilized to identify moving vehicles, and vehicle information, such as speed and direction can be estimated. In this paper, we focus on moving vehicle detection according to the displacement information and present a novel processing chain. The vehicle locations are extracted by an improved morphological detector based on the vehicle’s shape properties. To make better use of the time lag between MS1 and MS2, a band selection process is performed by both visual inspection and quantitative analysis. Moreover, three spectral-neighbor band pairs, which have a major contribution to vehicle identification, are selected. In addition, we improve the spatial and spectral analysis method by incorporating local ERGAS index analysis (ERGAS-SNS) to identify moving vehicles. The experimental results on WV2 images showed that the correctness, completeness and quality rates of the proposed method were about 94%, 91% and 86%, respectively. Thus, the proposed method has good performance for moving vehicle detection and information extraction. Full article
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Open AccessArticle Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa
Remote Sens. 2014, 6(7), 6472-6499; https://doi.org/10.3390/rs6076472
Received: 14 April 2014 / Revised: 7 July 2014 / Accepted: 8 July 2014 / Published: 15 July 2014
Cited by 38 | PDF Full-text (2056 KB) | HTML Full-text | XML Full-text
Abstract
Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual
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Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable. Full article
Open AccessArticle An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery
Remote Sens. 2014, 6(7), 6446-6471; https://doi.org/10.3390/rs6076446
Received: 15 January 2014 / Revised: 19 June 2014 / Accepted: 24 June 2014 / Published: 15 July 2014
Cited by 5 | PDF Full-text (3024 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this study is to assess the performance of an adaptive model (AM) in estimating chlorophyll‑a concentration (Chl‑a) in optically complex inland waters. Chl‑a modeling using remote sensing data is usually based on a single model that generally follows an exponential
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The purpose of this study is to assess the performance of an adaptive model (AM) in estimating chlorophyll‑a concentration (Chl‑a) in optically complex inland waters. Chl‑a modeling using remote sensing data is usually based on a single model that generally follows an exponential function. The estimates produced by such models are relatively accurate at high Chl‑a concentrations, but accuracy drops at low concentrations. Our objective was to develop an approach combining spectral response classification and three semi-empirical algorithms. The AM discriminates between three blooming classes (waters poorly, moderately, and highly loaded in Chl‑a), with discrimination thresholds set using the classification and regression tree (CART) technique. The calibration of three specific estimators for each class was achieved using a multivariate stepwise regression. Compared to published models (Floating Algae Index, Kahru model, and APProach by ELimination) using the same data set, the AM provided better Chl‑a concentration estimates (R2 of 0.96, relative RMSE of 23%, relative Bias of −2%, and a relative NASH criterion of 0.9). Moreover, the AM achieved an overall success rate of 67% in the estimation of blooming classes (corresponding to low, moderate, and high Chl‑a concentration classes). This was done using an independent data set collected from 22 inland water bodies for the period 2007–2010 and for which the only information available was the blooming class. Full article
Open AccessArticle Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data
Remote Sens. 2014, 6(7), 6423-6445; https://doi.org/10.3390/rs6076423
Received: 8 May 2014 / Revised: 4 July 2014 / Accepted: 8 July 2014 / Published: 14 July 2014
Cited by 7 | PDF Full-text (1217 KB) | HTML Full-text | XML Full-text
Abstract
This study used a reef-up approach to map coral reef benthos, substrates and bathymetry, with high spatial resolution hyperspectral image data. It investigated a physics-based inversion method for mapping coral reef benthos and substrates using readily available software: Hydrolight and ENVI. Compact Airborne
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This study used a reef-up approach to map coral reef benthos, substrates and bathymetry, with high spatial resolution hyperspectral image data. It investigated a physics-based inversion method for mapping coral reef benthos and substrates using readily available software: Hydrolight and ENVI. Compact Airborne Spectrographic Imager (CASI) data were acquired over Heron Reef in July 2002. The spectral reflectance of coral reef benthos and substrate types were measured in-situ, and using the HydroLight 4.2 radiative transfer model a spectral reflectance library of subsurface reflectance was simulated using water column depths from 0.5–10.0 m at 0.5 m intervals. Using the Spectral Angle Mapper algorithm, sediment, benthic micro-algae, algal turf, crustose coralline algae, macro-algae, and live coral were mapped with an overall accuracy of 65% to a depth of around 8.0 m; in waters deeper than 8.0 m the match between the classified image and field validation data was poor. Qualitative validation of the maps showed accurate mapping of areas dominated by sediment, benthic micro-algae, algal turf, live coral, and macro-algae. A bathymetric map was produced for water column depths 0.5–10.0 m, at 0.5 m intervals, and showed high correspondence with in-situ sonar data (R2 value of 0.93). Full article
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