Radar remote sensing of forest and wetland ecosystems in the Central American tropics

Pope, Kevin; Benayas, José M. Rey; Paris, J. F.

doi:10.1016/0034-4257(94)90142-2

Cited by 149 publications

(90 citation statements)

References 15 publications

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“…Clustering represents a common method to perform image segmentation under consideration of multi-spectral and multi-temporal data [63]. Cluster algorithms have already been used as segmentation methods for the derivation of TFV [64][65][66]. The grouping of pixels into segments was implemented for the study area using a k-means cluster algorithm.…”

Section: Clustering Approach For Segment Generationmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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Abstract:The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

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Section: Clustering Approach For Segment Generationmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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“…This index is best used for vegetated terrain. BMI and VSI respond similarly to biomass changes [10]. The use of BMI does not imply that it is the best measure of biomass, only that biomass is the most relevant biophysical parameter associated with this polarization combination.…”

Section: Computation Of Various Indicesmentioning

confidence: 96%

“…Various indices apart from the co and cross polarized ratios viz. VSI, CSI, and BMI have been computed to aid in discriminating forest species [10]. We attempted to evaluate these indices for Agricultural crops, to study their feasibility in discriminating crops varying in growth pattern and canopy architecture.…”

Section: Computation Of Various Indicesmentioning

confidence: 99%

“…High values of VSI result when cross polarized backscatter (CS refers to average of cross polarized backscatter) is large with respect to the like polarized average (LK). VSI is an indicator of canopy thickness or density [10].…”

Section: Computation Of Various Indicesmentioning

confidence: 99%

“…BMI combined with VSI resulted in 84% overall accuracy. As BMI measures the change in biomass, it is high for forests with woody biomass as compared to leafy evergreen trees [10].…”

Section: Sar-derived Indices Based Classificationmentioning

confidence: 99%

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Jute and Tea Discrimination Through Fusion of Sar and Optical Data

Haldar¹,

Patnaik²,

Mohan³

et al. 2012

PIER B

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Abstract-Remote sensing approaches based on both optical and microwave region of EM spectra have been widely adapted for large scale crop monitoring and condition assessment. Visible, infrared and microwave wavelengths are sensitive to different crop characteristics, thus data from optical and radar sensors are complementary. Synthetic Aperture Radar (SAR) responds to the large scale crop structure (size, shape and orientation of leaves, stalks, and fruits) and the dielectric properties of the crop canopy. Research is needed to assess the saturation effects of SAR data and to investigate the synergy between the optical and SAR imagery for exploring various dimensions of crop growth which is not possible with any one of them singly with higher degree of accuracy. An attempt has been made to study the potential of SAR and optical data individually and by fusing them to separate various landcover classes. Two-date and three-date SAR data could distinguish jute and tea crop with 70-85% accuracy, while cloud free optical data (green, red and infrared bands) resulted in accuracy 80-85%. On fusing the optical and SAR single date data of May, 29 2010 using Brovey method, an accuracy of 85% was obtained. PCA and HSV with munsell based approaches resulted in similar accuracies but HSV performed the best among these. This emphasizes on the synergistic effect of SAR and optical data. Also the fused data could be used to delineate the crop condition and age by inputs like NDVI from optical and XPR (Cross polarization ratio) from SAR data.The co-and cross polarization ratios along with various indices viz. Biomass Index (BMI), Volume Scattering Index (VSI) and canopy structural index (CSI) were used to discriminate tea from jute. Due to differences in structural component of tea and jute at early season as manifested by the indices, there is clear separability as observed from the mean values. Among the dual polarization combinations, HV/VV performed the best (70%) followed by HV/HH (62%) and lastly HH/VV (42%). Among the single best indices for discrimination BMI performed the best. Combination of Co, Cross-polarization and BMI yields around 80% classification accuracy. BMI and VSI combination yielded the best classification accuracy of 84%. This level of accuracy obtained was much superior to that of multidate HH polarization SAR data.

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