Journal ArticleDOI
Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data
Cristina Freitas,Luciana de Souza Soler,S.J.S. Sant'Anna,Luciano Vieira Dutra,J.R. dos Santos,José Claudio Mura,A.H. Correia +6 more
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TLDR
The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization.Abstract:
In September 2000, an airborne synthetic aperture radar (SAR) mission acquired unprecedented full polarimetric P-band data over the Tapajos National Forest (Para State), which is an area in the Brazilian Amazon which has been continuously monitored in the last three decades. Eight land use/cover classes were identified, namely, primary forest, regeneration older than 25 years, regeneration between 12 and 25 years, regeneration between 6 and 12 years, regeneration younger than six years, crops/pasture, bare soil, and floodplain (FP). The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization. The iterated conditional mode (ICM) contextual classifier was applied to amplitude, intensity images, biomass index, and some polarimetric parameters (entropy, alpha angle, and anisotropy) extracted from the polarimetric P-band data. As the accuracy obtained for eight classes was not acceptable, another two sets, with five and four classes, were formed by the combination of the previous ones. They were defined by confusion matrix analysis and by the graphical analysis of average backscatter values, entropy, [alpha] angle, and anisotropy images and by the H/alpha plans of the land use samples. The classification accuracy with four classes (three levels of biomass plus FP) was then considered acceptable with a Kappa value of 76.81%, using the ICM classification with the adequate bivariate distribution for the HV and VV channels.read more
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Journal ArticleDOI
Challenges in using land use and land cover data for global change studies
TL;DR: In this article, the authors consider the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment.
Journal ArticleDOI
A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data
TL;DR: Investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all theFour components have important contribution to the classification.
Journal ArticleDOI
Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions
TL;DR: This study shows for the first time how land use and land cover classifications in cloud-prone monsoon regions with small-scale agriculture and multiple cropping patterns can be improved by combining Sentinel-1 and Sentinel-2 data.
Journal ArticleDOI
Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity
Jaime Paneque-Gálvez,Jaime Paneque-Gálvez,Jean-François Mas,G. Moré,Jordi Cristóbal,Martí Orta-Martínez,Ana Catarina Luz,Maximilien Guèze,Manuel J. Macía,Victoria Reyes-García +9 more
TL;DR: The homogeneity index, which has so far been neglected in land use/cover classification efforts, is focused on, and it is found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM.
Journal ArticleDOI
Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data
Francis Canisius,Jiali Shang,Jiangui Liu,Xiaodong Huang,Bao-Luo Ma,Xianfeng Jiao,Xiaoyuan Geng,John M. Kovacs,Dan Walters +8 more
TL;DR: In this paper, the authors investigated the sensitivity of Synthetic Aperture Radar (SAR) signatures to crop biophysical parameters or phenological stages, such as emergence, flowering, fruiting, maturing and senescence, for crop production surveillance and yield prediction.
References
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