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Wide swath synthetic aperture radar (SAR) has been commonly used for oceanographic applications, such as maritime safety, fisheries management and coastal monitoring, including dark target detectio...

The main objective of this study is to evaluate different methods to integrate (fusion and combination) Synthetic Aperture Radar (SAR) Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band SAR (PALSAR-1) (Fine Beam Dual mode-FDB) and LANDSAT images in order to identify those which lead to higher accuracy of land-use and land-cover (LULC) mapping in an agricultural frontier region in Amazon. One method used to integrate the multipolarized information in SAR images before the fusion process was also evaluated. In this method, the first principal component (PC1) of SAR data was used. Color compositions of fused data that presented better LULC classification were visually analyzed. Considering the proposed objective, the following fusion methods must be highlighted: Ehlers , Wavelet a trous, Intensity, Hue and Saturation (IHS), and selective principal component analysis (SPC). These latter three methods presented good results when processed using PC1 from ALOS/PALSAR-1 FBD backscatter filtered image or three SAR extracted and selected features. These results corroborate with the applicability of the proposed method for SAR data information integration. Distinct methods better discriminate different LULC classes. In general, densely forested classes were better characterized by the Ehlers _TM6 fusion method, in which at least the polarization HV was used. Intermediate and initial regeneration classes were better discriminated using SPC-fused data with PC1 of ALOS/PALSAR-1 FBD data. Bare soil and pasture classes were better discriminated in optical features and the PC1 of ALOS/PALSAR-1 FBD data fused by the IHS method. Soybean with approximately 40 days from seeding was better discriminated in image classification obtained from ALOS/PALSAR-1 FBD image.

In tropical biomes such as the Amazon, the cloud cover is frequent. The use of synthetic aperture radar (SAR) sensor systems is important to monitor and study these biomes because they can acquire data under cloud coverage. In this paper, an advanced land observing system phased array L-band synthetic aperture radar fine-beam dual (ALOS/PALSAR-FBD) image was evaluated for land use and land cover (LULC) classification of an Amazon test site. The features extracted from this image were also evaluated. To perform this task, a method for feature selection, considering the desired classes, was proposed. In order to better understand the applicability of this type of data in Brazilian Government projects (such as DETER-B and TerraClass), the results obtained with SAR images were compared with those from LANDSAT5/TM. The results show that the PALSAR-FBD image and the features selected are not suitable for the discrimination of densely forested classes. They presented, however, a good discrimination among the group of forested and agropastoral classes, as well as among nondensely forested classes (i.e., pastures, bare soil, and new regeneration). Therefore, these data present good applicability for mapping and monitoring of both deforestation and LULC and they can be used in the above mentioned projects. The classification of selected features, with eight classes of interest, achieved an increase of about 133% and 69% in the Kappa index (0.32) and an Overall Accuracy (0.54) regarding the PALSAR-FBD classification (0.136 and 0.322, respectively). This result shows the applicability of the proposed method. It is also expected that the features selected in this paper will improve the classification of similar study sites.

Made available in DSpace on 2018-12-11T16:55:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-09-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha-1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide. University of Exeter Brazilian National Institute for Space Research-INPE Department of Geography Federal University of Rio de Janeiro Department of Forest Engineering Santa Catarina State University (UDESC) Ecosystem Dynamics Observatory Institute of Geosciences and Exact Sciences São Paulo State University (UNESP) Ecosystem Dynamics Observatory Institute of Geosciences and Exact Sciences São Paulo State University (UNESP) CNPq: #301118/2017-5