Vegetation cover monitoring in tropical regions using SAR-C dual-polarization index: seasonal and spatial influences

ABSTRACT

An approach to monitor vegetation without the influence of cloud cover and employing Synthetic Aperture Radar (SAR) data was developed by the DPSVI (Dual Polarization SAR Vegetation Index), based on Sentinel-1 mission data. However, DPSVI performance in areas of dense vegetation and variation in vegetation classes is unknown. Thus, this paper aimed to investigate the performance of DPSVI in the monitoring of vegetation in the Brazilian Atlantic Forest biome, as well as to propose modifications to improve its capacity to monitor the vegetation and investigate seasonal and spatial influences on the performance of the proposed index (DPSVIm). Three approaches were adopted: 1) use Sentinel-1 and Landsat 8 scenes obtained from four hydrological years (2015–2016 to 2018–2019), to compute monthly DPSVI, DPSVIm, NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) indices, to investigate seasonal influences; 2) perform topographic correction in Landsat 8 and Sentinel-1 data to verify the influence of relief on DPSVIm performance; and 3) confronting DPSVIm with an attribute of Atlantic Forest fragments: the above ground biomass (AGB). The scenes were processed on the Google Earth Engine platform. The DPSVIm was able to better distinguish the vegetation, compared to the DPSVI, mainly in forest planting areas and Atlantic Forest, which evidences the proposed improvements. The rainfall regime made the DPSVIm performance seasonal in comparison with the NDVI and EVI (Normalized Difference Vegetation Index and Enhanced Vegetation Index), presenting better agreements in the dry season and worse in the rainy season. There was a greater agreement between DPSVIm and EVI in the results to monitor dense vegetation since EVI does not saturate as NDVI does. Finally, the DPSVIm has a good fit to AGB, being able to distinguish different levels of AGB from 50 to 200 Mg ha, approximately. The study allowed improving the DPSVI making the DPSVIm adequate to quantify vegetation in the study area, even considering the effects of topography and rainfall regime. The latter environmental factor should be addressed in future studies, searching to mitigate this source of temporal uncertainty in DPSVIm.

Acknowledgements

: We thank the anonymous reviewers for their careful reading and comments, which helped us to improve the manuscript quality. We thank the Departments of Agricultural Engineering (DEA) and Forest Engineering (DEF), and the Center of Reference in Water Resources (CRRH) of the Universidade Federal de Viçosa for supporting the researchers, also to the Celulose Nipo-Brasileira S.A. (CENIBRA) that have been providing the field data to this research.

Data availability statement

: The data that support the findings, essentially from Spearman Correlation’s test and rainfall data, of this study are openly available in Mendeley Data repository at http://dx.doi.org/10.17632/rb9pj2c89k.1, reference number.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Also by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).