Space-temporal evaluation of biophysical parameters in the High Ipanema watershed by remote sensing

Abstract

The High Ipanema watershed is located in a semiarid region and because of this, becomes more vulnerable and susceptible to the effects of environmental changes and the degradation process, it has serious economic and socio-environmental implications. In recent years with the advancement of remote sensing based on satellite imagery or other platforms, it has become possible to monitor different and large areas of the various biomes in the world. The objective of this study was to identify changes in the vegetation cover conditions in the Alto Ipanema watershed, using spectral analyzes of Landsat-8 OLI / TIRS satellite images, using remote sensing techniques. Landsat-8 OLI / TIRS satellite images were obtained from the United States Geological Survey – USGS, on 10/12/2013, 14/01/2015 and 12/08/2016, where they were processed from ERDAS IMAGINE® Software, version 9.1. The thematic maps of biophysical parameters were processed by ArcGis® 10.2.2 Software. With the biophysical parameters analyzed, it was found that the northwest portion of the watershed presents a considerable area of exposed soils with indication of a high degree of susceptibility to degradation and that the biophysical parameters evaluated by the SEBAL algorithm are efficient in understanding the dynamics of spatial and temporal areas of semiarid environments.

Spatial-temporal dynamics of the Caatinga vegetation cover by remote sensing in municipality of the Brazilian semi-arid

“…In the last decades, geotechnology has been prominent in the geotechnologies development and has opened a new area of action for environmental monitoring of semi-arid regions from Brazil and the world, in particular the technologies involved in the detection of spectral responses in airborne and orbital sensors, part of the scope of remote sensing. Among the most used applications in this segment, the studies on detection, analysis and monitoring of the Caatinga biome vegetation cover stand out (Silva & Galvíncio, 2012;Vieira et al, 2013;Bezerra et al, 2014;Vieira et al, 2015;Lins et al, 2017;Oliveira et al, 2017;Barbosa et al, 2018;Campos et al, 2019).…”

“…In particular, the Soil Adjusted Vegetation Index (SAVI), a biophysical parameter that seeks to attenuate the previous effects of the soil, correcting it by the adjustment factor limitations imposed by other indexes in the semi-arid; and also the leaf area index (LAI), important in highlighting in Caatinga vegetation the highest/lowest photosynthetic activity, thus indicating the quality of plant biomass. Based on this information, it is possible proposing appropriate management and guiding the exploitation of plant resources (Allen et al, 2007;Silva & Galvíncio, 2012;Ribeiro et al, 2016;Leite et al, 2017;Lins et al, 2017;Oliveira et al, 2017).…”

Caracterização de eventos extremos e de suas causas climáticas com base no Índice Padronizado de Precipitação Para o Leste do Nordeste

“…Certamente os desastres naturais mais frequentes no país são as estiagens, as inundações e os deslizamentos. Desastres naturais ganharam maior destaque na mídia eletrônica e impressa devido aos impactos socioambientais ocasionados nas grandes áreas urbanas, a citar, elevado número de mortes, feridos e desabrigados, perdas econômicas onerosas, proliferação de doenças, impactos ao meio ambiente, dentre outros (Oliveira et al, 2017;Anjos et al, 2016;Loureiro et al, 2014).…”

Quantification Caatinga vegetable coverage and water availability by remote sensing in the Brazilian semiarid

“…Some indices can be applied to observe Caatinga natural vegetation in the semiarid region, highlighting the patterns of vegetation coverage changes in the different soil uses both spatially and temporally as, for example, Normalized Difference Vegetation Index (NDVI) (Silva and Galvíncio, 2012;Oliveira et al, 2017). In addition to analyzing vegetation, also exist indices that highlight the water condition using the principles of the radiation interaction with water as in the case of the Normalized Difference Water Index (NDWI).…”