Hyperspectral analysis in areas of Caatinga degraded in the municipality of Sertânia - PE

Abstract

The brazilian region semiarid has as predominant vegetation the caatinga. The Caatinga Forest has one of its striking features its excellent adaptation to arid conditions. This exclusively brazilian ecosystem has a great biodiversity, but promote their preservation is a major obstacle to be overcome, one is the best knowledge of their chemical and physical processes. Thus the hyperspectral remote sensing can help enough in the characterization and monitoring of this ecosystem, where the hyperspectral data collected by these sensors can be transformed into different information biophysical aspects of vegetable toppings. In this work was used images hyperspectral of the Hyperion Satellite Earth Observing1 sensor (EO-1). The software utilized for analysis and the pre-processing of the image was the ENVI 4.4 software, where we generated the NDVI - vegetation index (Normalized Difference Vegetation Index) and the indexes of leaf pigments PRI (Photochemical Reflectance Index), SIPI (Structure Insensitive pigment Index) and CRI (1 Carotenoid Reflectance Index).The indices generated answered well to the structure of the cover and characteristics vegetation. Allowing this way, get more accurate data of vegetation indices and leaf pigments such, as chlorophyll and carotenoids.

Utilização de técnicas de análise multivariadas no estudo da diversidade genética em quixabeira (Sideroxylon obtusifolium)

“…Segundo a classificação de Köpen, o clima da região é do tipo Bsh' -semiárido quente, com média de precipitação pluviométrica anual de 635 mm e temperatura de 25 °C. A altimetria da área está em torno de 558 metros (GOMES et al, 2016).…”

Multitemporal analysis of vegetation indices for the superior region of the Moxotó River hydrographic basin

“…The predominant vegetation is the hyperxerophilic caatinga. The rainy season is comprised from March to July, during the rest of the year, drought predominates (Melo, 2011;Gomes et al, 2016).…”

Analysis of errors in the digital elevation model - SRTM for the Sobradinho reservoir/BA

“…O sensoriamento remoto também colabora na gestão destas áreas, com o fornecimento das imagens, análises e monitoramento. No geral, essas ferramentas conseguem, na impossibilidade de trabalhos in loco ou em grandes áreas, o diagnóstico e o acompanhamento vegetacional; a geopotencialidade de áreas agricultáveis ou regiões susceptíveis a desertificação, erosão, dentre outros parâmetros (Santos e Galvíncio, 2013;Ferreira et al, 2016;Gomes et al, 2016aGomes et al, , 2016bSilva et al, 2016aSilva et al, , 2016b.…”