Evolução Espaço-Temporal do Risco de Degradação da Cobertura Vegetal de Petrolina-PE

Abstract

Resumo A disponibilidade de imagens de satélites em diferentes resoluções espacial, temporal, radiométrica e espectral estão sendo empregadas para monitorar grandes áreas dos diversos biomas existentes no mundo. O monitoramento por imagens orbitais possibilita detectar, analisar e inferir sobre alterações da superfície da Terra provocadas por processos naturais ou antrópicos. Objetivou-se com o presente estudo monitorar e analisar a evolução espacial e temporal na ocupação e uso da terra no Município de Petrolina, Pernambuco, Brasil, utilizando-se dados de superfície e imagens do Landsat-8, para os anos de 2013 a 2015. Técnicas de sensoriamento remoto foram utilizadas para estimar o índice de Vegetação por Diferença Normalizada (NDVI), albedo da superfície (α) e índice de Desvio Padrão Móvel (MDSI). A técnica de detecção de mudança e a classificação por árvore de decisão baseadas em regras predefinidas no NDVI, albedo e MDSI foram aplicadas para inferir sobre ocupação e uso da terra. Os resultados mostraram que os índices de vegetação e as técnicas de monitoramento da degradação e detecção de mudanças identificaram satisfatoriamente o comportamento da vegetação circundante no Município de Petrolina, destacando-se como indicadores de áreas em processo de degradação no semiárido. Conclui-se que a técnica de detecção de mudança e classificação por árvore de decisão foram eficientes na identificação de áreas antropizadas durante o período de estudo.

Monitoramento Espaço-Temporal do Risco de Degradação Ambiental e Desertificação por Sensoriamento Remoto em Região Semiárida

“…The SEBAL methodology was described according to Bastiaanssen (2000) and Allen et al (2002). In the algorithm, mathematical processing operations were developed, to estimate components of the energy balance of the Earth's surface, such as albedo and surface temperature, vegetation indices and thematic maps of the risk of degradation in the semiarid region (Bastiaanssen, 2000;Allen et al, 2002;Silva et al, 2019). The determination of spectral radiance and monochromatic reflectance in each band of Landsat-5 TM and Landsat-8 OLI followed the procedures of Chander et al (2009) and Silva et al (2016), respectively.…”

“…where N is the number of pixels in the 3 x 3 filter (N = 9); DNi is the digital number of the pixel "i" in each nine-pixel window; DN ̅̅̅̅̅ is the value of the average digital number for each nine-pixel window. Through pre-established rules for the study area, a local methodology by decision tree was developed using the response patterns of indicators such as SAVI, albedo, and MSDI to classify the risk of environmental degradation in different degrees (low degradation; moderate; high and severe, Table 6) through the heterogeneity of soil uses in the region (Silva et al, 2019).…”

“…In this context, the values were preestablished and tabulated to compose the rules of classification of the risk of degradation in different degrees according to their indicator indices (SAVI; MSDI and Albedo), for the semiarid region of study (Table 6). The surface albedo (αsur) was determined based on studies developed in different semiarid regions, including Brazil (Allen et al, 2002;Bezerra et al, 2014;Silva et al, 2016;Silva et al, 2019). It is an important indicator that determines the energy balance and the change in micrometeorological conditions (temperature, aridity, humidity, etc) of the surface affected by degradation and/or desertification, Equation 8 (Xu et al, 2009;Vorovencii, 2015;Silva et al, 2016;Silva et al, 2019).…”

“…Several studies involving remote sensing usually assess and monitor the risk of degradation and desertification, characterized mainly by the change in vegetation biomass, landscape pattern and micrometeorological conditions, based on important indicators for detecting changes in the soil surface of semiarid regions, highlighting the biophysical parameters albedo and surface temperature, as well as the Soil Adjusted Vegetation Index (SAVI), which is important for mitigating the antecedent effects of the soil, considering from the adjustment factor limitations imposed by other vegetation indexes in the Brazilian semiarid, giving reliability to the information on the different uses of the Caatinga soil and vegetation in the regions (Xu et al, 2009;Arraes et al, 2012;Silva and Galvíncio, 2012;Bezerra et al, 2014;Vorovencii, 2015;Ribeiro et al, 2016;Silva et al, 2019).…”

“…This study brings as main innovation, the semiarid regions of Brazil, in addition to the quantification of micrometeorological biophysical parameters of the Earth's surface, the creation of maps of the risk of environmental degradation in different degrees, using satellite images to propose new techniques for environmental monitoring subsidized by indicators that express the Spatialtemporal heterogeneity of the Caatinga biome, with high precision and on a large scale, with remote sensing being a promising tool to facilitate management in decision making to achieve sustainability in the use of hydric and natural resources, aiming mainly the protection and conservation of the remnants of natural Caatinga vegetation (Xu et al, 2009;Arraes et al, 2012;Giongo and Vettorazzi, 2014;Zheng et al, 2016;Silva et al, 2019).…”