Using Remote Sensing to Quantify the Joint Effects of Climate and Land Use/Land Cover Changes on the Caatinga Biome of Northeast Brazilian

• Autor(a) principal: Jardim, Alexandre Maniçoba da Rosa Ferraz
• Data de Publicação: 2022
• Outros Autores: Araújo Júnior, George do Nascimento, Silva, Marcos Vinícius da, Santos, Anderson dos, Silva, Jhon Lennon Bezerra da, Pandorfi, Héliton, Oliveira-Júnior, José Francisco de, Teixeira, Antônio Heriberto de Castro, Teodoro, Paulo Eduardo, Lima, João L. M. P. de, Silva Junior, Carlos Antonio da, Souza, Luciana Sandra Bastos de, Silva, Emanuel Araújo, Silva, Thieres George Freire da
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/100474; https://doi.org/10.3390/rs14081911
• topic: tropical dry forest; surface energy balance; Brazilian semi-arid; SEBAL; actual evapotranspiration

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region’s area had LST between 48.0 and 52.0 C, raising ETa rates (~4.7 mm day1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin’s concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions.