Land Use and Land Cover Change Patterns from Orbital Remote Sensing Products: Spatial Dynamics and Trend Analysis in Northeastern Brazil

Jhon Lennon Bezerra da Silva (), Marcos Vinícius da Silva, Pabrício Marcos Oliveira Lopes, Rodrigo Couto Santos, Ailton Alves de Carvalho, Geber Barbosa de Albuquerque Moura, Thieres George Freire da Silva, Alan Cézar Bezerra, Alexandre Maniçoba da Rosa Ferraz Jardim, Maria Beatriz Ferreira, Patrícia Costa Silva, Josef Augusto Oberdan Souza Silva, Marcio Mesquita, Pedro Henrique Dias Batista, Rodrigo Aparecido Jordan and Henrique Fonseca Elias de Oliveira
Additional contact information
Jhon Lennon Bezerra da Silva: Cerrado Irrigation Graduate Program, Goiano Federal Institute—Campus Ceres, GO-154, km 218-Zona Rural, Ceres 76300-000, GO, Brazil
Marcos Vinícius da Silva: Department of Engineering Agricultural, Centro of Sciences Chapadinha, Federal University of Maranhão, BR-222, Chapadinha 65500-000, MA, Brazil
Pabrício Marcos Oliveira Lopes: Department of Engineering Agricultural, Federal Rural University of Pernambuco (UFRPE), Rua Dom Manoel de Medeiros, Dois Irmãos, Recife 52171-900, PE, Brazil
Rodrigo Couto Santos: Faculty of Agricultural Sciences, Federal University of Grande Dourados (UFGD), Rodovia Dourados-Itahum, km 12, Campus Universitário (Unidade II), Dourados 79804-970, MS, Brazil
Ailton Alves de Carvalho: Academic Unit of Serra Talhada (UAST), Federal Rural University of Pernambuco (UFRPE), Gregório Ferraz Nogueira Ave., Serra Talhada 56909-535, PE, Brazil
Geber Barbosa de Albuquerque Moura: Department of Engineering Agricultural, Federal Rural University of Pernambuco (UFRPE), Rua Dom Manoel de Medeiros, Dois Irmãos, Recife 52171-900, PE, Brazil
Thieres George Freire da Silva: Department of Engineering Agricultural, Federal Rural University of Pernambuco (UFRPE), Rua Dom Manoel de Medeiros, Dois Irmãos, Recife 52171-900, PE, Brazil
Alan Cézar Bezerra: Academic Unit of Serra Talhada (UAST), Federal Rural University of Pernambuco (UFRPE), Gregório Ferraz Nogueira Ave., Serra Talhada 56909-535, PE, Brazil
Alexandre Maniçoba da Rosa Ferraz Jardim: Department of Engineering Agricultural, Federal Rural University of Pernambuco (UFRPE), Rua Dom Manoel de Medeiros, Dois Irmãos, Recife 52171-900, PE, Brazil
Maria Beatriz Ferreira: Postgraduate Program in Forest Sciences, Federal Rural University of Pernambuco (UFRPE), Rua Dom Manoel de Medeiros, Dois Irmãos, Recife 52171-900, PE, Brazil
Patrícia Costa Silva: Department of Agricultural Engineering, State University of Goiás, Santa Helena de Goiás 75920-000, GO, Brazil
Josef Augusto Oberdan Souza Silva: Cerrado Irrigation Graduate Program, Goiano Federal Institute—Campus Ceres, GO-154, km 218-Zona Rural, Ceres 76300-000, GO, Brazil
Marcio Mesquita: Cerrado Irrigation Graduate Program, Goiano Federal Institute—Campus Ceres, GO-154, km 218-Zona Rural, Ceres 76300-000, GO, Brazil
Pedro Henrique Dias Batista: Ceará State Technical Assistance and Rural Extension Company, Tenente José Vicente Avenue, 1017, Center, Itapipoca 62504-095, CE, Brazil
Rodrigo Aparecido Jordan: Faculty of Agricultural Sciences, Federal University of Grande Dourados (UFGD), Rodovia Dourados-Itahum, km 12, Campus Universitário (Unidade II), Dourados 79804-970, MS, Brazil
Henrique Fonseca Elias de Oliveira: Cerrado Irrigation Graduate Program, Goiano Federal Institute—Campus Ceres, GO-154, km 218-Zona Rural, Ceres 76300-000, GO, Brazil

Land, 2025, vol. 14, issue 10, 1-38

Abstract: Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km 2 and a total loss of −74,117.06 km 2 . Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km 2 and a total gain of +79,577.96 km 2 , indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km 2 . Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB.

Keywords: MODIS sensor product; MapBiomas Brazil; Mann–Kendall and Sen’s slope; LULC; land degradation; regional climate variability; Google Earth Engine (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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