Temporal analysis of desertification vulnerability in Northeast Brazil using Google Earth Engine

Abstract

Intensive agricultural production has negative environmental impacts, increasing predisposition to desertification in areas with inadequate soil management. To evaluate the effects of agriculture, we aimed to perform a temporal analysis and analyze the development of desertification in Northeast Brazil. Initially, the region was separated into four climatic zones, grouping the locations with similar characteristics of air temperature and rainfall. In the analyses, images of the northeast region acquired from the Landsat 5 (1989, 1999, and 2009) and Landsat 8 (2019) satellites were processed using the Google Earth Engine platform. The periods were separated into dry and rainy periods, according to the climatic zones. The evaluation of vegetation index, characterization of the remaining vegetation cover, landscape pattern, and meteorological conditions were performed, respectively, through the Normalized Difference Vegetation Index, Modified Soil‐Adjusted Vegetation Index, and albedo of the terrestrial surface. The desertification index showed an increase in susceptible areas when comparing the interval from 1989 to 2019. In general, our results showed that there is less predisposition for desertification to occur during the rainy season. Also, when the results of our study were compared to official data released by the National Program of Action to Combat Desertification and Mitigate the Effects of Drought, there was disagreement regarding the classification of areas.

Vulnerability Identification and Analysis of Contributors to Desertification in Inner Mongolia

“…Based on the annual change rate of RUE, PT RUE , and HA RUE , the driving effects of PT and HA on desertification vulnerability were divided into eight scenarios for discussion, but whether there is a more logical way to divide the scenarios needs to be explored further. Studies have shown that wind [43], overgrazing, deforestation [44], and overcultivation [45] are also important contributors to desertification. Anthropogenic activity is a double-edged sword for vegetation change [46].…”

Analisis Kerapatan Vegetasi Kota Ambon Menggunakan Data Citra Satelit Sentinel-2 dengan Metode MSARVI Berbasis Machine Learning pada Google Earth Engine

“…Analisis Kerapatan Vegetasi Menggunakan Data Citra Satelit Sentinel-2 dengan metode MSARVI dapat dilakukan secara efektif dan cepat dengan menggunakan platform Google Earth Engine (GEE) yang mendukung pemrosesan dan analisis data citra satelit berbasis cloud computing [10]. GEE juga menyediakan berbagai alat dan fungsi untuk melakukan analisis citra dan machine learning yang dapat digunakan untuk meningkatkan akurasi hasil analisis kerapatan vegetasi.…”

“…Analisis kerapatan vegetasi Kota Ambon menggunakan data citra satelit sentinel-2 dengan metode MSARVI berbasis machine learning pada google earth engine. Metode MSARVI adalah salah satu metode untuk mengukur kerapatan vegetasi yang didasarkan pada perbedaan reflektansi antara spektrum inframerah dekat (Near Infrared/NIR) dan merah (Red) pada cahaya matahari yang dipantulkan oleh permukaan bumi [10]. MSARVI merupakan pengembangan dari metode Normalized Difference Vegetation Index (NDVI), yang telah lama digunakan untuk mengukur kerapatan vegetasi.…”

Spatiotemporal Dynamics of Land Use and Land Cover through Physical–Hydraulic Indices: Insights in the São Francisco River Transboundary Region, Brazilian Semiarid Area

“…The northeast region is among the areas susceptible to the desertification process, with semiarid and sub-humid climates and subject to intense annual rainfall variations and vegetation suppression [10][11][12][13][14]. Removal of the vegetation cover causes soil erosion, which reduces the water retention capacity and consequently decreases the soil organic matter, leading to soil degradation, threatening biodiversity, and reducing plant biomass [15].…”