Land Cover Transformations in Mining-Influenced Areas Using PlanetScope Imagery, Spectral Indices, and Machine Learning: A Case Study in the Hinterlands de Pernambuco, Brazil

Admilson da Penha Pacheco, João Alexandre Silva do Nascimento, Antonio Miguel Ruiz-Armenteros (), Ubiratan Joaquim da Silva Junior, Juarez Antonio da Silva Junior, Leidjane Maria Maciel de Oliveira, Sylvana Melo dos Santos, Fernando Dacal Reis Filho and Carlos Alberto Pessoa Mello Galdino
Additional contact information
Admilson da Penha Pacheco: Department of Cartographic and Surveying Engineering, Center for Technology and Geosciences, Federal University of Pernambuco, Av. Prof. Moraes Rego, 1235, Cidade Universitária, Recife 50670-901, Brazil
João Alexandre Silva do Nascimento: Department of Cartographic and Surveying Engineering, Center for Technology and Geosciences, Federal University of Pernambuco, Av. Prof. Moraes Rego, 1235, Cidade Universitária, Recife 50670-901, Brazil
Antonio Miguel Ruiz-Armenteros: Department of Cartographic, Geodetic and Photogrammetry Engineering, University of Jaén, Campus Las Lagunillas s/n, 23071 Jaén, Spain
Ubiratan Joaquim da Silva Junior: PostGraduate Program in Civil Engineering (PPGEC), Department of Civil and Environmental Engineering, Federal University of Pernambuco, Recife 50050-000, Brazil
Juarez Antonio da Silva Junior: PostGraduate Program in Civil Engineering (PPGEC), Department of Civil and Environmental Engineering, Federal University of Pernambuco, Recife 50050-000, Brazil
Leidjane Maria Maciel de Oliveira: PostGraduate Program in Civil Engineering (PPGEC), Department of Civil and Environmental Engineering, Federal University of Pernambuco, Recife 50050-000, Brazil
Sylvana Melo dos Santos: PostGraduate Program in Civil Engineering (PPGEC), Department of Civil and Environmental Engineering, Federal University of Pernambuco, Recife 50050-000, Brazil
Fernando Dacal Reis Filho: Department of Cartographic and Surveying Engineering, Center for Technology and Geosciences, Federal University of Pernambuco, Av. Prof. Moraes Rego, 1235, Cidade Universitária, Recife 50670-901, Brazil
Carlos Alberto Pessoa Mello Galdino: Department of Cartographic and Surveying Engineering, Center for Technology and Geosciences, Federal University of Pernambuco, Av. Prof. Moraes Rego, 1235, Cidade Universitária, Recife 50670-901, Brazil

Land, 2025, vol. 14, issue 2, 1-25

Abstract: The uncontrolled expansion of mining activities has caused severe environmental impacts in semi-arid regions, endangering fragile ecosystems and water resources. This study aimed to propose a decision-making model to identify land use and land cover changes in the semi-arid region of Pernambuco, Brazil, caused by mining through a spatiotemporal analysis using high-resolution images from the PlanetScope satellite constellation. The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. As a result, a 3.28% reduction in vegetated areas and a 6.62% increase in urban areas were identified over five years, suggesting landscape transformation, possibly influenced by the expansion of mining and development activities. The application of kNN yielded an Overall Accuracy (OA) greater than 99% and a Kappa index of 0.98, demonstrating the effectiveness of the adopted methodology. However, challenges were encountered in distinguishing between constructions and bare soil, with the Jeffries–Matusita distance (JMD) analysis indicating a value below 0.34, while the similarity between water and vegetation highlights the need for more comprehensive training data. The results indicated that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built-up areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in the affected areas. This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas.

Keywords: mining; land use and land cover; remote sensing; PlanetScope; spectral indices; kNN (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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