Modeling the Spatial Distribution of Acacia decurrens Plantation Forests Using PlanetScope Images and Environmental Variables in the Northwestern Highlands of Ethiopia

• Published in: Forests 2024-02, Vol.15 (2), p.277
• Main Authors: Alemayehu, Bireda, Suarez-Minguez, Juan, Rosette, Jacqueline
• Format: Article
• Language: English
• Subjects: Acacia decurrens; Agricultural land; Agriculture; Algorithms; Biodiversity; Carbon sequestration; Climate change; Deforestation; Ecosystems; Energy requirements; Environmental economics; Environmental impact; Fagita Lekoma; Forests; Geographical distribution; Highlands; Land degradation; Land use; Land use management; Land use planning; Machine learning; Nonnative species; Performance evaluation; PlanetScope image; plantation forests; Plantations; Remote sensing; SDM; Spatial distribution; Sustainable agriculture; Vegetation; Vegetation index; Water conservation
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15020277

Small-scale Acacia decurrens plantation forests, established by farmers on degraded lands, have become increasingly prevalent in the Northwestern Highlands of Ethiopia. This trend has been particularly notable in Fagita Lekoma District over the past few decades. Such plantations play a significant role in addressing concerns related to sustainable agricultural land use, mitigating the adverse effects of deforestation, and meeting the livelihood and energy requirements of a growing population. However, the spatial distribution of Acacia decurrens and the essential remote sensing and environmental variables that determine its distribution are not well understood. This study aimed to model the spatial distribution of Acacia decurrens plantation forests using PlanetScope data and environmental variables combined with a species distribution model (SDM). Employing 557 presence/absence points, noncollinear variables were identified and utilized as input for six SDM algorithms, with a 70:30 split between training and test data, and 10-fold bootstrap replication. The model performance was evaluated using the receiver operation characteristic curve (AUC) and true skill statics (TSS). The ensemble model, which combined results from six individual algorithms, was implemented to predict the spatial distribution of Acacia decurrens. The highest accuracy with the values of 0.93 (AUC) and 0.82 (TSS) was observed using random forest (RF), followed by SVM with values of 0.89 (AUC) and 0.71 (TSS), and BRT with values of 0.89 (AUC) and 0.7 (TSS). According to the ensemble model result, Acacia decurrens plantation forests cover 22.44% of the district, with the spatial distribution decreasing towards lower elevation areas in the northeastern and western parts of the district. The major determinant variables for identifying the species were vegetation indices, specifically CVI, ARVI, and GI, with AUC metric values of 39.3%, 16%, and 7.1%, respectively. The findings of this study indicate that the combination of high-resolution remote sensing-derived vegetation indices and environmental variables using SDM could play a vital role in identifying Acacia decurrens plantations, offering valuable insights for land use planning and management strategies. Moreover, comprehending the spatial distribution’s extent is crucial baseline information for assessing its environmental implications at a local scale.