Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia
نویسندگان
چکیده
Abstract Background Species Distribution Modelling (SDM) coupled with freely available multispectral imagery from Sentinel-2 (S2) satellite provides an immense contribution in monitoring invasive species. However, attempts to evaluate the performances of SDMs using S2 spectral bands and Radiometric Indices (S2-RIs) biophysical variables, particular, were limited. Hence, this study aimed at evaluating performance six commonly used one ensemble model for S2-based variables modelling current distribution Prosopis juliflora lower Awash River basin, Ethiopia. Thirty-five computed Sentinel-2B level-2A, out twelve significant selected Variable Inflation Factor (VIF). A total 680 presence absence data collected train validate tenfold bootstrap replication approach R software “sdm” package. The models was evaluated sensitivity, specificity, True Skill Statistics (TSS), kappa coefficient, area under curve (AUC), correlation. Results Our findings demonstrated that except bioclim all machine learning regression provided successful prediction. Among tested models, Random Forest (RF) performed better 93% TSS 99% AUC followed by Boosted Regression Trees (BRT), ensemble, Generalized Additive Model (GAM), Support Vector Machine (SVM), Linear (GLM) decreasing order. relative influence vegetation indices highest soil indices, water According RF prediction, 16.14% (1553.5 km 2 ) invaded alien Conclusions results highlighted S2-RIs combined have a higher capacity species distribution. Besides, use algorithms such as algorithm is highly essential remote sensing-based SDM.
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ژورنال
عنوان ژورنال: Ecological processes
سال: 2021
ISSN: ['2097-1311', '2192-1709']
DOI: https://doi.org/10.1186/s13717-021-00285-6