Published: Jan. 1, 2024

The fall armyworm (Spodoptera frugiperda, FAW) is a significant migratory agricultural pest under the global warning of Food and Agriculture Organization United Nations (FAO). Habitat suitability monitoring analysis for FAW can help support more scientific management dynamics decision-making on prevention control. In this study, we proposed monthly habitat model that integrates multi-source remote sensing data such as climate, land use, vegetation, soil, taking Africa, which seriously affected by FAW, study area. First, exploratory factor (EFA) was employed to reconstruct climate variables obtain three factors characterizing temperature, humidity, wind respectively. Then, indicators were constructed combining with other environmental variables, in Africa developed using random forest algorithm. Finally, based model, distribution month 2023 analyzed, along temporal spatial variation characteristics suitability. results indicate that: (1) effectively extracted information raw demonstrated good interpretability. African closely associated use/land cover (LULC), normalized difference vegetation index (NDVI), humidity (F2). (2) algorithm exhibited high precision (>0.9) across various metrics including accuracy, sensitivity, specificity, F1-score, AUC, Kappa, TSS. presence points verified validity model. (3) 2023, mainly distributed West East south Sahara Desert, Nile Delta north Desert. With seasonal changes, suitable unsuitable areas shift, movement pattern center basically consistent precipitation zone. use types grasslands, savannas, croplands are hotspots infestation rainy season every year. demonstrate approach data, realize dynamic high-precision provide references control insect pests, promote sustainable development agriculture.

Language: Английский