Grassland locusts harm a large amount of grassland every year. Grassland locusts have caused devastating disasters across grassland resources and have greatly impacted the lives of herdsmen. Due to the impacts of climate change and human activity, the distribution of grassland locust habitats changes constantly. The monitoring and identification of locust habitats is of great significance for the production and utilization of grassland resources. In order to further understand the behavior of these grassland pests and carry out precise prevention and control strategies, researchers have often used survey points to reveal the distribution of habitat-suitability areas or establish the high density of locusts (more than 15 locusts/m²) to identify the different risk levels of habitat-suitability areas for grassland locusts. However, the results of these two methods have often been too large, which is not conducive to the precise control of grassland locusts in large areas. Starting from the sample points of our locust investigation, we conducted a hierarchical prediction of the density of locusts and used the probability value of locust occurrence, as predicted by a maximum entropy model, to categorize the habitat-suitability areas according to the probability thresholds of suitable species growth. The results were in good agreement with the actual situation and there was little difference between the prediction results for locust densities greater than 15 locusts/m² in the middle- and high-density habitat-suitability areas and those for all survey points, while there was a big difference between the prediction results for densities in the middle- and low-density habitat-suitability areas and those for all survey points. These results could provide a basis for the efficient and accurate control of grassland locusts and could have practical significance for future guidance.

Grasshoppers can significantly disrupt agricultural and livestock management because they reproduce and develop quickly in friendly environments. Xilingol League is the region most severely affected by grasshopper infestations. The region’s extensive grasslands are considered valuable, a critical component of the local ecosystem, a vital resource for the region’s key economic activity of livestock farming, and crucial for supporting diverse flora and fauna, carbon sequestration, and climate regulation. Oedaleus decorus asiaticus (O. d. asiaticus) is highly harmful in Xilingol League in the Inner Mongolia Autonomous Region of China. Therefore, early warning is crucial for projecting O. d. asiaticus’s regional spread and detecting the impacts of critical environmental elements. We systematically identified 26 major contributing elements by examining four categories of environmental factors—meteorology, vegetation, soil, and topography—encompassing the three growth phases of grasshoppers. Furthermore, the MaxEnt and frequency ratio (FR) approaches, coupled with multisource remote sensing data, were used to predict a potentially appropriate distribution (habitat suitability) of O. d. asiaticus in Xilingol League. The research found nine key habitat factors influencing O. d. asiaticus distribution: the mean specific humidity during the adult stage (ASH), vegetation type (VT), above-ground biomass during the nymph stage (NAB), soil sand content (SSAND), mean precipitation during the egg stage (EP), mean precipitation during the nymph stage (NP), soil bulk density (SBD), elevation, and soil type (ST). Additionally, our analysis revealed that the most suitable and moderately suitable habitats for O. d. asiaticus are predominantly located in the southern and eastern parts of Xilingol League, with significant concentrations in West Ujumqin, East Ujumqin, Xilinhot, Zhenglan, Zheng Xiangbai, Duolun, and Taipusi. Based on the suitable habitat results, policymakers may make judgments about future management actions to preserve the ecological security of grasslands and their sustainable growth. This study indicates that the Maxent approach exhibited superior accuracy (receiver operating characteristic) compared to the FR approach for assessing the habitat suitability for O. d. asiaticus in Xilingol League.

Grasshopper outbreaks severely threaten pastoral agriculture and grassland ecosystems in Inner Mongolia, China. Identifying suitable habitats and understanding their monthly dynamics are critical for early warning and targeted control. This study integrated multi-source remote sensing data (Moderate Resolution Imaging Spectroradiometer land surface temperature (MODIS LST), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI)), meteorological data (precipitation and temperature), soil and topographic variables, and field survey data (May–July 2023) to construct a coupled Random Forest (RF) and Maximum Entropy (MaxEnt) model for the dominant grasshopper species Dasyhippus barbipes. Key objectives were to identify environmental variables at different developmental stages, map monthly suitable habitats, quantify inter-monthly spatiotemporal-shift patterns of suitable habitats using a suitability class transition matrix, and analyze association patterns of NDVI, precipitation, and LST with suitability transitions. The results showed the following: (1) Key drivers varied monthly—vegetation/soil types, precipitation, and FVC dominated in May; FVC, SOC, slope, and NDVI increased in June; and soil, vegetation, aspect, and Max. The temperature of Warmest Month (Bio5) dominated in July. (2) Highly suitable areas expanded eastward from central Xilingol League and Chifeng City, following west-to-east and center-to-north-and-south diffusion patterns. (3) Vegetation green-up, precipitation (as a limiting moisture driver), and LST (threshold effects on development rates) jointly formed a vegetation–water–heat synergistic system. (4) The coupled Random Forest (RF) and Maximum Entropy (MaxEnt) model achieved high accuracy (AUC > 0.91), with variable importance matching observed spatiotemporal patterns. This study elucidates the monthly scale evolution of D. barbipes-suitable habitats and multi-factor associations between environmental predictors and habitat suitability, providing a scientific basis for refined monitoring, early warning, and green control in Inner Mongolia’s typical steppes.