4:30 PM - 4:45 PM
[U04-11] Remote Sensing-Based Analysis of Mongolian Gazelle Migration and Habitat Selection in Relation to Vegetation Productivity
Keywords:Remote sensing, NDVI, vegetation biomass, geopatial analysis
Introduction: The Mongolian gazelle (Procapra gutturosa) is a nomadic ungulate species adapted to the vast grasslands of Eastern Mongolia. The selection of calving grounds and seasonal migration routes is closely tied to vegetation productivity. This study applies remote sensing techniques to assess how NDVI (Normalized Difference Vegetation Index) and vegetation biomass influence habitat selection, with a specific focus on identifying calving areas using geospatial analysis.
Methods: We integrated multi-temporal MODIS NDVI satellite imagery with ground-based plant biomass measurements and Disk Plate Meter (DPM) data. GPS collar tracking data from Mongolian gazelles were analyzed to assess spatial movement patterns in response to seasonal variations in vegetation productivity. Statistical analyses, including ANOVA and multiple regression modeling, were performed to quantify the relationship between remote sensing-derived NDVI values and field-measured biomass. A geospatial habitat suitability model was developed using GIS to map preferred calving sites.
Results: ANOVA results revealed significant differences in plant biomass across sites (F = 29.87, p < 0.001). Tukey’s HSD post-hoc analysis identified Khar Ymaat and Menengiin Tsagaan Khooloi as the regions with the highest NDVI values and biomass, while Jaran Togoo and Toson Khulstai exhibited the lowest vegetation productivity (p < 0.001). Correlation analysis confirmed NDVI as a stronger predictor of biomass (r = 0.411) compared to DPM (r = 0.195). Regression modeling further indicated that NDVI significantly influences biomass distribution (p < 2e-16), suggesting that gazelles prioritize areas with higher NDVI values when selecting calving sites.
Discussion: The integration of GPS tracking data with remote sensing-derived NDVI and biomass measurements provides critical insights into Mongolian gazelle habitat selection. The results indicate that gazelles prefer areas with higher NDVI and biomass for calving, likely due to improved forage quality and reduced predation risk. Geospatial modeling highlights spatial patterns of habitat use, emphasizing the importance of remote sensing in large-scale ungulate migration studies.
Conclusion & Implications: This study demonstrates the effectiveness of remote sensing techniques in evaluating habitat selection and movement ecology of Mongolian gazelles. The use of NDVI and GIS-based modeling provides a scalable approach for monitoring rangeland conditions and predicting key calving areas. Future research should explore seasonal and interannual vegetation changes to refine habitat suitability models further. Conservation strategies should focus on preserving high-biomass regions with minimal anthropogenic disturbance to support sustainable gazelle populations.
Methods: We integrated multi-temporal MODIS NDVI satellite imagery with ground-based plant biomass measurements and Disk Plate Meter (DPM) data. GPS collar tracking data from Mongolian gazelles were analyzed to assess spatial movement patterns in response to seasonal variations in vegetation productivity. Statistical analyses, including ANOVA and multiple regression modeling, were performed to quantify the relationship between remote sensing-derived NDVI values and field-measured biomass. A geospatial habitat suitability model was developed using GIS to map preferred calving sites.
Results: ANOVA results revealed significant differences in plant biomass across sites (F = 29.87, p < 0.001). Tukey’s HSD post-hoc analysis identified Khar Ymaat and Menengiin Tsagaan Khooloi as the regions with the highest NDVI values and biomass, while Jaran Togoo and Toson Khulstai exhibited the lowest vegetation productivity (p < 0.001). Correlation analysis confirmed NDVI as a stronger predictor of biomass (r = 0.411) compared to DPM (r = 0.195). Regression modeling further indicated that NDVI significantly influences biomass distribution (p < 2e-16), suggesting that gazelles prioritize areas with higher NDVI values when selecting calving sites.
Discussion: The integration of GPS tracking data with remote sensing-derived NDVI and biomass measurements provides critical insights into Mongolian gazelle habitat selection. The results indicate that gazelles prefer areas with higher NDVI and biomass for calving, likely due to improved forage quality and reduced predation risk. Geospatial modeling highlights spatial patterns of habitat use, emphasizing the importance of remote sensing in large-scale ungulate migration studies.
Conclusion & Implications: This study demonstrates the effectiveness of remote sensing techniques in evaluating habitat selection and movement ecology of Mongolian gazelles. The use of NDVI and GIS-based modeling provides a scalable approach for monitoring rangeland conditions and predicting key calving areas. Future research should explore seasonal and interannual vegetation changes to refine habitat suitability models further. Conservation strategies should focus on preserving high-biomass regions with minimal anthropogenic disturbance to support sustainable gazelle populations.