Predictive Mapping of Plant Species and Communities Using GIS and Landsat Data in a Southern Mongolian Mountain Range

摘要: We assessed presence/absence prediction of plant species and communities in a southern Mongolian mountain range from geospatial data using randomized sampling approach. One hundred vegetation samples (3 × 3 m) were collected within the 2 km summit region Dund Saykhan range, which forms part core zone Gobi Gurvan National Park arid Mongolia. Using logistic regression, habitat preference models for all abundant (n = 52) 5) constructed; predictors derived Landsat 5 imagery digital elevation model. Nagelkerkes r2 was used an initial mining, significant validated by splitting one half accuracy assessment based on AUC (Area Under receiver operating characteristic Curve)-values. Significant could be built species. Altitude proved to most important predictor followed variables data. The clear altitudinal distribution patterns definitely reflect precipitation; overall biodiversity this environment is widely controlled moisture availability. chosen approach may prove valuable applied studies wherever spatial distributions are required conservation efforts.