Related Experiment Video
Updated: Jun 13, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published on: October 11, 2016
Analysis of Spatiotemporal Changes and Driving Forces of Vegetation Coverage in the Upper Reaches of the Hanjiang
Yongkang Ding1, Jianwei Sun1, Hao Zhao1
1Xi'an Mineral Resources Survey, China Geological Survey, Xi'an, China.
Abstract:
The upper reaches of the Hanjiang River Basin (HJRB) are a crucial water source hub that connects the water resources between the south and north of China. Quantifying the spatiotemporal patterns and underlying drivers of vegetation dynamics in this region is indispensable for formulating targeted ecological management strategies. This study used MOD13Q1 (250 m) remote sensing data from May to September during 2000-2020, and comprehensively applied Theil-Sen + Mann-Kendall and Hurst index methods to analyze the spatiotemporal changes of NDVI, which was verified by stability analysis. When considering the impacts of natural and human driving forces, an innovative optimal parameter geographic detector (OPGD) model was utilized, effectively addressing the modifiable areal unit problem (MAUP) often overlooked by traditional methods, thereby enhancing the rigor of statistical evaluation and the precision of identifying NDVI drivers. It solves the deficiencies of traditional methods in handling spatially heterogeneous data and enhances the reliability of the model. The findings revealed the following: (1) The regional NDVI significantly increased from 2000 to 2020 (slope = 0.0033 a-1, p < 0.05) and is anticipated to maintain this upward trend going forward. (2) Altitude, surface temperature, slope, and air temperature are the main driving factors for NDVI changes (with q values uniformly surpassing 0.45). (3) The explanatory power of two-factor interactions on NDVI changes substantially exceeded that of single factors. Among these factors, the interaction between surface temperature and slope exerts the greatest influence on NDVI changes (q = 0.7266), and altitude interactions with other factors are dominant. (4) Except for specific combinations, the vast majority of the interactions of two factors have significant differences with NDVI. At the same time, the optimal threshold intervals or types of each factor conducive to vegetation growth have been determined.
Related Concept Videos
Manipulation and Analysis
Design Example: Analyzing Capacity Contours for Flood Risk Assessment