{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:13:04Z","timestamp":1774548784483,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program","award":["2021xjkk0303"],"award-info":[{"award-number":["2021xjkk0303"]}]},{"name":"National Key Research and Development Program","award":["2018YFB0505301"],"award-info":[{"award-number":["2018YFB0505301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the driving mechanisms of vegetation development is critical for maintaining terrestrial ecosystem function in mountain areas, especially under the background of climate change. The Qinba Mountains (QBM), a critical north\u2013south transition zone in China, is an environmentally fragile area that is vulnerable to climate change. It is essential to characterize how its ecological environment has changed. Currently, such a characterization remains unclear in the spatiotemporal patterns of the nonlinear effects and interactions between environmental factors and vegetation changes in the QBM. Here, we utilized the Normalized Difference Vegetation Index (NDVI), obtained from Google Earth Engine (GEE) platform, as an indicator of terrestrial ecosystem conditions. Then, we measured the spatiotemporal heterogeneity for vegetation variation in the QBM from 2003 to 2018. Specifically, the Geodetector method, a new geographically statistical method without linear assumptions, was employed to detect the interaction between vegetation and environmental driving factors. The results indicated that there is a trend of a general increase in vegetation growth amplitude (the average NDVI increased from 0.810 to 0.858). The areas with an NDVI greater than 0.8 are mainly distributed in the Qinling Mountains and the Daba Mountains, which account for more than 76.39% of the QBM area. For the entire region, the global Moran\u2019s index of the NDVI is greater than 0.95, indicating that vegetation is highly concentrated in the spatial domain. The Geodetector identified that landform type was the primary factor in controlling vegetation changes, contributing 24.19% to the total variation, while the explanatory powers of the aridity index and the wetness index for vegetation changes were 22.49% and 21.47%, respectively. Furthermore, the interaction effects between any two factors outperformed the influence of a single environmental variable. The interaction between air temperature and the aridity index was the most significant element, contributing to 47.10% of the vegetation variation. These findings can not only improve our understanding in the interactive effects of environmental forces on vegetation change, but also be a valuable reference for ecosystem management in the QBM area, such as ecological conservation planning and the assessment of ecosystem functions.<\/jats:p>","DOI":"10.3390\/rs14225794","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T03:27:44Z","timestamp":1668655664000},"page":"5794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Using the Geodetector Method to Characterize the Spatiotemporal Dynamics of Vegetation and Its Interaction with Environmental Factors in the Qinba Mountains, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Shuhui","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2559-0241","authenticated-orcid":false,"given":"Yuke","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[{"name":"State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"Information Centre, Department of Natural Resources of Sichuan Province, Chengdu 610072, China"}]},{"given":"Ruixin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"}]},{"given":"Wenlong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105694","DOI":"10.1016\/j.catena.2021.105694","article-title":"Three-dimensional dynamic characteristics of vegetation and its response to climatic factors in the Qilian Mountains","volume":"208","author":"Ma","year":"2022","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, W., Ma, X., Moazzam Nizami, S., Tian, C., and Guo, F. 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