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Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model

文献类型：期刊论文

作者Fang, Shu2; He, Zhibin1; Zhao, Minmin3
刊名FOREST SCIENCE
出版日期2022-10-11
页码11
ISSN号0015-749X
关键词Vegetation distribution Terrain data Scale Qilian Mountains
DOI10.1093/forsci/fxac040
通讯作者Fang, Shu(201930@slxy.edu.cn)
英文摘要Predicting vegetation distribution strengthens ecosystem management, protection, and restoration in arid and degraded areas. However, data quality and incomplete data coverage limit prediction accuracy for Picea crassifolia Kom. (Qinghai spruce) forest in the Qilian Mountains of China. Here, we used a logistic regression model combined with high-resolution vegetation distribution data for different sampling scales and digital elevation models (DEMs) to determine the potential distribution of P. crassifolia forest in the Dayekou catchment in the Qilian Mountains. We found that the model with the best simulation accuracy was based on data with a DEM scale of 30 m and a sampling accuracy of 90 m (Nagelkerke's R-2 = 0.48 and total prediction accuracy = 83.89%). The main factors affecting the distribution of P. crassifolia forest were elevation and potential solar radiation. We conclude that it is feasible to calculate the distribution of arid mountain forests based on terrain and that terrain data at 30 m spatial resolution can fully support the simulation of P. crassifolia forest distribution. Study Implications Data used for species predictions in mountainous areas are often scarce. Terrain data can be obtained relatively easily, and many factors, including temperature, soil moisture, solar radiation, and soil fertility, are influenced by and change with topography. Therefore, modeling vegetation distribution with topographic data alone may be highly desirable. However, data quality and scale limit the prediction accuracy of a model. Thus, we applied high-definition remote sensing data of Picea crassifolia Kom. forest at different digital elevation models (DEMs) and sampling scales to establish a DEM-based basic model of P. crassifolia forest distribution in the Qilian Mountains using a logistic regression equation.
WOS关键词SPATIAL-DISTRIBUTION ; NORTHWESTERN CHINA ; PICEA-CRASSIFOLIA ; QINGHAI SPRUCE ; TREE LINE ; TOPOGRAPHY ; PREDICTION ; VEGETATION ; SCALE ; RESOLUTION
资助项目Shangluo University[19SKY027] ; The Youth Innovation Team of Shaanxi Universities, The Strategic Priority Research Program of the Chinese Academy of Sciences[Y92C782001]
WOS研究方向Forestry
语种英语
出版者OXFORD UNIV PRESS INC
WOS记录号WOS:000865993800001
资助机构Shangluo University ; The Youth Innovation Team of Shaanxi Universities, The Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/185681]  
专题中国科学院地理科学与资源研究所
通讯作者Fang, Shu
作者单位1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Chinese Ecosyst Res Network,Linze Inland River Ba, Lanzhou 730000, Peoples R China
2.Shangluo Univ, Coll Urban Rural Planning & Architectural Engn, Shangluo 726000, Peoples R China
3.China Geol Survey, Key Lab Hydrogeol, Ctr Hydrogeol & Environm Geol Survey, Baoding 071051, Peoples R China
推荐引用方式
GB/T 7714		 Fang, Shu,He, Zhibin,Zhao, Minmin. Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model[J]. FOREST SCIENCE,2022:11.
APA Fang, Shu,He, Zhibin,&Zhao, Minmin.(2022).Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model.FOREST SCIENCE,11.
MLA Fang, Shu,et al."Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model".FOREST SCIENCE (2022):11.

入库方式： OAI收割

来源：地理科学与资源研究所

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