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基于贝叶斯层次时空模型的甘肃省土地利用程度演变分析
引用本文:于明雪,孙建国,杨维涛,谢甫,吕建康. 基于贝叶斯层次时空模型的甘肃省土地利用程度演变分析[J]. 地理科学, 2022, 42(5): 918-925. DOI: 10.13249/j.cnki.sgs.2022.05.017
作者姓名:于明雪  孙建国  杨维涛  谢甫  吕建康
作者单位:兰州交通大学测绘与地理信息学院,甘肃兰州730070;地理国情监测技术应用国家地方联合研究中心,甘肃兰州730070;甘肃省地理国情监测工程实验室,甘肃兰州730070
基金项目:甘肃省科技计划项目(20YF3GA013);兰州交通大学优秀平台项目资助(201806)
摘    要:以甘肃省为例,在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上,利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明:① 研究期间内甘肃省土地利用程度呈增长趋势,其中1995―2000年和2010―2015年增长速度较明显;② 土地利用程度空间格局“东高西低”,热点区域主要分布在陇中、陇东和陇南地区;③ 土地利用程度局部变化呈现明显区域差异,整体表现为“东弱西强”,局部变化热点区域主要分布在河西地区;④ 影响土地利用程度变化的主要因素是经济规模和产业结构,其中经济因素影响程度最高。

关 键 词:土地利用  贝叶斯层次时空模型  Google Earth Engine  甘肃省
收稿时间:2021-08-10
修稿时间:2022-01-01

Evolution of Land Use Degree in Gansu Province Based on Bayesian Hierarchical Spatio-temporal Model
Yu Mingxue,Sun Jianguo,Yang Weitao,Xie Fu,Lyu Jiankang. Evolution of Land Use Degree in Gansu Province Based on Bayesian Hierarchical Spatio-temporal Model[J]. Scientia Geographica Sinica, 2022, 42(5): 918-925. DOI: 10.13249/j.cnki.sgs.2022.05.017
Authors:Yu Mingxue  Sun Jianguo  Yang Weitao  Xie Fu  Lyu Jiankang
Affiliation:1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, Gansu, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, Gansu, China
Abstract:Land use change has always been an important content of global change research. An in-depth understanding of the temporal and spatial characteristics of land use change can not only provide a direct decision-making basis for the optimal allocation of land resources, but also provide important data support for regulating ecosystem management and improving human social well-being. However, previous studies on land use change lack the analysis of the spatio-temporal coupling process. Taking Gansu Province as an example, based on the Google Earth Engine (GEE) platform to achieve land change monitoring in 1995, 2000, 2005, 2010, 2015 and 2020, and uses Bayesian hierarchical spatio?temporal model (BHM) to analyze the characteristics of temporal and spatial changes of land use degree. The results show that: 1) During the study period, the land use degree of Gansu Province showed an increasing trend, among which the growth rate was obvious from 1995 to 2000 and 2010 to 2015; 2) The spatial pattern of land use degree is “high in the east and low in the west”, hot spots mainly distributed in Longzhong, Longdong and Longnan regions; 3) The local changes of land use degree show obvious regional differences, and the overall performance is “weak in the east and strong in the west”. The hot spots of local changes are mainly distributed in the Hexi region; 4) The main factors affecting changes of land use degree are economic scale and industrial structure, among which economic factors have the highest degree of influence.
Keywords:land use  Bayesian hierarchical spatio-temporal model  Google Earth Engine  Gansu Province  
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