贵州省土地利用变化频数分布及驱动因素

谢丽钧; 杨广斌; 王仁儒; 杨丽

doi:10.13961/j.cnki.stbctb.2024.04.034

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贵州省土地利用变化频数分布及驱动因素

试验研究

- 贵州省土地利用变化频数分布及驱动因素
- Frequency Distribution and Driving Factors of Land Use Changes in Guizhou Province
- 水土保持通报 2024年44卷第4期页码：330-339
- 作者机构：
  
  贵州师范大学地理与环境科学学院,贵州,贵阳,550025
- 作者简介：
- 基金信息：
  
  贵州省基础研究(自然科学)项目“基于深度学习与遥感的贵州省喀斯特地区水土保持碳汇能力评估的智能核算方法研究”(黔科合基础-ZK[2024]一般445);贵州省科技计划项目“基于遥感大数据的自然资源统计和资产价值评估”(黔科合重大专项[2022]001)
- DOI：10.13961/j.cnki.stbctb.2024.04.034
  中图分类号： {F301.5}
- 纸质出版：2024
- 稿件说明：
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谢丽钧, 杨广斌, 王仁儒, 等. 贵州省土地利用变化频数分布及驱动因素[J]. 水土保持通报, 2024,44(4):330-339.

Xie Lijun, Yang Guangbin, Wang Renru, et al. Frequency Distribution and Driving Factors of Land Use Changes in Guizhou Province[J]. Bulletin of Soiland Water Conservation, 2024, 44(4): 330-339.
谢丽钧, 杨广斌, 王仁儒, 等. 贵州省土地利用变化频数分布及驱动因素[J]. 水土保持通报, 2024,44(4):330-339. DOI： 10.13961/j.cnki.stbctb.2024.04.034.

Xie Lijun, Yang Guangbin, Wang Renru, et al. Frequency Distribution and Driving Factors of Land Use Changes in Guizhou Province[J]. Bulletin of Soiland Water Conservation, 2024, 44(4): 330-339. DOI： 10.13961/j.cnki.stbctb.2024.04.034.

摘要

[目的] 研究贵州省土地利用动态转移变化频数的空间格局及驱动因素，为该区土地调查工作与相关政策提供理论参考。[方法] 基于2000—2020年贵州省土地利用数据，采用土地利用变化频数统计、核密度分析、空间自相关性等方法分析贵州省土地利用变化频率的时空分布特征，并借助地理探测器对其影响因素进行研究。[结果] ①贵州省2000—2020年土地利用变化频数中土地发生变化的面积仅为2%，但资金和人力的投入却相对较高，因此两者的投入关系极不协调。②贵州省2000—2020年土地利用变化频数中已变化的土地在空间分布上具有显著的空间异质性。③贵州省2000—2020年已变化土地核密度结果呈现“西高东低”的分布特征，土地变化1次的分布密度最大，土地变化3次的分布密度最小。④贵州省2000—2020年已变化土地和未变化土地皆呈显著空间集聚特征，已变化热点区域为赫章、大方等，未变化热点区域为威宁、从江等地区。⑤2000—2020年贵州省土地利用变化频数驱动因素的交互探测结果显示，坡度和坡向的交互作用对贵州省土地利用变化频数空间分异的解释力最强。[结论] 贵州省土地利用变化频率分布具有显著的空间异质性，应建立贵州省土地利用变化频数监管机制，实现有效利用社会资源和减轻社会财政负担。

Abstract

[Objective] The spatial patterns and driving factors of the frequency of land-use dynamic transfer changes in Guizhou Province were analysed in order provide theoretical references for land survey work and related policies in the region. [Methods] Based on the land-use data of Guizhou Province from 2000 to 2020

the spatial and temporal distribution characteristics of the frequency of land-use change in Guizhou Province were analysed using land-use change frequency statistics

kernel density analysis

and spatial autocorrelation

and the influencing factors were studied using geographic probes. [Results] ① The area of land changed in the land use change frequency in Guizhou Province from 2000 to 2020 was only 2%

but the inputs of capital and manpower were relatively high; as a result

there was a relationship between the two inputs and discordance. ② The land that changed in land-use change frequency in Guizhou Province from 2000 to 2020 had significant spatial heterogeneity in spatial distribution. ③ The kernel density of changed land in Guizhou Province from 2000 to 2020 showed the distribution characteristic of “higher in the west and lower in the east”

with the distribution density of one land change being the largest and the distribution density of three land changes being the smallest. ④ In 2000 to 2020

both changed and unchanged land in Guizhou Province showed significant spatial agglomeration characteristics

with changed hotspots in areas such as

Hezhang

and Dafang and unchanged hotspots in areas such as Xianning and Congjiang area. ⑤ The results of the interaction detection of the driving factors of land-use change frequency in Guizhou Province from 2000 to 2020 showed that the interaction of slope and slope direction had the strongest explanatory power for the spatial variation in land-use change frequency in Guizhou Province. [Conclusion] An obvious spatial heterogeneity exists in the distribution of land-use change frequency in Guizhou Province. A mechanism to regulate the frequency of land-use change in Guizhou Province should be established to realise the effective use of social resources and reduce the financial burden on society.

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references

Mooney H A, Duraiappah A, Larigauderie A. Evolution of natural and social science interactions in global change research programs [J]. Proceedings of the National Academy of Sciences of the United States of America, 2013,110(Suppl 1):3665-3672.

Sterling S M, Ducharne A, Polcher J. The impact of global land-cover change on the terrestrial water cycle [J]. Nature Climate Change, 2013,3(4):385-390.

Verburg P H, Crossman N, Ellis E C, et al. Land system science and sustainable development of the earth system: A global land project perspective [J]. Anthropocene, 2015,12:29-41.

傅家仪,臧传富,吴铭婉.1990—2015年海河流域土地利用时空变化特征及驱动机制研究[J].中国农业资源与区划,2020,41(5):131-139. Fu Jiayi, Zang Chuanfu, Wu Mingwan. Spatial and temporal variability characteristics and driving mechanism of land use in Haihe River Basin from 1990 to 2015[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2020,41(5):131-139.

史涵,李蒙,王向东.1980—2017年吉林省土地利用变化及驱动力分析[J].国土与自然资源研究,2019(4):14-16. Shi Han, Li Meng, Wang Xiangdong. Analysis of land use change and driving force in Jilin Province from 1980 to 2017[J]. Territory & Natural Resources Study, 2019(4):14-16.

杨梅,张广录,侯永平.区域土地利用变化驱动力研究进展与展望[J].地理与地理信息科学,2011,27(1):95-100. Yang Mei, Zhang Guanglu, Hou Yongping. Advances and prospects of the driving force of regional land use change researches [J]. Geography and Geo-Information Science, 2011,27(1):95-100.

卜祥,张永福,赵玉,等.资源型城市的土地利用变化及驱动力分析:以克拉玛依市为例[J].天津师范大学学报(自然科学版),2023,43(2):65-73. Bu Xiang, Zhang Yongfu, Zhao Yu, et al. Analysis of land use change and driving forces in resource-based cities: A case study of Karamay City [J]. Journal of Tianjin Normal University (Natural Science Edition), 2023,43(2):65-73.

张倩,黄昕,张良培.多尺度同质区域提取的高分辨率遥感影像分类研究[J].武汉大学学报(信息科学版),2011,36(1):117-121. Zhang Qian, Huang Xin, Zhang Liangpei. Multiscale image segmentation and classification with supervised ECHO of high spatial resolution remotely sensed imagery [J]. Geomatics and Information Science of Wuhan University, 2011,36(1):117-121.

张倩,黄昕,张良培.高分辨率遥感影像的全变分分割模型[J].测绘科学,2012,37(5):81-83. Zhang Qian, Huang Xin, Zhang Liangpei. Segmentation of high spatial resolution remote sensing image with total variation model [J]. Science of Surveying and Mapping, 2012,37(5):81-83.

黄昕,李平湘,张良培.基于多层形状特征提取与融合的城市高光谱影像解译[J].测绘科学,2009,34(6):62-64. Huang Xin, Li Pingxiang, Zhang Liangpei. Multilevel shape feature extraction and fusion for classification of urban hyperspectral imagery [J]. Science of Surveying and Mapping, 2009,34(6):62-64.

张乐飞,黄昕,张良培.高分辨率遥感影像的支持张量机分类方法[J].武汉大学学报(信息科学版),2012,37(3):314-317. Zhang Lefei, Huang Xin, Zhang Liangpei. Classification of high spatial resolution imagery using support tensor machine [J]. Geomatics and Information Science of Wuhan University, 2012,37(3):314-317.

窦世卿,宋莹莹,徐勇,等.基于随机森林的高分影像分类及土地利用变化检测[J].无线电工程,2021,51(9):901-908. Dou Shiqing, Song Yingying, Xu Yong, et al. High resolution image classification and land use change detection based on random forest [J]. Radio Engineering, 2021,51(9):901-908.

梁锦涛,陈超,张自力,等.一种融合指数与主成分分量的随机森林遥感图像分类方法[J].自然资源遥感,2023,35(3):35-42. Liang Jintao, Chen Chao, Zhang Zili, et al. A random forest-based method integrating indices and principal components for classifying remote sensing images [J]. Remote Sensing for Natural Resources, 2023,35(3):35-42.

冯权泷,牛博文,朱德海,等.土地利用/覆被深度学习遥感分类研究综述[J].农业机械学报,2022,53(3):1-17. Feng Quanlong, Niu Bowen, Zhu Dehai, et al. Review for deep learning in land use and land cover remote sensing classification [J]. Transactions of the Chinese Society for Agricultural Machinery, 2022,53(3):1-17.

省人民政府办公厅关于印发贵州省“十四五”自然资源保护和利用规划的通知[R].贵州省人民政府公报,2022. Notice of the General Office of the Provincial People’s Government on the Issuance of Guizhou Province’s “14th Five-Year Plan” for the Protection and Utilization of Natural Resources [R]. Gazette of the People’s Government of Guizhou Province, 2022.

Yang Jie, Huang Xin. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021,13(8):3907-3925.

Chen Jiandong, Gao Ming, Cheng Shulei, et al. Global 1 km×1 km gridded revised real gross domestic product and electricity consumption during 1992—2019 based on calibrated nighttime light data [J]. Scientific Data, 2022,9:202.

Zhao Naizhuo, Liu Ying, Cao Guofeng, et al. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images [J]. GIScience & Remote Sensing, 2017,54(3):407-425.

Zhang Yujia. Characterizing land changes over several points in time [D]. Massachusetts: Clark University, 2011.

张杰,唐根年.浙江省制造业空间分异格局及其影响因素[J].地理科学,2018,38(7):1107-1117. Zhang Jie, Tang Gennian. Spatial differentiation pattern of manufacturing industry in Zhejiang and its influencing factors [J]. Scientia Geographica Sinica, 2018,38(7):1107-1117.

何钊全,尚雪,张铜会,等.近20年陕北黄土丘陵区景观生态风险时空变化及其冷热点格局[J].生态学杂志,2023,42(10): 2514-2525. He Zhaoquan, Shang Xue, Zhang Tonghui, et al. Spatiotemporal variations of landscape ecological risk and its cold-hot spot pattern in the loess hills of northern Shaanxi over the past 20 years [J]. Chinese Journal of Ecology, 2023,42(10):2514-2525.

朱柏露,杨奇勇,谢运球,等.漓江流域土地石漠化空间分布及驱动因子分析[J].广西师范大学学报(自然科学版),2021,39(3):139-150. Zhu Bailu, Yang Qiyong, Xie Yunqiu, et al. Spatial distribution and driving factors of Karst rocky desertification in Lijiang River Basin [J]. Journal of Guangxi Normal University (Natural Science Edition), 2021,39(3):139-150.

王劲峰,徐成东.地理探测器: 原理与展望[J].地理学报,2017,72(1):116-134. Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective [J]. Acta Geographica Sinica, 2017,72(1):116-134.

朱青,崔宏浩,张钦,等.绿肥阻控贵州山区坡耕地水土流失的应用[J].水土保持研究,2016,23(2):101-105. Zhu Qing, Cui Honghao, Zhang Qin, et al. The application of green manure controlling soil erosion of slope farmland in Guizhou Mountain Area [J]. Research of Soil and Water Conservation, 2016,23(2):101-105.

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