半干旱地区矿区土地利用时空演变与预测

doi:10.13866/j.azr.2022.01.28

Abstract

Abstract:

Land use and land cover change (LUCC) is a critical factor contributing to regional land planning and ecological environmental protection. LUCCs associated with human activities, such as coal mining, have increased the tension in human-environment interactions. This study explored the spatio-temporal LUCC and its driving factors from 1985 to 2015 in the Datong mining area, which is one of the eight major coal production bases in China. Furthermore, the random forests (RF)-future land use simulation (FLUS) model was proposed to explicitly simulate the spatial trajectories of LUCCs for the year 2025. The results showed that (1) forestland, cropland, and watershed areas kept decreasing, while grasslands and construction lands kept increasing from 1985 to 2015. (2) Climate, elevation, and distance were the most influential factors for the distribution of croplands, forestlands, and grasslands, while precipitation was the most important factor for the distribution of watersheds. Coal production capacity and distance from facilities were the most influential factors for the distribution of construction lands. (3) Both the FLUS and RF-FLUS models had a good fitting accuracy, whereas the RF-FLUS models had a satisfactory kappa index and OA index. (4) Land use simulations for the year 2025 based on the RF-FLUS model indicated that croplands, forestlands, and grasslands will decrease, while construction lands will increase. This study provides a scientific reference for understanding the complex and dynamic evolution of LUCC, exploring the possibilities for the way of land resources, and promoting the sustainable development of coal mining areas.

Key words: coal mining area, land use change, model prediction, random forest model, driving factors

LIU Chang,ZHANG Hong,ZHANG Xiaoyu,YANG Guoting,LIU Yong. Spatio-temporal evolution and prediction of land use in semi-arid mining areas[J].Arid Zone Research, 2022, 39(1): 292-300.

Figures/Tables 9

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References 28

[1]	李胜鹏, 柳建玲, 林津, 等. 基于1980—2018年土地利用变化的福建省生境质量时空演变[J]. 应用生态学报, 2020, 31(12): 4080-4090.
	[Li Shengpeng, Liu Jianling, Lin Jin, et al. Spatial and temporal evolution of habitat quality in Fujian Province, China based on the land use change from 1980 to 2018[J]. Chinese Journal of Applied Ecology, 2020, 31(12): 4080-4090. ]
[2]	刘纪远, 张增祥, 庄大方, 等. 20世纪90年代中国土地利用变化时空特征及其成因分析[J]. 地理研究, 2003, 22(1): 1-12.
	[Liu Jiyuan, Zhang Zengxiang, Zhuang Dafang, et al. A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s[J]. Geographical Research, 2003, 22(1): 1-12. ]
[3]	Veldkamp A, Lambin E F. Predicting land-use change[J]. Agriculture, Ecosystems and Environment, 2001, 85(1): 1-6. doi: 10.1016/S0167-8809(01)00199-2
[4]	李保杰, 顾和和, 纪亚洲. 基于CLUE-S模型的矿区土地利用变化情景模拟——以徐州市贾汪矿区为例[J]. 热带地理, 2018, 38(2): 274-281.
	[Li Baojie, Gu Hehe, Ji Yazhou. Simulation of land use change in coal mining area under different scenarios based on the CLUE-S model: A case study of jiawang mining area in Xuzhou city[J]. Tropical Geography, 2018, 38(2): 274-281. ]
[5]	刘彦文, 刘成武, 何宗宜, 等. 基于地理加权回归模型的武汉城市圈生态用地时空演变及影响因素[J]. 应用生态学报, 2020, 31(3): 987-998.
	[Liu Yanwen, Liu Chengwu, He Zongyi, et al. Spatial-temporal evolution of ecological land and influence factors in Wuhan urban agglomeration based on geographically weighted regression model[J]. Chinese Journal of Applied Ecology, 2020, 31(3): 987-998. ]
[6]	孙毅中, 杨静, 宋书颖, 等. 多层次矢量元胞自动机建模及土地利用变化模拟[J]. 地理学报, 2020, 75(10): 2164-2179. doi: 10.11821/dlxb202010009
	[Sun Yizhong, Yang Jing, Song Shuying, et al. Modeling of multilevel vector cellular automata and its simulation of land use change[J]. Acta Geographica Sinica, 2020, 75(10): 2164-2179. ] doi: 10.11821/dlxb202010009
[7]	徐红涛, 陈春波, 郑宏伟, 等. 基于相关分析和自适应遗传算法的盐渍化建模变量和参数优选[J]. 地球信息科学学报, 2020, 22(7): 1497-1509.
	[Xu Hongtao, Chen Chunbo, Zheng Hongwei, et al. Correlation analysis and adaptive genetic algorithm based feature subset and model parameter optimization in salinization monitoring[J]. Journal of Geo-information Science, 2020, 22(7): 1497-1509. ]
[8]	王梓洋, 石培基, 张学斌, 等. 基于多智能体模型的兰州市城镇用地扩展模拟[[J]. 应用生态学报, 2021, 32(6): 2169-2179.
	[Wang Ziyang, Shi Peiji, Zhang Xuebin, et al. Simulation of Lanzhou urban land expansion based on multi-agent model[J]. Chinese Journal of Applied Ecology, 2021, 32(6): 2169-2179. ]
[9]	戴尔阜, 马良. 土地变化模型方法综述[J]. 地理科学进展, 2018, 37(1): 152-162. doi: 10.18306/dlkxjz.2018.01.016
	[Dai Erfu, Ma Liang. Review on land change modeling approaches[J]. Progress in Geography, 2018, 37(1): 152-162. ] doi: 10.18306/dlkxjz.2018.01.016
[10]	周成虎, 欧阳, 马廷, 等. 地理系统模拟的CA模型理论探讨[J]. 地理科学进展, 2009, 28(6): 833-838.
	[Zhou Chenghu, Ou Yang, Ma Ting, et al. Theoretical perspectives of CA-based geographical system modeling[J]. Progress in Geography, 2009, 28(6): 833-838. ]
[11]	黎夏, 刘小平. 基于案例推理的元胞自动机及大区域城市演变模拟[J]. 地理学报, 2007, 75(10): 1097-1109.
	[Li Xia, Liu Xiaoping. Case-based cellular automaton for simulating urban development in a large complex region[J]. Acta Geographica Sinica, 2007, 75(10): 1097-1109. ]
[12]	Wu F L. Calibration of stochastic cellular automata: The application to rural-urban land conversions[J]. International Journal of Geographical Information Science, 2002, 16(8): 795-818. doi: 10.1080/13658810210157769
[13]	杨青生, 黎夏. 基于遗传算法自动获取CA模型的参数——以东莞市城市发展模拟为例[J]. 地理研究, 2007, 26(2): 229-237.
	[Yang Qingsheng, Li Xia. Calibrating urban cellular automata using genetic algorithms[J]. Geographical Research, 2007, 26(2): 229-237. ]
[14]	张大川, 刘小平, 姚尧, 等. 基于随机森林CA的东莞市多类土地利用变化模拟[J]. 地理与地理信息科学, 2016, 32(5): 29-36.
	[Zhang Dachuan, Liu Xiaoping, Yao Yao, et al. Simulating spatiotemporal change of multiple land use types in Dongguan by using random forest based on cellular automata[J]. Geography and Geo-Information Science, 2016, 32(5): 29-36. ]
[15]	马慧娟, 高小红, 谷晓天. 随机森林方法支持的复杂地形区土地利用/土地覆被分类研究[J]. 地球信息科学学报, 2019, 21(3): 359-371. doi: 10.12082/dpxxkx.2019.180346
	[Ma Huijuan, Gao Xiaohong, Gu Xiaotian. Random forest classification of Landsat 8 imagery for the complex terrain area based on the combination of spectral, topographic and texture information[J]. Journal of GeoInformation Science, 2019, 21(3): 359-371. ] doi: 10.12082/dpxxkx.2019.180346
[16]	陈凯, 刘凯, 柳林, 等. 基于随机森林的元胞自动机城市扩展模拟——以佛山市为例[J]. 地理科学进展, 2015, 34(8): 937-946. doi: 10.18306/dlkxjz.2015.08.001
	[Chen Kai, Liu Kai, Liu Lin, et al. Urban expansion simulation by random-forest-based cellular automata: A case study of Foshan City[J]. Progress in Geography, 2015, 34(8): 937-946. ] doi: 10.18306/dlkxjz.2015.08.001
[17]	卞正富, 张燕平. 徐州煤矿区土地利用格局演变分析[J]. 地理学报, 2006, 61(4): 349-358.
	[Bian Zhengfu, Zhang Yanping. Land use changes in Xuzhou coal mining area[J]. Acta Geographica Sinica, 2006, 61(4): 349-358. ]
[18]	段语凤, 张玉秀, 余创. 煤炭井工开采对干旱荒漠区植被动态变化的影响[J]. 生态学报, 2020, 40(23): 8717-8728.
	[Duan Yufeng, Zhang Yuxiu, Yu Chuang. Effects of the underground coal mining on the dynamic changes of vegetation in arid desert area[J]. Acta Ecologica Sinica, 2020, 40(23): 8717-8728. ]
[19]	史晓琼, 杨泽元, 张艳娜, 等. 陕北高强度采煤对生态环境影响的研究进展[J]. 煤炭技术, 2016, 35(1): 314-316.
	[Shi Xiaoqiong, Yang Zeyuan, Zhang Yanna, et al. Reviews of influence by high-intensity coal mining on ecological environment in Northern Shanxi[J]. Coal Technology, 2016, 35(1): 314-316. ]
[20]	邹雅婧, 闫庆武, 谭学玲, 等. 渭北矿区土壤侵蚀评估及驱动因素分析[J]. 干旱区地理, 2019, 42(6): 1387-1394.
	[Zou Yajing, Yan Qingwu, Tan Xueling, et al. Evaluation of soil erosion and driving factors analysis in Weibei mining area[J]. Arid Land Geography, 2019, 42(6): 1387-1394. ]
[21]	Ye T T, Zhao N Z, Yang X C, et al. Improved population mapping for China using rely sensed and points-of-interest data within a random forests model[J]. Science of the Total Environment, 2019, 658: 936-946. doi: 10.1016/j.scitotenv.2018.12.276
[22]	Liu X P, Liang X, Li X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and Urban Planning, 2017, 168: 94-116. doi: 10.1016/j.landurbplan.2017.09.019
[23]	Li X, Anthony Gar-On Yeh. Neural-network-based cellular automata for simulating multiple land use changes using GIS[J]. International Journal of Geographical Information Science, 2002, 16(4): 323-343. doi: 10.1080/13658810210137004
[24]	张经度, 梅志雄, 吕佳慧, 等. 纳入空间自相关的FLUS模型在土地利用变化多情景模拟中的应用[J]. 地球信息科学学报, 2020, 22(3): 531-542. doi: 10.12082/dqxxkx.2020.190359
	[Zhang Jingdu, Mei Zhixiong, Lyu Jiahui, et al. Simulating multiple land use scenarios based on the FLUS model considering spatial autocorrelation[J]. Journal of GeoInformation Science, 2020, 22(3): 531-542. ] doi: 10.12082/dqxxkx.2020.190359
[25]	秦埼瑞, 李雪梅, 陈庆伟, 等. 基于FLUS模型的天山山区未来土地利用变化预估[J]. 干旱区研究, 2019, 36(5): 1270-1279.
	[Qin Qirui, Li Xuemei, Chen Qingwei, et al. Estimation of future land use change in the Tianshan mountainous based on FLUS model[J]. Arid Zone Research, 2019, 36(5): 1270-1279. ]
[26]	孙鸿超, 张正祥. 吉林省松花江流域景观格局脆弱性变化及其驱动力[J]. 干旱区研究, 2019, 36(4): 1005-1041.
	[Sun Hongchao, Zhang Zhengxiang. Changes of landscape pattern vulnerability of Songhua River Basin in Jilin Province and its drivingforces[J]. Arid Zone Research, 2019, 36(4): 1005-1041. ]
[27]	Mariana B, Lucian D. Random forest in remote sensing: A review of applications and future directions[J]. Journal of Photogrammetry and Remote Sensing, 2016, 114: 24-31. doi: 10.1016/j.isprsjprs.2016.01.011
[28]	Hadi M, Siva K B, Jamal B T, et al. Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia[J]. Journal of Geographic Information System, 2012, 4(6): 542-554. doi: 10.4236/jgis.2012.46059

数据类型	数据名称	数据来源
自然因子	高程	中国科学院资源环境科学数据中心（http://www.resdc.cn）
	坡度、坡向	基于高程（DEM）数据提取
	气温、降水	中国气象数据网（http://data.cma.cn）
社会经济因子	煤矿生产能力	采用实际调查获取
社会经济因子	人口	中国科学院资源环境科学数据中心（http://www.resdc.cn）
距离因子	距公路、铁路、水系距离	采用ArcGIS 10.5中欧氏距离算法计算
距离因子	距设施点、居民点距离	POI数据采用网络爬虫技术获取服务设施
土地利用数据	1985年、1995年、2005年、2015年遥感影像	中国科学院资源环境科学数据中心（http://www.resdc.cn）

年份	土地利用类型	林地	草地	耕地	水域	建设用地	其他
1985—1995	林地	86.88	3.69	8.54	0.45	0.44	-
	草地	5.26	77.63	16.49	0.31	0.20	0.11
	耕地	14.87	8.82	73.60	0.58	1.94	0.19
	水域	17.31	23.64	5.41	52.47	1.17	-
	建设用地	6.72	3.18	6.27	4.02	77.55	2.25
	其他	26.68	1.64	-	-	2.35	69.33
1995—2005	林地	10.44	55.93	31.2	0.10	2.07	0.27
	草地	35.7	49.14	13.26	0.14	1.07	0.68
	耕地	13.36	72.58	11.59	0.12	1.75	0.60
	水域	21.23	31.28	33.39	0.33	13.47	0.30
	建设用地	4.17	32.89	27.64	0.05	32.92	2.33
	其他	7.02	47.12	7.08	0.37	28.75	9.66
2005—2015	林地	99.20	-	0.06	-	0.30	0.44
	草地	-	97.54	0.05	-	2.24	0.18
	耕地	-	1.03	85.99	-	12.97	0.01
	水域	-	2.12		3.52	-	94.35
	建设用地	-	-	-	-	100.00	-
	其他	-	-	-	-	-	100.00

	RF-FLUS模型							FLUS模型
	林地	草地	耕地	水域	建设用地	其他		林地	草地	耕地	水域	建设用地	其他
林地	95.03	1.73	1.35	0.00	0.00	0.20	林地	93.25	2.14	1.80	0.00	0.00	0.33
草地	2.35	95.71	2.60	0.00	0.00	5.71	草地	2.94	95.33	2.32	10.00	0.00	6.03
耕地	2.02	1.20	94.57	0.00	0.00	0.26	耕地	2.54	1.62	93.52	0.00	0.00	0.26
水域	0.00	0.00	0.00	100.00	0.00	0.00	水域	0.00	0.00	0.00	90.00	0.00	0.00
建设用地	0.28	1.17	1.40	0.00	100.00	0.13	建设用地	0.66	0.85	2.05	0.00	100.00	0.20
其他	0.32	0.19	0.08	0.00	0.00	93.70	其他	0.61	0.06	0.31	0.00	0.00	93.18
总计	100.00	100.00	100.00	100.00	100.00	100.00	总计	100.00	100.00	100.00	100.00	100.00	100.00

Spatio-temporal evolution and prediction of land use in semi-arid mining areas

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