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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 155-165     DOI: 10.6046/zrzyyg.2021343
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Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas
WANG Yu1,2,3(), ZHOU Zhongfa1,2(), WANG Lingyu1,3, LUO Jiancheng4, HUANG Denghong1,3, ZHANG Wenhui1,2,3
1. School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550001, China
2. The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province, Guiyang 550001, China
3. State Engineering Technology Institute for Karst Desertification Control, Guiyang 550001, China
4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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Abstract  

Karst mountainous areas are influenced by complex cloudy and rainy weather. This brings great difficulties to the extraction of planting structure information using the remote sensing technology. Sentinel-1-based crop identification has unique advantages in precision agriculture. It can obtain the information on regional main crops in time and accurately, thus playing a significant role in formulating agricultural policies and guiding agricultural production. This study investigated Guanling County based on Google images in 2020, Sentinel-1 time series data from April to August, and UAV remote sensing data. First, the plots were extracted using the D_LinkNet model. Then, the planting structures were classified based on the LightGBM module. Finally, the spatial differentiation characteristics of main crops and the influencing mechanism of planting structures in the study area were explored combined with geographic detectors. The results are as follows. ① The crops in Guanling County showed an uneven spatial distribution pattern of more crops in the northwest and less crops in the southeast. ② The influence of factor interaction was greater than that of single factors. The distribution of cultivated land was mainly influenced by traffic location and drainage capacity, followed by factors such as elevation and traffic location. ③ The extraction results of crop planting structures are consistent with the proportions shown in the statistical yearbook, with confusion-matrix overall precision of 0.87 and Kappa coefficient of 0.83. The results can help understand the formation mechanisms and differences in the spatial differentiation of different crop planting structures in Karst mountainous areas. Therefore, this study can provide a scientific basis for the optimization and adjustment of planting structures and the analysis of influencing factors.
Keywords Karst      cultivated land utilization      planting structure      Sentinel-1      space differentiation     
ZTFLH:  TP79  
Issue Date: 27 December 2022
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Yu WANG
Zhongfa ZHOU
Lingyu WANG
Jiancheng LUO
Denghong HUANG
Wenhui ZHANG
Cite this article:   
Yu WANG,Zhongfa ZHOU,Lingyu WANG, et al. Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas[J]. Remote Sensing for Natural Resources, 2022, 34(4): 155-165.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021343     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/155
Fig.1  Overview of the study area
序号 数据级别 成像卫星 成像日期 成像模式 轨道号
1 SLC Sentinel-1A 20200423 IW 32261
2 SLC Sentinel-1A 20200517 IW 32611
3 SLC Sentinel-1A 20200623 IW 33136
4 SLC Sentinel-1A 20200728 IW 33661
5 SLC Sentinel-1A 20200822 IW 34186
Tab.1  Sentinel-1 satellite image information
Fig.2  Extraction technology flow of crop planting structure information
Fig.3  Precise boundaries of agricultural land
类型 变量 指标说明与计算方法 分类
地形条件T 高程T1 统计各网格内高程平均值 自然断点法分为5类
坡度T2 统计各网格内坡度平均值 自然断点法分为5类
石漠化等
级T3		 统计各网格中心石漠化等级 5类:非喀斯特耕地、无石漠化、轻度石漠化、中度石漠化、重度石漠化
土壤条件S 土壤质地S1 统计各网格中心点土壤质地类型 3类:黏土、壤土、砂土
区位条件L 市场区位L1 统计各网格城镇欧氏距离平均值 自然断点法分为5类
交通区位L2 统计各网格距公路欧氏距离平均值 自然断点法分为5类
管理条件M 灌溉潜力M1 统计各网格距河流欧氏距离平均值 自然断点法分为5类
排涝能力M2 统计各网格距沟渠欧氏距离平均值 自然断点法分为8类
耕作便利
度M3		 统计各网格距居民点欧氏距离平均值 自然断点法分为5类
Tab.2  Variable selection and classification of influencing factors of cultivated land planting structure
Fig.4  Statistical chart of cultivated land planting structure type
Fig.5  Confusion matrix verification diagram
Fig.6  Distribution of planting structure
Fig.7  Radar diagram of single factor influence on planting structuree

因子		 地形条件 土壤条件 区位条件 管理条件
T1 T2 T3 S1 L1 L2 M1 M2 M3
T1 0.004 033
T2 0.016 962 0.004 081
T3 0.008 429 0.007 411 0.000 502
S1 0.007 304 0.006 166 0.001 678 0.000 432
L1 0.041 717 0.009 812 0.004 105 0.004 063 0.002 004
L2 0.088 243 0.091 404 0.012 567 0.011 400 0.029 229 0.007 132
M1 0.005 254 0.018 111 0.008 057 0.006 923 0.037 400 0.090 287 0.003 847
M2 0.005 476 0.018 063 0.006 681 0.006 242 0.033 295 0.048 414 0.005 150 0.002 984
M3 0.004 914 0.017 787 0.007 538 0.006 601 0.039 840 0.087 272 0.005 002 0.005 008 0.003 504
Tab.3  Detection results of factor interaction on spatial differentiation of rice planting structure in the study area

因子		 地形条件 土壤条件 区位条件 管理条件
T1 T2 T3 S1 L1 L2 M1 M2 M3
T1 0.040 830
T2 0.041 058 0.040 814
T3 0.052 371 0.051 900 0.000 202
S1 0.984 690 0.984 639 0.001 472 0.000 435
L1 0.041 024 0.041 027 0.002 324 0.004 109 0.001 010
L2 0.140 668 0.140 636 0.002 541 0.004 039 0.008 257 0.001 061
M1 0.054 685 0.054 736 0.047 543 0.196 965 0.030 768 0.164 113 0.029 639
M2 0.044 739 0.044 789 0.021 952 0.051 857 0.023 443 0.070 335 0.044 740 0.014 718
M3 0.984 641 0.984 693 0.002 291 0.002 524 0.057 919 0.006 701 0.984 642 0.051 806 0.000 631
Tab.4  Detection results of factor interaction on spatial differentiation of maize planting structure in the study area

因子		 地形条件 土壤条件 区位条件 管理条件
T1 T2 T3 S1 L1 L2 M1 M2 M3
T1 0.002 609
T2 0.003 882 0.002 337
T3 0.004 615 0.003 920 0.000 204
S1 0.003 755 0.003 153 0.000 590 0.000 173
L1 0.009 419 0.004 228 0.002 063 0.002 039 0.000 939
L2 0.013 027 0.012 356 0.000 518 0.000 462 0.001 596 0.000 000
M1 0.072 612 0.061 499 0.002 317 0.002 061 0.021 771 0.002 132 0.000 871
M2 0.075 283 0.061 623 0.002 438 0.002 157 0.026 500 0.002 224 0.001 197 0.000 909
M3 0.002 980 0.004 030 0.003 738 0.003 597 0.009 049 0.009 653 0.032 625 0.034 493 0.002 309
Tab.5  Detection results of factor interaction on spatial differentiation of vegetable planting structure in the study area

因子		 地形条件 土壤条件 区位条件 管理条件
T1 T2 T3 S1 L1 L2 M1 M2 M3
T1 0.003 118
T2 0.007 874 0.000 759
T3 0.009 174 0.003 663 0.000 877
S1 0.008 547 0.003 322 0.002 618 0.000 739
L1 0.023 256 0.007 752 0.006 098 0.005 618 0.002 274
L2 0.020 000 0.012 987 0.009 174 0.008 475 0.023 810 0.004 471
M1 0.004 444 0.008 197 0.008 850 0.008 547 0.025 641 0.020 408 0.003 162
M2 0.047 619 0.007 143 0.010 989 0.008 475 0.055 556 0.028 571 0.058 824 0.003 033
M3 0.066 667 0.007 299 0.011 236 0.008 547 0.066 667 0.031 250 0.062 500 0.004 386 0.003 222
Tab.6  Detection results of factor interaction on spatial differentiation of sugarcane planting structure in the study area

因子		 地形条件 土壤条件 区位条件 管理条件
T1 T2 T3 S1 L1 L2 M1 M2 M3
T1 0.002 177
T2 0.003 548 0.000 289
T3 0.006 186 0.001 564 0.000 303
S1 0.005 096 0.001 643 0.001 889 0.000 447
L1 0.005 040 0.004 283 0.004 341 0.005 138 0.002 137
L2 0.005 296 0.001 042 0.001 219 0.001 099 0.004 450 0.000 167
M1 0.003 960 0.004 271 0.005 064 0.004 937 0.004 846 0.005 397 0.002 177
M2 0.005 215 0.005 537 0.005 653 0.005 196 0.004 689 0.005 738 0.005 360 0.002 735
M3 0.004 816 0.004 555 0.004 899 0.005 385 0.005 554 0.005 346 0.004 755 0.004 885 0.002 341
Tab.7  Detection results of factor interaction on spatial differentiation of main crop planting structures in the study area
[1] 唐惠燕, 包平. 基于GIS江苏水稻种植面积与产量的空间重心变迁研究[J]. 南京农业大学学报(社会科学版), 2014, 14(1):118-124.
[1] Tang H Y, Bao P. Change of spatial gravity center of rice planting area and yield in Jiangsu Province based on GIS[J]. Journal of Nanjing Agricultural University(Social Science Edition), 2014, 14(1):118-124.
[2] 杨彩云, 杨广斌, 戴丽. 贵州省耕地利用效益时空分异特征研究[J/OL]. 湖南师范大学自然科学学报:1-10[2022-03-10]. http://kns.cnki.net/kcms/detail/43.1542.N.20210114.1321.002.html .
url: http://kns.cnki.net/kcms/detail/43.1542.N.20210114.1321.002.html
[2] Yang C Y, Yang G B, Dai L. Guizhou Province cultivated land utilization benefit of time and space differentiationcharacteristics research[J/OL]. Hunan Normal University Journal of Natural Science:1- 10 [2021-12-22]. http://kns.cnki.net/kcms/detail/43.1542.N.20210114.1321.002.html .
url: http://kns.cnki.net/kcms/detail/43.1542.N.20210114.1321.002.html
[3] 苟志宏, 胥桂凤, 樊佳俐. 贵州省耕地资源利用效率评价及障碍因子识别[J]. 国土与自然资源研究, 2021(5):24-29.
[3] Gou Z H, Xu G F, Fan J L. Cultivated land resource use efficiency evaluation in Guizhou and obstacle factor recognition[J]. Journal of Land and Natural Resources Research, 2021(5):24-29.
[4] 易其国, 高妍, 陈慧婷. 喀斯特贫困山区耕地资源生态安全分析——以贵州省为例[J]. 农村经济与科技, 2021, 32(1):19-20.
[4] Yi Q G, Gao Y, Chen H T. Ecological security analysis of cultivated land resources in Karst poor mountainous areas:A case study of Guizhou Province[J]. Rural Economy and Science and Technology, 2021, 32(1):19-20.
[5] Moran M S, Inoue Y, Barnes E M. Opportunities and limitations for image-based remote sensing in precision crop management[J]. Remote Sensing of Environment, 1997, 61(3):319-346.
doi: 10.1016/S0034-4257(97)00045-X url: https://linkinghub.elsevier.com/retrieve/pii/S003442579700045X
[6] 郑长春, 王秀珍, 黄敬峰. 基于特征波段的SPOT-5卫星影像水稻面积信息自动提取的方法研究[J]. 遥感技术与应用, 2008, 23(3):294-299.
[6] Zheng C C, Wang X Z, Huang J F. Automatic extraction of rice area information from SPOT-5 satellite imagery based on feature bands[J]. Remote Sensing Technology and Application, 2008, 23(3):294-299.
[7] Mathur A, Foody G M. Crop classification by support vector machine with intelligently selected training data for an operational application[J]. International Journal of Remote Sensing, 2008, 29(8):2227-2240.
doi: 10.1080/01431160701395203 url: https://www.tandfonline.com/doi/full/10.1080/01431160701395203
[8] Jia K, Wu B, Li Q. Crop classification using HJ satellite multispectral data in the North China Plain[J]. Journal of Applied Remote Sensing, 2013, 7(1):073576.
doi: 10.1117/1.JRS.7.073576 url: http://remotesensing.spiedigitallibrary.org/article.aspx?doi=10.1117/1.JRS.7.073576
[9] 张健康, 程彦培, 张发旺. 基于多时相遥感影像的作物种植信息提取[J]. 农业工程学报, 2012, 28(2):134-141.
[9] Zhang J K, Cheng Y P, Zheng F W. Extraction of crop planting information based on multi-temporal remote sensing image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(2):134-141.
[10] Foerster S, Kaden K, Foerster M, et al. Crop type mapping using spectral-temporal profiles and phenological information[J]. Computers and Electronics in Agriculture, 2012, 89:30-40.
doi: 10.1016/j.compag.2012.07.015 url: https://linkinghub.elsevier.com/retrieve/pii/S0168169912002013
[11] 李鑫川, 徐新刚, 王纪华, 等. 基于时间序列环境卫星影像的作物分类识别[J]. 农业工程学报, 2013, 29(2):169-176.
[11] Li X C, Xu X G, Wang J H, et al. Crop classification and recognition based on time series environmental satellite image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(2):169-176.
[12] Claire B. Monitoring US agriculture:The US department of agriculture,national agricultural statistics service,cropland data layer program[J]. Geocarto International, 2011, 26(5) :341-358.
doi: 10.1080/10106049.2011.562309 url: http://www.tandfonline.com/doi/abs/10.1080/10106049.2011.562309
[13] Fiorillo E, Di Giuseppe E, Fontanelli G, et al. Lowland rice mapping in Sédhiou Region(Senegal) using Sentinel 1 and Sentinel 2 data and random forest[J]. Remote Sensing, 2020, 12(20):3403.
doi: 10.3390/rs12203403 url: https://www.mdpi.com/2072-4292/12/20/3403
[14] Jiao X, Kovacs J M, Shang J, et al. Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data[J]. ISPRS Journal Photogrammetry Remote Sensing, 2014, 96:38-46.
doi: 10.1016/j.isprsjprs.2014.06.014 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271614001695
[15] Waske B, Schiefer S, Braun M. Random feature selection for decision tree classification of multi-temporal SAR data[C]// Proceedings of the 2006 IEEE International Symposium on Geoscience and Remote Sensing,Denver,CO,USA, 2006:168-171.
[16] 李露, 徐维祥. 农村人口老龄化效应下农业生态效率的变化[J]. 华南农业大学学报(社会科学版), 2021, 20(2):14-29.
[16] Li L, Xu W X. Changes of agricultural eco-efficiency under the effect of rural population aging[J]. Journal of South China Agricultural University(Social Science Edition), 2021, 20(2):14-29.
[17] 郭萍, 赵敏, 张妍, 等. 基于水足迹的河套灌区多目标种植结构优化调整与评价[J]. 农业机械学报, 2021, 52(12):346-357.
[17] Guo P, Zhao M, Zhang Y, et al. Optimization adjustment and evaluation of multi-objective planting structure in Hetao irrigation area based on water footprint[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(12):346-357.
[18] 叶初升, 马玉婷. 人力资本及其与技术进步的适配性何以影响了农业种植结构[J]. 中国农村经济, 2020(4):34-55.
[18] Ye C S, Ma Y T. How human capital and its adaptation to technological progress affect agricultural planting structure[J]. Chinese Rural Economy, 2020(4):34-55.
[19] 刘琼, 肖海峰. 农地经营规模与财政支农政策对农业碳排放的影响[J]. 资源科学, 2020, 42(6):1063-1073.
doi: 10.18402/resci.2020.06.05
[19] Liu Q, Xiao H F. Effects of agricultural land management scale and financial policies on agricultural carbon emissions[J]. Resources Science, 2020, 42(6):1063-1073.
[20] 陈源源. 气候变化对中国粮食生产的影响[J]. 中国农学通报, 2021, 37(12):51-57.
[20] Chen Y Y. Effects of climate change on grain production in China[J]. Chinese Agricultural Science Bulletin, 2021, 37(12):51-57.
[21] 邓振镛, 王强, 张强, 等. 中国北方气候暖干化对粮食作物的影响及应对措施[J]. 生态学报, 2010, 30(22):6278-6288.
[21] Deng Z Y, Wang Q, Zhang Q, et al. Effects of climate warming and drying on grain crops in northern China and countermeasures[J]. Acta Ecologica Sinica, 2010, 30(22):6278-6288.
[22] 周惠成, 彭慧. 基于水资源合理利用的多目标农作物种植结构调整与评价[J]. 农业工程学报, 2007(9):45-49.
[22] Zhou H C, Peng H. Multi-objective crop planting structure adjustment and evaluation based on rational utilization of water resources[J]. Transactions of the Chinese Society of Agricultural Engineering, 2007(9):45-49.
[23] 武雪萍, 蔡典雄, 梅旭荣. 黄河流域农业水资源与水环境问题及技术对策[J]. 生态环境, 2007(1):248-252.
[23] Wu X P, Cai D X, Mei X R. Problems and technical countermeasures of agricultural water resources and water environment in The Yellow River Basin[J]. Ecological Environment, 2007(1):248-252.
[24] 郭晓佳, 周荣, 李京忠. 黄河流域农业资源环境效率时空演化特征及影响因素[J]. 生态与农村环境学报, 2021, 37(3):332-340.
[24] Guo X J, Zhou R, Li J Z. Problems and technical countermeasures of agricultural water resources and water environment in the Yellow River Basin[J]. Ecological Environment, 2021, 37(3):332-340.
[25] 张伟, 陈洪松, 王克林. 喀斯特峰丛洼地土壤养分空间分异特征及影响因子分析[J]. 中国农业科学, 2006(9):1828-1835.
[25] Zhang W, Chen H S, Wang K L. Spatial differentiation characteristics and influencing factors of soil nutrients in Karst peak-depression[J]. Scientia Agricultura Sinica, 2006(9):1828-1835.
[26] 彭建. 喀斯特生态脆弱区土地利用/覆被变化研究[D]. 北京: 北京大学, 2006.
[26] Peng J. Study on land use/cover change in Karst ecologically fragile region[D]. Beijing: Peking University, 2006.
[27] 张惠远, 赵昕奕, 蔡运龙, 等. 喀斯特山区土地利用变化的人类驱动机制研究——以贵州省为例[J]. 地理研究, 1999(2):25-31.
[27] Zhang H Y, Zhao X Y, Cai Y L, et al. Human driving mechanism of land use change in Karst mountainous areas:A case study of Guizhou Province[J]. Geographical Research, 1999(2):25-31.
[28] 杨颖频, 吴志峰, 骆剑承. 时空协同的地块尺度作物分布遥感提取[J]. 农业工程学报, 2021, 37(7):166-174.
[28] Yang Y P, Wu Z F, Luo J C. Remote sensing extraction of spatio-temporal synergistic crop distribution at plot scale[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(7):166-174.
[29] 吴志峰, 骆剑承, 孙营伟. 时空协同的精准农业遥感研究[J]. 地球信息科学学报, 2020, 22(4):731-742.
doi: 10.12082/dqxxkx.2020.190726
[29] Wu Z F, Luo J C, Sun Y W. Remote sensing research of precision agriculture based on spatio-temporal coordination[J]. Journal of Geo-Information Science, 2020, 22(4):731-742.
[30] Zhu M, Zhou Z F, Huang D H, et al. Extraction method for single Zanthoxylum bungeanum in Karst mountain area based on UAV visible light images[J]. Journal of Applied Remote Sensing, 2021, 15(2):026501.
[31] Xia L, Zhang X, Zhang J, et al. Building extraction from very-high-resolution remote sensing images using semi-supervised semantic edge detection[J]. Remote Sensing, 2021, 13(11):2187.
doi: 10.3390/rs13112187 url: https://www.mdpi.com/2072-4292/13/11/2187
[32] 黄登红, 周忠发, 彭睿文, 等. 西南高原山区作物低空遥感挑战与研究进展——以贵州省为例[J]. 贵州师范大学学报(自然科学版), 2021, 39(5):53-61.
[32] Huang D H, Zhou Z F, Peng R W, et al. Low altitude remote sensing challenge and research progress of crops in mountainous areas of southwest Plateau:A case study of Guizhou Province[J]. Journal of Guizhou Normal University(Natural Science), 2021, 39(5):53-61.
[33] 赵馨, 周忠发, 王玲玉. 喀斯特山区石漠化耕地遥感精准提取与分析——以贵州省北盘江镇与花江镇为例[J]. 热带地理, 2020, 40(2):289-302.
doi: 10.13284/j.cnki.rddl.003233
[33] Zhao X, Zhou Z F, Wang L Y. Remote sensing precision extraction and analysis of rocky desertification cultivated land in Karst mountainous areas:A case study of Beipanjiang and Huajiang towns in Guizhou Province[J]. Tropical Geography, 2020, 40(2):289-302.
doi: 10.13284/j.cnki.rddl.003233
[34] Lu H, Fu X, Liu C, et al. Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning[J]. Journal of Mountain Science, 2017, 14(4):731-741.
doi: 10.1007/s11629-016-3950-2 url: http://link.springer.com/10.1007/s11629-016-3950-2
[35] Hu X, Li X. Information extraction of subsided cultivated land in high-groundwater-level coal mines based on unmanned aerial vehicle visible bands[J]. Environmental Earth Sciences, 2019, 78(14):1-11.
doi: 10.1007/s12665-018-7995-0 url: https://doi.org/10.1007/s12665-018-7995-0
[36] 崔鸿雁, 徐帅, 张利锋. 机器学习中的特征选择方法研究及展望[J]. 北京邮电大学学报, 2018, 41(1):1-12.
[36] Cui H Y, Xu S, Zhang L F. Research and prospect of feature selection method in machine learning[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(1):1-12.
[37] Xu Y, Pei J, Lai L. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction[J]. Journal of Chemical Information and Modeling, 2017, 57(11):2672-2685.
doi: 10.1021/acs.jcim.7b00244 pmid: 29019671
[38] Chen C, Zhang Q, Ma Q, et al. LightGBM-PPI:Predicting protein-protein interactions through LightGBM with multi-information fusion[J]. Chemometrics and Intelligent Laboratory Systems, 2019, 191:54-64.
doi: 10.1016/j.chemolab.2019.06.003 url: https://linkinghub.elsevier.com/retrieve/pii/S016974391930262X
[39] 马晓君, 沙靖岚, 牛雪琪. 基于LightGBM算法的P2P项目信用评级模型的设计及应用[J]. 数量经济技术经济研究, 2018, 35(5):144-160.
[39] Ma X J, Sha J L, Niu X Q. Design and application of P2P credit rating model based on LightGBM algorithm[J]. Journal of Quantitative and Technical Economics, 2018, 35(5):144-160.
[40] 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.
doi: 10.11821/dlxb201701010
[40] Wang J F, Xu C D. Geographical detectors:Principles and prospects[J]. Acta Geographica Sinica, 2017, 72(1):116-134.
[41] Wang J F, Li X H, Christakos G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region,China[J]. International Journal of Geographical Information Science, 2010, 24(1):107-127.
doi: 10.1080/13658810802443457 url: http://www.tandfonline.com/doi/abs/10.1080/13658810802443457
[42] Liang J, Pan S, Chen W, et al. Cultivated land fragmentation and its influencing factors detection:A case study in Huaihe River Basin,China[J]. International Journal of Environmental Research and Public Health, 2021, 19(1):138.
doi: 10.3390/ijerph19010138 url: https://www.mdpi.com/1660-4601/19/1/138
[43] Wang Z, Liang L, Sun Z, et al. Spatiotemporal differentiation and the factors influencing urbanization and ecological environment synergistic effects within the Beijing-Tianjin-Hebei urban agglomeration[J]. Journal of Environmental Management, 2019, 243:227-239.
doi: S0301-4797(19)30555-9 pmid: 31096175
[44] 关岭自治县统计局. 关岭自治县2018年国民经济和社会发展统计公报[R]. 关岭: 关岭自治县统计局, 2018.
[44] Guanling Autonomous County Statistics Bureau. Statistical bulletin of national economic and social development in Guanling Autonomous County in 2018[R]. Guanling: Guanling Autonomous County Stastics Bureau, 2018.
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