Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas

WANG Yu; ZHOU Zhongfa; WANG Lingyu; LUO Jiancheng; HUANG Denghong; ZHANG Wenhui

doi:10.6046/zrzyyg.2021343

2022 Vol. 34, No. 4

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 155-165

Next Article Previous Article

WANG Yu, ZHOU Zhongfa, WANG Lingyu, LUO Jiancheng, HUANG Denghong, ZHANG Wenhui. 2022. Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas. Remote Sensing for Natural Resources, 34(4): 155-165. doi: 10.6046/zrzyyg.2021343

Citation:

WANG Yu, ZHOU Zhongfa, WANG Lingyu, LUO Jiancheng, HUANG Denghong, ZHANG Wenhui. 2022. Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas. Remote Sensing for Natural Resources, 34(4): 155-165. doi: 10.6046/zrzyyg.2021343

Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas

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

Received Date: 18 October 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

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.
- Karst /
- cultivated land utilization /
- planting structure /
- Sentinel-1 /
- space differentiation

FullText(HTML)

References

[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. Google Scholar
[2]	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. Google Scholar
[3]	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 . Google Scholar
[4]	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 . Google Scholar
[5]	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. Google Scholar
[6]	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. Google Scholar
[7]	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. Google Scholar
[8]	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. Google Scholar
[9]	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. Google Scholar
[10]	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. Google Scholar
[11]	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. Google Scholar
[12]	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. Google Scholar
[13]	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. Google Scholar
[14]	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. Google Scholar
[15]	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. Google Scholar
[16]	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. Google Scholar
[17]	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. Google Scholar
[18]	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. Google Scholar
[19]	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. Google Scholar
[20]	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. Google Scholar
[21]	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. Google Scholar
[22]	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. Google Scholar
[23]	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. Google Scholar
[24]	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. Google Scholar
[25]	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. Google Scholar
[26]	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. Google Scholar
[27]	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. Google Scholar
[28]	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. Google Scholar
[29]	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. Google Scholar
[30]	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. Google Scholar
[31]	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. Google Scholar
[32]	Chen Y Y. Effects of climate change on grain production in China[J]. Chinese Agricultural Science Bulletin, 2021, 37(12):51-57. Google Scholar
[33]	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. Google Scholar
[34]	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. Google Scholar
[35]	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. Google Scholar
[36]	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. Google Scholar
[37]	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. Google Scholar
[38]	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. Google Scholar
[39]	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. Google Scholar
[40]	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. Google Scholar
[41]	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. Google Scholar
[42]	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. Google Scholar
[43]	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. Google Scholar
[44]	Peng J. Study on land use/cover change in Karst ecologically fragile region[D]. Beijing: Peking University, 2006. Google Scholar
[45]	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. Google Scholar
[46]	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. Google Scholar
[47]	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. Google Scholar
[48]	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. Google Scholar
[49]	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. Google Scholar
[50]	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. Google Scholar
[51]	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. Google Scholar
[52]	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. Google Scholar
[53]	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. Google Scholar
[54]	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. Google Scholar
[55]	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. Google Scholar
[56]	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. Google Scholar
[57]	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. Google Scholar
[58]	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. Google Scholar
[59]	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. Google Scholar
[60]	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. Google Scholar
[61]	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. Google Scholar
[62]	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. Google Scholar
[63]	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. Google Scholar
[64]	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. Google Scholar
[65]	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. Google Scholar
[66]	Wang J F, Xu C D. Geographical detectors:Principles and prospects[J]. Acta Geographica Sinica, 2017, 72(1):116-134. Google Scholar
[67]	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. Google Scholar
[68]	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. Google Scholar
[69]	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. Google Scholar
[70]	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. Google Scholar
[71]	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. Google Scholar