A method for automatic mapping of the remote sensing monitoring results of national nature reserves based on ArcPy and map optimization

doi:10.6046/zrzyyg.2023250

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(2024 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Remote sensing monitoring in national-level nature reserves covers a land area of approximately 1.7 million km². This process involves the delineation of numerous features that indicate variations in the nature reserves, requiring specialized expertise. As a result, ensuring the accuracy and normalization of mapping is challenging even using substantial human and material resources. This affects the quality and effectiveness of monitoring result applications and relevant services. To address this issue, employing geometric techniques like the Sutherland-Hodgman clipping algorithm based on the ArcPy package, along with the customized ArcToolbox tools for encapsulating automated mapping scripts, this study automatically extracted the information and images of features from a geographic database. Subsequently, this study automatically generated the distribution maps of features that reflected variations in national-level nature reserves. Over 50000 maps were plotted using the proposed method, with an accuracy of 100%. Practical application demonstrates that the automatic mapping for a single map can be completed within 29.06 s on average, significantly less than manual mapping. The proposed method can meet practical production needs, with the automated mapping scripts proving stable, reliable, and widely applicable. The proposed method can significantly enhance the efficiency of the applications of the monitoring results reflecting variations in the national-level nature reserves, holding great practical significance.

Keywords monitoring of a national-level nature reserve ArcPy remote sensing image Sutherland-Hodgman algorithm

ZTFLH:

TP79

Issue Date: 17 February 2025

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Tixin WANG
	Jinzhong YANG
	Yu XING
	Kaijian WANG

Cite this article:

Tixin WANG,Jinzhong YANG,Yu XING, et al. A method for automatic mapping of the remote sensing monitoring results of national nature reserves based on ArcPy and map optimization[J]. Remote Sensing for Natural Resources, 2025, 37(1): 252-259.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023250 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/252

Fig.1 Design architecture for automatic mapping

Tab.1 Field attribute information and usage description for automatic mapping

Fig.2 Flow chart of automatic mapping

Fig.3 Distribution map of mapped features for monitoring changes in national natural reserves

Fig.4 Comparison of spatial information optimization before and after mapping

Fig.5 Detailed flowchart of spatial information optimization

Tab.2 Automatic mapping time

[1]	新华社. 中共中央办公厅国务院办公厅印发《关于建立以国家公园为主体的自然保护地体系的指导意见》[J]. 中华人民共和国国务院公报, 2019(19):16-21.
[1]	Xinhua News Agency. The general office of the central committee of the CPC and the general office of the state council print and issue the guiding opinions on establishing a system of natural reserves with National Parks as the main body[J]. Gazette of the State Council of the People’s Republic of China, 2019(19):16-21.
[2]	国家林业局自然保护区研究中心. 首届中国自然保护国际论坛召开[EB/OL].(2019-11-01) [2023-10-17].http://www.fnrrc.com/chinese/hydt/showcolumns.asp?ID=6109. url: http://www.fnrrc.com/chinese/hydt/showcolumns.asp?ID=6109
[2]	Nature reserve research center of national forestry and grassland administration. The 1st China international forum on nature conservation was held[EB/OL].(2019-11-01) [2023-10-17].http://www.fnrrc.com/chinese/hydt/showcolumns.asp?ID=6109. url: http://www.fnrrc.com/chinese/hydt/showcolumns.asp?ID=6109
[3]	梅安新. 遥感导论[M]. 北京: 高等教育出版社,2001:5-6.
[3]	Mei A X. An introduction to remote sensing[M]. Beijing: Higher Education Press,2001:5-6.
[4]	杨金中, 秦绪文, 张志. 矿山遥感监测理论方法与实践[M]. 北京: 测绘出版社,2011:1-2.
[4]	Yang J Z, Qin X W, Zhang Z. Theory and practice on remote sensing monitoring of mine[M]. Beijing: Sino Maps Press,2011:1-2.
[5]	陈栋, 姚维岭. 基于ArcPy与定制ArcToolbox的矿山新增图斑自动编号及方法改进[J]. 国土资源遥感, 2021, 33(2):262-269.doi:10.6046/gtzyyg.2020181.
[5]	Chen D, Yao W L. Automatic numbering and method improvement of mine patches based on ArcP_y and custom ArcToolbox[J]. Remote Sensing for Land and Resources, 2021, 33(2):262-269.doi:10.6046/gtzyyg.2020181.
[6]	武蓓蓓, 徐青, 李华孝杨, 等. 常用GIS制图软件地图符号设计现状分析[J]. 测绘与空间地理信息, 2022, 45(12):106-109.
[6]	Wu B B, Xu Q, Li H X Y, et al. Analysis of the current situation of map symbol design in commonly used GIS cartographic software[J]. Geomatics & Spatial Information Technology, 2022, 45(12):106-109.
[7]	许娟, 张翼, 周琪, 等. 基于QGIS的四川地震应急专题图精细化设计与实现[J]. 四川地震, 2022(1):24-29.
[7]	Xu J, Zhang Y, Zhou Q, et al. Refined design and implementation of the thematic map of earthquake emergency in Sichuan based on QGIS[J]. Earthquake Research in Sichuan, 2022(1):24-29.
[8]	张伟, 邱崇涛, 谢明宏, 等. 利用Python自动化生成地质解释图件[J]. 物探与化探, 2021, 45(1):186-191.
[8]	Zhang W, Qiu C T, Xie M H, et al. Automatic drawing of geological interpretation map by Python[J]. Geophysical and Geochemical Exploration, 2021, 45(1):186-191.
[9]	马红利, 李红文, 王祖亮. 某省区县级应急图快速制图技术研究[J]. 测绘技术装备, 2021, 23(3):57-61.
[9]	Ma H L, Li H W, Wang Z L. Rapid mapping technology of district and county level emergency map in A Province[J]. Geomatics Technology and Equipment, 2021, 23(3):57-61.
[10]	张清林. 基于1∶10 000 DLG的图库一体化数据生产实践[J]. 测绘与空间地理信息, 2022, 45(8):259-261.
[10]	Zhang Q L. Practice of graph-database integration data production based on 1∶10 000 DLG[J]. Geomatics & Spatial Information Technology, 2022, 45(8):259-261.
[11]	王祥. 利用ArcGIS数据驱动页面实现快速编制图集的方法研究[J]. 测绘与空间地理信息, 2023, 46(7):79-81.
[11]	Wang X. Research on the method of quickly compiling atlas using ArcGIS data-driven pages[J]. Geomatics & Spatial Information Technology, 2023, 46(7):79-81.
[12]	汪永华, 蔚远江, 温志新, 等. 基于ArcGIS软件的油气资源与盆地评价工业制图技术与应用[J]. 地球科学与环境学报, 2022, 44(4):675-684.
[12]	Wang Y H, Yu Y J, Wen Z X, et al. Industrial mapping technology and application of oil & gas resources and basin evaluation based on ArcGIS software[J]. Journal of Earth Sciences and Environment, 2022, 44(4):675-684.
[13]	刁明光, 薛涛, 李建存, 等. 基于ArcGIS的矿山遥感监测成果编制系统[J]. 国土资源遥感, 2016, 28(3):194-199.doi: 10.6046/gtzyyg.2016.03.30.
[13]	Diao M G, Xue T, Li J C, et al. Production data compilation system of mine remote sensing monitoring based on ArcGIS[J]. Remote Sensing for Land and Resources, 2016, 28(3):194-199.doi: 10.6046/gtzyyg.2016.03.30.
[14]	刁明光, 刘芳, 谭专条, 等. 矿山遥感监测解译记录表自动生成方法研究与实现[J]. 国土资源遥感, 2018, 30(4):212-217.doi: 10.6046/gtzyyg.2018.04.32.
[14]	Diao M G, Liu F, Tan Z T, et al. Research and implement on automatic production method of mine remote sensing monitoring interpretation record table[J]. Remote Sensing for Land and Resources, 2018, 30(4):212-217.doi: 10.6046/gtzyyg.2018.04.32.
[15]	车紫进, 李会宾, 贾曲, 等. 土壤调查成果制图软件设计与实现[J]. 中国农业信息, 2022, 34(6):59-66.
[15]	Che Z J, Li H B, Jia Q, et al. Design and implementation of soil survey results mapping software[J]. China Agricultural Informatics, 2022, 34(6):59-66.
[16]	ESRI. What is ArcPy?—ArcMap \| Documentation[EB/OL] [2023-07-13].https://desktop.arcgis.com/en/arcmap/latest/analyze/arcpy/what-is-arcpy-.htm. url: https://desktop.arcgis.com/en/arcmap/latest/analyze/arcpy/what-is-arcpy-.htm
[17]	李霄, 刘莹, 姚维岭, 等. 基于ArcPy的违法矿山自动出图方法[J]. 国土资源遥感, 2020, 32(3):247-251.doi: 10.6046/gtzyyg.2020.03.32.
[17]	Li X, Liu Y, Yao W L, et al. Automatic plotting of illegal mines based on ArcPy[J]. Remote Sensing for Land and Resources, 2020, 32(3):247-251.doi: 10.6046/gtzyyg.2020.03.32.
[18]	吴厚清, 熊维康, 聂晨晖, 等. ArcPy脚本工具在新型基础测绘质量控制中的应用[J]. 测绘通报, 2022(8):160-164. doi: 10.13474/j.cnki.11-2246.2022.0250
[18]	Wu H Q, Xiong W K, Nie C H, et al. Application of ArcP_y scripting tool in quality control of new fundamental surveying and mapping[J]. Bulletin of Surveying and Mapping, 2022(8):160-164.
[19]	张天成, 何昊, 李丹丹, 等. 基于GIS的作物生产力专题图系统设计与实现[J]. 中国农学通报, 2023, 39(7):153-157. doi: 10.11924/j.issn.1000-6850.casb2022-0261
[19]	Zhang T C, He H, Li D D, et al. Design and implementation of crop productivity thematic map system based on GIS[J]. Chinese Agricultural Science Bulletin, 2023, 39(7):153-157. doi: 10.11924/j.issn.1000-6850.casb2022-0261
[20]	王淑玲, 彭红春, 虎志伟. 基于Arcpy的地块分布图批量化处理[J]. 现代测绘, 2023, 46(2):61-64.
[20]	Wang S L, Peng H C, Hu Z W. Mass production of plot distribution map based on arcpy[J]. Modern Surveying and Mapping, 2023, 46(2):61-64.
[21]	欧丁丁, 张琪, 尹祺卿, 等. 基于Arcpy的长顺县国储林实施方案小班设计图自动出图方法优化[J]. 中南林业调查规划, 2023, 42(1):37-42.
[21]	Ou D D, Zhang Q, Yin Q Q, et al. Optimization of automatic drawing method for sub-compartment design drawings of Changshun County national reserve forest implementation plan based on arcpy[J]. Central South Forest Inventory and Planning, 2023, 42(1):37-42.
[22]	林金宝. 基于ArcPy的ArcGIS数据驱动制图及批量出图——以耕地质量等别评定制图为例[J]. 闽西职业技术学院学报, 2021, 23(3):104-108.
[22]	Lin J B. ArcGIS data-driven mapping and batch mapping based on ArcPy:Taking the evaluation of cultivated land quality as an example[J]. Journal of Minxi Vocational and Technical College, 2021, 23(3):104-108.
[23]	刁明光, 薛涛, 李建存, 等. 基于地质信息元数据标准的多源空间数据管理系统[J]. 国土资源遥感, 2013, 25(1):165-170.doi: 10.6046/gtzyyg.2013.01.29.
[23]	Diao M G, Xue T, Li J C, et al. The multi-source spatial data management system based on geological information metadata standard[J]. Remote Sensing for Land and Resources, 2013, 25(1):165-170.doi:10.6046/gtzyyg.2013.01.29.
[24]	刁明光, 赵元元, 赵丹宁, 等. 矿山遥感监测成果数据报表自动生成方法[J]. 科学技术与工程, 2020, 20(3):1107-1112.
[24]	Diao M G, Zhao Y Y, Zhao D N, et al. Automatic generation method of production data report of mine remote sensing monitoring[J]. Science Technology and Engineering, 2020, 20(3):1107-1112.
[25]	Sutherland I E, Hodgman G W. Reentrant polygon clipping[J]. Communications of the ACM, 1974, 17(1):32-42.
[26]	Weiler K, Atherton P. Hidden surface removal using polygon area sorting[C]// Proceedings of the 4th annual conference on Computer graphics and interactive techniques.San Jose California. ACM,1977:214-222.
[27]	Vatti B R. A generic solution to polygon clipping[J]. Communications of the ACM, 1992, 35(7):56-63.
[28]	张钧, 王鹏. 一种新的矢量数据多边形的快速裁剪算法[J]. 中国图象图形学报, 2008, 13(12):2409-2413.
[28]	Zhang J, Wang P. A new fast polygon clipping algorithm for vector data[J]. Journal of Image and Graphics, 2008, 13(12):2409-2413.
[29]	孙家广, 胡事民. 计算机图形学基础教程[M]. 北京: 清华大学出版社,2005:23-29,36-39.
[29]	Sun J G, Hu S M. Basic course of computer graphics[M]. Beijing: Tsinghua University Press,2005:23-29,36-39.

[1]	XU Xinyu, LI Xiaojun, GE Junfei, LI Yikun. A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter[J]. Remote Sensing for Natural Resources, 2025, 37(1): 24-30.
[2]	CHEN Jiaxue, XIAO Dongsheng, CHEN Hongyu. A boundary guidance and cross-scale information interaction network for water body extraction from remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(1): 15-23.
[3]	QU Haicheng, LIANG Xu. Building extraction from high-resolution images using a hybrid attention mechanism combined with multi-scale feature enhancement[J]. Remote Sensing for Natural Resources, 2024, 36(4): 107-116.
[4]	PAN Junjie, SHEN Li, YAN Xin, NIE Xin, DONG Kuanlin. An adversarial learning-based unsupervised domain adaptation method for semantic segmentation of high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(4): 149-157.
[5]	ZHAO Jinling, HUANG Jian, LIANG Zijun, ZHAO Xuedan, JIN Tao, GE Hanghang, WEI Xiaoyan, SHAO Yuanzheng. BDANet-based assessment of building damage from earthquake disasters[J]. Remote Sensing for Natural Resources, 2024, 36(4): 193-200.
[6]	TAI Jiayi, SHEN Li, QIAO Wenfan, ZHOU Wuzhen. Impacts of different proportions of contextual information on the construction of sample sets of remote sensing scene images for damaged buildings[J]. Remote Sensing for Natural Resources, 2024, 36(3): 154-162.
[7]	YU Miao, JING Hongbo, WANG Xiang, LI Xingjiu. Multi-directional target detection based on depth features[J]. Remote Sensing for Natural Resources, 2024, 36(3): 267-271.
[8]	WEN Quan, LI Lu, XIONG Li, DU Lei, LIU Qingjie, WEN Qi. A review of water body extraction from remote sensing images based on deep learning[J]. Remote Sensing for Natural Resources, 2024, 36(3): 57-71.
[9]	SONG Shuangshuang, XIAO Kaifei, LIU Zhaohua, ZENG Zhaoliang. A YOLOv5-based target detection method using high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 50-59.
[10]	ZHAO Binru, NIU Siwen, WANG Liyan, YANG Xiaotong, JIAO Hongbo, WANG Zike. An intelligent color enhancement method for high-resolution remote sensing images of the coastal zone of an island[J]. Remote Sensing for Natural Resources, 2024, 36(2): 70-79.
[11]	ZHAO Heting, LI Xiaojun, XU Xinyu, GAI Junfei. An ICM-based adaptive pansharpening algorithm for hyperspectral images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 97-104.
[12]	LIN Dan, LI Qiucen, CHEN Zhikui, ZHONG Fangming, LI Lifang. Research advances and challenges in multi-label classification of remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 10-20.
[13]	LIU Yujia, XIE Shizhe, DU Yang, YAN Jin, NAN Yanyun, WEN Zhongkai. A two-stage remote sensing image inpainting network combined with spatial semantic attention[J]. Remote Sensing for Natural Resources, 2024, 36(1): 58-66.
[14]	NIU Xianghua, HUANG Wei, HUANG Rui, JIANG Sili. A high-fidelity method for thin cloud removal from remote sensing images based on attentional feature fusion[J]. Remote Sensing for Natural Resources, 2023, 35(3): 116-123.
[15]	WANG Jianqiang, ZOU Zhaohui, LIU Rongbo, LIU Zhisong. A method for extracting information on coastal aquacultural ponds from remote sensing images based on a U²-Net deep learning model[J]. Remote Sensing for Natural Resources, 2023, 35(3): 17-24.

Viewed

Full text

Abstract

Cited

Shared

Discussed