基于北京二号与地理国情数据的林地提取

doi:10.13474/j.cnki.11-2246.2023.0007

测绘通报 ›› 2023, Vol. 0 ›› Issue (1): 39-44.doi: 10.13474/j.cnki.11-2246.2023.0007

基于北京二号与地理国情数据的林地提取

杜林丹^1,2, 董春², 赵荣², 张玉², 亢晓琛²

1. 辽宁工程技术大学测绘与地理科学学院, 辽宁阜新 123000;
2. 中国测绘科学研究院, 北京 100036

收稿日期:2022-02-14 发布日期:2023-02-08
通讯作者: 董春。E-mail:dongchun@casm.ac.cn
作者简介:杜林丹(1994-),女,博士生,主要研究方向为地理国情统计分析、地表覆盖提取、热岛效应。E-mail:416992933@qq.com
基金资助:
中国测绘科学研究院基本科研业务费(AR2122)

Forest land extraction based on Beijing No.2 and geographical national conditions data

DU Lindan^1,2, DONG Chun², ZHAO Rong², ZHANG Yu², KANG Xiaochen²

1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China;
2. Chinese Academy of Surveying and Mapping, Beijing 100036, China

Received:2022-02-14 Published:2023-02-08

摘要/Abstract

摘要： 由于地理国情林地数据不包含实地面积小于400m²的树木或四旁单排林,若仅利用地理国情的林地数据统计区域森林覆盖率,将对四旁树面积较大地区的林地统计结果产生较大误差。为提取区域内准确的林地覆盖与空间分布状况,本文借助地理国情地表覆盖数据,提出了一种基于北京二号高分辨率遥感影像的林地提取方法。首先,根据遥感影像光谱特征,将研究区按植被、道路、铁路、建筑用地进行地类划分,并基于遥感影像进行各地类的样本提取,通过可分离检验的样本利用最大似然分类提取研究区内植被覆盖范围;然后,借助地理国情地表覆盖数据,使用叠置分析剔除误分、错分地类,得到区域林地的空间分布。试验结果表明:①研究区内林地覆盖率为20.3%,尚未满足北京新一轮林地规划需求;②地理国情地表覆盖数据内林地面积占提取林地总面积的54.03%,说明在部分地区使用本文方法对地理国情林地数据进行补充是有必要的。通过将试验结果与遥感影像进行目视比对并结合外业调查结果发现,提取的林地空间分布情况与实际分布基本相符。本文为地理国情的应用提供了一种新方法,研究结果可辅助区域的绿色发展规划,有助于构建科学的生态空间格局。

关键词: 林地提取, 地理国情监测, 最大似然分类, 叠置分析, 北京二号

Abstract: The forest land in the geographic national conditions data does not include trees or single-row forests with a field area of less than 400 m². If we only use the forest land data of geographical conditions to calculate the regional forest coverage rate, there will be a large error in the statistical results of the forest land in the area with large surrounding trees. For the above problems, in order to accurately extract the forest land cover and spatial extent in the region, a forest land extraction method based on the Beijing No.2 high-resolution remote sensing image is proposed in this paper by using the geographical and national land cover data. Firstly, according to the spectral characteristics of remote sensing images, the land types are divided according to vegetation, roads, railways and construction land, and the remote sensing images are used to extract samples of each type, and the maximum likelihood classification is used to extract the vegetation coverage for the samples that have passed the separable test. Then, with the help of the geographical and national land cover data, the overlay analysis was used to eliminate the misclassification and misclassification, and obtain the regional forest land coverage rate and spatial distribution. The results show that: ① The forest land coverage rate in the study area is 20.3%, which has not yet met the needs of a new round of forest land planning in Beijing. ② The forest land area in the geographic and national conditions data accounts for 54.03% of the total extracted forest land area, indicating that the use of this method in some areas is not enough. It is necessary to supplement the geographical conditions of forest land data. The experimental result is based on the visual comparison with remote sensing images combined with the field survey results, and it is believed that the spatial distribution of the extracted forest land is basically consistent with the actual distribution. The research provides a new method for the application of geographic and national conditions. The results can assist the regional green development planning and help to construct a scientific ecological spatial pattern.

Key words: forest land extraction, geographical national conditions monitoring, maximum likelihood classification, overlay analysis, Beijing No.2

中图分类号:

P237

杜林丹, 董春, 赵荣, 张玉, 亢晓琛. 基于北京二号与地理国情数据的林地提取[J]. 测绘通报, 2023, 0(1): 39-44.

DU Lindan, DONG Chun, ZHAO Rong, ZHANG Yu, KANG Xiaochen. Forest land extraction based on Beijing No.2 and geographical national conditions data[J]. Bulletin of Surveying and Mapping, 2023, 0(1): 39-44.

参考文献

[1] 董君明.浅谈植被在生态系统中的作用[J].生物学通报,1992,27(6):19.
[2] 闫慧杰.植被在城市生态中的主要功能探究[J].科技经济导刊,2019,27(21):103.
[3] 曲绍厚,宋锡铭,李玉英,周明煜.北京城区的气象效应[J].地球物理学报,1981,24(2):229-237.
[4] 王喜全,王自发,郭虎.北京"城市热岛"效应现状及特征[J].气候与环境研究,2006,11(5):627-636.
[5] 丁军.生态城市是北京"四个中心"建设的落脚点[J].环境经济,2019(15):52-55.
[6] 周楚军.北京:生态优先助力高质量绿色发展[J].中国土地,2019(10):57-58.
[7] 石河,何建勇.北京"退烧"[J].绿化与生活,2019(9):4-12.
[8] 任志远,李晶.陕南秦巴山区植被生态功能的价值测评[J].地理学报,2003,58(4):503-511.
[9] 张云红,王道杰,江晓波,等.岷江上游土地利用变化及其对生态环境的影响[J].西南大学学报(自然科学版),2010,32(3):102-108.
[10] 颜梅春.高分辨率影像的植被分类方法对比研究[J].遥感学报,2007,11(2):235-240.
[11] 赵伟颖.极化SAR监督分类方法对植被分类效果比较分析[C]//第九届长三角科技论坛(测绘分论坛)论文集.连云港:[s.n.],2012:119-122.
[12] 王海龙,刘雪惠,温小荣,等.顾及植被季相节律的滨海湿地类型动态变化研究[J].林业资源管理,2017(2):58-64.
[13] 陈君颖,田庆久.高分辨率遥感植被分类研究[J].遥感学报,2007,11(2):221-227.
[14] 关韵桐,李金平.基于Sentinel-1A与Sentinel-2A融合的土地利用/覆盖分类研究:以昆明市呈贡区为例[J].测绘与空间地理信息,2018,41(7):95-98.
[15] 董春,张继贤,牛利斌,等.地理国情地表覆盖的城市建设用地扩张分析:以兰州新区为例[J].测绘科学,2017,42(2):28-34.
[16] 龚国辉,董春,亢晓琛.国土三调数据与地理国情数据融合可行性分析[J].遥感信息,2020,35(6):67-75.
[17] 国家林业和草原局.中国森林资源报告2014-2018[M].北京:中国林业出版社,2019.
[18] 张国峰,高松峰,谢存竹,等.关于山东省林业规划中几个技术问题的探析[J].山东林业科技,2001,31(S1):187-189.
[19] 刘纪平,董春,亢晓琛,等.大数据时代的地理国情统计分析[J].武汉大学学报(信息科学版),2019,44(1):68-76.

基于北京二号与地理国情数据的林地提取

Forest land extraction based on Beijing No.2 and geographical national conditions data

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