基于高分辨率影像的蔡家河流域人工林Crop Science单木提取与缺失检测
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摘要
以北京市延庆区蔡家河流域人工林为研究对象,以蔡家河流域平原造林区的高分辨遥感影像为数据源,利用ENVI 5.4的Crop Science工具包分别对阔叶林和针叶试验林样区进行单木提取、缺失单木检测,并对研究结果进行精度评价和对比分析。结果表明:Crop Science对人工幼林进行单木提取效果较好,总体精度达到94%以上;缺失单木的识别受林木排列规整程度影响较大,排列越规整,提取效果越好。本研究探索了一种基于高分辨率遥感数据进行人工林地单木定位、计数及缺失单木查找的简便可行的方法,有利于林业管护人员快速获取高精度林木监测信息。
Taking the plantation of Caijiahe Basin in Yanqing District of Beijing as the research object, the high-resolution remote sensing image of plain afforestation area of Caijiahe Basin was taken as the data source.Using the Crop Science toolkit of ENVI 5.4, extraction and detection of individual tree were performed on the broad-leaved forest and coniferous forest samples, and the results were evaluated and compared. The results show that the Crop Science toolkit has high efficiency and accuracy on extraction of individual tree for young plantation and the overall accuracy is over 94%. The identification of individual tree is greatly affected by the regularity of the trees distribution. The tree locations with structured rows, can get the better extraction result. In this study,we introduce an effective and convenient tool to quickly obtain the location, count at the individual tree scale,based on high resolution remote sensing data. It is beneficial for forestry management and maintenance personnel to quickly obtain high-precision forest monitoring information.
Taking the plantation of Caijiahe Basin in Yanqing District of Beijing as the research object, the high-resolution remote sensing image of plain afforestation area of Caijiahe Basin was taken as the data source.Using the Crop Science toolkit of ENVI 5.4, extraction and detection of individual tree were performed on the broad-leaved forest and coniferous forest samples, and the results were evaluated and compared. The results show that the Crop Science toolkit has high efficiency and accuracy on extraction of individual tree for young plantation and the overall accuracy is over 94%. The identification of individual tree is greatly affected by the regularity of the trees distribution. The tree locations with structured rows, can get the better extraction result. In this study,we introduce an effective and convenient tool to quickly obtain the location, count at the individual tree scale,based on high resolution remote sensing data. It is beneficial for forestry management and maintenance personnel to quickly obtain high-precision forest monitoring information.
引文
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[10]Jing L,Hu B,Noland T,et al.An individual tree crown delineation method based on multi-scale segmentation of imagery[J].ISPRS Journal of Photogrammetry andRemote Sensing,2012,70(3):88-98.
[11]Mohan M,Silva C A,Klauberg C,et al.Individual Tree Detection from Unmanned Aerial Vehicle(UAV)Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest[J].Forests,2017,8(9):1-17.
[12]冯钟葵,石丹,陈文熙,等.法国遥感卫星的发展:从SPOT到Pleiades[J].遥感信息,2007(4):87-92.
[13]ENVI-IDL技术殿堂的博客.ENVI精准农业工具包:ENVI Crop Science 1.0.1[EB/OL].(2007-09-04)[2018-02-01].http://blog.sina.com.cn/s/blog_764b1e9d0102y7tk.html.
[14]王二丽,李存军,周静平,等.基于多时相遥感影像的北京平原人工林树种分类[J].北京工业大学学报,2017,43(5):710-718.
[15]童庆禧,张兵,郑兰芬.高光谱遥感:原理、技术与应用[M].北京:高等教育出版社,2006.
[2]韩磊.北京延庆县平原造林的生态实践[J].农业科技与信息(现代园林),2015,12(11):815-820.
[3]张雪辉.北京市百万亩造林工程发展战略研究[D].北京:北京林业大学,2012.
[4]袁士保,赵云龙,孟佩.自主多源高分辨率卫星遥感监测技术在北京市平原造林工程中的应用[J].林业资源管理,2016(4):140-144.
[5]武红敢,石进,蒋丽雅.基于World View-2数据的单木长势监测初探[J].遥感信息,2013,28(5):64-68.
[6]甄贞,李响,修思玉,等.基于标记控制区域生长法的单木树冠提取[J].东北林业大学学报,2016,44(10):22-29.
[7]郭昱杉,刘庆生,刘高焕,等.基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取[J].地球信息科学学报,2016,18(9):1259-1266.
[8]王枚梅,林家元,林沂,等.基于无人机可见光影像的亚高山针叶林树冠参数信息自动提取[J].林业资源管理,2017(4):82-88.
[9]Ke Y,Quackenbush L J.Individual tree crown detection and delineation from high spatial resolution imagery using active contour and hill-climbing methods[C].Proceedings of 2009 ASPRS Annual Conference,American Society of Photogrammetry and Remote Sensing,2009:9-13.
[10]Jing L,Hu B,Noland T,et al.An individual tree crown delineation method based on multi-scale segmentation of imagery[J].ISPRS Journal of Photogrammetry andRemote Sensing,2012,70(3):88-98.
[11]Mohan M,Silva C A,Klauberg C,et al.Individual Tree Detection from Unmanned Aerial Vehicle(UAV)Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest[J].Forests,2017,8(9):1-17.
[12]冯钟葵,石丹,陈文熙,等.法国遥感卫星的发展:从SPOT到Pleiades[J].遥感信息,2007(4):87-92.
[13]ENVI-IDL技术殿堂的博客.ENVI精准农业工具包:ENVI Crop Science 1.0.1[EB/OL].(2007-09-04)[2018-02-01].http://blog.sina.com.cn/s/blog_764b1e9d0102y7tk.html.
[14]王二丽,李存军,周静平,等.基于多时相遥感影像的北京平原人工林树种分类[J].北京工业大学学报,2017,43(5):710-718.
[15]童庆禧,张兵,郑兰芬.高光谱遥感:原理、技术与应用[M].北京:高等教育出版社,2006.
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