Framework for video-based monitoring of forest insect defoliation and discoloration

被引:1
作者
Zhao, Feifei [1 ,2 ]
Wang, Yafei [2 ,3 ]
Qiao, Yanyou [1 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
来源
JOURNAL OF APPLIED REMOTE SENSING | 2015年 / 9卷
基金
国家高技术研究发展计划(863计划);
关键词
forest insect defoliation and discoloration; field survey; distant video surveillance; video positioning; remote sensing; DISTURBANCES;
D O I
10.1117/1.JRS.9.096093
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Pest damage is a general problem that disturbs the growth of forests, influencing carbon sequestration and causing economic losses. In the past decades, many studies have been conducted to monitor and detect forest insect damage using satellite remote sensing technology. Satellite remote sensing has a satellite or aerial vision allowing the monitoring of extensive forest areas, but it usually requires constant time periods and is prone to cloud interference. To enable more efficient and effective monitoring of forest pest damage, a video-based monitoring framework is presented. This framework comprises three key parts: (1) video positioning of forest insect damage based on digital elevation model (DEM) and the parameters obtained from the pan-tilt-zoom camera, (2) integration of two-dimensional/three-dimensional geographic information system and video surveillance to provide more intuitionistic monitoring and assistance for positioning, (3) on-site verification conducted by ground surveys and guided through global positioning system (GPS) integrated in the embedded devices. The experiment was carried out over two forest areas to validate the proposed method. Results showed that the framework bears a sound positioning accuracy and high detection ratio, which could be effectively used in detecting and monitoring forest insect defoliation and discoloration. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
引用
收藏
页数:15
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