Tree Condition and Analysis Program - Detecting Forest Disturbance at the Tree Level across the Contiguous United States with High Resolution Imagery

被引:1
|
作者
Wegmueller, Sarah A. [1 ]
Monahan, William B. [2 ]
Townsend, Philip A. [1 ]
机构
[1] Univ Wisconsin, Dept Forest & Wildlife Ecol, 1630 Linden Dr, Madison, WI 53706 USA
[2] USDA Forest Serv, Forest Hlth Assessment & Appl Sci Team, 2150A Ctr Ave,Suite 331, Ft Collins, CO 80526 USA
关键词
TreeCAP; NAIP; LiDAR; TCH; forest health; remote sensing; CALIBRATION; LANDSAT;
D O I
10.1093/jofore/fvad039
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Effective management of forest insects and diseases requires detection of abnormal mortality, particularly among a single species, sufficiently early to enable effective management. Remote detection of individual trees crowns requires a spatial resolution not available from satellites such as Landsat or Sentinel-2. In the United States, there are currently few operational systems capable of effectively and affordably detecting and mapping tree mortality over broad landscapes using high-resolution imagery. Here, we introduce the Tree Condition and Analysis Program (TreeCAP), an open-source system that uses freely available imagery from the National Agriculture Imagery Program (NAIP) to create maps of tree condition (healthy or damaged). We demonstrate the potential applications of TreeCAP in four study sites: (1) beetle-killed pines in California, (2) emerald ash borer progression in Wisconsin, (3) hemlock wooly adelgid mortality in Pennsylvania, and (4) drought damage in Texas. We achieved an average overall accuracy of 87% across all study sites.Study Implications: TreeCAP is a software program, ready for operational use, intended to help manage forest health in the contiguous United States at the individual tree level. Using freely available high-resolution NAIP airborne imagery and LiDAR data, TreeCAP maps tree crown condition, highlighting areas that may warrant further attention to forest managers. We demonstrate the potential applications of TreeCAP in four study sites: (1) beetle-killed pines in California, (2) emerald ash borer progression in Wisconsin, (3) hemlock wooly adelgid mortality in Pennsylvania, and (4) drought damage in Texas. We achieved an average overall accuracy of 87% across all study sites.
引用
下载
收藏
页码:31 / 53
页数:23
相关论文
共 42 条
  • [1] Watershed segmentation and classification of tree species using high resolution forest imagery
    Kanda, F
    Kubo, M
    Muramoto, K
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 3822 - 3825
  • [2] Analyzing the impacts of forest disturbance on individual tree diameter increment across the US Lake States
    Glasby, Macklin J.
    Russell, Matthew B.
    Domke, Grant M.
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2019, 191 (02)
  • [3] Analyzing the impacts of forest disturbance on individual tree diameter increment across the US Lake States
    Macklin J. Glasby
    Matthew B. Russell
    Grant M. Domke
    Environmental Monitoring and Assessment, 2019, 191
  • [4] Landscape correlates of forest plant invasions: A high-resolution analysis across the eastern United States
    Riitters, Kurt
    Potter, Kevin
    Iannone, Basil V., III
    Oswalt, Christopher
    Fei, Songlin
    Guo, Qinfeng
    DIVERSITY AND DISTRIBUTIONS, 2018, 24 (03) : 274 - 284
  • [5] Benchmarking and Calibration of Forest Vegetation Simulator Individual Tree Attribute Predictions Across the Northeastern United States
    Russell, Matthew B.
    Weiskittel, Aaron R.
    Kershaw, John A., Jr.
    NORTHERN JOURNAL OF APPLIED FORESTRY, 2013, 30 (02): : 75 - 84
  • [6] Detection of individual tree in artificial forest in japan using high-resolution remote sensing imagery
    Graduate School of Integrated Arts and Sciences, Kochi University, Japan
    不详
    不详
    Asian Conf. Remote Sens., ACRS, 1600, (1889-1893):
  • [7] Predicting the abundance of forest types across the eastern United States through inverse modelling of tree demography
    Vanderwel, Mark C.
    Rozendaal, Danae M. A.
    Evans, Margaret E. K.
    ECOLOGICAL APPLICATIONS, 2017, 27 (07) : 2128 - 2141
  • [8] Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery
    Kuzmin, Anton
    Korhonen, Lauri
    Manninen, Terhikki
    Maltamo, Matti
    EUROPEAN JOURNAL OF REMOTE SENSING, 2016, 49 : 239 - 259
  • [9] Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration
    Pouliot, DA
    King, DJ
    Bell, FW
    Pitt, DG
    REMOTE SENSING OF ENVIRONMENT, 2002, 82 (2-3) : 322 - 334
  • [10] Green Cover (Tree) Analysis of Chennai Metropolitan Area Using High Resolution Satellite Imagery
    Chinnappa, Venkatesan
    Rajiah, Murugasan
    Devarajan, Thirumalaivasan
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2021, 30 (05): : 3943 - 3954