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Title: Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements

Abstract

Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Additionally, compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposuremore » of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R- squared = 0.63, RMSE = 19.11%). The IS+LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Furthermore, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3]; ORCiD logo [4];  [1];  [5];  [6];  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental & Climate Sciences Department
  2. Univ. of Utah, Salt Lake City, UT (United States). Department of Geography
  3. Univ. of Maryland, College Park, MD (United States). Department of Geographical Sciences
  4. CSIRO, Agriculture and Food, St. Lucia, Brisbane (Australia)
  5. USDA Forest Service, Northeastern Area State & Private Forestry, Durham, NH (United States)
  6. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States). Biospheric Sciences Branch
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1456888
Report Number(s):
BNL-205774-2018-JAAM
Journal ID: ISSN 0034-4257
Grant/Contract Number:
SC0012704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 215; Journal Issue: C; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Forest infestation; MESMA; Invasive species; LiDAR; Hyperspectral; Data fusion

Citation Formats

Meng, Ran, Dennison, Philip E., Zhao, Feng, Shendryk, Iurii, Rickert, Amanda, Hanavan, Ryan P., Cook, Bruce D., and Serbin, Shawn P. Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements. United States: N. p., 2018. Web. doi:10.1016/j.rse.2018.06.008.
Meng, Ran, Dennison, Philip E., Zhao, Feng, Shendryk, Iurii, Rickert, Amanda, Hanavan, Ryan P., Cook, Bruce D., & Serbin, Shawn P. Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements. United States. doi:10.1016/j.rse.2018.06.008.
Meng, Ran, Dennison, Philip E., Zhao, Feng, Shendryk, Iurii, Rickert, Amanda, Hanavan, Ryan P., Cook, Bruce D., and Serbin, Shawn P. Tue . "Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements". United States. doi:10.1016/j.rse.2018.06.008.
@article{osti_1456888,
title = {Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements},
author = {Meng, Ran and Dennison, Philip E. and Zhao, Feng and Shendryk, Iurii and Rickert, Amanda and Hanavan, Ryan P. and Cook, Bruce D. and Serbin, Shawn P.},
abstractNote = {Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Additionally, compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposure of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R- squared = 0.63, RMSE = 19.11%). The IS+LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Furthermore, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.},
doi = {10.1016/j.rse.2018.06.008},
journal = {Remote Sensing of Environment},
number = C,
volume = 215,
place = {United States},
year = {Tue Jun 19 00:00:00 EDT 2018},
month = {Tue Jun 19 00:00:00 EDT 2018}
}

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