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

Meng, Ran; Dennison, Philip E.; Zhao, Feng; Shendryk, Iurii; Rickert, Amanda; Hanavan, Ryan P.; Cook, Bruce D.; Serbin, Shawn P.

doi:10.1016/j.rse.2018.06.008

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

Journal Article · Tue Jun 19 00:00:00 EDT 2018 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2018.06.008· OSTI ID:1456888

^[1]; Dennison, Philip E. ^[2];

^[3];

^[4]; Rickert, Amanda ^[1]; Hanavan, Ryan P. ^[5]; Cook, Bruce D. ^[6]; Serbin, Shawn P. ^[1]

Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental & Climate Sciences Department
Univ. of Utah, Salt Lake City, UT (United States). Department of Geography
Univ. of Maryland, College Park, MD (United States). Department of Geographical Sciences
CSIRO, Agriculture and Food, St. Lucia, Brisbane (Australia)
USDA Forest Service, Northeastern Area State & Private Forestry, Durham, NH (United States)
NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States). Biospheric Sciences Branch

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.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0012704

OSTI ID:: 1456888

Alternate ID(s):: OSTI ID: 1582906

Report Number(s):: BNL-205774-2018-JAAM

Journal Information:: Remote Sensing of Environment, Vol. 215, Issue C; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 45 works

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Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring Wulder, Michael A.; White, Joanne C.; Coops, Nicholas C. Remote Sensing of Environment, Vol. 112, Issue 6 https://doi.org/10.1016/j.rse.2008.01.010	journal	June 2008
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Cited By (2)

Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar Lin, Qinan; Huang, Huaguo; Wang, Jingxu Remote Sensing, Vol. 11, Issue 21 https://doi.org/10.3390/rs11212540	journal	October 2019
Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level Hao, Hongke; Li, Weizhong; Zhao, Xuan Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.00917	journal	July 2019

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Forest infestation
MESMA
Invasive species
LiDAR
Hyperspectral
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