A general Landsat model to predict canopy defoliation in broadleaf deciduous forests

Philip A. Townsend; Aditya Singh; Jane R. Foster; Nathan J. Rehberg; Clayton C. Kingdon; Keith N. Eshleman; Steven W. Seagle

doi:10.1016/j.rse.2011.12.023

A general Landsat model to predict canopy defoliation in broadleaf deciduous forests

Philip A. Townsend, Aditya Singh, Jane R. Foster, Nathan J. Rehberg, Clayton C. Kingdon, Keith N. Eshleman, Steven W. Seagle

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Abstract

Defoliation by insect herbivores can be a persistent disturbance affecting ecosystem functioning. We developed an approach to map canopy defoliation due to gypsy moth based on site differences in Landsat vegetation index values between non-defoliation and defoliation dates. Using field data from two study areas in the U.S. central Appalachians and five different years (2000, 2001, 2006, 2007, and 2008), we fit a sigmoidal model predicting defoliation as a function of the difference in the vegetation index. We found that the normalized difference infrared index (NDII, [Band 4-Band 5]/[Band 4+Band 5]) and the moisture stress index (Band 5/Band 4) worked better than visible-near infrared indices such as NDVI for mapping defoliation. We report a global 2-term fixed-effects model using all years that was at least as good as a mixed-effects model that varied the model coefficients by year. The final model was: proportion of foliage retained=1/(1+exp(3.057-31.483*[NDII _baseyear-NDII _{disturbanceyear}]). Cross-validation by dropping each year of data and subsequently refitting the remaining data generated an RMS error estimate of 14.9% defoliation, a mean absolute error of 10.8% and a cross-validation R ² of 0.805. The results show that a robust, general model of percent defoliation can be developed to make continuous rather than categorical maps of defoliation across years and study sites based on field data collected using different sampling methods.

Original language	English (US)
Pages (from-to)	255-265
Number of pages	11
Journal	Remote Sensing of Environment
Volume	119
DOIs	https://doi.org/10.1016/j.rse.2011.12.023
State	Published - Apr 16 2012

Bibliographical note

Funding Information:
The research and data presented in this paper was supported by funds from a number of grants dating to 1999. Primary funding for this research was provided by NASA Interdisciplinary Sciences Grant NNG04GL87G to PAT and KNE and NASA Carbon Cycle Science Grant NNX06AD45G to PAT. Additional funding supporting the collection of data presented in this paper include EPA Ecological Indicators STAR grant R826598 to SWS and PAT (2000 defoliation data) and NSF SGER grant DEB-0119581 to KNE and PAT (2001 defoliation data). Data analyses were also supported by US Forest Service Special Technology Development Program (STDP) grant 09-CA-11420004-021 to PAT. Numerous people were involved in the collection and analysis of the field data used in this study. In particular, we would like to express our appreciation to Brian Sturtevant, Robert Chastain, Jeremiah Sawma, Wendy Hunter, Michael Snyder, James Morgan, and Jeff Griffith. This research is also the result of innumerable discussions among the researchers at the Appalachian Laboratory and University of Wisconsin. We also thank Clay Baros, Kirsten de Beurs, Shawn Serbin, Brenden McNeil and Suming Jin for their intellectual contributions to this work. We gratefully acknowledge the helpful comments of three anonymous reviewers. None of the research or data presented in this paper has been published previously.

Keywords

Change detection
Defoliation
Gypsy moth
Landsat
NDII

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title = "A general Landsat model to predict canopy defoliation in broadleaf deciduous forests",

abstract = "Defoliation by insect herbivores can be a persistent disturbance affecting ecosystem functioning. We developed an approach to map canopy defoliation due to gypsy moth based on site differences in Landsat vegetation index values between non-defoliation and defoliation dates. Using field data from two study areas in the U.S. central Appalachians and five different years (2000, 2001, 2006, 2007, and 2008), we fit a sigmoidal model predicting defoliation as a function of the difference in the vegetation index. We found that the normalized difference infrared index (NDII, [Band 4-Band 5]/[Band 4+Band 5]) and the moisture stress index (Band 5/Band 4) worked better than visible-near infrared indices such as NDVI for mapping defoliation. We report a global 2-term fixed-effects model using all years that was at least as good as a mixed-effects model that varied the model coefficients by year. The final model was: proportion of foliage retained=1/(1+exp(3.057-31.483*[NDII baseyear-NDII disturbanceyear]). Cross-validation by dropping each year of data and subsequently refitting the remaining data generated an RMS error estimate of 14.9% defoliation, a mean absolute error of 10.8% and a cross-validation R 2 of 0.805. The results show that a robust, general model of percent defoliation can be developed to make continuous rather than categorical maps of defoliation across years and study sites based on field data collected using different sampling methods.",

keywords = "Change detection, Defoliation, Gypsy moth, Landsat, NDII",

author = "Townsend, {Philip A.} and Aditya Singh and Foster, {Jane R.} and Rehberg, {Nathan J.} and Kingdon, {Clayton C.} and Eshleman, {Keith N.} and Seagle, {Steven W.}",

note = "Funding Information: The research and data presented in this paper was supported by funds from a number of grants dating to 1999. Primary funding for this research was provided by NASA Interdisciplinary Sciences Grant NNG04GL87G to PAT and KNE and NASA Carbon Cycle Science Grant NNX06AD45G to PAT. Additional funding supporting the collection of data presented in this paper include EPA Ecological Indicators STAR grant R826598 to SWS and PAT (2000 defoliation data) and NSF SGER grant DEB-0119581 to KNE and PAT (2001 defoliation data). Data analyses were also supported by US Forest Service Special Technology Development Program (STDP) grant 09-CA-11420004-021 to PAT. Numerous people were involved in the collection and analysis of the field data used in this study. In particular, we would like to express our appreciation to Brian Sturtevant, Robert Chastain, Jeremiah Sawma, Wendy Hunter, Michael Snyder, James Morgan, and Jeff Griffith. This research is also the result of innumerable discussions among the researchers at the Appalachian Laboratory and University of Wisconsin. We also thank Clay Baros, Kirsten de Beurs, Shawn Serbin, Brenden McNeil and Suming Jin for their intellectual contributions to this work. We gratefully acknowledge the helpful comments of three anonymous reviewers. None of the research or data presented in this paper has been published previously.",

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AU - Singh, Aditya

AU - Foster, Jane R.

AU - Rehberg, Nathan J.

AU - Kingdon, Clayton C.

AU - Eshleman, Keith N.

AU - Seagle, Steven W.

N1 - Funding Information: The research and data presented in this paper was supported by funds from a number of grants dating to 1999. Primary funding for this research was provided by NASA Interdisciplinary Sciences Grant NNG04GL87G to PAT and KNE and NASA Carbon Cycle Science Grant NNX06AD45G to PAT. Additional funding supporting the collection of data presented in this paper include EPA Ecological Indicators STAR grant R826598 to SWS and PAT (2000 defoliation data) and NSF SGER grant DEB-0119581 to KNE and PAT (2001 defoliation data). Data analyses were also supported by US Forest Service Special Technology Development Program (STDP) grant 09-CA-11420004-021 to PAT. Numerous people were involved in the collection and analysis of the field data used in this study. In particular, we would like to express our appreciation to Brian Sturtevant, Robert Chastain, Jeremiah Sawma, Wendy Hunter, Michael Snyder, James Morgan, and Jeff Griffith. This research is also the result of innumerable discussions among the researchers at the Appalachian Laboratory and University of Wisconsin. We also thank Clay Baros, Kirsten de Beurs, Shawn Serbin, Brenden McNeil and Suming Jin for their intellectual contributions to this work. We gratefully acknowledge the helpful comments of three anonymous reviewers. None of the research or data presented in this paper has been published previously.

PY - 2012/4/16

Y1 - 2012/4/16

N2 - Defoliation by insect herbivores can be a persistent disturbance affecting ecosystem functioning. We developed an approach to map canopy defoliation due to gypsy moth based on site differences in Landsat vegetation index values between non-defoliation and defoliation dates. Using field data from two study areas in the U.S. central Appalachians and five different years (2000, 2001, 2006, 2007, and 2008), we fit a sigmoidal model predicting defoliation as a function of the difference in the vegetation index. We found that the normalized difference infrared index (NDII, [Band 4-Band 5]/[Band 4+Band 5]) and the moisture stress index (Band 5/Band 4) worked better than visible-near infrared indices such as NDVI for mapping defoliation. We report a global 2-term fixed-effects model using all years that was at least as good as a mixed-effects model that varied the model coefficients by year. The final model was: proportion of foliage retained=1/(1+exp(3.057-31.483*[NDII baseyear-NDII disturbanceyear]). Cross-validation by dropping each year of data and subsequently refitting the remaining data generated an RMS error estimate of 14.9% defoliation, a mean absolute error of 10.8% and a cross-validation R 2 of 0.805. The results show that a robust, general model of percent defoliation can be developed to make continuous rather than categorical maps of defoliation across years and study sites based on field data collected using different sampling methods.

AB - Defoliation by insect herbivores can be a persistent disturbance affecting ecosystem functioning. We developed an approach to map canopy defoliation due to gypsy moth based on site differences in Landsat vegetation index values between non-defoliation and defoliation dates. Using field data from two study areas in the U.S. central Appalachians and five different years (2000, 2001, 2006, 2007, and 2008), we fit a sigmoidal model predicting defoliation as a function of the difference in the vegetation index. We found that the normalized difference infrared index (NDII, [Band 4-Band 5]/[Band 4+Band 5]) and the moisture stress index (Band 5/Band 4) worked better than visible-near infrared indices such as NDVI for mapping defoliation. We report a global 2-term fixed-effects model using all years that was at least as good as a mixed-effects model that varied the model coefficients by year. The final model was: proportion of foliage retained=1/(1+exp(3.057-31.483*[NDII baseyear-NDII disturbanceyear]). Cross-validation by dropping each year of data and subsequently refitting the remaining data generated an RMS error estimate of 14.9% defoliation, a mean absolute error of 10.8% and a cross-validation R 2 of 0.805. The results show that a robust, general model of percent defoliation can be developed to make continuous rather than categorical maps of defoliation across years and study sites based on field data collected using different sampling methods.

KW - Change detection

KW - Defoliation

KW - Gypsy moth

KW - Landsat

KW - NDII

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A general Landsat model to predict canopy defoliation in broadleaf deciduous forests

Abstract

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Keywords

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