TY - JOUR
T1 - Integrating LiDAR-derived tree height and Landsat satellite reflectance to estimate forest regrowth in a tropical agricultural landscape
AU - Caughlin, T. Trevor
AU - Rifai, Sami W.
AU - Graves, Sarah J.
AU - Asner, Gregory P.
AU - Bohlman, Stephanie A.
N1 - Funding Information:
We thank D. Knapp, C. Anderson, T. Kennedy-Bowdoin and the entire Carnegie Airborne Observatory staff for data collection and processing support. T. T. Caughlin was supported by NSF grant #1415297 Science, Engineering and Education for Sustainability Fellow program. Airborne data collection, processing and analysis was funded by the Grantham Foundation for the Protection of the Environment and William R. Hearst III. The Carnegie Airborne Observatory has been made possible by grants and donations to G.P. Asner from the Avatar Alliance Foundation, Margaret A. Cargill Foundation, David and Lucile Packard Foundation, Gordon and Betty Moore Foundation, Grantham Foundation for the Protection of the Environment, W. M. Keck Foundation, John D. and Catherine T. MacArthur Foundation, Andrew Mellon Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr, and William R. Hearst III.
Publisher Copyright:
© 2016 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
PY - 2016/12
Y1 - 2016/12
N2 - Remotely sensed data have revealed ongoing reforestation across many tropical landscapes. However, most studies have quantified changes between discrete land cover categories that are difficult to relate to the continuous changes in forest structure that underlie reforestation. Here, we demonstrate how generalized linear models (GLMs) can predict tree height and tree canopy cover from Landsat satellite reflectance in a 109 882 ha tropical agricultural landscape of western Panama. We derived tree canopy cover and tree height from airborne Light Detection and Ranging (LiDAR) data, and related these variables to the fraction of photosynthetic vegetation (PV) in Landsat pixels. We found large gains in predictive accuracy from modeling tree canopy height with a gamma GLM and tree canopy cover with a binomial GLM, relative to modeling these variables using linear regression. Adding social and environmental covariates to our GLMs, including topography and parcel membership (representing different land owners), increased predictive accuracy, resulting in best-fit models with an R2 of 55.68% and RMSE of 23.69% for tree canopy cover, and an R2 of 51.24% and RMSE of 3.42 m for tree height. Finally, we applied the GLMs to predict tree height and tree canopy cover in Landsat images from c. 2000 to 2012, and used results to quantify changes in forest structure during this 12-year period. We found that >60% of pixels in our study area had increased in tree height and tree canopy cover, suggesting widespread forest regrowth. These increases were spatially widespread across the study area, yet subtle, with most pixels increasing <2 m in tree height. Our results suggest ecological and agricultural changes that could be overlooked if measuring land cover change with discrete forest and non-forest categories. Overall, we show the advantages of linking LiDAR and Landsat data to quantify forest regrowth in an agricultural landscape.
AB - Remotely sensed data have revealed ongoing reforestation across many tropical landscapes. However, most studies have quantified changes between discrete land cover categories that are difficult to relate to the continuous changes in forest structure that underlie reforestation. Here, we demonstrate how generalized linear models (GLMs) can predict tree height and tree canopy cover from Landsat satellite reflectance in a 109 882 ha tropical agricultural landscape of western Panama. We derived tree canopy cover and tree height from airborne Light Detection and Ranging (LiDAR) data, and related these variables to the fraction of photosynthetic vegetation (PV) in Landsat pixels. We found large gains in predictive accuracy from modeling tree canopy height with a gamma GLM and tree canopy cover with a binomial GLM, relative to modeling these variables using linear regression. Adding social and environmental covariates to our GLMs, including topography and parcel membership (representing different land owners), increased predictive accuracy, resulting in best-fit models with an R2 of 55.68% and RMSE of 23.69% for tree canopy cover, and an R2 of 51.24% and RMSE of 3.42 m for tree height. Finally, we applied the GLMs to predict tree height and tree canopy cover in Landsat images from c. 2000 to 2012, and used results to quantify changes in forest structure during this 12-year period. We found that >60% of pixels in our study area had increased in tree height and tree canopy cover, suggesting widespread forest regrowth. These increases were spatially widespread across the study area, yet subtle, with most pixels increasing <2 m in tree height. Our results suggest ecological and agricultural changes that could be overlooked if measuring land cover change with discrete forest and non-forest categories. Overall, we show the advantages of linking LiDAR and Landsat data to quantify forest regrowth in an agricultural landscape.
KW - Continuous change detection
KW - Landsat–LiDAR integration
KW - forest landscape restoration
KW - reforestation
KW - spectral mixture analysis
KW - tropical secondary forest
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U2 - 10.1002/rse2.33
DO - 10.1002/rse2.33
M3 - Article
AN - SCOPUS:85018666297
SN - 2056-3485
VL - 2
SP - 190
EP - 203
JO - Remote Sensing in Ecology and Conservation
JF - Remote Sensing in Ecology and Conservation
IS - 4
ER -