Open AccessIntegrating Li DAR ‐derived tree height and Landsat satellite reflectance to estimate forest regrowth in a tropical agricultural landscape
Author(s) -
Caughlin T. Trevor,
Rifai Sami W.,
Graves Sarah J.,
Asner Gregory P.,
Bohlman Stephanie A.
Publication year - 2016
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.33
Subject(s) - canopy , tree canopy , reforestation , generalized linear model , remote sensing , tree (set theory) , land cover , vegetation (pathology) , environmental science , thematic mapper , satellite imagery , geography , ecology , mathematics , land use , statistics , agroforestry , biology , medicine , mathematical analysis , pathology
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 ( GLM s) 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 (Li DAR ) 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 GLM s, including topography and parcel membership (representing different land owners), increased predictive accuracy, resulting in best‐fit models with an R 2 of 55.68% and RMSE of 23.69% for tree canopy cover, and an R 2 of 51.24% and RMSE of 3.42 m for tree height. Finally, we applied the GLM s 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 Li DAR and Landsat data to quantify forest regrowth in an agricultural landscape.