Integrating Li DAR ‐derived tree height and Landsat satellite reflectance to estimate forest regrowth in a tropical agricultural landscape

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...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing in ecology and conservation 2016-12, Vol.2 (4), p.190-203
Main Authors: Caughlin, T. Trevor, Rifai, Sami W., Graves, Sarah J., Asner, Gregory P., Bohlman, Stephanie A.
Format: Article
Language:English
Subjects:
Agricultural land
Biomass
Canopies
Data collection
Forests
Land use
Landsat
Landsat satellites
Landscape
Lidar
Observatories
Reflectance
Reforestation
Regrowth
Remote sensing
Time series
Trees
Tropical forests
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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
ISSN:2056-3485
2056-3485
DOI:10.1002/rse2.33