Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data

摘要

Landsat time-series multi-spectral data, GLAS (Geoscience Laser Altimeter System) height data and a regression tree model were used to estimate tree height for a transect in Sub-Saharan Africa ranging from the Sahara Desert through the Congo Basin to the Kalahari Desert (+22 to -22 degrees latitude and 23 to 24 degrees longitude). Objectives included comparing the performance of Landsat 7- and 8-derived inputs separately and combined in mapping tree height at a regional scale, assessing the relative value of good observation counts and different Landsat spectral inputs for tree height estimation across a range of environments, and describing tree height distributions and discontinuities in Sub-Saharan Africa. A total of 5371 images were processed and per pixel quality assessed to create a set of multi-temporal metrics for the 2013 and 2014 calendar years for Landsat 7 only, Landsat 8 only and both Landsat 7 and 8 combined. Differences in performance were slight between different sensor inputs. However, performance generally improved with increasing numbers of good observations. Metrics derived from red reflectance data contributed most in estimating tree height. The regression tree algorithm accurately reproduced theLiDAR-derived height training data with an overall mean absolute error (MAE) for tree height estimation of 2.45 m using integrated Landsat 7 and 8 data. Significant underestimations were quantified for tall tree cover (MAE of 4.65 m for >20 m heights) and overestimations for lowo tree cover (MAE 1.61 for <5 m heights). Resulting tree distributions were found to be discontinuous with a primary dry seasonal woodlands cluster of 510 m in height, a second cluster of primarily dry evergreen forest tree cover from 11-17 m, and a third cluster of humid evergreen forest tree cover >= 18 m. The integration of Landsat 7 and 8 and forthcoming Sentinel 2 time-series optical data to extend the value of LiDAR forest structure measurements is recommended. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license.