Modelling thicket above-ground biomass using LiDAR and SAR data, in the Addo Elephant National Park
Date
2021
Authors
Mkheswa, Bongani
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Abstract
Due to the ecological effects arising from climate change, it is necessary to quantify terrestrial
carbon stocks at various spatial and temporal scales for mitigation purposes. Whereas
deforestation is known to increase carbon dioxide emissions and negatively impact the fight
against global climate change, studies have also shown that increased carbon results in woody
thickening and the transformation from open savanna to bush encroached thickets.
Hence, this study sought to quantitatively model the Above Ground Biomass (AGB) within the
Addo Elephant National Park in South Africa. The dominant vegetation type of the park is the
unique and threatened Albany thicket. Field-based AGB measurements and LiDAR metrics
were used to derive a local AGB model that represented the Addo thicket vegetation type, and
could then be upscaled using regional-scale satellite-based Synthetic Aperture Radar (SAR)
backscatter values. Furthermore, this study sought to determine the performance of Sentinel-1
C band SAR data versus ALOS PALSAR-2 L band SAR data in retrieving the thicket AGB.
Specific objectives to the study were to use field-collected AGB plots and airborne LiDAR
data to develop and apply a LIDAR-based thicket AGB model to the LiDAR dataset; to use the
LiDAR-based AGB product as calibration and validation data in exploring different modelling
techniques for estimating regional scale SAR-based AGB and to investigate the effects of SAR
wavelength in retrieving thicket AGB of the Albany thicket biome by comparing Sentinel-1
multi-temporal model against the Global mosaic ALOS PALSAR-2 L band model.
The coefficient of determination (R2
), the Root Mean Square Error (RMSE) and the relative
Root Mean Square Error (rRMSE) were used to evaluate and compare the performance of the
models. Field-based AGB measurements were successfully upscaled using LiDAR metric and
the best Simple Linear Regression (SLR) model established was AGB= 0.258 (CC * MH)
with an R2
of 89%, RMSE of 3.779t/ha and rRMSE of 35.6%. Then Simple linear regression
and Random Forest modelling techniques were used to derive a regional AGB model. The
Random Forest model yielded respectable results (R2
of 67% and rRMSE of 38.4%) and
outperformed the Simple linear regression model. Furthermore, the time series of Sentinel-1 C
band data showed higher retrieval results (give best result, R2 & rRMSE) than the one-off
ALOS PALSAR-2 L band data (give best result, R2
, rRMSE) in retrieving AGB using both
Random Forest and Simple Linear Regression techniques. Importantly, the study demonstrated the effective use of machine learning predictive mapping
techniques for thicket AGB using freely available Sentinel-1 C band remote sensing data by
taking advantage of its relatively high spatial and temporal resolution.
Description
A research report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirement for the degree of Master of Science in Geographical Information Systems and Remote Sensing, 2021