Assessing the utility of remotely sensed data and integrated topographic characteristics for determining tree stand structural complexity in a re-forested urban landscape.
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Date
2017
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Abstract
Transformation of natural landscapes into impervious built-up surfaces through urbanisation is
known to significantly interfere with urban ecological integrity and its ability to provide
environmental goods and services as well as accelerate climate change and associated impacts.
Urban reforestation is widely promulgated as an ideal mitigation practice against impacts
associated with urbanisation, however reforestation often has to compete with multiple and
more “lucrative” urban land uses. This necessitates the optimisation of ecological benefits
derived from reforestation within the limited available land. Such optimisation demands
spatially explicit monitoring and evaluation (M&E). The recent proliferation of tree stand
structural complexity (SSC) – a multidimensional index of the ecological performance of tree
stands - offers great potential as an alternative indicator of ecological performance, instead of
the one-dimensional traditional indicators such as Leaf Area Index, stem diameter and tree
height. Furthermore, the recent advancements in remote sensing (RS) technology offers an
improved potential of determining ecological performance across an urban reforested
landscape. However, remotely sensed data costs and reliability often hinder their operational
adoption. Consequently, the recent advancements in the freely available Sentinel 2 (S-2) data
offer great potential for a cost effective operational M&E of SSC. The aim of this study was to
i) Examine the utility of the freely available S-2 multispectral instrument imagery to determine
SSC using the Partial Least Squares (PLS) regression technique within a re-forested urban
landscape ii) Explore the potential of integrating topographic datasets with the S-2 data to
determine SSC and iii) To rank the value of these variables in determining SSC. Tree structural
data from a re-forested urban area was collected and a SSC index used to determine the area’s
ecological performance. Multiple vegetation indices (VIs) were derived from the S-2 imagery
while topographic variables (i.e. Topographic Wetness Index (TWI), slope, Area Solar
Radiation (ASR), and elevation) were derived from a Digital Elevation Model (DEM). Results
showed that the PLS model (n = 90) using the most important S-2 VIs (S2 REP, REIP, IRECI,
GNDVI) produced a moderate predictive accuracy (0.215 NRMSECV) while topographybased
model produced a high prediction accuracy (0.147 NRMSECV). Integrating the S-2 data
with topographic information produced the highest prediction accuracy (0.13 NRMSECV).
Furthermore, results indicate that SSC significantly varied across all topographic variables,
with TWI and slope as the most important determinants of SSC. These results provide valuable
spatially explicit information about the ecological performance of the reforested urban areas.
Additionally, the study demonstrates the value of topographic data as an alternative predictor
of SSC as well as the value of integrating the S-2 data with topographic characteristics in
determining the performance of reforested areas.
Description
Master of Science in Environmental Science. University of KwaZulu-Natal 2017.
Keywords
Theses - Environmental science., Reforestation - South Africa., Urban ecology (Biology) - South Africa.