Assessing the utility of Landsat 8 multispectral sensor and the MaxEnt species distribution model to monitor Uromycladium acaciae damage in KwaZulu-Natal, South Africa.
Date
2020
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Abstract
South Africa has approximately 1.27 million hectares of plantation forests, with the forestry
industry contributing 1% to the state’s Gross Domestic Product (GDP). A major threat to the
industry is an escalating number of tree-damaging insect pests and pathogens. Uromycladium
acaciae is a pathogen which causes wattle rust in black wattle (Acacia mearnsii) plantation
forests; after its first appearance in 2013 in KwaZulu-Natal, it has since spread to most areas
in South Africa where suitable hosts are present, causing severe economic losses to the
industry. Traditional field-based methods of assessing forest damage can be labour intensive
and time consuming. The effective management of these biotic threats requires quick and
efficient methods of assessing forest health. Remote sensing has the potential to assess vast
areas of forest plantations in a timely and efficient manner. Therefore, the primary aim of this
research is to assess U. acaciae canopy damage using freely available Landsat 8 multispectral
satellite imagery and the partial least squares discriminant analysis algorithm (PLS-DA). The
study was done on two plantation farms near Richmond, KwaZulu-Natal which are managed
by NCT Forestry. The model detected forest canopy damage with an accuracy of 88.24%
utilising seven bands and the PLS-DA algorithm. The Variable Importance in Projection (VIP)
method was used to optimise the variables to be included in the model by selecting the most
influential bands. These were identified as coastal aerosol band (430 nm - 450 nm), red band
(640 nm - 670 nm), near infrared (850 nm - 880 nm) and NDVI. The model was run with only
the VIP selected bands and an accuracy of 82.35% was produced. The study highlighted the
potential of remote sensing to (1) detect canopy damage caused by U. acaciae and (2) provide
a monitoring framework for analysing forest health using freely available Landsat 8 imagery.
The secondary aim of this study is to use the maximum entropy species distribution model
(SDM) to determine potential forestry areas that may be at risk of U. acaciae infection. Species
distribution modelling using bioclimatic predictors can define the climatic range associated
with the disease caused by this pathogen. The climatic range will help identify high risk areas
and forecast potential outbreaks. This study assessed the capacity of the MaxEnt species
distribution model (SDM) and bioclimatic variables to estimate forestry areas that have a
suitable climate for U. acaciae development. The model was developed using 19 bioclimatic
variables sourced from WorldClim. The variables are used as predictors of risk for U. acaciae
infection and are applied to the landscape occupied by black wattle plantations. The results produced an area under the curve (AUC) value of = 0.97 suggesting strong discriminatory
power of the model. The potential distribution of U. acaciae under future climate conditions
was also assessed by applying the model to the bioclimatic variables developed from future
climate surfaces acquired from WorldClim. The results emphasized (1) the usefulness of
species distribution models for forest management and (2) highlighted how climate change can
influence the distribution of U. acaciae due to the expansion and contraction of suitable
climatic ranges.
Overall, the results from the study indicate (1) Landsat 8 multispectral imagery can be used to
detect forest canopy damage caused by U. acaciae, (2) PLS-DA variable importance in the
projection can successfully select the subset of multispectral bands that are most important in
detecting damage caused by U. acaciae, (3) the MaxEnt species distribution model and
bioclimatic variables can be used to identify geographic locations at risk of U. acaciae infection
and (4) the variable permutation metric successfully identified the most important bioclimatic
variables for U. acaciae development and highlighted the climatic patterns associated with the
occurrence of the disease caused by this pathogen.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.