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Dlamini W.M.,Swaziland National Trust Commission
International Journal of Remote Sensing

This article describes the use of a Bayesian network (BN) for the classification of land cover from satellite imagery in northern Swaziland. The main objective of this work was to apply and evaluate the efficacy of a BN for land-cover classification using gap-filled and terrain-corrected Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery acquired on 15 May 2007. The posterior probabilities (parameters) were estimated using the expectation-maximization (EM) and conjugate gradient descent (CGD) algorithms. A comparison of the results obtained from the algorithms indicates similar and excellent overall classification accuracies of 93.01%, and kappa coefficient values of 0.9143. The main result obtained in this study is that both algorithms considered here provide relatively similar and accurate solutions for the classification of the multispectral image although the EM algorithm is marginally competitive relative to CGD algorithm when measured in terms of the Brier score and the logarithmic loss. © 2011 Taylor & Francis. Source

Dlamini W.M.,Swaziland National Trust Commission
Environmental Modelling and Software

The impacts of wildfires on ecosystems and the factors contributing to their occurrence are increasingly receiving global attention. Advances in satellite remote sensing and information technology provide an opportunity to study these complex interrelationships. A Bayesian belief network (BBN) model was developed from a set of 12 biotic, abiotic and human variables to determine factors that influence wildfire activity in Swaziland using wildfire data from the Terra and Aqua satellites' Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2001-2007. These were geospatially integrated in the geographic information system (GIS) software ArcView and input into the software Netica for BBN analyses. Land cover, elevation, and climate (mean annual rainfall and mean annual temperature) were found to be strong predictors of wildfire occurrence, while aspect had the least influence on the wildfire occurrence. The model had a high predictive accuracy with an error rate of 9.62%, and an area under the receiver-operating characteristic (ROC) curve of 0.961. The study demonstrates how domain or field knowledge and limited empirical and GIS data can be combined within a BBN model to assist in determining key fire management interventions and lays the foundation for the future development of advanced and dynamic models. © 2009 Elsevier Ltd. All rights reserved. Source

Dlamini W.M.,Swaziland National Trust Commission

This study uses Bayesian networks (BNs) to simulate the spatial distribution of southern African biomes and bioregions using bioclimatic variables. Two Tree-Augmented Naïve (TAN) BN models were parameterized from 23 bioclimatic variables using the expectation-maximization (EM) algorithm. Using sensitivity analyses, the relative influence of each variable was determined using the mutual information from which six bioclimatic variables were selected for the final models. Precipitation of the warmest quarter and extra-terrestrial solar radiation was found to be the most influential variables on both bioregion and biome distributions. Isothermality was the least influential bioclimatic variable at both bioregion and biome levels. Overall correspondence was very high at 93.8 and 87.1% for biomes and bioregions, respectively, whereas classification errors were obtained in transition areas indicating the uncertainties associated with vegetation mapping around margins. The findings indicate that southern African bioregions and biomes can be classified and mapped according to key bioclimatic variables. Spatio-temporal, in particular, monthly and quarterly variations in both precipitation and temperature are found to be ecologically significant in determining the spatial distribution of biomes and bioregions. The findings also reflect the hierarchical relationship of biomes and bioregions as a function of local bioclimatic gradients and interactions. The results indicate the ecological significance of bioclimatic conditions in ecosystem science and offer the opportunity to utilize the models for predicting future responses and sensitivities to climatic changes. © 2011 Springer Science+Business Media, LLC. Source

Dlamini W.M.,Swaziland National Trust Commission
Ecological Informatics

This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation. © 2011 Elsevier B.V. Source

Dlamini W.,Swaziland National Trust Commission
Global Change Biology

In a spatially explicit climate change impact assessment, a Bayesian network (BN) model was implemented to probabilistically simulate future response of the four major vegetation types in Swaziland. Two emission scenarios (A2 and B2) from an ensemble of three statistically downscaled coupled atmosphere-ocean global circulation models (CSIRO-Mk3, CCCma-CGCM3 and UKMO-HadCM3) were used to simulate possible changes in BN-based environmental envelopes of major vegetation communities. Both physiographic and climatic data were used as predictors representing the 2020s, 2050s and the 2080s periods. A comparison of simulated vegetation distribution and the expert vegetation map under baseline conditions showed an overall correspondence of 97.7% and a Kappa coefficient of 0.966. Although the ensemble projections showed comparable trends during the 2020s, the results from the A2 storyline were more drastic indicating that grassland and the Lebombo bushveld will be impacted negatively as early as the 2020s with about 1°C temperature increase. The bioclimatically suitable areas of all but one vegetation type decline drastically after about 2°C warming, more so under the more severe A2 scenario and in particular during the 2080s. The sour bushveld is the only vegetation type that initially responds positively to warming by possibly encroaching to the highly vulnerable grassland areas. Vulnerability of vegetation is increased by the limited ability to migrate into suitable climates due to close affinity to certain geological formations and the fragmentation of the landscape by agriculture and other land uses. This is expected to have serious impacts on biodiversity in the country. Under warmer climates, the likely vegetation types to emerge are uncertain due to future novel combinations of climate and bedrock lithology. The strengths and limitations of the BN approach are also discussed. © 2010 Blackwell Publishing Ltd. Source

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