Browsing by Author "Dube, Timothy."
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Item A Bayesian geo-statistical approach for plantation forest productivity assessment after the fast-track land reform in Zimbabwe.(2023) Chinembiri, Tsikai Solomon.; Mutanga, Onisimo.; Dube, Timothy.The principal objective of the current study was to investigate how the new generation multispectral remote sensing, along with variants of the Bayesian hierarchical geostatistical methodology, could handle prediction uncertainty of carbon (C) stock. The assessment of C stock prediction uncertainty was conducted in a managed and disturbed plantation forest ecosystem located in Manicaland province of Zimbabwe. To achieve this, the study made use of ancillary data from the multispectral (Landsat-8 and Sentinel-2) remote sensing platforms, which informed the application of different inferential and methodological variants within the Bayesian hierarchical geostatistical framework. Allometric equations suited for the target plantation tree species in the sampled region were used to derive C stock from Above ground Biomass (AGB) sampled on 500 m2 circular supports. These Bayesian geostatistical models utilized a combination of Landsat-8 and Sentinel-2 derived vegetation indices, along with climatic and topographic variables. The study found that the Normalized Difference Vegetation Index (𝑁𝐷𝑉𝐼), Distance to settlements (𝐷𝐼𝑆𝑇), and Soil Adjusted Vegetation Index (𝑆𝐴𝑉𝐼) played crucial roles in influencing the spatial distribution of C stock in the studied region. Enhanced Vegetation Index (𝐸𝑉𝐼) is an insignificant predictor for both Landsat-8 and Sentinel-2 driven C stock predictions. Among the tested Bayesian approaches, the spatially varying coefficient (SVC) model, the multi-source data-driven Bayesian geostatistical approach, and the frequentist geostatistical framework were examined. Regardless of the various specifications for independent variables in the predictive C stock modelling within the Bayesian framework, 𝑁𝐷𝑉𝐼 and 𝐷𝐼𝑆𝑇 emerged as significant predictors of the modelled response variable. The non-stationary and Sentinel-2 driven Bayesian hierarchical model, with 𝑁𝐷𝑉𝐼 and DIST covariables, proved to be the most effective prediction model in the studied plantation forest ecosystem in Zimbabwe. This best-performing C stock predictive model was subsequently used to predict C stock under both current (1970-2000) and future (SSP5-8.5) 2075 climate scenarios. The results of the Bayesian constructed hierarchical model indicate a significant shrinkage of forest C stock density and distribution under the future SSP5-8 (2075) business-as-usual climate projection. Basically, the findings of this study highlight the critical role of new generation multispectral remote sensing and Bayesian geostatistical approaches in assessing and predicting carbon stock uncertainty in forest ecosystems. These insights have significant implications for informed land management strategies, aligning with the goals and recommendations of the Intergovernmental Panel on Climate Change (IPCC) to effectively address climate challenges and enhance sustainable land management practices.Item Modeling and explaining the distribution of Lantana camara in South African savanna ecosystems.(2019) Maluleke, Xivutiso Glenny.; Mutanga, Onisimo.; Dube, Timothy.Globally, the Invasive Alien Plant (IAPs) species pose a great threat to global biodiversity, agro-ecological systems and socio-economic development. In particular, Lantana camara (L. camara) is amongst the most notorious and problematic of all invasive plants globally. Its threats and effects are undeniably recognizable and it is ranked amongst the world’s ten worst weeds. As a result, it is one of the most documented weeds in the world. Most studies have focused mainly on detecting and mapping the spatial distribution of L. camara. Although its spatial distribution remains rudimentary, the mechanisms driving its distribution are not yet fully understood, especially in savanna rangelands. This study aimed at modelling and explaining the distribution of L. camara in South African savanna ecosystems (the Kruger National Park and Bushbuckridge communal lands). Specifically, the study sought to identify the environmental factors influencing the spatial distribution of L. camara in savanna ecosystems using the Maximum Entropy (Maxent) algorithm, coupled with remotely-sensed derivatives from Sentinel-2 satellite data. The performance of the model was assessed by using the Area Under Curve (AUC), the True Skills Statistic (TSS) and the Kappa Statistic. From the findings, the Bushbuckridge communal lands had the highest L. camara infestations, with the weed covering an area of 10%, when compared to the Kruger National Park, which had an estimated coverage of 7%. The derived spatial distribution maps from Maxent revealed that communal lands of Bushbuckridge are more vulnerable to L. camara invasion than the protected area. The study also demonstrates that bioclimatic factors influence the occurrence, spread and infestation of this invasive weed species. Comparatively-speaking, elevation was found to have the greatest influence on the infestation and spatial distribution of L. camara. The model that was derived from a composite of all the variables yielded the highest AUC score of 0.96. Subsequently, the model based on indices alone (Model 4) achieved the lowest accuracies, with an AUC score of 0.85. This work is critical for providing the necessary information to assist in effective management and clearing practices by informing the strategic planning, control and rehabilitation of the affected areas.Item Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.(2017) Chemura, Abel.; Mutanga, Onisimo.; Dube, Timothy.; Odindi, John Odhiambo.Abstract available in PDF file.Item Multi-date remote sensing assessment of invasive Lantana camara species distribution in semi-arid savanna rangelands of South Africa.(2019) Madileng, Ngwanamapotu Paschaline.; Mutanga, Onisimo.; Dube, Timothy.Invasive alien species pose a massive threat to biodiversity globally. A comprehensive analysis of the spatial distribution of invasive species, like Lantana camara, is essential for providing appropriate management strategies on both a local and regional scale. The main aim of the study was to map and assess the spatial distribution of Lantana camara invasion in savanna rangeland ecosystems over time in Agincourt, South Africa, using high-resolution SPOT 6 data. The first objective of the study is to focus on reviewing the progress in the remote sensing of Lantana camara. A review of the literature shows that previous studies on mapping and monitoring invasive Lantana camara have relied on traditional methods, such as visual interpretations and field surveys, which have been insufficient, particularly for large-scale monitoring. The use of commercial satellite data with a high-resolution has demonstrated the potential for providing fine spectral and spatial resolution capabilities that are essential for offering precise and reliable data on the spatial distribution of invasive species. The challenges encountered in remote sensing of Lantana camara include the problem of similarity in the spectral signatures of the weed and other vegetation species, which leads to a low classification accuracy. The second objective was to map the spatial distribution and rate of change of Lantana camara in savanna ecosystems over time and space. To achieve this objective, a supervised maximum likelihood classification was used for the SPOT 6 satellite images acquired over a period of three years (2014, 2016 and 2018). The results showed that, Lantana camara was distributed over almost the whole study area for all the three years, yet it decreased with time, due to the clearing and disaster programs. Furthermore, the weed then increased and re-established itself between 2016 and 2018, due to failure to do follow-up control after the initial attempts to eradicate it and the failure to remove rootstock. It was again observed that Lantana camara species can be accurately detected and mapped with an overall classification accuracy of >80% for all the three years. However, research with the enhanced spatial and spectral capabilities such as SPOT 6, has shown the importance of remotely sensed data in predicting lantana camara distribution.Item Optical remote sensing of aboveground forest biomass and carbon stocks in resource-constrained African environments.(2015) Dube, Timothy.; Mutanga, Onisimo.No abstract available.Item Use of remote sensing in landscape-scale vegetation degradation assessment in the semi- arid areas of the Save catchment, Zimbabwe.(2019) Matarira, Dadirai.; Mutanga, Onisimo.; Dube, Timothy.The deteriorating condition of land in parts of the world is negatively affecting livelihoods, especially, in rural communities of the developing world. Zimbabwe has experienced significant vegetation cover losses, particularly, in low and varied rainfall areas of the Save catchment. The concern that Save catchment is undergoing huge vegetation losses has been largely expressed, with the causes being environmental and anthropogenic. Given the magnitude of the problem, research studies have been undertaken to assess the extent of the problem in the south eastern region of Zimbabwe, which, nevertheless, have been mainly localized. The present study seeks to identify and quantify vegetation degradation at a landscape scale in the Save catchment of Zimbabwe, using remote sensing technologies. To achieve this, two objectives were set. The first objective provided a review of the application of satellite earth observations in assessing vegetation degradation, the causes, as well as associated impacts at different geographical scales. A review of literature has revealed the effectiveness of satellite information in identifying changes in vegetation condition. A second objective sought to establish the extent of vegetation degradation in the Save catchment. Moderate Resolution Imaging Spectroradiometer- Normalised Difference Vegetation Index (MODIS NDVI) datasets were used for mapping NDVI trends over the period 2000-2015. Further analysis involved application of residual trend (RESTREND) method to separate human influences from climatic signal on vegetation degradation. RESTREND results showed an increasing trend in NDVI values in about 33.6% of the Save catchment and a decreasing trend in about 18.3% from 2000 to 2015. The results of the study revealed that about 3,609,955 hectares experienced significant human induced vegetation degradation. Approximately 38.8% of the Save Catchment was significantly degraded (p< 0.05), 3.6%, 12.8%, and 22.4% of which were classified as severely, moderately, and lightly degraded, respectively. Severe degradation was mainly found in the central districts of the Save Catchment, mainly Bikita, Chipinge and northern Chiredzi. The results of this study support earlier reports about ongoing degradation in the catchment. Vegetation changes observed across the landscape revealed different degrees of the impacts of land use activities in altering the terrestrial ecosystems. The study demonstrated the usefulness of the RESTREND method in identifying vegetation loss due to human actions in very low rainfall areas.