UWCScholar :: Browsing by Author "Dube, Timothy"

Browsing by Author "Dube, Timothy"

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A multi-source data approach to carbon stock prediction using bayesian hierarchical geostatistical models in plantation forest ecosystems
(Taylor and Francis Ltd., 2024) Dube, Timothy; Chinembiri, Tsikai Solomon; Mutanga, Onisimo
Modeling of environmental phenomena is usually confounded by the influence of multiple factors existing at different time and spatial scales. Bayesian modeling is presumed to be the best approach for modeling such complex systems. Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. We therefore investigated how two related multi-data sources of new generation remote sensing derived ancillary information influence C stock prediction required for building sustainable capacity in C monitoring and reporting. Two mainstream models constructed from Landsat-8 and Sentinel-2 derived vegetation indices coupled with climatic and topographic covariates were used to predict C stocks using forest inventory data collected using spatial coverage sampling. A multi-source data driven approach to C stock prediction yielded slightly lower predictions for both the Landsat-8 ((Formula presented.) and the Sentinel-2 ((Formula presented.) -based C stock models than C stock predictions published in related studies. Distance to settlements ((Formula presented.)) and (Formula presented.) are significant predictors of C stock with the Sentinel-2-based C stock model outperforming its Landsat-8 model variant in terms of prediction accuracy. The Sentinel-2-based C stock model resulted in a 1.17 MgCha−1 Root Mean Square Error (RMSE) with a ((Formula presented.) 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha−1 RMSE with a ((Formula presented.) associated 95% credible interval. Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. Both climatic and topographic derived ancillary data are not significant predictors of C stock under the present modeling conditions. Accurate and precise accounting of C stock for climate change mitigation and action can best be done at landscape scales rather than local scale as the scale of variation for climate-change-related variables vary at larger spatial scales than the ones utilized in the present study.
Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions
(Taylor and Francis Ltd., 2024) Sigopi, Maria; Dube, Timothy; Shoko, Cletah
This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.
Advancements in satellite remote sensing for mapping and monitoring ofalien invasive plant species (AIPs)
(Elsevier, 2019) Royimani, Lwando; Mutanga, Onisimo; Dube, Timothy
Detecting and mapping the occurrence, spatial distribution and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. This work, therefore, provides an overview of advancements in satellite remote sensing for mapping and monitoring of AIPs and associated challenges and opportunities. Satellite remote sensing techniques have been successful in detecting and mapping the spatial and temporal distribution of AIPs in rangeland ecosystems. Also, the launch of high spatial resolution and hyperspectral remote sensing sensors marked a major breakthrough to precise characterization of earth surface feature as well as optimal resource monitoring. Although essential, the improvements in spatial and spectral properties of remote sensing sensors presented a number of challenges including the excessive acquisition and limited temporal resolution. Therefore, the use of high spatial and hyperspectral datasets is not a plausible alternative to continued and operational scale earth observation, especially in financially constrained countries. On the other hand, literature shows that image classification algorithms have been instrumental in compensating the poor spatial and spectral resolution of remote sensing sensors. Furthermore, the emergence of robust and advanced non-parametric image classification algorithms have been a major development in image classification algorithms.
Advancements in the satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands
(Elsevier, 2022) Maphanga, Thabang; Dube, Timothy; Shoko, Cletah
An increase in shrubs or woody species is likely, directly or indirectly, to significantly affect rural livelihoods, wildlife/livestock productivity and conservation efforts. Poor and inappropriate land use management practices have resulted in rangeland degradation, particularly in semi-arid regions, and this has amplified the bush encroachment rate in many African countries, particularly in key savannah rangelands. The rate of encroachment is also perceived to be connected to other environmental factors, such as climate change, fire and rainfall variability, which may influence the structure and density of the shrubs (woody plants), when compared to uncontrolled grazing. Remote sensing has provided robust data for global studies on both bush encroachment and climate variability over multiple decades, and these data have complemented the local and regional evidence and process studies. This paper thus provides a detailed review of the advancements in the use of remote sensing for the monitoring of bush encroachment on the African continent, which is fuelled by climate variability in the rangeland areas.
Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa
(Taylor and Francis Group, 2022) Thamaga, Kgabo Humphrey; Dube, Timothy; Shoko, Cletah
Wetlands are highly productive systems that act as habitats for avariety of fauna andflora. Despite their ecohydrological signifi-cance, wetland ecosystems are severely under threat from globalenvironmental changes as well as pressure from anthropogenicactivities. Such changes results in severe disturbances of plantspecies composition, spatial distribution, productivity, diversity,and their ability to offer critical ecosystem goods and services .However, wetland degradation varies considerably from place toplace with severe degradation in developing countries, especiallyin sub-Saharan Africa due to poor management practices thatleads to underutilization and over reliance on them for liveli-hoods. The lack of monitoring and assessment in this region hastherefore led to the lack of consolidated detailed understandingon the rate of wetland loss.
An assessment of long-term and large-scale wetlands change dynamics in the Limpopo transboundary river basin using cloud-based earth observation data
(Springer Science and Business Media B.V., 2024) Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic
Significant progress has been made in monitoring and assessing the effects of land use and land cover (LULC) changes on wetland extent. However, our understanding of wetland within the transboundary basins has been limited by the scarcity of available data on their dynamic changes over time. This study aimed to address this gap by analyzing the long-term and large-scale spatio-temporal extent of wetland in the Limpopo transboundary river basin (LTRB) over a 20-year period (2000–2020). To achieve this, we utilized the Google Earth Engine (GEE) cloud-computing platform and various remotely sensed data. The study had two primary objectives; (1) to examine LULC changes over time using machine learning algorithms applied to multisource remotely sensed data in GEE, and (2) to assess the relationship between LULC changes and the extent of wetlands in the basin. A total of nine land cover classes were identified, including shrublands, croplands, bare-surface, wetlands, sparse vegetation, tree cover, built-up areas, and grasslands. Shrublands covered 76–82% of the LTRB. On the other hand, wetlands and sparse vegetation were the least dominant, with proportions ranging from 0.3 to 2%. The overall accuracy of the classification results was within acceptable ranges, ranging from 77 to 78%. The study further revealed a continuing decline in wetlands extent and sparse vegetation, with average rates of 19% and 44%, respectively. Conversely, shrublands, croplands, and tree cover showed an increase, with average rates of 0.4% and 12.4% respectively. A significant finding was the replacement of a substantial portion (40%) of wetland areas with built-up areas, indicating that urban expansion is a major driver of wetland shrinkage in the study area. These results provide valuable insights into the declining extent of wetlands in the LTRB. Such findings are crucial for environmental management efforts, as they provide information on which wetlands should be prioritized when implementing strategies to prevent the negative impacts of LULC changes on wetlands in the area. Therefore, contributing towards achieving sustainable development goals relating to freshwater ecosystems protection and management.
An assessment of wetland vulnerability to artisanal mining in Zimbabwe
(Universty of the Western Cape, 2024) Dube, Thandekile; Dube, Timothy
The preservation of wetlands and pristine riverine eco-hydrological systems in sub-Saharan Africa, is crucial for biodiversity, ecosystem stability, and water availability. These face escalating threats from factors such as rapid population growth, agricultural expansion, and, more importantly, emerging illegal artisanal mining. To address these challenges, this study comprehensively evaluates the impact of artisanal mining on wetland ecosystems in Zimbabwe and proposes possible management strategies for mitigating environmental degradation. To achieve this goal, the research begins with a comprehensive literature review focused on the impact of artisanal mining on wetlands in semi-arid environments of sub-Saharan Africa. The findings underscore the detrimental effects of artisanal mining on wetland ecosystems, including habitat loss, biodiversity decline, riverbed sedimentation, and heavy metal pollution. Subsequently, the study investigated in the Umzingwane Catchment, located in southern Zimbabwe as a case study, to analyse variations in water nutrient and metal concentrations in wetlands affected by illegal mining activities along riparian zones (wetland-dominated areas).
Assessing edge effect on the spatial distribution of selected forest biochemical properties derived using the worldview data in Dukuduku forests, South Africa
(Wiley, 2019) Mutanga, Onisimo; Dube, Timothy; Omer, Galal
This work explores the potential of the high‐resolution WorldView‐2 sensor in quan‐tifying edge effects on the spatial distribution of selected forest biochemical proper‐ties in fragmented Dukuduku forest in KwaZulu‐Natal, South Africa. Specifically, we sought to map fragmented patches within forested areas in Dukuduku area, using very high spatial resolution WorldView‐2 remotely sensed data and to statistically determine the effect of these fragmented patches on the spatial distribution of se‐lected forest biochemical properties.
Assessing the seasonal water requirement of fully mature Japanese plum orchards: A systematic review
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Mashabatu, Munashe; Motsei, Nonofo; Jovanović, Nebojša; Dube, Timothy; Mathews, Ubaidullah; Nqumkana, Yolanda
Japanese plums have relatively high water requirements, which depend on supplementing rainfall volumes with accurately quantified irrigation water. There is a lack of knowledge on the seasonal water requirements of plum orchards. This gap in the literature poses an imminent threat to the long-term sustainability of the South African plum industry, which is particularly plagued by climate change and diminishing water resources. The systematic literature review conducted in this study aimed to provide a foundation for supporting water management in irrigated Japanese plum [Prunus salicina Lindl.] orchards. Seventeen peer-reviewed articles obtained from the literature were analyzed. Approximately 66% of the cultivars were cultivated under different regulated deficit irrigation regimes for water-saving purposes and to increase fruit quality. This review of our knowledge provided benchmark figures on the annual water requirements of Japanese plums. The full-year plum crop water requirements obtained from the literature ranged between 921 and 1211 mm a−1. Canopy growth, pruning and growing season length were the most common causes of differences in the water requirement estimates. Further research is required to measure the water requirement of plums from planting to full-bearing age and the response of plum trees to water stress, especially in the South African context.
Assessing the surface material quality of unpaved rural roads to understand susceptibility to surface deterioration. A case study of four rural areas in KwaZulu-Natal, South Africa
(Elsevier, 2019) Nkomo, Lucky S'phumelele; Desai, Sumaiya Amod; Dube, Timothy
Road surface deterioration is one of the most common problems of unpaved road networks worldwide. It is areduction in the performance of a road due to a decline in road surface material quality. Accumulated damagefrom vehicles, environmental and physical effects may contribute to a decline in the surface material quality andhence deterioration on an unpaved road surface. This study assesses the surface material quality of unpaved ruralroads in four rural areas in the KwaZulu-Natal Province, South Africa in order to understand susceptibility tosurface deterioration. The study further establishes other possible factors such as slope gradient and rainfall, thatcould determine the surface material quality. Soil samples were collected from R3, R4, and R5 road classes infour rural areas which are: Emazabekweni, Dukuza, Mkhunya and Mhlwazini Area. Laboratory analyses wereconducted in order to determine the performance of the material as potential wearing course. Material per-formance was then determined using the Standard Methods of Testing Road Construction Materials (TMH1:1976) classification method. The results obtained imply that there is a need for better material selection duringthe construction of unpaved road networks. All road classes in Mkhunya, Emazabekweni and Mhlwazini areasexhibited grading coefficient (Gc) values less than 16 and some of the shrinkage product (Sp) values in excess of365, corresponding to a classification of Class D, A and B. These results indicate material that is susceptible toslippery conditions, easily erodible and prone to the formation of ravels and corrugations. Correlation analysisresults conducted to assess the individual relationship between measured rainfall and slope with field shrinkageproduct and grading coefficient values in each area indicated that variation in slope better explains shrinkageproduct values in each area with an R2of 0.62 when compared to rainfall producing a lower R2of 0.57. Forgrading coefficient, slope and rainfall produced similar R2of 0.65 and 0.67, respectively.
An assessment of river and wetland morphology dynamics using geospatial techniques
(University of the Western Cape, 2023) Mbambi, Yamkela; Dube, Timothy
Land surface modification has intensified in recent years, and it continues to be an ongoing process. This raises serious concerns about changes in land use and land cover (LULC) since some of these changes have led to catchment degradation. The degradation of catchments has been observed to have adverse effects on natural resources, such as water bodies, resulting in food insecurity, water scarcity, and the degradation of ecosystems and the environment. Therefore, to effectively sustain life and the environment, it is crucial to monitor LULC changes for sustainable development and planning that can help alleviate pressures on water resources. This study aims to assess the impacts of LULC changes on the morphology dynamics and area changes of water resources in the Heuningnes catchment in South Africa. The findings from this assessment can offer valuable insights for water resource conservation in this catchment. Remote sensing and GIS techniques were employed to map and detect LULC changes, morphology dynamics, and area changes of water resources from 1990 to 2020.
Assessment of the spatiotemporal dynamics of the hydrological state of non-perennial river systems and identification of flow-contributing areas
(South African Water Research Commission, 2024) Maswanganye, Sagwati E; Dube, Timothy; Jovanovic, Nebo; Kapangaziwiri, Evison; Mazvimavi, Dominic
Non-perennial rivers (NPRs) have three hydrological states; each state has its importance, function and implication for water resource management. The dynamics of these states have been inadequately assessed and understood. Hence, this study sought to determine the spatiotemporal variations in the hydrological conditions of NPRs, focusing on the Touws river–karoo drylands and Molototsi river within the semi-arid region of the Limpopo province of South Africa. Additionally, the study aimed to delineate and characterize the primary areas contributing to runoff in these two river systems. Sentinel-1 and sentinel-2 satellite data sources were employed in this study. Specifically, the modified normalized difference water index (MNDWI) derived from sentinel-2 was utilized to delineate water surface areas along the two rivers. Subsequently, these derived datasets were utilized to assess the hydrological states over a 32-month period (2019–2022). Based on the presence of water, the river’s state was classified as flowing, pooled, or dry. The results showed that remote sensing can be used to determine the hydrological state of the two river systems with ~90% overall accuracy. However, there is about a 30% chance that a flow event can be missed using Sentinel-2 due to clouds and temporal resolution. Some of these gaps can be filled using synthetic aperture radar (SAR) data (Sentinel-1), as demonstrated with the Molototsi river. In the Molototsi catchment, the upper catchment contributes the majority of flows. For the Touws river, the southwestern part of the catchment was determined as the major contributing area for the observed flows. This suggests that the chosen observation site might not be representative of upper catchment dynamics; therefore, a monitoring site in the upper catchment is required. This study provided hydrological information and an approach that can be used to monitor the hydrological states for better understanding and management of NPRs and catchments
Available satellite data for monitoring small and seasonally flooded wetlands in semi-arid environments of Southern Africa
(John Wiley and Sons Ltd, 2024) Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic
Time-series monitoring of wetland eco-hydrological dynamics using remote sensing continues to be an attractive and practical tool, mainly due to its ability to overcome challenges related to in situ data availability. However, acquiring seamless and cloud-free data for accurate and routine wetlands monitoring remains a persistent challenge. In this study, we aimed to evaluate the availability of satellite scenes in the google earth engine (GEE) catalogue that could facilitate the monitoring of eco-hydrological dynamics in small and seasonally flooded wetlands within the semi-arid environments of southern Africa. The study covered a 20-year period from 2000 to 2020, with a specific focus on the Nylsvley floodplain as a case study. The study conducted a comprehensive assessment of available products on the GEE platform, including Landsat thematic mapper (TM), enhanced thematic mapper plus (ETM+), operational land imager (OLI), sentinel-1 and sentinel-2. The identified images underwent rigorous filtering and screening based on varying cloud-cover percentages (0%, 1%–10%, 11%–25% and 26%–50%). The results revealed a considerable number of satellite products (1376) available for the study period. Specifically, there were 492 landsat images, 394 sentinel-1 images and 490 sentinel-2 images. Amongst these, sentinel-2 and landsat-7 had the highest number of images (69% and 76%, respectively) with cloud-cover percentages ranging from 0% to 20%. However, images with cloud cover exceeding 26% were excluded from the analysis. Further analysis indicated that using satellite images with 0% cloud cover resulted in an overall accuracy (OA) ranging between 69% and 72%, while 1%–10% cloud cover had an OA ranging between 68% and 70%, and 11%–25% cloud cover had an OA ranging between 69% and 80.55% for both the dry and wet seasons. Overall, the classification results demonstrated satisfactory OAs (68%–82%) for all scenes, with some inaccuracies observed for certain classes, notably bare surface and long grass. These inaccuracies were particularly evident when using landsat-7 scenes, attributable to the spatial resolution of the data. The findings emphasised the availability of a substantial amount of archival satellite data, capable of monitoring small and seasonally flooded wetlands, providing valuable insights into the eco-hydrological dynamics of these ecosystems. Moreover, the study highlighted the benefits of cloud-computing platforms like GEE in addressing challenges associated with big data filtering, processing and analytics, thereby enhancing environmental monitoring and assessments, which may have been limited by the unavailability of advanced processing tools and seamless cloud-free data.
Bush encroachment with climate change in protected and communal areas: a species distribution modelling approach
(Elsevier B.V., 2025) Maphanga, Thabang; Dube, Timothy; Sibanda, Mbulisi
Savanna rangelands have experienced widespread degradation due to bush encroachment, raising significant concerns among conservationists and rural communities. In the context of climate change, these ecosystem shifts are likely to intensify, especially in South Africa's semi-arid regions. Understanding the impacts of climate variability and change on species distribution within these rangelands is crucial for mitigating further ecosystem disruption. Environmental factors, along with climatic variables, can accelerate the process of bush encroachment, threatening both biodiversity and land use. Early identification of areas vulnerable to invasion is key to developing effective and cost-efficient management strategies. This study aims to model the distribution of invasive species across protected and communal landscapes under long-term climate change projections. A Random Forest (RF) model produced the highest accuracy metrics for Area under the curve (AUC) = 0.99 and True Skill Statistic (TSS)=0.97, while a MaxEnt model recorded the second highest AUC (0.98) and TSS (0.97). The results show a clear difference between the current and future scenarios of the spatial distribution in all the models. Applying a species distribution model (SDM) using both MaxEnt and RF produced a higher degree of prediction accuracy because RF is susceptible to overfitting training data while MaxEnt can produce predictable and complex results. Moreover, the overall predictions using the ensemble model demonstrated an increase in areas suitable for encroachment under RCP 8.5 but a decrease in the bush encroachment rate under RCP 2.6. These findings underscore the critical need for proactive management strategies to mitigate bush encroachment, particularly under high-emission scenarios, ensuring the sustainability of semi-arid savanna rangelands in the face of climate change.
Carbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metrics
(MDPI, 2023) Chinembiri, Tsikai Solomon; Mutanga, Onisimo; Dube, Timothy
The study compares the performance of a hierarchical Bayesian geostatistical methodology with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for predicting C stock using prediction aides from the Landsat-8 and Sentinel-2 multispectral remote sensing platforms. The frequentist geostatistical approach’s reliance on the long-run frequency of repeated experiments for constructing confidence intervals is not always practical or feasible, as practitioners typically have access to a single dataset due to cost constraints on surveys and sampling. We evaluated two approaches for C stock prediction using two new generation multispectral remote sensing datasets because of the inherent uncertainty characterizing spatial prediction problems in the unsampled locations, as well as differences in how the Bayesian and frequentist geostatistical paradigms handle uncertainty.
Characterizing the spatio-temporal variations of C3 and C4 dominated grasslands aboveground biomass in the Drakensberg, South Africa
(Elsevier, 2018) Shoko, Cletah; Mutanga, Onisimo; Dube, Timothy; Slotow, Rob
C3 and C4 grass species composition, with different physiological, morphological and most importantly phenological characteristics, influence Aboveground Biomass (AGB) and their ability to provide ecosystem goods and services, over space and time. For decades, the lack of appropriate remote sensing data sources compromised C3 and C4 grasses AGB estimation, over space and time. This resulted in uncertainties in understanding their potential and contribution to the provision of services. This study therefore examined the utility of the new multi-temporal Sentinel 2 to estimate and map C3 and C4 grasses AGB over time, using the advanced Sparse Partial Least Squares Regression (SPLSR) model. Overall results have shown the variability in AGB between C3 and C4 grasses, estimation accuracies and the performance of the SPLSR model, over time. Themeda (C4) produced higher AGB from February to April, whereas from May to September, Festuca produced higher AGB. Both species also showed a decrease in AGB in August and September, although this was most apparent for Themeda than its counterpart. Spectral bands information predicted species AGB with lowest accuracies and an improvement was observed when both spectral bands and vegetation indices were applied. For instance, in the month of May, spectral bands predicted species AGB with lowest accuracies for Festuca (R2=0.57; 31.70% of the mean), Themeda (R2=0.59; 24.02% of the mean) and combined species (R2=0.61; 15.64% of the mean); the use of spectral bands and vegetation indices yielded 0.77; (18.64%), 0.75 (14.27%) and 0.73 (16.47%), for Festuca, Themeda and combined species, respectively. The red edge (at 0.705 and 0.74 μm) and derived indices, NIR and SWIR 2 (2.19 μm) were found to contribute more to grass species AGB estimation, over time. Findings have also revealed the potential of the SPLSR model in estimating C3 and C4 grasses AGB using Sentinel 2 images, over time. The AGB spatial variability maps produced in this study can be used to quantify C3 and C4 forage availability or accumulating fuel, as well as for developing operational management strategies.
Comprehensive analysis of land use and cover dynamics in djibouti using machine learning technique: A multi-temporal assessment from 1990 to 2023
(Elsevier B.V., 2024) Pandit, Santa; Dube, Timothy; Shimada, Sawahiko
Understanding land use and land cover (LULC) dynamics in semi-arid regions is vital for unraveling complex environmental processes and resource management. This study delves into the intricate interplay of land patterns and resource dynamics, offering indispensable insights into the environmental repercussions of these changes. The study aims to quantify land use categories in Djibouti's semi-desert region using remote sensing. It analyzes temporal changes and evaluates Random forest (RF) algorithms for land use classification. Through meticulous quantification and comprehensive temporal analysis, the research contributes significantly to remote sensing and environmental science by enhancing understanding of land use dynamics and informing sustainable land management practices. Leveraging machine learning supervised classification on the google earth engine (GEE) platform using lands at data spanning four time periods (1990, 2002, 2012, and 2023), alongside spectral indices and digital elevation model (DEM) data, our study achieves unprecedented insights. Our findings reveal a significant landscape transformation, delineating seven major land cover classes: mangroves, bushes, farmland, built-up areas, water bodies, barren land, and salt plains. With overall accuracy ranging from 89 % to 95 %, our assessments demonstrate significant changes in land use types over the studied period. Notably, mangroves, bushes, farmland, and salt areas witnessed declines, while built-up areas, water bodies, and barren lands expanded.
Correction: sibanda et al. application of drone technologies in surface water resources monitoring and assessment: a systematic review of progress, challenges, and opportunities in the global south. drones 2021, 5, 84
(Drones, 2022) Dube, Timothy; Mazvimavi, Dominic; Sibanda, Mbulisi; Mutanga, Onisimo
In the original publication [1], “Fahad Alawadi. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI)”, “Proc. SPIE 7825, Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2010, 782506 (18 October 2010); https://doi.org/10.1117/12.862096” [2] was not cited. The citation has now been inserted in “3.5. The Role of Drone Data Derived Vegetation Indices and Machine Algorithms in Remote Sensing Water Quality and Quantity” as reference [60] and should read: “Numerous vegetation indices were derived from drone remotely sensed data for characterizing surface water quality and quantity. The most widely used sections of the electromagnetic spectrum in detecting water quality parameters were the visible section (blue and green) and the NIR wavebands. In this regard, vegetation indices such as the red and near-infrared (NIR), Surface Algal Bloom Index (SABI) [60], two-band algorithm (2BDA) [26], NDVI, and Green NDV [33], as well as band combinations and differencing such as (R+NIR/G) were used mostly in characterizing chlorophyll content as well as TSS.
Derivation of allometric equations and carbon content estimation in mangrove forests of Malaysia
(Elsevier B.V., 2025) Dube, Timothy; Khan, Waseem Razzaq; Giani, Michele
Mangrove forests play a vital role in carbon sequestration and climate change mitigation, yet comprehensive data on their carbon storage capacity in Malaysia remain limited. This study investigated allometric relationships and carbon content in Malaysian mangrove forests, aiming to develop site species-specific allometric equations, determine carbon content in tree components, and assess total carbon stock. Research was conducted in four compartments of the Sg. Pulai Permanent Reserved Forest, representing a mixed-species mangrove stand. We measured 1403 trees across ten species, with Rhizophora apiculata identified as the dominant species. Using diameter at breast height (DBH) and tree height, we developed site species-specific allometric equations to estimate aboveground biomass. The total aboveground biomass ranged from 183.30 t ha⁻1 to 187.06 t ha⁻1 across the study area. We calculated the total carbon stock at 91.01 t C ha⁻1, incorporating measurements from trees below 5 cm in diameter, dead and downed wood, and litter. An economic valuation of carbon storage was conducted using two approaches: the social cost of carbon method estimated a value of USD 4054.76 per hectare. In contrast, the market price approach yielded USD 1064.34 per hectare. This study provides essential data for improving biomass and carbon stock estimation methods in Malaysian mangrove ecosystems. Our findings highlight these forests' economic and ecological importance, supporting their integration into climate change mitigation strategies and informing sustainable management and conservation policies for mangrove forests in Malaysia and similar regions.
Determining optimal new generation satellite derived metrics for accurate C3 and C4 grass species aboveground biomass estimation in South Africa
(MDPI, 2018) Shoko, Cletah; Mutanga, Onisimo; Dube, Timothy
While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However,WorldView-2 derived variables yielded better predictive accuracies (R2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 m for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity.

Browsing by Author "Dube, Timothy"

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