Browsing by Author "Mpakairi, Kudzai Shaun"
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Item Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir(Springer Science and Business Media Deutschland GmbH, 2024) Mpakairi, Kudzai Shaun; Muthivhi, Faith; Dondofema, FaraiEffective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wallItem Decorum in nature: impala (Aepyceros melampus melampus) dung middens follow spatial point patterns in Mukuvisi Woodland, Zimbabwe(Geology, Ecology, and Landscapes, 2023) Mpakairi, Kudzai Shaun; Tagwireyi, Paradzayi; Muhoyi, HardlifeGuided by the Optimum Foraging Theory,the Avoidance Concept, and assuming that the impala Aepyceros melampus melampus defecate purposevely at dung middens, we hypothesized that the impala’s dung midden locations do not: (1) follow complete spatial randomness; (2) cluster along park tracks; and (3) cluster along the waterways. Using geolocation data for all impala dung middens in the Mukuvisi Woodland, Zmbabwe, the G(r) function revealed a clustered pattern at 0–100 m. Additionally, the 2nd Order Gcross function showed evidence of spatial aggregation of dung middens to within 25 m of park tracks, but no evidence of spatial aggregation between impala dung middens and waterways. Our findings give insight into possible evolutionary decorum for optimum olfaction, energy-saving, disease,pest avoidance, and contamination avoidance.Item Decorum in nature: Impala (Aepyceros melampus melampus) dung middens follow spatial point patterns in Mukuvisi Woodland, Zimbabwe(Taylor and Francis Group, 2023) Tagwireyi, Paradzayi; Muhoyi, Hardlife; Mpakairi, Kudzai ShaunGuided by the Optimum Foraging Theory,the Avoidance Concept, and assuming that the impala Aepyceros melampus melampus defecate purposevely at dung middens, we hypothe-sized that the impala’s dung midden locations do not: (1) follow complete spatial randomness; (2) cluster along park tracks; and (3) cluster along the waterways. Using geolocation data for all impala dung middens in the Mukuvisi Woodland, Zmbabwe, the G(r) function revealed a clustered pattern at 0–100 m. Additionally, the 2nd Order Gcross function showed evidence of spatial aggregation of dung middens to within 25 m of park tracks, but no evidence of spatial aggregation between impala dung middens and waterways. Our findings give insight into possible evolutionary decorum for optimum olfaction, energy-saving, disease,pest avoidance, and contamination avoidance.Item Earth observation technologies for improved agricultural decision support systems in South Africa(University of the Western Cape, 2025) Mpakairi, Kudzai ShaunThis work investigates the application of remotely sensed data and advanced machine learning techniques in enhancing sustainable agricultural practices in South Africa, focusing on crop monitoring, water use efficiency, and land management. Firstly, a systematic review of remote sensing applications in Southern African agriculture, evaluating key advancements, challenges, and opportunities was conducted to document the key scientific knowledge gaps that then informed the focus of this study. The findings of the review revealed that the adoption of remotely sensed data and machine learning algorithms in agriculture remains in its infancy. Building on these insights, this study proposed a methodological framework for delineating irrigated and rainfed croplands in South Africa. By leveraging high-resolution Sentinel-2 satellite imagery and advanced machine learning techniques—including Deep Learning Neural Networks (DNN) and Random Forest (RF)—the study demonstrated the effectiveness of these models in generating accurate, large-scale agricultural land-use maps, achieving an overall classification accuracy of 0.71. Further, a novel approach for crop classification was also introduced by integrating unsupervised learning techniques and spectral matching algorithms, enabling accurate identification (OA = 0.84, p-value = 0.01) of major crop species across South Africa’s diverse agricultural landscapes. Additionally, the study employed multi-temporal MODIS satellite imagery to quantify annual crop water use (CWU) and crop water productivity (CWP), revealing substantial spatiotemporal variations between irrigated and rainfed croplands. Irrigated croplands generally had higher annual CWP (>0.002 kg/mm3/yr), while rainfed croplands consistently showed low CWP in forestry (0.001 kg/mm3/yr) and sugar (0.0012 kg/mm3/yr) agricultural regions.Item Mapping human fatalities from megafauna to inform coexistence strategies(Scientific Reports, 2025) Mpakairi, Kudzai Shaun; Kavhu, Blessing; Mutema, CourageHuman fatalities from human–wildlife conflict (HWC) represent a critical dimension of conservation, often triggering retaliatory actions and post-traumatic stress in affected communities. However, most studies focus on the economic implications of HWC, neglecting human fatalities which may have far-reaching long-term implications. This study investigates the spatial and temporal patterns of human fatalities caused by megafaunal species in Zimbabwe, using data collected from 2016 to 2022. Through spatial and statistical analyses based on the Getis-Ord Gi* hotspot analysis and Mann–Kendall trend test, we assess fatalities caused by six megafaunal species: Nile crocodile (Crocodylus niloticus), African elephant (Loxodonta africana), hippopotamus (Hippopotamus amphibius), African buffalo (Syncerus caffer), African lion (Panthera leo) and spotted hyena (Crocuta crocuta). The results of the study showed that crocodiles and elephants account for over 80% of human fatalities in Zimbabwe. These fatalities also significantly increased over the study period (p < 0.03). In contrast, fatalities involving lions, hyenas, hippos, and buffaloes showed no significant increase, indicating more stable but still concerning risks. Fatality hotspots were concentrated in Kariba, Binga and Hwange districts in northern and western Zimbabwe, highlighting areas needing urgent interventions. These insights have broader implications for HWC management across Africa, where megafaunal species frequently interact with human populations. By adopting data-driven, species-specific strategies, other countries facing similar conflicts can foster human–wildlife coexistence and improve conservation outcomes.Item Remote sensing crop water productivity and water use for sustainable agriculture during extreme weather events in South Africa(Elsevier B.V., 2024) Mpakairi, Kudzai Shaun; Dube, Timothy; Sibanda, MbulisiThe impact of climate variability and extreme weather events on agricultural productivity in arid environments has become a focal point in contemporary research. Monitoring crop water productivity (CWP) is critical and urgently required especially in the arid regions where agriculture consumes an above-average portion of the available fresh water resources. In this context, this study aimed to demonstrate the utility of remotely sensed data in assessing CWP and water use dynamics across diverse crop types in South Africa during the El Niño (2018/19) and non-El Niño (2021/22) events. In addressing the objective, the study also assessed the intra- and inter-annual variations in crop water productivity for diverse crop types including, grains, grapes, citrus fruits, teas, planted pastures, and oil seeds. The study used potential evapotranspiration and biomass derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to estimate CWP from 2017 to 2021. This period included El Niño (2018/19) and non-El Niño (2021/22) years. The results showed that potential evapotranspiration (PET) derived from MODIS was related to the PET estimated from weather stations (R2 > 0.6; RMSE < 21.90; p-value < 0.001). In terms of water use, planted pastures had the highest water use 114 mm/month), while teas and citrus fruits had the lowest water use (6 mm/month). Citrus fruits, grapes and teas consistently had the lowest annual mean crop water productivity (<0.02 kg/m3/annually), while oil seeds had the highest annual mean crop water productivity (>0.1 kg/m3/annually). Lastly, there were no significant differences (p-value > 0.05) between the CWP for all the crops observed between El Niño (2018/19) and non-El Niño (2021/22) periods, suggesting the effectiveness of adaptation measures and interventions during this period. These results provide a simple, spatially explicit framework, relevant to understanding crop-water use, laying the groundwork for informed decision-making and sustainable agricultural practices. Integrating these findings into policy frameworks and agricultural strategies is paramount for ensuring food security and resilience in a changing climate. © 2024Item Spatial characterisation of vegetation diversity in groundwater-dependent ecosystems using in-situ and sentinel-2 msi satellite data(MDPI, 2022) Mpakairi, Kudzai Shaun; Dube, Timothy; Dondofema, FaraiGroundwater-Dependent Ecosystems (GDEs) are under threat from groundwater overabstraction, which significantly impacts their conservation and sustainable management. Although the socio-economic significance of GDEs is understood, their ecosystem services and ecological significance (e.g., biodiversity hotspots) in arid environments remains understudied. Therefore, under the United Nations Sustainable Development Goal (SDG) 15, characterizing or identifying biodiversity hotspots in GDEs improves their management and conservation. In this study, we present the first attempt towards the spatial characterization of vegetation diversity in GDEs within the Khakea-Bray Transboundary Aquifer. Following the Spectral Variation Hypothesis (SVH), we used multispectral remotely sensed data (i.e., Sentinel-2 MSI) to characterize the vegetation diversity.Item Spatial monitoring and reporting tool (smart) in mid-Zambezi valley, Zimbabwe: Implementation challenges and practices(Wiley Open Access, 2021) Kavhu, Blessing; Mpakairi, Kudzai ShaunBiodiversity monitoring and data-management technologies can enhance the protection of persecuted species, such as African elephants (Loxodonta africana), through providing management-relevant information. Implementing these technologies, however, often presents several capacity and resource challenges for field staff in protected areas. In the Mid-Zambezi Valley, Zimbabwe, the Zimbabwe Parks and Wildlife Management Authority (ZPWMA) is in the process of adopting the Spatial Monitoring and Reporting Tool (SMART) as law enforcement and data management tool for adaptive management. With the support of several conservation partners, ZPWMA was able to acquire SMART equipment (computers and handheld cyber-tracker devices) as well as train rangers and officers on how to use SMART in the region.Item Trends in elephant poaching in the Mid-Zambezi Valley, Zimbabwe: Lessons learnt and future outlook(African Journal of Ecology, 2023) Mpakairi, Kudzai Shaun; Ngorima, Patmore; Blessing, Kavhu; Gara, Tawanda WinmoreBackground: The conservation of African elephants (Loxodonta africana) has important ecological, economical, cultural and aesthetic values, at both local and global levels (Pittiglio et al., 2014). Despite the important role elephants play as keystone species, their populations have been dwindling due to human activities (Sibanda et al., 2016). The most serious threats to elephant's survival across most of its range include illegal wildlife trade which has been exacerbated by an increase in organized poaching (Ouko, 2013). Poaching for both meat and ivory is by far the most acute problem across Africa according to data derived from the Monitoring the Illegal Killing of Elephants (MIKE) and Elephant Trade Information System (ETIS; WWF, 2017). This is a complex global threat to the survival of the African elephant across most of its range (Dejene et al., 2021; Ouko, 2013; Wittemyer et al., 2014).