Browsing by Author "Nasolwa, Emmanuel"

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Comparative Assessment of UAV and Sentinel-2 NDVI and GNDVI for Preliminary Diagnosis of Habitat Conditions in Burunge Wildlife Management Area, Tanzania
(MDPI, 2022-06-28) Mangewa, Lazaro; Ndakidemi, Patrick; Alward, Richard; Kija, Hamza; Bukombe, John; Nasolwa, Emmanuel; Munishi, Linus
Habitat condition is a vital ecological attribute in wildlife conservation and management in protected areas, including the Burunge wildlife management areas in Tanzania. Traditional techniques, including satellite remote sensing and ground-based techniques used to assess habitat condition, have limitations in terms of costs and low resolution of satellite platforms. The Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) have potential for assessing habitat condition, e.g., forage quantity and quality, vegetation cover and degradation, soil erosion and salinization, fire, and pollution of vegetation cover. We, therefore, examined how the recently emerged Unmanned Aerial Vehicle (UAV) platform and the traditional Sentinel-2 differs in indications of habitat condition using NDVI and GNDVI. We assigned 13 survey plots to random locations in the major land cover types: three survey plots in grasslands, shrublands, and woodlands, and two in riverine and mosaics cover types. We used a UAV-mounted, multi-spectral sensor and obtained Sentinel-2 imagery between February and March 2020. We categorized NDVI and GNDVI values into habitat condition classes (very good, good, poor, and very poor). We analyzed data using descriptive statistics and linear regression model in R-software. The results revealed higher sensitivity and ability of UAV to provide the necessary preliminary diagnostic indications of habitat condition. The UAV-based NDVI and GNDVI maps showed more details of all classes of habitat conditions than the Sentinel-2 maps. The linear regressions results showed strong positive correlations between the two platforms (p < 0.001). The differences were attributed primarily to spatial resolution and minor atmospheric effects. We recommend further studies to test other vegetation indices.
Connectivity between land, water, and people: integrating process concepts and assessment evidence across disciplines for co-design of soil erosion solutions
(Authorea, 2020-04-20) Blake, William; Kelly, Claire; Wynants, Mona; Patrick, Aloyce; Lewin, Shaun; Lawson, Joseph; Nasolwa, Emmanuel; Page, Annabel; Nasseri, Mona; Marks, Carey; Gilvear, David; Mtei, Kelvin; Munishi, Linus; Ndakidemi, Patrick
Soil resources in East Africa are being rapidly depleted by erosion, threatening food-, water- and livelihood security in the region. Here we demonstrate how integration of evidence from natural and social sciences has supported community-led change in land management in an agro-pastoral community in northern Tanzania impacted by soil erosion. Drone survey data and geospatial analysis of erosion extent and risk, supported by communication of ‘process’ and ‘structural’ hydrological connectivity, was integrated with local environmental knowledge within participatory community workshops. Rill density data were compared between cultivated plots that had been converted from pastoral land recently and more established plots where slow-forming terrace boundaries were more established. Slope length and connectivity between plots were key factors in development of rill networks. At the two extremes, recently converted land had a rill density ca 14 times greater than equivalent established slow forming terraces. Direction of cultivation, regardless of plot boundary orientation with contours, also enhanced rill development. Evidence of this critical time window of hillslope-scale rill erosion risk during early phases of slow-forming terrace development successfully underpinned and catalysed a community-led tree planting and grass seed sowing programme to mitigate soil erosion by water. This was grounded in an implicit community understanding of the need for effective governance mechanisms at both community and District levels, to enable community-led actions to be implemented effectively. The study demonstrates the wide-reaching impact of integrated and interdisciplinary ‘upslope-downslope’ thinking to tackle global soil erosion challenges.
Integrating land-water-people connectivity concepts across disciplines for co-design of soil erosion solutions
(John Wiley & Sons, Inc., 2020-10-02) Blake, William; Kelly, Claire; Wynants, Maarten; Patrick, Aloyce; Lewin, Shaun; Lawson, Joseph; Nasolwa, Emmanuel; Page, Annabel; Nasseri, Mona; Marks, Carey; Gilvear, David; Mtei, Kelvin; Munishi, Linus; Ndakidemi, Patrick
Soil resources in East Africa are being rapidly depleted by erosion, threatening food, water and livelihood security in the region. Here we demonstrate how the integration of evidence from natural and social sciences has supported a community-led change in land management in an agro-pastoral community in northern Tanzania. Geospatial analysis of erosion risk and extent (based on a drone survey across a 3.6 km2 sub-catchment) revealed that recently converted land had ca 12-times greater rill density than established slow-forming terraced plots (987 ± 840 m2 ha−1 vs. 79 ± 110 m2 ha−1). Slope length and connectivity between plots were key factors in the development of rill networks rather than slope per se wherein slope length was augmented by weak boundaries between newly formed plots. Erosion evidence, supported by communication of 'process' and 'structural' hydrological connectivity, was integrated with local environmental knowledge within participatory community workshops. Demonstration of the critical time window of hillslope-scale rill erosion risk during early phases of slow-forming terrace development catalysed a community-led tree planting and grass seed sowing programme to mitigate soil erosion by water. This was grounded in an implicit farmer understanding of the need for effective governance mechanisms at both community and District levels, to enable community-led actions to be implemented effectively. The study demonstrates the wide-reaching impact of integrated and interdisciplinary 'upslope-downslope' thinking to tackle global soil erosion challenges.
Land Use/Cover Classification of Large Conservation Areas Using a Ground-Linked High-Resolution Unmanned Aerial Vehicle
(MDPI, 2024-08-22) Mangewa, Lazaro; Ndakidemi, Patrick; Alward, Richard; Kija, Hamza; Nasolwa, Emmanuel; Munishi, Linus
High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient (k) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study’s novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas. Keywords: community wildlife management areas; random forest algorithm; remote sensing technologies; Sentinel-2; pre-UAV flight ground data; unmanned aerial vehicles