Avocado (Persea americana Mill.) production contributes to the economic growth of East Africa. However, poor fruit quality caused by infestations of tephritid fruit flies (Tephritidae) and the false codling moth, Thaumatotibia leucotreta (Meyrick), hampers access to lucrative export markets. Remote sensing and spatial analysis are increasingly applied to crop pest studies to develop sustainable and cost-effective control strategies. In this study, we assessed pest abundance in Muranga, Kenya, across three vegetation productivity classes, viz., low, medium and high, which were estimated using the normalised difference vegetation index at a landscape scale. Population densities of the oriental fruit fly, Bactrocera dorsalis (Hendel) and T. leucotreta in avocado farms were estimated through specific baited traps and fruit rearing. The population density of T. leucotreta varied across the vegetation productivity classes throughout the study period, although not significantly. Meanwhile, B. dorsalis showed a clear trend of decrease over time and was significantly lower in high vegetation productivity class compared to low and medium classes. Ceratitis cosyra (Walker) was the most abundant pest reared from fruit with few associated parasitoids, Pachycrepoideus vindemmiae (Rondani) and Toxeumorpha nigricola (Ferriere).

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Forest biomass and carbon are critical for ecological monitoring, and yet poorly modelled in complex ecosystems such as the tropical rainforests. To overcome this challenge incurred due to the complex biophysical properties of tropical forests, Airborne and Terrestrial LiDAR (Light Detection and Ranging) technologies have been used combinedly. Airborne LiDAR data 'from above' are largely restricted to analyses of lower canopy layer trees. Its combination with Terrestrial LiDAR allows the assessment of tree crowns under the upper canopy layer, thus opening up new possibilities for a more complete assessment of all the trees in a multi-layer stand. In this study, Airborne LiDAR was used for upper canopy tree measurements while Terrestrial LiDAR was complimented for lower canopy layer trees. The result showed that LiDAR-based tree measurements of DBH and height were highly accurate. We highly improved the accuracy of estimated above-ground biomass (AGB)/ carbon from 87% of Terrestrial and 90% of Airborne LiDAR-based estimates to 97% through combining the use of the two technologies. This approach contributes to the development of efficient techniques for forest monitoring systems and bears the potential to extend the modelling options from remote sensing data to understory layer trees.

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Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role played by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in general. In Gedaref landscape of Eastern Sudan, there is limited or no knowledge of LULC structure and size, degree of change, transition, intensity and future outlook. Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988–2018) using Landsat imageries and the random forest classifier, (2) determine the underlying dynamics that caused the changes in the landscape structure using intensity analysis, and (3) predict future LULC outlook for the years 2028 and 2048 using cellular automata-artificial neural network (CA-ANN). The results exhibited drastic LULC dynamics driven mainly by cropland and settlement expansions, which increased by 13.92% and 319.61%, respectively, between 1988 and 2018. In contrast, forest and grassland declined by 56.47% and 56.23%, respectively. Moreover, the study shows that the gains in cropland coverage in Gedaref state over the studied period were at the expense of grassland and forest acreage, whereas the gains in settlements partially targeted cropland. Future LULC predictions showed a slight increase in cropland area from 89.59% to 90.43% and a considerable decrease in forest area (0.47% to 0.41%) between 2018 and 2048. Our findings provide reliable information on LULC patterns in Gedaref region that could be used for designing land use and environmental conservation frameworks for monitoring crop produce and grassland condition. In addition, the result could help in managing other natural resources and mitigating landscape fragmentation and degradation.