The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

Upgrading all slums in Lagos by 2030 will an ambitious task, given that more than 70% of its residents resides in slums. Furthermore, there is no recent study identifying neither the slums nor their temporal growth/development pattern in Lagos that can backstop any slum management initiative. This study aims to contribute by applying object-based image analysis and intensity analysis to map and link patterns and processes of slum growth in Lagos. RapidEye imagery from 2009 and 2015 were used to create maps for each time point for six land use categories (water, vegetated area, open space, road, slum, and other urban). Intensity analysis was applied to quantify the annual intensity of changes at the category and transition level. An overall accuracy (and kappa coefficient) of 94% (0.9) and 89% (0.86) were achieved for the 2009 and 2015 land use and land cover maps, respectively. This study showed that slums in Lagos have increased spatially during the time interval studied, with a total net gain of 9.18 square kilometers, influenced by the increase in population, mainly due to in-migration to Lagos. However, this study also revealed that slums were actively losing and gaining land area between 2009 and 2015, with an annual gain and loss intensity of 10.08 and 6.41, respectively, compared to the uniform intensity of 3.15. The gain was due to poor maintenance of buildings and encroachment onto available spaces (water and open space), while the loss was attributed to gentrification and demolition processes. A systematic process of transition was observed between slums and other urban (and open space) areas in the interval studied, and this process was mainly influenced by the Lagos state government. This analysis is crucial for designing policy interventions to manage slum growth in Lagos.

Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith), damage was monitored at a regional scale using time series data in Western and Southern African countries. The study employed the normalized difference vegetation index (NDVI) computed from Landsat 8 imagery using the Google Earth Engine (GEE) using image composites for the years 2013 to 2020 for the study areas. The index was then reclassified based on the NDVI threshold values into low, sparse, moderate, and dense classes. FAW prevalence data were then utilized to validate the correlation between the FAW infestation and NDVI values. FAW was associated with a decrease in vegetation productivity between the years 2016, 2017, and 2018 when the pest infestation was reported in the study areas. The validation results showed that there is a correlation between FAW infestation and NDVI (R²0.83). Our study highlighted that NDVI can be used as a proxy to quantify pest damage to vegetation productivity.

Grasslands are a major component of the natural vegetation in South Africa, it covers almost one-third of the country’s land surface. The current study focuses on assessing the impact of drought stress on communal and commercial grasslands in Eastern Cape Province using multi-temporal analysis of Sentinel-1 SAR data. Data analysis included retrieval of biophysical parameters of grasslands using Sentinel-1 SAR backscattering coefficients. Correlation analysis was performed between SAR data and NDVI values derived from Landsat-8 optical data. A linear mixed-effects regression analysis was performed for detecting the impact of drought on communal and commercial grasslands during a drought and non-drought growing seasons. Our results indicate that vegetation dynamics in grasslands in the study area are highly responsive to the climatic fluctuations. A significant correlation between backscattering coefficients of Sentinel-1 SAR data (VH and VV) and NDVI was observed for both communal and commercial grasslands (R² = 0.89). Results also demonstrated that communal grasslands are more affected by drought impact than commercial grasslands. In commercial grasslands, management activities can improve the growing conditions of grasslands, reduce the impact of drought, and subsequently increase the resilience and productivity of this ecosystem.

The fall armyworm (FAW), Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), an invasive agricultural pest, has significantly impacted crop yields across Africa. This study investigated the relationship between temperature and FAW life history traits, employing life cycle modeling at temperatures of 20, 25, 28, 30, and 32°C. The development time for eggs, larvae, and pupae varied from 0–3 days, 10–18 days, and 7–16 days, respectively. The optimal temperature range for immature stage survival and female fecundity was identified as 21–25°C, with the intrinsic rate of increase (rm) and gross reproductive rate (GRR) peaking at 25–28°C. Model validation confirmed the accuracy of these findings. The research further projected the Establishment Risk Index (ERI), Activity Index (AI), and Generation Index (GI) for FAW under current and future climates (2050 and 2070) using RCP 2.6 and RCP 8.5 scenarios. Results indicate that RCP 2.6 leads to a reduction in high-risk FAW areas, particularly in central Africa. Conversely, RCP 8.5 suggests an increase in areas conducive to FAW activity. These findings highlight the impact of climate policy on pest dynamics and the importance of incorporating climatic factors into pest management strategies. The study predicts a potential decrease in FAW prevalence in West Africa by 2070 under aggressive climate mitigation, providing a basis for future FAW management approaches.