The Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays an important role in supplementing field methods to fill in data gaps. Traditional satellite and manned aircraft remote sensing platforms have their own advantages and limitations. The advent of unmanned aerial systems (UASs) has made it possible to acquire forest data at unprecedented spatial and temporal scales. UASs are adaptable to various forest applications in terms of providing flexibility in data acquisition with different sensors (RGB, multispectral, hyperspectral, thermal and light detection and ranging (lidar)) at a convenient time. To highlight possible applications in the Miombo woodlands, we first provide an overview of the Miombo woodlands and recent progress in remote sensing with small UASs. An overview of some potential forest applications was undertaken to identify key prospects and challenges for UAS applications in the Miombo region, which will provide expertise and guidance upon which future applications in the Miombo woodlands should be based. While much of the potential of using UASs for forest data acquisition in the Miombo woodlands remains to be realized, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of forest applications.

Accurate maps of tree species distributions are necessary for the sustainable management of forests with desired ecological functions. However, image classification methods to produce species distribution maps for supporting sustainable forest management are still lacking in the Miombo woodland ecoregion. This study used multi-date multispectral Unmanned Aerial Systems (UAS) imagery collected at key phenological stages (leaf maturity, transition to senescence, and leaf flushing) to classify five dominant canopy species of the wet Miombo woodlands in the Copperbelt Province of Zambia. Object-based image analysis (OBIA) with a random forest algorithm was used on single date, multi-date, and multi-feature UAS imagery for classifying the dominant canopy tree species of the wet Miombo woodlands. It was found that classification accuracy varies both with dates and features used. For example, the August image yielded the best single date overall accuracy (OA, 80.12%, 0.68 kappa), compared to October (73.25% OA, 0.59 kappa) and May (76.64% OA, 0.63 kappa). The use of a three-date image combination improved the classification accuracy to 84.25% OA and 0.72 kappa. After adding spectral indices to multi-date image combination, the accuracy was further improved to 87.07% and 0.83 kappa. The results highlight the potential of using multispectral UAS imagery and phenology in mapping individual tree species in the Miombo ecoregion. It also provides guidance for future studies using multispectral UAS for sustainable management of Miombo tree species.

The ability to collect precise three-dimensional (3D) forest structural information at a fraction of the cost of airborne light detection and ranging (lidar) makes uncrewed aerial systems-lidar (UAS-lidar) a remote sensing tool with high potential for estimating forest structural attributes for enhanced forest management. The estimation of forest structural data in area-based forest inventories relies on the relationship between field-based estimates of forest structural attributes (FSA) and lidar-derived metrics at plot level, which can be modeled using either parametric or non-parametric regression techniques. In this study, the performance of UAS-lidar metrics was assessed and applied to estimate four FSA (above ground biomass (AGB), basal area (BA), diameter at breast height (DBH), and volume (Vol)) using multiple linear regression (MLR), a parametric technique, at two wet Miombo woodland sites in the Copperbelt province of Zambia. FSA were estimated using site-specific MLR models at the Mwekera and Miengwe sites and compared with FSA estimates from generic MLR models that employed combined data from the two sites. The results revealed that the model fit of site-specific MLR models was marginally better (Adj-R²: AGB = 0.87–0.93; BA = 0.88–0.89; DBH = 0.86–0.96; and Vol = 0.87–0.98 than when using a generic combined data model (AGB = 0.80; BA = 0.81; DBH = 0.85; and Vol = 0.85). However, the rRMSE (2.01 – 20.89%) and rBias (0.01-1.03%) of site specific MLR models and combined data model rRMSE (3.40-16.71%) and rBias (0.55-1.16%) were within the same range, suggesting agreement between the site specific and combined data models. Furthermore, we assessed the applicability of a site-specific model to a different site without using local training data. The results obtained were inferior to both site-specific and combined data models (rRMSE: AGB = 36.29%–37.25%; BA = 52.98–54.52%; DBH = 55.57%–64.59%; and Vol = 26.10%–30.17%). The results obtained from this indicate potential for application in estimating FSA using UAS-lidar data in the Miombo woodlands and are a stepping stone towards sustainable local forest management and attaining international carbon reporting requirements. Further research into the performance of UAS-lidar data in the estimation of FSA under different Miombo vegetation characteristics, such as different age groups, hilly terrain, and dry Miombo, is recommended.

To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R² = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R² = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands.

Tropical forests in sub‐Saharan Africa (SSA) harbor around one‐third of the world's species but are becoming more fragmented due to the expansion of human settlements and small‐scale agricultural (SCA) areas. This study systematically reviewed the approaches and methods used to analyze forest fragmentation and its impact on biodiversity in SSA, with a focus on their suitability in SCA landscapes. The main findings revealed that most studies in SSA employed generalized approaches to assess fragmentation, failing to account for the distinct contributions of various land cover changes. The other key findings indicate that the majority of studies (i) depended on low‐resolution remote sensing imagery, (ii) neglected to assess landscape metrics accuracy, and (iii) had limited focus on socioeconomic aspects. These limitations impede precise and thorough evaluation of SCA's contribution to forest fragmentation. To address these gaps, we recommend the following: (i) adoption of high‐resolution images, (ii) integration of socioeconomic data, and (iii) rigorous validation of landscape metrics. Concerning biodiversity implications, 55.2% (21 out of 38) of the reviewed studies reported negative impacts, and 68.2% (26 out of 38) exhibited a taxonomic bias toward avian and tree/plant species. We recommend the following: (i) enhanced utilization of remote sensing data for biodiversity monitoring, (ii) greater emphasis on neglected taxa (e.g., small mammals and amphibians), and (iii) focused conservation initiatives for small forest fragments, which are essential for biodiversity conservation, and reducing emissions from deforestation and forest degradation in SSA.