Decorum in nature: Impala (Aepyceros melampus melampus) dung middens follow spatial point patterns in Mukuvisi Woodland, Zimbabwe

Guided 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

Tree Aboveground Carbon Mapping in an Indian Tropical Moist Deciduous Forest Using Object-Based Image Analysis and Very High Resolution Satellite Imagery

Forests’ capability to sequester and store a large amount of carbon makes it imperative to assess the carbon stocked in them. The present study aimed to map the tree aboveground carbon stock of sal (Shorea robusta) forests of Doon valley, India using object-based image analysis (OBIA) of WorldView-2, a very high resolution satellite imagery (VHRS). The study evaluated different pan-sharpening techniques for improving the spatial resolution of WorldView-2 multispectral imagery and found that the high pass filter resolution merge technique was better compared to others. OBIA was used for image segmentation and classification. It enabled the delineation of tree crowns and canopy projection area (CPA) calculation. The overall accuracy of image segmentation and classification were found to be 72.12% and 84.82% respectively. The study unveiled that there exists a strong relationship between diameter at breast height and the CPA of trees as well as CPA and tree carbon. The average forest carbon density in the study area was found to be 108 Mg ha−1. The study highlighted that OBIA of VHRS imagery coupled with field inventory can be efficiently used to quantify and map the tree carbon stock.</p

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane-dominated African savannah dry forest

This study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north-western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite-derived metrics, a two-pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands

Estimating Tree Crown Area and Aboveground Biomass in Miombo Woodlands From High-Resolution RGB-Only Imagery

Quantification of tree canopy area and aboveground biomass is essential for monitoring ecosystems' ecological functionalities, e.g., carbon sequestration and habitat provision. Miombo woodlands are vastly existent in developing countries that often lack resources to acquire LiDAR data or high spatiospectral resolution remote sensing data that have been proven to accurately estimate these structural attributes. This study explored the utility of freely available (via Google Maps) high (1-m) resolution red, green, and blue (RGB) satellite imagery in combination with object-based image analysis (OBIA) for estimating tree canopy area and aboveground biomass in Miombo woodlands. We randomly established 41 225-m 2 plots in Mukuvisi Woodland, Zimbabwe, and used RGB data with OBIA to estimate tree canopy area in those plots. We also field measured the height, canopy area, and trunk diameter at breast height of all trees that fell in those plots, then used the field data and a published allometric equation to estimate aboveground tree biomass (AGB). OBIA classification accuracy was high (Jaccard similarity index = 0.96). Data analysis showed significant positive linear relationship between AGB and field-measured canopy area (R 2 = 0.87, p <; 0.003), and significant exponential relationships between: 1) OBIA-derived canopy area and AGB (R 2 = 0.56, p <; 0.0001); and 2) field-measured canopy area and OBIA-derived canopy area (R 2 = 0.63, p <; 0.0001), and no significant differences (t = 19.67, df = 78, p = 0.28) between field-measured canopy area (×̅ = 187.11 m 2 , σ = 127.03) and OBIA-derived canopy area (×̅ = 163.00 m 2 , σ = 50.08). We conclude that RGB data with OBIA are suitable for estimating tree canopy area in Miombo woodlands for various low-accuracy purposes (e.g., biomass estimation)

An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa

The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019