Spatial characterization of vegetation diversity with satellite remote sensing in the khakea-bray transboundary aquifer

>Magister Scientiae - MScThere have been increasing calls to monitor Groundwater-Dependent Ecosystems (GDEs) more effectively, since they are biodiversity hotspots that provide several ecosystem services. The accurate monitoring of GDEs is an indispensable under Sustainable Development Goal (SDG) 15, because it promotes the existence of phreatophytes. It is imperative to monitoring GDEs, since their ecological significance (e.g., as biodiversity hotspots) is not well understood in most environments they exist. For example, vegetation diversity in GDEs requires routine monitoring, to conserve their biodiversity status and to preserve the ecosystem services in these environments. Such monitoring requires robust measures and techniques, particularly in arid environments threatened by groundwater over–abstraction, landcover and climate change. Although in–situ methods are reliable, they are challenging to use in extensive transboundary groundwater resources such as the Khakea-Bray Transboundary Aquifer

Spatial monitoring and reporting tool (smart) in mid-Zambezi valley, Zimbabwe: Implementation challenges and practices

Biodiversity monitoring and data-management technologies can enhance the protection of persecuted species, such as African elephants (Loxodonta africana), through providing management-relevant information. Implementing these technologies, however, often presents several capacity and resource challenges for field staff in protected areas. In the Mid-Zambezi Valley, Zimbabwe, the Zimbabwe Parks and Wildlife Management Authority (ZPWMA) is in the process of adopting the Spatial Monitoring and Reporting Tool (SMART) as law enforcement and data management tool for adaptive management. With the support of several conservation partners, ZPWMA was able to acquire SMART equipment (computers and handheld cyber-tracker devices) as well as train rangers and officers on how to use SMART in the region

Spatial characterisation of vegetation diversity in groundwater-dependent ecosystems using in-situ and sentinel-2 msi satellite data

Groundwater-Dependent Ecosystems (GDEs) are under threat from groundwater overabstraction, which significantly impacts their conservation and sustainable management. Although the socio-economic significance of GDEs is understood, their ecosystem services and ecological significance (e.g., biodiversity hotspots) in arid environments remains understudied. Therefore, under the United Nations Sustainable Development Goal (SDG) 15, characterizing or identifying biodiversity hotspots in GDEs improves their management and conservation. In this study, we present the first attempt towards the spatial characterization of vegetation diversity in GDEs within the Khakea-Bray Transboundary Aquifer. Following the Spectral Variation Hypothesis (SVH), we used multispectral remotely sensed data (i.e., Sentinel-2 MSI) to characterize the vegetation diversity

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

Spatio–temporal variation of vegetation heterogeneity in groundwater dependent ecosystems within arid environments

Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI)

Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

Trends in elephant poaching in the Mid-Zambezi Valley, Zimbabwe: Lessons learnt and future outlook

Background: The conservation of African elephants (Loxodonta africana) has important ecological, economical, cultural and aesthetic values, at both local and global levels (Pittiglio et al., 2014). Despite the important role elephants play as keystone species, their populations have been dwindling due to human activities (Sibanda et al., 2016). The most serious threats to elephant's survival across most of its range include illegal wildlife trade which has been exacerbated by an increase in organized poaching (Ouko, 2013). Poaching for both meat and ivory is by far the most acute problem across Africa according to data derived from the Monitoring the Illegal Killing of Elephants (MIKE) and Elephant Trade Information System (ETIS; WWF, 2017). This is a complex global threat to the survival of the African elephant across most of its range (Dejene et al., 2021; Ouko, 2013; Wittemyer et al., 2014)

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

African elephant (Loxodonta africana) select less fragmented landscapes to connect core habitats in human‐dominated landscapes

African elephants (Loxodonta africana) utilise corridors to access limited resources, that is forage and water scattered across heterogeneous habitats they roam. The existence of small elephant metapopulations depend on the intactness of these corridors to access the scarce resources. Due to the sedentarisation of the previously nomadic Maasai people, elephant corridors have been exposed to increased fragmentation from human-induced activities across the Amboseli ecosystem in Kenya. In this study, we sought to compare the scale of fragmentation between corridors and their immediate landscapes (noncorridors) in the Amboseli ecosystem, Kenya. We used a Brownian Bridge Movement Model (BBMM) to identify corridors used by elephants from global positioning system (GPS) collar data. The scale of fragmentation between corridors and noncorridors was determined using the effective mesh size fragmentation metric (m eff). Our results showed that elephant corridors were significantly less fragmented (Wilcoxon sum rank test: W = 6,121.5, p < 0.05) when compared to the noncorridors. The presence of fragmentation geometries in the corridors remains a major cause of concern for wildlife managers as they have the potential to invade and constrict the existing corridors. Our results underscore the need to extend management of elephant habitats to migration corridors outside protected areas

Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape.

When wildfires are controlled, they are integral to the existence of savannah ecosystems and play an intrinsic role in maintaining their structure and function. Ample studies on wildfire detection and severity mapping are available but what remains a challenge is the accurate mapping of burnt areas in heterogenous landscapes. In this study, we tested which spectral bands contributed most to burnt area detection when using Sentinel-2 and Landsat 8 multispectral sensors in two study sites. Post-fire Sentinel 2A and Landsat 8 images were classified using the Random Forest (RF) classifier. We found out that, the NIR, Red, Red-edge and Blue spectral bands contributed most to burned area detection when using Landsat 8 and Sentinel 2A. We found out that, Landsat 8 had a higher classification accuracy (OA = 0.92, Kappa = 0.85 and TSS = 0.84)) in study site 1 as compared to Sentinel-2 (OA = 0.86, Kappa = 0.74 and TSS = 0.76). In study site 2, Sentinel-2 had a slightly higher classification accuracy (OA = 0.89, Kappa = 0.67 and TSS = 0.64) which was comparable to that of Landsat 8 (OA = 0.85, Kappa = 0.50 and TSS = 0.41). Our study adds rudimentary knowledge on the most reliable sensor allowing reliable estimation of burnt areas and improved post-fire ecological evaluations on ecosystem damage and carbon emission