Browsing by Subject "NDVI"
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- ItemOpen AccessExploring the environmental drivers of waterfowl movement in arid landscapes using first-passage time analysis(2016) Henry, Dominic A W; Ament, Judith M; Cumming, Graeme SBackgroundThe movement patterns of many southern African waterfowl are typified by nomadism, which is thought to be a response to unpredictable changes in resource distributions. Nomadism and the related movement choices that waterfowl make in arid environments are, however, poorly understood. Tracking multiple individuals across wide spatiotemporal gradients offers one approach to elucidating the cues and mechanisms underpinning movement decisions. We used first-passage time (FPT) to analyse high spatial and temporal resolution telemetry data for Red-billed Teal and Egyptian Geese across a 1500km geographical gradient between 2008 and 2014. We tested the importance of several environmental variables in structuring movement patterns, focusing on two competing hypotheses: (1) whether movements are driven by resource conditions during the current period of habitat occupation (reactive movement hypothesis), or (2) whether movements are structured by shifts in the magnitude and direction of environmental variables at locations prior to occupation (prescient movement hypothesis).ResultsAn increase in rainfall at a 32day lag (i.e., prior to wetland occupancy), along with tagging site, were significant predictors of FPT in both waterfowl species. There was a positive relationship between NDVI and FPT for Egyptian Geese during this 32day period; the relationship was negative for Red-billed Teal. Consistent with findings for migratory grazing geese, Egyptian Geese prioritised food quality over food biomass. Red-billed Teal showed few immediate responses to wetland filling, contrary to what one would predict for a dabbling duck, suggesting high dietary flexibility. Our results were consistent with the prescient movement hypothesis.ConclusionsUsing FPT analysis we showed that the proximate drivers of southern African waterfowl movement are the dynamics of rainfall and primary productivity. Waterfowl appeared to be able to perceive and respond to temporal shifts in resource conditions prior to habitat patch occupation. This in turn suggests that their movements in semi-arid landscapes may be underpinned by intimate knowledge of the local environment; waterfowl pursue a complex behavioural strategy, locating suitable habitat patches proactively, rather than acting as passive respondents.
- ItemOpen AccessLand Use Change in The Knysna River Catchment and Its Impacts on the Geomorphological Characteristics of the Estuary between 1984 and 2019(2022) Taylor, Salwah; Meadows, Michael EdwardThe Knysna catchment and estuary are recognized in South Africa for their conservation significance as a sanctuary for marine species and biodiversity alike. The economy of the area which is dependent on tourism of the Knysna Estuary and catchment. The services obtained from the ecosystem include integral biodiversity value and is of significant importance for residents and tourists. Land use and land use cover dynamics remain some of the most crucial and obvious changes that has happened in the Knysna Estuary and catchment. Such changes severely affect ecosystems health, catchment areas, estuaries and the degradation of nature reserves. A ‘cloud-based platform for scientific analysis and visualization of geospatial datasets.' (Liu et al., 2020) method namely Google Earth Engine (GEE) is applied utilizing multi-temporal satellite imagery and Sentinel as interpretation to understand land use change over 35 years. A timeseries associated with land use changes and biodiversity loss were considered between the years 1984 and 2019. Additionally, Normalized Difference Vegetation Index (NDVI) and Supervised classification were performed using GEE software and Arc Geographical information systems (GIS) to identify land cover dynamics. The images are classified into three major land use classes, waterbodies, urban areas and vegetation. Vegetation is further classified into various classes namely fynbos, thicket, plantation forestry, salt marsh and agriculture. An accuracy assessment together with ground truthing, were conducted to verify and assess the overall classification accuracy of the results. The results indicated that over the study period urban growth and cultivated land makes up the most common land use category to have impacted the Knysna Estuary. Urban areas have increased significantly in reaction to the rapid increase in population ranging from 2.5% - 2.8% during the years 1984-1992, which reached to 2.9% - 4.1% during the years 1993-2007, and finally augmented to 4.3% to 6.3% during the years 2008-2019. The major reason behind the altering in the land use and geomorphology based on the research of the Knysna Estuary is human activities that have led to key determinantal impacts on surface runoff and land degradation. Overall, the noticeable changes in surface runoff and land degradation are strongly related to land use/land cover changes brought about by human impacts.
- ItemOpen AccessRemote sensing for detecting rapid post-fire recovery as Groundwater-Dependent Ecosystems in the Cape Floristic Region(2021) Chenge, Simcelile; Smit, Julian; West, Adam G; Singh, KaveerGroundwater Dependent Ecosystems (GDEs) concentrate high levels of biodiversity and several species not found anywhere else. They prevail in the landscape through the ecological contribution of groundwater. They, GDEs, are vulnerable to drastic changes in groundwater depth. If, for example, bulk groundwater pumping drastically increases the groundwater depth and GDEs can no longer access it, they would die out. In the Cape Floristic Region (CFR), South Africa, there is limited information about the spatial distribution of groundwater dependent ecosystems. With the CFR having multiple locations with current and subsequent bulk groundwater pumping, identifying the spatial distribution of GDEs is a prerequisite for establishing their groundwater requirements. This dissertation presents a proposed novel method to identify rapid recovering wetlands predicted to be GDEs and uses Random Forest (RF) to predict their spatial distribution. The proposed novel approach leveraged the periodic fire disturbances in the CFR and applied the remote sensing index; Normalised Difference Vegetation Index (NDVI) extracted from high spatial resolution (1 m) aerial orthoimages. The proposed novel approach involves three levels of analysis. The first two levels used a one-way Analysis of Variance (ANOVA) to analyse the sensitivity of mean NDVI to discriminate wetland and non-wetland classes in burned and unburned study sites, and a post-hoc test: Tukey's Honest Significant Differences (HSD) pair-wise comparison to detect differences between the wetland and non-wetland mean NDVI and infer an NDVI threshold of wetland classes. In unburned sites, ANOVAshowed no statistical significance between wetland and non-wetland classes, F (2,15) = 3.53, p = 0.055. In burned sites, however, ANOVA showed there was a significant difference between wetland and non-wetland classes, F (2,15) = 9.66, p = 0.002. ANOVA and Tukey showed there were significant differences betweenwetland and non-wetland classes, with wetlands having between 0.22 and 0.37 greater NDVI than non-wetlands. The last level of analysis employed a kernel density estimator function to assess the recovery rate post-burn and use it to detect faster recovery as potential of wetlands to be GDEs; results showed that potential wetland GDEs experience rapid NDVI recovery > 236 days post-fire. In the fire prone CFR, leveraging fire data to detect GDEs provides a potentially simple and efficient way of building a local database for GDEs. The proposed novel approach showed leveraging fire data is a simple alternative to laborious field data to identify and map GDEs in the CFR. But because of the finite spectral bands in aerial orthoimages, Sentinel-2A multi-epochs dataset was utilised to carry out random forest for predicting the spatial distribution of potential wetland GDEs in the Kogelberg Nature Reserve. Sentinel-2A bands: Short-Wave Infrared (SWIR), NearInfrared (NIR), Red-edge, Red, Green, NDVI and Normalised Difference Wetness Index (NDWI) predictors and the potential wetland GDEs/non-wetland classes as the response. I tuned RF using five-fold repeated spatial cross-validation instead of the typical cross-validation tuning to account for the spatial structure of the data. The overall predictive accuracy of RF was between 59%-71%. This predictive accuracy may have been reduced by the application of spatial cross-validation that accounted for the spatial autocorrelation in the multi-date data. The dissertation showed that Sentinel-2A multi-date data applies in predicting the distribution of potential wetland GDEs but might not be effective for smaller (< 100 m2) wetlands. These small wetlands showed rapid post-fire recovery (less than a year post-fire) and were effectively detected with high resolution aerial orthoimages (1 m) spatial resolution.