Browsing by Author "Visser, Vernon"

Now showing 1 - 7 of 7
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    Effects of fire frequency and seasonality on the population dynamics of the critically endangered Clanwilliam cedar
    (2019) Ncube, Thinabakho R. L.; Visser, Vernon
    The Clanwilliam cedar, (Widdringtonia wallichii, formerly W. cedarbergensis) is a threatened conifer endemic to the fire-adapted fynbos vegetation of the Cederberg mountains, South Africa. Here its population size has drastically declined, and its conservation status subsequently escalated to critically endangered in 2013 by IUCN Red List of Plants. Studies have hypothesised that excessive exploitation for timber products, climate change and unfavourable fire regimes (frequency, intensity and season) have contributed to this species’ decline. This decline led to the overarching aim of the study to gain a better understanding of the effects of fire frequency and seasonality on the life history of Clanwilliam cedar. To characterise fire patterns in the Cederberg Wilderness Area, I used a latent class analysis on fire indices calculated from a fire history database. To explore the effects of fire seasonality on the cedar count numbers I used a negative binomial hurdle model using seasonal fire indices and environmental data. To examine the impact of fire frequency and seasonality on the life-history of the Clanwilliam cedar, I used a stochastic demographic model based on parameter values obtained from the literature. Findings from the latent class model indicated that the main axes of variation in fire frequency were the fire indices representing total fire frequency, summer fires, autumn fires in the last 30 years and fires in the last 30 years. Although these fire indices were able to distinguish relatively well between the three latent classes, it however was difficult to disentangle the relative importance of each fire index due to their strong covariation. This points to a more general pattern, suggesting that it is necessary to examine the entire fire frequency history and the seasonality pattern in order to understand the current state of the population of the Clanwilliam cedar. The linear count model revealed autumn fires as being positively associated, whereas mean annual precipitation and mean annual temperature and precipitation seasonality were negatively associated with the cedar numbers. The stochastic demographic model showed both summer and winter fires induce positive growth rates at firereturn intervals greater than 10 years, but winter fires always permitted a higher population growth rate. The sensitivity analysis of the stochastic population growth rate (log λs) to changes in the life-history parameters at fire-return intervals of 10 and 20 years showed that fire mortality was most important for a summer fire regime, and growth rates of adult trees were most important for a winter fire regime. The different methods used in this study provided different but complementary results, and thus insights from these various models could potentially contribute to the development of fire management strategies that reflect the complexities of fire frequency and seasonality on the population dynamics and thus persistence of the Clanwilliam cedar.
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    Mapping desertification: towards an approach for mapping regional land degradation in drylands
    (2020) Bell, Wesley; Hoffman, Michael Timm; Visser, Vernon
    Land degradation in drylands (desertification) is an issue that potentially impacts nearly half of the world's human population living on over a third of the Earth's land surface. Despite global concern of the impact of desertification on people and the environment, there is no universal method to assess and map desertification. Methods to assess desertification at the local to regional scale that can fit into a broader global desertification narrative are more appropriate. The overall objective of this thesis is to assess regional desertification using field and Earth observation data for the Namaqualand Hardeveld bioregion of South Africa. Field data on the condition of the land from 277 plots was analysed using Latent Class Analysis (LCA) and found to cluster into three separate states. The first state (S1) was comprised primarily of degraded plots. The third state (S3), on the other hand, was comprised primarily of non-degraded plots, while the plots in state two (S2) generally fell between those which were assigned to S1 and S3. Through the LCA, each plot was assigned a probability of belonging to each state, and the most important variables in distinguishing the three states (perennial plant cover and bare ground cover) were identified. A total of 16 remote sensing variables were determined for the project area. Five vegetation indices (NDVI, EVI, SAVI, OSAVI, MSAVI), as well as spectral mixture analysis (SMA) cover estimates for perennial vegetation, bare ground and bare rock were calculated using both Landsat 8 and Sentinel-2A data. These variables were used in a series of Partial Least Squares regression (PLSr) models to predict either the probability of a plot belonging to one of the three latent states, or the field estimated perennial plant and bare ground cover. The best performing PLSr model had ten remote sensing variables predicting the field estimates of cover (R2Ycum = 0.592; Q2cum = 0.554). Both Sentinel-2A and Landsat 8 SMA cover estimates were better at predicting field cover than any of the vegetation indices. Estimates of bare ground and perennial plant cover were projected over the project area using the PLSr model and ground truthed using data from 61 independent field test plots. There was a significant correlation between the PLSr estimates and the field estimates for both perennial plant cover and bare ground cover for the test plots with the best correlation found to be between the PLSr estimate of bare ground and field estimated bare ground cover (r = 0.827, p < 0.001, CI [0.727, 0.893]). The trendline slope and percentile range of a time series of the Landsat SMA bare ground estimate were used to create raster images. These images, along with images for the PLSr bare ground and perennial plant cover estimates, were converted into images representing membership values between zero and one for the habitat condition archetype. These three images were then combined to produce one raster representing the overall membership of the project area to the habitat condition archetype. The importance of five potential drivers of land degradation (elevation, slope aspect, slope steepness, rainfall trend, and land tenure) in predicting PLSrestimated perennial plant and bare ground cover were evaluated using a random forest model. All drivers were found to be important predictors of cover and were included in the construction of the final, multi-band archetype image. If habitat condition classes are designated according to the mean archetype membership value ± one / two standard deviations, then 17% of the project area could be considered moderately degraded, with just over 3% severely degraded. This novel method of assessing and mapping desertification leads to improved accuracy in predicting habitat condition in the context of potential drivers of change. The utility of SMA over traditional vegetation indices is supported for this particular environment. This methodology can be improved with better endmember designation as well as improved spatial data on the potential drivers of change in drylands. The archetype approach ensures less subjectivity in map production, and the retention of pertinent information in map products. The approach developed in this thesis will allow for more accurate desertification reporting for UNCCD member states and will ultimately improve efforts to combat desertification globally.
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    The potential impact of climate change on the genetic diversity of the endangered western leopard toad, Sclerophrys pantherina
    (2017) Casola, Sarah; Tolley, Krystal A; Spottiswoode, Claire; Visser, Vernon
    Climate change is now considered to be one of the greatest threats to the persistence of biodiversity. Much work has focused on the potential for climatic shifts to alter species' ranges, phenology, physiology, and behaviour, addressing higher level units of biodiversity from populations to biomes. However, the potential effects of climate change on the most fundamental unit of biodiversity, intraspecific genetic diversity, has only recently received research attention. Studies to date suggest that the accelerated climatic changes we currently face could cause a loss of intraspecific diversity, hampering the ability of populations to respond to further environmental change. Amphibians are considered to be one of the most vulnerable taxa to climate change. The amphibians of the Western Cape of South Africa provide a powerful opportunity to study the impact of climate change on genetic diversity, as many are endemic, threatened, and generally considered to be poor dispersers, limiting their ability to respond to climatic changes through range shifts. This project had two aims: first, to explore the potential impact of climatic shifts on the genetic landscape of the endemic and Endangered western leopard toad, Sclerophrys pantherina, a species with a disjunct distribution on either side of the Cape Flats. Second, I aimed to test the hypothesis that climatic fluctuations drive genetic divergence, a mechanism which may explain the potential overlap of high diversity areas with areas of high climatic instability. Population genetic analyses supported the findings of previous genetic work on S. pantherina, that populations in the Cape Metropole and the Overstrand Municipality (to the west and east of the Cape Flats, respectively) are genetically distinct, and thus should be treated as separate conservation units. Higher haplotype diversity was identified in the populations in the Cape Metropole when compared with the Overstrand, highlighting the importance of urban habitat patches in harbouring diversity in the species. Distinct pockets of low haplotype diversity were identified at Observatory and Hout Bay, suggesting a lack of connectivity between these and adjacent breeding sites, likely due to urban-associated habitat fragmentation. Species distribution modelling revealed that the species could lose a substantial amount of climatically suitable space in its current area of occurrence by 2070. Furthermore, the degree of loss was not uniform across the species' distribution. The populations of the Cape Metropole were predicted to experience greater losses in climatically suitable space than populations in the Overstrand. Additionally, the change in climatic suitability between the mid-Holocene (6,000 years ago) and present as well as the change in suitability between future (2050 and 2070) and present were significant predictors of genetic diversity, where areas of the greatest change in suitability between time periods were associated with the highest genetic diversity. Future efforts to conserve the species should focus on establishing connectivity between breeding sites to allow for the rescue of genetically depauperate sites. Efforts to mitigate the drastic negative effects of climate change predicted by the species distribution models should prioritise the breeding sites in the Cape Metropole, which are both higher in diversity and at greater risk from climate change. Mitigation efforts will likely require the application of engineered solutions to promote the maintenance of suitable wetland habitat for the species.
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    Remote sensing evaluation of Cape parrot habitat in the Eastern Cape: implications for conservation
    (2023) Wright, Emma; Visser, Vernon; Hoffman Timm
    The Cape parrot is the only endemic parrot of South Africa and is currently nationally threatened. One of the biggest threats to the Cape parrot is the past and present degradation of indigenous forest. The Amathole Mistbelt Forest in the Eastern Cape is the primary habitat for Cape parrot and has historically been heavily degraded. In order to conserve the Cape parrot effectively, there is a need to understand the spatial distribution of indigenous forest patches and their quality. There is currently not a sufficiently accurate landcover map available to fulfil this need. Thus, this study uses remotely sensed imagery at a 10 m resolution and random forest classification to (1) produce a land cover map of the indigenous forest in the Amathole region; (2) determine habitat quality of the indigenous forest, and (3) determine whether forest loss, as reported by Global Forest Watch (GFW), reflects the loss of indigenous forest or the clearing of plantations and woody alien invasives. The overall accuracy of the classification was very high at 82%. Cross validated accuracies were all high ranging from 95 – 100%, with water having the highest accuracy and indigenous forest, eucalyptus spp., pine spp., and infrastructure having the lowest accuracies. F1 scores ranged from 0.78 – 1.0, with indigenous forest ranking the second lowest at 0.80 and grassland ranking the second highest at 0.91. Indigenous forest covered 26% of the study area. Black wattle, pine spp. and eucalyptus spp. covered a combined 35% of the study area. The detailed map of indigenous forest shows the extent of its fragmentation and outlines some of the management implications associated with small forest patches. Secondly, habitat quality for Cape parrot is questioned as there is a lack of emergent canopy tree species and 30% of the matrix between forest patches is invaded by invasive alien species. Thus, it is suggested that a strong focus is put into clearing and managing invasive alien species. Lastly, GFW ‘forest cover loss' is shown to be comprised primarily of plantation felling and invasive clearing. It is suggested that there has been little loss of indigenous forest in the last 30 years. Further research will include creating an open and accessible product in the form of a Google Earth Engine App to share with conservation managers in the area.
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    Satellite change detection in the albany thicket biome
    (2025) Mahlasi, Craig; Moncrief, Glenn; Visser, Vernon; Altwegg, Andreas; Slingsby, Jasper
    The Albany Thicket Biome has been subject to widespread transformation, with as much as 63% of the biome being severely degraded. The primary land use activity responsible for much of the transformation of the biome has been pastoralism and commercial agriculture land expansion (Mills et al., 2005; Powell, 2008; Stickler and Shackleton, 2015). There are primarily four traditional remote sensing based change detection frameworks: algebra, transformations, classification, and advanced models (van Oort, 2007, Asokan and Anitha, 2019). While these methods are able to detect changes in bi-temporal datasets they are inherently limited in that they are based on the assumption that each pixel's spectral signature is a linear combination of the features on the corresponding physical surface (Salih et al., 2017, Sun et al., 2015). These methods also suffer from the propensity for false positives resulting from differences in atmospheric conditions, viewing angles and illumination and soil moistures between the two images; another limitation of these methods is the observation interval between the initial and post-change or successive observations are often weeks or up to years apart. which makes the detection of transient changes difficult. Finally they are unable to provide information on changes in land cover that allows for timely intervention by the authorities. Continuous change detection on the other hand uses all available and usable observations to detect changes. Continuous change detection classifiers allocate pixels throughout a time series to predefined classes using labelled training data. The majority of tools that seek to perform continuous land cover change detection have been developed for forests and thus tend to perform poorly in thicket ecosystems. This study aims to use multi-temporal satellite imagery to detect transformation of Albany Thicket in near-real time. The first chapter seeks to generate a Thicket transformation map documenting the changes in the Thicket biome between 2016 and 2019 and to produce an online application to visualise and interpret these changes. Chapter two focuses on developing a change detection protocol for identifying clearings of Thickets using Temporal Convolution Neural Networks and comparing it against the Continuous Change Detection and Classification (CCDC) algorithm. Finally chapter three sets out to develop a Domain adaptive Temporal Convolution Neural Network for continuous change detection in the Albany Thicket biome. The study concluded that using medium resolution satellite imagery changes in Albany Thicket vegetation can be reliably detected and discerned from changes in other land cover types. The ability to continuously detect changes using TempCNNs was shown to outperform a state of art algorithm namely, CCDC. Albany Thicket cover dynamics were shown to be embedded within geographical contexts and that geographical gradients in biophysical variables influence the contextual representations learned by the TempCNNs and therefore fusing TempCNNs with biophysical variables such as surface dryness information can improve their performance. Finally, it was shown that using meta-learning the TempCNN can be adapted to be robust in shifting domains by learning the most optimal parameter initializations that allow for capturing the invariant embeddings that facilitate generalisation across domains possible.