The collection of published papers is mostly about the state of the ecosystems for the South African National Parks’ conservation assets, with one paper reporting research results from one of the ...private game reserves. In the quest to understand river systems, evidence in the geomorphic characterization of Sabie River through mapping geomorphic patterns and change, sediment dynamics and mobility as well as dating periods of sediment deposition (Knight & Evans 2022), diversity and distribution of benthic invertebrates, which are crucial for bio-monitoring (Majdi et al. 2022), and the enigmatic floodplain water snake’s phylogenetic placement (Keates et al. 2022) were presented. ...the reflections of over 60 years of research on herbivore exclosures in the Kruger National Park (KNP) were reported.
Rangelands are natural ecosystems that serve as essential sources of forage for domesticated livestock and wildlife. Therefore, accurately mapping nutrient levels in rangelands is crucial for ...sustainable development and effective management of grazing animals. Remote sensing tools offer a reliable means to explore nutrient concentrations across large spatial areas. This study aimed to estimate and map seasonal foliar concentrations of nitrogen (N), phosphorus, and neutral detergent fibre (NDF) in mesic tropical rangelands of Limpopo using Sentinel-1, Sentinel-2, and the integration of S1 and S2 data. Fieldwork was conducted to collect samples for seasonal foliar nutrients (N, P, and NDF) during early-summer (November-January 2020), winter (July-August 2021), and late-summer (February-March 2022). Various conventional and red-edge-based vegetation indices were computed. The results demonstrate that integration data from S1 and S2 can effectively estimate and predict foliar concentrations of N, P, and NDF in mesic rangelands throughout the seasons, achieving R
2
values of 0.76, 0.78, and 0.71, with corresponding RMSE values of 0.13, 0.04, and 2.52. Notably, red-edge variables emerged as the most significant parameters for predicting seasonal N, P, and NDF concentrations. Additionally, factors such as season and slope significantly influenced the distribution and occurrence of these foliage nutrients, with higher foliage production observed during late-summer and on steeper slopes. The study concludes that the integration of S1 and S2 data can effectively monitor the seasonal dynamics of biochemical parameters. This finding holds significant implications for policymakers and rangeland users, offering a comprehensive understanding of the intricate variations within rangeland ecosystems. Further research could expand on these findings by applying the knowledge to various datasets, exploring different rangelands, and examining additional ecological factors such as slope altitude to detect foliar fibre biochemicals. Finally, the applications of this research extend beyond individual properties, providing practical tools for sustainable rangeland management and informed decision-making in resource utilization and conservation.
Estimation of biophysical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) at high spatiotemporal resolution is important for managing natural and heterogeneous ...environments. However, accurate estimation of biophysical variables particularly over heterogeneous environments remains a challenge. The objective of the study was to develop locally parameterized grass LAI and CCC empirical models using the Sentinel-2 variables combined with the Stepwise multiple linear regression (SMLR) and Random forest (RF) at the Golden Gate Highlands National Park (GGHNP) and Marakele National Park (MNP) in South Africa. Results showed that in MNP, SMLR yielded better LAI estimation with root mean squared error (RMSE) of 0.67 m
2
.m
−2
and mean adjusted error (MAE) of 0.54, explaining 48% of LAI variability, when bands and indices are combined. In contrast, RF gave better CCC estimation i.e. RMSE and MAE of 17.08 µg.cm
−2
and 13.18 respectively, explaining about 40% of CCC variability with Sentinel-2 bands only. In GGHNP, the RF models provided the best estimates of both LAI and CCC compared to SMLR models. Furthermore, the CCC and LAI estimation models of GGHNP showed improved model accuracies when 50% and 75% of the MNP field samples were transferred to the GGHNP models. In contrast, the CCC and LAI estimation models of MNP showed a decline in model performance across all scenarios where the GGHNP field samples were transferred to the MNP models. These findings have significant implications for the development of locally parameterized types of models that can provide improved and consistent site-specific accurate estimates of grass biophysical parameters over heterogeneous environments.
The tree Acacia mearnsii is native to south-eastern Australia but has become an aggressive invader in many countries. In South Africa, it is a significant threat to the conservation of biomes. ...Detecting and mapping its early invasion is critical. The current ground-based methods to map A. mearnsii are accurate but are neither economical nor practical. Remote sensing (RS) provides accurate and repeatable spatial information on tree species. The potential of RS technology to map A. mearnsii distributions remains poorly understood, mainly due to a lack of knowledge on the spectral properties of A. mearnsii relative to co-occurring native plants. We investigated the spectral uniqueness of A. mearnsii compared to co-occurring native plant species within the South African landscape. We explored full-range (400-2500 nm), leaf and canopy hyperspectral reflectance of the species. The spectral reflectance was collected biweekly from December 23, 2016 and May 31, 2017. We conducted a time series analysis, to assess the effect of seasonality on species discrimination. For comparison, two classification models were employed: parametric interval extended canonical variate discriminant (iECVA-DA) and nonparametric random forest discriminant classifiers (RF-DA). The results of this paper suggest that phenology plays a crucial role in discriminating between A. mearnsii and sampled species. The RF classifier discriminated A. mearnsii with slightly higher accuracies (from 92% to 100%) when compared with the iECVA-DA (from 85% to 93%). The study showed the potential of RS to discriminate between A. mearnsii and co-occurring plant species.
Aims
Remote‐sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because they are cost‐effective and allow for coverage of large areas over a short period. ...This study investigated the relationship between multispectral remote‐sensing data from Landsat 8 and Sentinel‐2 and species richness and diversity in mountainous and protected grasslands.
Locations
Golden Gate Highlands National Park, Free State, South Africa.
Methods
In‐situ data of plant species composition and cover from 142 plots with 16 relevés each were distributed across the study site and used to calculate species richness and Shannon–Wiener species diversity index (species diversity). We used a machine‐learning random forest algorithm to optimize the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression.
Results
This research found weak relationships between remote‐sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine‐learning random forest, the multispectral data sets exhibited strong predictive powers. In this investigation, near‐infrared (NIR) seemed to be the most selected band for both sensors to explain species diversity in mountainous grasslands.
Main conclusions
This finding further ascertains the efficiency of optimizing high spatial resolution spectral information to estimate plant species richness and diversity. This research shows that NIR, Soil‐Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies. Plant phenology and the choice of sensor affect the relationship between spectral information and species diversity variables.
•June 10–26 is the best time to assess tree-cover in South African savanna from NDVI.•Tree-cover maps derived from Julian day 161 were used to assess woody encroachment.•Farm abandonment is ...contributing to woody encroachment in South Africa.
The varying proportions of tree and herbaceous cover in the grassland and savanna biomes of Southern Africa determine their capacity to provide ecosystem services. The asynchronous phenologies e.g. annual NDVI profiles of grasses and trees in these semi-arid landscapes provide an opportunity to estimate percentage tree-cover by determining the period of maximum contrast between grasses and trees. First, a 16-day NDVI time series was generated from MODIS NDVI data, i.e. MOD13A2 16-day NDVI composite data. Secondly, percentage tree-cover data for 100 sample polygons (4 × 4) pixels for areas that have not undergone change in tree cover between 2001 and 2018 were derived using high resolution Google Earth imagery. Next, a time series consisting of the coefficients of determination (R2) for the NDVI/tree-cover linear regression were computed for the 100 polygons. Lastly, a threshold R2 > 0.5 was used to determine the optimal period of the year for mapping tree-cover. It emerged that the narrow period from Julian day 161–177 (June 10–26) was the most consistent period with R2 > 0.5 in the region. 18 tree-cover maps (2001–2018) were generated using linear regression model coefficients derived from Julian day 161 for each year. Kendall correlation coefficient (tau) was used to determine areas of significant (p < 0.05 and p < 0.01) increasing or decreasing trend in tree-cover. Areas (polygons) that showed increasing tree-cover appeared to be more widespread in the trend map as compared to areas of decreasing tree-cover. An accuracy assessment of the map of increasing tree-cover was conducted using Google Earth high resolution images. Out of 330 and 200 mapped polygons verified using p < 0.05 and 0.01 thresholds, respectively, 180 (54% accuracy) and 132 (65% accuracy) showed evidence of tree recruitment. Farm abandonment appeared to have been the most important factor contributing to increasing tree-cover in the region.
•The invasive Acacia mearnsii and Acacia dealbata was successfully mapped in a heterogeneous ecosystem.•Sentinel-2 spectral reflectance and spectral indices were used to map the invaders.•Sentinel-2 ...vegetation indices mapped invaders with high accuracy.•Red-edge based vegetation indices and bands were most important on Acacia species detection and mapping.•Senescence, Dry season, Flowering and leaf green up seasons are optimal for mapping invaders.
The spread of invasive Australia native Acacia tree species threatens biodiversity and adversely affecting on vegetative structure and function, including plant community composition, quantity and quality worldwide. It is essential to provide researchers and land managers for biological invasion science and management with accurate information of the distribution of invasive alien species and their dynamics. Remotely sensed data that reveal spatial distribution of the earth’s surface features/objects provide great potential for this purpose. Consistent satellite monitoring of alien invasive plants is often difficult because of lack of sufficient spectral contrast between them and co-occurring plants species. Time series analysis of spectral properties of the species can reveal timing of their variations among adjacent species. This information can improve accuracy of invasive species discrimination and mapping using remote sensing data at large scale. We sought to identify and better understand the optimal time window and key spectral features sufficient to detect invasive Acacia trees in heterogeneous forested landscape in South Africa. We explored one-year (January to December 2018) time series spectral bands and vegetation indices derived from optical Copernicus Sentinel-2 data. The attributes correspond to geographical information of invasive Acacia and native species recorded during a field survey undertaken from 21 February to 25 February 2018 over Kwa-Zulu Natal grasslands landscape, in South Africa. The results showed comparable separability prospects between times series of spectral bands and that of vegetation indices.
Substantial differences between Acacia species and native species were observed from spectral indices and spectral bands which are sensitive to Leaf Area Index, canopy chlorophyll and nitrogen concentrations. The results further revealed spectral differences between Acacia species and co-occurring native vegetation in April (senescence for deciduous plants), June-July (dry season), September (peak flowering period of Acacia spp) and December (leaf green-up) with vegetation indices (overall accuracy > 80 %). While spectral bands showed the beginning of the growing season (November–January) and peak vegetation productivity (February-March) as the optimal seasons or dates for image acquisition for discriminating Acacias from its co-occurring native species (overall accuracy > 80 %). In general, the use of Sentinel-2 time series spectral bands and vegetation indices has increased our understanding of Australian Acacias spectral dynamics, and proved that the sentinel-2 data is useful for characterization and monitoring Acacias over a large scale. Our results and approach could assist in deriving detailed geographic information of the species and assessment of a spread invasive plant species and severity of invasion.
Mapping and tracking invasive alien plant species (IAPS) and their invasiveness can be achieved using remote sensing (RS) and geographic information systems (GIS). Continuous monitoring using RS, GIS ...and modelling are fundamental tools for informing invasion and management strategies. Using systematic comparisons, we look at three remote sensing imagery platforms and how accurately they can be classified within the Vhembe biosphere reserve, Limpopo Province, South Africa. Supervised classification of National Geospatial Information Colour Digital Aerial Imagery, DigitalGlobe Worldview 2 and CNES SPOT 6 was performed. The Spectral Angle Mapper (SAM) algorithm was used to identify the best satellite for species-level classification. The accuracy of the classifications produced an overall accuracy (OA) of 71% with a Kappa coefficient (KC) of 0.76 for CDA photographs, an OA of 81% and a KC of 0.80 for Worldview 2, and an OA of 89% with a KC of 0.86 for SPOT 6 imagery. Therefore, SPOT 6 imagery came out as the most suitable for species-level classification. The classification results from the SPOT 6 imagery were used as input data for further species distribution modelling of Mauritius Thorn and River Red Gum in the VBR.
The efficient use of land, water, and energy resources in Africa is crucial for achieving sustainable food systems (SFSs). A SFS refers to all the related activities and processes from farm to fork ...and the range of actors contributing to the availability of food at all times. This study aimed to analyse the growth in the land–water–energy (LWE) nexus integration in sustainable food system research. The focus was on publication growth, the thematic areas covered, and how the research addressed the policies, programmes, and practices using a socio-economic lens. The study utilised a systematic literature review approach, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The study underscored the limited emphasis on the socio-economic perspective in the examination of the LWE nexus within sustainable food system research in Africa. Policies, governance, institutional influences, and social inclusion are crucial for addressing the region-specific challenges and achieving sustainable outcomes, but they seemed to be underrepresented in current research efforts. More so, this review revealed a paucity of research on key influencing factors like gender, conflict, culture, and socio-political dynamics. Ignoring these social factors might contribute to an inadequate management of natural resources, perpetuating issues related to food security and equity in resource use and decision-making. Additionally, the dominance of non-African institutions in knowledge production found in this review highlighted a potential gap in locally owned solutions and perspectives, which are crucial for effective policy development and implementation, often leading to failures in addressing region-specific challenges and achieving sustainable outcomes. Overall, the study highlighted the need for a more holistic approach that not only considers the technical aspects of the LWE nexus but also the social, cultural, and institutional dimensions. Additionally, fostering collaboration with local institutions and ensuring a diverse range of influencing factors can contribute to more comprehensive and contextually appropriate solutions for achieving sustainable food systems in Africa.
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