95 research outputs found
A reflection about the recent Koedoe publications (Volume 64, No 1, 2022)
No abstract available.http://www.koedoe.co.zaam2023Geography, Geoinformatics and Meteorolog
Hybrid retrieval of grass biophysical variables based-on radiative transfer, active learning and regression methods using Sentinel-2 data in Marakele National Park
DATA AVAILABILITY STATEMENT :
We understand that the publication of the data is becoming a good practice in research.Biophysical variables such as leaf area index (LAI) and leaf chlorophyll content (LCC) are cited as essential biodiversity variables. A comprehensive comparison and integration of retrieval methods is needed for the estimation of biophysical variables such as LAI and LCC over a multispecies grass canopy. This study tested an assortment of five potentially robust, nonparametric regression methods (NPRMs) for inversion of radiative transfer model (RTM) to retrieve grass LAI and LCC in the Marakele National Park (MNP) of South Africa. The NPRMs used were, namely (i) Partial least squares regression (PLSR), (ii) Principle components regression (PCR), (iii) Kernel ridge regression (KRR), (iv) Random forest regression (RFR), and (v) K-nearest neighbours regression (KNNR). Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a large pool of RTM simulations. Results show the most accurate grass LAI and LCC retrievals had lower relative root mean squared errors (RRMSEs) of 39.87% and 16.58% respectively. These findings have significant implications for the development of transferable rangeland monitoring systems in protected mountainous regions.Research development programme of the University of Pretoria; National Research Foundation (NRF) of South Africa AND Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL).https://www.tandfonline.com/journals/TGEIhj2024Geography, Geoinformatics and MeteorologySDG-13:Climate actionSDG-15:Life on lan
Assessing natural resource change in Vhembe biosphere and surroundings
South Africa is a custodian of an immense wealth of natural and biodiversity resources in Africa. Natural resources are continually changing in different South African biospheres based on anthropogenic and non-anthropogenic causes. Land use activities like agriculture, cultivation, livestock rearing, commercial plantations, urbanisation and mining are among the major drivers of natural resource change and transformation. In this study, land cover change assessment was used to assess natural resource change in Vhembe biosphere and surroundings. To assess natural resource change in Vhembe biosphere, land use land cover change assessment was conducted using South African’s national land-cover dataset, generated from multi-seasonal Landsat 5 and Sentinel-2 images. The 72× class land cover map was re-classified into 12× classes to fit the study objectives. Eight out of twelve classes quantified in hectares: indigenous forests, thicket/dense bush, natural woodland, shrubland, grassland, water bodies and wetlands were categorised as natural resources for which the natural resource change assessment for this study was based. Assessment findings established that land use and its related activities have contributed substantially to natural resource change where cultivated commercial, natural woodland and built-up residential contributed the most significant upward change in hectarage and percentage, from 132,246.9 to 365,644.92 (ha)—percentage change of 176%; from 94,665.42 to 257,889.68 (ha)—percentage changes of 172% and from 74,070.27 to 147,701.88(ha)—percentage change of 99% respectively. Shrubland, thicket/dense bush and indigenous forests registered the highest downward changes from 263,070.6 to 977.72 (ha); from 338,723.7 to 23,166.92 and from 13,211.91 to 7402.92 (ha) with percentage changes of −100%, −93% and −44% respectively in Vhembe biosphere and the surroundings from 1990 to 2018. The study showed how natural resources are changing and the use of remote sensing for environmental monitoring and assessment in the Vhembe district.http://link.springer.com/journal/10661hj2022Geography, Geoinformatics and Meteorolog
Assessing the effect of seasonality on leaf and canopy spectra for the discrimination of an alien tree Species, Acacia Mearnsii, from co-occurring native species using parametric and nonparametric classifiers
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.The Council for Scientific and Industrial Research and the National Research Foundation (NRF).http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hj2020Plant Production and Soil Scienc
Mechanisms and consequences of benefit sharing from oil palm agribusiness plantations establishment in South Comoé Region, Côte d’Ivoire
One of the main difficulties facing agribusiness development in Cote d’Ivoire, is the issue of benefit sharing. Although communal land is expected to be equitably beneficial to all who have a stake on the land, unclear benefit sharing principles and modalities affect the implementation of benefit sharing to the disadvantage of the rural communities. Using a qualitative research approach, the study investigates if the benefit sharing approach practiced by oil palm plantations investors in South Comoé align with globally established standards of access and benefit sharing (ABS). To this end data for the study was collected from 50 participants: rural community members (N=36), agribusiness developers (N=6) and local government authorities (N=8). The results revealed inequality in the land acquisition and benefit sharing negotiation process in favour of local elites. Lack of fairness experienced in the benefit sharing approach in the districts of Aboisso, Bonoua and Adiaké was attributed to the lack of institutional, policy and legal frameworks to guide a fair benefit sharing. Inequality in benefit sharing scheme affects the working relationship between the parties thus generating tensions with consequences on the stability of commercial farming. The study contributes to the debate on the marginalisation of smallholders in the distribution of benefits from agribusiness investments. Therefore, the designing of policies and practical measures that bring together rural communities and agribusiness developers to negotiate fair benefit sharing terms in line with international standards including honesty, inclusive participation of rural community in land acquisition process are recommended
Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data
DATA AVAILABILITY : All the Sentinel data are free of cost and
are in the open domain, and field data port the published claims
and fulfill with field requirements.Rangelands play a vital role in developing
countries’ biodiversity conservation and economic
growth, since most people depend on rangelands for
their livelihood. Aboveground-biomass (AGB) is an
ecological indicator of the health and productivity of
rangeland and provides an estimate of the amount of
carbon stored in the vegetation. Thus, monitoring seasonal
AGB is important for understanding and managing
rangelands’ status and resilience. This study
assesses the impact of seasonal dynamics and fire
on biophysical parameters using Sentinel-1 (S1) and
Sentinel-2 (S2) image data in the mesic rangeland of
Limpopo, South Africa. Six sites were selected (3/
area), with homogenous vegetation (10 plots/site of
30m2).
The seasonal measurements of LAI and biomass
were undertaken in the early summer (December
2020), winter (July–August 2021), and late
summer (March 2022). Two regression approaches,
random forest (RF) and stepwise multiple linear
regression (SMLR), were used to estimate seasonal
AGB. The results show a significant difference (p <
0.05) in AGB seasonal distribution and occurrence
between the fire (ranging from 0.26 to 0.39 kg/m2)
and non-fire areas (0.24–0.35 kg/m2). In addition,
the seasonal predictive models derived from random
forest regression (RF) are fit to predict disturbance
and seasonal variations in mesic tropical rangelands.
The S1 variables were excluded from all models due
to high moisture content. Hence, this study analyzed
the time series to evaluate the correlation between
seasonal estimated and field AGB in mesic tropical
rangelands. A significant correlation between
backscattering, AGB and ecological parameters was
observed. Therefore, using S1 and S2 data provides
sufficient data to obtain the seasonal changes of biophysical
parameters in mesic tropical rangelands after
disturbance (fire) and enhanced assessments of critical
phenology stages.Open access funding provided by University of Pretoria.http://link.springer.com/journal/10661am2024Geography, Geoinformatics and MeteorologyPlant Production and Soil ScienceSDG-15:Life on lan
Exploring the integration of the land, water, and energy nexus in sustainable food systems research through a socio-economic lens : a systematic literature review
DATA AVAILABILITY STATEMENT : The data presented in this study are available on request from the
corresponding author.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.The Bill & Melinda Gates Foundation.https://www.mdpi.com/journal/sustainabilityam2024Animal and Wildlife SciencesGeography, Geoinformatics and MeteorologySDG-02:Zero Hunge
Ecosystem service valuation for a critical biodiversity area : case of the Mphaphuli community, South Africa
The study of ecosystem services and the valuation of their contribution to human wellbeing is gaining increasing interest among scientists and decision-makers. The setting of this study was a critical biodiversity area on a portion of land largely presided over by a traditional leadership structure on behalf of a relatively poor local community in South Africa. The study identified several ecosystem services and performed an economic valuation of these services, and their importance both locally and globally using the Co528,280,256.00, whereas hazard mitigation potential was found to be US5577.54. The values of the ecosystem services differed across the eleven land use land cover classes. The outcomes of the study focused on a very local scale, which was a departure from other studies previously carried out in South Africa, which focused more on the identification and valuation of regional and national scale ecosystem services.https://www.mdpi.com/journal/landam2023Geography, Geoinformatics and Meteorolog
Prediction of species richness and diversity in sub-alpine grasslands using satellite remote sensing and random forest machine-learning algorithm
SUPPORTING INFORMATION : APPENDIX S1. Random forest predicted species richness for January (top left), February (top right), March (bottom left), and January–February (bottom right) using Landsat 8 optimal variables.
APPENDIX S2. Random forest predicted species diversity for January (top left), February (top right), March (bottom left), and January–February (bottom right) using Landsat 8 optimal variables.
APPENDIX S3. Random forest predicted species richness for January (top left), February (top right), March (bottom left), and January–February (bottom right) using Sentinel-2 optimal variables.
APPENDIX S4. Random forest predicted species diversity for January (top left), February (top right), March (bottom left), and January–February (bottom right) using Sentinel-2 optimal variables.DATA AVAILABILITY STATEMENT : The data that support the findings of this study are openly available in Google Earth Engine at https://code.earthengine.google.com/ 0a7251d85e04c56d261069189cbc17ff.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.https://onlinelibrary.wiley.com/journal/1654109xGeography, Geoinformatics and MeteorologySDG-09: Industry, innovation and infrastructureSDG-15:Life on lan
Assessing species richness, diversity and assemblage of forest patches within a grassland matrix in the Afrotemperate ecosystems
Changes in species diversity have been widely used in environmental monitoring and global change studies as an indicator of vegetation change over time. High mountain ecosystems such as the Golden Gate Highlands National Park (GGHNP) host a relatively high number of plant species due to less human disturbances compared to the surrounding lowland areas. This study investigated the species richness and diversity in the Afrotemperate forest and woodland communities of the GGHNP. For vegetation classification, the TWINSPAN algorithm was firstly used to do a floristic analysis of thirty-two sampling plots and refined further using the Braun-Blanquet procedures and JUICE programmes. The Detrended Correspondence Analysis (DCA) and the phytosociological analysis of the vegetation data resulted in five plant communities and one sub-community across various topographic gradient. The Olinia emarginata–Podocarpus latifolius forest was found to be the most diverse forest whereas the Kiggelaria africana forest showed relatively lower species diversity. Species richness was also relatively high in the Olinia emarginata–Podocarpus latifolius forest plots, compared to the Leucosidea sericea–Buddleja salviifolia woodland, and the Euclea crispa–Protea caffra–roupelliae savannas. Data on plant assemblages and classification provide invaluable information for studies focussing on climate change, species distribution models and the associated bioclimatic variables. Understanding the importance and complexities of high mountains and forest ecosystems is therefore essential for developing effective conservation strategies.http://www.schweizerbart.de/journals/phytohj2024Geography, Geoinformatics and MeteorologySDG-15:Life on lan
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