Habitat suitability assessments for sable antelope

Relationships between occurrence of a species and features of habitats occupied are central to establish factors that influence its distribution. Within large protected areas extinction processes may cause retractions of species distributions to areas that are still suitable or to locations least affected by a negative influence. The aim of the project was to identify factors that influence the suitability of areas where sable antelope occur. Climate and geographic barriers have overriding influences over biotic factors to identify regions that lie outside a species range. Abiotic factors (e.g. geology and rainfall) indicate places with environmental conditions that allow a species to persist (spatial extent of a fundamental niche). However, biotic interactions can constrain occupation to a limited proportion of those conditions (subset of fundamental niche). I used aerial census data (1977-93) in Kruger National Park to: (1) model distribution patterns commonly exhibited by large ungulate species with the objective of identifying methods most suitable for assessing different aspects of species distributions; (2) assess how distribution patterns of 12 antelope species have apparently changed since around 1960 and how these changes may be related to sable distribution shifts or abundance decline; (3) assess whether a climate effect could have caused contractions of range and abundance of sable and other rare antelopes and (4) identify features that restricted a widespread distribution of sable in KNP using logistic regression models. In chapter 2, I compared and contrasted performance of LoCoH and kernel methods for constructing distributions for species exhibiting (i) wide and continuous distributions with a few gaps, (ii) broad distribution with local concentrations and absences, (iii) linear distribution pattern associated with rivers, and (iv) a patchy distribution pattern. The methods have valuable capabilities for assessing different objectives of species distributions. The type of spatial distribution exhibited by a species influences the performance of these methods. This contrasts generalizations from home range studies that suggest superiority of one method over the other. The LoCoH method tends not to include areas where a species was not recorded. In contrast, kernel method exhibited the opposite bias. However, their differences were not large enough to lead to a diverse interpretation of range extents or occupancy patterns. Automatic procedures of choosing h appeared not adequate for mapping distribution patterns of species that occur in patches where outlines of outer boundaries are not clearly defined or for those species exhibiting clumped occurrences in places and widespread occurrences elsewhere. A different h value may be necessary for each section of such a distribution with fixed kernel method. This is achieved by dividing a study area into separate sections and mapping the ranges independently. The LoCoH is suitable for indicating gaps and/or fine-scale range shifts. However, LoCoH method may have to be applied with caution for species exhibiting continuous distributions because there is a possibility of emphasizing unimportant gaps. Despite the fact that distribution patterns around 1960 were vaguely complied, it appeared that common species have increased occupation of northern half of KNP and several species (impala, buffalo, wildebeest, warthog, and waterbuck) have been sighted during dry season (1980-1993) in areas indicated around 1960 as wet season range. The 1980-1993 distribution of impala, warthog, and waterbuck appeared more widespread away from rivers than around 1960. Distributions of sable, tsessebe, eland, and roan contracted in northern half and in central region of KNP. Fences that blocked migrations of wildebeest and zebra outside the park to the west of central KNP appeared associated with distributional changes of herbivores in this area. Augmenting surface waterpoints was a key influence in expansions of distributions of common species into northern half of KNP and for occurrences of some species during dry season in areas previously used during wet season. The contractions of distributions of rarer antelopes occurred concurrently with expansions of common species into northern half of KNP where rare antelopes mainly occur. The above suggests that some areas of northern half of KNP may have become less suitable to rare antelopes. Despite that the exact influence of climate on rarer antelopes could not be established, distribution pattern changes were characteristic of an influence consistent with that of climate. Range contractions were evident for all three species (sable, roan and Tsessebe), associated with local herd extirpations, especially following the severe 1991/2 drought. Herds of sable, roan, or tsessebe that occurred in isolated locations disappeared and ranges contracted even in the relatively wetter southern section of the park. Sable herds persisted in discrete patches after a widespread contraction of their formerly contiguous range in northern section. Sable prevalence was highest on nutrient poor granite and sandstone rather than nutrient rich basalt and gabbro. Distances from perennial water sources did not have overriding influences on where sable herds occurred. Sable prevalence was higher in mopane savanna woodland and sour bushveld than shrubland, dense bush savanna, or grassland with few trees. Sable herds were prevalent in localities that exhibited relatively low predation risk and low effects of competition from abundant grazers, implying that at the edge of a range, interactions involving biotic factors appear more important than searching for areas which potentially have more forage resources. Predation risk appeared more influential to sable distribution than competition. Findings showed that biotic factors strongly modify effects of abiotic factors on where rare and sedentary species establish

Determination of soil electrical conductivity and moisture on different soil layers using electromagnetic techniques in irrigated arid environments in South Africa

DATA AVAILABILITY : Available on request.Precise adjustments of farm management activities, such as irrigation and soil treatment according to site-specific conditions, are crucial. With advances in smart agriculture and sensors, it is possible to reduce the cost of water and soil treatment inputs but still realize optimal yields and highprofit returns. However, achieving precise application requirements cannot be efficiently practiced with spatially disjointed information. This study assessed the potential of using an electromagnetic induction device (EM38-MK) to cover this gap. An EM38-MK was used to measure soil apparent electrical conductivity (ECa) as a covariate to determine soil salinity status and soil water content θ post irrigation at four depth layers (Hz: 0–0.25 m; Hz: 0–0.75 m; Vz: 0.50–1 m). The inverse distance weighting method was used to generate the spatial distribution thematic layers of electrical conductivity. The statistical measures showed an R2 = 0.87; r > 0.7 and p ≤ 0.05 on correlation of ECa and SWC. Based on the South African salinity class of soils, the area was not saline ECa < 200 mS/m. The EM38-MK can be used to estimate soil salinity and SWC variability using ECa as a proxy, allowing precise estimations with depths and in space. These findings provide key information that can aid in irrigation scheduling and soil management.The Agricultural Research Council-Natural Resources and Engineering, Department of Science and Innovation; National Research Foundation and the Water Research Commission of South Africahttps://www.mdpi.com/journal/waterGeography, Geoinformatics and Meteorolog

Delineating smallholder maize farms from Sentinel-1 coupled with Sentinel-2 data using machine learning

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.The Agricultural Research Council, the National Research Foundation and the University of Pretoria.https://www.mdpi.com/journal/sustainabilitydm2022Geography, Geoinformatics and Meteorolog

Mapping smallholder maize farms using multi-temporal Sentinel-1 data in support of the sustainable development goals

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.The Agricultural Research Council, University of Pretoria and National Research Foundation.http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog

Modeling the spatial distribution of soil nitrogen content at smallholder maize farms using machine learning regression and Sentinel-2 data

Nitrogen is one of the key nutrients that indicate soil quality and an important component for plant development. Accurate knowledge and management of soil nitrogen is crucial for food security in rural communities, especially for smallholder maize farms. However, less research has been done on generating digital soil nitrogen maps for these farmers. This study examines the utility of Sentinel-2 satellite data and environmental variables to map soil nitrogen at smallholder. maize farms. Three machine learning algorithms—random forest (RF), gradient boosting (GB), and extreme gradient boosting (XG) were investigated for this purpose. The findings indicate that the RF (R 2 = 0.90, RMSE = 0.0076%) model performs slightly better than the GB (R2 = 0.88, RMSE = 0.0083%) and XG (R2 = 0.89, RMSE = 0.0077%) models. Furthermore, the variable importance measure showed that the Sentinel-2 bands, particularly the red and red-edge bands, have a superior performance in comparison to the environmental variables and soil indices. The digital maps generated in this study show the high capability of Sentinel-2 satellite data to generate accurate nitrogen content maps with the application of machine learning. The developed framework can be implemented to map the spatial pattern of soil nitrogen. This will also contribute to soil fertility interventions and nitrogen fertilization management to improve food security in rural communities. This application contributes to Sustainable Development Goal number 2.The Agricultural Research Council, the National Research Foundation and the University of Pretoria.https://www.mdpi.com/journal/sustainabilityGeography, Geoinformatics and Meteorolog

Socio-economic benefits stemming from bush clearing and restoration projects conducted in the D’Nyala Nature Reserve and Shongoane Village, Lephalale, South Africa

This study aimed to investigate the socio-economic benefits stemming from bush clearing and restoration projects conducted in the Lephalale municipality, within the Limpopo Province of South Africa. The study was conducted at two sites: the D’Nyala Nature Reserve and a nearby local village, Shongoane. A qualitative thematic content analysis approach and semi-structured interviews were used to gather data from 14 purposively selected participants between the ages of 22 and 55 (male = 9 and female = 5). The results indicated that the nature reserve benefited from the project via the improved visibility of the landscape features and game viewing, which made the reserve more attractive for tourists and resulted in increased revenue. The costs of buying feed for game could also be curbed since the grazing capacity increased. Since the nature reserve sourced temporary labour from the local village to execute the project, the community benefited in terms of members being able to earn a wage, which led to an improvement in their livelihoods. Another indirect benefit was the morale and behavioural changes observed amongst community members. It was obvious that the socio-economic benefits derived from projects such as these far outweigh the negatives and that there is every reason to institute projects of a similar nature elsewhere.The Department of Environment, Forestry and Fisheries (DEFF), formerly known as the Department of Environmental Affairs (DEA), the department’s Natural Resource Management (NRM), Expanded Public Works (EPWP), Female Empowerment (FEM Power) programmes, the Agricultural Research Council, Pretoria, South Africa and North-West University, Potchefstroom, South Africa.http://www.mdpi.com/journal/sustainabilityam2021Geography, Geoinformatics and Meteorolog

A remote sensing-based approach to investigate changes in land use and land cover in the lower uMfolozi floodplain system, South Africa

The goal of this study was to understand land use and land cover (LULC) changes within the lower uMfolozi floodplain system, South Africa, and relate those changes to wetland loss. Changes in LULC were assessed using a geographic object-based image analysis (GEOBIA) algorithm to classify multi-date Landsat images into eight cover types over a period of 20 years, between 1997 and 2017. Post-classification accuracy assessment of all map-outputs was conducted by compiling confusion matrixes and calculating producer, user, and global accuracies and kappa coefficients (K) for each map-output. Levels of accuracy for all map-outputs were within acceptable limits, ranging between 79% and 88% (K = 0.76 and 0.86, respectively). Thereafter, paired t-tests were applied to determine whether the changes in LULC over the study period were significant. Results of this investigation showed a significant (p-value, < 0.01) conversion of wetland to cultivation, by 14%. This finding is important because it demonstrates that in this environment, human agency is one of the major drivers of a persistent decrease in the wetland ecosystem. The major insight from this observation is that there is an urgent need to formulate and implement objectively informed interventions to enhance the sustainability of the uMfolozi floodplain system and that of others elsewhere.https://www.tandfonline.com/loi/ttrs20hj2022Geography, Geoinformatics and Meteorolog

Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Monitoring wheat growth under different weather and ecological conditions is vital for a reliable supply of wheat yield estimations. Remote sensing techniques have been applied in the agricultural sector for monitoring crop biophysical properties and predicting crop yields. This study explored the application of Land Surface Temperature (LST)-vegetation index relationships for winter wheat in order to determine indices that are sensitive to changes in the wheat health status. The indices were derived from Landsat 8 scenes over the wheat growing area across Bloemfontein, South Africa. The vegetation abundance indices evaluated were the Normalised Difference Vegetation Index (NDVI) and the Green Normalised Difference Vegetation Index (GNDVI). The moisture indices evaluated were the Normalised Difference Water Index (NDWI) and the Normalised Difference Moisture Index (NDMI). The results demonstrated that LST exhibited an opposing trend with the vegetation abundance indices and an analogous trend with the moisture indices. Furthermore, NDVI proved to be a better index for winter wheat abundance as compared to the GNDVI. The NDWI proved to be a better index for determining water stress in winter wheat as compared to the NDMI. These results indicate that NDVI and NDWI are very sensitive to LST. These indices can be comprehensive indicators for winter wheat health status. These pilot results prove that LST-vegetation index relationships can be used for agricultural applications with a high level of accuracy.The Agricultural Research Councilhttp://www.sajg.org.za/index.php/sajgam2017Geography, Geoinformatics and Meteorolog

Characterizing leaf nutrients of wetland plants and agricultural crops with nonparametric approach using Sentinel-2 imagery data

In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops.Department of Higher Education, Science and Technology and Agricultural Research Council.http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog

Linking agricultural index insurance with factors that influence maize yield in rain-fed smallholder farming systems

Weather extremes pose substantial threats to food security in areas where the main source of livelihood is rain-fed crop production. In most of these areas, agricultural index insurance (AII) is recognized as being capable of securitizing food production by providing safety nets against weather-induced crop losses. Unfortunately, however, AII does not indemnify farmers for non weather-related crop losses. This study investigates how this gap can be filled by exploring strategies through which AII can be linked with non-weather factors that influence crop production. We do this by using an improvised variable ranking methodology to identify these factors in the O.R. Tambo District Municipality, South Africa. Results show that key agrometeorological variables comprising surface moisture content, growing degree-days, and precipitation influence maize yield even under optimal weather conditions, while seed variety, fertilizer application rate, soil pH, and ownership of machinery play an equally important role. This finding is important because it demonstrates that although AII focuses more on weather elements, there are non-weather variables that may expose farmers to production risk even under optimal weather conditions. As such, linking AII with critical non-weather, yield-determining factors can be a better risk management strategy.The Department of Science and Innovation through the Agricultural Research Council.https://www.mdpi.com/journal/sustainabilityGeography, Geoinformatics and Meteorolog