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Informative and scalable cartography plays a pivotal role in curbing urban pollution, waste management, and mitigating environmental damage in the development of informal settlements. The contemporary capabilities of cloud computing facilitate streamlined access to comprehensive data repositories, computational infrastructure, and proficient tools that have rapidly advanced the execution of sprawl mapping procedures. This study tests the performance of four machine-learning algorithms, namely: Gradient Boost, K Nearest Neighbor [KNN], Random Forest [RF], and Support Vector Machine [SVM] with data extracted from cloud computing repositories for delineating informal settlements in Gcuwa, Eastern Cape, South Africa, using low-cost datasets. A systematic approach comprising iterative phases, encompassing data acquisition, the development of a training dataset, modeling, and evaluation was employed. The delineation process involved the extraction of both spectral and textural features from Sentinel-2 imagery. The Random Forest algorithm emerged as the top performer, exhibiting the highest levels of accuracy and F1 score, followed by the gradient boosting, support vector machine, and then the K-nearest neighbor algorithms. Consequently, this innovative use of machine learning algorithms with low-cost datasets and the scalable, resilient approach for detecting informal settlements offers a promising avenue for enhancing urban planning and addressing sustainable development challenges. [ABSTRACT FROM AUTHOR]

Bush encroachment threatens rangelands' biodiversity and productivity, impacting savannah ecosystems based on location, management practices, and factors like erratic rainfall, climate change, and environmental variability. Considering these challenges, this study therefore seeks to evaluate bush encroachment changes over-time (1992-2022) in the Southern part of Kruger National Park and surrounding communities of South Africa. The study estimated the proportion and extent of encroacher plants in relation to native bush species. To achieve this objective, bioclimatic variables, and a digital elevation model in conjunction with the Random Forest model were utilized. Classified satellite imageries achieved an overall accuracy of 72 and 93%, respectively. A gradual increase in bush encroachment was observed from 41,947 hectares (ha) (3.4%) in 1992 to 61,225 ha (10%) in 2022. Additionally, this study observed a decline in the spatial extent of native plant species by 178,163.4 ha, while invasive species have expanded by 44,022.17 ha from 1992 to 2022 wet season. There is a significant increase in the invasive woody species across the years; because the p-value (p<0.000) is <5% (p<0.05), the significance level is <5%. The findings of this study offer a significant understanding of the historical patterns of bush encroachment over the past three decades. This information serves as a baseline for formulating strategic and sustainable management approaches to combat bush encroachment and promote effective rangeland preservation in predominantly savannah ecosystems. [ABSTRACT FROM AUTHOR]

In-air sounds of pinnipeds are important for interactions with conspecifics and threat displays. However, little is known about the in-air acoustic repertoire and associated behaviour of crabeater seals Lobodon carcinophaga. We investigated the in-air acoustic repertoire and associated behaviour of two male and one female wild juvenile crabeater seals that beached separately, and were rehabilitated in Durban and Gqeberha, South Africa. In-air sounds were visually identified and categorised into five classes validated via random forest model classification: brief, intermediate, and long moan calls, croaks, and hisses. Hisses were common (n = 25,105 sounds from 136 hrs of acoustic recordings) and detected during heightened arousal states and interactions with animal care staff and a conspecific. Furthermore, hisses were also recorded in low arousal states during exhalation. Moan calls (n = 241) were only detected from two of the three seals. During rehabilitation, the female seal ceased producing moan calls and later produced a series of croaks (n = 204). Acoustic characteristics of in-air moan calls differed from published underwater moan calls according to duration, minimum, and maximum frequencies, and pulse repetition rate. Our study shows that the in-air acoustic repertoire and associated behaviour of these Antarctic seals are dynamic, vary inter-individually and are context dependent. [ABSTRACT FROM AUTHOR]

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 m2.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. [ABSTRACT FROM AUTHOR]

Population growth and environmental shifts have elevated the pressure on land use and cover (LULC), necessitating vital management and adaptive strategies to preserve the balance between ecosystem services and human well-being in watersheds. It's pivotal to understand the implications of human-induced shifts from natural to human-dominated surroundings This study utilized Google Earth Engine (GEE) to analyze 31 years of LULC changes in Letaba watershed. Using GEE's random forest, LULC classes were mapped with 93% to 99% accuracy across four timeframes (1990, 2000, 2010, and 2021). Trends revealed declining water bodies (-4%), bare surfaces (-2%), natural forest (-3%), and grassland (-3%), while shrublands, plantations, and built-up areas increased at annual rates of 41%, 24%, and 47% respectively. This transformation reflects population-driven shifts, necessitating adaptive strategies. Given the importance of plantations for income, embracing climate-smart agriculture could ensure long-term food and environmental security, thus addressing the evolving dynamics in Letaba watershed. [ABSTRACT FROM AUTHOR]

Hyperspectral sensors have the potential of discerning subtle differences among wetland plant species. Assessment of wetland plant species is critical for the effective management of wetland ecosystems. We evaluated the utility of the forthcoming nSight-2 hyperspectral sensor in wetland plant species differentiation, by testing the utility of its spectral settings and compared its performance to EnMap and WorldView-2 sensors in classifying four dominant wetland plant species in Verloren Vallei Nature Reserve, South Africa. For this purpose, we used the Random Forest classifier and field spectrometer data of various wetland plant species. The results showed that the spectral settings of nSight-2, EnMap, and WorldView-2 yielded high overall accuracies of 84.09%, 81.82% and 77.77%, respectively. The best accuracies were achieved with spectral bands sampled across the visible, red-edge and near-infrared spectral regions. Overall, the findings demonstrate the potential of the upcoming hyperspectral satellite missions for wetland ecosystems monitoring and management. [ABSTRACT FROM AUTHOR]

This study explored the use of multi-source remotely sensed data in monitoring the spatial distribution of pools and pool dynamics in two distinct semi-arid sites in South Africa. The factors that control the pool dynamics were also examined. Three water extraction indices were used, these included Normalised Difference Water Index (NDWI), Modified NDWI and Normalised Difference Vegetation Index. In addition, random forest classifier and Sentinel-1 SAR data were used in mapping pools and pools dynamics for both sites. Overall, the remotely-sensed methods detected and mapped pools with acceptable accuracy, except for small pools (<400 m²). The results suggest that flow occurrences and rainfall are key in controlling temporal changes in pools sizes, and there was no interaction between pools and groundwater. The study showed that remote sensing methods are essential for filling ground monitoring gaps in non-perennial rivers and determining hydrological processes and water availability from pools in semi-arid environments. [ABSTRACT FROM AUTHOR]

Fire is a key regulator of tree cover in grassy ecosystems, but century-long changes in fire regimes have not been explicitly quantified in South Africa. This study aimed to determine changes in the fire regimes of South Africa's grassy biomes over the past century in response to widespread human-induced changes. Using spatially explicit data, we employed random forest models to examine the importance of climatic and human drivers on the extent, frequency and intensity of fire. We then modelled how fire regimes are likely to have changed over the past century using historical trends in key human drivers (population growth, livestock density, road density and land cover change) as inputs. Results showed that human drivers, particularly land transformation and human population density, were important determinants of fire regime. Random forest predictions showed a decline in burnt area (mean of 10.5% in 1910 to 5.1% in 2020), and a shift to fire regimes with smaller burnt areas, longer fire return intervals and lower intensity fires over the past century, particularly in the mesic east. These dynamics have important implications for our understanding of the widespread increases in woody cover observed in the region over this period. [ABSTRACT FROM AUTHOR]

Crop diseases monitoring is critical in understanding the effects of diseases on crop production and associated implications on food security. The aim of this study was to assess the utility of the 10 m resolution Sentinel 2 data set, in detecting and mapping Maize Streak Virus (MSV) disease in Ofcolaco farms in Tzaneen, South Africa. Specifically, the study sought to spectrally discriminate and map maize infected with MSV from other land-cover classes. To achieve this objective two analysis approaches were used: spectral analysis (Test I: spectral bands; Test II: spectral bands + spectral vegetation indices) using random forest algorithm in a supervised classification approach. The indices combined with spectral bands were EVI, SAVI, NDVI, GNDVI, GLI and MSAVI. Results indicated that infected maize was highly separable from health maize and other land cover classes (TDSI > 1.8). The mapping accuracy was high using spectral data (Overall accuracy = 85.29% and Kappa = 0.79) and even higher when spectral bands were combined with derived vegetation indices (Overall accuracy = 89.43% and Kappa = 0.84). The results of the study show that the 10 m resolution multispectral Sentinel 2 data set can be used to detect and map maize infected by MSV. The findings are important in showing the value of combining 10 m spectral data with derived indices from Sentinel 2 in improving monitoring of maize steak virus in resource-constrained nations. [ABSTRACT FROM AUTHOR]