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Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9’s potential in LULC classifications. [ABSTRACT FROM AUTHOR]

Leaf Area Index (LAI) remains one of the most important forest structural attributes, as accurate estimation of LAI is crucial for predicting the growth of different species. Using the Partial Least Squares Regression (PLS-R) algorithm, this study investigated three image processing techniques to determine the best technique for estimating LAI using WorldView-3 imagery in the Midlands, KwaZulu-Natal province of South Africa. The PLS-R texture ratios model achieved the highest accuracy of R2 = 0.70, RMSE = 1.21 (2.32% of the mean measured LAI) and R2 = 0.72, RMSE = 1.26 (2.41% of the mean measured LAI) for the wet and dry seasons, respectively. This was followed by the PLS-R single texture band model that produced an accuracy of R2 = 0.65, RMSE = 1.35 (2.58% of the mean measured LAI) and R2 = 0.67, RMSE = 1.32 (2.52% of the mean measured LAI) for the wet and dry seasons, respectively. The PLS-R model using a combination of vegetation indices had the lowest estimation accuracy of R2 = 0.59, RMSE = 1.38 (2.64% of the mean measured LAI) and R2 = 0.60, RMSE = 1.40 (2.67% of the mean measured LAI) for the wet and dry seasons, respectively. The results of this study provided evidence that image texture ratios can be used to estimate LAI effectively. [ABSTRACT FROM AUTHOR]

More than 75% of the global land has already suffered degradation, leading to the recognition of land degradation as one of the foremost challenges society faces. This recognition stems from its profound adverse impacts on natural ecosystem functioning, biodiversity, soil productivity, and food availability. Consequently, understanding the spatial distribution of land degradation across all scales becomes imperative. This study employed land cover change and soil organic carbon (SOC) stock assessments to analyse land degradation within the eThekwini Municipality beyond the baseline period (2000–2015). Utilizing remote sensing and machine learning techniques, this research examined land degradation within the eThekwini Municipality over the period spanning 2000 to 2022. Landsat 7 (Enhanced Thematic Mapper Plus – ETM+), Landsat 8 (Operational Land Imager 1 - OLI1), and Landsat 9 (Operational Land Imager 2 - OLI2) images were employed to extract variables for both land cover change and SOC stock prediction through XGBoost, LightGBM, Random Forest (RF), and Support Vector Machine (SVM) models. Among these models, LightGBM demonstrates superior performance, achieving an overall accuracy of 80.646 in land cover predictions and 77.869 in SOC stock predictions. Analysis of land cover change within the eThekwini Municipality unveiled a shift from forests and shrubland landscapes to cropland and built-up areas. This shift results in the municipality encountering losses in SOC stock between 2015 and 2022. The model predicted that most SOC stock losses occur at the 20–50 cm depth (9.27%), in comparison to the 7.21% loss at the 0–20 cm depth. These findings underscore the pivotal role of remote sensing and machine learning in aiding policymakers to assess land degradation and implement pertinent measures to enhance the landscape. [ABSTRACT FROM AUTHOR]

The assessment of carbon storage in natural forests is paramount in the ongoing efforts against climate change. While traditional field-based methods for quantifying carbon storage pose challenges, recent advancements in remote sensing and machine learning offer efficient and innovative alternatives. This systematic literature review investigates the latest developments in utilising optical, radar, and light detection and ranging (LiDAR) remote sensing data, coupled with cutting-edge machine learning algorithms, to estimate carbon storage in natural forests. Non-parametric machine-learning algorithms commonly applied to multispectral datasets have emerged as prominent tools for predicting aboveground carbon storage. Nonetheless, accurately assessing forest carbon storage using remote sensing data can be arduous in regions characterised by complex terrain and diverse species where dataset noise may be pronounced. Alternatively, the adoption of freely available optical sensors with moderate resolution has showcased reliability in estimating forest carbon storage. Hence, leveraging the integration of multi-sensor data with machine learning techniques has yielded substantial improvements in the accuracy of carbon storage estimation. This study identifies the most sensitive remote sensing variables that correlate with measurable biophysical parameters, thus highlighting the pivotal role of geospatial technologies in estimating terrestrial aboveground carbon storage. The study also delineates gaps and limitations inherent in current practices, underscoring the need for further investigations in this rapidly evolving field. Through the unification of conventional methods with state-of-the-art technologies, this study contributes to the advancement of accurate and efficient carbon storage assessments. By assuming such a transformative role, this research holds substantial promise in bolstering global climate change mitigation efforts. Ultimately, the purpose of this study was to demonstrate to researchers, policy makers and practitioners the importance of embracing the combined power of remote sensing and machine learning as a tool for safeguarding our natural forests and fight against climate change. [ABSTRACT FROM AUTHOR]

Commercial forest plantations in South Africa are homogeneous monocultures of highly bred exotic species grown to deliver timber products of the best potential quality. As such, these stands are susceptible to adverse effects of biotic and abiotic factors, and therefore require intense management to mitigate these risks. A sustainable forest monitoring system that can detect real-time changes in the physiological state of these plantations is needed for timeous management intervention to reduce losses. The use of machine learning algorithms has recently become popular, with acceptable levels of success. This study explores the application of deep learning neural networks for early detection of damage caused by baboons in evergreen plantations of Pinus species. Using PlanetScope imagery (spectral band 590–860 nm), which is captured by a constellation of Dove nanosatellites, with a high temporal resolution available daily at 3 m spatial resolution, the study achieved an overall accuracy of 81.54%, with a kappa value of 0.69, using a deep neural network. In comparison, using a random-forest classifier produced 74.04% accuracy and a kappa value of 0.62. The study successfully mapped different levels of baboon damage within commercial pine forests. We provide a repeatable method for daily monitoring initiatives, and attest to the utility of higher-resolution imagery such as PlanetScope for mapping health and damage severity at the tree level. [ABSTRACT FROM AUTHOR]

Commercial forest species discrimination is valuable for optimal management of commercial forests. Therefore, second-order image texture combinations computed from a 0.5 m WorldView-2 pan-sharpened image integrated with sparse partial least squares discriminant analysis (SPLS-DA) and partial least squares discriminant analysis (PLS-DA) were used to discriminate commercial forest species. The findings show that the SPLS-DA model, which is characterised by concurrent variable selection and reduction of data dimensionality, produced an overall classification accuracy of 86%, with an allocation disagreement of 9 and a quantity disagreement of 5. Conversely, the PLS-DA model with variable importance in projection (VIP) produced an overall classification accuracy of 81%, with an allocation disagreement of 12 and a quantity disagreement of 7. Overall, this study demonstrates the value of second-order image texture combinations in discriminating commercial forest species and presents an opportunity for improved commercial forest species delineation. [ABSTRACT FROM AUTHOR]

Drought has become a more frequent phenomenon under changing climatic conditions, particularly in Sub Saharan Africa. This study tested the utility of a newly proposed Temperature-Vegetation Water Stress Index (T-VWSI) in detecting drought severity using Landsat data for the years 2008, 2012, 2016 and 2018. This index was created using both NDVI and LST to detect drought severity within the region. The results show that the year 2016 experienced the most severe levels of drought, with the northern areas of the uMsinga region being most severely affected. SPI was used to corroborate the findings of the T-VWSI index and also established that the year 2016 was the year of severe drought in uMsinga. The results of this study have illustrated the potential of the T-VWSI index in effectively mapping and detecting drought over large spatial areas. [ABSTRACT FROM AUTHOR]

The detection and monitoring of lethal pathogenic funguses are important for effectively deploying suppressive measures to sudden and severe outbreaks in plantation forestry. This study successfully investigated the utility of Landsat 8 multispectral satellite imagery to map Uromycladium acacia (wattle rust) induced canopy defoliation across black wattle plantations. The framework developed for the provincial assessment of rust damage proved to be effective by using data collected from field monitoring plots over the year 2015 and 2016. Using a powerful Gradient Boosting Machine (GBM) approach, rust occurrences were mapped at accuracies of 69% for March and 72% for November when using the 2015 dataset and 77% for March and 81% for November using the 2016 dataset. Individual class accuracies for varying levels of defoliation were also evaluated. When aggregating the field and image datasets, a two-year probability map revealed the likelihood of rust defoliation across the black wattle plantation region. Overall, the study showcased the robustness and cost-effectiveness of using multispectral remote sensing methodologies for repeatable forest health monitoring in key commercial forest plantations. [ABSTRACT FROM AUTHOR]

Fisheries in Sub-Saharan Africa are vulnerable to the effects of climate change, which include changes in mean temperature, increasing rainfall variability and the occurrence of extreme weather events. This article reviews evidence on the magnitude of climate change and the nature of its impacts on both inland and marine fisheries. In addition, the review also focuses on the impacts of climate change on fishery-dependent communities and finally highlights climate change adaptation strategies adopted by fishers. A systematic approach was employed to search and select relevant published literature used in this review paper. The reviewed literature indicated the challenge of declining fish catches in several fishing communities, which was associated with climate change and variability. Changes in mean temperature, rainfall quantities and patterns, sea-level changes, water salinity, and lake levels affect fish productivity. Fishers in sub-Saharan Africa adopt different strategies to sustain their livelihoods and food security, which are being threatened by climate change. Adaptation strategies employed by fishers to cope with the impacts of climate change include, changing fishing gear, migration, targeting new species, and increasing fishing grounds and time spent fishing. To promote socio-ecological adaptation and fisheries sustainability, it is crucial to understand the impacts of climate change at a local level, which would inform policy and fisheries management practices. [ABSTRACT FROM AUTHOR]

Drought frequency is expected to increase in the coming decades due to climate change. Droughts are one of the most devastating natural disasters affecting food production, water resources and causing widespread human and animal mortalities. Sub-Saharan Africa is considered the most vulnerable region to climate change, due to low rainfall, long dry seasons and high levels of poverty. A systematic approach was used to search for published literature from 2008 to 2018 on the impacts of droughts on small-scale farmers in Sub-Saharan Africa from Ebscohost, Google Scholar and Web of Science internet search engines. To acquire relevant studies, the following keywords and Boolean operation combinations were used: GIS AND Remote Sensing OR drought OR small-scale farmers OR sub-Saharan Africa and only studies from 2008 to 2018 were selected. The impacts of droughts are far-reaching and affect the environment, societies and the economy of a country. There is limited reliable and comprehensive information on the impacts of droughts, which calls for research aimed at explaining the impacts of effect and how they can be predicted and mitigated. Improving drought preparedness and mitigation is an important precondition reducing the vulnerability of small-scale farmers and rural communities to the impacts of droughts. [ABSTRACT FROM AUTHOR]

Accurate spatial information on the location of invasive alien plants (IAPs) in riparian environments is critical to fulfilling a comprehensive weed management regime. This study aimed to automatically map the occurrence of riparian bugweed (Solanum mauritianum) using airborne AISA Eagle hyperspectral data (393 nm–994 nm) in conjunction with LiDAR derived height. Utilising an unsupervised random forest (RF) classification approach and Anselin local Moran's I clustering, results indicate that the integration of LiDAR with minimum noise fraction (MNF) produce the best detection rate (DR) of 88%, the lowest false positive rate (FPR) of 7.14% and an overall mapping accuracy of 83% for riparian bugweed. In comparison, utilising the original hyperspectral wavebands with and without LiDAR produced lower DRs and higher FPRs with overall accuracies of 79% and 68% respectively. This research demonstrates the potential of combining spectral information with LiDAR to accurately map IAPs using an automated unsupervised RF anomaly detection framework. [ABSTRACT FROM AUTHOR]

is a beetle causing severe defoliation to South Africa's eucalyptus plantations. This defoliation induced by the beetle inhibits the eucalypts ability to photosynthesize, by affecting its chlorophyll content. Therefore, this study integrates spatially optimized and the single 0.5 m resolution vegetation indices with sparse partial least squares regression (SPLS-R) and partial least squares regression (PLS-R) to detect and map leaf chlorophyll content of defoliated eucalyptus plantations. The optimized vegetation indices were spatially resampled to resolutions that best paralleled varying levels of G. scutellatus defoliation. From the results, the 0.5 m resolution SPLS-R model (R2 = 0.76; RMSE of 1.50 (2.88% of the mean measured chlorophyll)) outcompeted the 0.5 m resolution PLS-R (R2 = 0.73; RMSE of 1.54 (2.95% of the mean measured chlorophyll)) model. Furthermore, the spatially optimized SPLS-R (R2 = 0.81; RMSE of 1.44 (2.76% of the mean measured chlorophyll) model was more superior in detecting and mapping chlorophyll content of defoliated eucalyptus plantations when compared to the 0.5 m resolution SPLS-R model. The most significant variables selected by the optimized SPLS-R model were DMI, ARI, NDRE, GNDVI, and NDVI. In essence, this study has illustrated the significance of the spatial resolution in effectively detecting and mapping chlorophyll content of defoliated eucalyptus plantations. [ABSTRACT FROM AUTHOR]

Gonipterus scutellatusoutbreaks may severely defoliateEucalyptusplantations growing in South Africa. Therefore, detecting and mapping the severity and extent ofG. scutellatusdefoliation is essential for the deployment of suppressive measures. In this study, we tested the utility of spatially optimized vegetation indices and an artificial neural network in detecting and mappingG. scutellatus-induced vegetation defoliation, using both visual estimates of percentage defoliation and optical leaf area index (LAI) measures. We tested both field methods to determine which of the two were more superior in detecting vegetation defoliation using optimized vegetation indices. These indices were computed from a WorldView-2 pan-sharpened image, which is characterized with a 0.5-m spatial resolution and eight spectral bands. The indices were resampled to spatial resolutions that best represented levels ofG. scutellatus-induced defoliation. The results showed that levels of defoliation, using visual percentage estimates, were detected with anR2of 0.83 and an RMSE of 1.55 (2.97% of the mean measured defoliation), based on an independent test data-set. Similarly, LAI subjected to defoliation was detected with anR2of 0.80 and an RMSE of 0.03 (0.06% of the mean measured LAI), based on an independent test data-set. Therefore, the results indicate that the cheaper less-complicated visual percentage estimates of defoliation was the more superior model of the two. A sensitivity analysis revealed that NDRE, MCARI2 and ARI ranked as the top three most influential indices in developing both percentage defoliation and LAI models. Furthermore, we compared the optimized model with a model developed using the original image spatial resolution. The results indicated that the optimized model performed better than the original 0.5-m spatial resolution model. Overall, the study showed that vegetation indices optimized to specific spatial resolutions can effectively detect and map levels ofG. scutellatus-induced defoliation and LAI subjected to defoliation. [ABSTRACT FROM AUTHOR]

Bracken fern is one of the major invasive plants distributed all over the world currently threatening socio-economic and ecological systems due to its ability to swiftly colonize landscapes. The study aimed at reviewing the progress and challenges in detecting and mapping of bracken fern weeds using different remote sensing techniques. Evidence from literature have revealed that traditional methods such as field surveys and modelling have been insufficient in detecting and mapping the spatial distribution of bracken fern at a regional scale. The applications of medium spatial resolution sensors have been constrained by their limited spatial, spectral and radiometric capabilities in detecting and mapping bracken fern. On the other hand, the availability of most of these data-sets free of charge, large swath width and their high temporal resolution have significantly improved remote sensing of bracken fern. The use of commercial satellite data with high resolution have also proven useful in providing fine spectral and spatial resolution capabilities that are primarily essential to offer precise and reliable data on the spatial distribution of invasive species. However, the application of these data-sets is largely restricted to smaller areas, due to high costs and huge data volumes. Studies on bracken fern classification have extensively adopted traditional classification methods such as supervised maximum likelihood classifier. In studies where traditional methods performed poorly, the combination of soft classifiers such as super resolution analysis and traditional methods of classification have shown an improvement in bracken fern classification. Finally, since high spatial resolution sensors are expensive to acquire and have small swath width, the current study recommends that future research can also consider investigating the utility of the freely available recently launched sensors with a global footprint that has the potential to provide invaluable information for repeated measurement of invasive species over time and space. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1. [ABSTRACT FROM AUTHOR]

SPOT-5 multispectral and panchromatic image data were used to compute texture measures to estimate the road edge effect on adjacent Eucalyptus grandis forests. Employing a stepwise selection algorithm enabled the selection of optimal texture measures that were input into a backpropagation artificial neural network. The R2 of best models ranged from 0.67 to 0.89 for DBH, TH, BA, Volume and LAI on an independent test data set, with a root mean square error (RMSE) range of 0.01–5.36% for the respective variables. The result is critical for understanding and spatially predicting the road edge effect on adjacent vegetation. [ABSTRACT FROM AUTHOR]

Urban reforestation mitigates climate change by sequestering carbon, but quantifying carbon gains requires accurate aboveground biomass estimation. This study estimated carbon sequestration in a reforested urban landscape using PlanetScope, Sentinel-1A, Sentinel-2A, SRTM data, and field measurements. Non-parametric machine learning algorithms (k-nearest neighbor, support vector machines, extreme gradient boosting, random forests) with 39 predictor features generated aboveground biomass density maps. The extreme gradient boosting model performed best, predicting 4.1–286.5t ha-1 aboveground biomass, demonstrating its effectiveness for modeling reforested biomass with multi-source data. Findings highlight extreme gradient boosting’s promise for urban biomass estimation, the importance of multi-source data, and machine learning’s potential in addressing environmental challenges like climate change. [ABSTRACT FROM AUTHOR]