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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]

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]

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]

Solanum mauritianum is a highly destructive and resourceful plant invader, resulting in severe economic and ecological damage. Detecting and mapping the spatial distribution of S. mauritianum is a priority for effective management of commercial forest plantations. Therefore, image texture computed from a 2 m WorldView-2 image with sparse partial least squares discriminant analysis (SPLS-DA) and partial least squares discriminant analysis (PLS-DA) were developed and applied to detect and map co-occurring S. mauritianum within a commercial forest plantation. The results indicated that SPLS-DA successfully performed simultaneous variable selection and dimension reduction to yield an overall classification accuracy of 77%. In contrast, the PLS-DA model in conjunction with variable importance in the projection (VIP) yielded an overall classification accuracy of 67%. The most significant texture parameters selected by the SPLS-DA model were correlation, homogeneity and second moment, which were predominantly computed from the red, red edge and NIR bands. Overall, this study validates the potential of image texture integrated with SPLS-DA to effectively detect and map co-occurring S. mauritianum in a commercial forest plantation. [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]

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]

Gonipterus scutellatus Gyllenhal is a leaf feeding weevil that is a major defoliator of the genus Eucalyptus . Understanding the relationship between levels of weevil induced vegetation defoliation and the optimal spatial resolution of satellite images is essential for effective management of plantation resources. The objective of this study was to identify appropriate spatial resolutions for predicting levels of weevil induced defoliation. We resampled the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Enhanced Vegetation Index (EVI) images computed from a WorldView-2 pan-sharpened image, which is characterised with a 0.5 m spatial resolution and 8 spectral bands. Within each plantation compartment 30 × 30 m plots were established, representing different levels of defoliation. From the centre of each plot, the spatial resolution of the original image was progressively resampled from 1.5 to 8.5 m, with 1 m increments. The minimal variance for each level of defoliation was then established and used as an indicator for quantitatively selecting the optimal spatial resolution. Results indicate that an appropriate spatial resolution was established at 1.25, 1.25, 1.75 and 2.25 m for low, medium, high and severe levels of defoliation, respectively. In addition, an Artificial Neural Network was run to determine the relationship between the appropriate spatial resolution and levels of Gonipterus scutellatus induced defoliation. The model yielded an R 2 of 0.80, with an RMSE of 1.28 (2.45% of the mean measured defoliation) based on an independent test dataset. We then compared this model to a model developed using the original 0.5 m image spatial resolution. Our results suggest that optimising the spatial resolution of remotely sensed imagery essentially improves the prediction of vegetation defoliation. In essence, this study provides the foundation for multi-scale defoliation mapping using high spatial resolution imagery. [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]

Sub-Saharan Africa's freshwater fisheries contribute significantly to the livelihoods and food security of millions of people within the region. However, freshwater fisheries are experiencing multiple anthropogenic stressors such as overfishing, illegal fishing, pollution, and climate change. There is a substantial body of literature on the effects of climate change on freshwater fisheries in Sub-Saharan Africa. This study reviews the existing literature and highlights the effects of climate change on freshwater fisheries, the adaptation strategies of fishery-dependent households in response to the effects, and fisheries' management and mitigation efforts in the face of climate change. The general effects of climate change on freshwater environments include warming water temperatures, increased stratification, modified hydrological processes, and increased pollutants. These effects adversely affect the physiological processes of fish and the overall wellbeing of fishery-dependent people. To cope with the effects of fluctuating fishery resources due to climate change, fishery-dependent people have adopted several adaptation strategies including livelihood diversification, changing their fishing gear, increasing their fishing efforts, and targeting new species. Several management attempts have been made to enhance the sustainability of fishery resources, from local to regional levels. This study recommends the participation of the resource users in the formulation of policies aimed at promoting climate change adaptation and the resilience of freshwater fisheries for sustainable development. [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]

Drought is a major challenge threatening agricultural productivity in uMsinga. The occurrence of drought is expected to increase in coming decades, intensifying in severity, duration and the way people are affected by drought. The objective of this study is to understand small-scale farmers' and rural communities' perceptions of drought, its environmental and socio-economic impacts, adaptive and mitigation measures at household level and their satisfaction with the government's role in drought management in the community. The study utilized a combination of quantitative and qualitative research methods, in the form of questionnaires, focus groups and key informant interviews. The sample size for the research study was 180 respondents for the questionnaire component and a total of 30 respondents for the focus groups and key informant interviews. The results show that increased levels of poverty, food insecurity and increased migration were the main socio-economic impacts perceived by respondents. Water scarcity, crop failure, forest degradation and an increase in average temperatures were perceived by respondents as the main environmental impacts caused by drought in uMsinga. Respondents perceived drought as a serious threat to agricultural production and adopted various indigenous adaptive strategies. A majority of respondents adopted a reactive approach to drought management, and therefore did not adopt many mitigation measures. [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]

Highlights • A novel three-band texture ratio technique to map forest AGB variation. • Based on an image texture data computed from SPOT-6 pan-sharpened image. • Accurately mapped forest AGB at various successional stages. • Maps indicate increased levels of forest AGB over a long-term period. Abstract Developing models for estimating aboveground biomass (AGB) in naturally growing forests is critical for climate change modelling. AGB models developed using satellite imagery varies with study area, depending on the complexity of vegetation and landscape structure, which affects the upwelling radiance. We assessed the potential of SPOT-6 imagery in predicting AGB of trees planted at different time periods, using image texture combinations. Image texture variables were computed from the SPOT6 pan-sharpened image data, which is characterised by a 1.5 m spatial resolution. In addition, we incorporated the minimal variance technique to select the optimum window sizes that best captures AGB variation in our study area. The results showed that image texture was able to detect AGB for both mature and young trees, however, models detecting mature trees were more superior, with accuracies of R 2 = 0.70 and 0.25 for 2009–2011 and 2011–2013 plantation phases, respectively. In addition, our results showed that the three band texture ratios yielded the highest accuracy (R 2 = 0.88 and RMSE = 54.54 kg m−2) compared to two texture (R 2 = 0.85 and RMSE = 60.65 kg m−2) and single texture band combinations (R 2 = 0.64 and RMSE = 94.13 kg m−2). A frequency analysis was also run to determine which bands appeared more frequently in the selected texture band models. The frequency analysis revealed that both the red and green bands appeared more frequently on the selected texture band variables, indicating that they were more sensitive to the variation of AGB in our study area. The results showed high variation in AGB within the Buffelsdraai reforestation site, especially due to varying tree plantation phases as well as topography. In essence, the study demonstrated the possibility of image texture combinations computed from the SPOT-6 image in estimating AGB. [ABSTRACT FROM AUTHOR]

The invasive weed Solanum mauritianum (bugweed) has infested large areas of plantation forests in KwaZulu-Natal, South Africa. Bugweed often forms dense infestations and rapidly capitalises on available natural resources hindering the production of forest resources. Precise assessment of bugweed canopy cover, especially at low abundance cover, is essential to an effective weed management strategy. In this study, the utility of AISA Eagle airborne hyperspectral data (393–994 nm) with the new generation Worldview-2 multispectral sensor (427–908 nm) was compared to detect the abundance of bugweed cover within the Hodgsons Sappi forest plantation. Using sparse partial least squares discriminant analysis (SPLS-DA), the best detection results were obtained when performing discrimination using the remotely sensing images combined with LiDAR. Overall classification accuracies subsequently improved by 10% and 11.67% for AISA and Worldview-2 respectively, with improved detection accuracies for bugweed cover densities as low as 5%. The incorporation of LiDAR worked well within the SPLS-DA framework for detecting the abundance of bugweed cover using remotely sensed data. In addition, the algorithm performed simultaneous dimension reduction and variable selection successfully whereby wavelengths in the visible (393–670 nm) and red-edge regions (725–734 nm) of the spectrum were the most effective. [ABSTRACT FROM AUTHOR]

The accurate detection and mapping of plant invasions is important for an effective weed management strategy in forest plantations. In this study, the utility of WorldView-2 was investigated to automatically map the occurrence of Solanum mauritianum (bugweed) found as an anomaly in forest margins, open areas and riparian zones. The unsupervised methodology developed, proved to be an effective and an accurate framework in detecting and mapping the invasive alien plant (IAP). Using the random forest (RF) proximity matrix, similarity measures between pixels were successfully transformed into scores (Eigen weights) for each pixel using eigenvector analysis. Neighbourhood windows with minimum variance revealed the most important information from localized surrounding pixels to detect potential anomalous pixels. Bugweed occurrence in forest margins, open areas and riparian zones were successfully mapped at accuracies of 91.33%, 85.08%, and 67.90%, respectively. This research has demonstrated the unique capability of using an automated unsupervised RF approach for mapping IAPs using new generation multispectral remotely sensed data. [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]

Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control. [ABSTRACT FROM AUTHOR]