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Non-perennial rivers (NPRs) have three hydrological states; each state has its importance, function and implication for water resource management. The dynamics of these states have been inadequately assessed and understood. Hence, this study sought to determine the spatiotemporal variations in the hydrological conditions of NPRs, focusing on the Touws River--Karoo drylands and Molototsi River within the semi-arid region of the Limpopo Province of South Africa. Additionally, the study aimed to delineate and characterize the primary areas contributing to runoff in these two river systems. Sentinel-1 and Sentinel-2 satellite data sources were employed in this study. Specifically, the modified normalized difference water index (MNDWI) derived from Sentinel-2 was utilized to delineate water surface areas along the two rivers. Subsequently, these derived datasets were utilized to assess the hydrological states over a 32-month period (2019-2022). Based on the presence of water, the river's state was classified as flowing, pooled, or dry. The results showed that remote sensing can be used to determine the hydrological state of the two river systems with ~90% overall accuracy. However, there is about a 30% chance that a flow event can be missed using Sentinel-2 due to clouds and temporal resolution. Some of these gaps can be filled using synthetic aperture radar (SAR) data (Sentinel-1), as demonstrated with the Molototsi River. In the Molototsi catchment, the upper catchment contributes the majority of flows. For the Touws River, the southwestern part of the catchment was determined as the major contributing area for the observed flows. This suggests that the chosen observation site might not be representative of upper catchment dynamics; therefore, a monitoring site in the upper catchment is required. This study provided hydrological information and an approach that can be used to monitor the hydrological states for better understanding and management of NPRs and catchments. [ABSTRACT FROM AUTHOR]

[Display omitted] Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs. [ABSTRACT FROM AUTHOR]

Groundwater dependent vegetation (GDV) is increasingly threatened by the transformation of the natural environment. An understanding of the nature of GDV at the appropriate scale helps environmental managers make suitable decisions. This study assesses the potential for mapping the distribution of GDV within the Heuningnes Catchment using multispectral remotely sensed data (i.e., Landsat 8 (L8) and Sentinel 2 (S2)), the derived vegetation indices (Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI)) and in-situ data. The GDV distribution maps were produced by integrating vegetation productivity, landcover, and topographic layers as GDV indicators. The findings of the study revealed that the spectral indices had a significant influence on the sensor's GDV classification performance. Specifically, the S2-derived SAVI mapped the GDV areas with the highest overall accuracy (97%), followed by the S2-derived NDVI, with an accuracy of 95%. Comparatively, the L8(NDVI) GDV map was achieved with an overall accuracy of 92% and the L8(SAVI) map had an overall accuracy of 96%. The estimated coverage of potential GDV within the catchment ranges between 2.34 and 2.60%. This work demonstrated the capabilities of a combined remote sensing and GIS methodological framework, which can improve our knowledge on GDV. [ABSTRACT FROM AUTHOR]

Our research provides a detailed overview of the progress in remotely sensed fire monitoring techniques, which have been developed and employed for fire occurrence and detection. Our overview is provided from a literature search of English‐peer reviewed articles, conference proceedings and scientific book sections published between the periods of 1980 and 2019. Literature reveals that historically, fire detection through remotely sensed techniques has mainly occurred through ground‐based, airborne and satellite systems. Mathematical models, such as decision tree models, Species Distribution Models, Dynamic Global Vegetation Models and Global Climate Models, have also been employed alongside satellite systems to facilitate a greater understanding of fire dynamics and its susceptibility to changes in ecological and climatic variables. Fire frequency and severity are known to be influenced by atmospheric conditions, fuel load and ignitions. However, the literature suggests that targeting inappropriate wildfires with these techniques may still result in wildfires outside of the natural fire regime. Most studies regarding fire occurrence and/or monitoring focus on satellite‐based techniques as they provide the greatest coverage of wildfires at varying spatial and temporal resolutions depending on the sensor used. Satellite systems are advantageous for fire monitoring as they provide extensive coverage inexpensively. Finally, fire occurrence is explicitly influenced by moisture‐limited climatic conditions and/or fuel load in the form of leaf‐litter or water‐stressed plants. [ABSTRACT FROM AUTHOR]

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity. [ABSTRACT FROM AUTHOR]

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [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]

Identifying and delineating groundwater-dependent ecosystems (GDEs) is critical in understanding their location, distribution and groundwater allocation. However, this information is inadequately understood due to limited available data for most areas where they occur. Thus, this study aims to address this gap using remotely sensed, analytical hierarchy process (AHP) and in situ data to identify and delineate GDEs in the Khakea–Bray transboundary aquifer region. The study tested various spatial-explicit GDE indices that integrates environmental factors that predict occurrence of GDEs. These include the normalized difference vegetation index as a proxy for vegetation productivity and modified normalized difference water index as proxy for moisture availability, land-use and landcover, topographical factors such as slope, topographic wetness index, flow accumulation and curvature. The GDEs were delineated using the weighted overlay tool in a Geographic Information System (GIS) environment. The thematic output layer was then spatially classified into two classes, namely, GDEs and non-GDEs. The results showed that only 1.34% of the area is characterised by GDEs covering 721,908 ha. Overall, identified GDEs were found mostly on a gentle slope on the large portion of shrubland and grassland. The derived GDEs map was then statistically compared with groundwater level (GWL) data from 22 boreholes that occur in the area. Our results indicated that: GDEs are concentrated at the northern, central and south-western part of the study area. The validation results showed significant overlapping of GDEs classes with both the groundwater level (GWL) and rainfall in the study area. The results show a possible delineation of GDEs in the study area using remote sensing and GIS techniques along with AHP and is transferable to other arid and semiarid environments. The results of this study contributes to identifying and delineating priority areas where appropriate water conservation programmes for sustainable groundwater development can be implemented. [ABSTRACT FROM AUTHOR]

The impact of land use land cover (LULC) change and climate variability on water resources poses as a major threat in semi-arid environments, especially in the sub-Saharan Africa. Countries in sub-Saharan Africa are vulnerable to water scarcity. Hence, there is an urgent need for understanding the various methods for LULC change and climate variability assessment, to aid in water resources management at various scales. Various studies have modelled and assessed the effect of LULC change and climate variability on hydrological responses, using different approaches. In this regard, this paper provides a detailed review on the progress of various remote sensing techniques in modelling and assessing the effect of LULC change and climate variability on hydrological processes. The review also highlights the critical scientific strides and challenges of remotely-sensed applications in LULC change characterization and total evaporation estimation. Specifically, research gaps in the estimation of total evaporation in response to LULC change and climate variability, using remote sensing are also highlighted. The study demonstrated remotely-sensed methods used in hydrologic models such as the SCS-CN, WetSpa, JULES and the SWAT model that are used to determine run-off and streamflow. These methods have a component of total evaporation however evapotranspiration (ET) is not the sole focus. The study showed that the application of the remotely sensed SEBS tool has been widely accepted as a viable method to estimate total evaporation. However, it has been observed that there is limited focus on the impact of LULC change and climate variability on total evaporation. [ABSTRACT FROM AUTHOR]

The study explored the impact of Land Use and Land Cover (LULC) change dynamics in relation to the condition and status of an unprotected wetland located in the arid-tropical parts of the Limpopo Province, South Africa. The long-term Landsat archival data series was used to map and quantify the impacts of LULC change on the wetland over a period of 36 years (1983-2019). A multi-source satellite image analysis was performed, using the support vector machine (SVM) algorithm and advanced spatially-explicit geographic information system tools. Landsat data series covering the entire study area was used to assess, map and monitor LULC change that occurred over-time. Post-classification maps for the Maungani wetland area were analysed to provide a quantitative assessment and a detailed overview of the rate of change. The generated wetland detection maps for four temporal phases (i.e., 1983-1992, 1992-2001, 2002-2010) were analysed. This study found that the spatial extent of the wetland area declined severely during the period under study with 728 300 ha. The findings of this work provide critical insights and baseline information about the state of unprotected wetlands in the rural parts. This information is useful for the development of tailor-made wetland management strategies and a possible rehabilitation framework for unprotected wetland ecosystems. [ABSTRACT FROM AUTHOR]

Understanding how the spatial configuration of land cover patterns of built-up areas and urban vegetation affect urban surface temperatures is crucial for improving the sustainability of cities as well as optimizing urban design and landscape planning. Because of their capability to detect distinct surface thermal features, satellite data have proved useful in exploring the impacts of spatial configuration of land cover on land surface temperature (LST). In this study, we examine how the spatial configuration of built-up and urban vegetation affects the LST in the Harare metropolitan city, Zimbabwe. In order to achieve this objective, we combined the LST, local spatial statistics of Getis-Ord Gi* and local Moran's I statistic, Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI) derived from multi-date Landsat satellite data (1994, 2001 and 2017). The results of local Moran's I statistic showed moderate and negative correlations between LST and Landsat derived NDVI. Overall, these results of local Moran's I statistic demonstrate that clustered vegetation tend to lower LST, providing thermal comfort conditions. In contrast, clustered spatial arrangements of NDBI based on the Getis-Ord Gi* elevate LST, implying that continued clustered built-up expansion has the potential to increase urban surface temperatures. [ABSTRACT FROM AUTHOR]

Terrestrial groundwater dependent vegetation (TGDV) are crucial ecosystems which provide important goods and services such as carbon sequestration, habitat, water purification and aesthetic benefits in semi-arid environments. Global climate change and anthropogenic impacts on surface water resources have led to increased competing claims on groundwater resources to meet an exponential water demand for environmental needs, agricultural and developmental needs. This has led to the unsustainable exploitation of groundwater resources, resulting in groundwater table declines, threatening the sustainability of TGDV. It is on this premise that the review aims to provide a detailed overview on the progress in remote sensing of TGDV. More specifically, the paper provides a background on TGDV and threats, and then further explores recent knowledge on vegetation response to groundwater variability and climate change impacts on TGDV. This review also focuses on recent progress in remote sensing and geographic information systems (GIS) based techniques for mapping and monitoring of TGDV and explores the available satellite products and delineation techniques. Finally, the challenges of remote sensing and future research direction are explored. To date, research on TGDV has gained considerable interest with the year 2020 resulting in the most scientific journal publications. Of significant importance is an increase in studies integrating field measurements, model-based techniques with remotely sensed estimates. Despite this progress, only 0.06% of groundwater dependent ecosystems (GDE) research has utilized remote sensing techniques in the past 20 years, with the top three publishing countries namely, Australia, USA, and China. The literature reveals that TDGV are highly heterogenous, complex ecosystems with unique responses to varying groundwater levels. The vegetation responses differ with the landscape, vegetation type, and seasonality at specific groundwater table thresholds. Despite significant progress in TGDV scientific research, further remote sensing studies are required to understand the annual and inter-annual vegetation response to groundwater variability at local scales. Further, climate impacts are difficult to discriminate from other influences such as disturbances, management, and anthropogenic activities. Moreover, new generation remote sensing products integrated with machine learning techniques have the potential to improve TGDV delineation. Despite these challenges, the development of cloud computing technologies such as google earth engine (GEE) and artificial intelligence (AI) provide advanced computer-processing capabilities for long-term monitoring and integration of multi-source datasets required to capture the effects of climate and groundwater variability on TGDV. [ABSTRACT FROM AUTHOR]

Wetlands are highly productive systems that act as habitats for a variety of fauna and flora. Despite their ecohydrological significance, wetland ecosystems are severely under threat from global environmental changes as well as pressure from anthropogenic activities. Such changes results in severe disturbances of plant species composition, spatial distribution, productivity, diversity, and their ability to offer critical ecosystem goods and services. However, wetland degradation varies considerably from place to place with severe degradation in developing countries, especially in sub-Saharan Africa due to poor management practices that leads to underutilization and over reliance on them for livelihoods. The lack of monitoring and assessment in this region has therefore led to the lack of consolidated detailed understanding on the rate of wetland loss. For example, the lack of up-to-date and reliable spatial explicit information further complicates the management of wetland ecosystems in semi-arid tropical environments. To monitor, understand and document wetland degradation rate, the use of remote sensing for accurate estimation and precise mapping of present and historic information remains imperative. Similarly, there is a need to develop robust methodologies to precisely assess and monitor wetland degradation, ecohydrological processes and wetland condition over space and time. This work thereof, provides a comprehensive overview of remote sensing applications in monitoring and mapping the wetland ecosystem. It also highlights the strength and challenges associated with the use of satellite data for purposes of wetland monitoring. Spatial explicit and periodic information offered by satellite remote sensing demonstrate a unique opportunity for documenting and understanding of wetlands, their ecohydrological processes, and environmental conditions. [ABSTRACT FROM AUTHOR]

This study assessed the benefits and challenges associated with local community involvement in biodiversity conservation in the Blouberg Nature Reserve (BNR) of South Africa. To achieve this, a descriptive research design was used in the study. Three hundred and thirty‐five households from four villages scattered around the nature reserve were selected using a stratified systematic sampling procedure to participate in a household questionnaire survey. The BNR Manager was purposefully selected for an in‐depth structured interview so that an overview of socio‐economic benefits and challenges to the community from the park's perspective could be known. Field data collection was conducted during the month of June 2019. Descriptive and inferential statistical methods as well as thematic analysis were used to analyse the field data. The main study findings indicated that the community members do not obtain sufficient socio‐economic benefits from the nature reserve. The majority of the household respondents (89.6%) were not participating in biodiversity conservation in the nature reserve, despite possessing knowledge about nature conservation. The chi‐square test results showed a significant association between household respondents' conservation involvement and access to natural benefits (p = 0.008), and not cultural benefits (p = 0.740). Moreover, the chi‐square test results further show no significant association between household attributes (gender and age) and knowledge of the role of nature reserve (p > 0.05), whereas education had a bearing on the knowledge possessed by households (p < 0.05). Overall, the findings indicate the need for more community involvement to support biodiversity conservation within nature reserves. [ABSTRACT FROM AUTHOR]

We mapped and modelled the potential areas vulnerable to Lantana camara (L. camara) invasion in semi‐arid savannah ecosystems in the communal lands of Bushbuckridge and Kruger National Park, South Africa. Specifically, we modelled potentially vulnerable areas based on remotely sensed data and environmental variables. The Maximal Entropy (Maxent) algorithm was used to model the vulnerable area. The reliability of the modelled results was assessed using Skills Statistic (TSS), Area Under Curve (AUC) and Kappa statistics. According to the results, Bushbuckridge communal lands are more susceptible to L. camara invasions than Kruger National Park. The risk of L. camara invasion in the study site was modelled with high accuracy (AUC score of 0.95) using the best model (Model 7), which is a composite of all model variables (remote sensing and environmental variables). The spatial distribution maps derived from Maxent showed that L. camara was more likely to invade communal lands than protected areas. Using remotely sensed spectral indices as standalone model variables (Model 4) showed the lowest accuracy, with an AUC score of 0.85. Overall, model input variables such as elevation had a significant influence on the spatial distribution of L. camara in the study area. [ABSTRACT FROM AUTHOR]

With a growing global population and accelerating climate change, systematic assessment and monitoring of crop diseases is urgently required to ensure food security and production. However, current dietary transitions inclined towards vegetables such as tomatoes are expected to increase while effective crop disease monitoring and assessment methods are still limited. Therefore, a state-of-the-art review of progress in the assessment and monitoring of tomato crop diseases using geospatial technologies is presented. Results show that tomato crop diseases and their severity could be detected and discriminated from healthy ones more effectively using various remote sensing systems. Furthermore, the recent advances in RS technologies have greatly facilitated its integration with climatic and topo-edaphic factors to determine the possible drivers of disease infection. Although the use of remotely sensed variables and their integration with bioclimatic factors in understanding tomato crop diseases is still at its infancy, it is one of the most promising technologies. [ABSTRACT FROM AUTHOR]

Groundwater-Dependent Ecosystems (GDEs) are under threat from groundwater over-abstraction, which significantly impacts their conservation and sustainable management. Although the socio-economic significance of GDEs is understood, their ecosystem services and ecological significance (e.g., biodiversity hotspots) in arid environments remains understudied. Therefore, under the United Nations Sustainable Development Goal (SDG) 15, characterizing or identifying biodiversity hotspots in GDEs improves their management and conservation. In this study, we present the first attempt towards the spatial characterization of vegetation diversity in GDEs within the Khakea-Bray Transboundary Aquifer. Following the Spectral Variation Hypothesis (SVH), we used multispectral remotely sensed data (i.e., Sentinel-2 MSI) to characterize the vegetation diversity. This involved the use of the Rao's Q to measure spectral diversity from several measures of spectral variation and validating the Rao's Q using field-measured data on vegetation diversity (i.e., effective number of species). We observed that the Rao's Q has the potential of spatially characterizing vegetation diversity of GDEs in the Khakea-Bray Transboundary Aquifer. Specifically, we discovered that the Rao's Q was related to field-measured vegetation diversity (R2 = 0.61 and p = 0.00), and the coefficient of variation (CV) was the best measure to derive the Rao's Q. Vegetation diversity was also used as a proxy for identifying priority conservation areas and biodiversity hotspots. Vegetation diversity was more concentrated around natural pans and along roads, fence lines, and rivers. In addition, vegetation diversity was observed to decrease with an increasing distance (>35 m) from natural pans and simulated an inverse piosphere (i.e., minimal utilization around the natural water pans). We provide baseline information necessary for identifying priority conservation areas within the Khakea-Bray Transboundary Aquifer. Furthermore, this work provides a pathway for resource managers to achieve SDG 15 as well as national and regional Aichi biodiversity targets. [ABSTRACT FROM AUTHOR]

The study sought to assess the impacts of land use and land cover (LULC) changes on two wetland systems (Makuleke and Nylsvley Nature Reserve) in the Limpopo Transfrontier River Basin (LTRB) in South Africa between 2014 and 2018. To fulfil this objective, multi-date Landsat images were used. Furthermore, the maximum likelihood classification algorithm was used to identify various LULC classes within delineated wetlands. The LULC changes were mapped from the two wetlands, with high overall accuracies, ranging from 80% to 89% for both study areas. The spatial extent of the Makuleke wetland declined by 2% between 2014 and 2018, whereas the Nylsvley wetland decreased by 3%. Built-up areas have increased slightly over the 2014 and 2018 period because of population growth and infrastructure development, which occupy a portion of the wetland. In Nylsvley wetland, it was evident that during the 5-year monitoring period, croplands increased steadily in the Nylsvley catchment. Overall, the results demonstrated a steady decline in natural vegetation cover in both wetlands. This information can aid in enforcing wetland legislations and LULC management practices that can help protect them from further encroachment and degradation. [ABSTRACT FROM AUTHOR]

We use remotely sensed data to estimate species diversity in Blouberg Nature Reserve (BNR) in the Limpopo province, South Africa to understand the state of biodiversity since communities' involvement in conservation initiatives. To achieve this objective, Landsat series data collected before and after community involvement in biodiversity conservation were used in conjunction with selected diversity indices i.e., Shannon-Wiener Index (H') and Simpson Index (D). Thirty 15 m × 15 m field plots were selected and all trees within each plot were identified, with the help of Botanists. Further, we applied regression analysis to determine the relationship between satellite derived tree species diversity and field observations. The results of the study demonstrated a significant (p < 0.5) variation in tree species diversity between 1990 and 2019. The highest relationship was obtained between H' and the combined remotely sensed spectral data and as well as Indices (VIs) when compared to other derived satellite data. Further, the results showed positive significant relationship (p < 0.05) between the combined remotely sensed data and observed H' index with r2 = 0.36 and r2 = 0.34 for the period before and after involving local communities in biodiversity conservation, respectively. Thus, the findings of the study indicate that the ecological condition of the reserve was slightly affected by the involvement of local communities in biodiversity conservation, for instance, volunteering in bush-encroachment eradication and decision-making. Overall, findings of the study underscore the relevance of remotely sensed data in assessing the ecological condition of protected areas and this information can help in decision-making. [ABSTRACT FROM AUTHOR]

Numerous initiatives and programmes were introduced to enable biodiversity conservation, and community involvement was among these propositions. In line with such initiatives, several frameworks were thus developed to ensure the sustainable implementation and realisation of such programmes. This saw the establishment of the Community‐Based Natural Resource Management (CBNRM). As such, numerous calls were made to explore community involvement strategies and associated socio‐economic impacts in nature conservation. Most studies and reports emanating from such calls demonstrate cases of failure of inter alia including poaching. This work, therefore, provides a detailed synthesis of the implications of involving local communities in conservation in protected areas (PAs) and further examines how spatial explicit methodologies can be adopted in enhancing nature conservation. Overall, the study has shown that community attitudes and perceptions towards nature conservation in PAs point towards the failure of the CBNRM approach. Weak, conflicted and sometimes corrupt governance and poor institutional arrangements seem to have contributed towards its failure. Overall, the review further indicates that most studies on community involvement in nature conservation have not fully considered the spatial dimension, and this has hampered holistic and effective monitoring and assessment of biodiversity in PAs to ensure sustainable management and the possible implications of such an approach towards conservation. This work, therefore, recommends a shift towards collaborative modelling, transdisciplinary monitoring and assessment approaches to improve biodiversity conservation as well as to determine the success and limitations of the CBNRM approach in Africa and the world over. [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]

Accurate information on the amount of aboveground grass biomass (AGBg) stored in savannah rangelands remains unknown. Therefore, in this study we assessed the capability of a new multispectral sensor (Landsat‐8 OLI) in estimating aboveground grass biomass stored within two different land use management units in savannah rangelands using the stepwise multiple linear regression methods. Specifically, we evaluated the performance of different Landsat‐8 OLI derivatives (i.e. (a) spectral bands, (b) derived vegetation indices and (c) a combination of spectral bands and vegetation indices) to quantify aboveground grass biomass. The results highlighted that the use of spectral bands as stand‐alone model variables yielded considerably high accuracies in terms of the coefficient of determination (r2) and the root mean square error (RMSE). An r2 of 0.73 (72.81%) and RMSE of 37.88% for the protected area and an r2 of 0.75 (75.18%) and with a RMSE of 33.16% for the nonprotected area were attained. The use of vegetation indices, however, demonstrated a very weak model performance yielding an r2 of 0.31 (30.68%) and a RMSE of 51.51%, for the protected area and an r2 of 0.50 (50.43%) and RMSE of 40% for the nonprotected areas. Comparatively, combining Landsat‐8 OLI derived spectral bands and vegetation indices demonstrated improved model performance, yielding an r2 of 0.92 (91.90%), and RMSE of 41.3% for the protected area and an r2 of 0.94 (93.82%) with a RMSE of 33.14% for the nonprotected area. Therefore based on the observed results, AGBg in savannah rangelands can be satisfactorily estimated using broadband multispectral derivatives. [ABSTRACT FROM AUTHOR]

Non-perennial rivers (N-PRs) make up two thirds of all rivers in South Africa, yet many are ungauged. Traditionally, it has been assumed that when a flow is recorded, there is water throughout that river. These assumptions have led to incorrect estimations of available water resources. This work thus aimed at developing a new spatially explicit framework, for monitoring river water availability in a N-PR system. The Tankwa River in South Africa was used for testing this approach. The length of the river reach with water was determined using the Sentinel-1 and Sentinel-2 data derived indices. Image thresholding was applied to Sentinel-1, and the normalised difference water index (NDWI) to Sentinel-2. Sentinel-2 yielded an overall accuracy (OA) of 85%, whereas Sentinel-1 yielded an OA of 38%. The analysed reach of the Tankwa River had an actual length of 9 244 m. Based on the performance of Sentinel-2 data, further analysis was undertaken using Sentinel images acquired during the months of February, May and July of 2016. The results indicated that the lengths of the reaches of inundated Tankwa River were 2 809 m, 3 202 m and 2 890 m, respectively. Overall, the findings of this study show that an estimated length of a river inundated by water can be determined using new-generation Sentinel data and these results provide new insights on the dynamics of N-PRs - a previously challenging task with broadband multispectral satellite datasets. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive literature review exploring the effects of artisanal mining on wetlands in semi-arid regions of sub-Saharan Africa. The review encompasses relevant literature sourced from Google Scholar, SCOPUS and Web of Science databases, using specific key search terms. The findings reveal a substantial body of research dedicated to artisanal mining. The study findings reveal that wetlands are currently facing threats, and extensive scientific research has been conducted on wetland monitoring and assessment. Ecohydrological and geomorphological assessment studies are found to be critical in understanding wetland vulnerability. Among the frequentist-interpretation methods used in most wetland assessment studies, Bayesian belief networks (BBNs), geospatial and earth observation analysis, and expert elicitation are prominently highlighted. In addition, many countries, including Congo, Niger, South Africa, and Zimbabwe, have experienced extensive artisanal mining activities, leading to notable wetland and habitat losses, compromised eco-hydrology integrity, riverbed siltation, and severe mercury pollution, resulting in neurological poisoning of both invertebrate and vertebrate wetland species. The review underscores the urgency of adopting a transdisciplinary scientific approach to wetland monitoring and assessment to prevent further degradation of these ecosystems. By embracing such an approach, scientists can gain a comprehensive understanding of the impacts of human activities on wetlands and develop more effective strategies for mitigating these adverse effects. • Illegal artisanal mining has a regional impact on the integrity and functioning of wetlands. • Wetlands are extremely vulnerable due to their size, hydrology, and ecological features. • Wetland monitoring and assessment require a multidisciplinary scientific approach. • In most wetland assessment studies, frequentist interpretation methods are used. [ABSTRACT FROM AUTHOR]

Invasive Alien Plants (IAPs) pose major threats to biodiversity, ecosystem functioning and services. The availability of moderate resolution satellite data (e.g. Sentinel‐2 Multispectral Instrument and Landsat‐8 Operational Land Imager) offers an opportunity to map and monitor the occurrence and spatial distribution of IAPs. The use of two multispectral remote sensing data sets to map and monitor IAPs in the Heuningnes Catchment, South Africa, was therefore investigated using the maximum likelihood classification algorithm. It was possible to identify areas infested with IAPs using remote sensing data. Specifically, IAPs were mapped with a higher overall accuracy of 71%, using Sentinel‐2 MSI as compared to using Landsat 8 OLI, which produced 63% accuracy. However, both sensors showed similar patterns in the spatial distribution of IAPs within the hillslopes and riparian zones of the catchment. This work demonstrates the utility of the two multispectral data sets in mapping and monitoring the occurrence and distribution of IAPs, which contributes to improved ecological modelling and thus to improved management of invasions and biodiversity in the catchment. [ABSTRACT FROM AUTHOR]

Hydrological monitoring networks do not adequately cover non-perennial rivers in most parts of Africa partly because river flows do not frequently occur on these rivers. However, in the arid and semi-arid regions flows occurring on these rivers are an important resource that have to be managed judiciously. The lack of data on magnitudes, timing and frequency of non-perennial river flows constrain sustainable management of their water resources. The availability of moderate resolution satellite data (e.g. Sentinel-2 Multispectral Instrument and Landsat 8 Operational Land Imager) offers an opportunity to monitor the surface water availability specifically changes in surface areas covered with water in pools along these rivers. In this study, we investigated the use of remote sensing data to detect and monitor changes of water surface areas of pools along three non-perennial rivers and one perennial river in the Western Cape, South Africa, from 2016 to 2017, using Landsat 8 and Sentinel-2 datasets. We assessed a range of image pre-processing and image classification techniques to determine the most suitable method for surface water identification and pool area estimation along the selected rivers. The following indices were used; Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEI sh), non-shadowed regions (AWEI nsh), and the Multi-Band Water Index (MBWI). The NDWI computed from Sentinel-2 TOA reflectance datasets was found to be the most suitable index for mapping pools, with an overall accuracy ranging between 60 and 86%. This index was used successfully to establish the variations of surface areas of pools covered with water along the Breede, Nuwejaars, Tankwa and Touws Rivers during the 2016–2017 period. The temporal variations of pool areas could be explained as expected by changes of rainfall and potential evapotranspiration. Overall, the study demonstrated the feasibility of using moderate resolution datasets to monitor pool changes of areas covered with water in non-perennial rivers, which contributes to improved management of water resources particularly in arid and semi-arid environments. [ABSTRACT FROM AUTHOR]

This work explores the potential of the high‐resolution WorldView‐2 sensor in quantifying edge effects on the spatial distribution of selected forest biochemical properties in fragmented Dukuduku forest in KwaZulu‐Natal, South Africa. Specifically, we sought to map fragmented patches within forested areas in Dukuduku area, using very high spatial resolution WorldView‐2 remotely sensed data and to statistically determine the effect of these fragmented patches on the spatial distribution of selected forest biochemical properties. Edge effects on carbon, LAI and foliar nitrogen were quantified based on the models derived by Omer et al. (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8, 4825). Edge effect statistical results on the spatial distribution of carbon, LAI and nitrogen showed significant (α = 0.05) variations with change in distance from fragmented patches (>150 m2). Forest foliar carbon concentrations significantly (p‐value = 0.016) increased from 44.8% to 45.3% with increasing distance (25–375 m) from fragmented patches. A similar trend was observed for LAI. Nevertheless, for nitrogen the results show that its concentration significantly (p = 0.016) decreased with increase in distance from the fragmented patches. Overall, the findings of this work underscore the invaluable potential and strength of WorldView‐2 data set in assessing edge effect on the spatial distribution of selected forest biochemical properties. [ABSTRACT FROM AUTHOR]

As the risk of climate change increases, robust fire monitoring methods become critical for fire management purposes. National-scale spatiotemporal patterns of the fires and how they relate to vegetation and environmental conditions are not well understood in Zimbabwe. This paper presents a spatially explicit method combining satellite data and spatial statistics in detecting spatiotemporal patterns of fires in Zimbabwe. The Emerging Hot Spot Analysis method was utilized to detect statistically significant spatiotemporal patterns of fire occurrence between the years 2002 and 2021. Statistical analysis was done to determine the association between the spatiotemporal patterns and some environmental variables such as topography, land cover, land use, ecoregions and precipitation. The highest number of fires occurred in September, coinciding with Zimbabwe's observed fire season. The number of fires significantly varied among seasons, with the hot and dry season (August to October) recording the highest fire counts. Additionally, although June, July and November are not part of the official fire season in Zimbabwe, the fire counts recorded for these months were relatively high. This new information has therefore shown the need for revision of the fire season in Zimbabwe. The northern regions were characterized by persistent, oscillating, diminishing and historical spatiotemporal fire hotspots. Agroecological regions IIa and IIb and the Southern Miombo bushveld ecoregion were the most fire-prone areas. The research findings also revealed new critical information about the spatiotemporal fire patterns in various terrestrial ecoregions, land cover, land use, precipitation and topography and highlighted potential areas for effective fire management strategies. [ABSTRACT FROM AUTHOR]

• A comprehensive review of remote sensing applications in water resources monitoring and management in Africa is provided. • Evolution of remote sensing techniques, particularly in hydrological modeling is summarized. • Role of sensors, observations, and analytical models in monitoring Africa's water resources at different scales are highlighted. • In-situ hydrological monitoring networks and limited long-term rainfall and climate data remains a challenge in Africa. • The adoption of multi-source data integration, and big data analytics provides new opportunities to improve water resource management in Africa. This paper provides an overview of the progress made in remote sensing of water resources in Africa, focusing on various applications such as precipitation estimation, land surface temperature analysis, soil moisture assessment, surface water extent measurement, surface runoff and streamflow analysis, water quality evaluation, land cover/land use mapping, and groundwater analysis. Specifically, the study sheds light on the remarkable progress made in remote sensing applications, showcasing scientific advancements and highlighting the challenges encountered. Moreover, it identifies crucial knowledge gaps that need to be addressed in order to further advance this field. The review's key findings indicate that the availability of sensors and observations, along with analytical models, has contributed to monitoring Africa's water resources at various scales. The availability and accessibility of hydrological data for monitoring and assessing water resources in Africa have been partially improved through the adoption of satellite data and processing technologies. Additionally, the emergence of various international remote sensing initiatives, North-South research collaborations, and projects has contributed to the research progress. Prominent satellite data series such as Landsat, MODIS, and GRACE have played significant roles in African hydrological research. However, the limited and malfunctioning in-situ hydrological monitoring networks in Africa have affected the accurate calibration and validation of remotely sensed hydrological models. Insufficient long-term rainfall and climate data pose challenges to long-term earth observation research on African water systems. The lack of high-resolution spatial and temporal data hampered accurate monitoring of hydrological processes at smaller scales. Despite the widespread use of rainfall satellite products, validation attempts over Africa, particularly in western and southern regions, have been limited. Furthermore, the reliability of hydrological satellite datasets is affected by factors such as the number and coverage of surface stations, retrieval algorithms, data integration techniques, and cloud cover. Overall, this work demonstrates the importance of earth observation in understanding Africa's hydrology, previously hindered by the lack of in-situ data. Nevertheless, efforts are therefore needed to enhance the adoption and application of remote sensing, particularly in groundwater and surface water estimation at smaller scales. Future research should focus on multi-source data integration, assimilation, and big data analytics using cloud computing and machine learning to address complex hydrological research questions at various scales. [ABSTRACT FROM AUTHOR]

Surface runoff modelling is crucial, particularly in regions with inadequate hydrometric networks such as southern Africa. In southern Africa, rainfall patterns are erratic with frequent droughts being common. Yet due to population growth, water consumption has increased, posing significant challenges to water resources management to meet demand. It is also anticipated that climate change will increase rainfall variability and drought and this is likely to create challenges for water resources management. In this regard, understanding different water balance components is thus critical to conserve and improve water management strategies. In this paper we present progress and challenges in the modelling of surface runoff, which is one of the key components of water balance, affecting surface water availability. For decades, ground-based measurements and hydrological models have been used to understand surface runoff. Nevertheless, the emergences and advances in remote sensing provide a new horizon to model different water balance components. Specifically, the aim of this work is to provide a detailed overview of the different approaches for modelling surface runoff, which include the use of empirical models and remotely sensed data, in southern Africa. Further, the strengths and weaknesses of the highlighted approaches in understanding the variations in surface runoff are also discussed. The paper also recommends possible directions in which future studies may fill existing gaps in rainfall-runoff literature. [ABSTRACT FROM AUTHOR]

In savannah ecosystems, termites drive key ecosystem processes, such as primary production through creation of patchiness in soil nutrients availability around their nests. In this study, we evaluated the role of termites in altering the soil seed bank size, an important ecosystem component that has often been overlooked in previous work. Data on above ground vegetation and soil seed bank samples were collected from four microhabitats, that is, the wooded mound, unwooded mound, tree sub‐canopy and the open grassland matrix in a protected game reserve in south‐central Zimbabwe. The seedling emergence method was then used to identify species present in the soil samples. One‐way analysis of variance followed by Tukey's multiple comparison tests was executed to test for significant differences in plant species richness among the four microhabitats. The results indicate that plant species richness was high on wooded termite mound but did not differ between the unwooded and the sub‐canopy microhabitats. The open grassland microhabitat had the lowest plant species richness. The influence of termites on the soil seed bank composition was also life form specific. The herb and woody life forms had significantly (α = 0.05) higher species richness in the soil seed bank at wooded and unwooded termite mounds when compared to the other two microhabitats. Overall, these results imply that termites alter the soil seed bank and the findings enhance our understanding of the significant role termites play in regulating processes in savannah ecosystem. [ABSTRACT FROM AUTHOR]

Abstract Urban growth is a key process affecting the functioning of natural ecosystems, and consequently the global land-surface process. This work aimed at establishing the link between land cover changes around Harare Metropolitan city and the proliferation of aquatic hyacinth (Eichhornia crassipes) in Lake Chivero. Remotely sensed Landsat series acquired in the year 1973, 1981, 1994, 1998, 2008, 2009 and 2014 was used. Image classification was implemented to map the associated changes over time using discriminant analysis algorithm. Derived thematic land cover maps showed that agricultural land increased from 2% in 1973 to a 5% in 1981 reaching up to 30% in 2014, whereas the city's land area significantly (p < 0.05) increased between 1973 and 1994. However, water hyacinth constantly increased over time. The spatial and temporal resolution of Landsat images detected land cover changes and the proliferation of aquatic hyacinth (Eichhornia crassipes) in the Lake Chivero over time. Highlights • Fast urban expansion is a key factor affecting global climate change. • Remote sensing shows the potential in monitoring and understanding complex urban land use and land cover changes. • Post hoc test showed that water hyacinth can be accurately be discriminated from other land cover types. • Band 4, 5, 6 and 7 of the Landsat series data can be used to successfully map land cover changes. [ABSTRACT FROM AUTHOR]

Early detection and mapping of the spatio-temporal distribution of invasive water hyacinth (Eichhornia crassipes) in inland hydrological systems are vital for a number of water resource management-related reasons. Field surveys and current climate change projections (associated with longer dry spells, and shortened rain seasons) have shown that climate change and the rapid spread of aquatic invasive species are increasingly affecting inland surface water availability in semi-arid regions of Southern Africa. It is upon this premise that accurate, reliable, and timely information on the spatio-temporal distribution and configuration of water hyacinth is required in tracing their evolution and propagation in affected areas as well as in potential vulnerable areas. This work, therefore, attempts to test two robust push-broom multispectral sensors: Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) in identifying, detecting, and mapping the spatial distribution and configuration of invasive water hyacinth in a river system. The results of the study show that water hyacinth in small reservoirs can be mapped with an overall accuracy of 68.44% and 77.56% using Landsat 8 and Sentinel-2 data, respectively. The results further demonstrated the blue, red, red edge (RE) 1, short-wavelength infrared (SWIR)-1, and SWIR-2 of both satellite data sets as the critical and outstanding spectral regions in detecting and mapping water hyacinth from other land-cover types. Overall, the study highlights the unexploited prospects of the new noncommercial multispectral sensors in monitoring invasive species infestation from inland surface waterbodies in semi-arid regions (i.e. smaller reservoirs). [ABSTRACT FROM AUTHOR]

Abstract Water is integral for the survival of humans, animals, as well as vegetation and information on its spatial distribution is important for water resources management. Various methods have been applied to detect and map land surface water, especially due to the availability of a large range of satellite sensors. The main methods involved are either general feature classification techniques or thematic water body information extraction. Thematic water body extraction methods make use of band information and in this study; spectral indices are used to delineate land surface water for Zimbabwe using Landsat 8 OLI images. Multiple algorithms i.e. spectral indices were used to extract surface water bodies from Landsat 8 imagery. Then overall accuracies and Kappa coefficients and average reliability were then applied to assess the performance of the seven applied models. The results have shown that the LSWI plus VI can map surface water bodies with an overall accuracy of 86%, kappa statistic of 0.70 and a reliability accuracy of 89%. Comparatively, the index produced good results followed by the mNDWI which had an overall classification accuracy of 84% and an average reliability of 87%. Following on the list in terms of performance was the AWEI sh , mMDWI plus VI, NDWI and NDWI plus VI. However, other indices had slightly lower results comparatively. The study noted that LSWI plus VI applied on Landsat 8 OLI data can reliably delineate land surface water in a subtropical environment. Highlights • Up-to-date surface water information is important for water resources management. • FAO Aquastat database provides a better representation of Zimbabwe's surface water bodies. • MNDWI derived from Landsat 8 band 6 can reliably delineate land surface water. • Surface water distribution can be accurately derived using water indices. [ABSTRACT FROM AUTHOR]

Remote sensing of groundwater-dependent ecosystems (GDEs) has increased substantially in recent years. Of significant prominence, is the delineation and mapping of groundwater-dependent vegetation (GDV), species diversity, and water quality in these ecosystems. Groundwater-dependent ecosystems provide several ecological services such as habitat for wildlife fauna, carbon sequestration and water purification. The recent technological advancements and readily accessibility of new satellite sensors with improved sensing characteristics have resulted in numerous state-of-the-art applications for GDEs assessment and monitoring. These studies were done at varying scales, essentially in light of global climate change and variability. In this study, we review and assess the progress on the remote sensing of GDEs in semi-arid environments. We present the key trends in GDEs remote sensing that underpin many of the recent scientific research milestones and application developments. In addition, we observed a considerable shift towards the use of advanced spatial modelling techniques, using high-resolution remotely sensed data to further improve the characterisation and understanding of GDEs. Thus, literature shows the successful use of freely available remotely sensed data in mapping GDEs. We conclude that the advancement in remote sensing provides unique opportunities for the assessment and monitoring of GDEs in environments currently influenced by climate change and other anthropogenic impacts. Although remarkable progress has been made, this review revealed the need for further remote sensing and geospatial analysis studies to map other GDEs aspects including water quality and species diversity. Furthermore, the mapping and characterizing of the seasonal and yearly variability and changes in GDEs is required, mainly in the face of climate change and human impacts as well as water scarcity, particularly in data-limited tropical environments. • Numerous algorithms have been applied in the delineation of Groundwater-Dependent Ecosystems (GDEs). • Remotely sensed derived spectral indices perform better in the delineation of GDEs than raw spectral data. • The NDVI and MNDWI are the commonly used spectrally derived indices. • The advent of new satellite data provide new opportunities for delineating Groundwater-Dependent Ecosystems. [ABSTRACT FROM AUTHOR]

Monitoring key pollinator taxa such as the genus Ceratina requires precise near real-time predictions to facilitate better surveillance. The potential habitat suitability of Ceratina moerenhouti was predicted in the Eastern Afromontane biodiversity hotspot (EABH) in Kenya using presence-only data, to identify their potential distribution and vulnerability due to climate change. Bioclimatic, edaphic, terrain, land surface temperature, and land use and land cover (LULC) variables were used as predictors. Three machine learning techniques, together with their ensemble model, were evaluated on their suitability to predict current and future (the shared socioeconomic pathways (SSPs), i.e., SSP245 and SSP585) habitat suitability. Predictors were subjected to variable selection using the variance inflation factor resulting in a few (n = 9) optimum variables. The area under the curve (AUC) and true skill statistic (TSS) were used for the accuracy assessment of the modeling outputs. The results indicated that 30% and 10% of the EABH in Murang'a and Taita Taveta counties are currently suitable for C. moerenhouti occurrence, respectively. However, future projections show a ±5% decrease in C. moerenhouti habitats in the two counties. Further, the ensemble model harnessed the algorithm differences while the random forest had the highest individual predictive power (AUC = 0.97; TSS = 0.96). Clay content, LULC, and the slope were the most relevant variables together with temperature and precipitation. Integrating multi-source data in predicting suitable habitats improves model prediction capacity. This study can be used to support the maintenance of flowering plant communities around agricultural areas to improve pollination services. • Monitoring key pollinator taxa facilitates better surveillance and the development of empirical interventions. • The potential habitat suitability of Ceratina moerenhouti was established in the Eastern Afromontane biodiversity hotspot. • Three robust machine learning algorithms and their ensemble had a very high prediction accuracy. • Integrating multi-source data improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

Water quality problems continue on a global scale and this creates the need for regular monitoring using cheaper technologies to inform management. The objective of this study was to test for significant relationships between the field-measured and Landsat 8 OLI sensor-retrieved water quality parameters. The study was carried out in two reservoirs with contrasting trophic states in Zimbabwe. Results show that the Blue/Red ratio had strong predictive relationships with Secchi disc transparency (R2 > 0.70) and turbidity (R2 ≥ 0.65). The Near-infrared/Red ratio was a strong predictor of chlorophyll-a in Mazvikadei (R2 > 0.84) whereas in Lake Chivero, which is more polluted, the red band was the most useful predictor (R2 = 0.69). Overall, our work demonstrates the utility of using Landsat 8 band ratios for remote assessment of water quality in African reservoirs as a value-addition to the traditional field-based methods, which are expensive resulting in data scarcity. [ABSTRACT FROM AUTHOR]

Encosternum delegorguei spinola (edible stink bugs) is renowned for its high protein and contribution to the local economies of the people in Africa. Although many studies have evaluated the economic and nutritional importance of E. delegorguei, little is known about its geographic distribution and habitat yet the insects are an important source of protein and money for many people in Southern Africa. In this study maximum entropy model was used to predict the probability of presence of E. delegorguei in southern Zimbabwe. The environmental factors governing its geographic distribution in Zimbabwe were also evaluated. Presence/absence data were selected along thirty-five randomly selected transects. The climatic and topographic variables used to predict the distribution of E. delegorguei were: maximum temperature of the warmest month; minimum temperature of the coldest month; the normalised difference vegetation index (NDVI); altitude; slope; and aspect. It was found that E. delegorguei is most likely to occur on steep slopes with high NDVI located at an altitude ranging of 856 and 1450 m above sea level. These suitable habitats are characterised by mild temperatures ranging from 17 °C to 28 °C. These results are in agreement with previous studies indicating that E. delegorguei is sensitive to temperature, as well as tree cover and may contribute towards conserving its habitat, which is being fragmented by anthropogenic disturbance. [ABSTRACT FROM AUTHOR]

Accurate and reliable soil organic carbon stock estimation is critical in understanding forest role to regional carbon cycles. So far, the total carbon pool in dry Miombo ecosystems is often under-estimated. In that regard this study sought to model the relationship between the aboveground woody carbon pool and the soil carbon pool, using both ground-based and remote sensing methods. To achieve this objective, the Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and the Soil Adjusted Vegetation Index (SAVI) computed from the newly launched Landsat 8 OLI satellite data were used. Correlation and regression analysis were used to relate Soil Organic Carbon (S.O.C), aboveground woody carbon and remotely sensed vegetation indices. Results showed a soil organic carbon in the upper soil layer (0–15 cm) was positively correlated with aboveground woody carbon and this relationship was significant (r = 0.678; P < 0.05) aboveground carbon. However, there were no significant correlations (r = −0.11, P > 0.05) between SOC in the deeper soil layer (15–30 cm) and aboveground woody carbon. These findings imply that (relationship between aboveground woody carbon and S.O.C) aboveground woody carbon stocks can be used as a proxy to estimate S.O.C in the top soil layer (0–15 cm) in dry Miombo ecosystems. Overall, these findings underscore the potential and significance of remote sensing data in understanding savanna ecosystems contribution to the global carbon cycle. [ABSTRACT FROM AUTHOR]

Abstract: We tested whether the recently proposed two‐part measure of degree of invasion (DI) of a community relating exotic proportion of cover to exotic proportion of richness can characterize patterns of plant invasions at multiple savannah sites in Southern Africa. Regression analysis was performed on transformed data to assess how this two‐part measure of DI compares to other metrics of community invasibility. The results indicate that at the plot level, the absolute cover of exotics was not significantly related to native cover for three sites out of four assessed (R2 ≤ 0.17; P > 0.05). Also, at all four sites, no significant relationships were detected between native and exotic plant richness at both the 1‐m2 and 400‐m2 plot scales. By contrast, significant (P < 0.05) positive linear relationships were observed between exotic proportion of richness and exotic proportion of cover at all sites (R2 was as high as 0.67 and 0.97 for two sites). Our results indicate that the new two‐part measure of DI is able to characterize patterns of plant invasions across plant communities in African savannahs. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R2of 0.67 and a RMSE-CV of 2.2 t ha−1) when compared to when the red-edge band was included as a co-variate (R2of 0.73 and a RMSE-CV of 2.04 t ha−1). Also, similar results were obseved when all WorldView-2 vegetation indices(without the red-edge computed ones), producing slightly low accuracies (R2of about 0.67 and a RMSE-CV of 2.20 t ha−1), when compared to those obtained using all indices and RE-computed indices(R2of 0.76 and a RMSE-CV of 1.88 t ha−1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint. [ABSTRACT FROM PUBLISHER]

The estimation of total evaporation is fundamental for water accounting, considering its influence on water availability. Moreover, the current increase in water consumption (e.g.in sub-Saharan Africa and the world over), land cover/use changes, deteriorating water quality and the climate change projections in most regions of the world underscore the need to understand water loss. So far, different approaches have been developed and implemented in estimating the variations of total evaporation, with varying accuracies. The aim of this work was therefore, to provide a review of these different approaches for estimating total evaporation, as well as a detailed discussion of their strengths and weaknesses. Findings from this review have shown that total evaporation estimates derived, using ground-based meteorological and micro-meteorological methods are inadequate for representing its large-scale spatial variations. On the other hand, remote sensing technology, which acquires data at different resolutions (i.e.radiometric, spectral, spatial and temporal), provides timely, up-to-date and relatively accurate spatial estimates of total evaporation over large geographic coverage, for sustainable and effective water accounting, which is key for well-informed and improved management of water resources at both catchment and regional scales. In this regard, more details on the remote sensing-based methods of estimating total evaporation are provided, especially considering the robust technological advancements and its potential in characterizing earth features over time and space. This work has also managed to identify research gaps and challenges in the accurate estimation of total evaporation, using remote sensing, especially with the emergence of more advanced sensors and the characteristics of the landscape. [ABSTRACT FROM PUBLISHER]

The invasion of agro-ecosystems, as well as natural ecosystems byOpuntia fulgida,compromises the ability of these systems to provide goods and services to the society and this has a direct bearing on human livelihoods. Yet, not much is known about the current, as well as potential distribution of this invasive alien species in invaded landscapes, such as the southwestern districts of Zimbabwe. In this study, we apply maximum entropy species distribution modelling to georeferenced occurrence data and a set of predictor variables (that is, climatic, ecological and demographic variables) in order to generate the first distribution maps ofO. fulgidain Zimbabwe. Our results suggest that a total of 17 districts in Zimbabwe are vulnerable to invasion byO. fulgidaand communal lands are most susceptible to invasion relative to other land tenure systems. We discuss the possible mechanisms of spread and highlight threats to livestock production systems posed by this invasive alien species. [ABSTRACT FROM AUTHOR]

The invasion of agro-ecosystems, as well as natural ecosystems by Opuntia fulgida, compromises the ability of these systems to provide goods and services to the society and this has a direct bearing on human livelihoods. Yet, not much is known about the current, as well as potential distribution of this invasive alien species in invaded landscapes, such as the southwestern districts of Zimbabwe. In this study, we apply maximum entropy species distribution modelling to georeferenced occurrence data and a set of predictor variables (that is, climatic, ecological and demographic variables) in order to generate the first distribution maps of O. fulgida in Zimbabwe. Our results suggest that a total of 17 districts in Zimbabwe are vulnerable to invasion by O. fulgida and communal lands are most susceptible to invasion relative to other land tenure systems. We discuss the possible mechanisms of spread and highlight threats to livestock production systems posed by this invasive alien species. [ABSTRACT FROM AUTHOR]

Forest stand volume is one of the crucial stand parameters, which influences the ability of these forests to provide ecosystem goods and services. This study thus aimed at examining the potential of integrating multispectral SPOT 5 image, with ancillary data (forest age and rainfall metrics) in estimating stand volume between coppiced and planted Eucalyptus spp. in KwaZulu-Natal, South Africa. To achieve this objective, Partial Least Squares Regression (PLSR) algorithm was used. The PLSR algorithm was implemented by applying three tier analysis stages: stage I: using ancillary data as an independent dataset, stage II: SPOT 5 spectral bands as an independent dataset and stage III: combined SPOT 5 spectral bands and ancillary data. The results of the study showed that the use of an independent ancillary dataset better explained the volume of Eucalyptus spp. growing from coppices (adjusted R2 (R 2 Adj ) = 0.54, RMSEP = 44.08 m 3 /ha), when compared with those that were planted (R 2 Adj = 0.43, RMSEP = 53.29 m 3 /ha). Similar results were also observed when SPOT 5 spectral bands were applied as an independent dataset, whereas improved volume estimates were produced when using combined dataset. For instance, planted Eucalyptus spp . were better predicted adjusted R2 (R 2 Adj ) = 0.77, adjusted R 2 Adj = 0.59 , RMSEP = 36.02 m 3 /ha) when compared with those that grow from coppices (R 2 = 0.76, R 2 Adj = 0.46, RMSEP = 40.63 m 3 /ha). Overall, the findings of this study demonstrated the relevance of multi-source data in ecosystems modelling. [ABSTRACT FROM AUTHOR]

Reliable and accurate quantification of plant Leaf Area Index (LAI) is critical in understanding its role in reducing runoff. The main aim of the present study was to evaluate the ability of the Red Edge (RE) band derived from RapidEye in estimating LAI for applications in quantifying canopy interception at landscape scale. To achieve this objective, the study also compares the predictive power of two machine learning algorithms (Random Forest-RF and Stochastic Gradient Boosting-SGB) in estimating LAI. Comparatively, the results of the study have demonstrated that the inclusion of spectral information derived from the Red Edge band yields high accurate LAI estimates, when compared to the use of traditional traditional Red, Green, Blue and Near Infra-Red (traditional RGBNIR) spectral information. The results indicate that the use of the four traditional RGBNIR bands yielded comparatively lower R 2 values and high Root Mean Squares, Mean Absolute Error ( Pinus taeda : R 2 of 0.60; the lowest RMSE (0.35 m 2 /m 2 ) and MAE of 28); whereas the use of integration of traditional RGBNIR + RE in more accurate LAI estimates ( Pinus taeda: R 2 = 0.65; RMSE = 0.30 m 2 /m 2 ) and the lowest MAE of 0.23). These findings therefore underscores the importance of new generation multispectral sensors with strategically-position bands and machine learning algorithms in estimating LAI for quantifying canopy interception, especially in resource-poor areas. [ABSTRACT FROM AUTHOR]

The remote sensing of freshwater resources is increasingly becoming important, due to increased patterns of water use and the current or projected impacts of climate change and the rapid invasion by lethal water weeds. This study therefore sought to explore the potential of the recently-launched Landsat 8 OLI/TIRS sensor in mapping invasive species in inland lakes. Specifically, the study compares the performance of the newly-launched Landsat 8 sensor, with more advanced sensor design and image acquisition approach to the traditional Landsat-7 ETM+ in detecting and mapping the water hyacinth ( Eichhornia crassipes ) invasive species across Lake Chivero, in Zimbabwe. The analysis of variance test was used to identify windows of spectral separability between water hyacinth and other land cover types. The results showed that portions of the visible (B3), NIR (B4), as well as the shortwave bands (Band 8, 9 and 10) of both Landsat 8 OLI and Landsat 7 ETM, exhibited windows of separability between water hyacinth and other land cover types. It was also observed that on the use of Landsat 8 OLI produced high overall classification accuracy of 72%, when compared Landsat 7 ETM, which yielded lower accuracy of 57%. Water hyacinth had optimal accuracies ( i.e. 92%), when compared to other land cover types, based on Landsat 8 OLI data. However, when using Landsat 7 ETM data, classification accuracies of water hyacinth were relatively lower ( i.e. 67%), when compared to other land cover types ( i.e. water with accuracy of 100%). Spectral curves of the old, intermediate and the young water hyacinth in Lake Chivero based on: (a) Landsat 8 OLI, and (b) Landsat 7 ETM were derived. Overall, the findings of this study underscores the relevance of the new generation multispectral sensors in providing primary data-source required for mapping the spatial distribution, and even configuration of water weeds at lower or no cost over time and space. [ABSTRACT FROM AUTHOR]

Land degradation as a result of inappropriate land use practices, such as overgrazing and cultivation on steep slopes, etc. is one of the major global environmental challenges. Specifically, land degradation threatens the productivity and sustainability of the natural environment, agriculture, and most importantly rural economies in most developing countries, particularly the sub-Saharan region. The main aim of this study was therefore, to assess the potential and strength of using the free or readily available Landsat series data in mapping degraded land areas at the King Sabata Dalindyebo local municipality in the Eastern Cape, South Africa (1984–2010). Data analysis was done using a robust non-parametric classification ensemble; Discriminant Analysis (DA). The results show that degraded areas vary over the years. For example, the results show that the year 1994 and 2004 incurred high degradation levels, when compared to the year 1984 and 2010. Moreover, the observed degradation significantly (α = 0.05) varies with soil type. The chromic acrisols have the highest levels of erosion (approx. 80% in 1984), when compared to humic-umbric acrisols (less than 10% for the entire period under study). It can also be observed that considerable part of degradation occurred in the northern part of the municipal district. Overall, the findings of this research underlines the importance and efficacy of multispectral Landsat series data-set in mapping and monitoring levels of land degradation in data-scarce catchments. [ABSTRACT FROM AUTHOR]