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Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations. [ABSTRACT FROM AUTHOR]

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the difference between potential yield and actual farmer yield). To this end, crop yield gap practices in African countries need to be understood to fill this gap while decreasing the environmental impacts of agricultural systems. For instance, the variability of yields has been demonstrated to be strongly controlled by soil fertilizer use, irrigation management, soil attribute, and the climate. Consequently, the quantitative assessment and mapping information of soil attributes such as nitrogen (N), phosphorus (P), potassium (K), soil organic carbon (SOC), moisture content (MC), and soil texture (i.e., clay, sand and silt contents) on the ground are essential to potentially reducing the yield gap. However, to assess, measure, and monitor these soil yield-related parameters in the field, there is a need for rapid, accurate, and inexpensive methods. Recent advances in remote sensing technologies and high computational performances offer a unique opportunity to implement cost-effective spatiotemporal methods for estimating crop yield with important levels of scalability. However, researchers and scientists in Africa are not taking advantage of the opportunity of increasingly available geospatial remote sensing technologies and data for yield studies. The objectives of this report are to (i) conduct a review of scientific literature on the current status of African yield gap analysis research and their variation in regard to soil properties management by using remote sensing techniques; (ii) review and describe optimal yield practices in Africa; and (iii) identify gaps and limitations to higher yields in African smallholder farms and propose possible improvements. Our literature reviewed 80 publications and covered a period of 22 years (1998-2020) over many selected African countries with a potential yield improvement. Our results found that (i) the number of agriculture yield-focused remote sensing studies has gradually increased, with the largest proportion of studies published during the last 15 years; (ii) most studies were conducted exclusively using multispectral Landsat and Sentinel sensors; and (iii) over the past decade, hyperspectral imagery has contributed to a better understanding of yield gap analysis compared to multispectral imagery; (iv) soil nutrients (i.e., NPK) are not the main factor influencing the studied crop productivity in Africa, whereas clay, SOC, and soil pH were the most examined soil properties in prior papers. [ABSTRACT FROM AUTHOR]

This study focuses on the suspended sediment delivery of 17 rivers and streams of various sizes to the sea over a wide geographical area covering most of the Greek peninsula, utilizing two Moderate Resolution Imaging Spectroradiometer (MODIS) products. Equal-area polygons ("plume" polygons), were delineated at the mouths of each selected river. These polygons were utilized to estimate the suspended sediment load of each river through the application of suspended sediment indices, ratios, and masks. To achieve this, 669 Level 1B MODIS images (MOD02) and their corresponding MODIS cloud products (MOD35) were downloaded and processed for a 10-water-year period (2004–2014). During this period of 669 days, there were 58 flood events (episodes) ranging in duration from 5 to 45 days. Relative atmospheric correction was applied to the images based on four selected bright invariant areas (PIFs) scattered along mainland Greece. The second product used in this study was MOD09Q1, an atmospherically corrected 8-day composite processed for the entire record period (2000–2019). Suspended sediment indices, ratios, and masks were developed using all three visible channels and near-infrared (NIR) for the MOD02 dataset, while only Red and Near-InfraRed (NIR) channels were available from the MOD09Q dataset. The resulting rankings from the remote sensing analysis were compared with the predictions of soil loss models, and the outcomes were largely consistent. While the remote sensing results can be considered as a type of experimental data or measurements, they come with inherent limitations. These include infrequent access to cloud-free data on stormy days, the influence of wind and currents, and the potential impact of dust storms originating from Africa, among others. On the other hand, soil loss models are sensitive to the parameter values used, and in some cases, the uncertainties are significant. Hence, the ranking derived from remote sensing can serve as a calibration of the models, particularly for the BQART model, which provides information on the catchment's sink capacity. An index of "sediment productivity per square kilometer and mm of rainfall" was developed. This index can be considered a "sediment delivery ratio" and is crucial for accurately quantifying the phenomenon. [ABSTRACT FROM AUTHOR]

The type of algorithm employed to classify remote sensing imageries plays a great role in affecting the accuracy. In recent decades, machine learning (ML) has received great attention due to its robustness in remote sensing image classification. In this regard, random forest (RF) and support vector machine (SVM) are two of the most widely used ML algorithms to generate land cover (LC) maps from satellite imageries. Although several comparisons have been conducted between these two algorithms, the findings are contradicting. Moreover, the comparisons were made on local-scale LC map generation either from high or medium resolution images using various software, but not Python. In this paper, we compared the performance of these two algorithms for large area LC mapping of parts of Africa using coarse resolution imageries in the Python platform by the employing Scikit-Learn (sklearn) library. We employed a big dataset, 297 metrics, comprised of systematically selected 9-month composite FegnYun-3C (FY-3C) satellite images with 1 km resolution. Several experiments were performed using a range of values to determine the best values for the two most important parameters of each classifier, the number of trees and the number of variables, for RF, and penalty value and gamma for SVM, and to obtain the best model of each algorithm. Our results showed that RF outperformed SVM yielding 0.86 (OA) and 0.83 (k), which are 1–2% and 3% higher than the best SVM model, respectively. In addition, RF performed better in mixed class classification; however, it performed almost the same when classifying relatively pure classes with distinct spectral variation, i.e., consisting of less mixed pixels. Furthermore, RF is more efficient in handling large input datasets where the SVM fails. Hence, RF is a more robust ML algorithm especially for heterogeneous large area mapping using coarse resolution images. Finally, default parameter values in the sklearn library work well for satellite image classification with minor/or no adjustment for these algorithms. [ABSTRACT FROM AUTHOR]

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries. [ABSTRACT FROM AUTHOR]

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents' poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents' welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced. [ABSTRACT FROM AUTHOR]

Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield. [ABSTRACT FROM AUTHOR]

Interfaces between protected areas and their peripheries in southern Africa are subject to interactions between wildlife and livestock that vary in frequency and intensity. In these areas, the juxtaposition between production and conservation land uses in a context of increasing anthropisation can create issues associated with human-wildlife coexistence and raises concerns for biodiversity conservation, local development and livelihoods. This literature review aimed at addressing the need to consolidate and gather in one article current knowledge on potential uses of satellite remote sensing (SRS) products by movement ecologists to investigate the sympatry of wildlife/domestic ungulates in savanna interface environments. A keyword querying process of peer reviewed scientific paper, thesis and books has been implemented to identify references that (1) characterize the main environmental drivers impacting buffalo (Syncerus caffer caffer) and cattle (Bos taurus & Bos indicus) movements in southern Africa environments, (2) describe the SRS contribution to discriminate and characterize these drivers. In total, 327 references have been selected and analyzed. Surface water, precipitation, landcover and fire emerged as key drivers impacting the buffalo and cattle movements. These environmental drivers can be efficiently characterized by SRS, mainly through open-access SRS products and standard image processing methods. Applying SRS to better understand buffalo and cattle movements in semi-arid environments provides an operational framework that could be replicated in other type of interface where different wild and domestic species interact. There is, however, a need for animal movement ecologists to reinforce their knowledge of remote sensing and/or to increase pluridisciplinary collaborations. [ABSTRACT FROM AUTHOR]

The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone. [ABSTRACT FROM AUTHOR]

The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively. [ABSTRACT FROM AUTHOR]

Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface phenological response in the semi-arid savanna of Southern Africa. Various land surface phenological metrics for the green-up and senescing periods of the vegetation were retrieved from leaf index area (LAI) seasonal time series (2001 to 2015) maps for a study region in South Africa. Tree cover (%) data for 100 randomly selected polygons grouped into three tree cover classes, low (<20%, n = 44), medium (20-40%, n = 22) and high (>40%, n = 34), were used to determine the influence of varying tree cover (%) on the phenological metrics by means of the t-test. The differences in the means between tree cover classes were statistically significant (t-test p < 0.05) for the senescence period metrics but not for the green-up period metrics. The categorical data results were supported by regression results involving tree cover and the various phenological metrics, where tree cover (%) explained 40% of the variance in day of the year at end of growing season compared to 3% for the start of the growing season. An analysis of the impact of rainfall on the land surface phenological metrics showed that rainfall influences the green-up period metrics but not the senescence period metrics. Quantifying the contribution of tree cover to the day of the year at end of growing season could be important in the assessment of the spatial variability of a savanna ecological process such as the risk of fire spread with time. [ABSTRACT FROM AUTHOR]

The present study analyses the new Visible Infrared Imaging Radiometer Suite (VIIRS) lights data to determine whether it can provide more accurate proxies for socioeconomic data in areas with poor quality data than proxies based on stable lights. Our analysis indicates that VIIRS lights are a promising supplementary source for standard measures on population and economic output at a small scale, especially for low population and economic density areas in Africa. The current analysis also suggests that in comparison to stable lights generated by the DMSP-OLS system, data generated by the VIIRS system provide more information to estimate population than output index. However, further analysis and formal statistical models are needed to evaluate the usefulness of VIIRS lights versus other lights products. With more advanced methods, there is also a potential to generate a synthetic index by combining different lights products to produce a better proxy measure for other indexes. [ABSTRACT FROM AUTHOR]

Hydro-climatic resilience is an essential element of food security. The miombo ecosystem in Southern Africa supports varied land uses for a growing population. Albedo, Leaf Area Index (LAI), Fractional Vegetation Cover (FVC), Solar-Induced chlorophyll Fluorescence (SIF), and precipitation remote-sensing data for current climate were jointly analyzed to explore vegetation dynamics and water availability feedbacks. Changes in the surface energy balance tied to vegetation status were examined in the light of an hourly albedo product with improved atmospheric correction derived for this study. Phase-space analysis shows that the albedo's seasonality tracks the landscape-scale functional stability of miombo and woody savanna with respect to precipitation variations. Miombo exhibits the best adaptive traits to water stress which highlights synergies among root-system water uptake capacity, vegetation architecture, and landscape hydro-geomorphology. This explains why efforts to conserve the spatial structure of the miombo forest in sustainable farming of seasonal wetlands have led to significant crop yield increases. Grass savanna's high vulnerability to water stress is illustrative of potential run-away impacts of miombo deforestation. This study suggests that phase-space analysis of albedo, SIF, and FVC can be used as operational diagnostics of ecosystem health. [ABSTRACT FROM AUTHOR]

Understanding trends or changes in biomass and biodiversity around conservation areas in Africa is important and has economic and societal impacts on the surrounding communities. Gorongosa National Park, Mozambique was established under unique conditions due to its complex history. In this study, we used a time-series of Normalized Difference Vegetation Index (NDVI) to explore seasonal trends in biomass between 2000 and 2016. In addition, vegetation directional persistence was created. This product is derived from the seasonal NDVI time series-based analysis and represents the accumulation of directional change in NDVI relative to a fixed benchmark (2000–2004). Trends in precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was explored from 2000–2016. Different vegetation covers are also considered across various landscapes, including a comparison between the Lower Gorongosa (savanna), Mount Gorongosa (rainforest), and surrounding buffer zones. Important findings include a decline in precipitation over the time of study, which most likely drives the observed decrease in NDVI. In terms of vegetation persistence, Lower Gorongosa had stronger positive trends than the buffer zone, and Mount Gorongosa had higher negative persistence overall. Directional persistence also varied by vegetation type. These are valuable findings for park managers and conservationists across the world. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture at the Earth'surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time). [ABSTRACT FROM AUTHOR]