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This review examines high-quality research evidence that synthesises the effects of extreme heat on human health in tropical Africa. Web of Science (WoS) was used to identify research articles on the effects extreme heat, humidity, Wet-bulb Globe Temperature (WBGT), apparent temperature, wind, Heat Index, Humidex, Universal Thermal Climate Index (UTCI), heatwave, high temperature and hot climate on human health, human comfort, heat stress, heat rashes, and heat-related morbidity and mortality. A total of 5, 735 articles were initially identified, which were reduced to 100 based on a set of inclusion and exclusion criteria. The review discovered that temperatures up to 60°C have been recorded in the region and that extreme heat has many adverse effects on human health, such as worsening mental health in low-income adults, increasing the likelihood of miscarriage, and adverse effects on well-being and safety, psychological behaviour, efficiency, and social comfort of outdoor workers who spend long hours performing manual labour. Extreme heat raises the risk of death from heat-related disease, necessitating preventative measures such as adaptation methods to mitigate the adverse effects on vulnerable populations during hot weather. This study highlights the social inequalities in heat exposure and adverse health outcomes. [ABSTRACT FROM AUTHOR]

Moreover, resources such as Copernicus not only make access to imagery simple, but also provide analysis-ready data and even data products so that specialist knowledge on extracting information from the remotely sensed data is sometimes no longer necessary. At that time, researchers were essentially constrained to data acquired by airborne systems such as aerial photography and multispectral scanners together with only limited options for satellite sensor imagery, notably from Landsat sensors and the NOAA AVHRR. Many articles have made use of Landsat sensor data (Moris et al. [32]; Hopkins et al. [23]) notably taking advantage of the relatively long time series of data that has now been formed (Zhao et al. [57]; Bost et al. [5]; Jung et al. [24]; Fisher et al. [16]; Yu et al. [55]). Graph The Allerton Park workshop essentially defined landscape ecology as a field of study that cuts across multiple natural and social sciences (Risser et al. [42]). [Extracted from the article]

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the "spectral species"—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept. [ABSTRACT FROM AUTHOR]

Land-based fish-processing activities in coastal fringe areas and their social-ecological impacts have often been overlooked by marine scientists and antislavery groups. Using remote sensing methods, the location and impacts of fish-processing activities were assessed within a case study of Bangladesh's Sundarbans mangrove forests. Ten fish-processing camps were identified, with some occurring in locations where human activity is banned. Environmental degradation included the removal of mangroves, erosion, and the destruction of protected areas. Previous studies have identified cases of labour exploitation and modern slavery occurring within the Sundarbans, and remote sensing was used to triangulate these claims by providing spatial and temporal analysis to increase the understanding of the operational trends at these locations. These findings were linked to the cyclical relationship between modern slavery and environmental degradation, whereby environmental damage is both a driver and result of workers subjected to modern slavery. Remote sensing can be used as an additional methodological tool to support the achievement of the Sustainable Development Goals (SDGs) and provide evidence to support the promotion of the "freedom dividend" which would have far-reaching economic, social, cultural, and environmental benefits. Satellite remote sensing is likely to play an important role going forward for understanding these issues but should be augmented with ground-based data collection methods. [ABSTRACT FROM AUTHOR]

Neural networks are attractive tools for the derivation of thematic maps from remotely sensed data. Most attention has focused on the multilayer perceptron (MLP) network but other network types are available and have different properties that may sometimes be more appropriate for some applications. Here a MLP, radial basis function (RBF) and probabilistic neural network (PNN) were used to classify remotely sensed data of an agricultural site. The accuracy of these classifications ranged from 86.25–91.25%. The accuracy of the PNN classification could be increased through the incorporation of prior probabilities of class membership but the accuracy of each classification could also be degraded by the presence of an untrained class. Post-classification analyses, however, could be used to identify potentially misclassified cases, including those belonging to an untrained class, to increase accuracy. The effect of the post-classification analysis on the accuracy of the classification derived from each of the three network types investigated differed and it is suggested that network type be selected carefully to meet the requirements of the application in-hand. [ABSTRACT FROM AUTHOR]

Abstract. Remote sensing is the only feasible means of mapping and monitoring land cover at regional to global scales. Unfortunately the maps are generally derived through the use of a conventional 'hard' classification algorithm and depict classes separated by sharp boundaries. Such approaches and representations are often inappropriate particularly when the land cover being represented may be considered to be fuzzy. The definition of boundaries between classes can therefore be difficult from remotely sensed data, particularly for continuous land cover classes which are separated by a fuzzy boundary which may also vary spatially in time. In this paper a neural network was used to derive fuzzy classifications of land cover along a transect crossing the transition from moist semi-deciduous forest to savanna in West Africa in February and December 1990. The fuzzy classifications revealed both sharp and gradual boundaries between classes located along the transect. In particular, the fuzzy classifications enabled the definition of important boundary properties, such as width and temporal displacement. [ABSTRACT FROM AUTHOR]