In developing countries, information on the area and spatial distribution of paddy rice fields is an essential requirement for ensuring food security and facilitating targeted actions of both technical assistance and restoration of degraded production areas. In this study, Sentinel 1 (S1) and Sentinel 2 (S2) imagery was used to map lowland rice crop areas in the Sédhiou region (Senegal) for the 2017, 2018, and 2019 growing seasons using the Random Forest (RF) algorithm. Ground sample datasets were annually collected (416, 455, and 400 samples) for training and testing yearly RF classification. A procedure was preliminarily applied to process S2 scenes and yield a normalized difference vegetation index (NDVI) time series less affected by clouds. A total of 93 predictors were calculated from S2 NDVI time series and S1 vertical transmit–horizontal receive (VH) and vertical transmit–vertical receive (VV) backscatters. Guided regularized random forest (GRRF) was used to deal with the arising multicollinearity and identify the most important predictors. The RF classifier was then applied to the selected predictors. The algorithm predicted the five land cover types present in the test areas, with a maximum accuracy of 87% and kappa coefficient of 0.8 in 2019. The broad land cover maps identified around 12,500 (2017), 13,800 (2018), and 12,800 (2019) ha of lowland rice crops. The study highlighted a partial difficulty of the classifier to distinguish rice from natural herbaceous vegetation (NHV) due to similar temporal patterns and high intra-class variability. Moreover, the results of this investigation indicated that S2-derived predictors provided more valuable information compared to VV and VH backscatter-derived predictors, but a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs. An example is finally provided that illustrates how the maps obtained can be combined with ground observations through a ratio estimator in order to yield a statistically sound prediction of rice area all over the study region.

Niamey, the capital of Niger, is particularly prone to floods, since it is on the banks of the Niger River, which in its middle basin has two flood peaks: one in summer (the red flood) and one in winter (the black flood). In 2020, the Niger River in Niamey reached its all-time highest levels following an abundant rainy season. On the other hand, the floods in Niamey have been particularly frequent in the last decade, a symptom of a change in hydroclimatic behaviour already observed since the end of the great droughts of the 1970s and 1980s and which is identified with the name of Sahelian Paradox. This study, starting from the analysis of the 2020 flood and from the update of the rating curve of the Niamey hydrometric station, analyses the rainfall–runoff relationship on the Sahelian basins of the Medium Niger River Basin (MNRB) that are at the origin of the local flood. The comparative analysis of runoffs, annual maximum flows (AMAX) and runoff coefficients with various rainfall indices calculated on gridded datasets allowed to hydroclimatically characterise the last decade as a different period from the wet one before the drought, the dry one and the post-drought one. Compared to the last one, the current period is characterised by a sustained increase in hydrological indicators (AMAX +27%) consistent with the increase in both the accumulation of precipitation (+11%) and the number (+51%) and magnitude (+54%) of extreme events in the MNRB. Furthermore, a greater concentration of rainfall and extremes (+78%) in August contributes to reinforcing the red flood’s positive anomalies (+2.23 st.dev in 2020). The study indicates that under these conditions the frequency of extreme hydrological events in Niamey will tend to increase further also because of the concurrence of drivers such as river-bed silting and levee effects. Consequently, the study concludes with the need for a comprehensive flood-risk assessment on the Niamey city that considers both recent hydroclimatic trends and urbanisation dynamics in flood zones hence defining the most appropriate risk-reduction strategies.

Physiological equivalent temperature (PET) is one of most used indices for outdoor human well-being evaluation; its determination is particularly helpful for adaptation strategies in built-up areas affected by the urban heat island (UHI) phenomenon. In this work, we presented a methodology to compute spatially and temporally resolved PET values during a heatwave at the city level, based on a combination of satellite products, in situ measurements and Envi-met model runs upscaled from specific test areas to the broader city. The method exploits the ECOSTRESS sensor to detect surface thermal patterns at different diurnal times by developing an hourly based index called hUHTI (hourly urban heatwave thermal index) that serves as a proxy. A case study on Prato (Italy) municipality during the 2021 summer heatwave events is presented. Based on the available satellite products, a set of six hourly diurnal PET maps at 10 m spatial resolution were derived and daytime outdoor thermal patterns and trends were investigated according to land cover. hUHTI index resulted a more suitable tool as PET proxy compared to the sole ECOSTRESS land surface temperature (LST) product, especially for morning and evening times. Hourly PET maps were summarized by the use of an average exceedance map providing public administrations and stakeholders a synthetic tool for urban regeneration purposes at city scale.

Flood risk mapping is spreading in the Global South due to the availability of high-resolution/high-frequency satellite imagery, volunteered geographic information, and hydraulic models. However, these maps are increasingly generated without the participation of exposed communities, contrary to the Sendai Framework for Disaster Risk Reduction 2015–2030 priorities. As a result, the understanding of risk is limited. This study aims to map flood risk with citizen science complemented by hydrology, geomatics, and spatial planning. The Niger River floods of 2024–2025 on a 113 km² area upstream of Niamey are investigated. The novelty of the work is the integration of local and technical knowledge in the micro-mapping of risk in a large area. We consider risk the product of a hazard and damage in monetary terms. Focus groups in flooded municipalities, interviews with irrigation perimeter managers, and statistical river flow and rainfall analysis identified the hazard. The flood plain was extracted from Sentinel-2 images using MNDWI and validated with ground control points. Six classes of assets were identified by visual photo interpretation of very high-resolution satellite imagery. Damage was ascertained through interviews with a sample of farmers. The floods of 2024–2025 may occur again in the next 12–19 years. Farmers cannot crop safer sites, raising significant environmental justice issues. Damage depends on the strength of the levees, the crop, and the season. From January to February, horticulture is at a higher risk. Flooding does not bring benefits. Risk maps highlight hot spots, are validated, and can be linked to observed flood levels.

Although environmental sustainability and economic feasibility frameworks have been developed to evaluate the impact of insect farms, significant studies on the development of territorial suitability methods specifically tailored to insect production have not yet been performed. This work proposes a GIS-based multi criteria decision making analysis to evaluate the suitability of a territory to the installation of insect farms. A case study developed specifically for black soldier flyHermetia illucens(HI) insect farms in Emilia-Romagna region (North Italy) is presented. This is an area which, due to its agro-industrial nature and the consequent large production of related waste and by-products, is an optimal candidate for the installation of insect farms. Nine physical, environmental, and economic criteria were assessed. A raster spatial layer for each criterion was obtained, and their values were standardised. The criteria were weighted using the analytical hierarchical process, based on expert opinion recorded via an online questionnaire. The resulting weights were used to combine the single criterion maps using a weighted linear combination procedure and generate, after reclassification, the final suitability map. This map indicates the areas suitable for insect farms at the local level and provides indications for estimating suitability at regional and provincial level. The results showed that 56.2% of the study area was deemed unsuitable, and 43.8% was determined suitable for an HI insect farm; best locations were found around the main agro-industrial centres of the Po valley. The proposed methodology can be easily adapted to other breeding models, insects or study areas and adds valuable information in the development of guidelines for industrial-scale insect farms.