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Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed a small fire dataset (SFD) for Sub-Saharan Africa for 2016, this paper presents a new version of the dataset for 2019 using the two Sentinel-2 satellites (A and B) and VIIRS active fires. Total estimated BA was 4.8 Mkm

In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user’s decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-score

Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s–1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no noteworthy human effect on vegetation dynamics at sub continental scale from the 1980s to late 1990s. However, several regional/local crises related to natural resources occurred in the last decades despite the re-greening thus underlying that more detailed studies are needed. In this study we used time-series (1998–2010) of SPOT–VGT NDVI and FEWS–RFE rainfall estimates to analyse vegetation – rainfall correlation and to map areas of local environmental anomalies where significant vegetation variations (increase/decrease) are not fully explained by seasonal changes of rainfall. Some of these anomalous zones (hot spots) were further analysed with higher resolution images Landsat TM/ETM+ to evaluate the reliability of the identified anomalous behaviour and to provide an interpretation of some example hot spots. The frequency distribution of the hot spots among the land cover classes of the GlobCover map shows that increase in vegetation greenness is mainly located in the more humid southern part and close to inland water bodies where it is likely to be related to the expansion/intensification of irrigated agricultural activities. On the contrary, a decrease in vegetation greenness occurs mainly in the northern part (12°–15°N) in correspondence with herbaceous vegetation covers where pastoral and cropping practices are often critical due to low and very unpredictable rainfall. The results of this study show that even if a general positive re-greening due to increased rainfall is evident for the entire Sahel, some local anomalous hot spots exist and can be explained by human factors such as population growth whose level reaches the ecosystem carrying capacity as well as population displacement leading to vegetation recovery.

Since fire is a major threat to forests and wooded areas in the Mediterranean environment of Southern Europe, systematic regional fire monitoring is a necessity. Satellite data constitute a unique cost-effective source of information on the occurrence of fire events and on the extent of the area burned. Our objective is to develop a (semi-)automated algorithm for mapping burned areas from medium spatial resolution (30 m) satellite data. In this article we present a multi-criteria approach based on Spectral Indices, soft computing techniques and a region growing algorithm; theoretically this approach relies on the convergence of partial evidence of burning provided by the indices. Our proposal features several innovative aspects: it is flexible in adapting to a variable number of indices and to missing data; it exploits positive and negative evidence (bipolar information) and it offers different criteria for aggregating partial evidence in order to derive the layers of candidate seeds and candidate region growing boundaries. The study was conducted on a set of Landsat TM images, acquired for the year 2003 over Southern Europe and pre-processed with the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) processing chain for deriving surface spectral reflectance ρ i in the TM bands. The proposed method was applied to show its flexibility and the sensitivity of the accuracy of the resulting burned area maps to different aggregation criteria and thresholds for seed selection. Validation performed over an entire independent Landsat TM image shows the commission and omission errors to be below 21% and 3%, respectively.

The lakes of the European perialpine region constitute a large water reservoir, which is threatened by the anthropogenic pressure altering water quality. The Water Framework Directive of the European Commission aims to protect water resources and monitoring is seen as an essential step for achieving this goal. Remote sensing can provide frequent data for large scale studies of water quality parameters such as chlorophyll-a (chl-a). In this work we use a dataset of maps of chl-a derived from over 200 MERIS (MEdium Resolution Imaging Spectrometer) satellite images for comparing water quality of 12 perialpine lakes in the period 2003-2009. Besides the different trophic levels of the lakes, results confirm that the seasonal variability of chl-a concentration is particularly pronounced during spring and autumn especially for the more eutrophic lakes. We show that relying on only one sample for the assessment of lake water quality during the season might lead to misleading results and erroneous assignments to quality classes. Time series MERIS data represents a suitable and cost-effective technology to fill this gap, depicting the dynamics of the surface waters of lakes in agreement with the evolution of natural phenomena.

The determination of sample size before collecting experimental data is fundamental to obtain reliable estimates of variables describing agroecosystem development. In order to analyze the influence of experimental factors (artificially-induced variability) on rice sample size, an experiment was carried out in 2004 in northern Italy. In particular, different sample size determinations were carried out for different fertilization levels, varieties (Indica and Japonica type), development stages, sowing techniques and typologies of the sampling unit. The obtained sample sizes were compared to investigate the influence of each factor, keeping the others constant (for example, we have compared the sample sizes computed for different fertilization levels within the same variety, the same phenological stage and the same sampling unit). Since original data were often not normally distributed and the variances of the original samples were not homogeneous, a new approach for sample size determination based on a visual evolution of the jackknife was preferred to classical techniques. Results (expressed as number of plants) showed that (i) sample sizes computed in an early phenological stage (between 21 and 27) are higher than those calculated for later stages (15–21); (ii) fertilization hides soil N content variability with the consequence that larger sample sizes are required for unfertilized plots (21–27) compared to fertilized plots (15–27) and (iii) for the early sampling, the Indica type variety required larger sample size (always 27) with respect to the Japonica type variety (21–24). For row-seeded rice, the number of plants instead of linear centimeters as the sampling unit led to lower sample sizes (18–27 versus 30–33). These results highlight the influence of experimental factors and development stage on within-plot variability, and therefore the importance of preliminary samplings for sample size determination.

Radiometric analysis of SPOT-VEGETATION (VGT) images acquired over Australia was carried out as a basis for the development of an algorithm to map burnt areas in woodland savannas. We analysed the variability of daily ground reflectance and its relationship with illumination and viewing geometry. Finding that the geometrical effects can be parameterised by the phase angle (angle between the illumination and the viewing directions) and the viewing zenith angle (VZA), we fit a simple linear model to the observations. The results show that about 60–70% of the variability in the daily reflectance is caused by geometrical effects. The residual 30–40% of the variability is probably due to changes in vegetation condition, such as senescence, and residual atmospheric contamination. We tested temporal compositing as a practical method of reducing the variability in the reflectance whilst retaining the burnt area signal. We inspected the radiometric and geometrical effects of four different compositing criteria and showed that minimum near infrared (NIR) is the most appropriate for burnt area mapping over the study area. In order to analyse the sensitivity of the VGT spectral bands and derived indices to changes induced by fire, we extracted burnt area spectral signatures for different vegetation types. The persistence of the burnt signal, as observed with each band and index, was analysed. Among the bands, NIR is shown to be the most sensitive to fire occurrence. There is a clear drop in the reflectance immediately after the fire and it remains very low during subsequent weeks. On the other hand, the burnt signal in the short-wave infrared (SWIR) band is showed to be strongly dependent on the vegetation cover type and on the age of the burnt area. Among the indices, the Global Environment Monitoring Index (GEMI) is identified as the most suitable for detecting changes induced by fire on the vegetation cover.