A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses

Abstract Cloud detection in optical remote sensing images is a crucial problem because undetected clouds can produce misleading results in the analyses of surface and atmospheric parameters. Sentinel-2 provides high spatial resolution satellite data distributed with associated cloud masks. In this paper, we evaluate the ability of Sentinel-2 Level-1C cloud mask products to discriminate clouds over a variety of biogeographic scenarios and in different cloudiness conditions. Reference cloud masks for the identification of misdetection were generated by applying a local thresholding method that analyses Sentinel-2 Band 2 (0.490 μm) and Band 10 (1.375 μm) separately; histogram-based thresholds were locally tuned by checking the single bands and the natural color composite (B4B3B2); in doubtful cases, NDVI and DEM were also analyzed to refine the masks; the B2B11B12 composite was used to separate snow. The analysis of the cloud classification errors obtained for our test sites allowed us to get important inferences of general value. The L1C cloud mask generally underestimated the presence of clouds (average Omission Error, OE, 37.4%); this error increased (OE > 50%) for imagery containing opaque clouds with a large transitional zone (between the cloud core and clear areas) and cirrus clouds, fragmentation emerged as a major source of omission errors (R2 0.73). Overestimation was prevalently found in the presence of holes inside the main cloud bodies. Two extreme environments were particularly critical for the L1C cloud mask product. Detection over Amazonian rainforests was highly inefficient (OE > 70%) due to the presence of complex cloudiness and high water vapor content. On the other hand, Alpine orography under dry atmosphere created false cirrus clouds. Altogether, cirrus detection was the most inefficient. According to our results, Sentinel-2 L1C users should take some simple precautions while waiting for ESA improved cloud detection products

Integrated Indicators for the Estimation of Vulnerability to Land Degradation

In this chapter we approach the assessment of the vulnerability to land degradation of a typical Mediterranean environment using a modified version of the ESA model. This approach combines analyses of the socio-economic component with analyses of the vegetation trends. According to the standard ESA strategy, different indicators representing the impact of agricultural and grazing activities are used. The main feature of these indicators is that they are census-based and consequently suitable only for the analysis at municipal scale. Therefore we have also elaborated a mechanization index (proxy for soil compaction induced by agricultural machineries) that uses land cover and morphological data [36], enabling high spatial resolution and faster rate of update. The indicators related to the anthropic impact are integrated into an overall Land Management Index (LMI) and in each area it is possible to enhance the main contributing factors to highlight the prevailing forces that drive human-induced degradation processes. In order to include vegetation in the vulnerability map we analyze satellite vegetation index NDVI (Normalized Difference Vegetation Index) which is recognized as ideal tool for monitoring long term trends of degradation phenomena and assessing different values of severity of the concerned processes [37,38]. The final result of our analyses is an integrated vulnerability map of the investigated region, accounting for management and vegetation factors, which allows us to identify priority sites where restoration/rehabilitation interventions are urgent. The adopted procedure can be easily applied to geographic contexts characterized by high complexity in terms of land cover type and economic vocation (intensive agriculture, grazing, industrial activities) thus enabling an early detection of the areas most vulnerable to land degradation

Receptor-mediated endocytosis and trafficking between endosomal–lysosomal vacuoles in Giardia lamblia

The early branching Giardia lamblia has highly polarized vacuoles, located underneath the plasma membrane, which have at least some of the characteristics of endosomes and of lysosomes. These peripheral vacuoles (PVs) are necessary for nutrient uptake and the maintenance of plasma membrane composition, but whether they carry out sorting and segregation of receptors and ligands is a matter of debate. Here, we showed that the internalization of low-density lipoprotein (LDL) to the PVs is highly dynamic in trophozoites with a rate similar to the internalization of the low-density lipoprotein receptor-related protein 1. Moreover, by analyzing receptor-mediated and fluid-phase endocytosis in living cells, we showed that after endocytosis LDL but not dextran moved laterally between the PVs. We speculate on PV functional heterogeneity and maturation in this parasite.Fil: Rivero, Maria Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Investigaciones Médicas Mercedes y Martín Ferreyra; ArgentinaFil: Jausoro, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Investigaciones Médicas Mercedes y Martín Ferreyra; ArgentinaFil: Bisbal, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Investigaciones Médicas Mercedes y Martín Ferreyra; ArgentinaFil: Feliziani, Constanza. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Investigaciones Médicas Mercedes y Martín Ferreyra; ArgentinaFil: Lanfredi Rangel, Adriana. Centro de Pesquisas Gonçalo Moniz, Serviço de Microscopia Eletrônica; BrasilFil: Touz, Maria Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Investigaciones Médicas Mercedes y Martín Ferreyra; Argentin

Investigating climate variability and long-term vegetation activity across heterogeneous Basilicata agroecosystems

The Basilicata region summarizes many basic features of the biogeographic complexity characterizing Mediterranean countries. The intricate geomorphology and the long history of human management generated the current landscapes, which include both high-value ecosystems and areas prone to desertification. Preserving goods and services provided by such composite land cover mosaics poses many problems due to the interference/overlap of diverse natural and anthropic factors which make the correct selection of relevant parameters and the interpretation of observational data rather difficult. Here, we study interconnections between local climate and vegetation activity by correlating parameters characterizing the interannual statistics of the NDVI (Normalized Difference Vegetation Index), derived from satellite data, with a recently devised multivariate statistical index of meteoclimatic variability. We used a 15-year sequence of remote images concerning a set of plots located around meteorological ground stations of the central-eastern part of the region to pick up spatial structures in the vegetation–climate relationships. Our analyses were able to correlate spatial heterogeneity to variations in water exchanges between vegetation and atmosphere. This study represents a first step to improve the description of relevant processes to protect natural habitats and quality agriculture, therefore combating land degradation and climate change detrimental effects

Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests

The use of satellite data to detect forest areas impacted by extreme events, such as droughts, heatwaves, or fires is largely documented, however, the use of these data to identify the heterogeneity of the forests’ response to determine fine scale spatially irregular damage is less explored. This paper evaluates the health status of forests in southern Italy affected by adverse climate conditions during the hot and dry summer of 2017, using Sentinel-2 images (10m) and in situ data. Our analysis shows that the post-event—NDVI (Normalized Difference Vegetation Index) decrease, observed in five experimental sites, well accounts for the heterogeneity of the local response to the climate event evaluated in situ through the Mannerucci and the Raunkiaer methods. As a result, Sentinel-2 data can be effectively integrated with biological information from field surveys to introduce continuity in the estimation of climate change impacts even in very heterogeneous areas whose details could not be captured by lower resolution observations. This integration appears to be a successful strategy in the study of the relationships between the climate and forests from a dynamical perspective

Wavelet analysis as a tool to characteriseand remove environmental noisefrom self-potential time series

Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctuations in electrical signals, which might be addressed to meteorological variability. In the time-scale domain of the wavelet transform, rain data are used as markers to single out those wavelet coefficients of the electric signal which can be considered relevant to the environmental disturbance. Then these coefficients are filtered out and the signal is recovered by anti-transforming the retained coefficients. Such methodological approach might be applied to characterise unwanted environmental noise. It also can be considered as a practical technique to remove noise that can hamper the correct assessment and use of electrical techniques for the monitoring of geophysical phenomena