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

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

A Smart Procedure for Assessing the Health Status of Terrestrial Habitats in Protected Areas: The Case of the Natura 2000 Ecological Network in Basilicata (Southern Italy)

Natura 2000 is the largest coordinated network of protected areas in the world, which has been established to preserve rare habitats and threatened species at the European Community level. Generally, tools for habitat quality assessment are based on the analyses of land-use/land-cover changes, thus, highlighting already overt habitat modifications. To evaluate the general quality conditions of terrestrial habitats and detect habitat degradation processes at an early stage, a direct and cost-effective procedure based on satellite imagery (Landsat data) and GIS (Geographic Information System) tools is proposed. It focuses on the detection of anomalies in vegetation matrix (stress/fragmentation), estimated for each habitat at the level of both a single protected site and local network, to identify habitat priority areas (HPA), i.e., areas needing priority interventions, and to support a rational use of resources (field surveys, recovery actions). By analyzing the statistical distributions of standardized NDVI for all the enclosed habitats (at the site or network level), the Degree of Habitat Consistency (DHC) was also defined. The index allows the assessment of the general status of a protected site/network, and the comparison of the environmental conditions of a certain habitat within a given protected site (SCI, SAC) with those belonging to the other sites of the network. The procedure was tested over the Natura 2000 network of the Basilicata region (Southern Italy), considered as a hotspot of great natural and landscape interest. An overall accuracy of ~97% was obtained, with quite low percentages of commission (~8%) and omission (~6%) errors. By examining the diachronic evolution (1985–2009) of DHC and HPA, it was possible to track progress or degradation of the analyzed areas over time and to recognize the efficaciousness/failure of past managements and interventions (e.g., controlled disturbances), providing decision-makers with a thorough understanding for setting up the most suitable mitigation/contrast measures

Editorial for the Special Issue “Advances of Remote Sensing in the Analysis of the Spatial and Temporal Variability of Land Surface”

Land systems have taken a central role in major environmental/climatic issues of the Anthropocene, as they are the result of interacting natural and anthropic processes that are crucial for life on Earth [...

Analyzing Space–Time Coherence in Precipitation Seasonality across Different European Climates

Seasonality is a fundamental feature of environmental systems which critically depend on the climate annual cycle. The regularity of the precipitation regime, in particular, is a basic factor to sustain equilibrium conditions. An incomplete or biased understanding of precipitation seasonality, in terms of temporal and spatial properties, could severely limit our ability to respond to climate risk, especially in areas with limited water resources or fragile ecosystems. Here, we analyze precipitation data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) at 0.050 resolution to study the spatial features of the precipitation seasonality across different climate zones in Central-Southern Europe during the period 1981–2018. A cluster analysis of the average annual precipitation cycle shows that seasonality under the current climate can be synthesized in the form of a progressive deformation process of the annual cycle, which starts from the northernmost areas with maximum values in summer and ends in the south, where maximum values are recorded in winter. Our analysis is useful to detect local season-dependent changes, enhancing our understanding of the geography of climate change. As an example of application to this issue, we discuss the seasonality analysis in a simulated scenario based on IPCC projections