A Possible Land Cover EAGLE Approach to Overcome Remote Sensing Limitations in the Alps Based on Sentinel-1 and Sentinel-2: The Case of Aosta Valley (NW Italy)

Abstract

Land cover (LC) maps are crucial to environmental modeling and define sustainable management and planning policies. The development of a land cover mapping continuous service according to the new EAGLE legend criteria has become of great interest to the public sector. In this work, a tentative approach to map land cover overcoming remote sensing (RS) limitations in the mountains according to the newest EAGLE guidelines was proposed. In order to reach this goal, the methodology has been developed in Aosta Valley, NW of Italy, due to its higher degree of geomorphological complexity. Copernicus Sentinel-1 and 2 data were adopted, exploiting the maximum potentialities and limits of both, and processed in Google Earth Engine and SNAP. Due to SAR geometrical distortions, these data were used only to refine the mapping of urban and water surfaces, while for other classes, composite and timeseries filtered and regularized stack from Sentinel-2 were used. GNSS ground truth data were adopted, with training and validation sets. Results showed that K-Nearest-Neighbor and Minimum Distance classification permit maximizing the accuracy and reducing errors. Therefore, a mixed hierarchical approach seems to be the best solution to create LC in mountain areas and strengthen local environmental modeling concerning land cover mapping.

Snow Metrics as Proxy to Assess Sarcoptic Mange in Wild Boar: Preliminary Results in Aosta Valley (Italy)

“…Therefore, the results obtained must be considered as preliminary and deserve more in-depth studies and insights by the scientific community in order to achieve solid evidence on samples obtained over several years and in larger areas confirming or confuting the geospatially based results here obtained. Having said this, the use of remote sensing and GIS tools in the veterinary and eco-epidemiological fields can certainly constitute an added value to ordinary diagnostic techniques [59][60][61][62][63][64][65]. The technological transfer offered by spatial analyzes in terms of repercussions on the veterinary sector as highlighted in this study can certainly be positive.…”

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

“…Though it is the smallest in Italy, its geomorphology makes it one of the most complex. It is situated in the Western Alps [25].…”

“…The reference year 2020 was considered. Land cover (LC) complies with EAGLE guidelines and is realized according to the following methods [30] and it is based on Sentinels missions (S1-S2) [31][32][33][34] adopting Aosta Valley land cover [25,30]. It is worth to note that, to assist the semantic and technological foundation of a European harmonized information management capability for land monitoring, the EAGLE Group has been working on a solution and proof of concept since 2008.…”

Integrating GIS, Remote Sensing, and Citizen Science to Map Oak Decline Risk across the Daniel Boone National Forest

“…However, due to the lack of spatially explicit report data on the oak decline sites and associated environmental characteristics, risk assessment and mapping at landscape and regional scales are challenging, as researchers in other systems have also noted [22,23]. Other studies have successfully combined remote sensing and ground truthing to strengthen resulting models in a variety of systems and for different purposes, from mapping land cover [24] to testing models of habitat suitability [25]. Risk maps for tree health issues that use GIS and satellite information have been developed in a number of contexts, such as forest fire and disease spread [26,27].…”