Wildfires represent one of the major agent of change as far as forest ecosystems are concerned. These natural hazards are monitored and studied at different stages, exploiting many and innovative tools. For example, post-fire studies are mainly targeted in quantify the impact of wildfire events on forests and monitor the recovery of natural environments. The study of post-fire vegetation restoration is of great importance for decision makers and landscape planners, as it can provide useful information to update landscape vulnerability maps, monitor forest recovery processes and identify forest repopulation areas. In this contest, satellite remote sensing represents a time- and cost-effective alternative to the traditional methods used for post-fire vegetation dynamics monitoring, especially over large areas. In particular, the Sentinel-2 MSI sensors, thanks to free data access with unprecedented trade-off in spatial-temporal resolutions (10-60 m pixel size and 5-days revisit time), represents a great occasion of improvement on such topic.
The objective of this contribution is to identify post-fire vegetation restoration dynamics for the study area surrounding Naples (Italy). This study case was in fact interested by severe wildfire events during summer 2017. Specific objectives are: (i) identify representative trajectories of vegetation restoration and (ii) evaluate how land use vulnerability (e.g. forest resilience, landslide susceptibility) can be better evaluate from identified dynamics.
A database of 218 Sentinel-2 A and B acquisitions was processed in order to produce smoothed temporal series of Leaf Area Index (LAI) values for the period 2016-2018. LAI time series were smoothed using a Whittaker approach, to avoid the residual noise rate affecting time series due to cloud contamination, and masked using a reference burned area map. During the time series analyses phase, each burned area was considered as a single Region of Interest (ROI) and a representative LAI time signature was extracted in the pre-fire, fire and post-fire years. Successively, a set of temporal features were extracted from time series and a clustering procedure was implemented with the aim to isolate clusters of post-fire LAI response behavior. In the last methodological step, clusters were compared against already available topographic and ancillary information, trying to qualitatively investigate which environmental drivers could be related to specific fire vegetation restoration processes.
Results lead to identify different vegetation recover responses and to associate them to the main environmental drivers. Future perspectives of this research study lies in the development of automatic approaches to analyze Sentinel-2 time series data and operatively map vegetation restoration typologies over burn-affected areas.

The establishment of the Copernicus Programme by the European Commission created a new paradigm in the availability and accessibility of data information, offering services based on satellite Earth Observation and in situ data under 6 thematic Copernicus services. The Copernicus Programme became the world largest space data provider, providing complete, free and open access to satellite data, mainly acquired by Sentinel satellites. Main advantages offered by Sentinel data are the improved spatial resolution and high revisit frequency, that makes them useful for a wide range of applications.
Sentinel-1 satellites constellation acquires SAR data in single or dual polarization with a revisit time of 6 days. Sentinel-1 Level 1 data are distributed by the Copernicus Open Access Hub under two product types: Ground Range Detected (GRD) and Single Look Complex (SLC). While few research applications need Sentinel-1 GRD data with few corrections applied, the wider part of the users needs products with a standard set of corrections applied. In order to facilitate the exploitation of Sentinel-1 GRD products, there is the need to standardise procedures to preprocess SAR data to a higher processing level.
A standard generic workflow to preprocess Copernicus Sentinel-1 GRD data is here presented. The workflow aims to apply a series of standard corrections, and precisely to apply precise orbit of acquisition, remove thermal and image border noise, perform radiometric calibration, apply range doppler and terrain correction. Additionally, the workflow allows to spatially snap Sentinel-1 GRD products to Sentinel-2 MSI data grids, in order to promote the use of satellite virtual constellations by means of data fusion techniques.
The presented workflow allows to produce a set of preprocessed Sentinel-1 GRD data, offering a benchmark for the development of new products and operational downstreaming services based on Copernicus Sentinel-1 GRD data, with the aim of providing reliable information of interest to a wide range of communities.

The increasing trend of larch forests burning in the permafrost zone (60–65° N, 95–105°E) is observed in Siberia. More than 10% of entire larch forests were damaged by wildfire during the last 15 years. Post-fire effect might determine long-term dynamics of the seasonal thawed layer.

The increasing trend of larch forests burning in the permafrost zone (60–65° N, 95–105°E) is observed in Siberia. More than 10% of entire larch forests were damaged by wildfire during the last 15 years. Post-fire effect might determine long-term dynamics of the seasonal thawed layer.

Current research analysed the reflectance and thermal anomalies of the post-pyrogenic sites under the conditions of permafrost. Studies are based on long-term Terra, Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer) survey for 2006–2018. We used IR thermal range data of 10.780–11.280 microns (MOD11A1 product) and we evaluated NDVI from MOD09GQ product as well. The averaged temperature and NDVI dynamics were investigated in total for 50 post-fire plots under different stages of succession (1, 2, 5 and 10 years after burning) in comparison with non-disturbed vegetation cover sites under the same conditions.

We recorded higher temperatures (20–47% higher than average background value) and lower NDVI values (9–63% lower than non-disturbed vegetation cover) persisting for the first 10 years after the fire. Under conditions of natural restoration background temperature anomalies of the ground cover remained significant for more than 15 years, which was reflected on long-term satellite data and confirmed by ground-based measurements.

To estimate impact of thermal anomalies on soil profile temperature and thawed layer depth we used the Stefan’s solution for the thermal conductivity equation. According to results of numerical simulation, depth of the seasonal thawed layer could increase more than 20% in comparison with the average statistical norm under the conditions of excessive heating of the underlying layers. This is a significant factor in the stability of Siberian permafrost ecosystems requiring long-term monitoring.

This research was supported by the Russian Foundation for Basic Research, project No 17-04-00589, Government of the Krasnoyarsk region, project No 18-41-242003.

Earth observation capabilities provided by satellite missions constitute useful tools in agricultural management, particularly in the context of forecasting yields. Recent advances in sensors onboard harvesting machines allow accessing the intra-plot variability of yields, spatial scale fully compatible with numerous on-going satellite missions. In this context, the aim of this study is to estimate the sunflower yield at the intra-plot spatial scale using the multi-temporal satellite images provided by the Landsat-8 and Sentinel-2 missions. The proposed approach is based on random forest or artificial neural networks, testing different sampling strategies to partition the dataset into independent training and testing sets: a random selection by testing different ratio of data, a systematic selection by focusing on different plots, and a forecast procedure by using an increasing number of satellite images. Emphasis is put on the use of high spatial and temporal resolution satellite data acquired throughout the agricultural seasons 2016 and 2017, on a study site located in southwestern France, near Toulouse. Ground measurements consist in intra-plot yields collected over ~250 hectares by a surveying harvesting machine with GPS system on track mode. Interesting accurate statistical performances are obtained regardless the considered sampling strategy, providing complementary information useful for the yield retrieval. The results based on the random selection are satisfactory for a large range of tested ratio, with for instance R² upper than 0.64 and RMSE lower than 0.45 tons per hectare (t ha^-1), on the 50% of independent data used to validate the approach. While the systematic selection allows analyzing the plot representativeness, the forecast of yield throughout the agricultural season provides early accurate estimation during the crop flowering (two months before the harvest), with R² equal to 0.59 or 0.66 and RMSE of 0.47 or 0.34 t ha^-1, for the agricultural seasons 2016 and 2017 respectively.