Over the last decades, climate change has triggered an increase in the frequency of spruce bark beetle (Ips typographus L.) in Central Europe. More than 50% of forests in the Czech Republic are seriously threatened by this pest, leading to high ecological and economic losses. The exponential increase of bark beetle infestation hinders the implementation of costly field campaigns to prevent and mitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent and spatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two models have been developed to test the ability of this data source to map bark beetle damage and severity. All models were based on a change detection approach, and required the generation of previous forest mask and dominant species maps. The first damage mapping model was developed for 2019 and 2020, and it was based on bi-temporal regressions in spruce areas to estimate forest vitality and bark beetle damage. A second model was developed for 2020 considering all forest area, but excluding clear-cuts and completely dead areas, in order to map only changes in stands dominated by alive trees. The three products were validated with in situ data. All the maps showed high accuracies (acc > 0.80). Accuracy was higher than 0.95 and F1-score was higher than 0.88 for areas with high severity, with omission errors under 0.09 in all cases. This confirmed the ability of all the models to detect bark beetle attack at the last phases. Areas with no damage or low severity showed more complex results. The no damage category yielded greater commission errors and relative bias (CEs = 0.30–0.42, relB = 0.42–0.51). The similar results obtained for 2020 leaving out clear-cuts and dead trees proved that the proposed methods could be used to help forest managers fight bark beetle pests. These biotic damage products based on Sentinel-2 can be set up for any location to derive regular forest vitality maps and inform of early damage.

Bark beetle outbreaks and forest mortality have rocketed in European forests because of warmer winters, intense droughts, and poor management. The methods developed to predict a bark beetle outbreak have three main limitations: (i) a small-spatial-scale implementation; (ii) specific field-based input datasets that are usually hard to obtain at large scales; and (iii) predictive models constrained by coarse climatic factors. Therefore, a methodological approach accounting for a comprehensive set of environmental traits that can predict a bark beetle outbreak accurately is needed. In particular, we aimed to (i) analyze the influence of environmental traits that cause bark beetle outbreaks; (ii) compare different machine learning architectures for predicting bark beetle attacks; and (iii) map the attack probability before the start of the bark beetle life cycle. Random Forest regression achieved the best-performing results. The predicted bark beetle damage reached a high robustness in the test area (F1 = 96.9, OA = 94.4) and showed low errors (CE = 2.0, OE = 4.2). Future improvements should focus on including additional variables, e.g., forest age and validation sites. Remote sensing-based methods contributed to detecting bark beetle outbreaks in large extensive forested areas in a cost-effective and robust manner.

The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OA_MSF = 96.3%; DC_MSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OE_MSF = 2.4%; CE_MSF = 4.5%; relB_MSF = 2%), while the other products showed on average lower accuracies (OA_HRL = 89.2%; OA_FNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OE_MSF = 9.5%; OE_HRL = 59.5%; OE_FNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets.

Deforestation reduction is recognized as the best short-term strategy to reduce carbon emissions. We propose using Earth Observation to assess these land cover changes using the Continuous Change Detection and Classification (CCDC) framework to decompose and classify Landsat and Sentinel-1 time series (over 20,000 satellite im- ages). We separated forest, other woodlands, oil palm, cocoa, grassland, cropland, urban, bare soil, water, and wetland in two highly dynamic areas on the Peruvian Amazon. Both Landsat-only (OA= 98.3%, F1 = 91.6%) and combined Landsat - Sentinel-1 (OA = 98.3%, F1 = 91.7%) classifications were highly accurate and stable. However, both classifications tended to detect changes past the dry season, indicating that the results are driven by the availability of cloud free data, even when Sentinel- 1 features are included, indicating an over-reliance on Landsat features. When the CCDC segments from both sensors were examined, Landsat’s segments tended to end during the dry season, whereas Sentinel-1 segment ends preceded the peaks by some months, highlighting the sensor potential for change detection. These findings underscore the useful of both sensors in terms of information content (Landsat) and timeliness (Sentinel-1), underlining the need for more research on their combined use.

Climate change is one of the grand challenges of the XXI century.In this context, deforestation reduction is recognized as the best short-term strategy to reduce carbon emissions. Sustainability-linked bonds (SLBs) and loans (SLLs) are financial instruments tying incentives to objectives such as deforestation rate reduction. Hence, it becomes necessary an accurate measurement of land cover transitions. We propose using Earth Observation to assess these transitions, using the Continuous Change Detection And Classification (CCDC) framework to decompose and classify Landsat and Sentinel-1 time series (over 20,000 satellite images). We separated forest, other woodlands, oil palm, cocoa, grassland, cropland, urban, bare, water, wetland in two highly dynamic areas on the Peruvian Amazon. Both Landsat-only (OA= 98.3%, F1 = 91.6%) and combined Landsat - Sentinel-1 (OA = 98.3%, F1 = 91.7%) classifications were highly accurate and stable, showing a timely detection of the main disturbances. However, both classifications tended to detect changes past the dry season, indicating that the results are driven by the availability of cloud free data, even when Sentinel-1 features are included, indicating an over-reliance on Landsat features. Nonetheless, our design will reflect changes earlier than annual products if there is a sufficient number of observations, and allows disaggregating forest-losses by post-change land cover. Transitions to other woodlands and oil palm correlated better with macroeconomic indicators than the aggregated deforestation estimate. This indicates our methodology can aid the attribution of economic incentives and supporting the empirical basis for sustainability-linked financing mechanisms.