Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

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Bioenergy represents the highest share of renewable energies consumed in the European Union and is still expected to grow. This could be possible by exploring bioenergy production on Marginal, Underutilised, and Contaminated lands (MUC) that are not used for agricultural purposes and therefore, present no competition with food/feed production. In this paper, the viability and sustainability of bioenergy value chains on these lands is investigated and measures for market uptake were developed. Using three case study areas in Italy, Ukraine, and Germany, a screening of MUC lands was conducted, then an agronomic assessment was performed to determine the most promising crops. Then, techno-economic assessments followed by sustainability assessments were performed on selected value chains. This concept was then automated and expanded through the development of a webGIS tool. The tool is an online platform that allows users to locate MUC lands in Europe, to define a value chain through the selection of bioenergy crops and pathways, and to conduct sustainability assessments measuring a set of environmental, social, and economic sustainability indicators. The findings showed positive results in terms of profitability and greenhouse gas emissions for bioethanol production from willow in Ukraine, heat and power production from miscanthus, and biogas and chemicals production from grass in Germany. The webGIS tool is considered an important decision-making tool for stakeholders, which gives first insights on the viability and sustainability of bioenergy value chains.

This study aims at identifying underutilized land potentially suitable for bioenergy production in Europe by means of remote sensing time series analysis. The background is the Revised Renewable Energy Directive (REDII) requesting that 32% of Europe’s energy production shall come from renewable energy sources until 2030. In order to avoid the food versus fuel debate, we only considered land that has not been used in the previous five years. Satellite remote sensing is the only technique that allows for the assessment of the usage of land for such a long time span at the pan-European scale with reasonable efforts. We used Landsat 8 (L8) data for the full five year time period 2015–2019 and included additional Sentinel-2 (S2) data for 2018 and 2019. The analysis was based on a stratified approach for biogeographical regions and countries using Google Earth Engine. To our knowledge, this is the first work that employs high resolution time series data for pan-European mapping of underutilized land. The average patch size of underutilized land was found to be between 23.2 ha and 49.6 ha, depending on the biogeographical region. The results show an overall accuracy of more than 85% with a confidence interval (CI) of 1.55% at the 95% confidence level (CL). The classification suggests that at total of 5.3 million ha of underutilized land in Europe is potentially available for agricultural bioenergy production.