For monitoring protected forest landscapes over time it is essential to follow changes in tree species composition and forest dynamics. Data driven remote sensing methods provide valuable options if terrestrial approaches for forest inventories and monitoring activities cannot be applied efficiently due to restrictions or the size of the study area. We demonstrate how species can be detected at a single tree level utilizing a Random Forest (RF) model using the Black Forest National Park as an example of a Central European forest landscape with complex relief. The classes were European silver fir (Abies alba, AA), Norway spruce (Picea abies, PA), Scots pine (Pinus sylvestris, PS), European larch (Larix decidua including Larix kampferii, LD), Douglas fir (Pseudotsuga menziesii, PM), deciduous broadleaved species (DB) and standing dead trees (snags, WD). Based on a multi-temporal (leaf-on and leaf-off phenophase) and multi-spectral mosaic (R-G-B-NIR) with 10 cm spatial resolution, digital elevation models (DTM, DSM, CHM) with 40 cm spatial resolution and a LiDAR dataset with 25 pulses per m2, 126 variables were derived and used to train the RF algorithm with 1130 individual trees. The main objective was to determine a subset of meaningful variables for the RF model classification on four heterogeneous test sites. Using feature selection techniques, mainly passive optical variables from the leaf-off phenophase were considered due to their ability to differentiate between conifers and the two broader classes. An examination of the two phenological phases (using the difference of the respective NDVIs) is important to clearly distinguish deciduous trees from other classes including snags (WD). We also found that the variables of the first derivation of NIR and the tree metrics play a crucial role in discriminating PA und PS. With this unique set of variables some classes can be differentiated more reliably, especially LD and DB but also AA, PA and WD, whereas difficulties exist in identifying PM and PS. Overall, the non-parametric object-based approach has proved to be highly suitable for accurately detecting (OA: 89.5%) of the analyzed classes. Finally, the successful classification of complex 265 km2 study area substantiates our findings.
<p>Understanding disaster risk is the first priority for action of the Sendai Framework for Disaster Risk Reduction (SFDRR) and is the essential information needed to guide disaster governance and achieve disaster risk reduction. Flooding is a natural hazard that causes the highest number of affected people due to disasters. In Ecuador from 1970 to 2019 flooding caused the highest amount of loss and damage to housing, and from 2016 to 2019 there were 1263 flood events reported. However, the differentiated impacts in flood exposed areas and what can be done to reduce risk and its impacts are still not well understood. In this research, we explored the different dimensions of flood risk, namely hazard, exposure, and vulnerability, and investigated the drivers of risk in different ecological regions of Ecuador. The assessment was conducted at the parish level, the smallest administrative scale, for three selected provinces of Bolivar, Los R&#237;os, and Napo, representing not only the country&#8217;s three main ecological regions but also commonly affected territories due to flooding. Using an automated flood detection procedure based on Sentinel-1 synthetic aperture radar data, flood hazard information was derived from flood frequency and flood depth for the years 2017, 2018, and 2019. The drivers of exposure and vulnerability were derived from scientific literature and further evaluated and complemented during a participatory workshop with over 50 local experts from the different regions. Centered on this exercise, an indicator library was created to inform the data selection from various sources and provides the basis for deriving a spatially explicit flood risk assessment using an indicator-based approach. Impact data are available to validate the risk assessment at the parish level and with this reveal key drivers of flood risk in different ecological regions of Ecuador. This information will provide the basis to derive targeted measures for disaster risk reduction.</p>
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