Manuela Hirschmugl

Global Change Research in the Carpathian Mountain Region

Abstract The Carpathian Mountains in Europe are a biodiversity hot spot; harbor many relatively undisturbed ecosystems; and are still rich in seminatural, traditional landscapes. Since the fall of the Iron Curtain, the Carpathians have experienced widespread land use change, affecting biodiversity and ecosystem services. Climate change, as an additional driver, may increase the effect of such changes in the future. Based on a workshop organized by the Science for the Carpathians network, this paper reviews the current status of global change research in the Carpathians, identifies knowledge gaps, and suggests avenues for future research.

Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods.

Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation

Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods.

Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review

Purpose of ReviewThis paper presents a review of the current state of the art in remote sensing-based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview and tabular description of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing mapping approaches: first, classical image-to-image change detection and second, time series analysis.Recent FindingsWith the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa.SummaryThe review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high-resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real-time disturbance mapping in support of operational reactive measures.

Reflectance of various snow types: measurements, modeling, and potential for snow melt monitoring

Seasonal snow covers large parts of the northern hemisphere annually. It can change the albedo of the surfaces from dark to bright overnight (and back), causing significant climate feedback (Manninnen and Stenberg, 2008; Flanner and Zender, 2006; Pirazzini, 2008; Nolin and Frei, 2001; Roesch et al., 2001). It forms large energy reservoirs which can be exploited by hydro energy power plants, and is the source of big floods when melting. It can significantly impact traffic and construction safety. It changes living and environmental conditions radically, and has major recreational value.

Information Services to Support Disaster and Risk Management in Alpine Areas

The concept for an operational service for natural disaster situations requires a scenario driven data access to different sensor information for all phases of a disaster management. This also includes the actual availability of image information of the earth surface concerning the specific requirements of each phase. From the temporal point of view, spaceborne data acquisition does not offer a sufficient data availability in order to support all different phases in specific crisis situations. Especially the event phase cannot be supported as required.

Remote Sensing for Alpine Forest Monitoring

Forest areas cover large parts of the Alps. In the mountains, the main functions of the forests are referring to the protection of people and infrastructure against natural hazards such as avalanches, landslides and rockfalls. However, the protective effect of many forests is threatened by their poor condition and poor forest regeneration capabilities. Information on the state of the forest therefore is an important basis for the sustainable management of protected forests. The key forest parameters that can be mentioned in this context are the forest border line, tree species distribution, age of the forests, biomass as well as structural parameters such as vertical stand structure and forest density. The paper presents the possibilities and limits of satellite image data as well as of Airborne LiDAR data for the assessment of these parameters. The results presented are achieved by the EUFODOS project financed by the 7th Frame Work Programme of the European Commission [4] and the project ALS—Steiermark financed by Waldverband Steiermark. EUFODOS was focussing on the development of methods for pan-European identification of forest damage whereas the aim of ALS—Steiermark was to apply these methods to the entire area of the province of Styria.

Updating lidar-derived crown cover density products with sentinel-2

Crown cover density (CCD) is one important forest attribute used in forest management. With remote sensing, crown cover density maps can be derived from the spectral information of optical satellite imagery or from a normalized digital surface model (nDSM). LiDAR data based applications provide the most accurate results, but LiDAR campaigns are expensive and available data are often outdated. We propose a new method to update LiDAR-derived CCD products and to map forest change. The method is based on Sentinel-2 imagery and an outdated LiDAR nDSM used to train a kNN classifier. CCD estimations are derived for two tests sites in Austria. Results are compared with the LiDAR CCD values in unchanged forest and with the latest tree cover density product of the European Copernicus High Resolution Layers Forest. Results demonstrate the operability of the workflow. User accuracies for forest change detection are very high with 87.3% and 94.8%.

Humid tropical forest monitoring with multi-temporal L-, C- and X-band SAR data

Humid tropical forest monitoring with EO is limited by frequent cloud cover and rapid forest regrowth. Both can be overcome by using temporally dense SAR image stacks. We present a method that uses the coefficient of variation of multi-temporal SAR data stacks to map tropical forest disturbances. The SAR data pre-processing and the forest change detection workflows are described and illustrated. The method is tested at a humid tropical forest site in the Republic of Congo. At this test site we use data from three different SAR sensors: ALOS PALSAR, Sentinel-1 and TerraSAR-X. The forest disturbance maps are validated by visual interpretation and compared to the Landsat based Humid Tropical Forest Disturbance Alerts available from Global Forest Watch. Change mapping accuracies for plots larger than 0.5 ha are very high: 76% for ALOS PALSAR, 96% for TerraSAR-X and 98% for Sentinel-1. For Sentinel-1, producer accuracies were derived for different forest disturbance types. The overall accuracy is 81.8%, with highest values for deforestation in oil palm plantations and burnt areas. The results are similar to the accuracy of the Humid Tropical Forest Disturbance Alert layer, which detects 85.6% of all reference areas. We also show that fusion of the disturbance maps on a result level is possible. The presented method could be adapted to near real-time processing and to a combined processing with optical EO data.

Support of Wind Resource Modeling Using Earth Observation

This contribution outlines the potential of remote sensing data to support wind resource modelling especially through improved input parameterization regarding the state and characterization of the land surface. Wind speed and wind flow is strongly influenced by land surface properties. Three different remote sensing based parameters can help to characterize wind resources: a) land cover and land use; b) digital elevation models (DEM); c) phenological information. Earth observation data are used already in wind resource models to some extent. However, the new advances and especially the possibilities which open up through the Copernicus Sentinel satellites are not considered yet. Opportunities include seasonal mapping of land cover which will allow a precise quantification of vegetation cover which has a direct influence on heat fluxes. The use of newest DEMs like Tandem-X with a 12 m resolution allows detecting also small landscape feature like rows of hedges and trees. Further, elevation models derived by either photogrammetric approaches or airborne laser scanning can further refine the information. By using EO-based information on the surface, e.g. roughness, and in-situ wind measurements, realistic wind fields for sufficiently large areas can be derived by considering also shadowing effects and wind shear.