Abstract
The availability of accurate and timely information on timber volume is important for supporting operational forest management. One option is to combine statistical concepts (e.g., small area estimates) with specifically designed terrestrial sampling strategies to provide estimations also on the level of administrative units such as forest districts. This may suffice for economic assessments, but still fails to provide spatially explicit information on the distribution of timber volume within these management units. This type of information, however, is needed for decision-makers to design and implement appropriate management operations. The German federal state of Rhineland-Palatinate is currently implementing an object-oriented database that will also allow the direct integration of Earth observation data products. This work analyzes the suitability of forthcoming multi- and hyperspectral satellite imaging systems for producing local distribution maps for timber volume of Norway spruce, one of the most economically important tree species. In combination with site-specific inventory data, fully processed hyperspectral data sets (HyMap) were used to simulate datasets of the forthcoming EnMAP and Sentinel-2 systems to establish adequate models for estimating timber volume maps. The analysis included PLS regression and the k-NN method. Root Mean Square Errors between 21.6% and 26.5% were obtained, where k-NN performed slightly better than PLSR. It was concluded that the datasets of both simulated sensor systems fulfill accuracy requirements to support local forest management operations and could be used in synergy. Sentinel-2 can provide meaningful volume distribution maps in higher geometric resolution, while EnMAP, due to its hyperspectral coverage, can contribute complementary information, e.g., on biophysical conditions.
Highlights
Forests, with large storage of biomass and their function as an important terrestrial carbon dioxide sink, cover about 4 billion hectares worldwide, which is almost one third of the Earth’s land surface [1,2,3].Forest ecosystems cover almost one third of the territory of Germany and provide a wide range of economic benefits and ecosystem services [4]
The relationship between the spectral response in the sensitive spectral regions and the forest information database (FID)-derived timber volume for Norway spruce reveals that the three stands may qualify as outliers
While Ardö [66] had identified the SWIR region around 1600 nm as the most sensitive spectral range when trying to map timber volume based on Landsat-TM data in Scandinavian conifer forests, in our work the highest sensitivity occurs in the NIR region, which conforms to other studies (e.g., [67,68])
Summary
With large storage of biomass and their function as an important terrestrial carbon dioxide sink, cover about 4 billion hectares worldwide, which is almost one third of the Earth’s land surface [1,2,3].Forest ecosystems cover almost one third of the territory of Germany and provide a wide range of economic benefits and ecosystem services [4]. The strategic objectives of the German forest policy for the coming decades are focused on developing a balance between the diverging demands on ecological, economic, and socioeconomic functions of forests and their sustainable performance [5]. These strategic objectives, in combination with national and international commitments for reporting on forest resources, are triggering an increasing demand for expanded information on forest resources [6,7]. In the federal state of Rhineland-Palatinate, forested areas cover more than 42% of the land and, notwithstanding the ecological, social, and cultural services they are providing, they represent important economic value. Since remote sensing data products may compensate for such shortcomings [9], the federal state of Rhineland-Palatinate has begun to explore pathways for a direct integration of Earth observation data products into an innovative object-oriented data base concept, which is currently being implemented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
