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Open AccessArticle

Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume

1
Environmental Remote Sensing and Geoinformatics, University of Trier, Behringstraße 21, 54286 Trier, Germany
2
Landesforsten Rheinland-Pfalz, Zentralstelle der Forstverwaltung, Südallee 15-19, 56068 Koblenz, Germany
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Author to whom correspondence should be addressed.
Academic Editors: Josef Kellndorfer and Prasad S. Thenkabail
Remote Sens. 2015, 7(9), 12009-12040; https://doi.org/10.3390/rs70912009
Received: 28 July 2015 / Revised: 3 September 2015 / Accepted: 10 September 2015 / Published: 18 September 2015
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. View Full-Text
Keywords: forest management; timber volume; Norway spruce (Picea abies); hyperspectral imaging; PLS regression; k-NN estimator; EnMAP; Sentinel-2 forest management; timber volume; Norway spruce (Picea abies); hyperspectral imaging; PLS regression; k-NN estimator; EnMAP; Sentinel-2
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Nink, S.; Hill, J.; Buddenbaum, H.; Stoffels, J.; Sachtleber, T.; Langshausen, J. Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume. Remote Sens. 2015, 7, 12009-12040.

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