Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume
Abstract
:1. Introduction
- Integration of routinely acquired forest resource assessments and spectral information for estimating the spatial distribution of timber volume in managed Norway spruce (Picea abies) forests.
- Identification of sensitive wavelengths for volume estimation in intensively managed Norway spruce forests.
- Assessment of concepts (PLSR vs. k-NN) for producing spatially explicit maps of Norway spruce timber volume within administrative forest units.
- Evaluation of the spectral and spatial resolution characteristics of Sentinel-2 and EnMAP for mapping Norway spruce timber volume.
2. Material and Methods
2.1. Study Area
2.2. Data
2.2.1. Airborne HyMap imagery
2.2.2. EnMAP and Sentinel-2 Data Simulation
2.2.3. Timber Volume Reference Data
2.3. Methods
2.3.1. Extraction of Spectral References
2.3.2. Reference Data and Identification of Sensitive Spectral Regions
2.3.3. Predictive Modeling
2.3.4. Model Validation
2.3.5. Model Application
2.3.6. Prediction Maps
3. Results
3.1. Identification of Sensitive Spectral Regions
3.2. Principal Component Analysis
3.3. PLSR
Estimation | Reference | ||||||
---|---|---|---|---|---|---|---|
loo-cv | Administrative Forest Unit | ||||||
Mean Timber Volume (m³/ha) | Biascv (m³/ha) | RMSEcv (%) | Mean Timber Volume (m³/ha) | Biascv (m³/ha) | RMSEcv (%) | Mean Timber Volume (m³/ha) | |
PLSR (EnMAP) | 283.61 | –0.08 | 28.19 | 283.63 | –0.06 | 26.48 | 283.69 |
PLSR (Sentinel-2) | 282.39 | –1.29 | 26.81 | 278.08 | –5.61 | 25.60 |
3.4. k-NN
Principal Component | No. of Nearest Neighbours (k) | RMSEloo-cv (%) | RMSEloo-cv (m�/ha) | |||||
EnMAP | PC 1 | PC 2 | PC 3 | PC 7 | ||||
R� | 0.69 | 0.35 | 0.63 | 0.37 | ||||
feature band weighting | 1 | 4 | 30.22 | 85.72 | ||||
1 | 4 | 41.41 | 117.49 | |||||
1 | 1 | 3 | 28.49 | 80.81 | ||||
1 | 1 | 1 | 3 | 28.48 | 80.80 | |||
1 | 1 | 1 | 3 | 28.43 | 80.65 | |||
1 | 1 | 1 | 1 | 3 | 28.44 | 80.67 | ||
optimized feature band weighting | 0.25 | 2 | 3 | 25.70 | 72.93 | |||
Principal Component | No. of Nearest Neighbours (k) | RMSEloo-cv [%] | RMSEloo-cv [m�/ha] | |||||
Sentinel-2 | PC 1 | PC 3 | PC 9 | PC 10 | ||||
R� | 0.68 | 0.69 | 0.27 | 0.28 | ||||
feature band weighting | 1 | 5 | 32.66 | 92.65 | ||||
1 | 3 | 37.27 | 105.73 | |||||
1 | 1 | 4 | 28.40 | 80.56 | ||||
1 | 1 | 1 | 4 | 28.32 | 80.34 | |||
1 | 1 | 1 | 1 | 4 | 28.18 | 79.93 | ||
optimized feature band weighting | 0.5 | 1.75 | 4 | 27.37 | 77.44 |
LOO-cv | Administrative Forest Unit | Reference | ||||||
---|---|---|---|---|---|---|---|---|
Mean Timber Volume (estim.) (m³/ha) | Biascv (m³/ha) | RMSEcv (%) | Mean Timber Volume (estim.) (m³/ha) | Biascv (m³/ha) | RMSEcv (%) | Mean Timber Volume (m³/ha) | ||
k-NN (EnMAP); k = 3 | 280.16 | –3.53 | 25.7 | 287.33 | 3.64 | 23.11 | 283.69 | |
k-NN (Sentinel-2); k = 4 | 279.47 | –4.22 | 27.37 | 280.16 | –3.53 | 21.58 |
4. Discussion
4.1. Sensitive Spectral Ranges
4.2. Estimation Models and Sensors
4.3. Prediction Maps
4.4. Forest Reference Information
5. Conclusions
Acknowledgments
Author Contributions
Appendix
Conflicts of Interest
References
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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. https://doi.org/10.3390/rs70912009
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 Sensing. 2015; 7(9):12009-12040. https://doi.org/10.3390/rs70912009
Chicago/Turabian StyleNink, Sascha, Joachim Hill, Henning Buddenbaum, Johannes Stoffels, Thomas Sachtleber, and Joachim Langshausen. 2015. "Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume" Remote Sensing 7, no. 9: 12009-12040. https://doi.org/10.3390/rs70912009