Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden
Abstract
:1. Introduction
2. Study Area
3. Data and Methods
3.1. Field Measurements
3.2. Remotely Sensed Data
3.3. Vegetation Mapping Using Maximum-Likelihood Classification
3.4. Derivation of Amount of Trees and Their Heights from Airborne LiDAR
3.5. Reference Biomass Estimation
Biomass (kg) | DBH | H | n | R2 | Equation | a | b | c | d | |
---|---|---|---|---|---|---|---|---|---|---|
Spruce | ln(CR) | cm | - | 544 | 0.95 | a + b × [DBH/(DBH + 13)] | −1.280 | 8.524 | - | - |
Spruce | ln(CR) | cm | m | 544 | 0.95 | a + b × [DBH/(DBH + 13)] + c × H + d × ln(H) | −1.206 | 10.971 | −0.012 | −0.492 |
Spruce | ln(ST) | cm | - | 546 | 0.99 | a + b × [DBH/(DBH + 14)] | −2.057 | 11.334 | - | - |
Spruce | ln(ST) | cm | m | 546 | 0.99 | a + b × [DBH/(DBH + 14)] + c × H + d × ln(H) | −2.170 | 7.469 | 0.029 | 0.686 |
Pine | ln(CR) | cm | - | 482 | 0.90 | a + b ×[DBH/(DBH + 10)] | −2.860 | 9.102 | - | - |
Pine | ln(CR) | cm | m | 482 | 0.92 | a + b·[DBH/(DBH + 10)] + c × ln(H) | −2.541 | 13.396 | −1.196 | - |
Pine | ln(ST) | cm | - | 488 | 0.98 | a + b × [DBH/(DBH + 13)] | −2.339 | 11.326 | - | - |
Pine | ln(ST) | cm | m | 488 | 0.99 | a + b × [DBH/(DBH + 13)] + c × H + d × ln(H) | −2.677 | 7.594 | 0.015 | 0.880 |
Birch | AGB | mm | - | - | 0.99 | a × DBH b | 0.0009 | 2.2864 | - | - |
4. Results and Discussion
4.1. Vegetation Classes
Spruce Mature | Spruce Young | Pine Mature | Pine Young | Deciduous | Total | Producer’s accuracy (%) | Area (km2) | Area (%) | |
---|---|---|---|---|---|---|---|---|---|
Spruce Mature | 67 | 3 | 5 | 0 | 1 | 76 | 92 | 5.4 | 26.6 |
Spruce Young | 2 | 25 | 3 | 0 | 1 | 31 | 86 | 2.5 | 12.3 |
Pine Mature | 4 | 1 | 32 | 8 | 0 | 45 | 68 | 4.6 | 22.7 |
Pine Young | 0 | 0 | 7 | 10 | 0 | 17 | 50 | 3.2 | 15.7 |
Deciduous | 0 | 0 | 0 | 2 | 25 | 27 | 93 | 4.6 | 22.7 |
Total | 73 | 29 | 47 | 20 | 27 | 196 | |||
User’s accuracy (%) | 88 | 81 | 71 | 59 | 93 | Total accuracy (%) | 81 | ||
Kappa coefficient | 0.75 |
4.2. Reference Aboveground Biomass Estimates
4.3. LiDAR-Derived Forest Inventory Parameters
4.3.1. Tree Density
4.3.2. Tree Height
4.4. Aboveground Biomass Estimates over the Study Area
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References and Notes
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Shendryk, I.; Hellström, M.; Klemedtsson, L.; Kljun, N. Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden. Forests 2014, 5, 992-1010. https://doi.org/10.3390/f5050992
Shendryk I, Hellström M, Klemedtsson L, Kljun N. Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden. Forests. 2014; 5(5):992-1010. https://doi.org/10.3390/f5050992
Chicago/Turabian StyleShendryk, Iurii, Margareta Hellström, Leif Klemedtsson, and Natascha Kljun. 2014. "Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden" Forests 5, no. 5: 992-1010. https://doi.org/10.3390/f5050992