Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery
Abstract
:1. Introduction
- (i)
- Can high-resolution VNIR spectroscopy predict LMA, N, P, K, Ca and Mg in sun and shade leaves at the individual leaf level? While sun leaves have received a lot of attention in remote sensing studies due to their natural prominence in airborne imaging data, shade leaves (those located under the canopy in the understory) constitute the majority of tropical forest foliage and are known to have a strong influence on canopy spectral signatures in the near-infrared (NIR).
- (ii)
- Can the same foliar traits be quantified with similar accuracy and precision at the tree crown level using UAV-collected hyperspectral imagery?
- (iii)
- Can UAV-collected hyperspectral imagery be used to map these leaf traits across forest plots?
- (iv)
- Does the spatial distribution of traits and their relationships subscribe to general LES theory?
2. Materials and Methods
2.1. The Study Sites
2.2. Leaf Trait Sampling
2.3. Leaf Spectroscopy Measurements
2.4. Airborne Spectroscopy
2.5. Spectra–Trait Analyses
2.6. Mapping Foliar Traits
3. Results
3.1. Variation in Leaf Traits and Spectral Properties
3.2. PLSR Analyses
3.3. Mapping Foliar Traits Using UAV-Collected Hyperspectral Data
3.4. Leaf Economic Spectrum Trait Interactions
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
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Site Name | Ankasa Conservation Area | Bobiri Forest Reserve | Kogyae Strict Nature Reserve |
---|---|---|---|
Latitude (°) | 5.2680 | 6.6910 | 7.3012 |
Longitude (°) | −2.6955 | −1.3389 | −1.1649 |
Elevation (m.a.s.l) | 114 | 254 | 225 |
Mean annual air temperature (°C) | 25 | 25.7 | 26.4 |
Mean annual precipitation (mm yr−1) | 2050 | 1500 | 1200 |
Mean maximum climatological water deficit (mm) | −13 | −374 | −412 |
Soil pH | 4.27 | 6.05 | 6.07 |
Soil N (%) | 0.17 | 0.16 | 0.06 |
Soil C (%) | 2.61 | 1.71 | 0.72 |
Ptotal (mg kg−1) | 147 | 258 | 67 |
Caex | 1.34 | 32.81 | 18.91 |
Kex | 0.83 | 1.25 | 1.09 |
Mgex | 3.45 | 11.00 | 6.22 |
Alex | 18.44 | 0.89 | 0.02 |
Sand (%) | 63 | 47 | 83 |
Clay (%) | 22 | 29 | 2 |
Silt (%) | 15 | 24 | 15 |
Leaf photosynthesis rate (μmol m−2 s−1) | 5.87 | 7.75 | 7.74 |
Leaf residence time (months) | 9–10 | 4–6 | 6.5–8 |
Aboveground coarse wood residence time (months) | 99 ± 22.1 | 39. 65 ± 8.88 | 40.60 ± 9.08 |
NPP (Mg C ha−1 yr−1) | 13.12 ± 0.79 | 11.74 ± 0.95 | 10.19 ± 0.78 |
Leaf Type | Sun | Shade | UAV-Collected Spectra | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Spectral Range and Resolution | Individual Leaves (400–1050 nm) 1 nm FWHM | Individual Leaves (400–1050 nm) 1 nm FWHM | Tree Crowns (450–950 nm) 9 nm FWHM | ||||||||||||
Mean ± 1 Std | RMSE | %RMSE | R2-cal | R2-val | Mean ± 1 Std | RMSE | %RMSE | R2-cal | R2-val | Mean ± 1 Std | RMSE | %RMSE | R2-cal | R2-val | |
LMA (g m−2) | 102 ± 35 | 21.13 | 22 | 0.60 *** | 0.42 *** | 89 ± 33 | 19.44 | 20 | 0.70 *** | 0.55 *** | 98 ± 20 | 17.43 | 18 | 0.45 *** | 0.25 * |
N (%) | 2.05 ± 0.63 | 0.48 | 24 | 0.78 *** | 0.43 *** | 2.07 ± 0.64 | 0.47 | 23 | 0.83 *** | 0.48 *** | 2.15 ± 0.65 | 0.54 | 25 | 0.38 *** | 0.22 * |
P (%) | 0.12 ± 0.05 | 0.04 | 34 | 0.64 *** | 0.21 *** | 0.12 ± 0.05 | 0.05 | 38 | 0.83 *** | 0.33 *** | 0.10 ± 0.04 | 0.03 | 32 | 0.38 *** | 0.22 * |
K (%) | 0.89 ± 0.49 | 0.41 | 43 | 0.43 *** | 0.20 *** | 1.03 ± 0.55 | 0.48 | 51 | 0.52 *** | 0.20 *** | 0.91 ± 0.44 | 0.41 | 44 | 0.25 * | 0.15 |
Ca (%) | 1.39 ± 1.98 | 0.89 | 60 | 0.50 *** | 0.23 *** | 1.57 ± 1.11 | 0.94 | 63 | 0.66 *** | 0.28 *** | 0.87 ± 0.66 | 0.56 | 64 | 0.43 *** | 0.25 * |
Mg (%) | 0.34 ± 0.17 | 0.16 | 45 | 0.37 *** | 0.14 *** | 0.38 ± 0.19 | 0.18 | 48 | 0.45 *** | 0.16 *** | 0.31 ± 0.18 | 0.18 | 57 | 0.19 | 0.09 |
Predicted Trait (Bold) vs. Field-Collected Value | Plot | Statistics | ||||
---|---|---|---|---|---|---|
Ankasa 01 | Ankasa 03 | Bobiri 02 | RMSE | %RMSE | R2 | |
Field-sampled basal area (%) | 63 | 56 | 61 | |||
LMA (g m−2) | 90 ± 19 a | 100 ± 24 b | 98 ± 20 c | - | - | - |
Basal-area-weighted | 102 | 101 | 94 | 7 | 8 | 0.19 |
Stem-abundance-weighted | 101 | 101 | 92 | 7 | 8 | 0.11 |
Unweighted | 97 | 101 | 100 | 6 | 5 | 1 *** |
N (%) | 1.97 ± 0.64 a | 1.74 ± 0.76 b | 2.93 ± 0.50 c | - | - | - |
Basal-area-weighted | 2.06 | 1.93 | 2.74 | 0.16 | 7 | 1 *** |
Stem-abundance-weighted | 2.00 | 1.89 | 2.81 | 0.11 | 11 | 1 *** |
Unweighted | 1.91 | 2.03 | 2.45 | 0.33 | 15 | 0.85 |
P (%) | 0.07 ± 0.02 a | 0.07 ± 0.02 b | 0.08 ± 0.02 c | - | - | - |
Basal-area-weighted | 0.09 | 0.10 | 0.15 | 0.05 | 74 | 0.98 |
Stem-abundance-weighted | 0.09 | 0.10 | 0.15 | 0.05 | 74 | 0.98 |
Unweighted | 0.08 | 0.10 | 0.13 | 0.04 | 62 | 0.84 |
Ca (%) | 1.20 ± 0.36 a | 1.34 ± 0.48 b | 1.87 ± 0.50 c | - | - | - |
Basal-area-weighted | 0.72 | 0.74 | 1.82 | 0.45 | 30 | 0.97 |
Stem-abundance-weighted | 0.73 | 0.73 | 1.88 | 0.45 | 30 | 0.96 |
Unweighted | 0.71 | 0.86 | 1.54 | 0.44 | 44 | 1 *** |
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Thomson, E.R.; Malhi, Y.; Bartholomeus, H.; Oliveras, I.; Gvozdevaite, A.; Peprah, T.; Suomalainen, J.; Quansah, J.; Seidu, J.; Adonteng, C.; et al. Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery. Remote Sens. 2018, 10, 1532. https://doi.org/10.3390/rs10101532
Thomson ER, Malhi Y, Bartholomeus H, Oliveras I, Gvozdevaite A, Peprah T, Suomalainen J, Quansah J, Seidu J, Adonteng C, et al. Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery. Remote Sensing. 2018; 10(10):1532. https://doi.org/10.3390/rs10101532
Chicago/Turabian StyleThomson, Eleanor R., Yadvinder Malhi, Harm Bartholomeus, Imma Oliveras, Agne Gvozdevaite, Theresa Peprah, Juha Suomalainen, John Quansah, John Seidu, Christian Adonteng, and et al. 2018. "Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery" Remote Sensing 10, no. 10: 1532. https://doi.org/10.3390/rs10101532