Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China
Abstract
:1. Introduction
2. Theory
2.1. Effective PAI (), Effective WAI (), PAI, and WAI
2.2. Effective Woody-to-Total Area Ratio () and Woody-to-Total Area Ratio ()
3. Materials and Methods
3.1. Plots Description
3.2. Mean Element Eidth and Needle-to-Shoot Area Ratio () Measurement
3.3. DHP Images Acquisition and Processing
3.4. MCI Images Acquisition and Processing
3.5. The Measurements of The Representative Trees and Plots
4. Results
4.1. Estimates Obtained from The Destructive Method
4.2. Estimates Obtained from DHP and MCI
4.3. Estimates Obtained from DHP and MCI
5. Discussion
5.1. Factors That Affect the Accuracy of the Reference Estimates
5.2. Impact of Tree Age, Stand Density, Site Conditions, and Management Activities on the Reference of Forest Plots
5.3. Factors That Affect the Estimation of the Two Optical Methods
5.4. Determining Whether Accurate Estimates Can Be Obtained from DHP and MCI
5.5. Limitations and Perspectives
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix
References
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Plot 1 | Plot 2 | Plot 3 | Plot 4 | Plot 5 | |
---|---|---|---|---|---|
Longitude and latitude | 42°24’43” N, 117°19’4” E | 42°24’2” N, 117°18’40” E | 42°18’2” N, 117°18’9” E | 42°25’22” N, 117°19’32” E | 42°17’42” N, 117°16’53” E |
Mean tree height (m) * | 19.43 | 20.4 | 12.58 | 13.31 | 8.73 |
Average DBH** (cm) | 26.58 | 27.22 | 12.71 | 14.14 | 9.23 |
Mean element width (mm) | 21.66 | 23.34 | 17.91 | 21.09 | 17.60 |
Stand density (stems/ha) | 464 | 384 | 2320 | 1760 | 3904 |
Tree age (~years) | 54 | 55 | 21 | 22 | 13 |
Needle-to-shoot area ratio () | 1.30 | 1.17 | 1.14 | 1.17 | 1.28 |
Tree species | Larix gmelinii |
Proportions of the Woody Components Area of the Parts of the Woody Components to Woody Components Area of the Harvested Trees in Each Plot | Plot 1 | Plot 2 | Plot 3 | Plot 4 | Plot 5 |
---|---|---|---|---|---|
Woody components with heights of <1.2 m | 2% | 2% | 6% | 4% | 6% |
All the branches located below live canopies | 1% | 1% | 11% | 18% | 21% |
Plot Name | Tree 1 | Tree 2 | Tree 3 | Plot | |
---|---|---|---|---|---|
Measurement Height: 0 m | Measurement Height: 1.2 m | ||||
Plot 1 | 0.16 | 0.16 | 0.17 | 0.16 | 0.16 |
Plot 2 | 0.16 | 0.19 | 0.17 | 0.16 | |
Plot 3 | 0.22 | 0.17 | 0.36 | 0.21 | 0.20 |
Plot 4 | 0.27 | 0.21 | 0.28 | 0.25 | 0.24 |
Plot 5 | 0.19 | 0.25 | 0.28 | 0.25 | 0.23 |
Image Datasets | Leaf-on and Leaf-off Periods DHP Images | Leaf-on Period MCI Images | Leaf-on and Leaf-off Periods MCI Images | |||||
---|---|---|---|---|---|---|---|---|
Inversion model | 57.3 | Miller | LAI-2200 | 57.3 | MCI_0-85 | Percentage | 57.3 | MCI_0-85 |
Plot 1 | 0.59 | 0.61 | 0.68 | 0.17 | 0.17 | 0.36 | 0.65 | 0.59 |
Plot 2 | 0.68 | 0.53 | 0.74 | 0.16 | 0.17 | 0.35 | 0.55 | 0.56 |
Plot 3 | 0.57 | 0.49 | 0.64 | 0.28 | 0.28 | 0.54 | 0.59 | 0.63 |
Plot 4 | 0.67 | 0.85 | 0.73 | 0.29 | 0.31 | 0.59 | 0.55 | 0.62 |
Plot 5 | 0.64 | 0.79 | 0.67 | 0.30 | 0.28 | 0.59 | 0.65 | 0.62 |
Image Datasets | Inversion Model | R2 | Intercept | Slope | RMSE (in %) | MAE (in %) |
---|---|---|---|---|---|---|
Leaf-on and leaf-off periods DHP images | 57.3 | 0.19 | 0.58 | 0.26 | 0.44 (221%) | 0.43 (219%) |
Miller | 0.78 | 0.01 | 3.23 | 0.47 (238%) | 0.46 (231%) | |
LAI-2200 | −0.07 | 0.71 | −0.08 | 0.50 (250%) | 0.49 (249%) | |
Leaf-on period MCI images | 57.3 | 0.95 | −0.10 | 1.73 | 0.05 (26%) | 0.04 (21%) |
MCI_0-85 | 0.956 | −0.10 | 1.71 | 0.05 (26%) | 0.04 (22%) | |
Percentage | 0.97 | −0.14 | 3.16 | 0.30 (150%) | 0.29 (145%) | |
Leaf-on and leaf-off periods MCI images | 57.3 | −0.03 | 0.61 | −0.05 | 0.41 (205%) | 0.40 (203%) |
MCI_0-85 | 0.82 | 0.47 | 0.66 | 0.41 (205%) | 0.41 (205%) |
Inversion Model | and Estimation Algorithm | Plot 1 | Plot 2 | Plot 3 | Plot 4 | Plot 5 |
---|---|---|---|---|---|---|
57.3 | CC | 0.45 | 0.57 | 0.49 | 0.57 | 0.47 |
LX_5 | 0.41 | 0.57 | 0.50 | 0.53 | 0.46 | |
LX_15 | 0.41 | 0.59 | 0.50 | 0.55 | 0.47 | |
LX_30 | 0.44 | 0.60 | 0.51 | 0.55 | 0.47 | |
CLX_15 | 0.49 | 0.62 | 0.53 | 0.57 | 0.49 | |
CLX_30 | 0.47 | 0.62 | 0.50 | 0.57 | 0.46 | |
CLX_45 | 0.46 | 0.62 | 0.50 | 0.57 | 0.47 | |
Miller | CC | 0.50 | 0.49 | 0.45 | 0.72 | 0.59 |
LX_5 | 0.51 | 0.54 | 0.51 | 0.69 | 0.56 | |
LX_15 | 0.51 | 0.52 | 0.51 | 0.71 | 0.58 | |
LX_30 | 0.52 | 0.51 | 0.50 | 0.72 | 0.59 | |
CLX_15 | 0.54 | 0.56 | 0.54 | 0.72 | 0.59 | |
CLX_30 | 0.53 | 0.55 | 0.52 | 0.72 | 0.58 | |
CLX_45 | 0.52 | 0.54 | 0.50 | 0.71 | 0.58 | |
LAI_2200 | CC | 0.50 | 0.62 | 0.54 | 0.61 | 0.49 |
LX_5 | 0.50 | 0.64 | 0.56 | 0.59 | 0.48 | |
LX_15 | 0.50 | 0.65 | 0.57 | 0.61 | 0.49 | |
LX_30 | 0.51 | 0.66 | 0.58 | 0.61 | 0.50 | |
CLX_15 | 0.54 | 0.66 | 0.59 | 0.62 | 0.52 | |
CLX_30 | 0.53 | 0.66 | 0.57 | 0.62 | 0.50 | |
CLX_45 | 0.52 | 0.66 | 0.56 | 0.61 | 0.50 |
Inversion Model | and Estimation Algorithm | Leaf-on Period MCI Images | Leaf-on and Leaf-off Periods MCI Images | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Plot 1 | Plot 2 | Plot 3 | Plot 4 | Plot 5 | Plot 1 | Plot 2 | Plot 3 | Plot 4 | Plot 5 | ||
57.3 | CC | 0.10 | 0.11 | 0.17 | 0.20 | 0.16 | 0.32 | 0.37 | 0.54 | 0.40 | 0.37 |
LX | 0.15 | 0.15 | 0.28 | 0.28 | 0.22 | 0.42 | 0.45 | 0.55 | 0.50 | 0.45 | |
CLX | 0.16 | 0.16 | 0.31 | 0.31 | 0.26 | 0.44 | 0.47 | 0.57 | 0.55 | 0.48 | |
MCI_0-85 | CC | 0.10 | 0.16 | 0.20 | 0.23 | 0.18 | 0.36 | 0.38 | 0.46 | 0.46 | 0.36 |
LX | 0.14 | 0.16 | 0.28 | 0.30 | 0.24 | 0.45 | 0.47 | 0.56 | 0.54 | 0.44 | |
CLX | 0.15 | 0.17 | 0.30 | 0.33 | 0.28 | 0.47 | 0.48 | 0.60 | 0.57 | 0.48 |
Inversion Model | and Estimation Algorithm | R2 | Intercept | Slope | RMSE (in %) | MAE (in %) |
---|---|---|---|---|---|---|
57.3 | CC | 0.15 | 0.47 | 0.22 | 0.32 (160%) | 0.31 (158%) |
LX_5 | 0.10 | 0.46 | 0.17 | 0.30 (153%) | 0.30 (150%) | |
LX_15 | 0.08 | 0.48 | 0.15 | 0.31 (159%) | 0.31 (155%) | |
LX_30 | −0.03 | 0.52 | −0.05 | 0.32 (164%) | 0.32 (160%) | |
CLX_15 | −0.16 | 0.59 | −0.24 | 0.35 (176%) | 0.34 (173%) | |
CLX_30 | −0.13 | 0.57 | −0.25 | 0.33 (169%) | 0.33 (165%) | |
CLX_45 | −0.12 | 0.57 | −0.22 | 0.33 (169%) | 0.33 (165%) | |
Miller | CC | 0.76 | 0.13 | 2.12 | 0.36 (182%) | 0.35 (179%) |
LX_5 | 0.73 | 0.28 | 1.43 | 0.37 (186%) | 0.37 (185%) | |
LX_15 | 0.79 | 0.21 | 1.81 | 0.37 (187%) | 0.37 (185%) | |
LX_30 | 0.81 | 0.18 | 1.98 | 0.37 (189%) | 0.37 (187%) | |
CLX_15 | 0.74 | 0.3 | 1.46 | 0.40 (200%) | 0.40 (198%) | |
CLX_30 | 0.7 | 0.28 | 1.49 | 0.38 (194%) | 0.38 (192%) | |
CLX_45 | 0.72 | 0.25 | 1.61 | 0.38 (190%) | 0.37 (188%) | |
LAI_2200 | CC | −0.03 | 0.56 | −0.05 | 0.36 (183%) | 0.36 (180%) |
LX_5 | −0.19 | 0.62 | −0.34 | 0.37 (184%) | 0.36 (181%) | |
LX_15 | −0.14 | 0.61 | −0.24 | 0.37 (188%) | 0.36 (184%) | |
LX_30 | −0.14 | 0.62 | −0.25 | 0.38 (191%) | 0.37 (188%) | |
CLX_15 | −0.19 | 0.65 | −0.30 | 0.39 (199%) | 0.39 (196%) | |
CLX_30 | −0.22 | 0.65 | −0.37 | 0.38 (194%) | 0.38 (190%) | |
CLX_45 | −0.21 | 0.64 | −0.37 | 0.38 (191%) | 0.37 (187%) |
Inversion Model | and Estimation Algorithm | Leaf-on Period MCI Images | Leaf-on and Leaf-off Periods MCI Images | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
R2 | Intercept | Slope | RMSE (in %) | MAE (in %) | R2 | Intercept | Slope | RMSE (in %) | MAE (in %) | ||
57.3 | CC | 0.91 | −0.06 | 1.03 | 0.05 (28%) | 0.05 (27%) | 0.27 | 0.28 | 0.59 | 0.22 (109%) | 0.20 (103%) |
LX | 0.84 | −0.07 | 1.42 | 0.04 (19%) | 0.03 (14%) | 0.42 | 0.36 | 0.57 | 0.28 (141%) | 0.28 (140%) | |
CLX | 0.86 | −0.10 | 1.71 | 0.06 (31%) | 0.04 (22%) | 0.58 | 0.34 | 0.84 | 0.31 (155%) | 0.31 (154%) | |
MCI_0-85 | CC | 0.82 | −0.03 | 1.01 | 0.04 (18%) | 0.03 (13%) | 0.49 | 0.27 | 0.67 | 0.21 (107%) | 0.21 (105%) |
LX | 0.88 | −0.10 | 1.64 | 0.04 (22%) | 0.03 (16%) | 0.34 | 0.39 | 0.49 | 0.30 (149%) | 0.29 (147%) | |
CLX | 0.91 | −0.14 | 1.96 | 0.06 (33%) | 0.05 (26%) | 0.45 | 0.38 | 0.70 | 0.33 (165%) | 0.32 (163%) |
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Zou, J.; Leng, P.; Hou, W.; Zhong, P.; Chen, L.; Mai, C.; Qian, Y.; Zuo, Y. Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China. Forests 2018, 9, 746. https://doi.org/10.3390/f9120746
Zou J, Leng P, Hou W, Zhong P, Chen L, Mai C, Qian Y, Zuo Y. Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China. Forests. 2018; 9(12):746. https://doi.org/10.3390/f9120746
Chicago/Turabian StyleZou, Jie, Peng Leng, Wei Hou, Peihong Zhong, Ling Chen, Chunna Mai, Yonggang Qian, and Yong Zuo. 2018. "Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China" Forests 9, no. 12: 746. https://doi.org/10.3390/f9120746