An Approach on Estimating Canopy Closure via Digital Images

doi:10.13466/j.cnki.lyzygl.2020.06.024

Abstract

Abstract:

In this study,the whole sky photos taken at the Dongtai research site are used to separate leaves,trunks and sky by establishing an RGB classification model to extract the canopy closure accurately.It shows that the overall classification accuracy of this method is 0.94 and the Kappa coefficient is 0.89.The classification accuracy is high and it works well in distinguishing the main trunk of the forest.The overall classification accuracy reaches 0.94 and the Kappa coefficient is 0.84.In addition,this study finds that the calculation accuracy of photos with low canopy closure is higher than photos with high canopy closure.In comparison of the model estimation results with ocular estimate results,the R² is 0.77.The model estimate result is higher than the ocular estimate result when canopy closure is low,and it is lower than the ocular estimate result when canopy closure is high.In the comparative study of the canopy closure of two different age poplar forests,the results indicate that the canopy closure of the 14-year-old poplar is higher than that of the 9-year-old poplar.The p values of the two methods are less than 0.05 and 0.001,respectively.Using digital photos to obtain ground sample data accurately can further verify the forest canopy closure value retrieved by large-scale remote sensing.It has great application value in further improving the measurement accuracy of forest canopy closure on a large scale.

Key words: forest canopy, canopy closure, digital photos, classification, RGB pictures

CLC Number:

S758.58

PU Yihan, XU Dandan, WANG Haobin. An Approach on Estimating Canopy Closure via Digital Images[J]. FOREST RESOURCES WANAGEMENT, 2020, (6): 153-160.

Figures/Tables 8

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样地编号	实测郁闭度	模型郁闭度	样地编号	实测郁闭度	模型郁闭度
1	0.44	0.45	11	0.53	0.51
2	0.44	0.46	12	0.53	0.53
3	0.37	0.39	13	0.53	0.52
4	0.49	0.46	14	0.58	0.55
5	0.48	0.46	15	0.45	0.45
6	0.47	0.44	16	0.48	0.49
7	0.53	0.53	17	0.49	0.49
8	0.52	0.48	18	0.45	0.49
9	0.50	0.52	19	0.49	0.50
10	0.45	0.48

点数量/郁闭度	总体分类精度-a	Kappa-a	总体分类精度-b	Kappa-b	总体分类精度-c	Kappa-c
20	0.95	0.91	0.95	0.90	0.95	0.92
40	0.92	0.85	0.90	0.82	0.92	0.86
60	0.95	0.90	0.97	0.94	0.85	0.73
80	0.98	0.95	0.91	0.84	0.89	0.78
100	0.96	0.92	0.93	0.90	0.90	0.80