Identification of forest types based on ICESat-GLAS data and fuzzy pattern recognition algorithm

doi:10.12302/j.issn.1000-2006.202003008

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2021, Vol. 45 ›› Issue (4): 33-40.doi: 10.12302/j.issn.1000-2006.202003008

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Identification of forest types based on ICESat-GLAS data and fuzzy pattern recognition algorithm

CAI Longtao(), XING Tao^*(), XING Yanqiu, DING Jianhua, HUANG Jiapeng, CUI Yang, QIN Lei

College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China

Received:2020-03-03 Accepted:2021-03-22 Online:2021-07-30 Published:2021-07-30
Contact: XING Tao E-mail:cailongtao966@163.com;xt_hit@126.com

Abstract

Abstract:

【Objective】 Using space-borne LiDAR waveform data and the fuzzy pattern recognition algorithm, combined with the waveform characteristic parameters proposed in this paper, the forest type was identified and studied to improve the accuracy of forest type classification. 【Method】 First, using the differences in GLAS-received waveforms of the canopies of different forest types, the waveform characteristic parameters $R fi t 1$ , $K 1'$ and $K 1 ¯$ were acquired. Second, the extracted waveform feature parameters were combined with the other waveform feature parameters to establish a combination of waveform feature parameters. Thereafter, the waveform feature parameters of the sample data were subjected to index normalization and singularity detection processing to unify the dimensions of different waveform feature parameters and remove the sample data. Finally, combined with the fuzzy pattern recognition algorithm, the classification accuracy of different forest types was calculated. 【Result】 The total classification accuracies of coniferous and broadleaved forest types were 96.30%, of which the classification accuracies of coniferous and broadleaved forest types were 92.86% and 97.50%, respectively. Furthermore, the total accuracy was 84.51%, of which the classification accuracies of coniferous forest, broadleaved forest, and mixed forest types were 85.71%, 97.50% and 52.94%, respectively. 【Conclusion】 The fuzzy pattern recognition algorithm has certain advantages for the forest-type classification. This is especially so in the classification of coniferous and broadleaved forests, where the classification accuracy was higher.

Key words: geoscience laser altimeter system (GLAS), forest type, fuzzy pattern recognition, waveform characteristic parameters, singular point, degree of membership

CLC Number:

S771:S758

CAI Longtao, XING Tao, XING Yanqiu, DING Jianhua, HUANG Jiapeng, CUI Yang, QIN Lei. Identification of forest types based on ICESat-GLAS data and fuzzy pattern recognition algorithm[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(4): 33-40.

Figures/Tables 5

Fig.1

Fig.2

Table 1

Classification results of forest types in coniferous and broadleaved forests with different parameters"

特征参数组合 character metrics combination	分类总数 total number of types	正确分类数 correct classification number	分类总精度/% accuracy of classification	Kappa系数 Kappa coefficient
$R fi t 1$ 、K₁'、 $K 1 ¯$	54	50	92.59	0.820 3
AGS、SGS、MSGS	54	49	90.74	0.778 0
$R fi t 1$ 、K₁'、 $K 1 ¯$ 、AGS、SGS、MSGS	54	52	96.30	0.903 6

Table 1

Table 2

Table 3

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森林类型 forest type	分类结果 result of classification		类型总数 total number of types	分类精度/% accuracy of classification
森林类型 forest type	针叶林 coniferous forest	阔叶林 broadleaved forest	类型总数 total number of types	分类精度/% accuracy of classification
针叶林 coniferous forest	13	1	14	92.86
阔叶林 broadleaved forest	1	39	40	97.50

森林类型 forest type	分类结果 result of classification			类型总数 total number of types	分类精度/% accuracy of classification	分类总精度/% accuracy of total classification	Kappa系数 Kappa coefficient
森林类型 forest type	针叶林 coniferous forest	阔叶林 broadleaved forest	混交林 mixed forest	类型总数 total number of types	分类精度/% accuracy of classification	分类总精度/% accuracy of total classification	Kappa系数 Kappa coefficient
针叶林 coniferous forest	12	1	1	14	85.71
阔叶林 broadleaved forest	1	39	0	40	97.50	84.51	0.725 7
混交林 mixed forest	3	5	9	17	52.94

Identification of forest types based on ICESat-GLAS data and fuzzy pattern recognition algorithm

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