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

Previous Articles Next Articles

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

References 28

[1]	FIELD C B, RAUPACH M R, EBRARY I, et al. The global carbon cycle: integrating humans, climate, and the natural world[M]. Chicago: Island Press, 2004.
[2]	汤旭光. 基于激光雷达与多光谱遥感数据的森林地上生物量反演研究[D]. 北京:中国科学院研究生院(东北地理与农业生态研究所), 2013.
	TANG X G. Estimation of forest aboveground biomass by integrating ICESat/GLAS waveform and TM data[D]. Beijing: Graduate School of Chinese Academy of Sciences (Northeast Institute of Geography and Agricultural Ecology), 2013.
[3]	邱赛. ICESat-GLAS波形与HJ-1A高光谱影像联合反演森林地上生物量的研究[D]. 哈尔滨:东北林业大学, 2016.
	QIU S. The research of regional forest above ground biomass inversion combining ICESat-GLAS waveform and HJ-1A hyperspectral imageries[D]. Harbin:Northeast Forestry University, 2016.
[4]	吴文跃, 姚顺彬, 徐志扬. 基于森林资源清查数据的江西省主要森林类型净生产力研究[J]. 南京林业大学学报(自然科学版), 2019, 43(5):193-198.
	WU W Y, YAO S B, XU Z Y, et al. A study on the net productivity of main forest types in Jiangxi Province based on forest resources inventory data[J]. J Nanjing For Univ (Nat Sci Ed), 2019, 43(5):193-198. DOI: 10.3969/j.issn.1000-2006.201903045. doi: 10.3969/j.issn.1000-2006.201903045
[5]	MOHOLDT G, NUTH C, HAGEN J O, et al. Recent elevation changes of Svalbard glaciers derived from ICESat laser altimetry[J]. Remote Sens Environ, 2010, 114(11):2756-2767.DOI: 10.1016/j.rse.2010.06.008. doi: 10.1016/j.rse.2010.06.008
[6]	CARABAJAL C C, HARDING D J, SUCHDEO V P, et al. Development of AN ICESat geodetic control database and evaluation of global topographic assets[J]. American Geophysical Union, Fall Meeting, 2010(6):1907-1910. DOI: 10.1038/nmeth.2835. doi: 10.1038/nmeth.2835
[7]	LEFSKY M A. A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system[J]. Geophys Res Lett, 2010, 37(15):L15401.DOI: 10.1029/2010GL043622. doi: 10.1029/2010GL043622
[8]	KHALEFA E, SMIT I P J, NICKLESS A, et al. Retrieval of savanna vegetation canopy height from ICESat-GLAS spaceborne LiDAR with terrain correction[J]. IEEE Geosci Remote Sens Lett, 2013, 10(6):1439-1443.DOI: 10.1109/LGRS.2013.2259793. doi: 10.1109/LGRS.2013.2259793
[9]	XING Y Q, DE GIER A, ZHANG J J, et al. An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain:a case study in Changbai Mountains,China[J]. Int J Appl Earth Obs Geoinformation, 2010, 12(5):385-392.DOI: 10.1016/j.jag.2010.04.010. doi: 10.1016/j.jag.2010.04.010
[10]	WANG C, TANG F X, LI L W, et al. Wavelet analysis for ICESat/GLAS waveform decomposition and its application in average tree height estimation[J]. IEEE Geosci Remote Sens Lett, 2013, 10(1):115-119.DOI: 10.1109/LGRS.2012.2194692. doi: 10.1109/LGRS.2012.2194692
[11]	邱赛, 邢艳秋, 田静, 等. 基于ICESat-GLAS波形数据估测森林郁闭度[J]. 南京林业大学学报(自然科学版), 2016, 40(5):99-106.
	QIU S, XING Y Q, TIAN J, et al. Estimation of forest canopy density based on ICESat-GLAS waveform data[J]. J Nanjing For Univ (Nat Sci Ed), 2016, 40(5):99-106.DOI: 10.3969/j.issn.1000-2006.2016.05.016. doi: 10.3969/j.issn.1000-2006.2016.05.016
[12]	XI X H, HAN T T, WANG C, et al. Forest above ground biomass inversion by fusing GLAS with optical remote sensing data[J]. ISPRS Int J Geo-Inf, 2016, 5(4):45.DOI: 10.3390/ijgi5040045. doi: 10.3390/ijgi5040045
[13]	WANG Y, NI W J, SUN G Q, et al. Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass[J]. Remote Sens Environ, 2019, 224:386-400.DOI: 10.1016/j.rse.2019.02.017. doi: 10.1016/j.rse.2019.02.017
[14]	FATOYINBO T E, SIMARD M. Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM[J]. Int J Remote Sens, 2013, 34(2):668-681.DOI: 10.1080/01431161.2012.712224. doi: 10.1080/01431161.2012.712224
[15]	RANSON K J, SUN G, KOVACS K, et al. Landcover attributes from ICESat GLAS data in Central Siberia[C]// IGARSS 2004.2004 IEEE International Geoscience and Remote Sensing Symposium.September 20-24,2004,Anchorage,AK,USA.IEEE, 2004:753-756.DOI: 10.1109/IGARSS.2004.1368511. doi: 10.1109/IGARSS.2004.1368511
[16]	ZHANG J J, DE GIER A, XING Y Q, et al. Full waveform-based analysis for forest type information derivation from large footprint spaceborne lidar data[J]. Photogramm Eng Remote Sensing, 2011, 77(3):281-290.DOI: 10.14358/pers.77.3.281. doi: 10.14358/pers.77.3.281
[17]	LI L C, XING Y Q. Study on forest type classification based on ICESat-GLAS lidar data[C]// 2011 2nd International Conference on Artificial Intelligence,Management Science and Electronic Commerce (AIMSEC).August 8-10,2011,Deng Feng,China.IEEE, 2011:3379-3382.DOI: 10.1109/AIMSEC.2011.6011388. doi: 10.1109/AIMSEC.2011.6011388
[18]	刘美爽, 邢艳秋, 李立存, 等. 基于星载激光雷达数据和支持向量分类机方法的森林类型识别[J]. 东北林业大学学报, 2014, 42(2):124-128.
	LIU M S, XING Y Q, LI L C, et al. Forest type identification with spaceborne LIDAR data and C-support vector classification[J]. J Northeast For Univ, 2014, 42(2):124-128.DOI: 10.13759/j.cnki.dlxb.2014.02.030. doi: 10.13759/j.cnki.dlxb.2014.02.030
[19]	蔡龙涛, 邢艳秋, 黄佳鹏, 等 . 基于 ICESat-GLAS 数据和回波仿真原理识别森林类型[J]. 中南林业科技大学学报, 2021, 41(1):60-68.
	CAI L T, XING Y Q, HUANG J P, et al. Forest types identification based on ICESat-GLAS data and echo simulation principles[J]. J Central South Univ For Technol, 2021, 41(1):60-68.
[20]	WU H B, XING Y Q. Wavelet transform and its application to ICESat-GLAS full waveform data[C]// 2010 International Symposium on Intelligence Information Processing and Trusted Computing.October 28-29,2010,Huanggang,China.IEEE, 2010:373-377.DOI: 10.1109/IPTC.2010.38. doi: 10.1109/IPTC.2010.38
[21]	邱赛, 邢艳秋, 李立存, 等. 基于小波变换的ICESAT-GlAS波形处理[J]. 森林工程, 2012, 28(5):33-35,59.
	QIU S, XING Y Q, LI L C, et al. ICESAT-GLAS data processing based on wavelet transform[J]. For Eng, 2012, 28(5):33-35,59.DOI: 10.16270/j.cnki.slgc.2012.05.022. doi: 10.16270/j.cnki.slgc.2012.05.022
[22]	LI D, XU L J, LI X L, et al. Full-waveform LiDAR signal filtering based on Empirical Mode Decomposition method[C]// 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS.July 21-26,2013,Melbourne,VIC,Australia.IEEE, 2013:3399-3402.DOI: 10.1109/IGARSS.2013.6723558. doi: 10.1109/IGARSS.2013.6723558
[23]	YANG T, WANG C, LI G C, et al. Forest canopy height mapping over China using GLAS and MODIS data[J]. Sci China Earth Sci, 2015, 58(1):96-105.DOI: 10.1007/s11430-014-4905-5. doi: 10.1007/s11430-014-4905-5
[24]	MAHONEY C, KLJUN N, LOS S, et al. Slope estimation from ICESat/GLAS[J]. Remote Sens, 2014, 6(10):10051-10069.DOI: 10.3390/rs61010051. doi: 10.3390/rs61010051
[25]	PANG Y, LEFSKY M, SUN G Q, et al. Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar[J]. Remote Sens Environ, 2011, 115(11):2798-2809.DOI: 10.1016/j.rse.2010.08.025. doi: 10.1016/j.rse.2010.08.025
[26]	汪泉, 宋文龙, 张怡卓, 等. 基于改进VGG16 网络的机载高光谱针叶树种分类研究[J]. 森林工程, 2021, 37(3):79-87.
	WANG Q, SONG W L, ZHANG Y Z, et al. Study on hyperspectral conifer species classification based on improved VGG16 network[J]. For Eng, 2021, 37(3):79-87.
[27]	DUNCANSON L I, NIEMANN K O, WULDER M A. Estimating forest canopy height and terrain relief from GLAS waveform metrics[J]. Remote Sens Environ, 2010, 114(1):138-154.DOI: 10.1016/j.rse.2009.08.018. doi: 10.1016/j.rse.2009.08.018
[28]	JIHYUN H A, SEOK S, LEE J S. Robust outlier detection using the instability factor[J]. Knowl-Based Syst, 2014, 63:15-23.DOI: 10.1016/j.knosys.2014.03.001. doi: 10.1016/j.knosys.2014.03.001

Related Articles 15

[1]	HUANG Jian, WU Dasheng, FANG Luming. Identification of sub-compartment forest type based on multi-source data and three-tier models [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(1): 69-80.
[2]	ZHU Jiaqi, MAN Xiuling, WANG Fei. Organic carbon and nitrogen characteristics of soil aggregates in four forest types in frigid temperate zone [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(5): 71-83.
[3]	WANG Bing, ZHANG Pengjie, ZHANG Qiuliang. Characteristics of the soil aggregate and its organic carbon in different Larix gmelinii forest types [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(3): 15-24.
[4]	WU Guoxun, TANG Xuejun, RUAN Honghua, LUO Xifang. Carbon storage and carbon sequestration potential based on forest inventory data in Jiangxi Province, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 62(01): 105-110.
[5]	CHEN Lixin, LIANG Weiwei, DUAN Wenbiao, LI Gang, LI Yifei, LI Shaoran, MA Haijuan. Effects of low molecular weight organic acid on inorganic phosphorus fractions of typical temperate forest soils [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 61(04): 75-82.
[6]	ZHU Guangyu, HU Song, FU Liyong. Basal area growth model for oak natural forest in Hunan Province based on dummy variable [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 61(02): 155-162.
[7]	WANG Xing, CUI Xiaoyang, GUO Yafen. A study on free amino acid in different forest types soil of cold-temperate forest region [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2016, 59(04): 42-48.
[8]	WU Chuping, YE Jihua, HUANG Yujie, ZHU Jinru, SHEN Aihua, YUAN Weigao, JIANG Bo. Effects of different forest types on water quality in Zhoushan Island, Zhejiang Province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2015, 58(04): 75-80.
[9]	WEI Qiang,ZHANG Guangzhong,LING Lei,CHAI Chunshan. Water conservation function of litter and soil layer under main forest types in Xinglong Mountain of Gansu [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2013, 56(02): 78-84.
[10]	GU Yushu, XING Zhaokai, HAN Youzhi, LIU Hongmin, GAO Yingxu. Water-holding characteristics of four typical forests litter in upstream of Hunhe river [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2013, 56(01): 31-36.
[11]	TAO Baoxian,ZHANG Jinchi. Preliminary study of the variation of air anion in different forest stands in Nanjing city [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2012, 55(02): 147-150.
[12]	YI Lita, YAN Xiaosu, YU Shuquan, BAO Chunquan, TU Yonghai, PAN Changyao. The health index system of different forest types in Zhejiang province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2012, 55(01): 145-148.
[13]	JIANG Wen wei 1,2 ,JIANG Zhi lin 1,YU Shu quan 2,LIU An xin 3,QIAN Xin biao 2. Analysis and Evaluation on Water-holding Function of Main Forest Types in Anji Region [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2002, 45(04): 71-74.
[14]	Xu Shiyong Lin Jie ?Cheng Tao(Nanjing Forestry University\ Nanjing 210037). A Study on Forest Type Mapping with Digital Colour Infrared Photograph [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2000, 43(03): 49-52.
[15]	Zhang Pengchun. THE INVESTIGATION OF TWIG BEETLE RANGE OF DAHURIAN LARCH (LARIX GMELINI (RUPR.) RUPR) IN THE NORTH REGION OF THE GREATER KHINGAN MOUNTAINS [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 1990, 33(01): 44-50.

Metrics

Comments

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

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

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 28

Related Articles 15

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer