Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

doi:10.12302/j.issn.1000-2006.202010004

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2021, Vol. 45 ›› Issue (4): 49-57.doi: 10.12302/j.issn.1000-2006.202010004

Previous Articles Next Articles

Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

ZHAO Yinghui¹^,²(), GUO Xinlong¹, ZHEN Zhen¹^,²^,^*()

1. School of Forestry, Northeast Forestry University, Harbin 150040, China
2. Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China

Received:2020-10-02 Accepted:2021-05-06 Online:2021-07-30 Published:2021-07-30
Contact: ZHEN Zhen E-mail:zyinghui0925@126.com;zhzhen@syr.edu

Abstract

Abstract:

【Objective】 We explored feature variables extracted through a combination of airborne laser scanning (ALS) and Sentinel-2A data, and investigated with the aim of identifying the best variable combination mode and estimation method for estimating the forest aboveground biomass (AGB) of natural secondary forests. 【Method】 Based on ALS data from 2015, Sentinel-2A data, and the fixed sample plots of the continuous inventory of secondary forest resources from Maoershan Forest Farm in 2016, this study extracted height features from ALS data (all the LiDAR variables, AL), several vegetation indices from Sentinel-2A (all the optical variables, AO), and then combined the two kinds of variables into new variables (COLI₁ and COLI₂). Finally, five models were constructed, including the stepwise multiple linear regression (SMLR), K-nearest neighbor (K-NN), support vector regression (SVR), random forest (RF), and stack sparse auto-encoder (SSAE) of AGB for natural secondary forests using six feature combinations (AO+AL, COLI₁, COLI₂, COLI₁+AO+AL, COLI₂+AO+AL and COLI₁+ COLI₂+AO+AL). The influence of the COLIs variable, and that of different models, on the accuracy of the AGB was investigated. 【Result】The COLIs variable could efficiently improve the accuracy of AGB estimates and, compared to the other four models, SSAE had the highest accuracy regardless of the variables. The SSAE model with the combination of optical and ALS features (COLI₁+ COLI₂+AO+AL) had the best model performance of R² which was 0.83, RMSE was 11.06 t/hm², rRMSE was 8.23%. 【Conclusion】 The combined variable COLIs can effectively improve the estimation accuracy of natural secondary forest AGB, and one way of the deep learning model (SSAE) is superior to the other prediction models in estimating the AGB of a natural secondary forest. This conclusion will help to further apply the deep learning model to draw a large-area AGB spatial distribution map and estimate the other forest parameters. In general, the SSAE model with the combination of ALS and Sentinel-2A data could estimate AGB more accurately than the other models. This finding provides a technical support for AGB estimation and carbon evaluation of natural secondary forests.

Key words: airborne laser radar (ALS), Sentinel-2A, combined optical and LiDNR index (COLIs), stack sparse auto-encoder (SSAE), natural secondary forest, aboveground biomass

CLC Number:

S758

ZHAO Yinghui, GUO Xinlong, ZHEN Zhen. Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(4): 49-57.

Figures/Tables 3

Table 1

Table 2

Table 3

Accuracy results of each model"

模型 model	变量组合 variables combination	R²	σ(RMSE)/ (t·hm^-2)	σ(rRMSE)/ %
SMLR	A_O+A_L	0.22	39.67	29.52
	$I CO L 1$	0.39	30.43	22.65
	$I CO L 2$	0.35	32.58	24.25
	$I CO L 1$ +A_O+A_L	0.47	27.39	20.38
	$I CO L 2$ +A_O+A_L	0.44	28.96	21.55
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.52	26.37	19.62
K-NN	A_O+A_L	0.37	37.28	27.74
	$I CO L 1$	0.56	23.49	17.48
	$I CO L 2$	0.52	24.91	18.54
	$I CO L 1$ +A_O+A_L	0.61	25.73	19.15
	$I CO L 2$ +A_O+A_L	0.58	27.64	20.57
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.66	26.19	19.49
SVR	A_O+A_L	0.41	34.70	25.82
	$I CO L 1$	0.60	20.34	15.14
	$I CO L 2$	0.56	23.49	17.48
	$I CO L 1$ +A_O+A_L	0.67	21.67	16.13
	$I CO L 2$ +A_O+A_L	0.65	22.52	16.76
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.72	20.98	15.61
RF	A_O+A_L	0.45	33.61	25.01
	$I CO L 1$	0.64	19.38	14.42
	$I CO L 2$	0.62	21.29	15.84
	$I CO L 1$ +A_O+A_L	0.75	17.35	12.91
	$I CO L 2$ +A_O+A_L	0.72	18.64	13.87
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.79	16.07	11.96
SSAE	A_O+A_L	0.50	31.75	23.63
	$I CO L 1$	0.78	12.71	9.46
	$I CO L 2$	0.76	15.36	11.43
	$I CO L 1$ +A_O+A_L	0.81	13.68	10.18
	$I CO L 2$ +A_O+AL	0.79	15.07	11.22
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.83	11.06	8.23

Table 3

References 53

[1]	GOETZ S, DUBAYAH R. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change[J]. Carbon Manag, 2011, 2(3):231-244. DOI: 10.4155/cmt.11.18. doi: 10.4155/cmt.11.18
[2]	ZOLKOS S G, GOETZ S J, DUBAYAH R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing[J]. Remote Sens Environ, 2013, 128:289-298. DOI: 10.1016/j.rse.2012.10.017. doi: 10.1016/j.rse.2012.10.017
[3]	娄雪婷, 曾源, 吴炳方. 森林地上生物量遥感估测研究进展[J]. 国土资源遥感, 2011, 23(1):1-8.
	LOU X T, ZENG Y, WU B F. Advances in the estimation of above-ground biomass of forest using remote sensing[J]. Remote Sens Land Resour, 2011, 23(1):1-8. DOI: 10.1016/j.ejphar.2011.07.001. doi: 10.1016/j.ejphar.2011.07.001
[4]	TSUI O W, COOPS N C, WULDER M A, et al. Integrating airborne LiDAR and space-borne radar via multivariate Kriging to estimate above-ground biomass[J]. Remote Sens Environ, 2013, 139:340-352. DOI: 10.1016/j.rse.2013.08.012. doi: 10.1016/j.rse.2013.08.012
[5]	LI W, NIU Z, LIANG X L, et al. Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via strati-fied random sampling[J]. Int J Appl Earth Obs Geoinformation, 2015, 41:88-98. DOI: 10.1016/j.jag.2015.04.020. doi: 10.1016/j.jag.2015.04.020
[6]	CHEN X W, LI B L, LIN Z S. The acceleration of succession for the restoration of the mixed-broadleaved Korean pine forests in Northeast China[J]. For Ecol Manag, 2003, 117(1/2/3):503-514. DOI: 10.1016/S0378-1127(02)00455-3. doi: 10.1016/S0378-1127(02)00455-3
[7]	安利波, 毕会涛, 杨红震, 等. 河南不同立地条件下栓皮栎天然次生林群落生物量结构特征[J]. 河南农业大学学报, 2016, 50(5):624-628.
	AN L B, BI H T, YANG H Z, et al. Structure characteristics of community biomass of Quercus variabilis natural secondary forest under different site conditions in Henan Province[J]. J Henan Agric Univ, 2016, 50(5):624-628. DOI: 10.16445/j.cnki.1000-2340.2016.05.008. doi: 10.16445/j.cnki.1000-2340.2016.05.008
[8]	赵金龙, 王泺鑫, 韩海荣, 等. 辽河源不同龄组油松天然次生林生物量及空间分配特征[J]. 生态学报, 2014, 34(23):7026-7037.
	ZHAO J L, WANG L X, HAN H R, et al. Biomass and spatial distribution characteristics of Pinus tabulaeformis natural secondary forest at different age groups in the Liaoheyuan Nature Reserve, Hebei Province[J]. Acta Ecol Sin, 2014, 34(23):7026-7037. DOI: 10.5846/stxb201303060357. doi: 10.5846/stxb201303060357
[9]	申鑫, 曹林, 佘光辉. 高光谱与高空间分辨率遥感数据的亚热带森林生物量反演[J]. 遥感学报, 2016, 20(6):1446-1460.
	SHEN X, CAO L, SHE G H. Subtropical forest biomass estimation based on hyperspectral and high-resolution remotely sensed data[J]. J Remote Sens, 2016, 20(6):1446-1460. DOI: 10.11834/jrs.20165210. doi: 10.11834/jrs.20165210
[10]	LI Y C, LI M Y, LI C, et al. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms[J]. Sci Rep, 2020, 10:9952. DOI: 10.1038/s41598-020-67024-3. doi: 10.1038/s41598-020-67024-3
[11]	HUANG W L, SUN G Q, DUBAYAH R, et al. Mapping biomass change after forest disturbance: applying LiDAR footprint-derived models at key map scales[J]. Remote Sens Environ, 2013, 134:319-332. DOI: 10.1016/j.rse.2013.03.017. doi: 10.1016/j.rse.2013.03.017
[12]	MONTESANO P M, NELSON R F, DUBAYAH R O, et al. The uncertainty of biomass estimates from LiDAR and SAR across a boreal forest structure gradient[J]. Remote Sens Environ, 2014, 154:398-407. DOI: 10.1016/j.rse.2014.01.027. doi: 10.1016/j.rse.2014.01.027
[13]	NAESSET E, GOBAKKEN T, SOLBERG S, et al. Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: a case study from a boreal forest area[J]. Remote Sens Environ, 2011, 115(12):3599-3614. DOI: 10.1016/j.rse.2011.08.021. doi: 10.1016/j.rse.2011.08.021
[14]	HUDAK A T, STRAND E K, VIERLING L A, et al. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys[J]. Remote Sens Environ, 2012, 123:25-40. DOI: 10.1016/j.rse.2012.02.023. doi: 10.1016/j.rse.2012.02.023
[15]	PFLUGMACHER D, COHEN W B, KENNEDY R E. Using Landsat-derived disturbance history (1972-2010) to predict current forest structure[J]. Remote Sens Environ, 2012, 122:146-165. DOI: 10.1016/j.rse.2011.09.025. doi: 10.1016/j.rse.2011.09.025
[16]	ZHAO K G, POPESCU S, NELSON R. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers[J]. Remote Sens Environ, 2009, 113(1):182-196. DOI: 10.1016/j.rse.2008.09.009. doi: 10.1016/j.rse.2008.09.009
[17]	ASNER G P, POWELL G V, MASCARO J, et al. High-resolution forest carbon stocks and emissions in the Amazon[J]. PNAS, 2010, 107(38):16738-16742. DOI: 10.1073/pnas.1004875107. doi: 10.1073/pnas.1004875107
[18]	CAO L, COOPS N C, INNES J L, et al. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data[J]. Remote Sens Environ, 2016, 178:158-171. DOI: 10.1016/j.rse.2016.03.012. doi: 10.1016/j.rse.2016.03.012
[19]	FERRAZ A, SAATCHI S, MALLET C, et al. Airborne lidar estimation of aboveground forest biomass in the absence of field inventory[J]. Remote Sens, 2016, 8(8):653. DOI: 10.3390/rs8080653. doi: 10.3390/rs8080653
[20]	BROVKINA O, NOVOTNY J, CIENCIALA E, et al. Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe[J]. Ecol Eng, 2017, 100:219-230. DOI: 10.1016/j.ecoleng.2016.12.004. doi: 10.1016/j.ecoleng.2016.12.004
[21]	EDIRIWEERA S, PATHIRANA S, DANAHER T, et al. Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in north-eastern Australia[J]. J For Res, 2014, 25(4):761-771. DOI: 10.1007/s11676-014-0485-7. doi: 10.1007/s11676-014-0485-7
[22]	郭艺歌, 王新云, 何杰, 等. 基于多源遥感数据的森林生物量估算模型研究[J]. 人民长江, 2016, 47(3):17-22.
	GUO Y G, WANG X Y, HE J, et al. Study on forest biomass estimation model based on multisource remote sensing data[J]. Yangtze River, 2016, 47(3):17-22. DOI: 10.16232/j.cnki.1001-4179.2016.03.005. doi: 10.16232/j.cnki.1001-4179.2016.03.005
[23]	李明阳, 吴军, 余超, 等. 福建武夷山自然保护区森林碳储量遥感估测方法与空间分析[J]. 南京林业大学学报(自然科学版), 2014, 38(6):6-10.
	LI M Y, WU J, YU C, et al. Estimation and spatial analysis of forest carbon stocks based on remote sensing technology in Wuyi Mountain National Reserve of Fujian Province[J]. J Nanjing For Univ (Nat Sci Ed), 2014, 38(6):6-10. DOI: 10.3969/j.issn.1000-2006.2014.06.002. doi: 10.3969/j.issn.1000-2006.2014.06.002
[24]	潘磊, 孙玉军, 王轶夫, 等. 基于Sentinel-1和Sentinel-2数据的杉木林地上生物量估算[J]. 南京林业大学学报(自然科学版), 2020, 44(3):149-156.
	PAN L, SUN Y J, WANG Y F, et al. Estimation of aboveground biomass in a Chinese fir(Cunninghamia lanceolata)forest combining data of Sentinel-1 and Sentinel-2[J]. J Nanjing For Univ (Nat Sci Ed), 2020, 44(3):149-156. DOI: 10.3969/j.issn.1000-2006.201811012. doi: 10.3969/j.issn.1000-2006.201811012
[25]	TIAN X, LI Z Y, SU Z B, et al. Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data[J]. Int J Remote Sens, 2014, 35(21):7339-7362. DOI: 10.1080/01431161.2014.967888. doi: 10.1080/01431161.2014.967888
[26]	FASSNACHT F E, HARTIG F, LATIFI H, et al. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass[J]. Remote Sens Environ, 2014, 154:102-114. DOI: 10.1016/j.rse.2014.07.028. doi: 10.1016/j.rse.2014.07.028
[27]	蒙诗栎, 庞勇, 张钟军, 等. WorldView-2纹理的森林地上生物量反演[J]. 遥感学报, 2017, 21(5):812-824.
	MENG S L, PANG Y, ZHANG Z J, et al. Estimation of aboveground biomass in a temperate forest using texture information from WorldView-2[J]. J Remote Sens, 2017, 21(5):812-824. DOI: 10.11834/jrs.20176083. doi: 10.11834/jrs.20176083
[28]	SHAO Z F, ZHANG L J, WANG L. Stacked sparse autoencoder modeling using the synergy of airborne LiDAR and satellite optical and SAR data to map forest above-ground biomass[J]. IEEE J Sel Top Appl Earth Obs Remote Sens, 2017, 10(12):5569-5582. DOI: 10.1109/JSTARS.2017.2748341. doi: 10.1109/JSTARS.2017.2748341
[29]	ZHANG L J, SHAO Z F, LIU J C, et al. Deep learning based retrieval of forest aboveground biomass from combined LiDAR and landsat 8 data[J]. Remote Sens, 2019, 11(12):1459. DOI: 10.3390/rs11121459. doi: 10.3390/rs11121459
[30]	董利虎. 黑龙江省主要树种相容性生物量模型研究[D]. 哈尔滨: 东北林业大学, 2012.
	DONG L H. Compatible biomass models of main tree species in Heilongjiang Province[D]. Harbin: Northeast Forestry University, 2012.
[31]	ANDERSEN H E, MCGAUGHEY R J, REUTEBUCH S E. Estimating forest canopy fuel parameters using LIDAR data[J]. Remote Sens Environ, 2005, 94(4):441-449. DOI: 10.1016/j.rse.2004.10.013. doi: 10.1016/j.rse.2004.10.013
[32]	LU D S, CHEN Q, WANG G X, et al. Aboveground forest biomass estimation with landsat and LiDAR data and uncertainty analysis of the estimates[J]. Int J For Res, 2012(2):1-16. DOI: 10.1155/2012/436537. doi: 10.1155/2012/436537
[33]	CHEN Q, LAURIN G V, BATTLES J J, et al. Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass[J]. Remote Sens Environ, 2012, 121:108-117. DOI: 10.1016/j.rse.2012.01.021. doi: 10.1016/j.rse.2012.01.021
[34]	LUO S Z, WANG C, XI X H, et al. Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation[J]. Ecol Indic, 2017, 73:378-387. DOI: 10.1016/j.ecolind.2016.10.001. doi: 10.1016/j.ecolind.2016.10.001
[35]	徐婷, 曹林, 申鑫, 等. 基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算[J]. 植物生态学报, 2015, 39(4):309-321. doi: 10.17521/cjpe.2015.0030
	XU T, CAO L, SHEN X, et al. Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data[J]. Chin J Plant Ecol, 2015, 39(4):309-321. DOI: 10.17521/cjpe.2015.0030. doi: 10.17521/cjpe.2015.0030
[36]	曹霖, 彭道黎, 王雪军, 等. 应用Sentinel-2A卫星光谱与纹理信息的森林蓄积量估算[J]. 东北林业大学学报, 2018, 46(9):54-58.
	CAO L, PENG D L, WANG X J, et al. Estimation of forest stock volume with spectral and textural information from the Sentinel-2A[J]. J Northeast For Univ, 2018, 46(9):54-58. DOI: 10.13759/j.cnki.dlxb.2018.09.012. doi: 10.13759/j.cnki.dlxb.2018.09.012
[37]	ROUSE J W J, HAAS R H, SCHELL J A, et al. Monitoring ve-getation systems in the great plains with ERTS[EB/OL]. https://ntrs.nasa.gov/api/citations/19740022614/downloads/19740022614.pdf . 1974.
[38]	RICHARDSON A J, WIEGAND C L. Distinguishing vegetation from soil background information[J]. Photogramm Eng Remote Sens, 1977, 43(12):1541-1552. DOI: 10.1109/TGE.1977.294499. doi: 10.1109/TGE.1977.294499
[39]	HUETE A, DIDAN K, MIURA T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sens Environ, 2002, 83(1/2):195-213. DOI: 10.1016/S0034-4257(02)00096-2. doi: 10.1016/S0034-4257(02)00096-2
[40]	JORDAN C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4):663-666. DOI: 10.2307/1936256. doi: 10.2307/1936256
[41]	GITELSON A A, VIÑA A, CIGANDA V, et al. Remote estimation of canopy chlorophyll content in crops[J]. Geophys Res Lett, 2005, 32(8):L08403. DOI: 10.1029/2005GL022688. doi: 10.1029/2005GL022688
[42]	RONDEAUX G, STEVEN M, BARET F. Optimization of soil-adjusted vegetation indices[J]. Remote Sens Environ, 1996, 55(2):95-107. DOI: 10.1016/0034-4257(95)00186-7. doi: 10.1016/0034-4257(95)00186-7
[43]	GITELSON A A, MERZLYAK M N. Remote estimation of chlorophyll content in higher plant leaves[J]. Int J Remote Sens, 1997, 18(12):2691-2697. DOI: 10.1080/014311697217558. doi: 10.1080/014311697217558
[44]	GITELSON A A, GRITZ Y, MERZLYAK M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves[J]. J Plant Physiol, 2003, 160(3):271-282. DOI: 10.1078/0176-1617-00887. doi: 10.1078/0176-1617-00887
[45]	谢福明, 舒清态, 字李, 等. 基于K-NN非参数模型的高山松生物量遥感估测研究[J]. 江西农业大学学报, 2018, 40(4):743-750.
	XIE F M, SHU Q T, ZI L, et al. Remote sensing estimation of Pinus densata aboveground biomass based on K-NN nonparametric model[J]. Acta Agric Univ Jiangxiensis, 2018, 40(4):743-750. DOI: 10.13836/j.jjau.2018094. doi: 10.13836/j.jjau.2018094
[46]	GLEASON C J, IM J. Forest biomass estimation from airborne LiDAR data using machine learning approaches[J]. Remote Sens Environ, 2012, 125:80-91. DOI: 10.1016/j.rse.2012.07.006. doi: 10.1016/j.rse.2012.07.006
[47]	PANDIT S, TSUYUKI S, DUBE T. Estimating above-ground biomass in sub-tropical buffer zone community forests, Nepal, using Sentinel-2 data[J]. Remote Sens, 2018, 10(4):601. DOI: 10.3390/rs10040601. doi: 10.3390/rs10040601
[48]	STEPHEN A B, PHILIP H Y. The trouble with R²[J]. Journal of Parametrics, 2006, 25(1):87-114. DOI: 10.1080/10157891.2006.10462273. doi: 10.1080/10157891.2006.10462273
[49]	ASTOLA H, HÄME T, SIRRO L, et al. Comparison of Sentinel-2 and Landsat-8 imagery for forest variable prediction in boreal region[J]. Remote Sens Environ. 2019, 223:257-273. DOI: 10.1016/j.rse.2019.01.019. doi: 10.1016/j.rse.2019.01.019
[50]	郑阳, 吴炳方, 张淼. Sentinel-2数据的冬小麦地上干生物量估算及评价[J]. 遥感学报, 2017, 21(2):318-328.
	ZHENG Y, WU B F, ZHANG M. Estimating the above ground biomass of winter wheat using the Sentinel-2 data[J]. J Remote Sens, 2017, 21(2):318-328. DOI: 10.11834/jrs.20176269. doi: 10.11834/jrs.20176269
[51]	雷相东. 机器学习算法在森林生长收获预估中的应用[J]. 北京林业大学学报, 2019, 41(12):23-36.
	LEI X D. Applications of machine learning algorithms in forest growth and yield prediction[J]. J Beijing For Univ, 2019, 41(12):23-36. DOI: 10.12171/j.1000-1522.20190356. doi: 10.12171/j.1000-1522.20190356
[52]	孙雪莲. 基于Landsat 8-OLI的香格里拉高山松林生物量遥感估测模型研究[D]. 昆明: 西南林业大学, 2016.
	SUN X L. Biomass estimation model of Pinus densata forests in Shangri-La City based on Landsat 8-OLI by remote sensing[D]. Kunming: Southwest Forestry University, 2016.
[53]	HAME T, RAUSTE Y, ANTROPOV O, et al. Improved mapping of tropical forests with optical and SAR imagery, part II: above ground biomass estimation[J]. IEEE J Sel Top Appl Earth Obs Remote Sens, 2013, 6(1):92-101. DOI: 10.1109/JSTARS.2013.2241020. doi: 10.1109/JSTARS.2013.2241020

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

林分类型 forest types	样地数 plots number	胸径/cm DBH
林分类型 forest types	样地数 plots number	最大值 max.	最小值 min.	平均值 mean
白桦林 Betula phatyphylla forest	4	14.6	13.1	13.8
阔叶混交林 broad-leaved mixed forest	134	19.8	9.2	15.1
胡桃楸林 Juglans mandshurica forest	1	12.4	12.4	12.4
落叶松林 Larix gmelinii forest	8	19.7	14.2	15.5
山杨林 Populus davidiana forest	1	17.0	17.0	17.0
柞树林 Quercus mongolica forest	4	22.4	14.0	18.0
樟子松林 Pinus sylvestris forest	1	14.1	14.1	14.1
针阔混交林 mixed broadleaf-conifer forest	7	15.0	9.4	11.6
针叶混交林 coniferous mixed forest	1	16.0	16.0	16.0

特征变量 feature variable	R²	σ(RMSE)/ (t·hm^-2)	σ(rRMSE)/ %	特征变量 feature variable	R²	σ(RMSE)/ (t·hm^-2)	σ(rRMSE)/ %
h₁₀	0.39	40.95	30.48	h_max	0.20	47.13	35.07
h₂₀	0.42	39.97	29.75	h_min	0.15	48.86	36.36
h₃₀	0.44	39.52	29.41	h_skew	0.28	44.76	33.31
h₄₀	0.44	39.47	29.37	h_kurt	0.19	47.32	35.22
h₅₀	0.43	39.84	29.65	h_std	0.21	46.78	34.81
h₆₀	0.41	40.47	30.12	h_mean	0.45	39.16	29.14
h₇₀	0.39	41.11	30.59	h_cv	0.20	47.07	35.03
h₈₀	0.35	42.24	31.44	h_var	0.21	46.86	34.87
h₉₀	0.30	43.90	32.67	h_mode	0.27	44.96	33.46
h₉₉	0.22	46.27	34.43	C	0.19	47.07	35.03

Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 3

References 53

Related Articles 14

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer

[1]	DONG Yujie, MAO Lingfeng, ZHANG Min, LU Xudong, WU Xiuping. Relationship between aboveground biomass and environmental factors of subtropical typical evergreen broad-leaved forest in east China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(1): 74-80.
[2]	ZHOU Youfeng, XIE Binglou, LI Mingshi. Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(1): 169-178.
[3]	WANG Qisong, GUO Qingxi. The spatial distribution characteristics of light intensity attenuation under natural secondary forests in eastern Jilin Province, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(1): 101-108.
[4]	JU Yilin, JI Yongjie, HUANG Jimao, ZHANG Wangfei. Inversion of forest aboveground biomass using combination of LiDAR and multispectral data [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(1): 58-68.
[5]	ZHANG Guofei, YUE Cairong, LUO Hongbin, GU Lei, ZHU Bodong. Based polarization orientation angle compensation for Pinus kesiya var. langbianensis forest aboveground biomass estimation [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(6): 185-192.
[6]	ZHAO Yinghui, YANG Haicheng, ZHEN Zhen. Tree height estimations for different forest canopies in natural secondary forests based on ULS, TLS and ultrasonic altimeter systems [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(4): 23-32.
[7]	CHEN Chen, LIU Guangwu. The growth law for natural secondary forests of Pinus bungeana in the Huanglong Mountain forest region [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(6): 125-130.
[8]	PAN Lei, SUN Yujun, WANG Yifu, CHEN Liping, CAO Yuanshuai. Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2 [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 149-156.
[9]	DUAN Guangshuang,LI Xuedong, FENG Yan, FU Liyong. Generalized nonlinear mixed-effects crown base height model of Larix principis-rupprechtii natural secondary forests [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 42(02): 170-176.
[10]	QIAN Guoping,ZHAO Zhixia, LI Zhengcai,ZHOU Jungang, CHENG Caifang, ZHAO Ruiyu, SUN Jiaojiao. Effects of fire on soil organic carbon in natural secondary forest in north subtropical areas [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 41(06): 115-119.
[11]	YU Chao, SONG Liyi, LI Mingyang, Omidreza Shobairi SEYED, ZHANG Xiangyang. Spatio-temporal dynamics of forest aboveground biomass in Xixia County, Henan Province, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 41(06): 93-101.
[12]	ZHOU Hua, MENG Shengwang, LIU Qijing. Allometric equations for estimating aboveground biomass of broad-leaved forests saplings and shrubs in subtropical China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 41(06): 79-86.
[13]	GAO Yanping, DING Fangjun, PAN Mingliang, ZHOU Fengjiao, WU Peng. Carbon sequestration and distribution characteristics in natural secondary forests of Betula luminifera in west Guizhou [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2014, 38(04): 51-56.
[14]	WANG Xiaoming, ZHOU Benzhi, ZHONG Shaozhu, KONG Weijian, WANG Gang, XU Shenghua. Dynamic analysis of hydrological processes of natural secondary forest in different rainfall conditions [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2010, 34(06): 57-60.