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Forest and Grassland Resources Research >

2021 , Vol. 0 >Issue 1: 50 - 60

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2021.01.008

Scientific Research

Estimation on Forest Above-Ground Biomass Based on Simulated Large-Footprint LiDAR and Multi-Layer Perceptron

  • Changjian XV ,
  • Yingchun LIU ,
  • Lijun ZUO ,
  • Jiangeng LI ,
  • Ting ZHANG ,
  • Lumeng HAN ,
  • Yu FANG ,
  • Yin ZHANG ,
  • Tian WANG
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  • 1. Academy of Inventory and Planning,National Forestry and Grassland Administration,Beijing 100714,China
    2. Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
    3. Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China

Received date: 2020-11-09

  Revised date: 2020-12-12

  Online published: 2021-03-30

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Abstract

Forests are important global terrestrial ecosystems.Sample survey is a commonly used method by countries to assess their forest resources and biomass.With the development of LiDAR technology,spaceborne large-footprint ladar become an option to estimate forest above-ground biomass(AGB) in large areas.In order to develop the method to estimate forest AGB with large-footprint LiDAR,the study proposes an AGB estimation model based on simulated large-footprint LiDAR and multi-layer perceptron.Based on 13 groups of LiDAR waveform parameters,the multi-layer perceptron achieves higher accuracy than multiple linear regression to estimate AGB.Compared with the field measured AGB,the deviation range of the estimated AGB from the multiple linear regression is between -34.96 to 23.28 t/hm2 and the estimated deviation of the multi-layer perceptron is between -19.09 to 20.19 t/hm2.Therefore,multi-layer perceptron is better than multiple linear regression in estimating forest AGB.

Key words： above-ground biomass; LiDAR; simulated waveform; multi-layer perceptron

Cite this article

Changjian XV , Yingchun LIU , Lijun ZUO , Jiangeng LI , Ting ZHANG , Lumeng HAN , Yu FANG , Yin ZHANG , Tian WANG . Estimation on Forest Above-Ground Biomass Based on Simulated Large-Footprint LiDAR and Multi-Layer Perceptron[J]. Forest and Grassland Resources Research, 2021 , 0(1) : 50 -60 . DOI: 10.13466/j.cnki.lyzygl.2021.01.008

References

[1] Fang Jingyun, Yu Guirui, Liu Lingli, et al. Climate change,human impacts,and carbon sequestration in China[J]. Proceedings of the National Academy of Sciences, 2018,115(16):4015-4020.
[2] Nascimento H E M, Laurance W F. Total above-ground biomass in central Amazonian rainforests:a landscape-scale study[J]. Forest ecology and management, 2002,168(1-3):311-321.
[3] Nie Sheng, Wang Cheng, Zeng Hongcheng, et al. Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest[J]. Ecological Indicators, 2017,78:221-228.
[4] 刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015,19(1):62-74.
[5] 刘清旺, 李增元, 陈尔学, 等. 机载LIDAR点云数据估测单株木生物量[J]. 高技术通讯, 2010,7:765-770.
[6] 洪奕丰, 张守攻, 陈伟, 等. 基于机载激光雷达的落叶松组分生物量反演[J]. 林业科学研究, 2019(5):12.
[7] Margolis H A, Nelson R F, Montesano P M, et al. Combining satellite lidar,airborne lidar,and ground plots to estimate the amount and distribution of above-ground biomass in the boreal forest of North America[J]. Canadian Journal of Forest Research, 2015,45(7):838-855.
[8] Muss J D, Mladenoff D J, Townsend P A. A pseudo-waveform technique to assess forest structure using discrete lidar data[J]. Remote Sensing of Environment, 2011,115(3):824-835.
[9] 吴娇娇, 欧光龙, 舒清态. 基于BP神经网络模型思茅松天然林生物量遥感估测[J]. 中南林业科技大学学报, 2017,37(7):30-35.
[10] 孙翊, 姜树海, 陈至灵. 人工神经网络在林业上的应用研究进展[J]. 世界林业研究, 2019,32(3):7-12.
[11] 徐辉, 潘萍, 宁金魁, 等. 多元线性回归与神经网络模型在森林地上生物量遥感估测中的应用[J]. 东北林业大学学报, 2018,46(1):63-67.
[12] 徐奇刚, 雷相东, 国红, 等. 基于多层感知机的长白落叶松人工林林分生物量模型[J]. 北京林业大学学报, 2019,41(5):97-107.
[13] Le Cun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015,521(7553):436-444.
[14] 罗云建, 张小全, 王效科, 等. 华北落叶松人工林生物量及其分配模式[J]. 北京林业大学学报, 2009,6(1):13-18.
[15] 白静. 油松人工林生长特征及其与林分结构关系研究[D]. 呼和浩特:内蒙古农业大学, 2008.
[16] 吴俊民, 苏贵林, 刘成志, 等. 黑龙江省西部杨树人工林生物量的估算方法[J]. 林业科技, 2000(3):14-16.
[17] Wang Chuankuan. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests[J]. Forest Ecology and Management, 2006,222(1-3):9-16.
[18] 贾炜玮, 于爱民. 樟子松人工林单木生物量模型研究[J]. 林业科技情报, 2008,40(2):1-2
[19] Hancock S, Armston J, Hofton M, et al. The GEDI simulator:a large-footprint waveform Lidar simulator for calibration and validation of spaceborne missions[J]. Earth and Space Science, 2019,6(2):294-310.
[20] Chen J M, Pavlic G, Brown L, et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J]. Remote Sensing of Environment, 2002,80(1):165-184.
[21] Richardson J J, Moskal L M, Kim S H. Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR[J]. Agricultural and Forest Meteorology, 2009,149(6-7):1152-1160.
[22] Hyer E J, Goetz S J. Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation:assessments from a boreal forest site[J]. Agricultural and Forest Meteorology, 2004,122(3-4):157-174.
[23] 张志, 田昕, 陈尔学, 等. 森林地上生物量估测方法研究综述[J]. 北京林业大学学报, 2011,33(5):144-150.
[24] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. Cambridge,MA:MIT press, 2016.
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