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星载大光斑LiDAR与HJ-1A高光谱数据联合估测区域森林地上生物量
Estimation of regional forest aboveground biomass combining spaceborne large footprint LiDAR and HJ-1A Hyperspectral Images

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邱赛 1   邢艳秋 1 *   徐卫华 2   丁建华 1   田静 1  
文摘 以吉林省汪清林业局经营区为研究区,利用HJ-1A/HSI高光谱数据和ICESat-GLAS波形数据,估测区域森林地上生物量。从平滑后的GLAS波形数据中提取波形长度W和地形坡度参数TS,建立GLAS森林最大树高估测模型;从GLAS波形数据中提取能量参数I(植被回波能量E_v和回波总能量E之比),建立GLAS森林郁闭度估测模型;利用GLAS估测的森林最大树高和森林郁闭度联合建立森林地上生物量模型。由于GLAS呈离散条带状分布,无法实现区域估测,因此研究将GLAS波形数据与HJ-1A/HSI高光谱数据联合,基于支持向量回归机算法实现森林地上生物量区域估测,得到研究区森林地上生物量分布图。研究结果显示,基于W和TS建立的GLAS森林最大树高估测模型的adj. R~2 = 0.78,RMSE = 2.51m,模型验证的adj. R~2 = 0.85,RMSE = 1.67m。地形坡度参数TS能够有效的降低地形坡度的影响;当林下植被高度为2m时,得到的基于参数I建立的GLAS森林郁闭度估测模型效果最好,模型的adj. R~2 = 0.64,RMSE = 0.13,模型验证的adj. R~2 = 0.65,RMSE = 0.12。利用森林最大树高和森林郁闭度建立的森林地上生物量模型的adj. R~2 = 0.62,RMSE = 10.88 t /hm~2,模型验证的adj. R~2 = 0.60,RMSE = 11.52 t /hm~2。基于支持向量回归机算法,利用HJ-1A/HSI和GLAS数据建立的森林地上生物量SVR模型,生成了森林地上生物量分布图,利用野外数据对得到的分布图进行验证,验证结果显示森林地上生物量估测值与实测值存在很强的线性关系(adj.R~2 = 0.62,RMSE = 11.11 t /hm~2),能够满足林业应用的需要。因此联合ICESat-GLAS波形数据与HJ-1A高光谱数据,能够提高区域森林地上生物量的估测精度。
其他语种文摘 HJ-1A/HSI Hyperspectral images and ICESat-GLAS waveform were used to estimate regional forest aboveground biomass (AGB) in the Wangqing forestry area of Jilin Province,China. Waveform parameters (e. g.,waveform length W and the terrain slope parameter TS) extracted from GLAS waveform,were used to build the maximum forest height model. In addition,the energy parameter I (the ratio of vegetation energy and total energy) extracted from GLAS waveform was used to build the forest canopy density model. The final forest AGB model was built using both the maximum forest height and forest canopy density models. However,since GLAS footprints are geographically discrete,the AGB model was unable to produce the full regional coverage of forest AGB. To overcome the discontinuity limitations,HJ-1A/HSI Hyperspectral images were combined with GLAS waveforms to predict the regional forest AGB based on the support vector regression (SVR) method,to fully map the distribution of forest AGB. Results showed that the adj. R~2 and RMSE of the maximum forest height model were 0.78 and 2.51 m,respectively,with adj. R~2 of 0.85 and RMSE of 1.67 m as validation results. In the model,TS effectively reduced the impact of terrain slope. When the below vegetation height was set at 2 m,the forest canopy density model with I as dependent variable produced the best fit,with adj. R~2 and RMSE of 0.64 and 0.13,respectively,and adj. R~2 of 0.65 and RMSE of 0.12 as validation results. Overall,the adj. R~2 and RMSE of the forest AGB model were 0.62 and 10.88 t /hm~2,respectively,with validation results of adj. R~2 = 0.60 and RMSE = 11.52 t /hm~2. Estimated AGB had a strong linear relationship with field inventory AGB (adj. R~2 = 0.62,RMSE = 11.11 t /hm~2). This study demonstrates that combining GLAS waveform and HJ-1A/HSI hyperspectral images has significant potential to map the full coverage regional forest AGB distribution with a high degree of accuracy.
来源 生态学报 ,2016,36(22):7401-7411 【核心库】
DOI 10.5846/stxb201601050027
关键词 星载大光斑LiDAR ; ICESat-GLAS波形数据 ; HJ-1A高光谱数据 ; 森林最大树高 ; 森林郁闭度 ; 森林地上生物量 ; 支持向量回归算法
地址

1. 东北林业大学工程技术学院, 哈尔滨, 150040  

2. 中国科学院生态环境研究中心, 北京, 100085

语种 中文
文献类型 研究性论文
ISSN 1000-0933
基金 中央高校基本科研业务费专项资金资助项目 ;  国家自然科学基金
文献收藏号 CSCD:5870276

参考文献 共 34 共2页

1.  Piao S L. The carbon balance of terrestrial ecosystems in China. Nature,2009,458(7241):1009-1013 被引 442    
2.  刘双娜. 基于遥感降尺度估算中国森林生物量的空间分布. 生态学报,2012,32(8):2320-2330 被引 26    
3.  杜虎. 中国南方3种主要人工林生物量和生产力的动态变化. 生态学报,2014,34(10):2712-2724 被引 45    
4.  Houghton R A. Importance of biomass in the global Carbon cycle. Journal of Geophysical Research:Biogeosciences,2009,114(G2):G00E03 被引 36    
5.  黄金龙. 基于高分辨率遥感影像的森林地上生物量估算. 生态学报,2013,33(20):6497-6508 被引 10    
6.  娄雪婷. 森林地上生物量遥感估测研究进展. 国土资源遥感,2011,23(1):1-8 被引 22    
7.  汤旭光. 森林地上生物量遥感估算研究进展. 生态学杂志,2012,31(5):1311-1318 被引 25    
8.  Boudreau J. Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec. Remote Sensing of Environment,2008,112(10):3876-3890 被引 22    
9.  张慧芳. 遥感技术支持下的森林生物量研究进展. 世界林业研究,2007,20(4):30-34 被引 9    
10.  黄玫. 中国区域植被地上与地下生物量模拟. 生态学报,2006,26(12):4156-4163 被引 78    
11.  Zhu X L. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing,2015,102:222-231 被引 14    
12.  郭云. 甘肃黑河流域上游森林地上生物量的多光谱遥感估测. 林业科学,2015,51(1):140-149 被引 14    
13.  王立海. 基于人工神经网络的天然林生物量遥感估测. 应用生态学报,2008,19(2):261-266 被引 27    
14.  Laurin G V. Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing,2014,89:49-58 被引 11    
15.  Xing Y Q. An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain:a case study in Changbai mountains,China. International Journal of Applied Earth Observation and Geoinformation,2010,12(5):385-392 被引 13    
16.  Lefsky M A. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters,2005,32(22) 被引 56    
17.  Enble F. Accuracy of vegetation height and terrain elevation derived from ICESat /GLAS in forested areas. International Journal of Applied Earth Observation and Geoinformation,2014,31:37-44 被引 10    
18.  Zhang G. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sensing of Environment,2014,151:44-56 被引 11    
19.  Sun G. Forest vertical structure from GLAS:an evaluation using LVIS and SRTM data. Remote Sensing of Environment,2008,112(1):107-117 被引 42    
20.  Pflugmacher D. Regional applicability of forest height and aboveground biomass models for the Geoscience Laser Altimeter System. Forest Science,2008,54(6):647-657 被引 2    
引证文献 8

1 吴楠 国内外林业遥感应用研究概况与展望 世界林业研究,2017,30(6):34-40
被引 10

2 邱小雷 基于地基LiDAR高度指标的小麦生物量监测研究 农业机械学报,2019,50(10):159-166
被引 1

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