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
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摘要
森林生物量是衡量生态系统生产力的重要指标，也是研究森林生态系统物质循环的重要基础，作为陆地生物圈的主体，对全球气候变化研究具有重要意义。传统的森林生物量统计以实测数据为基础，需要进行大量的实地调查，工作量大、周期长，在推测大面积林分生物量时，待测林分每木检尺数据往往难以获得。而随着遥感技术的快速发展，包括航空像片、光学遥感影像、微波雷达与激光雷达等多源遥感数据已应用于森林类型、分布与结构特征的监测与信息提取，为大尺度森林生物量估算与长时间动态变化研究提供了一条快捷、经济、方便的途径。利用光学遥感数据进行区域性森林结构参数及生物量反演研究起步较早，但是其信号穿透性较差，因此主要记录了森林的水平结构信息。运行于特定波长范围的合成孔径雷达（SAR）对植被有一定的穿透能力，能够利用后向散射进行生物量的估算，但是微波受地形起伏干扰较大，并且当植被冠层密闭或生物量较高时易饱和，从而限制了在区域生物量估算中的应用。激光雷达是近年来迅速发展的主动遥感技术，对森林具有很强的穿透能力，在森林结构参数获取方面具有显著的优势。然而，小光斑激光雷达也存在着成本高、覆盖范围有限、数据量大等局限，限制了其在大面积森林空间结构信息提取中的应用。具有完整波形的大光斑激光雷达能够描述大面积森林冠层空间结构信息，记录光斑内随时间变化的能量值，获取比小光斑激光雷达更多的林分冠层信息，且理论上具有获取全球数据的能力。目前，以激光雷达为代表的新技术逐渐成为森林生态参数测量中的重要手段，正发挥着无可比拟的优势。但是，大光斑激光雷达传感器在空间上采样不连续，无法达到无缝覆盖，在大尺度应用上也存在着局限。而随着遥感技术手段的多样化，人们对如何有效利用多源遥感数据进行生态学研究寄予厚望。当前，将激光雷达数据与其他光学或微波遥感数据融合进行森林结构参数及生物量反演研究是热点，具有很大的发展潜力。
本研究针对长白山林区复杂的地形环境，探讨了联合多光谱TM数据与大光斑激光雷达GLAS数据进行森林冠层高度与生物量估算的可行性；实现了GLAS完整波形数据的处理算法，提出并建立了能适应复杂地形条件的森林冠层高度估算模型；针对ICESat/GLAS光斑数据空间离散，不具备成像能力的特点，融合光学遥感影像数据建立了区域尺度森林冠层高度反演模型，并分析其影响因素及不确定性；最终，联合GLAS最大冠层高度数据与光谱信息对研究区森林生物量进行空间反演。研究结果表明，联合光学遥感与激光雷达进行森林冠层高度与生物量估算可以充分发挥多源遥感数据的优势，并且具有广泛的适用性。
取得的主要结论如下：
1.基于TM遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI、WI等10个植被指数，并辅助于DEM、ASPECT、SLOPE等地形信息，在与植物冠层分析仪（TRAC）实测各森林类型叶面积指数相关性分析的基础上，对比分析多元线性回归与偏最小二乘法估算能力，构建了该区森林LAI最佳遥感反演模型，最终获得区域尺度森林LAI分布。研究发现，对于复杂的森林生态系统，仅依靠单个波段或植被指数很难达到建模反演LAI的需要，而融合各指数能够改善遥感估算的精度。同时还发现，引入地形指数并未能有效地提高模型的预测精度。从模型的反演能力来看，无论多元线性回归方法还是偏最小二乘法，针叶林优于阔叶林、针阔混交林，这可能是受森林群落结构复杂程度的影响。对比分析了各森林类型基于单变量的郁闭度最佳遥感反演模型及基于植被指数的像元二分模型模拟效果，研究发现后者能更好地把握森林郁闭度的动态变化，并且在应用像元二分模型时，通过对NDVIcrown与NDVInon-crown设置不同的阈值，能够在一定程度上提高模型估算的精度。
2.分析了研究区不同森林类型激光雷达脚点数据的空间分布情况，实现了GLAS完整波形数据处理算法。针对当前大多数研究利用Gaussian组分拟合原始波形获取信号始末位置及地面回波位置不足以准确把握细节信息、易造成地面回波“丢失”的缺陷，提出利用傅里叶变换进行低通滤波与波形拟合，从而为波形关键参数提取及森林冠层高度估算奠定基础。而后基于波形长度、地形指数及质心位置信息构建了适于复杂地形条件下的森林冠层高度估算模型。总体而言，对于平缓地形，GLAS估算的最大冠层高度精度较高（误差通常在0.5m左右），坡地条件下经过校正后的最大冠层高度总体RMSE也在2.021~2.674m之间，为有
3.在GLAS最大森林冠层高度获取的基础上，联合TM多光谱数据及转换生成的植被指数、叶面积指数、郁闭度信息，并考虑地形因素对林分高度的影响，构建了适用于区域扩展的针叶林、阔叶林及针阔混交林冠层高度最佳遥感反演模型，并利用野外实测数据进行独立验证。研究表明，融入地形因子可以弥补光谱信息的不足；各森林类型偏最小二乘法模型估算结果RMSE都在2m以内，虽仍有个别点存在高估或低估的现象，但总体一致性较好。
4.基于GLAS数据获取的最大森林冠层高度，建立了各森林类型地上生物量估算模型，分析发现针叶林GLAS冠层高度与生物量的相关系数达到0.903，回归模型的确定性系数R2也达到0.816，而阔叶林相关性也达到了0.589，回归模型R2达到了0.412，表明树高对于森林生物量具有重要的预测能力。而后，融合GLAS估算的最大冠层高度、TM数据转换生成的10个植被指数以及估算的LAI和冠层郁闭度，分别利用多元线性回归（MLR）方法及BP神经网络模型建立森林地上生物量反演模型。研究表明，融合光谱信息与林分高度信息的BP神经网络模型以其强大的非线性处理能力，能够较好地对各森林类型生物量进行空间反演。
Forest biomass is an important indicator for assessing the ecosystem productivity,and also the basis for analysis of substance circulation in forest ecosystem. As themain body of the Earth’s terrestrial biosphere, forest plays a major role in fixingatmospheric CO2and mitigating climate change. The traditional methods forcalculating biomass rely on a considerable amount of in-situ measurements thatinvolves extreme time and labour, which is also difficult to update spatial distributionin a large area. However, with the rapid development of remote sensing technique,multi-source remote sensing data including aerial photographs, optical images, radarand LiDAR have been extensively used for monitoring the forest types, spatialdistribution and structural features, which provides a fast, cheap, and convenient wayto estimate the large-scale forest biomass and long-term dynamic change. Usingoptical remote sensing data to retrieve regional forest structural parameters andbiomass started earlier. However, owing to weak penetrating power, it mainly reflectshorizontal forest structures. Synthetic Aperture Radar (SAR) could penetrate thevegetation canopies to a certain extent, but it was seriously disturbed by thetopographic relief and no longer sensitive when the crown was closed or the biomasswas very high, which restricted its application in regional estimation. LiDAR (LightDetect and Ranging) is an active remote sensing that developed rapidly in recent years,and has performed great potential and advantage in estimating forest vertical structureparameters due to its strong capacity to penetrate forest canopies. The small-footprintLiDAR can provide a wide variety of efficient stand parameters with high precision,but it still has plenty of limitations such as high cost, limited coverage, massive amounts of data, and so on. While large-footprint ICESat/GLAS waveform data canestimate spatial structure parameters of large-area forest, and acquire more standinformation than small-foortprint LiDAR through recording the time variation in theintensities of returned laser pulses, which resole elliptical areas approximately65m indiameter. Futhermore, the satellite coverage is even global scale in theory. CurrentlyICESat/GLAS data have being widely used in estimation of ecological parameters offorest. However, the major shortcoming of the large-footprint LiDAR is lack ofimaging capability, which could only provide vegetation samples in spatially discretefootprints and restrict its application in regional biomass estimation. The developmentof multi-source remote sensing techniques stimulates how to effectively use these datafor ecological studies owing to limited information from a single sensor. At present,there is a trend of using multi-sensor remote-sensing system integration and datafusion (such as GLAS waveform data and optical images or SAR), which indicatesgreat potential in forest biomass estimation.
In view of the complicated terrain in Changbai Mountains, the study explored thefeasibility of extraction of forest canopy height and above-ground biomass byintegrating multispectral data (TM) and GLAS waveform data. The algorithms ofprocessing the GLAS data were implemented and the models of estimating canopyheight in the area of flat slope and steep slope were established. Considering theGLAS waveform data were spatially discrete, the study brought forward theestimation model of regional forest canopy height using optical remote sensingimages. At last, the forest above-ground biomass in the study area was retrieved byintegrating the canopy height from GLAS with optical data. The results showed thatTM wide-swath data and GLAS waveform data can be combined to estimate forestcanopy height and above-ground biomass with good precision. The main findings areas follows:
1. Based on the object-oriented method from eCognition and corrected LandsatTM data, this study acquired the land use/cover data in2010. Then the data wasfurther divided into coniferous forest, broad-leaved forest, mixed broadleaf-conifer forest and non-forestry land. Meanwhile under the ArcGIS platform, six bands ofreflectance values, ten kinds of vegetation index including RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI and WI, and the topographic factors as DEM、ASPECTand SLOPE, were calculated to analyze the correlations between the correspondingforest LAI measured using TRAC with each factor. Then compared the modelperformance of multiple linear regression (MLR) and partial least squares (PLS)method, this paper established the optimal model to retrieve the forest LAI of eachforest type. At last, the distribution map of forest LAI in this study area was made byintegrating remote sensing inversion model with the forest classification data acquiredbeforehand. The results showed that it’s hard to retrieve forest LAI accurately with asingle band or vegetation index, but we could improve the model’s precision bycombining these variables. Meanwhile, the topographic factors cannot effectivelyenhance the model’s effect. From the perspective of inversion ability of each foresttype, PLS models were obviously superior to MLR. Forest crown density is animportant parameter for evaluating forest status and indicating possible managementinterventions. Based on the highly related vegetation index, this paper established theoptimal empirical model to retrieve the forest crown density of each forest type.Compared with the empirical approach, we also improved the dimidiate pixel modelby defining four-level thresholds of NDVI including2%,1%,0.5%, and0.2%minimum and maximum cut of points in the histograms of NDVI imagery. The resultsshowed that forest crown density estimation based on the dimidiate pixel model withthreshold of NDVI being0.5%was efficient with high precision over the in-situ fieldvalidation for coniferous forest and mixed forest whereas being1%for broad-leavedforest in Changbai Mountain area. And the model performed well for coniferousforest (R2of0.872and RMSE of0.019), followed by broad-leaved forest (R2of0.832and RMSE of0.016) and mixed forest (R2of0.799and RMSE of0.021).
2. After analyzing the distribution of these GLAS waveform data for each foresttype in the study area, the algorithms of processing GLAS data were implemented.Currently, many studies use Gauss function to fit the GLAS full waveform, and obtain the key parameters. However, this method might lead to ground echo signals missing.Therefore, Fourier transform was proposed to filter and fit the waveform, which builta basis for extraction of the key parameters and estimation of canopy height. Thenbased on the waveform length, topographic index and centroid position, this studyestablished the estimation model of forest canopy height in complicated terrain.Overall, the precision of GLAS forest canopy height was high in flat regions (about0.5m). The RMSE in high-slope terrain also reached2.021~2.674m after correction.
3. Based on the algorithm of forest canopy height for GLAS data, the models ofconifer, deciduous broadleaf forest, and mixed forest in complex terrain conditionswere established by integrating TM parameters (6spectral bands,10vegetationindexes, LAI and crown density), canopy height and the topographic factors, and thenvalidated with in-situ measured data. The results showed that the topographic factorscould supplement the shortcomings of spectral information. RMSE of the optimizedmodels for each forest type using PLS method were less than2m. Although there stillexisted overestimation or underestimation, the general trend held true.
4. The relationship between forest canopy height and above-ground biomass wasanalyzed. The result showed that the correlation coefficients between above-groundbiomass and the canopy height were0.903and0.589, and the R2of the regressionmodel reached0.816and0.412, respectively, which indicated the predictive ability ofcanopy height to forest biomass. Then the multiple linear regression model and BPneutral network model of forest above-ground biomass were established bycombining forest canopy height, vegetation indexes, LAI and crown density. Theresult showed that the characteristic of nonlinearity of BP neutral network model wasmore fitted to inverse the forest biomass.
本研究针对长白山林区复杂的地形环境，探讨了联合多光谱TM数据与大光斑激光雷达GLAS数据进行森林冠层高度与生物量估算的可行性；实现了GLAS完整波形数据的处理算法，提出并建立了能适应复杂地形条件的森林冠层高度估算模型；针对ICESat/GLAS光斑数据空间离散，不具备成像能力的特点，融合光学遥感影像数据建立了区域尺度森林冠层高度反演模型，并分析其影响因素及不确定性；最终，联合GLAS最大冠层高度数据与光谱信息对研究区森林生物量进行空间反演。研究结果表明，联合光学遥感与激光雷达进行森林冠层高度与生物量估算可以充分发挥多源遥感数据的优势，并且具有广泛的适用性。
取得的主要结论如下：
1.基于TM遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI、WI等10个植被指数，并辅助于DEM、ASPECT、SLOPE等地形信息，在与植物冠层分析仪（TRAC）实测各森林类型叶面积指数相关性分析的基础上，对比分析多元线性回归与偏最小二乘法估算能力，构建了该区森林LAI最佳遥感反演模型，最终获得区域尺度森林LAI分布。研究发现，对于复杂的森林生态系统，仅依靠单个波段或植被指数很难达到建模反演LAI的需要，而融合各指数能够改善遥感估算的精度。同时还发现，引入地形指数并未能有效地提高模型的预测精度。从模型的反演能力来看，无论多元线性回归方法还是偏最小二乘法，针叶林优于阔叶林、针阔混交林，这可能是受森林群落结构复杂程度的影响。对比分析了各森林类型基于单变量的郁闭度最佳遥感反演模型及基于植被指数的像元二分模型模拟效果，研究发现后者能更好地把握森林郁闭度的动态变化，并且在应用像元二分模型时，通过对NDVIcrown与NDVInon-crown设置不同的阈值，能够在一定程度上提高模型估算的精度。
2.分析了研究区不同森林类型激光雷达脚点数据的空间分布情况，实现了GLAS完整波形数据处理算法。针对当前大多数研究利用Gaussian组分拟合原始波形获取信号始末位置及地面回波位置不足以准确把握细节信息、易造成地面回波“丢失”的缺陷，提出利用傅里叶变换进行低通滤波与波形拟合，从而为波形关键参数提取及森林冠层高度估算奠定基础。而后基于波形长度、地形指数及质心位置信息构建了适于复杂地形条件下的森林冠层高度估算模型。总体而言，对于平缓地形，GLAS估算的最大冠层高度精度较高（误差通常在0.5m左右），坡地条件下经过校正后的最大冠层高度总体RMSE也在2.021~2.674m之间，为有
3.在GLAS最大森林冠层高度获取的基础上，联合TM多光谱数据及转换生成的植被指数、叶面积指数、郁闭度信息，并考虑地形因素对林分高度的影响，构建了适用于区域扩展的针叶林、阔叶林及针阔混交林冠层高度最佳遥感反演模型，并利用野外实测数据进行独立验证。研究表明，融入地形因子可以弥补光谱信息的不足；各森林类型偏最小二乘法模型估算结果RMSE都在2m以内，虽仍有个别点存在高估或低估的现象，但总体一致性较好。
4.基于GLAS数据获取的最大森林冠层高度，建立了各森林类型地上生物量估算模型，分析发现针叶林GLAS冠层高度与生物量的相关系数达到0.903，回归模型的确定性系数R2也达到0.816，而阔叶林相关性也达到了0.589，回归模型R2达到了0.412，表明树高对于森林生物量具有重要的预测能力。而后，融合GLAS估算的最大冠层高度、TM数据转换生成的10个植被指数以及估算的LAI和冠层郁闭度，分别利用多元线性回归（MLR）方法及BP神经网络模型建立森林地上生物量反演模型。研究表明，融合光谱信息与林分高度信息的BP神经网络模型以其强大的非线性处理能力，能够较好地对各森林类型生物量进行空间反演。
Forest biomass is an important indicator for assessing the ecosystem productivity,and also the basis for analysis of substance circulation in forest ecosystem. As themain body of the Earth’s terrestrial biosphere, forest plays a major role in fixingatmospheric CO2and mitigating climate change. The traditional methods forcalculating biomass rely on a considerable amount of in-situ measurements thatinvolves extreme time and labour, which is also difficult to update spatial distributionin a large area. However, with the rapid development of remote sensing technique,multi-source remote sensing data including aerial photographs, optical images, radarand LiDAR have been extensively used for monitoring the forest types, spatialdistribution and structural features, which provides a fast, cheap, and convenient wayto estimate the large-scale forest biomass and long-term dynamic change. Usingoptical remote sensing data to retrieve regional forest structural parameters andbiomass started earlier. However, owing to weak penetrating power, it mainly reflectshorizontal forest structures. Synthetic Aperture Radar (SAR) could penetrate thevegetation canopies to a certain extent, but it was seriously disturbed by thetopographic relief and no longer sensitive when the crown was closed or the biomasswas very high, which restricted its application in regional estimation. LiDAR (LightDetect and Ranging) is an active remote sensing that developed rapidly in recent years,and has performed great potential and advantage in estimating forest vertical structureparameters due to its strong capacity to penetrate forest canopies. The small-footprintLiDAR can provide a wide variety of efficient stand parameters with high precision,but it still has plenty of limitations such as high cost, limited coverage, massive amounts of data, and so on. While large-footprint ICESat/GLAS waveform data canestimate spatial structure parameters of large-area forest, and acquire more standinformation than small-foortprint LiDAR through recording the time variation in theintensities of returned laser pulses, which resole elliptical areas approximately65m indiameter. Futhermore, the satellite coverage is even global scale in theory. CurrentlyICESat/GLAS data have being widely used in estimation of ecological parameters offorest. However, the major shortcoming of the large-footprint LiDAR is lack ofimaging capability, which could only provide vegetation samples in spatially discretefootprints and restrict its application in regional biomass estimation. The developmentof multi-source remote sensing techniques stimulates how to effectively use these datafor ecological studies owing to limited information from a single sensor. At present,there is a trend of using multi-sensor remote-sensing system integration and datafusion (such as GLAS waveform data and optical images or SAR), which indicatesgreat potential in forest biomass estimation.
In view of the complicated terrain in Changbai Mountains, the study explored thefeasibility of extraction of forest canopy height and above-ground biomass byintegrating multispectral data (TM) and GLAS waveform data. The algorithms ofprocessing the GLAS data were implemented and the models of estimating canopyheight in the area of flat slope and steep slope were established. Considering theGLAS waveform data were spatially discrete, the study brought forward theestimation model of regional forest canopy height using optical remote sensingimages. At last, the forest above-ground biomass in the study area was retrieved byintegrating the canopy height from GLAS with optical data. The results showed thatTM wide-swath data and GLAS waveform data can be combined to estimate forestcanopy height and above-ground biomass with good precision. The main findings areas follows:
1. Based on the object-oriented method from eCognition and corrected LandsatTM data, this study acquired the land use/cover data in2010. Then the data wasfurther divided into coniferous forest, broad-leaved forest, mixed broadleaf-conifer forest and non-forestry land. Meanwhile under the ArcGIS platform, six bands ofreflectance values, ten kinds of vegetation index including RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI and WI, and the topographic factors as DEM、ASPECTand SLOPE, were calculated to analyze the correlations between the correspondingforest LAI measured using TRAC with each factor. Then compared the modelperformance of multiple linear regression (MLR) and partial least squares (PLS)method, this paper established the optimal model to retrieve the forest LAI of eachforest type. At last, the distribution map of forest LAI in this study area was made byintegrating remote sensing inversion model with the forest classification data acquiredbeforehand. The results showed that it’s hard to retrieve forest LAI accurately with asingle band or vegetation index, but we could improve the model’s precision bycombining these variables. Meanwhile, the topographic factors cannot effectivelyenhance the model’s effect. From the perspective of inversion ability of each foresttype, PLS models were obviously superior to MLR. Forest crown density is animportant parameter for evaluating forest status and indicating possible managementinterventions. Based on the highly related vegetation index, this paper established theoptimal empirical model to retrieve the forest crown density of each forest type.Compared with the empirical approach, we also improved the dimidiate pixel modelby defining four-level thresholds of NDVI including2%,1%,0.5%, and0.2%minimum and maximum cut of points in the histograms of NDVI imagery. The resultsshowed that forest crown density estimation based on the dimidiate pixel model withthreshold of NDVI being0.5%was efficient with high precision over the in-situ fieldvalidation for coniferous forest and mixed forest whereas being1%for broad-leavedforest in Changbai Mountain area. And the model performed well for coniferousforest (R2of0.872and RMSE of0.019), followed by broad-leaved forest (R2of0.832and RMSE of0.016) and mixed forest (R2of0.799and RMSE of0.021).
2. After analyzing the distribution of these GLAS waveform data for each foresttype in the study area, the algorithms of processing GLAS data were implemented.Currently, many studies use Gauss function to fit the GLAS full waveform, and obtain the key parameters. However, this method might lead to ground echo signals missing.Therefore, Fourier transform was proposed to filter and fit the waveform, which builta basis for extraction of the key parameters and estimation of canopy height. Thenbased on the waveform length, topographic index and centroid position, this studyestablished the estimation model of forest canopy height in complicated terrain.Overall, the precision of GLAS forest canopy height was high in flat regions (about0.5m). The RMSE in high-slope terrain also reached2.021~2.674m after correction.
3. Based on the algorithm of forest canopy height for GLAS data, the models ofconifer, deciduous broadleaf forest, and mixed forest in complex terrain conditionswere established by integrating TM parameters (6spectral bands,10vegetationindexes, LAI and crown density), canopy height and the topographic factors, and thenvalidated with in-situ measured data. The results showed that the topographic factorscould supplement the shortcomings of spectral information. RMSE of the optimizedmodels for each forest type using PLS method were less than2m. Although there stillexisted overestimation or underestimation, the general trend held true.
4. The relationship between forest canopy height and above-ground biomass wasanalyzed. The result showed that the correlation coefficients between above-groundbiomass and the canopy height were0.903and0.589, and the R2of the regressionmodel reached0.816and0.412, respectively, which indicated the predictive ability ofcanopy height to forest biomass. Then the multiple linear regression model and BPneutral network model of forest above-ground biomass were established bycombining forest canopy height, vegetation indexes, LAI and crown density. Theresult showed that the characteristic of nonlinearity of BP neutral network model wasmore fitted to inverse the forest biomass.
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