基于光学多光谱与SAR遥感特征快速优化的大区域森林地上生物量估测

针对基于多模式遥感手段的大区域森林地上生物量（AGB）定量反演效率低的问题，充分集成主、被动遥感对森林AGB多维观测特征，提高区域定量反演结果；针对两期反演结果分析，揭示区域森林AGB空间变化格局，为科学评估区域生态环境保护（如天然林保护）、提升国家生态环境遥感连续动态监测与预警能力提供支撑。以内蒙古大兴安岭林区为研究区，以2009年为主的光学LandsatTM5（TM）与ALOS-1 PALSAR，以及2014年为主的高分一号（GF-1）与ALOS-2 PALSAR两期主、被动遥感数据提取特征因子，利用快速迭代特征选择的k-NN方法（k-Nearest Neighbor with Fast Iterative Features Selection，KNN-FIFS），实现主、被动遥感特征组合快速优化及最优估测模型构建；基于第七次、第八次森林资源连续清查样地数据，对两期研究区森林（乔木）AGB进行定量反演与留一法（LOO）验证；根据两期反演结果叠加对比，在样地和区域尺度上定量分析研究区2009~2014年间森林AGB变化。在样地尺度上，基于森林资源清查样地结果与LOO法验证结果表明,2009年的AGB反演结果R²=0.56，RMSE=25.95 t/hm²；2014年R²=0.64；RMSE=24.55 t/hm²。2009年反演均值较样地计算结果均值偏高（预测：81.59 t/hm²，实测：78.64 t/hm²）；而2014年反演均值较样地计算结果偏低（预测：79.63 t/hm²；实测：82.48 t/hm²）。从区域尺度来看，2009年平均森林AGB为88.33 t/hm²；2014年的为94.61 t/hm²；平均AGB增长量为6.28 t/hm²；与前期研究利用扩展生物量因子法计算的结果接近（2008年和2013年分别为87.14 t/hm²、92.20 t/hm²）。采用基于快速迭代的KNN-FIFS方法，可大幅度提升高维度多模式遥感特征优选效率；充分融合主、被动遥感的多维观测特征，提高森林AGB反演精度及饱和点。在像素尺度上（30 m）利用LOO法对KNN-FIFS反演结果进行了验证，具有更强鲁棒性，避免了由于训练、检验样本抽选造成的随机误差。2009~2014年期间，内蒙古大兴安岭林区植被覆盖度整体呈现了明显的增长趋势；森林AGB也相应增加。自天然林保护工程实施以来，尽管森林火灾造成了局部较为严重的森林退化（覆盖度、AGB），但整体森林资源状况得到有效改善。     

基于光学多光谱与SAR遥感特征快速优化的大区域森林地上生物量估测

针对基于多模式遥感手段的大区域森林地上生物量(AGB)定量反演效率低的问题,充分集成主、被动遥感对森林AGB多维观测特征,提高区域定量反演结果;针对两期反演结果分析,揭示区域森林AGB空间变化格局,为科学评估区域生态环境保护(如天然林保护)、提升国家生态环境遥感连续动态监测与预警能力提供支撑。以内蒙古大兴安岭林区为研究区,以2009年为主的光学LandsatTM5(TM)与ALOS-1 PALSAR,以及2014年为主的高分一号(GF-1)与ALOS-2PALSAR两期主、被动遥感数据提取特征因子,利用快速迭代特征选择的k-NN方法(k-Nearest Neighbor with Fast Iterative Features Selection,KNN-FIFS),实现主、被动遥感特征组合快速优化及最优估测模型构建;基于第七次、第八次森林资源连续清查样地数据,对两期研究区森林(乔木)AGB进行定量反演与留一法(LOO)验证;根据两期反演结果叠加对比,在样地和区域尺度上定量分析研究区2009～2014年间森林AGB变化。在样地尺度上,基于森林资源清查样地结果与LOO法验证结果表明,2009年的AGB反演结果R2=0.56,RMSE=25.95 t/hm2;2014年R2=0.64;RMSE=24.55 t/hm2。2009年反演均值较样地计算结果均值偏高(预测:81.59 t/hm2,实测:78.64t/hm2);而2014年反演均值较样地计算结果偏低(预测:79.63 t/hm2;实测:82.48 t/hm2)。从区域尺度来看,2009年平均森林AGB为88.33 t/hm2;2014年的为94.61 t/hm2;平均AGB增长量为6.28 t/hm2;与前期研究利用扩展生物量因子法计算的结果接近(2008年和2013年分别为87.14 t/hm2、92.20 t/hm2)。采用基于快速迭代的KNN-FIFS方法,可大幅度提升高维度多模式遥感特征优选效率;充分融合主、被动遥感的多维观测特征,提高森林AGB反演精度及饱和点。在像素尺度上(30 m)利用LOO法对KNN-FIFS反演结果进行了验证,具有更强鲁棒性,避免了由于训练、检验样本抽选造成的随机误差。2009～2014年期间,内蒙古大兴安岭林区植被覆盖度整体呈现了明显的增长趋势;森林AGB也相应增加。自天然林保护工程实施以来,尽管森林火灾造成了局部较为严重的森林退化(覆盖度、AGB),但整体森林资源状况得到有效改善。     

主被动遥感特征优化的天然林森林蓄积量估测

结合国产主被动遥感数据高分六号（GF-6） PMS和高分三号（GF-3）双极化PolSAR估测森林蓄积量,并针对多源遥感数据的冗余问题进行特征组合优化。以新疆巩留县天然林地为研究区,提取GF-6 PMS数据的光谱信息、植被指数、纹理以及植被覆盖度信息和GF-3 PolSAR数据的后向散射系数、极化分解参数,结合地形因子,在森林样地调查数据的基础上,利用快速迭代特征选择的 K 最近邻法（K-Nearest Neighbor with Fast Iterative Features Selection,KNN-FIFS）估测研究区的森林蓄积量。对比国产主被动遥感数据和单一遥感数据源时的估测结果,基于最优特征组合反演研究区的森林蓄积量,结果表明：联合GF-3 PolSAR和GF-6 PMS数据估测研究区森林蓄积量的精度为 R2=0.72,RMSE=92.48 m3/hm2,相比于仅使用GF-6 PMS数据估测的精度（R2=0.56,RMSE=118.8 m3/hm2）,R2提高了0.16,提高了28.6%,RMSE降低了26.32 m3/hm2,降低22.2%。说明主被动遥感数据协同反演可以提高森林蓄积量估测精度,KNN-FIFS方法可以有效地估测天然林森林蓄积量。     

基于四尺度几何光学模型的森林地上生物量遥感估算

森林地上生物量(AGB)是评价森林生态系统功能的重要参数,遥感是获取区域尺度AGB的有效手段。以内蒙古根河市为研究区,利用TM遥感影像数据和33个森林样地调查数据,基于四尺度几何光学模型的森林AGB遥感估算方法,首先,基于样地观测数据建立树冠面积(SA)估算AGB的方程;再利用四尺度几何光学模型建立由冠层反射率反演SA的查找表,由TM影像反演SA,进而估算AGB。在全部33个样地,估算的AGB与观测数据的一致性(RMSE=20.8t·hm-2,R2=0.45)明显优于基于差值植被指数(DVI)(RMSE=27.7t·hm-2,R2=0.09)和混合像元分解(SMA)(RMSE=27.6t·hm-2,R2=0.02)方法建立的统计模型的估算结果。利用19个针叶林样地的观测数据验证表明,估算的AGB的RMSE和R2分别为20.8t·hm-2和0.53,利用DVI估算的AGB的RMSE和R2分别为31.5t·hm-2和0.18,利用SMA方法估算的AGB的RMSE和R2分别为31.8t·hm-2和0.14;对于14个阔叶林样地,估算的AGB的RMSE和R2分别为20.9t·hm-2和0.47,利用DVI估算的AGB的RMSE和R2分别为21.4t·hm-2和0.01,利用SMA方法估算的AGB的RMSE和R2分别为20.6t·hm-2和0.11。结果表明:通过反演与AGB紧密联系的SA,进行AGB的遥感估算是一种有效可行的技术方法。     

基于光学与SAR因子的森林生物量多元回归估算

基于福建省Landsat-8 OLI影像,利用混合像元分解模型从实测样地数据中筛选出“纯净”的植被像元,并将筛选出的样地分为针叶林、阔叶林和混交林3种植被类型,依次提取3种不同植被类型“纯净”植被像元的树高、林龄、坡度属性信息以及对应的光学NDVI、RVI植被因子和合成孔径雷达（SAR）HH、HV极化后向散射因子,分别构成不同植被类型的“含光学特征多元因子”（NDVI、RVI、树高、林龄、坡度）和“含SAR特征多元因子”（HH、HV、树高、林龄、坡度）,开展对比研究。采用含光学特征的多元因子回归模型先估测不同植被类型的森林叶生物量,然后根据叶生物量与地上生物量的关系间接估测森林地上生物量。同时,采用含SAR特征的多元因子回归模型直接估测森林的地上生物量。最后,对比分析这两组多元回归模型的估测精度。结果表明：不同植被类型的含光学特征多元回归模型的验证精度（针叶林：R²为0.483,RMSE为29.522 t/hm²;阔叶林：R²为0.470,RMSE为21.632 t/hm²;混交林：R²为0.351,RSME为25.253 t/hm²）比含SAR特征多元回归模型的验证精度（针叶林：R²为0.319,RMSE为28.352 t/hm²;阔叶林：R²为0.353,RMSE为18.991t/hm²;混交林：R²为0.281,RMSE为26.637 t/hm²）略高,说明在福建省森林生物量估算中采用含光学特征的多元回归模型（先估测叶生物量进而间接估测地上生物量）比利用含SAR特征的多元回归模型（直接估测地上生物量）更具优势。     

基于光学与SAR因子的森林生物量多元回归估算

基于福建省Landsat-8 OLI影像，利用混合像元分解模型从实测样地数据中筛选出“纯净”的植被像元，并将筛选出的样地分为针叶林、阔叶林和混交林3种植被类型，依次提取3种不同植被类型“纯净”植被像元的树高、林龄、坡度属性信息以及对应的光学NDVI、RVI植被因子和合成孔径雷达（SAR）HH、HV极化后向散射因子，分别构成不同植被类型的“含光学特征多元因子”（NDVI、RVI、树高、林龄、坡度）和“含SAR特征多元因子”（HH、HV、树高、林龄、坡度），开展对比研究。采用含光学特征的多元因子回归模型先估测不同植被类型的森林叶生物量，然后根据叶生物量与地上生物量的关系间接估测森林地上生物量。同时，采用含SAR特征的多元因子回归模型直接估测森林的地上生物量。最后，对比分析这两组多元回归模型的估测精度。结果表明：不同植被类型的含光学特征多元回归模型的验证精度（针叶林：R²为0.483，RMSE为29.522 t/hm²；阔叶林：R²为0.470，RMSE为21.632 t/hm²；混交林：R²为0.351，RSME为25.253 t/hm²）比含SAR特征多元回归模型的验证精度（针叶林：R²为0.319，RMSE为28.352 t/hm²；阔叶林：R²为0.353，RMSE为18.991t/hm²；混交林：R²为0.281，RMSE为26.637 t/hm²）略高，说明在福建省森林生物量估算中采用含光学特征的多元回归模型（先估测叶生物量进而间接估测地上生物量）比利用含SAR特征的多元回归模型（直接估测地上生物量）更具优势。     

基于KNN-FIFS的内蒙古根河森林郁闭度遥感估测研究

为探索国产高分一号宽幅（GF-1 Wide Field of View,GF-1 WFV）数据以及具有宽覆盖、红边波段（Red-Edge band,RE）的高分六号（GF-6）卫星数据在森林郁闭度（Forest Canopy Closure,FCC）定量反演中的潜力,本研究以GF-1 WFV多光谱数据为基础,添加哨兵2号（Sentinel-2A）红边波段,模拟GF-6红边波段特性,并提取相关纹理信息（Texture Information,TI）、植被指数（Vegetation Index,VI）和红边指数（Red- edge Index,RI）,同时添加太阳入射角的余弦值cosi和1/cosi进一步探究了地形因素（Topographic Factors,TF）对FCC估测的影响,利用快速迭代特征选择的k-NN（k-Nearest Neighbor with Fast Iterative Features Selection,KNN-FIFS）模型,实现了内蒙古大兴安岭根河研究区FCC的定量反演,并对比逐步多元线性回归（Stepwise Multiple Linear Regressions,SMLR）和支持向量机（Support Vector Machine,SVM）估测结果。通过44块调查样地实测数据验证发现：基于GF-1 WFV估测的FCC与实测数据具有很好的一致性,R²=0.52,RMSE=0.08;GF-1 WFV+VI+TI估测结果为R²=0.56,RMSE=0.08;GF-1 WFV+RE+RI+TI的精度明显提高,R²=0.63,RMSE=0.07;GF-1 WFV+RE+RI+TI+TF的精度最高,R²=0.68,RMSE=0.07,并高于SMLR（R²=0.39,RMSE=0.10）和SVM（R²=0.49,RMSE=0.10）方法。KNN-FIFS方法比SMLR和SVM方法更适用于FCC遥感估测,且添加红边信息经地形校正后,能有效提高FCC的估测精度。     

基于KNN-FIFS的内蒙古根河森林郁闭度遥感估测研究

为探索国产高分一号宽幅（GF-1 Wide Field of View，GF-1 WFV）数据以及具有宽覆盖、红边波段（Red-Edge band，RE）的高分六号（GF-6）卫星数据在森林郁闭度（Forest Canopy Closure，FCC）定量反演中的潜力，本研究以GF-1 WFV多光谱数据为基础，添加哨兵2号（Sentinel-2A）红边波段，模拟GF-6红边波段特性，并提取相关纹理信息（Texture Information，TI）、植被指数（Vegetation Index，VI）和红边指数（Red- edge Index，RI），同时添加太阳入射角的余弦值cosi和1/cosi进一步探究了地形因素（Topographic Factors，TF）对FCC估测的影响，利用快速迭代特征选择的k-NN（k-Nearest Neighbor with Fast Iterative Features Selection，KNN-FIFS）模型，实现了内蒙古大兴安岭根河研究区FCC的定量反演，并对比逐步多元线性回归（Stepwise Multiple Linear Regressions，SMLR）和支持向量机（Support Vector Machine，SVM）估测结果。通过44块调查样地实测数据验证发现：基于GF-1 WFV估测的FCC与实测数据具有很好的一致性，R²=0.52，RMSE=0.08；GF-1 WFV+VI+TI估测结果为R²=0.56，RMSE=0.08；GF-1 WFV+RE+RI+TI的精度明显提高，R²=0.63，RMSE=0.07；GF-1 WFV+RE+RI+TI+TF的精度最高，R²=0.68，RMSE=0.07，并高于SMLR（R²=0.39，RMSE=0.10）和SVM（R²=0.49，RMSE=0.10）方法。KNN-FIFS方法比SMLR和SVM方法更适用于FCC遥感估测，且添加红边信息经地形校正后，能有效提高FCC的估测精度。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型,利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而,地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足,不能完全支持LAI等地表参数的估算,易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息,并将其用于代价函数的构建,利用PROSAIL辐射传输模型和遗传算法,分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m,验证对应尺度LAI结果,评价引入先验信息对于提高LAI反演精度的作用。研究表明,引入先验信息有助于提高不同分辨率下LAI反演精度,且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比,以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65,RMSE由1.29下降至0.38。在250 m尺度,以500 m LAI反演结果作为先验信息反演的叶面积指数,其精度优于以MODIS LAI产品为先验知识的估算结果,验证精度R²增加了0.08,RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身,没有地面实测数据的参与,在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型，利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而，地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足，不能完全支持LAI等地表参数的估算，易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息，并将其用于代价函数的构建，利用PROSAIL辐射传输模型和遗传算法，分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m，验证对应尺度LAI结果，评价引入先验信息对于提高LAI反演精度的作用。研究表明，引入先验信息有助于提高不同分辨率下LAI反演精度，且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比，以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65，RMSE由1.29下降至0.38。在250 m尺度，以500 m LAI反演结果作为先验信息反演的叶面积指数，其精度优于以MODIS LAI产品为先验知识的估算结果，验证精度R²增加了0.08，RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身，没有地面实测数据的参与，在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon

The complicated forest stand structure and associated abundant tree species in the Amazon often induce difficulty in estimating aboveground biomass (AGB) using remotely sensed data. This paper explores AGB estimation using Landsat Thematic Mapper (TM) data in the eastern and western Brazilian Amazon, and discusses the impacts of forest stand structure on AGB estimation. Estimating AGB is still a challenging task, especially for the sites with complicated biophysical environments. The TM spectral responses are more suitable for AGB estimation in the sites with relatively simple forest stand structure than for the sites with complicated forest stand structure. Conversely, textures appear more important than spectral responses in AGB estimation in the sites with complicated forest stand structure. A combination of spectral responses and textures improves AGB estimation performance. Different study areas having various biophysical conditions affect AGB estimation performance.     

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.     

Retrieval of Forest above Ground Biomass Using 4-Scale GeometricalOptical Model and Remote Sensing Data

Forest Aboveground Biomass (AGB) is an important parameter for assessing the function of forest ecosystems.Remote sensing is an effective technique for retrieving AGB.A method for retrieving AGB from TM remote sensing data and field measurements taken at 33 plots in Genhe city,Inn Mongolia,China was developed.First,The empirical equations estimating AGB from canopy Surface Area (SA) was fitted using field measured data.Then,a look up table for the inversion of SA from canopy reflectance was set up through forward simulations of the 4\|scale geometrical optical model.SA determined from TM remote sensing data and the constructed look up table was used to estimate AGB.At all 33 plots,AGB estimated using the newly developed method was in good agreement with measured data,with RMSE=20.8 t·hm-2and R2=0.45,much better than the estimation using Difference Vegetation Index (DVI) (RMSE=27.7 t·hm-2,R²=0.09) and special mixture analysis (SMA) (RMSE=27.6 t·hm-2,R²=0.02) method.Validation at 19 conifer plots indicated that the RMSE and R² of AGB estimated using the method developed in this study were 20.8 t·hm-2 and 0.53,respectively.The corresponding values were 31.5 t·hm-2 and 0.18 for the DVI-based model and were 31.8 t·hm-2 and 0.14 for the SMA-based model.As to 14 broad-leaved plots,the RMSE and R² of AGB estimated using the method developed here were 20.9 t·hm--2 and 0.47.The corresponding values were 21.4 t·hm-2 and 0.01for the DVI-based model and were 20.6 t·hm-2 and 0.11 for the SMA-based model.The method which estimates AGB on the basis of SA inverted from optical remote sensing data was applicable for the retrieval of AGB.     

Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain

The amount and spatial distribution of aboveground forest biomass (AGB) are required inputs to forest carbon budgets and ecosystem productivity models. Satellite remote sensing offers distinct advantages for large area and multi-temporal applications, however, conventional empirical methods for estimating forest canopy structure and AGB can be difficult in areas of high relief and variable terrain. This paper introduces a new method for obtaining AGB from forest structure estimates using a physically-based canopy reflectance (CR) model inversion approach. A geometric-optical CR model was run in multiple forward mode (MFM) using SPOT-5 imagery to derive forest structure and biomass at Kananaskis, Alberta in the Canadian Rocky Mountains. The approach first estimates tree crown dimensions and stem density for satellite image pixels which are then related to tree biomass and AGB using a crown spheroid surface area approach. MFM estimates of AGB were evaluated for 36 deciduous (trembling aspen) and conifer (lodgepole pine) field validation sites and compared against spectral mixture analysis (SMA) and normalised difference vegetation index (NDVI) biomass predictions from atmospherically and topographically corrected (SCS+C) imagery. MFM provided the lowest error for all validation plots of 31.7 tonnes/hectare (t/ha) versus SMA (32.6 t/ha error) and NDVI (34.7 t/ha) as well as for conifer plots (MFM: 23.0 t/ha; SMA 27.9 t/ha; NDVI 29.7 t/ha) but had higher error than SMA and NDVI for deciduous plots (by 4.5 t/ha and 2.1 t/ha, respectively). The MFM approach was considerably more stable over the full range of biomass values (67 to 243 t/ha) measured in the field. Field plots with biomass > 1 standard deviation from the field mean (over 30% of plots) had biomass estimation errors of 37.9 t/ha using MFM compared with 65.5 t/ha and 67.5 t/ha error from SMA and NDVI, respectively. In addition to providing more accurate overall results and greater stability over the range of biomass values, the MFM approach also provides a suite of other biophysical structural outputs such as density, crown dimensions, LAI, height and sub-pixel scale fractions. Its explicit physical-basis and minimal ground data requirements are also more appropriate for larger area, multi-scene, multi-date applications with variable scene geometry and in high relief terrain. MFM thus warrants consideration for applications in mountainous and other, less complex terrain for purposes such as forest inventory updates, ecological modeling and terrestrial biomass and carbon monitoring studies.     

Estimating stand structural details using nearest neighbor analyses to link ground data, forest cover maps, and Landsat imagery

Forest information over a landscape is often represented as a spatial mosaic of polygons, separated by differences in species composition, height, age, crown closure, productivity, and other variables. These polygons are commonly delineated on medium-scale photography (e.g., 1:15,000) by a photo-interpreter familiar with the inventory area, and displayed and stored in a Geographic Information System (GIS) layer as a forest cover map. Forest cover maps are used for multiple purposes including timber and habitat supply analyses, and carbon inventories, at a regional or management unit level, and for parks planning, operational planning, and selection of stands for many purposes at a local level. Attribute data for each polygon commonly include the variables used to delineate the polygon, and other variables that can be measured or estimated using these medium-scale photographs. Additional measures that can only be obtained via expensive ground measures or possibly on high resolution photographs (e.g., volume per unit area, biomass components per unit area, tree-list of species and diameters) are available only for a sample of polygons, or may have been gathered independently using a sample survey over the land area. Improved linkages over a variety of data sources may help to support landscape level analyses. This study presents an approach to combine information from a systematic (grid) ground survey, forest cover (polygon) data, and Landsat Thematic Mapper (TM) imagery using variable-space nearest neighbor methods to estimate (i) mean ground-measured attributes for each polygon, in particular, volume per ha (m³/ha), stems per ha, and quadratic mean diameter for each polygon; and (ii) variation of these ground attributes within polygons. The approach was initially evaluated using Monte Carlo simulations with known measures of these attributes. Nearest neighbor methods were then applied to an approximate 5000 ha area (about 1000 polygons) of high productivity, mountainous forests located near the Pacific Coast of British Columbia, Canada. Based on the simulation results, the use of Landsat pixel reflectances to estimate volume per ha, average tree size (i.e., quadratic mean diameter), and stems per ha did not show great promise in improving estimates for each polygon over using forest cover data alone. However, in application, the use of remotely sensed data provided estimates of within-polygon variability. At the same time, the estimated means of these three imputed variables over the entire study area were very similar to the representative sample estimates using the ground data only. Extensions to other variables such as ranges of diameters and numbers of snags may also be possible providing useful data for habitat and forest growth analysis.     

Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique

This article explores a non-linear partial least square (NLPLS) regression method for bamboo forest carbon stock estimation based on Landsat Thematic Mapper (TM) data. Two schemes, leave-one-out (LOO) cross validation (scheme 1) and split sample validation (scheme 2), are used to build models. For each scheme, the NLPLS model is compared to a linear partial least square (LPLS) regression model and multivariant linear model based on ordinary least square (LOLS). This research indicates that an optimized NLPLS regression mode can substantially improve the estimation accuracy of Moso bamboo (Phyllostachys heterocycla var. pubescens) carbon stock, and it provides a new method for estimating biophysical variables by using remotely sensed data.     

Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Integration of multisensor data provides the opportunity to explore benefits emanating from different data sources. A fusion between fraction images derived from spectral mixture analysis of Landsat-7 ETM+ and phased array L-band synthetic aperture radar (PALSAR) is introduced. The aim of this fusion is to improve the estimation accuracy of above-ground biomass (AGB) in lowland mixed dipterocarp forest. Spectral mixture analysis was applied to decompose a mixture of spectral components of Landsat-7 ETM+ into vegetation, soil, and shade fractions. These fraction images were integrated with PALSAR data using the discrete wavelet transform (DWT) and Brovey transform. As a comparison, spectral reflectance of Landsat-7 ETM+ was fused directly with PALSAR data. Backscatter of horizontal–horizontal and horizontal–vertical polarizations was also used to estimate AGB. Forest inventory was carried out in 77 randomly distributed plots, the data being used for either model development or validation. A local allometric equation was applied to calculate AGB per plot. Regression models were developed by integrating field measurements of 50 sample plots with remotely sensed data, e.g. fraction images, reflectance of Landsat-7 ETM+, and PALSAR data. The models developed were validated using 27 independent sample plots. The results showed that not all fused images significantly improved the accuracy of AGB estimation. The model based on Brovey transform using the reflectance of Landsat-7ETM+ and PALSAR produced an R² of only 0.03–0.10. By contrast, fusion between PALSAR data and fraction images using Brovey transform improved the accuracy of R² to 0.33–0.46. Further improvement in the accuracy of estimating AGB was observed when DWT was applied to integrate PALSAR with the reflectance of Landsat-7ETM+ (R² = 0.69–0.72) and PALSAR with fraction images (R² = 0.70–0.75).