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遥感技术与应用  2019, Vol. 34 Issue (5): 925-938    DOI: 10.11873/j.issn.1004-0323.2019.5.0925
林业遥感专栏     
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
张少伟1,2(),惠刚盈1(),韩宗涛3,孙珊珊3,田昕3
1. 中国林业科学研究院林业研究所,北京 100091
2. 河南农业职业学院园艺园林学院,河南 郑州 451450
3. 中国林业科学研究院资源信息研究所,北京 100091
Estimation of Large-scale Forest Above-ground Biomass based on Fast Optimizing Remotely Sensed Features from Pptical Multi-spectral and SAR Data
Shaowei Zhang1,2(),Gangying Hui1(),Zongtao Han3,Shanshan Sun3,Xin Tian3
1. Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
2. College of Horticulture and Landscape, Henan Vocational College of Agriculture, Zhengzhou 451450, China
3. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
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摘要:

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。

关键词: 主、被动遥感多模式遥感特征快速迭代特征选择区域森林地上生物量变化格局    
Abstract:

Aiming at the problem of low efficiency for estimating large-area forest Above-Ground Biomass(AGB) using multi-mode remote sensing, this study fully integrated multi-dimensional observation characteristics of forest AGB from active and passive remotely sensed features, in order to improve the regional estimation result. Based on an analysis on two temporal estimation results, this study disclosed the spatial patterns of the regional forest AGB changes. It could provide data supports for the scientific assessments on the regional eco-environmental protection projects (i.e., the Natural Forest Protection Project) and for improving the ability of continuous dynamic monitoring and early warning the national eco-environment by use of remote sensing. The study area is located at the Great Khingan, the Inner Monolia. Based on the active and passive multi-mode remotely sensed features extracted from the Landsat-TM5(TM) and ALOS-1 PALSAR mainly acquired in 2009,and the Gaofen-1(GF-1)and ALOS-2 PALSAR data mainly acquired in 2014, respectively, the k- Nearest Neighbor with Fast Iterative Features Selection (KNN-FIFS) method was applied to fast select the features composition to establish the optimal estimating model. The 7th and 8th National Forest resource Inventory (NFI) data were applied to training and validating (by Leave One Out method, LOO) the optimal KNN-FIFS for estimating two-temporal forest(arbor forest) AGB over study area. Based on the comparison between the two-temporal AGB results, the local forest changes from 2009 to 2014 at pixel and regional scales were quantitatively analyzed. At pixel scale, the validation based on NFI and LOO method showed that, estimates obtained a R2=0.56 and Root-Mean-Square Error (RMSE) = 25.95 t/ha, and a R2=0.64; RMSE=24.55 t/ha for 2009 and 2014, respectively. Meanwhile, as compared with NFI measurements, the average of 2009 results was over-estimated (predictions: 81.59 t/ha VS NFI measurements:78.64 t/ha), but the average of 2014 was under-estimated (predictions: 79.63 t/ha VS NFI measurements:82.48 t/ha). At regional scale, the overall averages of 2009 and 2014 were 88.33 t/ha, 94.61 t/ha respectively, with a increment of 6.28 t/ha,which were closed to those from previous studies using the Biomass Expansion Factor method, 87.14 t/ha for 2008, and 92.20 t/ha for 2013, respectively. The KNN-FIFS method used in this study, could largely improve the efficiency for selecting the optimal composition from high-dimensional multi-mode remotely sensed features. Full integration of the multi-dimensional observation characteristics from active and passive remotely sensed information, could improve the estimating accuracy and saturation level of forest AGB. Validation based the LOO method at pixel scale made the KNN-FIFS more robust with avoiding the random errors brought form the selection of training and validation data set. From 2009 to 2014, the local vegetation fractional coverage got to increase obviously, as well as the local forest AGB. Thanks to the implement of National Forest Protection Project, the situation of the local forest resource was effectively improved, although some forest fire were occasionally witnessed by the study years.

Key words: Active and passive remote sensing    Multi-mode remotely sensed features    Fast iterative features selection    Regional forest above-ground biomass    Change pattern
收稿日期: 2018-10-28 出版日期: 2019-12-05
ZTFLH:  TP79  
基金资助: 中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2017QC005);中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2017MB039);国家重点研发计划项目(2017YFC0504005)
通讯作者: 惠刚盈     E-mail: hncazsw@126.com;hui@caf.ac.cn
作者简介: 张少伟(1981-),男,河南安阳人,博士,副教授,主要从事森林资源变化监测与经营研究。E?mail:hncazsw@126.com
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引用本文:

张少伟,惠刚盈,韩宗涛,孙珊珊,田昕. 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[J]. 遥感技术与应用, 2019, 34(5): 925-938.

Shaowei Zhang,Gangying Hui,Zongtao Han,Shanshan Sun,Xin Tian. Estimation of Large-scale Forest Above-ground Biomass based on Fast Optimizing Remotely Sensed Features from Pptical Multi-spectral and SAR Data. Remote Sensing Technology and Application, 2019, 34(5): 925-938.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.0925        http://www.rsta.ac.cn/CN/Y2019/V34/I5/925

图1  研究区土地利用类型图(2014年)
图2  KNN-FIFS算法流程图
图3  KNN-FIFS方法特征选择与特征组合遍历对比
图4  内蒙古大兴安岭地区2009年和2014年植被覆盖度
图5  内蒙古大兴安岭地区2009~2014年植被覆盖度变化图
图6  基于森林资源清查样地结果与LOO法检验两期森林AGB反演结果及散点密度分布图(灰色虚线为1∶1线;黑色点划线为拟合线)
图7  内蒙古大兴安岭地区两期森林AGB反演结果
图8  2009~2014年间内蒙古大兴安岭地区森林AGB变化格局
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