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遥感技术与应用  2021, Vol. 36 Issue (2): 453-462    DOI: 10.11873/j.issn.1004-0323.2021.2.0453
遥感应用     
基于高时空分辨率融合影像的红树林总初级生产力遥感估算
杨昊翔1,2(),张丽1(),闫敏1,林光辉3
1.中国科学院空天信息创新研究院 数字地球重点实验室,北京 100094
2.中国科学院大学,北京 100049
3.清华大学地球系统科学系暨东亚迁徙鸟类与栖息地生态学教育部野外观测研究站,北京 100084
Remote Sensing of Mangrove Gross Primary Production Estimation based on High Spatiotemporal Resolution Fused Images
Haoxiang Yang1,2(),Li Zhang1(),Min Yan1,Guanghui Lin3
1.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.Tsinghua University,Department of Earth System Science,National Field Research Station for East Asian Migratory Birds and Their Habitats,Beijing 100084,China
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摘要:

红树林是热带与亚热带地区潮间带具备高植被生产力和高储碳量的滨海湿地植被类型,在维系全球碳平衡过程中扮演着重要的角色。目前通量站点尺度的红树林生产力研究已取得了一定的进展,然而由于受到遥感影像时空分辨率和红树林斑块分布的限制,区域尺度红树林总初级生产力(Gross Primary Production,GPP)估算仍少有涉及。基于影像融合算法获得的高时空分辨率植被指数数据集,结合红树林通量观测数据开展光能利用率模型的参数估计和模型验证研究,实现了区域尺度的红树林GPP估算,获取了一套2012年广东省高桥红树林GPP高时空分辨率数据集。数据验证得到的决定系数R2 = 0.64,较现有的MOD17A2和GLASS产品GPP估算精度提高了48.9%。实验结果显示:高桥红树林最大光能利用率为3.07 g C MJ-1,研究区内全年GPP均值为1 915.4 g C m-2 a-1。红树林季节平均GPP夏、秋季大于春、冬季。该方法和估算数据可为区域尺度红树林生产力研究和红树林保护提供高精度数据支持。

关键词: 红树林总初级生产力遥感影像融合光能利用率模型    
Abstract:

Mangrove forests are characterized as high productivity and high carbon storage coastal vegetation inhabited in the intertidal zone of tropical and subtropical region. They play a significant role in global carbon balance. Previous studies have made achievements in estimating and analyzing mangrove primary production on eddy flux tower site scale, however, rare experiments were conducted on the estimation of mangrove Gross Primary Production(GPP) on regional scale due to the limited remote sensing image resolution and patchy distribution of mangrove. In this study, we first combined high spatiotemporal resolution vegetation index datasets produced by data fusion technique and eddy flux data to calibrate and validate light use efficiency model, and then applied the model to estimate mangrove GPP in our study region. Based on our method, a high spatiotemporal resolution dataset of mangrove GPP in Gaoqiao, Guangdong province in 2012 was established. The overall accuracy of our dataset (R2=0.64) outperformed MOD17A2 and GLASS GPP product with the increase of 48.9%. Experiments results showed that the maximum light use efficiency of mangrove in Gaoqiao is 3.07 g C MJ-1, and annually average GPP is 1 915.4 g C m-2 a -1 in our study site. Besides, seasonally average GPP of Gaoqiao mangrove is higher in summer and autumn than spring and winter. Our method and dataset can be served for the regional-scale mangrove production research, as well as are effective support for mangrove protection.

Key words: Mangrove    Gross primary production    Remote sensing image fusion    Light use efficiency model
收稿日期: 2019-12-18 出版日期: 2021-05-24
ZTFLH:  TP79  
基金资助: 国家自然科学基金面上项目(41771392);国家自然科学基金重点项目(30930017);国家重点基础研究发展计划(973)项目(2013CB956600)
通讯作者: 张丽     E-mail: yanghx@radi.ac.cn;zhangli@aircas.ac.cn
作者简介: 杨昊翔(1995-),男,湖北宜昌人,硕士研究生,主要从事植被生态遥感研究。E?mail:yanghx@radi.ac.cn
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引用本文:

杨昊翔,张丽,闫敏,林光辉. 基于高时空分辨率融合影像的红树林总初级生产力遥感估算[J]. 遥感技术与应用, 2021, 36(2): 453-462.

Haoxiang Yang,Li Zhang,Min Yan,Guanghui Lin. Remote Sensing of Mangrove Gross Primary Production Estimation based on High Spatiotemporal Resolution Fused Images. Remote Sensing Technology and Application, 2021, 36(2): 453-462.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.2.0453        http://www.rsta.ac.cn/CN/Y2021/V36/I2/453

图1  广东湛江红树林国家级自然保护区及高桥红树林通量观测站位置图(红树林分布图由全球变化科学研究数据出版系统提供) 审图号:GS(2019)1822
指标名称计算式参数释义
均值(Mean)Fpˉ=?i=1Nj=1MFp(i,?j)M×N

Fpi,?j为融合预测影像;

F(i,?j)为真实影像;

M?×?N为影像的大小;

融合质量越好的融合影像,其均值、标准差会越靠近

真实影像的均值和标准差;R2r越接近1;RMSE、

RMSEr、MAD和MAPE越接近于0

标准差(SD)σFp=1M×Ni=1Nj=1M(Fp(i,?j)-Fp?)2
均方根误差(RMSE)RMSE=i=1Mj=1N[Fp(i,?j)-F(i,?j)]2M×N
相对均方根误差(RMSEr)RMSEr=RMSEFpˉ
决定系数(R2R2=?(i=1Mj=1N(Fp(i,?j)-Fp?)(F(i,?j)-F˙))2i=1Mj=1N(Fp(i,?j)-Fp?)2i=1Mj=1N(F(i,?j)-F˙)2
相关系数(rr=?R2
平均绝对差(MAD)MAD=1M×Ni=1Nj=1MFp(i,?j)-F(i,?j)
平均绝对百分误差(MAPE)MAPE=1M×Ni=1Mj=1NFp(i,?j)-F(i,?j)F(i,?j)
耗时(Time)获取每一张预测影像所需时间
表1  ESTARFM影像融合算法精度评价指标
融合指数窗口大小MeanSDRMSERMSErR2rMADMAPETime
EVI参考影像0.3960.149
900.3710.1280.0780.2090.7590.8710.0601.06510 m 55 s
NDWI参考影像0.4810.126
700.4490.0940.0810.1800.6590.8120.0631.1395 m 50 s
表2  基于最佳滑动窗口大小的融合精度评价结果
图2  基于最佳滑动窗口大小的融合影像与真实影像红树林EVI与NDWI散点图
图3  基于最佳滑动窗口大小的融合影像与真实影像红树林EVI与NDWI空间分布图审图号:GS(2019)1822
图4  高桥站红树林最大光能利用率估计和验证结果
图5  模型估算GPP与其他卫星GPP产品的比较散点图与时序变化图
图6  高桥站站点观测光合有效辐射(PAR)与CMFD PAR的散点图和年时间序列图
图7  2012年高桥站红树林日均GPP空间分布图审图号:GS(2019)1822
图8  高桥站红树林逐月日均GPP空间分布图 审图号:GS(2019)1822
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