洞庭湖湿地净初级生产力估算研究
Net primary productivity estimation of Dongting Lake wetland
- 2023年27卷第6期 页码：1454-1466
纸质出版日期： 2023-06-07
DOI： 10.11834/jrs.20221744
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
湿地是地球上重要的“碳库”之一，针对湿地净初级生产力NPP（Net Primary Productivity）模拟中时空分辨率不高和估算精度不稳定等方面的问题，本文提出了一种修正的CASA（Carnegie-Ames-Stanford Approach）模型。首先采用遥感云计算下的时空融合算法快速、准确地获得了时间序列的Landsat 8多光谱影像，解决湿地NPP估算中高时空分辨率影像缺失问题。然后，利用Landsat 8数据集（光谱波段、陆表水体指数、归一化植被指数等）与自适应Stacking算法得到高精度的植被分类图，并结合植被分类图确定每个植被像元理想条件下最大光能利用率
ε
max
。同时，利用时序陆表水体指数及降水数据计算获得NPP估算中所需的水分胁迫因子。最后，基于归一化植被指数、水分胁迫因子、
ε
max
及气象数据等多种参数，驱动CASA模型对洞庭湖湿地NPP进行估测。研究结果显示，与其他模型相比，本文修正CASA模型估算的NPP与实测的NPP具有最高的相关系数（
R
2
=0.85）和最低的RMSE（20.16 g C/m
2
），表明该方法能有效、准确地模拟区域湿地生态系统NPP。洞庭湖区主要湿地植被类型芦苇与苔草的NPP均值分别为424.26 g C/m
2
和357.50g C/m
2
。
Abstract
Wetlands
which are an important carbon pool on Earth
is crucial for human beings and the environment. An accurate estimation of wetland carbon storage and its temporal and spatial changes are conducive to understanding the sustainable development of wetland ecosystems. Net Primary Productivity (NPP) is the net accumulation of organic matter fixed by photosynthesis per unit time and per unit area of green vegetation and is an important indicator to characterize the status of carbon flux. Therefore
accurate estimation of the spatial patterns and temporal dynamics of wetland NPP at a regional scale is crucial to improving our understanding of the carbon dynamics and sustainable development of terrestrial ecosystems. In China
similar studies have mapped wetlands or estimated wetland NPP using optical data. However
only a few studies have used dense high-spatiotemporal-resolution multispectral images for wetland mapping and considered the accuracy of the light-use efficiency (
ε
) of wetland vegetation types for NPP estimation.
In this study
we proposed an improved Carnegie-Ames-Stanford Approach (CASA) model to generate wetland NPP with high spatiotemporal resolution. First
spatiotemporal fusion algorithm process under remote sensing cloud computing was utilized to produce dense Landsat 8 reflectance images based on Landsat 8 and MOD09A1 images. Then
we explored the potential of the Landsat 8 dataset for vegetation type mapping in a subtropical wetland ecosystem using the adaptive stacking algorithm. Subsequently
the vegetation classification map was used to determine the final prior specification of a maximum
ε
(
ε
max
) of each vegetation pixel. Finally
wetland NPP with CASA was estimated using the normalized difference vegetation index
LSWI and wetland vegetation map.
Visually
the SpatioTemporal Fusion Algorithm (STFA) process based on Google Earth Engine (GEE) showed good performance in downscaling MODIS at low to high spatial resolutions
except for some minor flaws that did not affect the overall product. For the fused image
the STFA based on GEE produced an
R
2
value larger than 0.88
RMSE
less than 0.05
and
SAM
less than 3
which indicated that the fused image was nearly consistent with original Landsat spectrally and spatially. Therefore
STFA based on GEE is suitable for image fusion in areas experiencing rapid change
such as wetlands and city suburbs. The overall accuracy of the wetland map was above 88%
which indicates the potential of the improved stacking algorithm for delineating different land cover types. Additionally
the user and producer accuracies of vegetation types varied within 85%—92% and 83%—91%
respectively. The classification accuracy associated with the proposed method are notably higher than those of the classical methods (e.g.
SVM
RF
and kNN)
indicating the superiority of the adaptive stacking algorithm for discriminating land cover in a wetland with complex conditions. The measured NPP values derived from field aboveground biomass data were used to validate the accuracy of simulated NPP. The high correlation coefficient (
R
2
=
0.85) and low
RMSE
(20.16 g C/m
2
) between the estimated and measured NPP demonstrated a significant linear relationship
and thus the estimated NPP based on Landsat data using the CASA model with the input parameters described above is creditable. The average NPP of sedges and reed wetland were 357.50 and 424.26 g C/m
2
respectively. The mean NPP values of wetlands (reed and tussock) estimated by the modified CASA model in this study were also closer to those estimated by other models.
In this study
time-series Landsat data were obtained on the basis of the STFA based on GEE
and the modified CASA model estimated the NPP of the Dongting Lake wetlands with high spatiotemporal resolution. The NPP estimation method in this study is expected to provide scientific data support for quantitative research on regional wetland carbon reserves and sustainable development.
关键词
遥感湿地净初级生产力CASA时空融合分类洞庭湖湿地
Keywords
remote sensingwetlandnet primary productivityCASAspatio-temporal fusionclassificationDongting Lake wetland
references
Bandaru V, West T O, Ricciuto D M and Lzaurralde R C. 2013. Estimating crop net primary production using national inventory data and MODIS-derived parameters. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 61-71 [DOI: 10.1016/j.isprsjprs.2013.03.005http://dx.doi.org/10.1016/j.isprsjprs.2013.03.005]
Bao G, Bao Y H, Qin Z H, Xin X P, Bao Y L, Bayarsaikan S, Zhou Y and Chuntai B. 2016. Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model. International Journal of Applied Earth Observation and Geoinformation, 46: 84-93 [DOI: 10.1016/j.jag.2015.12.001http://dx.doi.org/10.1016/j.jag.2015.12.001]
Cai Y T, Li X Y, Zhang M and Lin H. 2020. Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data. International Journal of Applied Earth Observation and Geoinformation, 92: 102164 [DOI: 10.1016/j.jag.2020.102164http://dx.doi.org/10.1016/j.jag.2020.102164]
Cheng Q, Liu H Q, Shen H F, Wu P H and Zhang L P. 2017. A spatial and temporal nonlocal filter-based Data fusion method. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4476-4488 [DOI: 10.1109/TGRS.2017.2692802http://dx.doi.org/10.1109/TGRS.2017.2692802]
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218 [DOI: 10.1109/TGRS.2006.872081http://dx.doi.org/10.1109/TGRS.2006.872081]
Jönsson P and Eklundh L. 2004. TIMESAT-a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833-845 [DOI: 10.1016/j.cageo.2004.05.006http://dx.doi.org/10.1016/j.cageo.2004.05.006]
Liu Z H, Wang T, Qu Y H, Liu H M, Wu X F and Wen Y. 2019. Prediction of high-quality MODIS-NPP product data. Remote Sensing, 11(12): 1458 [DOI: 10.3390/rs11121458http://dx.doi.org/10.3390/rs11121458]
Long X R, Li X Y, Lin H and Zhang M. 2021. Mapping the vegetation distribution and dynamics of a wetland using adaptive-stacking and Google Earth Engine based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 102: 102453 [DOI: 10.1016/j.jag.2021.102453http://dx.doi.org/10.1016/j.jag.2021.102453]
Mao D H, Wang Z M, Du B J, Li L, Tian Y L, Jia M M, Zeng Y, Song K S, Jiang M and Wang Y Q. 2020. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 11-25 [DOI: 10.1016/j.isprsjprs.2020.03.020http://dx.doi.org/10.1016/j.isprsjprs.2020.03.020]
Mao D H, Wang Z M, Li L and Ma W H. 2014. Spatiotemporal dynamics of grassland aboveground net primary productivity and its association with climatic pattern and changes in Northern China. Ecological Indicators, 41: 40-48 [DOI: 10.1016/j.ecolind.2014.01.020http://dx.doi.org/10.1016/j.ecolind.2014.01.020]
Monteith J L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9(3): 747-766 [DOI: 10.2307/2401901http://dx.doi.org/10.2307/2401901]
Mu S J, Zhou S X, Chen Y Z, Li J L, Ju W M and Odeh I O A. 2013. Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in Inner Mongolian grassland, China. Global and Planetary Change, 108: 29-41 [DOI: 10.1016/j.gloplacha.2013.06.007http://dx.doi.org/10.1016/j.gloplacha.2013.06.007]
Piao S L, Fang J Y, Zhou L M, Zhu B, Tan K and Tao S. 2005. Changes in vegetation net primary productivity from 1982 to 1999 in China. Global Biogeochemical Cycles, 19(2): GB2027 [DOI: 10.1029/2004GB002274http://dx.doi.org/10.1029/2004GB002274]
Potter C S, Randerson J T, Field C B, Matson P A, Vitousek P M, Mooney H A and Klooster S A. 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 7(4): 811-841 [DOI: 10.1029/93GB02725http://dx.doi.org/10.1029/93GB02725]
Shang E P, Xu E Q, Zhang H Q and Liu F. 2018. Analysis of spatiotemporal dynamics of the Chinese vegetation net primary productivity from the 1960s to the 2000s. Remote Sensing, 10(6): 860 [DOI: 10.3390/rs10060860http://dx.doi.org/10.3390/rs10060860]
Singh D. 2011. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. International Journal of Applied Earth Observation and Geoinformation, 13(1): 59-69 [DOI: 10.1016/j.jag.2010.06.007http://dx.doi.org/10.1016/j.jag.2010.06.007]
Sun J K, Niu H P and Yuan M. 2020. Spatial pattern change and analysis of NPP in terrestrial vegetation ecosystem in China. Transactions of the Chinese Society for Agricultural Machinery, 51(6): 162-168
孙金珂, 牛海鹏, 袁鸣. 2020. 中国陆地植被生态系统NPP空间格局变迁分析. 农业机械学报, 51(6): 162-168 [DOI: 10.6041/j.issn.1000-1298.2020.06.017http://dx.doi.org/10.6041/j.issn.1000-1298.2020.06.017]
Wu C and Chen J M. 2012. The use of precipitation intensity in estimating gross primary production in four northern grasslands. Journal of Arid Environment, 82: 11-18 [DOI: 10.1016/j.jaridenv.2012.02.014http://dx.doi.org/10.1016/j.jaridenv.2012.02.014]
Wu P H, Shen H F, Zhang L P and Göttsche F M. 2015. Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment, 156: 169-181 [DOI: 10.1016/j.rse.2014.09.013http://dx.doi.org/10.1016/j.rse.2014.09.013]
Xiao X M, Hollinger D, Aber J, Goltz M, Davidson E A, Zhang Q Y and Moore III B. 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment, 89(4): 519-534 [DOI: 10.1016/j.rse.2003.11.008http://dx.doi.org/10.1016/j.rse.2003.11.008]
Xu J, Chen D, Li W L and Wei W. 2019. Study of vegetation net primary productivity in Gannan based on light use efficiency model. Pratacultural Science, 36(10): 2455-2465
许静, 陈迪, 李文龙, 魏巍. 2019. 基于光能利用率模型的甘南州植被净初级生产力研究. 草业科学, 36(10): 2455-2465 [DOI: 10.11829/j.issn.1001-0629.2018-0018http://dx.doi.org/10.11829/j.issn.1001-0629.2018-0018]
Yan Y C, Liu X P, Ou J P, Li X and Wen Y Y. 2018. Assimilating multi-source remotely sensed data into a light use efficiency model for net primary productivity estimation. International Journal of Applied Earth Observation and Geoinformation, 72: 11-25 [DOI: 10.1016/j.jag.2018.05.013http://dx.doi.org/10.1016/j.jag.2018.05.013]
Ye X C, Meng Y K, Xu L G and Xu C Y. 2019. Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China’s largest freshwater lake. Science of the Total Environment, 659: 302-313 [DOI: 10.1016/j.scitotenv.2018.12.331http://dx.doi.org/10.1016/j.scitotenv.2018.12.331]
Yin X J, Zhu H H, Gao J, Gao J, Guo L J and Gou Z H. 2020. NPP simulation of agricultural and pastoral areas based on Landsat and MODIS data fusion. Transactions of the Chinese Society for Agricultural Machinery, 51(8): 163-170
尹小君, 祝宏辉, Gao J, 高军, 郭丽洁, 苟贞珍. 2020. 基于Landsat和MODIS数据融合的农牧区NPP模拟. 农业机械学报, 51(8): 163-170 [DOI: 10.6041/j.issn.1000-1298.2020.08.018http://dx.doi.org/10.6041/j.issn.1000-1298.2020.08.018]
Yu T, Sun R, Xiao Z Q, Zhang Q, Liu G, Cui T X and Wang J M. 2018. Estimation of global vegetation productivity from global LAnd surface satellite data. Remote Sensing, 10(2): 327 [DOI: 10.3390/rs10020327http://dx.doi.org/10.3390/rs10020327]
Zhang M, Lin H, Long X R and Cai Y T. 2021. Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000‐2019 time-series Landsat data. Science of the Total Environment, 780: 146615 [DOI: 10.1016/j.scitotenv.2021.146615http://dx.doi.org/10.1016/j.scitotenv.2021.146615]
Zhang M and Zeng Y N. 2018. Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution. Journal of Remote Sensing, 22(1): 143-152
张猛, 曾永年. 2018. 融合高时空分辨率数据估算植被净初级生产力. 遥感学报, 22(1): 143-152 [DOI: 10.11834/jrs.20186499http://dx.doi.org/10.11834/jrs.20186499]
Zhang M, Zeng Y N and Zhu Y S. 2017. Wetland mapping of Donting Lake Basin based on time-series MODIS data and object-oriented method. Journal of Remote Sensing, 21(3): 479-492
张猛, 曾永年, 朱永森. 2017. 面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例. 遥感学报, 21(3): 479-492 [DOI: 10.11834/jrs.20176129http://dx.doi.org/10.11834/jrs.20176129]
Zhang M, Zhang H Q, Li X Y, Liu Y, Cai Y T and Lin H. 2020. Classification of paddy rice using a stacked generalization approach and the spectral mixture method based on MODIS time series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 2264-2275 [DOI: 10.1109/JSTARS.2020.2994335http://dx.doi.org/10.1109/JSTARS.2020.2994335]
Zhao Y G, Huang B and Song H H. 2018. A robust adaptive spatial and temporal image fusion model for complex land surface changes. Remote Sensing of Environment, 208: 42-62 [DOI: 10.1016/j.rse.2018.02.009http://dx.doi.org/10.1016/j.rse.2018.02.009]
Zhu W Q, Pan Y Z, He H, Yu D Y and Hu H B. 2006. Simulation of maximum light use efficiency for some typical vegetation types in China. Chinese Science Bulletin, 51(4): 457-463 [DOI: 10.1007/s11434-006-0457-1http://dx.doi.org/10.1007/s11434-006-0457-1]
Zhu W Q, Pan Y Z, Long Z H, Chen Y H, Li J and Hu H B. 2005. Estimating net primary productivity of terrestrial vegetation based on GIS and RS: a case study in Inner Mongolia, China. Journal of Remote Sensing, 9(3): 300-307
朱文泉, 潘耀忠, 龙中华, 陈云浩, 李京, 扈海波. 2005. 基于GIS和RS的区域陆地植被NPP估算—以中国内蒙古为例. 遥感学报, 9(3): 300-307 [DOI: 10.11834/jrs.20050344http://dx.doi.org/10.11834/jrs.20050344]
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2613 [DOI: 10.1016/j.rse.2010.05.032http://dx.doi.org/10.1016/j.rse.2010.05.032]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
中南林业科技大学林业遥感信息工程研究中心
中南林业科技大学林业遥感大数据与生态安全湖南省重点实验室
中南林业科技大学南方森林资源经营与监测国家林业与草原局重点实验室
中国林业科学研究院资源信息研究所
中山大学 地理科学与规划学院
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部