洞庭湖湿地净初级生产力估算研究

张猛; 陈淑丹; 林辉; 刘洋; 张怀清

doi:10.11834/jrs.20221744

湿地生态遥感 | 浏览量 : 0 下载量: 1664 CSCD: 4

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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
收稿：2021-12-03，

纸质出版：2023-06-07
DOI： 10.11834/jrs.20221744
稿件说明：

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张猛，陈淑丹，林辉，刘洋，张怀清.2023.洞庭湖湿地净初级生产力估算研究.遥感学报，27（6）： 1454-1466 DOI： 10.11834/jrs.20221744.

Zhang M，Chen S D，Lin H，Liu Y and Zhang H Q. 2023. Net primary productivity estimation of Dongting Lake wetland. National Remote Sensing Bulletin， 27（6）：1454-1466 DOI： 10.11834/jrs.20221744.

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

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、

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（

=0.85）和最低的RMSE（20.16 g C/m

），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

和357.50g C/m

。

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 (

) 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

(

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

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

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 (

0.85) and low

RMSE

(20.16 g C/m

) 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

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

Keywords

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