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Net primary productivity estimation of Dongting Lake wetland

  • role： First author第一作者
  • Affiliation：

    Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China

    Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Central South University of Forestry & Technology, Changsha 410004, China

    Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Central South University of Forestry & Technology, Changsha 410004, China

  • Email：mengzhang@csuft.edu.cn
  • Introduction：张猛，研究方向为湿地遥感。E-mail：mengzhang@csuft.edu.cn
ZHANG Meng123,  
  • Affiliation：

    Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China

    Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Central South University of Forestry & Technology, Changsha 410004, China

    Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Central South University of Forestry & Technology, Changsha 410004, China

CHEN Shudan123,  
  • Affiliation：

    Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China

    Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Central South University of Forestry & Technology, Changsha 410004, China

    Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Central South University of Forestry & Technology, Changsha 410004, China

LIN Hui123,  
  • Affiliation：

    Research Institute of Forest Resources Information Techniques, ChineseAcademy of Forestry, Beijing 100091, China

LIU Yang4,  
  • role： Corresponding author通信作者
  • Affiliation：

    Research Institute of Forest Resources Information Techniques, ChineseAcademy of Forestry, Beijing 100091, China

  • Email：zhang@ifrit.ac.cn
  • Introduction：张怀清，研究方向为林业遥感。E-mail：zhang@ifrit.ac.cn
ZHANG Huaiqing4*

resumen

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 (εmax) 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 R2 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 (R2=0.85) and low RMSE (20.16 g C/m2) 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/m2, 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.

palabra clave

remote sensing;wetland;net primary productivity;CASA;spatio-temporal fusion;classification;Dongting Lake wetland

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