三峡库区消落带植被NPP估算OA北大核心CHSSCDCSTPCD
Estimation of vegetation carbon sink in fluctuation zone of Three Gorges Reservoir Area based on CASA Model optimized by machine learning
三峡库区蓄水后,其生态效应受到广泛关注.消落带植被固碳量作为衡量库区生态系统健康状态的重要指标,对库区碳循环与生态净化具有重要意义.针对消落带不同高程植被接受光照的时间有所差异,且受河流水位变化影响,传统的CASA模型在计算消落带植被固碳量时,存在对植物的光能利用率计算不够精确等问题.以三峡库区香溪河陡坡消落带为研究区域,提出了一种耦合 RBFNN 模型(Radial Basis Function Neural Network)与 CASA 模型(Carnegie-Ames-Stanford approach)的新方法(RBF-CASA).基于RBFNN建立环境影响因子模型,借助高程数据及植被指数等特征计算适合消落带区域的环境影响因子.结合CASA模型中温度和水分胁迫因子,提高植被在像元尺度上的净初级生产力(Net Primary Productivity,NPP)的估算精度,并对反演结果进行验证.模型验证结果显示:RBF-CASA模型估算值与观测值的决定系数(Coefficient of determination,R2)为0.730(P<0.01,n=32).对比原始 CASA 模型,平均绝对误差(Mean absolute error,MAE)降低 10.991,均方根误差(Root mean square error,RMSE)降低了 23.861,相对均方根误差(Relative root mean square error,RRMSE)降低5.10%,平均绝对百分误差(Mean absolute percentage error,MAPE)降低1.12%.使用提出的RBF-CASA模型在库区水位落干期(7-8月份)进行固碳量估算,结果表明:NPP月均值在66.234-134.144g C/m2之间,NPP随着高程的增加呈现起伏变化,其总量在150-155m之间达到峰值,均值在170m以上区域最高.在2021年9月植被NPP均值为35.883g C/m2,2022年9月植被NPP均值为25.964g C/m2,由于降雨量减少、长江水位下降,在2021-2022年间植被恢复情况较差.研究结果可为库区碳循环、生态净化及生态修复等决策提供科学依据.
After the storage of the Three Gorges Reservoir,its ecological impact has received great attention.As an important indicator of ecosystem health in the reservoir area,carbon sequestration of vegetation in fluctuating zone is of great significance to carbon cycle and ecological purification in the reservoir area.Due to the differences in the time of receiving sunlight for vegetation at different elevations in the ebb and flow zone,and the impact of changes in river water levels,the traditional CASA model has problems such as inaccurate calculation of the light energy utilization rate of plants when calculating the carbon sequestration amount of vegetation in the ebb and flow zone.In this paper,a new method(RBF-CASA)for coupling the Radial Basis Function Neural Network(RBFNN)model with the Carnegie Ames Stanford approach(CASA)model is proposed based on the study area of the steep slope fluctuation zone of the Xiangxi River in the Three Gorges Reservoir Area.We have established an environmental impact factor model based on RBFNN.This model utilizes features such as elevation data and vegetation index to calculate environmental impact factors suitable for the region,and combines them with temperature and water stress factors to calculate environmental stress factors,in order to improve the accuracy of estimating vegetation net primary productivity(NPP)at the pixel scale.The results showed that the R2 between the estimated value and the observed value of the RBF-CASA model was 0.730(P<0.01,n=32).Compared with the CASA model,the MAE decreased by 10.991,RRMSE decreased by 5.10%,and MAPE decreased by 1.12%.Using the RBF-CASA model to estimate carbon sequestration in the typical steep slope and falling zone of the Three Gorges Reservoir area,the monthly average NPP of the study area in July,the most suitable month for vegetation growth,was between 66.234 and 134.144g C/m2.In the area of the ebb and flow zone,NPP fluctuated with the increase of elevation,with the total amount of NPP reaching a peak value between 150 and 155 m,and the area with the average value of NPP above 170 m had the highest value.In September 2021,the average NPP of vegetation was 35.883g C/m2,while in September 2022,the average NPP of vegetation was 25.964g C/m2.Due to the decrease in rainfall and the decline in the water level of the Yangtze River,the vegetation restoration situation was poor between 2021 and 2022.Therefore,this research can provide scientific basis for carbon cycle,ecological purification and ecological restoration decision-making.
靳专;胥焘;黄应平;肖敏;张家璇;周爽爽;席颖;熊彪
湖北省农田环境监测工程技术研究中心(三峡大学),宜昌 443002||三峡大学计算机与信息学院,宜昌 443002||三峡库区生态环境教育部工程研究中心(三峡大学),宜昌 443002三峡库区生态环境教育部工程研究中心(三峡大学),宜昌 443002湖北省农田环境监测工程技术研究中心(三峡大学),宜昌 443002||三峡库区生态环境教育部工程研究中心(三峡大学),宜昌 443002||三峡大学水利与环境学院,宜昌 443002
基于过程的遥感模型(CASA)机器学习植被净初级生产力(NPP)无人机环境影响因子模型
CASA modelmachine learningnet primary productivityunmanned aerial vehicleenvironmental impact factor model
《生态学报》 2024 (006)
2464-2478 / 15
国家自然科学基金(22136003,42177397);长江水环境教育部重点实验室(同济大学)开放基金项目(YRWEF202103);湖北省教育厅优秀中青年人才项目(Q20211205)
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