基于高分辨率遥感影像的NPP估算及驱动因子研究——以银川市为例

Estimation of Net Primary Productivity by High Resolution Remote Sensing Images and Its Influencing Factors in Yinchuan City

  • 摘要: 植被净初级生产力(net primary productivity, NPP)是监测区域陆地生态系统碳循环过程,评估区域碳源、汇的重要因子。估算植被NPP并揭示其演变特征及其对自然、人文因子的响应,对于区域生态系统可持续发展具有重要意义。以银川市为例,利用亚米级高分辨率遥感影像以及气候、土地利用等数据,基于改进的CASA(Carnegie-Ames-Stanford approach)估算研究区2015年和2021年不同季度的植被NPP并分析其时空演变特征,采用梯度提升决策树模型揭示自然因子、人类活动以及土地利用对植被NPP的非线性响应和阈值效应。结果表明,2021年银川市植被NPP在0~753.951 gC·m-2·a-1,高值区集中在黄河沿岸农业灌溉区。利用高分辨率影像估算植被NPP,可得到田块尺度和城市街区尺度植被NPP空间分布的细节图,提高估算精度。银川市植被NPP随季节的变化趋势与植被物候和农作物生长周期一致。2015—2021年第三季度植被NPP整体呈增加趋势,但城市扩张占用耕地导致局部NPP下降显著,而生态保护区内植被NPP上升显著。归一化植被指数(normalized difference vegetation index, NDVI)、土壤有机碳密度、高程、气温和降雨等自然因子对植被NPP存在显著阈值效益,其中NDVI的相对贡献最大,但其超过0.6后,对植被NPP的提升作用不明显。不同土地利用类型对植被NPP的非线性影响存在差异,如旱地对NPP的影响阈值高于水田,建设用地中城市绿色基础达到一定比例时对植被NPP起正向作用,即调控土地利用结构和布局对于植被NPP的提升至关重要。当人类活动在一定范围内时对植被NPP有一定程度的促进作用,而当人口数超过阈值后,人口越大,生态承载力越低,植被NPP则会下降。

     

    Abstract:
    Objectives Net primary productivity (NPP) of vegetation serves as a key indicator for monitoring the carbon cycle process of regional terrestrial ecosystem and regional carbon sources and sinks. Estimating NPP of vegetation,revealing its evolutionary characteristics, and clarifying its responses to natural and human factors are crucial for the sustainable development of regional ecosystem.
    Methods This paper takes Yinchuan City as the study area, employs Carnegie-Ames-Stanford approach (CASA), and combined with sub-meter high-resolution remote sensing images, climate, land use and other data, the NPP of vegetation in different seasons from 2015 to 2021 in the study area are estimated and its temporal and spatial evolution characteristics are analyzed. Gradient boosting decision tree (GBDT) model is used to reveal the nonlinear response and threshold effect of natural factors, human activities and land use on NPP of vegetation.
    Results The NPP of vegetation in Yinchuan City ranges from 0 to 753.951 gC·m-2·a-1, with high-value areas concentrated in the agricultural irrigation zones along the Yellow River. The integration of high-resolution images in NPP estimation yieldes the detailed spatial distribution maps at both field and urban block scales, while enhancing estimation accuracy. The seasonal variation trend of vegetation NPP in Yinchuan City aligns with vegetation phenology and crop growth cycle. From 2015 to 2021, vegetation NPP in the third quarter shows an overall upward trend. However, urban expansion-led cultivated land occupation causes a significant local NPP decline, whereas vegetation NPP in ecological protection areas increases remarkably. Natural factors, including normalized difference vegetation index (NDVI), soil organic carbon density, elevation, and rainfall, exert significant threshold effects on NPP. Among these factors, NDVI contributes the most relatively. If NDVI exceeds 0.6, it no longer significantly promotes vegetation NPP. The NPP threshold of dry land is higher than that of paddy fields. When urban green space accounts for a certain proportion in construction land, such land exerts a positive effect on NPP. Human activities within a certain scope can promote vegetation NPP to some extent. Once the population exceeds the threshold, a larger population leads to lower ecological carrying capacity and consequently reduces vegetation NPP.
    Conclusions In this paper, we use high-resolution images to estimate vegetation NPP, which enhance the spatial refinement of vegetation NPP distribution. Vegetation NPP in Yinchuan City exhibits distinct seasonal characteristics, and the seasonal variation trend is consistent with vegetation phenology and crop growth cycles. Natural factors, land use, and human activities in Yinchuan City all exert significant impacts on vegetation carbon sinks, with notable threshold effects. Notably, natural factors contribute more relatively to vegetation NPP than the other two types of factors.

     

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