As a powerful approach,the advantage of usig remote sensing to estimate and supervise net primary productivity of terrestrial vegetation lies not only in that it is free from a lot of trivially detailed field works,bu...As a powerful approach,the advantage of usig remote sensing to estimate and supervise net primary productivity of terrestrial vegetation lies not only in that it is free from a lot of trivially detailed field works,but also in that it realizes the estimation of NPP in a large region.So it largely pushes forward global change research.According to this kind of approach,this paper mainly discusses on the role of vegetation index,PAR and light energy efficiency on the estimation of NPP,and puts forward a few suggestions on improving the NPP models.展开更多
人类活动是NPP变化的重要影响因子,定量计算NPP人为影响值具有较重要的意义。提出基于变异系数法的NPP人为影响模型,对其基本概念、理论基础、计算流程等进行了阐述,并以石羊河流域为研究区,分析该流域NPP人为影响分布规律。研究结果表...人类活动是NPP变化的重要影响因子,定量计算NPP人为影响值具有较重要的意义。提出基于变异系数法的NPP人为影响模型,对其基本概念、理论基础、计算流程等进行了阐述,并以石羊河流域为研究区,分析该流域NPP人为影响分布规律。研究结果表明:(1)该模型基于一种间接计算的思想回避了人为作用的复杂过程,模型理论科学,以变异系数为参数,所需参数少,技术可行,计算结果为NPP值,易于定量评价。(2)2000—2010年期间,石羊河流域人类活动对植被NPP的影响广泛而严重,年均影响值大于40g C m^(-2)a^(-1)的面积占96.21%,影响程度严重以上占26.94%。NPP人为正负影响均较大,正影响年均为1.63×106g C m^(-2)a^(-1),负影响年均为1.21×106g C m^(-2)a^(-1),年均净增加4.20×105g C m^(-2)a^(-1);正向平均影响强度为136.84 g C m^(-2)a^(-1),负向平均影响强度为100.32 g C m^(-2)a^(-1),全流域表现为正影响。(3)凉州区是人为影响最为剧烈的地区,表现为强烈正影响;其次是天祝县,为强烈负影响;接下来是民勤县,表现为正影响;其它县区依次是永昌、古浪、肃南和金昌。(4)2000—2010期间,NPP人为影响值变化较大,人为活动减弱面积占53.90%,增加占46.10%;影响值正向减弱8.12×105g C m^(-2)a^(-1),负向减弱8.07×105g C m^(-2)a^(-1),正向增强8.02×105g C m^(-2)a^(-1),负向增强3.94×105g C m^(-2)a^(-1),人为活动影响净减少4.25×105g C m^(-2)a^(-1),人为作用总体呈减弱趋势。展开更多
Study on seasonal responses of terrestrial net primary production (NPP) to climate changes is to help understand feedback between climate systems and terrestrial ecosystems and mechanisms of increased NPP in the north...Study on seasonal responses of terrestrial net primary production (NPP) to climate changes is to help understand feedback between climate systems and terrestrial ecosystems and mechanisms of increased NPP in the northern middle and high latitudes. In this study, time series dataset of normalized difference vegetation index (NDVI) and corresponding ground-based information on vegetation, climate, soil, and solar radiation, together with an ecological process model, were used to explore the seasonal trends of terrestrial NPP and their geographical differences in China from 1982 to 1999. As the results,. seasonal total NPP in China showed a significant increase for all four seasons (spring, summer, autumn and winter) during the past 18 years. The spring NPP indicated the largest increase rate, while the summer NPP was with the largest increase in magnitude. The response of NPP to climate changes varied with different vegetation types. The increased NPP was primarily led by an advanced growing season for broadleaf evergreen forest, needle-leaf evergreen forest, and needle-leaf deciduous forest, whilst that was mainly due to enhanced vegetation activity (amplitude of growth cycle) during growing season for broadleaf deciduous forest, broadleaf and needle-leaf mixed forest, broadleaf trees with groundcover, perennial grasslands, broadleaf shrubs with grasslands, tundra, desert, and cultivation. The regions with the largest increase in spring NPP appeared mainly in eastern China, while the areas with the largest increase in summer NPP occurred in most parts of Northwestern China, Qinghai-Xizang Plateau, Mts. Xiaoxinganling-Changbaishan, Sanjiang Plain, Songliao Plain, Sichuan Basin, Leizhou Peninsula, part of the middle and lower Yangtze River, and southeastern mountainous areas of China. In autumn, the largest NPP increase appeared in Yunnan Plateau-Eastern Xizang and the areas around Hulun Lake. Such different ways of the NPP responses depended on regional climate attributes and their changes.展开更多
文摘As a powerful approach,the advantage of usig remote sensing to estimate and supervise net primary productivity of terrestrial vegetation lies not only in that it is free from a lot of trivially detailed field works,but also in that it realizes the estimation of NPP in a large region.So it largely pushes forward global change research.According to this kind of approach,this paper mainly discusses on the role of vegetation index,PAR and light energy efficiency on the estimation of NPP,and puts forward a few suggestions on improving the NPP models.
文摘人类活动是NPP变化的重要影响因子,定量计算NPP人为影响值具有较重要的意义。提出基于变异系数法的NPP人为影响模型,对其基本概念、理论基础、计算流程等进行了阐述,并以石羊河流域为研究区,分析该流域NPP人为影响分布规律。研究结果表明:(1)该模型基于一种间接计算的思想回避了人为作用的复杂过程,模型理论科学,以变异系数为参数,所需参数少,技术可行,计算结果为NPP值,易于定量评价。(2)2000—2010年期间,石羊河流域人类活动对植被NPP的影响广泛而严重,年均影响值大于40g C m^(-2)a^(-1)的面积占96.21%,影响程度严重以上占26.94%。NPP人为正负影响均较大,正影响年均为1.63×106g C m^(-2)a^(-1),负影响年均为1.21×106g C m^(-2)a^(-1),年均净增加4.20×105g C m^(-2)a^(-1);正向平均影响强度为136.84 g C m^(-2)a^(-1),负向平均影响强度为100.32 g C m^(-2)a^(-1),全流域表现为正影响。(3)凉州区是人为影响最为剧烈的地区,表现为强烈正影响;其次是天祝县,为强烈负影响;接下来是民勤县,表现为正影响;其它县区依次是永昌、古浪、肃南和金昌。(4)2000—2010期间,NPP人为影响值变化较大,人为活动减弱面积占53.90%,增加占46.10%;影响值正向减弱8.12×105g C m^(-2)a^(-1),负向减弱8.07×105g C m^(-2)a^(-1),正向增强8.02×105g C m^(-2)a^(-1),负向增强3.94×105g C m^(-2)a^(-1),人为活动影响净减少4.25×105g C m^(-2)a^(-1),人为作用总体呈减弱趋势。
文摘Study on seasonal responses of terrestrial net primary production (NPP) to climate changes is to help understand feedback between climate systems and terrestrial ecosystems and mechanisms of increased NPP in the northern middle and high latitudes. In this study, time series dataset of normalized difference vegetation index (NDVI) and corresponding ground-based information on vegetation, climate, soil, and solar radiation, together with an ecological process model, were used to explore the seasonal trends of terrestrial NPP and their geographical differences in China from 1982 to 1999. As the results,. seasonal total NPP in China showed a significant increase for all four seasons (spring, summer, autumn and winter) during the past 18 years. The spring NPP indicated the largest increase rate, while the summer NPP was with the largest increase in magnitude. The response of NPP to climate changes varied with different vegetation types. The increased NPP was primarily led by an advanced growing season for broadleaf evergreen forest, needle-leaf evergreen forest, and needle-leaf deciduous forest, whilst that was mainly due to enhanced vegetation activity (amplitude of growth cycle) during growing season for broadleaf deciduous forest, broadleaf and needle-leaf mixed forest, broadleaf trees with groundcover, perennial grasslands, broadleaf shrubs with grasslands, tundra, desert, and cultivation. The regions with the largest increase in spring NPP appeared mainly in eastern China, while the areas with the largest increase in summer NPP occurred in most parts of Northwestern China, Qinghai-Xizang Plateau, Mts. Xiaoxinganling-Changbaishan, Sanjiang Plain, Songliao Plain, Sichuan Basin, Leizhou Peninsula, part of the middle and lower Yangtze River, and southeastern mountainous areas of China. In autumn, the largest NPP increase appeared in Yunnan Plateau-Eastern Xizang and the areas around Hulun Lake. Such different ways of the NPP responses depended on regional climate attributes and their changes.
