复杂地形条件下提高BEPS模型模拟能力的途径

植被的净初级生产力(netprimaryproductivity,NPP)是全球变化和碳循环研究中的一个非常重要的环节。为了更精确地模拟植被的净初级生产力,选取中国长白山自然保护区作为研究区域,针对基于过程的北部森林生态系统生产力模拟模型(borealecosystemproductivitysimulator,BEPS)仅考虑平坦立地条件的特点,通过对该模型中地面接收到的太阳辐射部分进行地形修正,首先估算长白山自然保护区森林植被的NPP;然后采用高分辨率(30m)的ETM+遥感数据,利用修正前后的BEPS模型来模拟得到长白山自然保护区森林植被的净初级生产力;最后对模拟得到的NPP结果,再利用地面实测数据进行验证,其相关系数R分别为0.91659和0.92957,算术平均偏差分别为62.8gC/(m2.a)和44.2gC/(m2.a)。结果表明,通过对模型的进一步完善,BEPS对NPP的模拟精度有了一定程度的提高。     

海南省西北部植被净初级生产力的长时序精尺度遥感监测

植被净初级生产力（Net Primary Productivity, NPP）是反映生态系统碳循环状态和变化的有效指标，在“双碳”目标背景下，长时序精尺度数据对植被净初级生产力动态监测具有重要意义。海南省作为国家生态文明试验区，其NPP动态监测对于强化陆地生态系统碳汇建设具有典型作用。以海南省西北部为研究区，基于Landsat系列遥感影像数据，采用VPM光能利用率模型，估算得到海南省西北部长时序（2000～2020）精尺度（30 m空间分辨率）的NPP数据，并对其进行时空变化分析。研究结果表明：海南省西北部日平均NPP在年际尺度上呈现明显的波动上升趋势，与其他植被类型相比落叶阔叶林的NPP最高且增长趋势最快，灌丛的NPP最低且增长趋势最慢。海南省西北部NPP在空间上呈现南高北低分布，研究区南部阔叶林及稀树草原地区NPP较高，北部灌丛地区NPP较低。研究区域NPP的整体增长趋势主要是由南部植被所控制。     

稀土矿开采导致的植被净初级生产力损失遥感评估——以江西省赣州市为例

稀土矿的无序开采既造成了稀土资源的浪费,也导致了矿区及其周边生态环境的恶化。以江西省赣州市为例,建立了一套适用于稀土矿开采导致的植被净初级生产力(NPP)损失遥感评估方法,该方法充分利用了高、中、低3种分辨率遥感数据的优势,涵盖了基准参考区选择、植被受损范围界定、低分辨率植被NPP数据降尺度等关键技术环节。将该方法应用于研究区2013年的植被NPP损失评估,结果表明:1截止2013年,赣州市稀土开采造成的植被直接破坏面积为31.74km~2,间接受损面积为44.48km~2。随着距矿区的距离增大,植被间接受损面积呈指数下降(R~2=0.96,P0.01);2 2013年,赣州市稀土开采导致的植被NPP总损失量为3.87×10~(10) gC,其中直接损失占总损失的77.81%,间接损失占总损失的22.89%,说明在稀土矿开采导致的生态破坏评估中,间接受损量不容忽视。文章构建的植被NPP损失评估方法可为其他类似矿区的生态破坏评估提供解决思路,研究结果可为稀土矿区生态评估、稀土定价以及矿区的生态环境管理提供依据。     

数据融合技术在提高NPP估算精度中的应用

针对现有遥感数据不能同时满足在时间和空间上精确监测植被动态变化的问题,提出利用时空适应性反射率融合模型（STARFM）的方法对MODIS-NDVI和TM-NDVI影像数据进行融合处理获得30 m较高时空分辨率的融合NDVI影像,进而将多种尺度的MODIS-NDVI和融合NDVI数据分别输入到CASA模型,对锡林浩特地区进行植被净初级生产力（NPP）的多尺度估算。将不同尺度的NPP估算结果与地上生物量地面实测值进行验证比较,结果表明：随着输入NDVI空间分辨率的提高,NPP估算值与实测地上生物量之间的相关性也逐渐增大,[r]最大值达到了0.915。此外以融合NDVI影像作为输入数据之一的NPP估算值与实测地上生物量的相关性均比未融合NDVI的相关性高,说明融合NDVI估算NPP的效果较未融合NDVI好,并且以融合NDVI影像作为模型输入数据可提高NPP估算精度。     

基于多时相遥感数据的徐州市植被净初级生产力估算及相关性分析

以遥感数据和气象数据为主要数据源,应用改进的光能利用率模型估算徐州市2006、2008和2010年3年间6月份的植被净初级生产力(Net Primary Productivity,NPP),研究了该区域6月份NPP的时空变化及其与气象因子的相关性。结果表明：时间上,受气候和环境等因素综合变化的影响,研究区域6月份NPP呈逐年下降趋势;空间上,NPP的分布表现为在林地、草地和农田相对集中的区域偏高,且不同植被类型6月份的NPP大小关系在不同年份可能不同,其中在2006和2008年为农田>草地>林地,而2010年为农田>林地>草地。通过分析与气象因子的相关性和偏相关性,限制NPP的主要气象因子不是固定不变的,其中2006和2008年,限制NPP的主要气象因子为太阳辐射,而2010年为降雨量和温度。不同植被类型下NPP与气象因子相关性和偏相关性差异反映了不同类型植被生长对光、热、水条件要求的差异。
     

植被净初级生产力的遥感模型在武汉地区的应用

卫星遥感成为观测和估算地面植被净初级生产力的一种极其有利的工具,其原因不仅仅在于它将人们从繁重的地面实验中解放出来,同时也因为它能够实现对大范围区域的植被净初级生产力进行实时监测。为了更加充分有效地利用卫星数据获取植被信息,我们在模式分解方法的基础上,建立了一种新的植被指数(VIPD)。根据地面实验建立的相应光合成曲线估算模型,估算武汉地区地域性植被净初级生产力（NPP）,研究该地区植被生长年间变化状况。并通过和不同方法估算结果及地面调查结果进行比较,检验模型对该地区的适用情况,为该地区植被NPP长期监测研究提供可靠依据。     

基于Landsat TM/ETM+影像的武汉市冬季NPP估算及其时空变化分析

植被净第一生产力(NPP)作为反映植被固碳能力的重要指标,在全球CO2浓度上升的背景下,成为研究全球及区域生态系统对气候环境变化响应的热点之一。基于Landsat TM/ETM+遥感影像数据,采用改进的CASA模型,估算得到武汉市2001~2010年空间分辨率为30m的冬季NPP,并对其进行时空变化分析。研究结果表明:武汉市过去10a冬季平均NPP为8.55gC/m2·m。2001~2010年武汉市冬季NPP整体呈现波动上升的趋势,各区域具有不同的增长速率,其中以江夏区最快,而各植被类型中灌木林具有最快的增长速率和最高的平均NPP。武汉市冬季NPP均呈现从三环区域向四周增大的空间分布特征,过去10a武汉市冬季NPP最高的区域由黄陂区转移到了江夏区。     

植被净初级生产力的遥感模型在武汉地区的应用

卫星遥感成为观测和估算地面植被净初级生产力的一种极其有利的工具|其原因不仅仅在于它将人们从繁重的地面实验中解放出来|同时也因为它能够实现对大范围区域的植被净初级生产力进行实时监测。为了更加充分有效地利用卫星数据获取植被信息|我们在模式分解方法的基础上|建立了一种新的植被指数(VIPD)。根据地面实验建立的相应光合成曲线估算模型|估算武汉地区地域性植被净初级生产力（NPP）研究该地区植被生长年间变化状况。并通过和不同方法估算结果及地面调查结果进行比较|检验模型对该地区的适用情况|为该地区植被NPP长期监测研究提供可靠依据。     

东北地区区域植被净初级生产力估算

鉴于我国东北地区是全球变化最敏感的区域之一,以东北地区植被净初级生产力(Net Primary Productivity,NPP)为指标,监测该地区的生态环境状态变化。在东北三省选取了东、中、西3个研究区,基于CASA(Carnegie-Ames-Stanford Approach)模型,利用2007年和2010年两个时相的美国陆地卫星多光谱扫描仪(Landsat TM)遥感数据和气象观测数据实现了NPP估算,并对其空间分布进行了分析。结果表明:3个研究区在2个时相NPP的数值有一定的波动,农田、草地面积较大的中、西部2个研究区,NPP受到季节条件以及人为因素的影响较显著;森林面积较大的东部研究区,NPP主要受季节、水热条件影响;相对于中、西部研究区,东部研究区的NPP最大。该文以东北地区3个研究区为例,为利用多时相遥感与模型相结合的方式,开展区域NPP估算和研究碳储量动态变化提供了科学依据和方法借鉴。     

芦苇湿地植被NPP估算方法探索与应用

作为表征湿地生态系统健康的重要指标,湿地植被净初级生产力（NPP）的精准估算对于理解全球变化以及区域碳循环具有重要的支撑作用。基于Landsat 8 OLI遥感影像和大量实测数据,以光能利用率模型基本结构式为基础,构建和评价了芦苇湿地植被NPP估算的不同遥感驱动模型,并以东北3个典型芦苇湿地保护区为例进行了验证与应用。结果表明：以NPP = f（f（VI₁）） × f（VI₂） 结构与NDVI和MSAVI两个植被指数作为自变量的模型最优,模型精度为89.2%,明显高于NPP低空间分辨率产品和CASA模型的模拟结果。根据该模型估算的东北地区七星河、查干湖和双台河口芦苇湿地的NPP均值分别为3 001、3 050和3 621 gC·m^–2·a^–1。受水文条件和人类活动影响,各湿地样区间NPP具有典型的空间分布异质性。实验提出的框架模型可为小尺度上湿地生态系统健康评估或湿地生态系统恢复效果评价等指标获取提供方法借鉴。     

Assessing and predicting biodiversity in a floodplain ecosystem: Assimilation of net primary production derived from imaging spectrometer data into a dynamic vegetation model

New concepts for river management in northwestern Europe are being developed which aim at both flood protection and nature conservation. As a result, methods are required that assess the effect of management activities on the biodiversity of floodplain ecosystems. In this paper, we show that dynamic vegetation models (DVMs) in combination with regional scale derived remote sensing products can be adopted to assess both current and future ecosystem development and biodiversity status of a complex floodplain ecosystem in the Netherlands. The dynamic vegetation model SMART2-SUMO2 in combination with the nature valuation model NTM3 predicting potential floristic diversity was applied to simulate the biodiversity status of the Millingerwaard floodplain along the river Rhine in the Netherlands. Estimates of net primary production (NPP) derived from airborne HyMap imaging spectrometer data were used for validation of the simulated NPP by the DVM at the time of data acquisition in 2004. Imaging spectrometer derived NPP was in good agreement with the SMART2-SUMO2 modeled results. The NTM3 derived nature valuation in 2004 expressed as plant diversity for the floodplain was high and well in agreement with field observations. In a next step, the DVM was re-initialized using imaging spectrometer derived NPP in 2004 and a forecast of plant diversity and biomass development in 2050 was made. A comparison was performed for three pre-defined floodplain management scenarios using a data-assimilation based approach as well as one without. Significant differences in biomass development can be observed between the scenarios. Predicted plant diversity for individual ecosystems in 2050 shows increased variability for forest ecosystems compared to grass ecosystems. This shows that floodplain management should take advantage of spatiotemporal dynamics of the floodplain as a basis for fostering the development of increased biodiversity. The results of this study demonstrate that imaging spectrometer derived products can be used for validation and initialization of DVMs.     

Estimation of net primary productivity by integrating remote sensing data with an ecosystem model

This paper describes a method for integrating leaf area index (LAI) derived from remote sensing data with an ecosystem model for accurate estimation of net primary productivity (NPP). The ecosystem model used in this study was Sim-CYCLE, with which LAI retrieved from the data acquired by MODIS sensor (MODIS-LAI) was integrated. Global annual NPP was estimated as 59.6 Gt C year⁻¹ by MOD-Sim-CYCLE (Sim-CYCLE after integration of MODIS-LAI), whereas it was 62.7 Gt C year⁻¹ in case of Sim-CYCLE for the year 2001. Both models predicted highest NPP around the equator with another smaller peak occurring around 60°N. These two regions represented the tropical and boreal forests biomes, respectively. The NPP estimated by MOD-Sim-CYCLE exceeded the NPP estimated by Sim-CYCLE in these two regions. Other than the tropical and boreal forests biomes, NPP values estimated by the MOD-Sim-CYCLE were typically lower than Sim-CYCLE across the latitudes. Validations of results in Australia and USA showed that MOD-Sim-CYCLE estimated NPP more accurately than Sim-CYCLE. Our results demonstrate the utility of combining satellite-observation with an ecosystem process model to achieve improved accuracy in estimates and monitoring global net primary productivity.     

Topographic effects on estimating net primary productivity of green coniferous forest in complex terrain using Landsat data: a case study of Yoshino Mountain,Japan

In mountainous areas, irregular terrain significantly affects spatial variations of climatic variables and the reflectance of pixels in remote sensing imagery. Consequently, the variations may affect the estimation of net primary productivity (NPP). The light-use efficiency (LUE) model is used to analyse topographic influence on NPP by evaluating topographic effects on primary input data to the model, including both Normalized Difference Vegetation Index (NDVI) and climatic data. A typical green coniferous forest in Yoshino Mountain, Japan, was employed as the study area. The results show that the average NPP is significantly increased after removing topographic influences on NDVI; the average NPP has a relatively minimal change when only topographic effects on climatic data are considered. When both topographic effects on NDVI and climatic data are considered, the average NPP is 1.80 kg m^?2 yr^?1, which is very similar to the ground measurement result of 1.74 kg m^?2 yr^?1.     

Analysis of the Forest Dynamic Changes in Southeast Asia based on Remote Sensing and Meteorological Data

To further understand the relationship between dynamic changes of tropical forest and human activities as well as climate changes，we use methods of time series analysis and correlation analysis to study the temporal and spatial changes of forest net primary productivity(NPP) and their correlation with tree coverage(VCF)，temperature，precipitation and photosynthetically active radiation(PAR) in 11 countries in Southeast Asia from 2001 to 2013 based on MODIS remote sensing data and ERA-Interim reanalysis of meteorological data.The main conclusions are as follows：①the NPP in Southeast Asia is increasing from the equator to the north and the south;②NPP in most areas of the study area show a decreasing trend，and regions where have a more dramatic change of NPP usually have a higher coefficient of variation which showsa more unstable carbon sequestration capacity of forest ecosystem;③the tree cover in study areais generally high(60%~80%) and most of thearea have an increasing trend，in addition，the partial correlation coefficient between VCF and NPP was higher than correlation coefficient，indicating that human activities have a greater impacton forest NPP;④the temperature，precipitation and PAR in study area are relatively high，and as for the correlation between NPP and meteorological factors，countries with tropical forest climate have a better correlation than countries with tropical monsoon climate，whose NPP is generally negatively correlated with the temperature and positively correlated with precipitation and PAR.     

Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems

One of the most frequently applied methods for integrating controls on primary production through satellite data is the light use efficiency (LUE) approach, which links vegetation gross or net primary productivity (GPP or NPP) to remotely sensed estimates of absorbed photosynthetically active radiation (APAR). Eddy covariance towers provide continuous measurements of carbon flux, presenting an opportunity for evaluation of satellite estimates of GPP. Here we investigate relationships between eddy covariance estimated GPP, environmental variables derived from flux towers, Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GPP across African savanna ecosystems. MODIS GPP was found to underestimate GPP at the majority of sites, particularly at sites in the Sahel. EVI was found to correlate well with estimated GPP on a site-by-site basis. Combining EVI with tower-measured PAR and evaporative fraction (EF, a measure of water sufficiency) improved the direct relationship between GPP and EVI at the majority of the sites. The slope of this relationship was strongly related to site peak leaf area index (LAI). These results are promising for the extension of GPP through the use of remote sensing data to a regional or even continental scale.     

近10年榆林市植被净初级生产力时空分异特征

针对榆林市生态环境脆弱,植被净初级生产力分布及变化对其影响较大的问题,利用2000年、2005年和2010年的NPP数据,采用马尔科夫转移矩阵及GIS空间分析功能,揭示榆林市NPP的时空变化特征。结果表明：（1）榆林市NPP空间分布由西北向东南逐渐递增。（2）2000年～2010年NPP呈显著上升趋势：2000年～2005年NPP升高区域95.03%,增幅大于300gC/（m2·a）的占47.22%,主要分布在榆林市最北端和南部地区;2005年～2010年NPP升高区域面积93.96%,增幅大于300gC/（m2·a）的占54.10%,集中在东南大部分地区。（3）榆林市NPP增加的主要因素是1999年以来该区采取的多项植被恢复与重建工程等措施,植被覆盖度增加。     

Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS‐1

Synthetic aperture radar images, combined with field measurements, were used to estimate net primary productivity (NPP) of aquatic vegetation in the lower Amazon. Input data for a NPP model are (i) the total biomass of aquatic vegetation, determined by radar imagery and field measurements and (ii) the area occupied by aquatic vegetation, determined from radar imagery. After correction for monthly biomass losses, the NPP of one growth cycle of aquatic vegetation was calculated in the image domain. The total net primary productivity of Hymenachne amplexicaules, the dominant aquatic vegetation in the area, was on average 19×10¹¹ g C yr^?1 for the entire area. Spatially, lower values of produced organic carbon (<900 g C m^?2 yr^?1) are confined to regions where the plants developed only in the beginning of the rising phase of the hydrological cycle. In general, values are higher (>5000 g C m^?2 yr^?1) in areas closer to the Amazon River where the availability and influence of nutrient‐rich water is greater.     

Scaling net primary production to a MODIS footprint in support of Earth observing system product validation

Release of an annual global terrestrial net primary production (NPP) data layer has begun in association with the Moderate Imaging Spectroradiometer (MODIS) sensor, a component of the NASA Earth Observing System. The task of validating this product will be complicated by the mismatch in scale between ground-based NPP measurements and the coarse resolution (1?km) of the NPP product. In this paper we describe three relevant approaches to scaling NPP from the plot level to the approximately 25-km² footprint of the sensor, and discuss issues associated with operational comparisons to the MODIS NPP product. All approaches revealed considerable spatial heterogeneity in NPP at scales less than the resolution of the MODIS NPP product. The effort to characterize uncertainty in the validation data layers indicated the importance of treating the combination of classification error, sampling error, and measurement error. Generally, the optimal procedure for scaling NPP to a MODIS footprint will depend on local vegetation type, the scale of spatial heterogeneity, and available resources. In all approaches, high resolution remote sensing can play a critical role in characterizing land cover and relevant biophysical variables.