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1.
Christopher Potter Steven Klooster Robert Crabtree Shengli Huang Peggy Gross Vanessa Genovese 《Carbon balance and management》2011,6(1):1-16
Background
A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO2 is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.Results
Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr-1 (1 Tg = 1012 g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO2 on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO2 to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr-1 for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.Conclusions
Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape. 相似文献2.
气候变化对中国陆地植被净第一性生产力影响的初步研究 总被引：55，自引：0，他引：55
在假定气温平均升高1．5℃，降水平均增加5％，地表植被分布未发生变化的情况下，利用改进的光能利用率模型和1992年12个月的1km AVHRR-NDV1资料及地面气候资料对气候变化对中国陆地植被净第一性生产力（NPP）的影响进行了初步分析。结果表明，除了华南沿海NPP略在下降外，中国大部分地区NPP将有所增加，平均增加6．2％。其中从相对增加量来说，青藏高原NPP的增加幅度最大；而从绝对增加量来说，森林植被的增加量最大，荒漠地区NPP的增加量最小。 相似文献
3.
针对现有植被净初级生产力研究对城市圈、城市带尺度缺乏关注的问题,基于MODIS遥感数据、地面气象资料等,利用改进的CASA模型,结合回归分析、相关分析等方法探究了2000—2013年皖江城市带植被NPP的时空变化及其对气候因子的响应,为区域生态环境质量评价提供参考。结果表明:近14年来,皖江城市带植被NPP总体呈增加趋势;不同土地利用类型NPP差异显著,林地草地耕地建设用地未利用土地水体;年NPP均值呈现由南部向西北部减少的空间分布特征;植被NPP年际变化率较小,介于±10gC·m-2·a-1范围内;温度是影响研究区植被NPP时空变化的主要气候因子。 相似文献
4.
Since the estimate of moisture stress coefficients (MSC) in the current Carnegie-Ames-Stanford-Approach (CASA) model still requires considerable inputs from ground meteorological data and many soil parameters, here we present a modified CASA model by introducing the land-surface water index (LSWI) and scaled precipitation to model the vegetation net primary productivity (NPP) in the arid and semiarid climate of the Mongolian Plateau. The field-observed NPP data and a previously proposed model (the Yu-CASA model) were used to evaluate the performance of our LSWI-based CASA model. The results show that the NPP predicted by both the LSWI-based CASA model and the Yu-CASA model showed good agreement with the observed NPP in the grassland ecosystems in the study area, with coefficients of determination of 0.717 and 0.714, respectively. The LSWI-based CASA model also performed comparably with the Yu-CASA model at both biome and per-pixel scales when keeping other inputs unchanged, with a difference of approximately 16 g C in the growing-season total NPP and an average value of 2.3 g C bias for each month. This indicates that, unlike an earlier method that estimated MSC based entirely on climatic variables or a soil moisture model, the method proposed here simplifies the model structure, reduces the need for ground measurements, and can provide results comparable with those from earlier models. The LSWI-based CASA model is potentially an alternative method for modelling NPP for a wide range of vegetation types in the Mongolian Plateau. 相似文献
5.
Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities. 相似文献
6.
7.
Background
The amount of carbon dioxide in the atmosphere steadily increases as a consequence of anthropogenic emissions but with large interannual variability caused by the terrestrial biosphere. These variations in the CO2 growth rate are caused by large-scale climate anomalies but the relative contributions of vegetation growth and soil decomposition is uncertain. We use a biogeochemical model of the terrestrial biosphere to differentiate the effects of temperature and precipitation on net primary production (NPP) and heterotrophic respiration (Rh) during the two largest anomalies in atmospheric CO2 increase during the last 25 years. One of these, the smallest atmospheric year-to-year increase (largest land carbon uptake) in that period, was caused by global cooling in 1992/93 after the Pinatubo volcanic eruption. The other, the largest atmospheric increase on record (largest land carbon release), was caused by the strong El Niño event of 1997/98.Results
We find that the LPJ model correctly simulates the magnitude of terrestrial modulation of atmospheric carbon anomalies for these two extreme disturbances. The response of soil respiration to changes in temperature and precipitation explains most of the modelled anomalous CO2 flux.Conclusion
Observed and modelled NEE anomalies are in good agreement, therefore we suggest that the temporal variability of heterotrophic respiration produced by our model is reasonably realistic. We therefore conclude that during the last 25 years the two largest disturbances of the global carbon cycle were strongly controlled by soil processes rather then the response of vegetation to these large-scale climatic events. 相似文献8.
2009年武汉市植被净初级生产力估算 总被引：1，自引：0，他引：1
利用CASA模型,结合实测的光合有效辐射（PAR）数据、MODIS归一化植被指数（NDVI）和Land Cover数据、气象数据等资料,估算了2009年武汉市的植被净初级生产力（NPP）。结果显示,武汉市的植被平均单位面积年NPP产量达到464.19gC·m^-2·a^-1。6、7、8三个月NPP积累值最高,占全年的56.8%;12、1、2三个月NPP值最低,仅占5.6%。黄陂区由于林地较广,NPP值较大,在1 000gC·m^-2·a^-1以上;而城市周边由于植被覆盖面积较小,NPP值较低,在400gC·m^-2·a^-1以下。 相似文献
9.
Global warming associated with climate change is one of the greatest challenges of today’s world. Increasing emissions of the greenhouse gas CO2 are considered as a major contributing factor to global warming. One regulating factor of CO2 exchange between atmosphere and land surface is vegetation. Measurements of land cover changes in combination with modelling the Gross Primary Productivity (GPP) can contribute to determine important sources and sinks of CO2.The aim of this study is to accurately model the GPP for a region in West Africa with a spatial resolution of 250 m, and the differentiation of GPP based on woody and herbaceous vegetation. For this purpose, the Regional Biomass Model (RBM) was applied, which is based on a Light Use Efficiency (LUE) approach. The focus was on the spatial enhancement of the RBM from the original 1000–250 m spatial resolution (RBM+). The adaptation to the 250 m scale included the modification of two main input parameters: (1) the fraction of absorbed Photosynthetically Active Radiation (FPAR) based on the 1000 m MODIS MOD15A2 FPAR product which was downscaled to 250 m using MODIS NDVI time series; (2) the fractional cover of woody and herbaceous vegetation, which was improved by using a multi-scale approach. For validation and regional adjustments of GPP and the input parameters, in situ data from a climate station and eddy covariance measurements were integrated.The results of this approach show that the input parameters could be improved significantly: downscaling considerably reduces data gaps of the original FPAR product and the improved dataset differed less than 5.0% from the original data for cloud free regions. The RMSE of the fractional vegetation cover varied between 5.1 and 12.7%. Modelled GPP showed a slight overestimation in comparison to eddy covariance measurements. The in situ data was exceeded by 8.8% for 2005 and by 2.0% for 2006. The model results were converted to NPP and also agreed well with previous NPP measurements reported from different studies. Altogether a high accuracy and suitability of the regionally adjusted and downscaled model RBM+ can be concluded. The differentiation between vegetation growth forms allows a separation of long-term and short-term carbon storage based on woody and herbaceous vegetation, respectively. 相似文献
10.
2000—2010年神东矿区植被NPP的变化特征及影响因素分析 总被引：1，自引：0，他引：1
基于EOS/MODIS NPP数据集,对神东矿区植被净初级生产力(NPP)变化的时空特征及主要影响因素进行分析。研究表明,2000—2010年,神东矿区植被年NPP主要介于(98~160)g C/(m2·a)区间,11 a平均值为139.80 g C/(m2·a),低于同期全国植被年平均NPP值360.97 g C/(m2·a)约61.3%,低于同期矿区10 km缓冲区年平均NPP值142.49 g C/(m2·a)约2%,同时也低于同纬度对比区域年均NPP值161.97 g C/(m2·a)约13.7%。11 a NPP值一元线性回归分析表明,3个区域2000—2010年平均NPP变化趋势及斜率特征大致相符,相关系数均达到0.94以上;植被NPP与同期气候因子的相关性分析表明,神东矿区植被年NPP与年降水量相关系数较大,为0.716。 相似文献
11.
Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000–2013) of medium spatial resolution (250 m) MODIS-EVI time-series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000–2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012. Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models. 相似文献
12.
Christopher A Williams Niall P Hanan Jason C Neff Robert J Scholes Joseph A Berry Scott A Denning David F Baker 《Carbon balance and management》2007,2(1):3-13
The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications.
However, the sparse observation network in and around the African continent means that Africa is one of the weakest links
in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward
and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions
and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it
through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing
regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially
drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major
source of interannual variability in global atmospheric CO2. Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use
change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability
are likely to undergo substantial increases through the 21st century. 相似文献
13.
以山地植被生态系统NPP遥感估测与分析为研究内容,以地学和生态学知识为基础,利用RS,GIS技术手段,综合遥感数据、DEM数据、统计数据、定位点的实测数据等多源信息,构建了NPP遥感地形模型,计算出夏尔希里自然保护区7月份的植被NPP,最终完成了夏尔希里自然保护区植被生产力的定量化。 相似文献
14.
Christopher Potter Peggy Gross Vanessa Genovese Marie-Louise Smith 《Carbon balance and management》2007,2(1):1-11
Background
A simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate net primary productivity (NPP) of forest stands at the Bartlett Experiment Forest (BEF) in the White Mountains of New Hampshire.Results
Net primary production (NPP) predicted from the NASA-CASA model using 30-meter resolution Landsat inputs showed variations related to both vegetation cover type and elevational effects on mean air temperatures. Overall, the highest predicted NPP from the NASA-CASA model was for deciduous forest cover at low to mid-elevation locations over the landscape. Comparison of the model-predicted annual NPP to the plot-estimated values showed a significant correlation of R2 = 0.5. Stepwise addition of 30-meter resolution elevation data values explained no more than 20% of the residual variation in measured NPP patterns at BEF. Both the Landsat 7 and the 250-meter resolution MODIS derived mean annual NPP predictions for the BEF plot locations were within ± 2.5% of the mean of plot estimates for annual NPP.Conclusion
Although MODIS imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations as well as Landsat, the MODIS satellite data as inputs to the NASA-CASA model does accurately predict the average annual productivity of a site like the BEF. 相似文献15.
利用高分一号卫星数据建立小尺度CASA－NPP估算模型,反演及监测草原露天煤矿区草地NPP空间分布格局,以及煤矿区内部复垦植被的NPP与表层土壤之间的关系。利用归一化植被指数、归一化水体指数等获取CASA模型有效光辐射参数、水分胁迫系数等,从而简化并改进小尺度CASA模型,遥感反演数据与实测样点数据拟合度达到0．94。结果表明,该模型能够高精度地完成小区域NPP估测。从利用CASA模型反演得到的NPP空间格局可以看出,这一地区的植被退化以采矿、放牧等人类活动为主导,原地貌区域围栏的典型草甸草原NPP在300～400 gC／m2· yr 之间,高于放牧区域 NPP,矿区内 NPP 空间变化显著,矿区内部 NPP 低于100 gC／m2· yr,明显低于周边原地貌地区。对于矿区复垦排土场区域,复垦年限和表土的养分是决定植被生长和NPP的主要因素。研究得出国产高分卫星数据可以高精度地完成小尺度NPP的估测,从而为其在草原地区以及矿山环境中监测提供了研究基础。 相似文献
16.
Net primary productivity (NPP) is an important indicator of ecosystem health and its estimation and understanding of factors determining its spatial and temporal variations is critical. It is important to note that biophysical factors and human induced factors are interlinked in determining NPP patterns. Nevertheless, it is difficult to consider some aspects of human management systems in relation to NPP variations on a global scale analysis than on a local scale analysis. In this study, we tested the hypothesis that, at the local scale, particularly in highly intensive systems, land-use/land tenure types influence NPP variations by altering the biophysical conditions of the land. We estimated NPP between 2000 and 2009 using MODIS data and used ANOVA to test the abovementioned hypotheses. Results showed that NPP significantly (p < 0.05) varied by land-use/land tenure type. We also found that biophysical factors remained essential in explaining NPP variations even at local scales. These results exhibit the intricacies that exist between the biophysical and human-induced factors in explaining NPP variations within ecological landscapes. 相似文献
17.
R. P. Singh S. Rovshan S. K. Goroshi S. Panigrahy J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):345-353
The monitoring of terrestrial carbon dynamics is important in studies related with global climate change. This paper presents
results of the inter-annual variability of Net Primary Productivity (NPP) from 1981 to 2000 derived using observations from
NOAA-AVHRR data using Global Production Efficiency Model (GloPEM). The GloPEM model is based on physiological principles and
uses the production efficiency concept, in which the canopy absorption of photosynthetically active radiation (APAR) is used
with a conversion “efficiency” to estimate Gross Primary Production (GPP). NPP derived from GloPEM model over India showed
maximum NPP about 3,000 gCm−2year−1 in west Bengal and lowest up to 500 gCm−2year−1 in Rajasthan. The India averaged NPP varied from 1,084.7 gCm−2year−1 to 1,390.8 gCm−2year−1 in the corresponding years of 1983 and 1998 respectively. The regression analysis of the 20 year NPP variability showed significant
increase in NPP over India (r = 0.7, F = 17.53, p < 0.001). The mean rate of increase was observed as 10.43 gCm−2year−1. Carbon fixation ability of terrestrial ecosystem of India is increasing with rate of 34.3 TgC annually (t = 4.18, p < 0.001). The estimated net carbon fixation over Indian landmass ranged from 3.56 PgC (in 1983) to 4.57 PgC (in 1998). Grid
level temporal correlation analysis showed that agricultural regions are the source of increase in terrestrial NPP of India.
Parts of forest regions (Himalayan in Nepal, north east India) are relatively less influenced over the study period and showed
lower or negative correlation (trend). Finding of the study would provide valuable input in understanding the global change
associated with vegetation activities as a sink for atmospheric carbon dioxide. 相似文献
18.
Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVITHM), especially for agriculture and forest ecosystems. To apply NDVITHM, knowledge of the proportion of vegetation cover (PV) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the PV on the estimation of LSE and LST when using NDVITHM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The PV was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate PV and to compute LSE and LST. The results showed that the prediction accuracy of PV improved using an artificial neural network (R2CV = 0.64, RMSECV = 0.05) over classic vegetation indices (R2CV = 0.42, RMSECV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated PV affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST. 相似文献
19.
Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon and source-sink nature of the ecosystem. This study was carried out in a forest plantation area and aimed to (i) estimate the spatio-temporal patterns of NPP during 2009 and 2010 using Carnegie-Ames-Stanford Approach [CASA] model and (ii) study the effects of climate variables on the NPP using generalized linear modelling (GLM) approach. The total annual NPP varied from 157.21 to 1030.89 gC m?2 yr?1 for the year 2009 and from 154.36 to 1124.85 g C m?2 yr?1 for the year 2010. The annual NPP was assessed across four major plantation types, where maximum NPP gain (106 and 139 g C m?2 yr?1 ) in October was noticed in teak (Tectona grandis) and minimum (77 and 109 g C m?2 yr?1 ) in eucalyptus (Eucalyptus hybrid) during 2009 and 2010.The validation, using field-estimated NPP, showed under-estimation of modelled NPP, with maximum MAPE of 34% for eucalyptus and minimum of 13% for teak. The dominant influence of precipitation on the NPP was revealed by GLM explaining more than 20% of variation. CASA model efficiently estimated the annual NPP of plantations. The accuracy could be improved further with inclusion of higher resolution data. 相似文献
20.
以福建省为研究区,以中等分辨率MODIS NDVI遥感数据、气象数据及其他辅助数据为数据源,基于植被净初级生产力(net primary productivity,NPP)光能利用率估算模型——CASA,定量研究了该区域历史序列(2001—2012年)NPP时空变化格局,探索其主要影响因素。结果表明:2001—2012年该区域NPP总体呈现下降趋势,2003年和2005年为历年变化下降率最大的两年;该区域NPP时空分布特征明显,在空间上表现为由南向北递减的空间分布格局,且沿海经济发达区域NPP普遍较低;时间上表现为春秋两季具有相同的空间分布,夏季具有最高的NPP,占全年NPP的56%,冬季平均NPP在120gC·m~(-2)·a~(-1)以下;降水和温度与NPP的线性相关性较小,且线性相关性随空间位置的不同而有所差异;福建省NPP对气候因子的响应随空间位置的变化而变化,在不同的区域,其主要的胁迫因子不同,NPP总体受到辐射量的驱动因素要比其他胁迫因子强。 相似文献