Carnegie-Ames-Stanford Approach Model Research Articles

An Improved Gross Primary Production Model Considering Atmospheric CO2 Fertilization: The Qinghai–Tibet Plateau as a Case Study

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.

Estimation of Corn Net Primary Productivity and Carbon Sequestration Based on the CASA Model: A Case Study of the Fen River Basin

The utilization of remote sensing technology to assess changes in crop net primary productivity (NPP), biomass, and carbon sequestration within the Fen River Basin, a crucial agricultural region in China, is important for achieving agricultural modernization, enhancing ecological environment quality, and obtaining carbon neutrality objectives. This study employed satellite remote sensing and the Carnegie–Ames–Stanford approach (CASA) model as research methods to investigate the temporal and spatial distribution characteristics of corn NPP in the Fen River Basin. Correlation analysis was conducted to examine the response of corn NPP to various environmental factors in the region, while aboveground biomass and carbon sequestration of corn were estimated using a biomass inversion model driven by NPP and principles of photosynthesis in green plants. The findings revealed that, from a temporal perspective, corn NPP in the Fen River Basin exhibited a unimodal variation pattern, with an average value of 368.65 gC/m2. Spatially, the corn NPP displayed a discernible differentiation pattern, with the highest values primarily observed in the middle reaches of the Fen River Basin. Throughout the spatial and temporal variations in corn NPP during 2011–2020, the carbon sequestration capacity of corn exhibited an upward trend, particularly since 2017. The corn NPP displayed a positive correlation with temperature and precipitation. The response to solar radiation was mildly negative and a mildly positive correlation. In 2020, the aboveground biomass and carbon sequestration of corn followed a normal distribution, with the highest values concentrated in the northwestern part of the lower Fen River.

Net Primary Production of Ecoregions Across North America in Response to Drought and Wildfires From 2015 to 2022

AbstractEcosystem models are valuable tools to make climate‐related assessments of change when ground‐based measurements of water and carbon fluxes are not adequate to realistically capture regional variability. The Carnegie‐Ames‐Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems. CASA model predictions from 2015 to 2022 revealed several notable high and low periods in growing season NPP totals in certain biomes. Both Temperate Broadleaf and Boreal Forest production shifted from relatively high average NPP values in 2015 through 2019 to lower levels in 2020, typically representing a loss of 10%–14% of growing season NPP flux. This rapid decline in growing season NPP from 2019 to 2020–2021 was also estimated for the Temperate Grasslands and Savanna, Temperate Conifer Forest, and Tundra biomes. In contrast to the climate patterns in the temperate biomes that developed into severe widespread drought in 2020 and 2021 due to low precipitation totals and extreme hot temperatures, growing season NPP in the Tundra biome was depressed in these same years by colder temperature induced drought conditions at the high latitudes of North America. Drought severity classes were closely associated with different levels of decline in NPP in most biomes. Trends in NPP in areas of the largest wildfires in North America that burned between 2012 and 2021 were examined to assess recovery of vegetation and the resiliency of ecosystems during extreme drought periods.

TVDI-based water stress coefficient to estimate net primary productivity in soybean areas

Net Primary Productivity (NPP) is a major parameter to assess carbon (C) increments by crops. Many models based on Light Use Efficiency (LUE) have been developed to estimate NPP in different regions. LUE in different ecosystems is reduced by environmental stressors, such as those caused by water restrictions. However, few studies have used remote sensing data to estimate water stress coefficients. Thus, our goal was to evaluate the performance of the Carnegie-Ames-Stanford-Approach (CASA) model using Temperature-Vegetation Dryness Index (TVDI) as a water stress coefficient to quantify NPP dynamics in agricultural ecosystems in northwestern Rio Grande do Sul state, in Brazil. Weather data from the ERA5 and surface weather stations were used to estimate NPP. Surface temperature (LST), Normalized Difference Vegetation Index (NDVI), estimated (EET) and potential (PET) evapotranspiration data were retrieved from Landsat/OLI and Terra/MODIS and used as input in the model. The CASA model was evaluated using ground-based data and then applied to the agricultural region of study. NPP data obtained using the CASA model and remote sensing data were in accordance with the observed data (RMSE less than 30 gC m−2 month−1 and r higher than 0,97), highlighting the model's efficiency in representing the temporal variations of NPP in the experimental area. The high correlation between simulated and observed data indicates that the TVDI is suitable for use as a water stress index. This work may serve as a baseline for future studies, which could explore the use of other indices and enhance NPP estimates.

Multi-source data-driven estimation of urban net primary productivity: A case study of Wuhan

Amidst urban expansion, numerous ecological challenges have surfaced, notably the swift alterations in the net primary productivity (NPP), presenting complexities in discerning their scale and spatial distribution. Leveraging meteorological data, multi-modal remote sensing datasets, field sampling data, and a digital elevation model (DEM), this research amalgamates field assessments, model simulations, and cutting-edge remote sensing techniques to craft a city-scale NPP estimation model for vegetation rooted in the Carnegie-Ames-Stanford Approach (CASA). This improved CASA model facilitated the computation of monthly NPP for Wuhan over 2017–2020. Our analysis divulged NPP spatiotemporal trends and distribution nuances in Wuhan, and assessed meteorological influences on the same. Salient conclusions encompass: (1) Wuhan's annual NPP maximum between 984.40 and 1310.51 gC/(m2 a), averaging from 316.78 to 430.32 gC/(m2 a), and culminating in totals from 170.15 to 231.60 × 1010 gC/a. (2) Monthly NPP trajectories in Wuhan exhibited a unimodal pattern: ascending from January to June, peaking in July–August, and tapering off through September-December, with a spatial distribution characterized by lower NPP values centrally and higher values peripherally. (3) Regionally, temperature markedly influenced NPP over precipitation. This research underpins urban ecological planning, offering empirical insights and strategic guidance for fostering a sustainable coexistence between humans and their environment, and charting future urban development paradigms.

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

AbstractClimate change and the resulting natural vegetation succession can alter vegetation productivity. However, the mechanisms underlying future productivity changes under the two influences remain unclear. Here, we used the comprehensive sequence classification system to simulate changes in global potential natural vegetation under different climate scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5), and combined the Carnegie–Ames–Stanford Approach model with random forest to assess the response of net primary productivity (NPP) to climate change and vegetation succession from 2020 to 2100. Except for SSP126, terrestrial NPP in 2100 decreased by 0.86, 2.39, and 2.54 Pg C·a−1 versus 2020 under SSP2‐4.5, SSP5‐8.5, and SSP3‐7.0, respectively. Forest was the primary contributor to terrestrial NPP changes. The total forest area was projected to increase under all scenarios, with SSP2‐4.5 showing the largest increase (358.57 × 104 km2). However, expanding forest regions exhibited a relatively low mean NPP, while stable regions demonstrated a declining pattern. Consequently, forest NPP increased under SSP1‐2.6 but decreased by 4.03, 3.43, and 0.82 Pg C·a−1 in 2100 versus 2020 under SSP5‐8.5, SSP3‐7.0, and SSP2‐4.5, respectively. In comparison, grassland and desert exerted minor influence on terrestrial NPP changes, their total NPP decreased only under the SSP1‐2.6 scenario. The grassland area decreased, but the mean NPP increased, whereas the desert area expanded, resulting in consistent changes in both total and mean NPP. Our results analyzed the effects of climate change and vegetation distribution under its influence on the change of NPP, which can deepen our understanding of their relationship.

How to Simulate Carbon Sequestration Potential of Forest Vegetation? A Forest Carbon Sequestration Model across a Typical Mountain City in China

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation in various provinces and districts are helpful to the regional and global Carbon cycle. How to build an effective carbon sequestration potential model and reveal the spatiotemporal evolution trend and driving factors of carbon sequestration potential is an urgent challenge to be solved in carbon cycle simulation and prediction research. This study characterized the carbon sequestration status of forest vegetation using the modified CASA (Carnegie-Ames Stanford Approach) model and estimated the carbon sequestration potential from 2010 to 2060 using the FCS (Forest Carbon Sequestration) model combined with forest age and biomass under the four future Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, then proposes natural, social, and economic perspectives. This study found that the average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m2, and the carbon storage by 2060 was 269.94 Tg C. The carbon sequestration rate varied between <0.01 Tg C/a and 0.20 Tg C/a in various districts and counties. Over time, forest growth gradually slowed, and carbon sequestration rates also decreased. Under the four future climate scenarios, the SSP5-8.5 pathway had the highest carbon sequestration rate. Natural factors had the greatest influence on changes in carbon sequestration rate. This result provides data support and scientific reference for the planning and control of forests and the enhancement of carbon sequestration capacity in Chongqing.

Improvement of water yield and net primary productivity ecosystem services in the Loess Plateau of China since the “Grain for Green” project

The Loess Plateau of China is one of the regions with the most serious soil erosion globally. The ambitious “Grain for Green” project was there implemented to restore degraded ecosystems, profoundly changing the water yield and carbon sequestration ecosystem services. To understand the changes of water- and carbon-related ecosystem services (hereafter WCES) in the Loess Plateau, this study combined the CASA (Carnegie-Ames-Stanford Approach) model and InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to thoroughly reveal the changes and intrinsic linkages of water yield and net primary productivity since the “Grain for Green” project in the Loess Plateau. The results show that the WCES of the Loess Plateau were increasing since 2001, dominated by both the climate change and human afforestation. The water yield and net primary productivity showed strong synergistic effects in 80% of the Loess Plateau, mainly resulting from the rapid increase of precipitation in recent years. The increasing atmospheric precipitation contributed to more WCES provisions, which may have offset the trade-off between WCES caused by large-scale afforestation. In view of the synergistic effects of WCES, enhancing water input into the ecosystem may be an effective measure to respond with future WCES degradation in dry years, such as artificial basin-cross water transfer project. Despite the low correlation between teleconnection factors and WCES, the low water yield and reduction of net primary productivity may be associated with a strong El Niño event in 2015. This study assists in a wise decision-making on the future “Grain for Green” and ecological management in the Loess Plateau.

Impacts of Climate Change and Anthropogenic Activities on the Net Primary Productivity of Grassland in the Southeast Tibetan Plateau

Climate change and anthropogenic activities have had a profound effect on the variation in grassland productivity in the Tibetan Plateau in recent decades. Quantifying the impacts of climatic and anthropogenic variables on grassland productivity is a necessary step in making the management policies of a sustainable grassland ecosystem. Net primary productivity (NPP) is an important part of the terrestrial carbon cycle and can be used to assess vegetation growth. Based on the Carnegie–Ames–Stanford Approach model and statistical analysis method, in this study we estimated the variations in grassland potential NPP (PNPP), actual NPP (ANPP) and human-induced NPP (HNPP) in the Northwest Sichuan Plateau (NWSP) of the Southeast Tibetan Plateau from 2001 to 2020. Also, we assessed the contribution of climatic change and anthropogenic activities to grassland ANPP. The results showed that the average values of grassland ANPP, PNPP and HNPP in the whole NWSP increased at the rates of 3.81, 9.14 and 7.18 g C m−2 a−1, respectively. Grassland ANPP increased in 91.7% of the total area. Climate-oriented impacts led grassland ANPP to increase in 82.6% of the area, and temperature increase was the dominant factor. Additionally, anthropogenic activity was the major reason for the grassland ANPP’s decline (5.4% of the total area). Overall, our findings are beneficial for the formulation of practical countermeasures regarding climate change adaption and damaged grassland recovering in the plateau.

NPP and Vegetation Carbon Sink Capacity Estimation of Urban Green Space Using the Optimized CASA Model: A Case Study of Five Chinese Cities

Urban area is a major source of CO2 and other greenhouse gases. Urban green space (UGS) is an essential element to increase carbon sequestration directly and reduce emission indirectly. In this study, the net primary production (NPP) and net ecosystem productivity (NEP) was monitored in order to enhance the carbon sequestration function of UGS and promote urban low-carbon development. Based on the Sentinel-2 L2A satellite images, meteorological data, and vegetation type data in 2019, we used the optimized Carnegie Ames Stanford Approach (CASA) model to estimate the NPP values of UGS types including attached green space, park green space, protective green space, and regional space in Beijing, Guangzhou, Shanghai, Shenyang, and Xi’an. The NEP values were evaluated based on NPP and soil heterotrophic respiration (RH) to quantify the vegetation carbon sink capacity. The accuracy test shows that the estimated NPP values based on the optimized CASA model are effective. The results indicate that the average NPP values (1008.5 gC·m−2·a−1) and vegetation carbon sink capacity (771.49 gC·m−2·a−1) of UGS in Beijing rank first among the cities, which is followed by the values in Guangzhou. The regional green space and park green space in five cities function as carbon sinks with high NPP values and have vegetation carbon sink capacity, whereas the attached green space in Shanghai and Xi’an as well as the protective green space in Guangzhou and Xi’an function as carbon sources. Moreover, the NEP distribution shows obvious spatial aggregation characteristics, that is, the high NEP values of UGS are clustered in mountainous forest areas in the west and north of Beijing, Northeast Guangzhou, and South Xi’an whereas the low NEP values are mostly concentrated in the urban built-up areas under strong influences of human activities. This research provides a new method for NPP and NEP estimation of UGS at the city scale and the scientific basis for the improvement of the vegetation carbon sink capacity of UGS.

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Spatio-temporal patterns and driving mechanisms of rice biomass during the growth period in China since 2000

China is a major rice-producing nation and understanding changes in rice planting areas and growth periods and the mechanisms driving rice biomass is important to ensure effective agricultural strategies and food security. In this study, rice planting areas from 2000 to 2021 in China were extracted using Savitzky–Golay filtering and the absolute distance index. Using MODIS remote sensing image data, we determined the rice growth period based on the inflection points of the enhanced vegetation index. In addition, the cumulative biomass of rice during the growth period since 2000 was determined based on the Carnegie–Ames–Stanford approach model. Finally, the key driving mechanisms and coupling relationships among the driving factors of rice biomass during the growth period were determined using geographically weighted regression and structural equation models. The results showed that the accumulation of net primary productivity (NPP) during the rice growth period in Northeast China, the middle and lower reaches of the Yangtze River, and Northwest China increased significantly (P < 0.01). The accumulation of NPP during the rice growth period in Huang-Huai-Hai, South China, and Southwest China decreased significantly (P < 0.01). In addition, Central Inner Mongolia, Qinghai–Tibet Plateau, and Loess Plateau have less rice planting area and no significant variation pattern in the accumulation of NPP during the rice growth period. In the nine major agricultural regions of China, precipitation, temperature, and evapotranspiration were among the most influential meteorological factors affecting rice biomass. For the inland areas with less precipitation, drainage density was an increasingly important influencing factor. Areas with high topographic relief were more likely to be affected by the slope. In areas with relatively poor soil, the fertilizer application rate influenced rice biomass growth. Our results provide insights into the spatio-temporal patterns of rice biomass and its driving mechanisms, and demonstrate the efficacy of using remote sensing data at a macro scale.

Estimation of aerosol and cloud radiative effects on terrestrial net primary productivity over northeast Qinghai-Tibet plateau

Processes of plant growth are affected by solar radiation reaching the surface, which is regulated by atmospheric components such as aerosols and clouds. However, there are still large uncertainties in evaluating the impact of aerosol radiative effects (ARE) and cloud radiative effects (CRE) on ecosystems. This study estimated the ARE and CRE on vegetation productivity from 2001 to 2020 over the Northeast Qinghai-Tibet Plateau (QTP) based on MODIS satellite data coupled with the validated Radiative Transfer model (RTM) libRadtran and CASA (Carnegie-Ames-Stanford Approach)model. The results showed that both ARE and CRE have restrained vegetation growth in the last 20 years. The net primary productivity (NPP) of vegetation attenuation by ARE was strongest in summer with an increase of temperature and humidity which caused enhanced air particle transformation and hygroscopic growth, followed by spring and autumn, which were extremely affected by dust, and NPP attenuation by ARE was weakest in winter, accounting for 5.2% of the annual NPP. CRE plays an attenuating role on NPP with increasing cloudiness. The ARE reduced NPP by 18.8 g C/m2/yr, and CRE reduced NPP by 406.9 g C/m2/yr. NPP attenuation by CRE was more than ten times that of NPP attenuation by ARE, which indicated that the CRE has a much stronger weakening of NPP than ARE. The area where the trend of NPP at all-sky conditions increases/decreases had a good match with the areas where the trend of NPP by CRE increases/decreases with R2 being 0.89, indicating that CRE plays a dominant role in attenuating NPP. The increase in NPP is mainly attributed to the decrease in NPP attenuation by CRE over the past two decades. The study results could help to comprehensively understand the ARE and CRE on the terrestrial carbon cycle, and to further access and analyze vegetation ecosystem conditions.

Changes in the Spatiotemporal of Net Primary Productivity in the Conventional Lake Chad Basin between 2001 and 2020 Based on CASA Model

Accurate estimation of vegetation Net Primary Productivity (NPP) has important theoretical and practical significance for ecological environment governance, carbon cycle research, and the rational development and utilization of natural resources. In this study, the spatial characteristics, temporal changes, and driving factors of NPP in the Conventional Lake Chad Basin (CLCB) were based on MODIS data by constructing a Carnegie Ames Stanford Approach (CASA) model and using a combination of Residual trends (RESTREND) and correlation analysis. The results showed that from 2001 to 2020, the NPP of the CLCB decreased annually (1.14 g C/m2), mainly because of overgrazing, deforestation, and large-scale irrigation. We conducted a driving factor analysis and found that the main influencing factor of the NPP of the CLCB is high-intensity human activities, including farmland reclamation and animal husbandry. Although the impact of climate change on NPP is not obvious in the short term, climate change may help recover NPP in the long term. The continued reduction in NPP has greatly increased the difficulty of regreening the Sahel; the increase in population density and rapid urbanization have led are major contributing factors to this. Our findings have important implications for the continued implementation of stringent revegetation policies. However, owing to limited data and methods, only the overall change trend of NPP was obtained, and comprehensive follow-up studies are needed.

黄河流域生态功能区植被碳汇估算及其气候影响要素

PDF HTML阅读 XML下载 导出引用 引用提醒 黄河流域生态功能区植被碳汇估算及其气候影响要素 DOI: 10.5846/stxb202203110591 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 水沙科学与水利水电工程国家重点实验室(清华大学)2021年对外开放基金(sklhse-2021-A-06);辽宁省兴辽英才项目资助;国家自然科学基金项目(52079060) Estimation of vegetation carbon sink in the Yellow River Basin ecological function area and analysis of its main meteorological elements Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于改进的CASA模型和土壤呼吸模型,综合遥感数据、气象数据、植被数据等多源数据类型,以黄河流域为研究对象,估算生态功能区植被碳源/汇,探讨其时空变化及主要气候影响要素。结果显示:(1)黄河流域植被净初级生产力(NPP)在2000-2020年整体呈波动上升趋势,多年平均NPP值为317.18 gC m-2 a-1;林地＞耕地＞沼泽＞草地,水体、戈壁、风蚀劣地和裸沙地的NPP总体处于较低值。空间尺度上,黄河流域NPP呈现出南高北低的分布特点,高值区集中在黄土高原农业与草原生态区与汾渭盆地农业生态区交界处,空间差异显著。(2)土壤微生物呼吸量极大值出现2018年,为15.82 gC m-2 a-1,最小值出现在2004年为14.34 gC m-2 a-1;空间上呈现从东南往西北、从东往西依次递减的空间分布格局。(3)黄河流域总体呈碳汇属性(碳汇区域超过60%),年均固碳量约为111.02 MgC/a,年均排碳量为-9.96 MgC/a,年均净碳汇总量约为111.41 MgC/a。(4)黄河流域植被碳汇的形成与降水和太阳辐射的相关性最大,气温的影响面积仅占12.44%;当气温为30℃,降水150 mm,太阳辐射550 MJ/m2时,植被固碳能力效果最强。研究结果为区域全面准确地评估陆地生态系统碳源/汇,实现黄河流域高质量发展,达到碳中和目标提供参考依据。 Abstract:Based on the improved CASA (Carnegie-Ames-Stanford Approach) model and soil respiration model, this paper integrates multi-source data, such as remote sensing data, meteorological data and vegetation data, to estimate vegetation carbon sources/sinks in ecological function areas, and explore their spatio-temporal variations and main climate influencing elements in the Yellow River Basin. The results show that (1) the overall Net Primary Productivity (NPP) of vegetation in the Yellow River Basin presented a fluctuating upward trend from 2000 to 2020, with a multi-year average NPP value of 317.18 gC m-2 a-1. The NPP of various vegetation types showed that: forest land>cropland>swamp>grassland, and water bodies, Gobi, wind eroded poor land and bare sand land were at lower values overall. On the spatial scale, the NPP in the Yellow River Basin shows the distribution characteristics of high in the south and low in the north, and the high value areas are concentrated at the junction of the Loess Plateau agricultural and grassland ecological zone and the Fenwei Basin agroecological zone, with significant spatial differences. (2) The maximum value of soil microbial respiration appeared in 2018 at 15.82 gC m-2 a-1, and the minimum value appeared in 2004 at 14.34 gC m-2 a-1; spatially it showed a spatial distribution pattern decreasing from southeast to northwest and from east to west. (3) The Yellow River Basin has a carbon sink attribute in general (the carbon sink area exceeds 60%) The average annual carbon sequestration is about 111.02 MgC/a, the average annual carbon emission is -9.96 MgC/a, and the total annual net carbon sink is about 111.41 MgC/a. (4) The formation of vegetation carbon sink in the Yellow River Basin is mostly correlated with precipitation and solar radiation, and the influence area of air temperature only accounts for 12.44%; the effect of vegetation carbon sequestration capacity is the strongest when the temperature is 30℃, precipitation is 150 mm, and solar radiation is 550 MJ/m2. The results of the study provide a reference basis for a comprehensive and accurate regional assessment of carbon sources/sinks in terrestrial ecosystems to achieve high-quality development in the Yellow River Basin and meet the carbon neutrality target. 参考文献 相似文献 引证文献

An improved Carnegie-Ames-Stanford Approach model for estimating ecological carbon sequestration in mountain vegetation

The vegetation in mountainous areas is abundant, and its ecological carbon sequestration ability is of great significance to maintain the sustainable and healthy development of the ecological environment. However, when estimating the carbon sequestration of mountain vegetation, the Carnegie-Ames-Stanford Approach (CASA) model assigns a uniform value to the maximum light energy utilization (εmax = 0.389 gC/MJ), ignoring the influence of vegetation types and topographic factors on εmax, resulting in the low accuracy of the CASA model in estimating the carbon sequestration of mountain vegetation. In this paper, the improved CASA model was combined with Landsat 8 Operational Land Imager (OLI) remote sensing image data to improve the estimation accuracy of carbon sequestration of mountain vegetation. The first was the establishment of a linear link between the terrain characteristics (slope and aspect), vegetation types, and εmax in mountainous locations. The second was the improvement of the CASA model’s calculation method for key parameters. The different distributions of the estimation results from the two techniques in 2015 and 2016 are then compared using Landsat 8 data as the data source, and the impact of the terrain factors in the improved CASA model on the estimation results is confirmed. Finally, the improved CASA model and the CASA model are used to estimate the Net Primary Productivity (NPP) of the study area from 2000 to 2020, and the estimated results of the two models are compared with the computation results of the MODIS data NPP product. The findings indicate that the improved CASA model’s estimation results have a higher degree of fit and a better correlation. The improved CASA model aids in precisely understanding the ecological carbon sequestration potential of mountain areas and increases the estimation accuracy of vegetation carbon sequestration in mountainous areas.

Quantitative assessment of the contributions of climate change and human activities on vegetation degradation and restoration in typical ecologically fragile areas of China

Vegetation is a key component of terrestrial ecosystems, and its changes are very sensitive to climate change (CC) and human activities (HA), especially in ecologically fragile areas (EFA). However, the mechanism of relative contribution to vegetation degradation and restoration in EFA under the influence of CC and HA is still unclear. Based on the Carnegie-Ames-Stanford Approach (CASA) model and the Miami model, we estimated three key parameters of vegetation in China’s EFA: actual net primary productivity (ANPP), potential net primary productivity (PNPP), and human activity net primary productivity (HNPP). Using these variables, we quantitatively analyzed the relative contribution of CC and HA to vegetation restoration and degradation from 1982 to 2018 by the residual trend method. The results showed that the area ratio of vegetation restoration in China’s EFA was close to 71.6%, and the total ANPP increased by 174 Tg C, mainly concentrated in the Southwest Karst area and the Loess Plateau, but the Qinghai-Tibet Plateau showing a trend of degradation during 1982 to 2018. The climate background of each region was a key factor that can never be ignored to determine the positive or negative impact of regional CC and HA on vegetation activities. The CC and HA played a positive role in vegetation restoration areas, with a relative contribution of 59.1% and 16.4%, regional warming and humidification caused by the increase of temperature and precipitation was the main factor driving vegetation restoration. In vegetation degradation areas, The HA was the main driving force, especially the relative contribution of the Qinghai-Tibet Plateau, the arid and semi-arid areas and the Loess Plateau, which were as high as 96.8%, 83.3% and 80.2%, respectively. Finally, the annual average temperature (TEMP) and the annual solar radiation (SRAD) were relatively important and sensitive for the CASA model in the input environmental variables. This study provided a way to quantitatively understand the mechanism of climate change and human activities on the dynamic evolution of vegetation in EFA. In the future, the implementation of ecological protection and restoration projects need to fully consider the differences in climate background and strengthen the monitoring of the ecological environment.

Spatiotemporal variation of net primary productivity influenced by climatic variables in the karst area of China

Karst area is one of the most ecologically fragile regions. Monitoring spatial and temporal variation of net primary productivity (NPP) and its response to climate change are essential for vegetation restoration. In this study, we used the CASA (Carnegie-Ames-Stanford Approach) model to estimate the NPP in the karst area of China during 2001 to 2019. We analyzed the response of multi-scale NPP to climate change by using Theil-Sen Median trend analysis, partial correlation analysis and time lag analysis. The results showed that the average NPP was 399.06 gC·m−2, decreased from southeast to northwest, with an annual increase rate of 1.93 gC·m−2 from 2001 to 2019. The broadleaf forest had the highest NPP, and summer NPP accounted for 48.12% of annual NPP. Climate change significantly promotes the increase of NPP in the early growing season. Partial correlation analysis showed that NPP was more correlated with precipitation (0.20) than temperature (0.12), this phenomenon was more pronounced in some arid regions. Where precipitation caused a decrease in NPP during the rainy season. The time lag of NPP to temperature change was shorter than precipitation, indicating that NPP is more sensitive to temperature in most regions. The time lag of NPP to temperature changes was very short in spring. Whereas, even in arid regions, the time lag of NPP to precipitation changes was still long. This study can deepen our knowledge of the response of NPP to climate change in karst ecosystems.

Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland

Abstract. The Carnegie–Ames–Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scales. However, the CASA is still driven by multisource data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data can conveniently provide real-time regional information and may replace ground observation data to drive the CASA model. We attempted to improve the CASA model in this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) RS products, the GlobeLand30 RS product, and the digital elevation model data derived from radar RS. We applied it to simulate the NPP of alpine grasslands in the Qinghai Lake basin, which is located in the northeastern Qinghai–Tibetan Plateau, China. The accuracy of the RS-data-driven CASA, with a mean absolute percent error (MAPE) of 22.14 % and root mean square error (RMSE) of 26.36 g C m−2 per month, was higher than that of the multisource-data-driven CASA, with a MAPE of 44.80 % and RMSE of 57.43 g C m−2 per month. The NPP simulated by the RS-data-driven CASA in July 2020 shows an average value of 108.01 ± 26.31 g C m−2 per month, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapid simulation of grassland NPP to satisfy the requirements of accounting carbon stocks and other applications.

Evaluation of Ecological Carrying Capacity and Identification of Its Influencing Factors Based on Remote Sensing and Geographic Information System: A Case Study of the Yellow River Basin in Shaanxi

Ecological carrying capacity (ECC), which requires simple scientific evaluation methods, is an important evaluation index for assessing the sustainability of ecosystems. We integrate an innovative research method. Geographic information systems (GIS) and remote sensing (RS) were used to evaluate the ECC of the Yellow River Basin in Shaanxi (YRBS) and to identify the underlying factors that influence it. A calculation method that combines RS and GIS data to estimate ECC based on net primary productivity (NPP) was established. The Carnegie–Ames–Stanford approach model was applied to estimate NPP. The NPP of each land type was used as an indicator to determine the yield factors. The ECC of the watershed was calculated with the carrying capacities of each land-use type. The geographical detector model was used to study the influencing factors of ECC, which provides a scientific basis for the formulation of ecological management policies in YRBS. The results show that from 2000 to 2010, it first decreased by 45.46%, and then increased by 37.06% in 2020, an overall decrease of 13.49 × 105 wha in 20 years. Precipitation is the dominant factor that affects ECC, while the impact of human activities on ECC was significantly enhanced during the study period. The developed method based on RS data serves as a reference for ecological evaluation in other similar regions.