Variation In Net Primary Production Research Articles

Spatiotemporal Variations of Vegetation Net Primary Productivity and Its Response to Meteorological Factors Across the Yellow River Basin During the Period 1981–2020

Based on trend analysis, partial correlation analysis, and Mann–Kendall test, we analyzed the spatiotemporal variations of net primary productivity (NPP) in the Yellow River Basin and their responses to meteorological factors during the period 1981–2020. The results revealed that NPP had high values in the mid-south part but low values in the northwestern part of the Yellow River Basin. The average NPP was 195.3 g C·m−2·a−1 from 1981 to 2020, and the inter-annual fluctuation of NPP showed a significant increasing trend with an increasing rate of 2.35 g C·m−2·a−2 (p < 0.01). The annual mean temperature showed a positive correlation with NPP in 99.6% of the basin, and 91.4% of which passed the 0.01 significant test. NPP and annual precipitation positively correlated in 87.1% of the basin, and 41.7% of which passed the 0.01 significant test. In 75.2% of the basin, NPP was related negatively with annual sunshine hours, and 17.6% of which of which passed the 0.01 significant test.

Spatial-Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China.

In recent years, ecological concerns such as vegetation destruction, permafrost deterioration, and river drying have been paid much more attention to on the Yellow River Basin in China. Soil pH is regarded to be the fundamental variable among soil properties for vegetation growth, while net primary productivity (NPP) is also an essential indicator to reflect the healthy growth of vegetation. Due to the limitation of on-site samples, the spatial–temporal variations in soil pH and NPP, as well as their intrinsic mechanisms, remain unknown, especially in the Yellow River source area, China. Therefore, it is imperative to investigate the coupling relationship between soil pH and NPP of the area. The study coupled MODIS reflectance data (MOD09A1) with on-site soil pH to estimate spatial–temporal variations in soil pH, explore the response of NPP to soil pH, and assess the extent to which they contribute to grassland ecosystems, thus helping to fill knowledge gaps. Results indicated that the surface spectral reflectance for seven bands could express the geographic pattern of soil pH by applying a multiple linear regression equation; NPP exhibited an increasing trend while soil pH was the contrary in summer from 2000 to 2021. In summer, NPP was negatively correlated with soil pH and there was a lag effect in the response of NPP to soil pH, revealing a correlation between temperate steppes > montane meadows > alpine meadows > swamps in different grassland ecosystems. In addition, contribution indices for temperate steppes and montane meadows were positive whereas they were negative for swamps and alpine meadows, which are apparent findings. The contribution index of montane and alpine meadows was greater than that of temperate steppes and swamps. The approach of the study can enable managers to easily identify and rehabilitate alkaline soil and provides an important reference and practical value for ecological restoration and sustainable development of grassland ecosystems in alpine regions.

Modeling the daytime net primary productivity of maize at the canopy scale based on UAV multispectral imagery and machine learning

Net primary productivity (NPP) is an important index to evaluate the carbon absorption capacity in agricultural ecosystems, timely and accurate spatial–temporal variations of NPP play a significant role in guiding agricultural production. Currently, NPP observation at the canopy scale is primarily based on the chamber method. However, upscaling the spatial–temporal estimates of NPP at the canopy scale is still challenging. In this study, maize daytime NPP was measured by the chamber, and multispectral images of maize canopies were obtained via unmanned aerial vehicle (UAV) multispectral system. We explored the potential of multispectral images for estimating maize daytime NPP at the canopy scale. Four machine learning algorithms were employed to estimate daytime NPP using ground factors and vegetation indices (VIs) × photosynthetic active radiation (PAR) independently. NPP estimation models based on the gradient boosting regression (GBR), random forest (RF), and support vector regression (SVR) using ground factors outperformed the VIs × PAR-based model, barring the ridge regression (RR) model. Among them, the GBR-based models performed better, and the model using ground factors (R2 = 0.958) outperformed those using VIs × PAR (R2 = 0.899). However, the ground factor-based GBR model was complex and contained more input parameters, thus making it highly-time and labor-intensive, and highly destructive. Moreover, ground factor-based GBR model could not reflect the variation of NPP in maize canopy. The VIs × PAR-based GBR model could explain 89.9% of the daytime NPP, and it only required two parameters, including VIs and PAR. Therefore, the VIs × PAR-based GBR model can easily obtain high resolution and long time-series NPP information nondestructively for a large range. This study reveals that maize daytime NPP can be estimated using high-resolution UAV multispectral images and provides a good reference for promoting the monitoring and upscaling of NPP observations.

Study on Ecological Loss in Coal Mining Area Based on Net Primary Productivity of Vegetation

Opencast mining contributes to the severe disturbance of vegetation and serious damage to the ecological environment. Scientific assessment of the ecological losses may provide guidance for the sustainable land use in mining areas. This study uses the Carnegie Ames Stanford Approach (CASA) model to estimate the net primary productivity (NPP) of vegetation in the Pingshuo mining area from 2006 to 2020 and uses statistical analysis and mathematical models to study the impact of mining and land reclamation on NPP. The results show that NPP decreased slowly at a rate of 0.809 gC/(m2·month) per year. In 2006, the urban area of Pinglu and the opencast coal mining area were located in the western part of the study area, while the eastern part was distributed with a large amount of grassland, forested and cultivated land, and the NPP showed a high pattern in the east and low in the west. With the continued eastward movement of Antaibao and Anjialing mining areas, as well as the construction and continuous westward expansion of the East opencast mine, the pits of all three opencast mines moved to a central location in 2020, changing the spatial distribution of NPP to high in the surroundings and low in the middle. The NPP within the three opencast mines decreased significantly, but the change in NPP increased from −7.34 gC/(m2·month) to 7.15 gC/(m2·month) as the reclamation area increased, indicating that mining increased ecological losses while land reclamation mitigated them. The variation of NPP within 600 m outside the mining area is 2.06~7.45 gC/(m2·month), and 0.11~1.00 gC/(m2·month) from 600 m to 1000 m, indicating that NPP is sensitive to the influence of mining. The results may provide guidance for further analysis of the impact of mining on local environments and exploration of more appropriate land reclamation measures.

North‐South Differences in Under‐Ice Primary Production in the Chukchi Sea From 1988 to 2018

AbstractAs Arctic sea ice has thinned and begun to retreat earlier in the year, there have been substantial changes in the timing and magnitude of regional net primary production (NPP). Most notably, field, remote sensing, and model‐based studies have demonstrated that massive under‐ice phytoplankton blooms (UIBs) contribute substantially to annual NPP and can even drive increases in sedimentary nitrogen recycling and loss through coupled partial nitrification‐denitrification. In this study, we used a 1‐D biogeochemical model (CAOS‐GO) to compare the magnitude of NPP associated with UIBs in the northern and southern Chukchi Sea between 1988 and 2018. While UIBs were critical in driving interannual variation and secular increases in annual NPP and sedimentary nitrification and denitrification in the northern Chukchi Sea, UIBs were far less important at our southern site. As the length of the under‐ice (UI) period diminished between 1988 and 2018 in the southern Chukchi Sea, there was a decrease in the amount of NPP produced during the UI period. Despite higher rates of both annual NPP and denitrification at the southern site, there were no secular trends in these rates over time. Our results indicate that, as sea ice continues to retreat earlier, the impact of UIBs on the biogeochemistry of the Chukchi Sea is likely to diminish.

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Climate conditions can significantly alter the vegetation net primary productivity (NPP) in many of Earth’s ecosystems, although specifics of NPP–climate condition interactions, especially time-lag responses on seasonal scales, remain unclear in ecologically sensitive forest–grassland ecotones. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, we analyzed the relationship between NPP and precipitation, temperature, and drought during the growing season (April–August), considering the time-lag effect (0–5 months) at the seasonal scale in Hulunbuir, Inner Mongolia, China from 2000 to 2018. The results revealed a delayed NPP response to precipitation and drought throughout the growing season. In April, the precipitation in the 4 months before (i.e., the winter of the previous year) explained the variation in NPP. In August, the NPP in some areas was influenced by the preceding 1~2 months of drought. The time-lag effect varied with vegetation type and soil texture at different spatial patterns. Compared to grass and crop, broadleaf forest and meadow exhibited a longer legacy of precipitation during the growing season. The length of the time-lag effects of drought on NPP increased with increasing soil clay content during the growing season. The interaction of vegetation types and soil textures can explain 37% of the change in the time-lag effect of the NPP response to PPT on spatial pattern. Our findings suggested that preceding precipitation influences vegetation growth at the early stages of growth, while preceding drought influences vegetation growth in the later stages of growth. The spatial pattern of the time lag was significantly influenced by interaction between vegetation type and soil texture factors. This study highlights the importance of considering the time-lag effects of climate conditions and underlying drivers in further improving the prediction accuracy of NPP and carbon sinks in temperate semiarid forest–grassland ecotones.

The Different Impacts of Climate Variability and Human Activities on NPP in the Guangdong–Hong Kong–Macao Greater Bay Area

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPPp), actual NPP (NPPa), and human-induced NPP (NPPh), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPPa variability were quantitatively analyzed and identified. The results showed that the NPPp, NPPa, and NPPh had heterogeneous spatial distributions, and the average NPPp and NPPa values over the whole study area increased at rates of 3.63 and 6.94 gC·m−2·yr−1 from 2000 to 2020, respectively, while the NPPh decreased at a rate of −4.43 gC·m−2·yr−1. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPPa increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPPa values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPPa over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA.

Temporal and spatial variations of net primary productivity (NPP) and its climate driving effect in the Qinghai-Tibet Plateau, China from 2000 to 2020

Qinghai-Tibet Plateau is a "climate change laboratory" for China and the world. Driven by climate change, net primary productivity (NPP) in the Qinghai-Tibet Plateau has significant variations. Using the data of normalized difference vegetation index, digital elevation, annual precipitation, and annual temperature, we explored the temporal and spatial variation characteristics of NPP and its correlation with climate factors on the Qinghai Tibet Plateau from 2000 to 2020. The results showed that NPP of the Qinghai-Tibet Plateau increased significantly from 2000 to 2020, with an increase rate of 1.67 g C·m-2·a-1. The NPP was significantly positively correlated with temperature and precipitation. The climate trend of warming and wetting was an important driving force to promote the significant increase of NPP. The increases of NPP would continue if the climate become warmer and wetter.

Estimating estuarine primary production using satellite data and machine learning

• First attempt to use machine learning to estimate estuarine NPP from satellite data. • Machine learning algorithm greatly outperformed VGPM for NPP estimation. • Tampa Bay NPP shows seasonality and an increasing trend from 2003 to 2020. • Seasonality and long-term increasing trend are largely driven by water temperature. • NPP shows a spatial pattern that likely reflects differences in river discharge. Net primary production (NPP) plays an important role in estuarine carbon cycling, which has been increasingly impacted by human activities and global climate change. Spatiotemporal trends of NPP in the open ocean have been well studied using satellite data and standard primary production algorithms such as the Vertically Generalized Production Model (VGPM), but these algorithms are generally not suitable for estuarine and coastal waters. Previous remote sensing studies on estuarine NPP mainly focused on the tuning of the standard VGPM based on extensive local in situ data. Here we make a first attempt to use machine learning algorithms to estimate NPP in an estuarine environment from satellite measurements. Tampa Bay, the largest estuary in Florida (United States), has abundant in situ measurements of NPP, although the spatiotemporal variability of NPP within the bay remains unrevealed. Combining these data with concurrent MODIS/Aqua image data, we developed and evaluated seven machine learning algorithms (support vector regression, random forest, decision tree, bagging, adaptive boosting, gradient tree boosting, and neural network), and applied the one with the least root mean square error (RMSE) and highest correlation coefficient to establish a time-series NPP record for Tampa Bay from 2002 to 2020. Results of the best performing algorithm (Pearson’s r = 0.82, RMSE = 151.0 mgC m −2 d −1 ) showed substantial improvement over the standard or tuned VGPM (Pearson’s r = 0.51, RMSE = 364.9 mgC m −2 d −1 ), using the same independent variables for NPP ranging from 157.5 to 1368.8 mgC m −2 d −1 . MODIS NPP shows temporal variations that are largely driven by temperature: lowest values in winter, highest values in summer, and an increasing trend from 2003 to 2020, highlighting the impact of global warming on estuarine NPP. The spatial distribution of MODIS NPP shows higher values in Hillsborough Bay, Middle Tampa Bay and Lower Tampa Bay, and relatively lower values in Old Tampa Bay, a pattern that likely reflects differences in river discharge. The long-term NPP product derived from machine learning algorithms and satellite data can complement existing field-based monitoring programs and help to understand estuarine responses to climate changes and human impacts, and design relevant mitigation strategies.

Warm–Wet Climate Trend Enhances Net Primary Production of the Main Ecosystems in China during 2000–2021

A significant greening trend has been reported globally in recent decades. The greening indicates the improvement in net primary production (NPP) in vegetation. Adopting statistics-based regression models, we investigated the dynamics of NPP and its climatic drivers in main ecosystems (forest land, grass land, and unused land) over China during the period 2000–2021. The results confirmed an increasing NPP covering approximately 86% area in the main ecosystems. NPP exhibited an increase rate of 6.11 g C m−2 yr−1 in forest land, 4.77 g C m−2 yr−1 in grass land, and 1.25 g C m−2 yr−1 in unused land, respectively. Over the same period, warm–wet climate trend was observed covering approximately 90% of the main ecosystems. The warm–wet climate has had a positive effect rather than negative effect on NPP in the main ecosystems, judging by their significant positive correlation. Our results suggested that the increase in annual precipitation exerted much more important effect on the increasing NPP. The warm–wet climate trend contributes to the upward trend in NPP, even if variability in NPP might involve the influence of solar radiation, atmospheric aerosols, CO2 fertilization, nitrogen deposition, human intervention, etc.

ANALISIS KARAKTERISTIK NET PRIMARY PRODUCTIVITY DAN KLOROFIL-A DI LAUT BANDA DAN SEKITARNYA

Net primary productivity (NPP) and chlorophyll-a (Chl-a) are indicators of water productivity. In this study, an analysis of NPP and Chl-a characteristics in the Banda Sea was carried out using the Hovmöller diagram and Pearson’s correlation. The NPP data used comes from VGPM and Chl-a from Aqua MODIS satellite. The results of data analysis from January 2003-December 2020, NPP and Chl-a reached highest concentrations in dry season and lowest in wet season. For monthly data, the highest concentrations occurred in August and the lowest in April and December. The waters of the Banda Sea include mesotrophic waters with monthly average of NPP 429 mg C/m2/day and Chl-a 0.24 mg/m3. During La Niña and El Niño, there was a change (decrease/increase) the concentration of NPP and Chl-a in dry season and transition period II. NPP and Chl-a have a high correlation and a strong linear relationship. NPP and Chl-a have almost the same pattern/tendency temporally. The change of NPP concentration temporally corresponded to change of Chl-a concentration. Seasonal factors, La Niña and El Niño have a strong influence in influencing the variability of NPP and Chl-a concentrations. High productivity based on NPP and Chl-a didn’t affect for skipjack and tuna seasons (big pelagic), that occurs in wet season and transition period II. High productivity affects to flying fish season (small pelagic) that occurs in dry season.

Spatial and temporal variability of Net Primary Production on the Agulhas Bank, 1998–2018

Despite the importance of Agulhas Bank (AB) marine productivity in supporting South African coastal fisheries and shelf ecosystems, there are relatively few regional-scale assessments of its spatial and temporal variability, and most productivity studies have been limited in scale. Here we use satellite-derived Net Primary Production (NPP) rates calculated using the Vertically Generalized Production Model (VGPM) to examine the spatial and temporal dynamics of NPP over the 21-year satellite record (1998–2018) on the AB. In calculating VGPM NPP we used the OCCI Chlorophyll-a product, SST from Operational-Sea-Surface-Temperature-and-Sea-Ice-Analysis (OSTIA) and PAR from GlobColour level-3 mapped products as these represent the longest datasets that fit our extended study period. We examine spatial trends between the eastern and central AB, as well as three areas of the bank (around Port Alfred, the Tsitsikamma coast, and the ‘cold ridge’) that have been previously identified as contributing significantly to the overall productivity of the AB. The AB shows only a moderate degree of seasonality in NPP calculated from the VGPM, with NPP being highest in austral summer (1.7–1.8 g C m−2 d−1) and lowest in winter (0.9–1.0 g C m−2 d−1), and remains relatively high (>1 g C m−2 d−1) throughout the year, contrasting sharply with other shelf systems. Considered annually, NPP on the bank was 516 g C m−2 yr−1 (38 Mt C yr−1 when scaled to the total shelf area) which is higher than many other shelf systems though lower than the neighbouring Benguela system and is indicative of a moderately productive shelf system fuelled by perennial NPP. Comparing different sections of the AB from east to central bank, and including the three upwelling areas, highlighted that spatial differences in NPP were relatively limited; that these three upwelling areas made similar contributions to their relative proportion of the total shelf area, and that average rates of NPP are spatially similar across the bank, though notable high rates occur in some coastal upwelling areas. Interannual variability in NPP was relatively modest, varying between years by only ∼15% over the two decades assessed. Over the 21-year data set, there was a slight (∼0.26% yr−1) statistically-significant decline in calculated NPP over time for the AB as a whole, which, when examined on a pixel-by-pixel basis, indicated that most of the decline was on the central bank between 100 m and 200 m isobaths. In summer, an increase in NPP occurred on the EAB (26.5–28°E). In conclusion, the AB is a significant site of perennial moderate levels of NPP, varying little interannually and with only a slight decline in NPP over time. These factors lead to a stable environment in terms of ecosystem productivity so that the AB makes a significant contribution to the productivity of South African regional fisheries.

Daytime and nighttime temperatures exert different effects on vegetation net primary productivity of marshes in the western Songnen Plain

The western Songnen Plain has a large area of marshes that are able to modulate the regional carbon cycle. Net primary productivity (NPP) is an important ecological indicator of carbon storage within the marsh ecosystems. Although climate change could have significantly changed marsh vegetation NPP during the past decades, the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh vegetation NPP remain unclear in the western Songnen Plain. In this paper, we assessed the spatiotemporal variation in the NPP of marshes at the western Songnen Plain and evaluated its response to climate change from 2000 to 2020. The NPP of marshes over the western Songnen Plain increased significantly at a rate of 5.69 g C/m2/a during 2000 to 2020, with an average value of about 273.62 g C/m2/a. The largest increase of the NPP of marshes was mainly distributed in the northwest part of the Songnen Plain. Correlation analysis results showed that the increased precipitation in summer and autumn could enhance the NPP of marsh vegetation. By contrast, the increase of mean temperature in summer reduced the NPP of marshes. In addition, we first found the asymmetric impacts of summer minimum temperature (Tmin) and maximum temperature (Tmax) on marsh vegetation NPP. Notably, the increase in summer Tmax could significantly reduce NPP, while the increase in Tmin could increase NPP. Under the background of global asymmetric warming of daytime and nighttime temperatures, more attention should be paid to the different effects of Tmax and Tmin on marsh NPP in the western Sonegnen Plain. The findings of this study can provide theoretical guidance for ecological protection and the adaptive management of marsh vegetation at the western Songnen Plain.

Where Anthropogenic Activity Occurs, Anthropogenic Activity Dominates Vegetation Net Primary Productivity Change

Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver.

Quantitative estimation of the factors impacting spatiotemporal variation in NPP in the Dongting Lake wetlands using Landsat time series data for the last two decades

Wetlands are important ecosystems and constitute one of the major carbon reservoirs on Earth. Therefore, accurate estimation of wetland carbon stocks, quantitative analysis of spatial and temporal changes in wetland carbon stocks, and maintenance and enhancement of wetland carbon sink functions play a pivotal role in achieving China’s “carbon neutral” strategy. Vegetation net primary productivity (NPP) is one of the core components of wetland ecosystem carbon budgets. In this study, an improved CASA model and the spatiotemporal fusion algorithm were employed to obtain NPP data with good accuracy and high spatiotemporal resolution for the Dongting Lake wetland area for the last two decades. Quantitative methods based on partial derivatives were used to assess the contribution of climate and anthropogenic factors to NPP changes. The results demonstrate that the NPP of wetland vegetation in Dongting Lake showed a significant increasing trend from 2000 to 2019 (0.86 g C/m2-yr, P < 0.05). Temperature, precipitation and solar radiation contributed positively to the NPP changes in wetland vegetation, with solar radiation making the greatest contribution (10.31 g C/m2·year), followed by precipitation (1.61 g C/m2·year) and then temperature (0.03 g C/m2·year). The contributions of climate change and human activities to changes in wetland vegetation NPP were 1.08 and 0.58 g C/m2·year, respectively. Human activities dominate in terms of vegetation degradation, while climate dominates in terms of vegetation restoration.

Unequal weakening of urbanization and soil salinization on vegetation production capacity

Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of “climate-urbanization-soil-AMPs” on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of “climate-urbanization-soil” that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security.

Long-Term Spatiotemporal Variation of Net Primary Productivity and Its Correlation With the Urbanization: A Case Study in Hubei Province, China

Net primary productivity (NPP) is a critical component in terrestrial ecosystem carbon cycles. Thus, quantitatively estimating and monitoring the dynamics of NPP have become key aspects for exploring the carbon cycle of terrestrial ecosystems. Anthropogenic activity, such as urbanization, has significant effects on NPP and increases pressure on the natural resources of a specific region. However, to date, although many studies have focused on the relationship between NPP variation and urbanization, they usually ignored any differences at a long-term spatiotemporal variation of urbanization factors, which led to the insufficient understanding of the urbanization-induced impacts on NPP. As a result, this study effectively explored the spatiotemporal variation of NPP from 2001 to 2012 and its corresponding relationship with urbanization, taking the Hubei Province in China as a case study area. To clarify the degree of urbanization, the spatial distribution and temporal variation of population and gross domestic product (GDP) were simulated based on the elevation-adjusted human settlement index and nighttime lights data. The major results showed that high NPP areas were located in those highlands with widespread woodland, in which the NPP value continued to grow during the period. The low NPP areas were mainly distributed in urban areas, and the NPP value had a continued and visible loss. The population and GDP both had a strong correlation with NPP. The significant negative correlation was concentrated in the center of Hubei, with a dense population and developed economy. In order to further realize their complex relationship, the correlation coefficients between the annual NPP and the two factors from 2001 to 2012 were calculated, and the changing trends were investigated. Overall, the findings of this study may provide a reference for studies on the interaction between ecological environment and socioeconomic processes under the background of global rapid urbanization.

基于GEE平台的黄河流域森林植被净初级生产力时空变化特征

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征 DOI: 10.5846/stxb202104271113 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(31960330);宁夏自然科学基金(2020AAC03112) Spatio-temporal variation characteristics of forest net primary productivity in the Yellow River Basin based on Google Earth Engine cloud platform Author: Affiliation: Fund Project: National Natural Science Foundation of China (31960330)；Ningxia Natural Science Foundation (2020AAC03112) 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:黄河流域是我国重要的生态屏障,研究黄河流域森林植被净初级生产力(Net Primary Productivity,NPP)的时空变化特征及驱动机制,对解释黄河流域森林碳汇/源变化具有重要意义。基于Google Earth Engine (GEE)云平台,利用MOD17A3H V6 NPP数据、MCD12Q1 V6土地覆盖类型数据、ECMWF/ERA5气象数据和USGS/SRTMGL1_003高程数据,采用岭回归分析、Hurst指数和冗余分析(Redundancy Analysis,RDA)对黄河流域2001-2019年森林NPP的时空变化特征及影响因子进行分析。结果表明:(1)2001-2019年,黄河流域森林平均总面积为3.66万km2,其中阔叶林、针叶林、混交林平均面积分别为:2.64万km2、0.01万km2和1.01万km2,森林NPP年总量呈线性增加趋势,其均值为8.99Tg C,年均增速为0.36Tg C/a,19a增长率为173.60%;不同森林类型的NPP年总量均值分别为:4.79Tg C (阔叶林)、6.04×10-5Tg C (针叶林)和0.64Tg C (混交林),年均增速为:阔叶林(0.16Tg C/a)>混交林(0.04Tg C/a)>针叶林(6.98×10-6Tg C/a)。(2)2001-2019年,黄河流域森林年均NPP呈线性增加趋势,其均值为241.58g C m-2 a-1,年均增速为7.18g C m-2 a-1,19a增长率为108.63%;不同森林类型的年均NPP均值分别为:178.48g C m-2 a-1(阔叶林)、0.60g C m-2 a-1(针叶林)和62.49g C m-2 a-1(混交林),年均增速为:阔叶林(4.75g C m-2 a-1)>混交林(2.39g C m-2 a-1)>针叶林(0.04g C m-2 a-1)。(3)黄河流域森林NPP呈增加趋势的面积占94.50%,其中显著增加的面积占73.29%;呈减少趋势的面积占5.50%,其中显著减少的面积占1.57%。阔叶林NPP显著增加的面积最高(76.78%),其次为混交林(60.84%),针叶林最少(56.76%)。(4)黄河流域森林NPP的Hurst指数(H)介于0.38-1.00之间,平均值为0.87,其中H≥0.5的像元数约占99.34%,黄河流域森林NPP在未来一段时间内仍保持持续增加趋势。(5)归因分析表明环境因子对黄河流域森林NPP时空变化的总解释率为55.80%,显著影响的环境因子为经度(35.50%)、降水(8.00%)、气温(6.50%)和纬度(5.40%)。2001-2019年黄河流域森林NPP呈增加趋势,且呈现较强的可持续性;GEE云平台结合冗余分析可及时、高效获取黄河流域森林NPP的时空变化并对其进行归因分析。 Abstract:The Yellow River Basin is an important ecological barrier in China. To study the spatio-temporal variation characteristics and driving mechanisms of the Net Primary Productivity (NPP) of forest is of great significance to explain the change of forest carbon sink and source in the Yellow River Basin. Based on Google Earth Engine (GEE) cloud platform, MOD17A3H V6 NPP data, MCD12Q1 V6 land cover data, ECMWF/ERA5 weather data, USGS/SRTMGL1_003 elevation data, and ridge regression analysis, Hurst index, redundancy analysis (RDA) were used to analyze the spatio-temporal variation characteristics and influencing factors of the forest NPP in the Yellow River Basin from 2001 to 2019. The results showed that (1) from 2001 to 2019, the average total area of forest in the Yellow River Basin was 36600 km2, of which the average area of broadleaf forest, coniferous forest and mixed forest was 26400 km2, 100 km2 and 10100 km2. The annual total forest NPP showed a linear increasing trend, with an average value of 8.99 Tg C, an average annual growth rate of 0.36 Tg C/a, and a 19-year growth rate of 173.60%. The averagely annual total NPP of different forest types were:4.79 Tg C (broadleaf forest), 6.04×10-5 Tg C (coniferous forest) and 0.64 Tg C (mixed forest), and the averagely annual growth rate were:broadleaf forest (0.16 Tg C/a) > mixed forest (0.04 Tg C/a) > coniferous forest (6.98×10-6 Tg C/a). (2) From 2001 to 2019, the annual average NPP of the forest in the Yellow River Basin increased linearly, with an average value of 241.58 g C m-2 a-1, an average annual growth rate of 7.18 g C m-2 a-1, and a 19-year growth rate of 108.63%. The mean annual average NPP of different forest types were 178.48 g C m-2 a-1 (broadleaf forest), 0.60 g C m-2 a-1 (coniferous forest) and 62.49 g C m-2 a-1(mixed forest), and the averagely annual growth rate were:broadleaf forest (4.75 g C m-2 a-1) > mixed forest (2.39 g C m-2 a-1) > coniferous forest (0.04 g C m-2 a-1). (3) The area of forest NPP showed an increasing trend in the Yellow River Basin accounting for 94.50%, of which 73.29% was significantly increased; the area showed a decreasing trend accounting for 5.50%, of which the area with a significant decrease accounted for 1.57%. The area of the significantly increased of the broadleaf forest NPP was the highest (76.78%), followed by mixed forest (60.84%) and coniferous forest (56.76%). (4) The Hurst index (H) of the forest NPP in the Yellow River Basin was between 0.38-1.00, with an average value of 0.87. Among them, the amount of H ≥ 0.5 accounted for about 99.34%, and the forest NPP in the Yellow River Basin would continue to increase in the future. (5) Attribution analysis showed that the total interpretation rate of the environmental factors on the spatio-temporal variation of the forest NPP in the Yellow River Basin was 55.80%, and the environmental factors that had significant effects were longitude (35.50%), precipitation (8.00%), temperature (6.50%) and latitude (5.40%). From 2001 to 2019, the forest NPP of the Yellow River Basin increased and showed strong sustainability. The GEE cloud platform combined with redundant analysis can timely and efficiently obtain the spatio-temporal variation of the forest NPP of the Yellow River Basin and perform an attribution analysis. 参考文献 相似文献 引证文献

ВЛИЯНИЕ ИЗМЕНЕНИЙ КЛИМАТА НА БИОПРОДУКТИВНОСТЬ НАЗЕМНЫХ ЭКОСИСТЕМ В БЕЛОРУССКО-УКРАИНСКОМ ПОЛЕСЬЕ

The influence of interannual and long-term climate change impacts on net primary production (NPP) of terrestrial ecosystems in the central part of the Belarusian-Ukrainian Polesie region (50.7°-53° N, 25.5°-31.5° E) are investigated using satellite observations and reanalysis data. The results show that nowadays Polesie belongs to the zone of excessive heat and solar radiation resources and insufficient moisture resources. The interannual variations in NPP in Polesie are 2-3 times higher than in the other parts of Eastern Europe. Climate change in the recent 50 years led to the reduction of the total carbon stock in the vegetation cover of the region (at the rate of ~750000 t/year). However, a number of natural and anthropogenic processes not related to the climate compensate the climate-related carbon loss in the terrestrial biomass. Moreover, these processes cause a slow growth of carbon at the rate of 140000 t/year in the region.

Spatiotemporal variation of net primary productivity and its response to drought in Inner Mongolian desert steppe

The desert steppe in Inner Mongolia is an important part of the temperate grasslands in Central Asia and plays a key role in sustaining the regional pastoral livelihood and securing the ecological environment in northern China. Grassland net primary productivity (NPP) is an important indicator to ecosystem health, and is largely affected by the variation of precipitation, especially the frequent droughts in the desert steppe region. Monitoring the spatiotemporal changes of the NPP in relation with climate variation, especially with the drought events, is of great significance for assessing the total amount of grassland resources and predicting its future change trend, which is critically important for the development of grassland management regimes with the consideration of current and future climate changes. In the present study, we estimated the spatiotemporal variation of the NPP of the desert steppe in Inner Mongolia for the period from 2000 to 2019. We calculated the standardized precipitation evapotranspiration index (SPEI) of the study area, and analyzed its effects on vegetation NPP, especially for the drought effects. We found that the temporal trend line and Mann-Kendall statistical curve of vegetation NPP were basically consistent. Over the 20-year period, the NPP showed an overall downward trend during the plant growing season, or in summer and autumn period, but a slight upward trend in spring. Spatially, the plant growing season NPP slightly decreased on the 74.89% of the studied desert steppe area, the NPP in summer slightly decreased on the 72.25% of the area, while the NPP in spring slightly increased on the 61.8% of the area, and NPP in autumn significantly decreased on the 25.9% of the area. From the perspective of the persistence on different time scales, the proportion of moderate persistence-slight increase area covered 11.72% of the study area, and weak persistence-slight increase in spring covered 50.10% of the study area. The proportion of weak persistence-slight decrease in summer was 59.68%. The proportion of moderate persistence-slight decrease covered 18.83% in autumn. In the growing season, a positive correlation between NPP and SPEI accounted for 91.18% of the desert steppe area. Over 8% of the area showed a highly significant positive correlation, and 25.21% showed a significant positive correlation. Overall, 92.56% of the area showed a positive correlation in spring, and 87.26% showed a positive correlation in summer. A positive correlation was observed over 60.71% of the area in autumn. The research results show that the NPP results of CASA model simulating desert vegetation in Inner Mongolia are reliable. The SPEI drought index has good applicability in the study area. On the whole, the dynamic changes of NPP are closely related to drought. This research has important reference significance for regional ecosystem management and construction.