Net Primary Production Product Research Articles

Response of Southern Ocean Resource Stress in a Changing Climate

AbstractPhytoplankton underpin ocean net primary production (NPP) and Southern Ocean phytoplankton display different ecological‐biogeochemical traits, compared to temperate species. Climate models currently forecast consistent across‐model NPP increases due to climate change, yet neglect specific aspects of the Southern Ocean ecological‐biogeochemical system. We conducted experiments to evaluate how key regional traits, including multiple limiting nutrients, unique photophysiology and differential resource acquisition, drive changes in the projected response of resource stress, NPP and export production under a high emissions scenario. Although Southern Ocean iron limitation is widespread, it declines in the future and is replaced by growing manganese limitation, as concentrations cannot support increasing growth rates. Distinct phytoplankton traits either amplify or dampen climate‐driven changes, depending on whether they are those typical of Antarctic or temperate phytoplankton, respectively. Overall, future Southern Ocean NPP trends may be more uncertain than currently assumed and future efforts should focus on accounting for regional ecological‐biogeochemical differences.

Human modification of land cover alters net primary productivity, species richness and their relationship

AbstractAimHumans have altered ecosystem productivity and biodiversity worldwide by changing land‐cover types and management. High local species richness is commonly found in geographic areas and ecosystems with high net primary productivity (NPP), but the long‐term effects of modification on productivity and biodiversity change, and particularly on the relationship between the two, are poorly understood. Here we evaluate whether human modification tends to increase biodiversity in low‐productivity ecosystems (where human management is likely to increase productivity) and decrease biodiversity in ecosystems that were originally high‐productive.LocationGlobal.Time Period2001–2013.Major Taxa StudiedPlants, mammals, birds, hexapods, arachnids, other terrestrial invertebrates, amphibians, reptiles and fungi.MethodsWe assembled a large worldwide dataset of NPP and associated species richness from MODIS land cover and NPP products and the PREDICTS biodiversity database, involving 11,849 sites. This enabled comparisons of species richness and NPP differences between samples of relatively natural and human‐modified vegetation within the same geographic regions, considering 102 types of land‐cover transitions.Results(1) Modification from non‐forest vegetation to forests on average increased NPP by 7.76%, and vice versa. (2) Human modification of less productive areas tended to increase NPP and vice versa, with stronger effects of major modification. (3) However, site‐level species richness decreases were associated with nearly all land‐cover transitions from relatively natural to modified vegetation. (4) Despite expectations, we found no significant relationship between species richness differences (between relatively natural and modified vegetation) and productivity differences, when considering conversions across land‐cover types and excluding human‐appropriated productivity in croplands.Main ConclusionsHuman modification tends to increase NPP in low‐productivity ecosystems, but species richness declines are associated with most major human‐induced land‐cover changes. Therefore, increasing or decreasing NPP did not generate corresponding increases or decreases in species richness, contrary to expectations of the general species richness‐productivity relationship.

Assessment of ecosystem quality changes based on optimizing key indicators in Nanwenghe National Nature Reserve, Heilongjiang, China.

Net primary productivity (NPP) is an indicator to reflect the production capacity of terrestrial ecosystems, as well as a key indicator for ecological quality. NPP at large scale is difficult to be measured. At present, most of the assessment of ecosystem quality uses NPP products with low resolution, which cannot capture the detailed characteristics of the ecosystem and is not conducive to the assessment of ecosystem quality at small-scale. The establishment of a rapid and efficient assessment method for small-scale ecosystem quality will greatly promote the protection and restoration of ecosystems in China. We focused on the calculation method of ecosystem quality assessment and NPP, and optimized the calculation process of the NPP, and obtained NPP by remote sensing without ground observation data. We established a regression model for remote sensing inversion of leaf area index, and estimated the vegetation coverage by using dimidiate pixel model, developed a set of methods for rapid assessment of ecosystem quality by remote sensing. Taking Nanwenghe National Nature Reserve as an example, we evaluated the change of ecosystem quality from 2001 to 2022. The results showed that from 2001 to 2022, the ecosystem qua-lity of the reserve was good as a whole, and that the area with good and excellent quality accounted for more than 85% in 2022. High vegetation coverage was the backbone of the sustainable good ecosystem quality of the reserve. From 2001 to 2022, ecosystem quality of the reserve showed a trend of first decreasing and then increasing, with the lowest point of ecosystem quality in 2013. This method had achieved good results in the evaluation of ecosystem quality in Nanwenghe National Nature Reserve. The index optimization method proposed in this study could facilitate the rapid and accurate assessment of ecosystem quality in small-scale regions, and thus provides technical reference for the precise improvement of ecosystem quality.

Spatiotemporal Characteristics Prediction and Driving Factors Analysis of NPP in Shanxi Province Covering the Period 2001–2020

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm−2a−1. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage.

Analysis of Spatiotemporal Variation Characteristics and Driving Forces of NPP in Shanxi Province from 2000 to 2020 Based on Geodetector

The net primary productivity (NPP) of vegetation, as an indispensable element in carbon cycle studies, characterizes plant growth status. This study applied MODIS NPP products from 2000 to 2020 and multi-source data on elevation, slope, precipitation, temperature, land use, and population density in Shanxi province. We used trend analysis, correlation analysis, and geographic probes to explore the spatial and temporal evolution characteristics and driving factors of NPP in Shanxi province and its national planned coal-mining areas. The results showed that: ① the overall NPP exhibited a fluctuating upward trend from 2000 to 2020, with an average rate of increase (in terms of C) of 6.7 g·(m2·a)-1. The total NPP varied significantly among different land types, with arable land>forest land>grassland>construction land>water area>unused land. ② The spatial heterogeneity of NPP changes was obvious, with lower NPP values in the western and northern regions and higher average NPP values in the eastern and southern regions; the NPP comparison of three major coal bases showed that Jindong coal base>Jinzhong coal base>Jinbei coal base. ③ The correlation between NPP and precipitation was high, with 62.2% of regions having a significant correlation (P<0.05), mainly in central and eastern Shanxi province. The relationship between NPP changes and temperature was weak, with only 1.10% of regions having a significant correlation (P<0.05). ④ The comparison of the q-means of each factor in different years based on geographic probes showed that precipitation (0.165)>land use (0.124)>population density (0.085)>slope (0.080)>elevation (0.064)>air temperature (0.024), further indicating that precipitation was the dominant driver of NPP changes over the years. 5 The influence of the two-factor interaction was significantly higher than that of the single factor, and the influence of anthropogenic factors was gradually increasing. From 2000 to 2020, the interaction factor precipitation∩population density (0.275) with the highest explanatory power replaced precipitation∩temperature (0.385) as the interaction factor precipitation with the highest explanatory power.

Vulnerability of Primary Productivity and Its Carbon Use Efficiency to Unfavorable Climatic Conditions in Jambi Province, Indonesia

Climatic conditions and land cover play crucial roles in influencing the process of carbon uptake through vegetation. This study aimed to analyze the effect of climate variability on carbon uptake of four different land covers in Jambi Province, Indonesia. The four land cover types studied were: forest, shrub, grass, and irrigated soybean, based on Community Land Model version 5. Forest was found to have the highest net primary production (NPP) compared to the other land covers. Seasonal climate variability showed no major effect on NPP and gross primary production (GPP). However, GPP and NPP experienced significant declines during El Niño Southern Oscillation (ENSO), particularly in 2015. Carbon use efficiency (CUE = NPP/GPP) was also affected by ENSO, where CUE decreased during El Niño, particularly in October and November with an increased number of days without rainfall. In addition, the difference between latent (LE) and sensible heat (H) flux, denoted as (LE-H), decreased from August to November. This difference was highly correlated with NPP. This result indicates that when water supply is low, stomata will close, thereby reducing photosynthesis and transpiration, and allocating more of the available energy to sensible heat flux rather than latent heat flux.

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.

Identification of Coupling Relationship between Ecosystem Services and Urbanization for Supporting Ecological Management: A Case Study on Areas along the Yellow River of Henan Province

Urbanization has an important effect on ecosystem services (ESs) and identifying the relationship between urbanization and ESs can provide a decision-making reference for regional ecological protection and management. Taking the areas along the Yellow River of Henan Province (AYRHP) as a research area, a coupling system of ESs and urbanization is established in this study to reveal the coupling relationship between the two. ESs are estimated by using Carnegie–Ames–Stanford approach, revision universal soil loss equation, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. The urbanization level is evaluated from three dimensions, namely, population, economy, and land. The coupling coordination relationship between various ESs and urbanization in AYRHP is quantified from 2000 to 2018 on the county scale based on the coupling coordination degree (CCD) model. The lead–lag relationship between ESs and urbanization is identified by using the relative development degree model, and ecological management zoning is conducted. Results show that in the study period, net primary production (NPP), soil conservation, and food production are increased, whereas water yield is decreased. In the study period, population, economy, and land urbanization level are increasing, and the comprehensive urbanization level is increased by 51.63%. The total CCD between NPP, food production, and water yield and comprehensive urbanization is basic or moderate coordination, whereas that between soil conservation and comprehensive urbanization is moderate maladjustment. In the research period, the coupling coordination between NPP and food production and comprehensive urbanization is increasing; that between water yield and comprehensive urbanization is fluctuated; and that between soil conservation and comprehensive urbanization is decreasing. The result of the research into the relative development degree in 2018 showed that food production, water yield, and soil conservation lag behind the urbanization level in most regions and counties along the Yellow River of Henan Province. On the basis of the lead–lag relationship between different ESs and urbanization level, the AYRHP are divided into ecological reconstruction area, ecological and agricultural improvement area, and ecological conservation area. CCD and relative development degree models can be used to evaluate the coordination relationship between ESs and urbanization, which provides scientific support for regional ES management.

Effects of Climate Change and Human Activities on Aeolian Desertification Reversal in Mu Us Sandy Land, China

The aeolian desertification in Mu Us Sandy Land (MUSL) in northern China have been paid much attention, but the relative contributions of climate change and human activities to desertification dynamics are still not clear. Based on the Landsat MSS, TM, ETM+ and OLI images in 1975, 1990, 1995, 2000, 2005, 2010 and 2015, we developed a database of aeolian desertification land distribution, discussed the spatial and temporal variation of aeolian desertification, and discovered the relative contributions of climate change and human activities to desertification reversal, using the trends of the potential net primary productivity (NPP) and the human-influenced NPP with meteorological data and MODIS NPP products. The results indicated that aeolian desertification developed firstly from 1975 to 2000, with serious and severe aeolian desertification land continually increasing, and then changed into a reversal state from 2000 to 2015, as the serious aeolian desertification land decreased, although the severe, moderate and light aeolian desertification land lightly increased. Human activities were the dominant factor in desertification dynamics in MUSL and had different contributions to aeolian desertification reversal in different periods. This study will improve our understanding of the processes of aeolian desertification.

Spatio-temporal pattern of urban vegetation carbon sink and driving mechanisms of human activities in Huaibei, China.

Carbon neutrality is a strategic choice for the sustainable development of global cities. Quantitatively assessing the spatio-temporal patterns of urban vegetation carbon sink and the impact of human activities has become an essential basis for adjusting urban carbon balance. We used Huaibei, a typical city with vigorous human coal resource mining activities, as the case study area. We regarded the net ecosystem productivity (NEP) as an indicator parameter of vegetation carbon sink and calculated it based on the improved Carnegie-Ames-Stanford approach (CASA). We then revealed the spatial-temporal evolution of vegetation carbon sink through trend analysis, coefficient of variation, and standard direction. Finally, we used geographic detectors to evaluate the impact of human activities on NEP. We found that net primary productivity (NPP) accuracy was good, and the R2 value was 0.755 compared with MODIS NPP products. NEP was characterized by the first decrease and then increase, showing a slow increase overall, with an average trend coefficient of 0.15 gC·m-2·a-1. The average value in 2010 was the lowest at 18.30 gC·m-2·a-1. In terms of spatial characteristics, NEP showed a gradual decrease from north to south. High and severe fluctuations were distributed along the southeast, mainly concentrated in Duji District, Xiangshan District, and Lieshan District. The driving factors with reliable explanatory power for NEP were population density, GDP, and road density, while land use type, soil erosion intensity, and mining and collapse area had weak explanatory power. Meanwhile, factors of cooperative interaction enhanced the explanatory power of the results.

Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models

Earth system models (ESMs) have been widely used to simulate global terrestrial carbon fluxes, including gross primary production (GPP) and net primary production (NPP). Assessment of such GPP and NPP products can be valuable for understanding the efficacy of certain ESMs in simulating the global carbon cycle and future climate impacts. In this work, we studied the model performance of 22 ESMs participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) by comparing historical GPP and NPP simulations with satellite data from MODIS and further evaluating potential model improvement from CMIP5 to CMIP6. In CMIP6, the average global total GPP and NPP estimated by the 22 ESMs are 16% and 13% higher than MODIS data, respectively. The multi-model ensembles (MME) of the 22 ESMs can fairly reproduce the spatial distribution, zonal distribution and seasonal variations of both GPP and NPP from MODIS. They perform much better in simulating GPP and NPP for grasslands, wetlands, croplands and other biomes than forests. However, there are noticeable differences among individual ESM simulations in terms of overall fluxes, temporal and spatial flux distributions, and fluxes by biome and region. The MME consistently outperforms all individual models in nearly every respect. Even though several ESMs have been improved in CMIP6 relative to CMIP5, there is still much work to be done to improve individual ESM and overall CMIP performance. Future work needs to focus on more comprehensive model mechanisms and parametrizations, higher resolution and more reasonable coupling of land surface schemes and atmospheric/oceanic schemes.

The Impact of the Amazon on the Biological Pump and the Air‐Sea CO2 Balance of the Western Tropical Atlantic

AbstractThe Amazon River strongly modifies the biogeochemistry of the Western Tropical Atlantic (WTA). To disentangle the different mechanisms driving these modifications, we conduct a series of modeling experiments with a high‐resolution regional ocean model (ROMS) coupled to a biogeochemical/ecological model (BEC) that we augmented to include Diatom‐Diazotroph‐Assemblages (DDAs). In our model, the Amazon River increases net primary production (NPP) in the WTA by almost 10%, exceeding the stimulation expected from the supplied inorganic nitrogen and phosphorus by a factor of two. This amplification is fueled by new nitrogen stemming from DDA‐driven N2 fixation in the plume region, supported, in part, by the consumption of riverine dissolved organic phosphorus. The vertical export of organic carbon is enhanced by a shift of the phytoplankton community toward diatoms induced by the large amount of Si(OH)4 delivered by the Amazon. These changes in NPP and export production induce a strong uptake of atmospheric CO2. In contrast, the remineralization of the river‐delivered terrestrial organic matter leads to a release of CO2 over the WTA, which is partially offset by a net uptake induced by the riverine dissolved inorganic carbon and alkalinity. Overall, the Amazon reduces the strong outgassing of the WTA in our simulations by more than 50%. Our study demonstrates how rivers modify the marine biological pump and the air‐sea CO2 fluxes in the downstream ocean through a myriad of cascading effects, highlighting the need to fully consider the land‐ocean aquatic continuum in the modeling of the Earth System.

Quantitative Assessment of NPP Changes in the Yellow River Source Area from 2001 to 2017

It is of great significance to quantitatively evaluate the ecological degradation and restoration of the Yellow River source area from 2001 to 2017. Net primary productivity (NPP) is the core indicator used to indicate the health of terrestrial ecosystems. Based on the improved CASA (Carnegie-Ames-Stanford Approach) model and MOD17A3 NPP products, 174 measured sample sites were used to validate the NPP simulation results, select the model with higher accuracy as the actual NPP simulation, and using Theil-Sen median trend analysis and Mann-Kendall test to investigate the dynamics of NPP changes. The results are as follows: (1) the accuracy of the CASA model is higher than that of the MOD17A3 NPP product, and it is more suitable for the simulation of the actual NPP in the source area of the Yellow River; (2) the Yellow River Source region has shown a fluctuating upward trend in net primary productivity over the past 17 years, with 50% of the region showing a slight improvement, 36% showing a significant improvement, 13% showing a slight decline, and only 1% showing a significant decline, the average annual growth rate of NPP is 2.6 g C/yr-1. Compared with 2001, the net primary productivity in 2017 increased by 5.24×1012g C.

Quantitatively determine the dominant driving factors of the spatial—temporal changes of vegetation NPP in the Hengduan Mountain area during 2000–2015

The Hengduan mountain area, located in the upper reaches of the Yangtze River of China, is an important ecological barrier that significantly impacts the climate and ecological environment of the surrounding region and western China as a whole. This paper introduces the gravity center model used to analyze the spatial-temporal variation patterns of vegetation Net Primary Productivity (NPP) from 2000 to 2015, which were determined by the use of MOD17A3 NPP products. Additionally, the dominant driving factors of the spatial—temporal changes of vegetation NPP of the Hengduan Mountain area were quantitatively determined with a geographical detector over 2000–2015. The results revealed that: (1) From 2000 to 2015, there was an increasing trend of vegetation NPP in the Hengduan mountain area. Throughout the whole study region, the vegetation NPP with a mean value of 611.37 gC·m−2·a−1 indicated a decreasing trend from southeast to northwest in terms of spatial distribution. (2) The gravity centers of vegetation NPP in 2000–2015 were mainly concentrated in Zhongdian County. During the study period, the gravity center of vegetation NPP moved northward, which indicated that the increment and increasing rate of vegetation NPP in the northern parts were greater than that of the southern areas. (3) The vegetation NPP showed a moderately positive correlation with temperature, accumulated temperature (>10°C), and sunshine, while there was an overall negative relationship between NPP and precipitation. (4) The dominant factors and interactive dominant factors changed in different sub-regions over different segments of the study period. The dominant factors of most sub-regions in Hengduan mountain were natural factors, and the climate change factors played an increasingly greater role over the 16 years of the study period.

我国西南喀斯特地区2001—2018年植被净初级生产力时空演变

植被净初级生产力（Net primary productivity，NPP）是陆地生态系统碳循环的重要指标，定量评价NPP的变化规律是碳循环机理的主要研究内容之一。以我国西南地区为研究区域，基于土地覆盖和NPP产品分析了2001-2018年西南地区NPP的时空变化特征，利用土地转移矩阵和统计分析方法，定量描述了土地覆盖类型变化导致的NPP变化量，对NPP变化的主要原因进行分析。结果表明：（1）2001-2018年，西南地区NPP均值呈波动上升趋势。（2） NPP变化趋势在空间分布上呈南高北低，NPP呈增长趋势主要集中在四川的中东部、重庆、贵州西部和云南东部；NPP呈减少趋势出现在云南、四川中西部和贵州东部。（3）森林、草地和耕地转灌丛以及灌丛转森林是对NPP变化量影响较大的土地覆盖类型转变，土地类型转变导致的NPP净增量为20.643 TgC。（4）降水与NPP相关性低，气温与NPP有一定相关性，植被叶面积指数与NPP有显著相关关系，表明气候因子对NPP的影响较小，植被生长密度对NPP有显著影响。退耕还林还草等生态工程实施，导致耕地面积减少和森林面积大量增加，是西南地区NPP上升的主导因素，因此生态工程的实施是西南地区NPP增加的重要影响因素。研究可为生态工程实施背景下NPP的变化机理研究提供参考。;Net primary productivity (NPP) of vegetation is an important indicator of the carbon cycle of terrestrial ecosystems. Quantitative evaluation of the changing law of NPP is one of the main research contents of carbon cycle mechanism. This article analyzes the spatiotemporal changes of NPP in southwest China from 2001 to 2018 based on land cover and NPP products. We use land transfer matrix and statistical analysis methods to quantitatively describe the amount of NPP change caused by land cover changes and analyze the main reasons for the NPP changes. The results showed that:(1) from 2001 to 2018, the average value of the NPP in the southwest China presented a fluctuating upward trend. (2) The spatial distribution of NPP showed a high in the south and a low in the north. NPP showed a growing trend, mainly in the central and eastern parts of Sichuan, Chongqing, western Guizhou, and eastern Yunnan; NPP indicated a decreasing trend in Yunnan, central and western Sichuan and eastern Guizhou. (3) The conversion of forests, grasslands, and the cultivated land to shrubs and shrubs to forests were land cover types that had a greater impact on the amount of NPP changes. The net increase in NPP caused by land type conversions is 20.643 TgC. (4) The correlation between precipitation and NPP is low. There is some correlation between air temperature and NPP and the vegetation leaf area index has a significant correlation with NPP, indicating that climatic factors have a small impact on NPP, and vegetation growth density has a significant impact on NPP. The implementation of ecological projects such as returning farmland to forest and grassland has led to a decrease in cultivated land and a substantial increase in forest area, which is the leading factor in the NPP increase. This study can provide a reference for the change mechanism of NPP under the background of ecological engineering implementation.

Effect of Land Use and Land Cover Change on the Changes in Net Primary Productivity in Karst Areas of Southwest China: A Case Study of Huanjiang Maonan Autonomous County

Karst areas in southwest China have experienced significant land cover and land use change (LUCC) due to utilization for human activity and a comprehensive rocky desertification control project (RDCP) since 2008. It is important to quantify the effect of LUCC on ecosystem productivity in this region for assessing the overall benefit of this ecological restoration project. In this study, we used using MODIS land cover and NPP products to investigate the relative contribution of LUCC to the change in net primary productivity (NPP) during 2008–2013 in Huanjiang County, one of first one hundred pilot counties to implement RDCP. Our results show that NPP increased in 95.53% of the county, and the average growth of NPP in non-rocky desertification area was higher than in rocky desertification or potential rocky desertification areas. LUCC has an important contribution (25.23%) to the NPP increase in the county, especially in the LUCC area (70.97%), which increased the average NPP by 3.9% and 10.5%, respectively. Across the six RDCP regions in the county, the average increase in NPP for the vegetation restoration measure of governed karst area is significantly greater than in the ungoverned karst area, and the positive change in NPP increased with the increasing implementation area of the vegetation restoration measure.

Impact of climate change on net primary production (NPP) in south Iran.

Climate change is a natural hazard which threatens the sustainable development of human health, food security, economic well-being, and natural resources. It also affects photosynthesis, plant respiration, and decomposition of organic matter that contribute to atmospheric carbon flow. The net primary production (NPP) is one of the main components of carbon balance. This study investigated the impact of climatic change on the net production in the Hormozgan county in south Iran. To obtain NPP, MODIS NPP product (MOD17A3) was used and future temperature and precipitation values were obtained using the HadGEM2-ES model under the RCP4.5 scenario. These values were downscaled using the LARSWG 6 statistical model, and precipitation and temperature were simulated for the RCP4.5 scenario. For further analysis, NPP was simulated based on the BIOME-BGC model and compared with the NPP data obtained from the MODIS images. Comparison of the climatic parameters of the basic (2001-2015) and future (2016-2030) periods indicated an increase in precipitation, minimum temperature, and maximum temperature of the study area and subsequently an increase in the NPP value in all biomes (averagely 17.73%) in the future. The highest NPP values were observed in the central and western parts of the region in biomes 4 (mangrove forest cover), 10 (broadleaf forest vegetation), and 6, 5, and 1 (rangeland vegetation), respectively, and the lowest values were observed in the eastern parts. Results showed that the increase in future NPP could be due to the increase in precipitation.

A novel remote sensing-based method of ozone damage assessment effect on Net Primary Productivity of various vegetation types

Tropospheric ozone causes considerable damage to both agricultural crops and forest trees, leading to Net Primary Productivity (NPP) reduction. Ozone damage is conventionally investigated using experiments limited in scope and study area. This study attempted to develop a novel remote sensing-based method to assess the effect of regional ozone damage on the NPP of various vegetation types, combining AOT40 (Accumulated Ozone exposure over a Threshold of 40 ppb) and MODIS NPP products. Annual averages of multi-year (five periods during 2009–2013) cumulative AOT40 values and NPP data for each MODIS image pixel were compared to investigate the relationships between NPP and AOT40, and construct linear AOT40-based NPP response models for each of five vegetation types. Our results showed negative significant relationships between NPP and AOT40 for all vegetation types, using at least 4-year average AOT40 values. Estimated maps of ozone impacts on NPP showed a 4–48% reduction for the Kanto area of Japan in response to AOT40 for the year 2013, and a higher ozone sensitivity of crop species (with NPP decrease of 12–48%) than forest species (with NPP decrease of 4–39%). The largest decreases (over 40% in some locations) occurred in the agricultural lands in the central part of the study area. This remote sensing-based method was considered as suitable for regional ozone damage assessment with considerable precision based on comparison with the results of previous experiment-based studies.

Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom County, Iran

Net primary production (NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach (CASA) model for estimating NPP and its spatial and temporal changes in semi-arid rangelands of Semirom County, Iran. Using CASA model, we estimated the NPP values based on monthly climate data and the normalized difference vegetation index (NDVI) obtained from the MODIS sensor. Regression analysis was then applied to compare the estimated production data with observed production data. The spatial and temporal changes in NPP and light utilization efficiency (LUE) were investigated in different rangeland vegetation types. The standardized precipitation index (SPI) was also calculated at different time scales and the correlation of SPI with NPP changes was determined. The results indicated that the estimated NPP values varied from 0.00 to 74.48 g C/(m2·a). The observed and estimated NPP values had different correlations, depending on rangeland conditions and vegetation types. The highest and lowest correlations were respectively observed in Astragalus spp.-Agropyron spp. rangeland (R2=0.75) with good condition and Gundelia spp.-Cousinia spp. rangeland (R2=0.36) with poor and very poor conditions. The maximum and minimum LUE values were found in Astragalus spp.-Agropyron spp. rangeland (0.117 g C/MJ) with good condition and annual grasses-annual forbs rangeland (0.010 g C/MJ), respectively. According to the correlations between SPI and NPP changes, the effects of drought periods on NPP depended on vegetation types and rangeland conditions. Annual plants had the highest drought sensitivity while shrubs exhibited the lowest drought sensitivity. The positive effects of wet periods on NPP were less evident in degraded areas where the destructive effects of drought were more prominent. Therefore, determining vegetation types and rangeland conditions is essential in NPP estimation. The findings of this study confirmed the potential of the CASA for estimating rangeland production. Therefore, the model output maps can be used to evaluate, monitor and optimize rangeland management in semi-arid rangelands of Iran where MODIS NPP products are not available.

The relationship between primary production and export production in the ocean: Effects of time lags and temporal variability

We analyzed net primary production (NPP) and export production rates measured in the Southern Ocean (SO) and at Station ALOHA (22° 45′N, 158° 00′W) and discovered that in both cases there was a highly significant negative correlation between NPP and the ratio of export production to NPP (i.e., the export ratio). We show that in both cases this negative correlation can be explained by a time lag between the production and export of organic matter from the euphotic zone. The negative correlation appears (disappears) in simulated data if the estimates of the relationship between NPP and export production are based on measurements over timeframes that are comparable to or shorter (much longer) than the time lag between NPP and export production. At Station ALOHA, where data are available from a time series that extends back to 1988, the negative correlation disappears when the data are climatologies over monthly time intervals, which are long compared to the assumed lag of five days between the production and export of organic matter at low latitudes. We conclude that because of the temporal variability of NPP and the time lags between the production and export of organic matter, meaningful estimates of export ratios will need to be based on rates of NPP and export production averaged over timeframes that are long compared to the lag between production and export.