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

Changes in extent of open-surface water bodies in China's Yellow River Basin (2000–2020) using Google Earth Engine cloud platform

Global warming threatens ecosystem functions, biodiversity, and rangeland productivity in Mongolia. The study analyzes the spatial and temporal distributions of the Net Primary Production (NPP) and its response to climatic parameters. The study also highlights how various land cover types respond to climatic fluctuations from 2003 to 2018. The Boreal Ecosystem Productivity Simulator (BEPS) model was used to simulate the rangeland NPP of the last 16 years. Satellite remote sensing data products were mainly used as input for the model, where ground-based and MODIS NPP were used to validate the model result. The results indicated that the BEPS model was moderately effective (R2 = 0.59, the Root Mean Square Error (RMSE) = 13.22 g C m−2) to estimate NPP for Mongolian rangelands (e.g., grassland and sparse vegetation). The validation results also showed good agreement between the BEPS and MODIS estimates for all vegetation types, including forest, shrubland, and wetland (R2 = 0.65). The annual total NPP of Mongolia showed a slight increment with an annual increase of 0.0007 Pg (0.68 g C per meter square) from 2003 to 2018 (p = 0.82) due to the changes in climatic parameters and land cover change. Likewise, high increments per unit area found in forest NPP, while decreased NPP trend was observed in the shrubland. In conclusion, among the three climatic parameters, temperature was the factor with the largest influence on NPP variations (r = 0.917) followed precipitation (r = 0.825), and net radiation (r = 0.787). Forest and wetland NPP had a low response to precipitation, while inter-annual NPP variation shows grassland, shrubland, and sparse vegetation were highly sensitive rangeland types to climate fluctuations.

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China’s national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants’ growth and extreme climate, together with the reaction of plants’ growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants’ growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin.

As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Google Earth Engine平台与复杂网络的黄河流域土地利用/覆被变化分析 DOI: 10.5846/stxb202011132938 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学面上基金项目（42071144，41771118）；国家重点研发计划重点专项（2016YFC0501601）；中央高校基本科研业务费专项资金（GK202003060） Land use/cover change in the Yellow River Basin based on Google Earth Engine and complex network Author: Affiliation: Fund Project: the National Natural Science Foundation of China (42071144, 41771118); the National Key Research and Development Program of China (2016YFC0501601); and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK202003060). 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:黄河流域在中国社会经济发展和生态安全方面的地位十分重要，过去几十年的自然演变和人类活动对其造成了深远影响，系统科学地认识黄河流域的环境格局变化是实现黄河流域高质量发展的重要前提。基于Google Earth Engine平台和复杂网络分析方法，解译并分析了1986年至2018年黄河流域的连续年度土地利用/覆被变化（LUCC）。基于1000个独立验证点进行的评估显示解译的覆盖黄河流域33年的年度土地利用/覆被数据集的7个一级类的总体准确率为82.6%，15个二级类的总体准确率为74.7%。分析结果显示黄河流域的土地系统在整个研究期间呈现出复杂的时空变化，主要的LUCC模式包括：不变或很小的变化、伴随耕地流失的城市扩张、草地恢复、果园和梯田扩张和森林增加。基于复杂网络方法对黄河流域土地系统的分析表明，土地利用/覆被转移网络中的高覆盖草地、低植被覆盖地表和落叶常绿混交林与其他地类的转移较频繁，地类节点的中介中心性和度值较高，是黄河流域土地系统中的关键地类。另外与人类活动关系密切的果园和梯田、谷物耕地和城市及建设用地节点组成了较为活跃的网络社区结构，它们都有较高的结构多样性、接近中心性，这些地类之间的转移是土地系统中重要的地类转移类型。时间序列的转移网络分析表明黄河流域的土地系统在1993-1998年、2001-2007年、2011-2014年存在三个不稳定时期，这三个不稳定时期的出现可能分别由大量的农业土地开发、退耕还林还草工程和果园梯田的大量增加导致。对黄河流域的长时间土地利用/覆被动态的系统性分析提供了关于环境政策和社会经济活动对土地系统造成何种影响的见解，表明黄河流域正朝着实现农民生计安全，区域经济增长和生态环境保护的可持续发展的目标迈进，并为LUCC研究提供了新的数据处理方式和新的系统科学视角，有助于实现区域的高质量可持续发展和理解人与自然系统之间的相互作用关系。 Abstract:The Yellow River Basin plays an important role in China's economic and social development and ecological security. The natural evolution and human activities in past decades had a profound impact on the Basin. A systematic and scientific understanding of the environmental changes in the Yellow River Basin is an important premise to achieve the high-quality development of the Basin. Using landsat imagery (17080 scenes) and the classification and regression trees algorithm, mapped and analyzed the annual continuous land use/cover change (LUCC) of the Yellow River Basin based on the Google Earth Engine platform and complex network analysis methods, and provided a set of 90 m resolution continuous annual land use/cover maps from 1986 to 2018. The assessment based on 1000 independent validation points collected in Google Earth indicated that these maps achieved an overall accuracy of 82.6% for 7 first-degree classes and 74.7% for 15 second-degree classes. The analysis of the land system showed that the Yellow River Basin presented complex temporal and spatial changes during the study period. The main LUCC patterns included no change or little change, urban expansion with croplands loss, grasslands restoration, orchard and terrace expansion, and forests growth. In the land use/cover transfer network, high coverage grasslands, low-vegetated lands, and the mixed forests were the key land class nodes in the land system of the Yellow River Basin, with high degrees and frequent transfers with other classes. In addition, the nodes of orchard and terrace, croplands, and urban and built-up all had high diversity and closeness and form one relatively active network community structure. The transfers between them were more important land transfer types in the land system. Time series transfer network analysis showed that the land system of the Yellow River Basin had three unstable periods in 1993-1998, 2001-2007, and 2011-2014 with lower average shortest-path length and higher network density and network transitivity. The possible reasons for these three periods are the reclamation of a large amount of agricultural land, the implementation of the Grain for Green program, and the large increase in orchard and terrace, respectively. Based on the data and analysis results, we can further understand the role of the land system in the mutual feedback between society and the environment. The results of LUCC analysis revealed the land use/cover dynamics caused by frequent human activities in the Yellow River Basin, provided insights on the impact of environmental policies and socio-economic activities on the land system, which showed that the Yellow River is moving towards achieving the sustainable development goals of livelihood security, economic growth, and ecological protection. Research provided new data processing methods and new system science perspectives for LUCC analysis, which can help to achieve high-quality regional development and to understand the interaction between human and natural systems. 参考文献 相似文献 引证文献

Modeling Carbon and Water Budgets in the Lushi Basin with Biome-BGC

In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R2, RMSE) were: temperate grasses (0.94, 24.33 g C m−2), alpine meadows (0.94, 18.13 g C m−2), shrubs (0.94, 29.20 g C m−2), evergreen needle leaf forests (0.96, 27.88 g C m−2), deciduous broad leaf forests (0.94, 32.09 g C m−2), one crop a year (0.96, 16.19 g C m−2), and two crops a year (0.90, 38.15 g C m−2). After adaptability testing, the average R2 value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m−2. Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and simulating diverse vegetation types on a large scale.

Knowledge of the effects of climate factors on net primary production (NPP) is pivotal to understanding ecosystem processes in the terrestrial carbon cycle. Our goal was to evaluate four different categories of effects (physical, climatic, NDVI, and all effects[global]) as predictors of forest NPP in eastern China. We developed regression models with data from 221 NPP in eastern China and identified the best model with each of the four categories of effects. Models explained a large part of the variability in NPP, ranging from 46.8% in global model to 36.5% in NDVI model. In the most supported global model, winter temperature and sunshine duration negatively affected NPP, while winter precipitation positively affected NPP. Thus, winter climate conditions play an important role in modulating forest NPP of eastern China. Spring temperature had a positive affect on NPP, which was likely because a favorable warm climate in the early growing season promotes forest growth. Forest NPP was also negatively affected by summer and autumn temperatures, possibly because these are related to temperature induced drought stress. In the NDVI model, forest NPP was affected by NDVI in spring (positive), summer (negative) and winter (negative) seasons. Our study provides insight into seasonal effects of climate and NPP of forest in China, as well as useful knowledge for the development of climate-vegetation models.

Green plants play an important role in matter and energy transformations and are key messengers in the carbon and energy cycle. Vegetation net primary productivity (NPP) reflects the capability of plants to convert solar energy into photosynthate (fixed carbon). It can be used as a direct indicator of current status and future trend of ecological processes affected by human activities and climate variability, especially in fragile ecological environments such as karst areas where rock-desertification increasingly occurs. Using remote sensing and geographic information system technologies, research on spatio-temporal variations of NPP and its response to changes in climate variables (temperature, rainfall and solar radiation) in Guizhou province of China was conducted. Karst areas within the study area were delineated to explore the relationship between NPP and climate variables. The NPP model developed by Wang et al. (2010) was used. This model is a modification of the light use efficiency (LUE) model of Running et al. (2000), validated for this region in China. As input to the model, three climate scenarios were designed - one representing current conditions and two for hypothetical future conditions. Both future scenarios have a 2˚C increase in temperature and 6% increase in rainfall, applied spatially across the region using a constant scaling factor on the current condition scenario. The first future scenario has unchanged solar radiation; and the second has a 10% increase in solar radiation for investigating the effect of solar radiation on NPP. The model was run on a monthly time step for one year. Under the current condition scenario, the spatio- temporal patterns of monthly NPP were analysed using MODIS imagery, with the climate variables as model inputs. The relationships between NPP and temperature, and NPP and rainfall, were quantified using single and partial correlation. The hypothetical future condition scenarios were then used to explore the response of NPP to changes in these variables. This paper reports on the results of this modelling under all scenarios, with particular interest in the relationship between solar radiation and karst areas. Model results show that, under the current condition scenario, the mean annual NPP of terrestrial vegetation is 421gCm -2 with the NPP in karst areas being 11.9% lower than in non-karst areas. Maximum and minimum NPP values occur in July and January, respectively. The results show strong correlations between NPP and climatic variables: (1) Temperature is a key factor which significantly limits vegetation growth in the northwest. The correlation between NPP and temperature decreases from north to south, and is stronger in karst (than non-karst) areas. (2) The correlation between NPP and rainfall is most significant in the southeast and west in comparison with other areas, and is lower in karst (than non-karst) areas. Under the first future scenario, the total annual NPP decreases from 74×10 6 tC to 73×10 6 tC; whereas under the second future scenario, annual NPP increases from 74×10 6 tC to 79×10 6 tC. The results from both future scenarios indicate that the largest variation in NPP occurs in karst areas with severe rock-desertification, indicating solar radiation is a dominant factor affecting NPP in karst areas. There are obvious regional differences on spatio-temporal patterns of NPP between karst areas and non-karst areas. The analysis of the interactions among three climate variables - temperature, rainfall and solar radiation has contributed to our understanding of their contributions to NPP, especially in karst areas.

With climate change, frequent forest fires and prolonged fire period occur all over the world. Moreover, carbon emission from forest fire affects the carbon cycle of the forest ecosystem. However, this effect varies by region with no uniform conclusions, and fewer comparative studies exist on such differences between regions. In this paper, net primary productivity (NPP) data MOD17A3 were used as an important parameter of forest carbon absorption, along with MODIS fire spot data MCD14DL and burned area data MCD64A1. Forest carbon lost under forest fire interference in the northeast and southwest natural forest areas of China was studied to explore the role of forest fire in the carbon cycle process and its differences in the unlike regions of China. Here, by means of kernel density analysis and M-K trend test, the characteristics of forest fires in China’s southwest and northeast forests were calculated. Forest carbon emission under forest fire disturbance was quantified by reference to the forest fire emission factor list. We show that (1) the total number of forest fire spots in the southwest region from 2001 to 2020 was 1.06 × 105, 1.28 times that of Northeast China. However, the total burned area in the southwest was only 67.84% of that in the northeast. (2) The total carbon emissions from forest fires in the southwest from 2001 to 2020 was 37,559.94 Gg, 10.77% larger than the northeast forest, CH4 and CO2 were 13.52% and 11.29% larger respectively. Moreover, the carbon emissions of forest fire in the northeast showed a downward trend, R2 = 0.16 (p < 0.1), while it remained basically unchanged in the southwest. The contribution of carbon emissions from forest fires changed with forest types, it was shown as: evergreen needleleaf forest (14.98%) > evergreen broadleaf forest (10.81%) > deciduous needleleaf forest (6.52%) > deciduous broadleaf forest (5.22%). (3) From 2001 to 2020, under the premise that the NPP both manifested upward trends, the NPP of the burned areas showed a significant downward trend in the southwest forest, with R2 = 0.42 (p < 0.05), while it increased in the northeast forest, with R2 = 0.37 (p < 0.05). It showed negative correlation between NPP of burned areas and forest fire carbon emissions, and forest fire disturbance had no significant effect on forest NPP in Northeast China, while net carbon loss occurred in Southwest China. In general, under different forest fire characteristics, NPP, which represents forest carbon uptake, and carbon emissions from forest fires show differences. The impact of forest fire disturbance on forest carbon process varies with regions. The study can provide some ideas on the effects of forest fire disturbance on climate change.

This study innovatively utilizes the Google Earth Engine (GEE) cloud platform, combined with a multispectral index rule set, to address the challenges of extracting open-surface water bodies in the middle reaches of the Tarim River Basin (MROTR). To overcome the limitations of traditional index methods, particularly the reduced extraction accuracy caused by mixed pixels in arid environments, this research integrates indices such as MNDWI, NDVI, and EVI for automated water extraction, significantly enhancing the precision of water body delineation. By analyzing the spatiotemporal changes in open-surface water area (SWA) from 1990 to 2022, a notable increase in SWA was observed following the implementation of ecological gate-controlled water management, while a decreasing trend was identified within an 8-km range from the riverbanks. The results show a 93.7% accuracy in surface water identification and an SWA growth rate of 12.47 km2/year, with 96% of this growth attributed to seasonal water areas. Runoff loss decreased by 8.53% in the S1 and S2 regions but increased by 7.76% in the S3 and S4 regions. The mixed index rule method proved effective for large-scale water detection, offering new insights for managing arid region water resources.

Impacts of sea ice retreat, thinning, and melt-pond proliferation on the summer phytoplankton bloom in the Chukchi Sea, Arctic Ocean

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.

Tropical forests have the highest rates of net primary productivity (NPP) in terrestrial ecosystems and, therefore, may contribute significantly to the mitigation of global climate change. Although NPP is influenced by soil fertility, and recently, in some regions, mining activity in forest ecosystems has intensified. Little is known about how soils determine the restoration of NPP in forests degraded by mining. We evaluated the influence of soil nutrients on wood NPP of post-mining forests in the biogeographic Chocó region (Colombia), with emphasis on the effects of nitrogen (N) and phosphorus (P) limitations in post-mining forests under successional stages of 12-15 or 30-35 years. For this, permanent plots were established in secondary post-mining forests in Jigualito (Colombian Pacific), the wood NPP (accumulated and current) was evaluated, and it was related to soil properties such as organic matter (OM), acidity, Al, total N, available P, magnesium (Mg), potassium (K), calcium (Ca), and texture. An accumulated wood NPP of 0.72 t ha -1 yr -1 was recorded in post-mining forests 12-15 years old. Meanwhile, in post-mining forests of 30-35 years, the accumulated wood NPP was 6.52 t ha -1 yr -1 . The current wood NPP was 4.25 t ha -1 yr -1 in post-mining forests with 30-35 years of recovery. Accumulated NPP positively correlated with soil OM, total N, Ca, Mg, and effective cation exchange capacity–ECEC in post-mining forests. In post-mining forests, a slow recovery of the wood NPP was denoted in the first years. Soil nutrients determined the wood NPP, and a multiple limitation of nutrients with the succession was observed, which corroborates the need to restore the degraded ecosystem in the region.

Based on Moderate Resolution Imaging Spectoradiometer (MODIS) remote sensing data, meteorological observation data, multisource atmospheric circulation, and sea surface temperature (SST) data from NCEP/NCAR reanalysis, we estimated the net primary productivity (NPP) of terrestrial natural vegetation in China according to the CASA model and analyzed the linear trend and interannual fluctuation of NPP, as well as the spatial distribution characteristics of the annual NPP response to climatic factors. The obtained results revealed the impact of air–sea interaction on interannual NPP variability in key climatic areas. In China, the annual NPP of natural vegetation, linear NPP trend, and interannual NPP fluctuation showed significant regional characteristics. The annual NPP exhibited a significant increasing trend and interannual fluctuation in North China and Northeast China, with spatially consistent responses from NPP to precipitation and temperature. On the seasonal time scale, NPP in the key climatic area (105~135° E, 35~55° N) exhibited a strong response to both summer precipitation and mean temperature. In the summer atmospheric circulation, the circulation anomaly area is mainly distributed in the northeast cold vortex area in the middle- and high-latitude westerlies in East Asia and in the Sea of Okhotsk with dipole circulation. In the SST of the preceding winter and spring, the key SST anomaly area was the Kuroshio region, with an impact of the Kuroshio SST anomaly on the interannual variation in annual NPP in the key climatic area. The cold vortex in Northeast China played a pivotal role in the influence of the SST anomaly in the Kuroshio region on atmospheric circulation anomalies, resulting in abnormal summer precipitation in the key climatic region and affecting the annual accumulation of NPP of natural vegetation.