Spatiotemporal Variation in Driving Factors of Vegetation Dynamics in the Yellow River Delta Estuarine Wetlands from 2000 to 2020

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Natural Science Foundation of Shandong Province (grant number ZR2022QD118) and the National Natural Science Foundation of China (grant numbers 42201312 and 32271678)Peer ReviewedPrevious studies of vegetation dynamics in the Yellow River Delta (YRD) predominantly relied on sparse time series or coarse-resolution images, which not only overlooked the rapid and spatially heterogeneous changes, but also limited our understanding of driving mechanisms. Here, employing spatiotemporal data fusion methods, we constructed a novel fused enhanced vegetation index (EVI) dataset with a high spatiotemporal resolution (30-meter and 8-day resolution) for the YRD from 2000 to 2020, and we analyzed the vegetation variations and their driving factors within and outside the YRD Nation Natural Reserve (YRDNRR). The fused EVI effectively captured spatiotemporal vegetation dynamics. Notably, within the YRDNRR core area, the fused EVI showed no significant trend before 2010, while a significant increase emerged post-2010, with an annual growth of 7%, the invasion of Spartina alterniflora explained 78% of this EVI increment. In the YRDNRR experimental area, the fused EVI exhibited a distinct interannual trend, which was characterized by an initial increase (2000–2006, p 0.05); the dynamics of the fused EVI were mainly affected by the spring runoff (R2 = 0.71), while in years with lower runoff, it was also affected by the spring precipitation (R2 = 0.70). Outside of the protected area, the fused EVI demonstrated a substantial increase from 2000 to 2010 due to agricultural land expansion and human management practices, followed by stabilization post-2010. These findings enhance our comprehension of intricate vegetation dynamics in the YRD, holding significant relevance in terms of wetland preservation and management

Decreasing Cropping Intensity Dominated the Negative Trend of Cropland Productivity in Southern China in 2000–2015

As the country with the highest food consumption in the world, China’s food security has been drawing global attention. The inter-annual variability of agricultural productivity and its predominant driving factors play important roles in food security and sustainable agricultural development. Here, we used gross primary productivity (GPP), which was simulated using the vegetation photosynthesis model (VPM), to quantify the spatial-temporal heterogeneity of cropland productivity from 2000 to 2015. The results showed that the cropland GPP significantly increased in northern China and markedly decreased in southern China. Socioeconomic and climatic factors jointly promoted a rise in GPP in the Northeast region, Inner Mongolia and Great Wall region, Huang-Huai-Hai region, and Loess Plateau region, with contribution rates of 93.6%, 67.9%, 73.8%, and 78.1%, respectively. The negative GPP trend in southern China was mainly attributed to the decreasing cropping intensity, with direct contributions of 54.1%, 53.9%, and 48.7% for the Yangtze River region, Southwest region, and South China region, respectively. Despite the decline in cropping intensity, the policies of Cang-liang-yu-di and Cang-liang-yu-ji can help in ensuring food security in China

Satellite-Based Monitoring on Green-Up Date for Optimizing the Rest-Grazing Period in Xilin Gol Grassland

Ecological degradation has occurred in global grasslands and has impaired their ecosystem services severely, so ecological conservation of grasslands should be focused more on the effectiveness of management measures. The green-up process decides the year-round forage yield and ecological conditions of grasslands. Adopting rest-grazing during the green-up process can guarantee a successful green-up, thus realizing more economic benefits without grassland degradation. Therefore, studies should pay more attention to whether the green-up process is really covered by the rest-grazing period or not. We analyze the spatiotemporal variations and the stability of the annual green-up date in Xilin Gol Grassland from 2000 to 2018 based on MODIS time series images and compare the green-up date with the rest-grazing period to assess the effectiveness of the rest-grazing policy. The results show that the green-up date of Xilin Gol Grassland had advanced 15 days on average because of the increasing trend of both temperature and precipitation during 2000~2018. The green-up date is mostly 120~130 d in the east, about 10 days earlier than the west (130~140 d) and 20 days earlier than in the central areas (140~150 d), also because of the spatial variations of temperature and precipitation. The coefficient of variation (CV) of the green-up date showed a significant negative correlation with precipitation, so the green-up date is more unstable in the arid areas. The rest-grazing period started more than 45 days earlier than the green-up date and failed to cover it in several years, which occurred more frequently in southern counties. The average green-up date appeared after rest-grazing started in over 98% of areas, and the time gap is 15~45 days in 88% of areas, which not only could not avoid grassland degradation effectively but also increased herdsmen’s life burden. This study aims to accurately grasp the temporal and spatial variations of the green-up date in order to provide references for adjusting a more proper rest-grazing period, thus promoting ecological conservation and sustainable development of animal husbandry

Satellite-Based Monitoring on Green-Up Date for Optimizing the Rest-Grazing Period in Xilin Gol Grassland

Ecological degradation has occurred in global grasslands and has impaired their ecosystem services severely, so ecological conservation of grasslands should be focused more on the effectiveness of management measures. The green-up process decides the year-round forage yield and ecological conditions of grasslands. Adopting rest-grazing during the green-up process can guarantee a successful green-up, thus realizing more economic benefits without grassland degradation. Therefore, studies should pay more attention to whether the green-up process is really covered by the rest-grazing period or not. We analyze the spatiotemporal variations and the stability of the annual green-up date in Xilin Gol Grassland from 2000 to 2018 based on MODIS time series images and compare the green-up date with the rest-grazing period to assess the effectiveness of the rest-grazing policy. The results show that the green-up date of Xilin Gol Grassland had advanced 15 days on average because of the increasing trend of both temperature and precipitation during 2000~2018. The green-up date is mostly 120~130 d in the east, about 10 days earlier than the west (130~140 d) and 20 days earlier than in the central areas (140~150 d), also because of the spatial variations of temperature and precipitation. The coefficient of variation (CV) of the green-up date showed a significant negative correlation with precipitation, so the green-up date is more unstable in the arid areas. The rest-grazing period started more than 45 days earlier than the green-up date and failed to cover it in several years, which occurred more frequently in southern counties. The average green-up date appeared after rest-grazing started in over 98% of areas, and the time gap is 15~45 days in 88% of areas, which not only could not avoid grassland degradation effectively but also increased herdsmen’s life burden. This study aims to accurately grasp the temporal and spatial variations of the green-up date in order to provide references for adjusting a more proper rest-grazing period, thus promoting ecological conservation and sustainable development of animal husbandry

Estimating the grassland aboveground biomass in the Three-River Headwater Region of China using machine learning and Bayesian model averaging

Spatially and temporally explicit information on the biomass in terrestrial ecosystems is essential to better understand the carbon cycle and achieve vegetation resource conservation. As a climate-sensitive critical ecological function area, accurate monitoring of the spatiotemporal variation in the grassland aboveground biomass (AGB) is important in the Three-River Headwater Region (TRHR) of China. In this study, based on field observation, remote sensing, meteorological and topographical data, we estimated the grassland AGB in the TRHR and analyzed its spatiotemporal change and response to climatic factors. Four machine learning (ML) models (random forest (RF), cubist, artificial neural network and support vector machine models) were constructed and compared for AGB simulation purposes. The AGB results estimated with the four ML models were then applied in integrated analysis via Bayesian model averaging (BMA) to obtain more accurate and stable estimates. Our results demonstrated that the RF model performed better among the four ML models (testing dataset: correlation coefficient ( r ) = 0.84; root mean squared error = 76.99 g m ^−2 ), and BMA improved grassland AGB prediction based on the multimodel results. The spatial distribution of the grassland AGB in the TRHR was heterogeneous, with higher values in the southeast and lower values in the northwest. The interannual variation in the grassland AGB in most areas of the TRHR exhibited nonsignificant increasing trends from 2000 to 2018, and the sensitivity of the AGB to the annual precipitation was obviously modulated by regional water conditions. This study provides a more precise method for grassland AGB estimation, and these findings are expected to enable improved assessments to obtain a greater grassland AGB understanding

Climate Change and CO2 Fertilization Have Played Important Roles in the Recent Decadal Vegetation Greening Trend on the Chinese Loess Plateau

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint

Interannual variability of terrestrial net ecosystem productivity over China: regional contributions and climate attribution

China’s terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China’s terrestrial carbon sink and the attributions to climate variations are not well understood. Here we have investigated how terrestrial ecosystems in the four climate zones with various climate variabilities contribute to the IAV in China’s terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominates national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and half of the annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone makes the largest contribution (23%) to the IAV of NEP in China because of both the high sensitivity of terrestrial ecosystem carbon uptake to rainfall and the large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV can be mainly attributed to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China's grasslands. The four models were trained with two strategies: training for all of northern China's grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China's grasslands fairly well, while the SAE model performed best (R-2 = 0.858, RMSE = 0.472 gC m(-2) d(-1), MAE = 0.304 gC m(-2) d(-1)). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy

Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus

The carbon budgets in terrestrial ecosystems in China are strongly coupled with climate changes. Over the past decade, China has experienced dramatic climate changes characterized by enhanced summer monsoon and decelerated warming. However, the changes in the trends of terrestrial net ecosystem production (NEP) in China under climate changes are not well documented. Here, we used three ecosystem models to simulate the spatiotemporal variations in China's NEP during 1982-2010 and quantify the contribution of the strengthened summer monsoon and warming hiatus to the NEP variations in four distinct climatic regions of the country. Our results revealed a decadal-scale shift in NEP from a downtrend of -5.95 Tg C/yr(2) (reduced sink) during 1982-2000 to an uptrend of 14.22 Tg C/yr(2) (enhanced sink) during 2000-10. This shift was essentially induced by the strengthened summer monsoon, which stimulated carbon uptake, and the warming hiatus, which lessened the decrease in the NEP trend. Compared to the contribution of 56.3% by the climate effect, atmospheric CO2 concentration and nitrogen deposition had relatively small contributions (8.6 and 11.3%, respectively) to the shift. In conclusion, within the context of the global-warming hiatus, the strengthening of the summer monsoon is a critical climate factor that enhances carbon uptake in China due to the asymmetric response of photosynthesis and respiration. Our study not only revealed the shift in ecosystem carbon sequestration in China in recent decades, but also provides some insight for understanding ecosystem carbon dynamics in other monsoonal areas