Spatiotemporal Variations of Chinese Terrestrial Ecosystems in Response to Land Use and Future Climate Change

Terrestrial ecosystems in China are threatened by land use and future climate change. Understanding the effects of these changes on vegetation and the climate-vegetation interactions is critical for vegetation preservation and mitigation. However, land-use impacts on vegetation are neglected in terrestrial ecosystems exploration, and a deep understanding of land-use impacts on vegetation dynamics is lacking. Additionally, few studies have examined the contribution of vegetation succession to changes in vegetation dynamics. To fill the above gaps in the field, the spatiotemporal distribution of terrestrial ecosystems under the current land use and climate baseline (1970–2000) was examined in this study using the Comprehensive Sequential Classification System (CSCS) model. Moreover, the spatiotemporal variations of ecosystems and their succession under future climate scenarios (the 2030s–2080s) were quantitatively projected and compared. The results demonstrated that under the current situation, vegetation without human disturbance was mainly distributed in high elevation regions and less than 10% of the national area. For future vegetation dynamics, more than 58% of tundra and alpine steppe would shrink. Semidesert would respond to climate change with an expansion of 39.49 × 104 km2, including the succession of the steppe to semidesert. Although some advancement of the temperate forest at the expense of substantial dieback of tundra and alpine steppe is expected to occur, this century would witness a considerable shrinkage of them, especially in RCP8.5, at approximately 55.06 × 104 km2. Overall, a warmer and wetter climate would be conducive to the occurrence and development of the CSCS ecosystems. These results offer new insights on the potential ecosystem response to land use and climate change over the Chinese domain, and on creating targeted policies for effective adaptation to these changes and implementation of ecosystem protection measures

Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria

Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring

Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria

Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring

Aboveground Biomass Dynamics of a Coastal Wetland Ecosystem Driven by Land Use/Land Cover Transformation

Accurately estimating aboveground biomass (AGB) is essential for assessing the ecological functions of coastal wetlands, and AGB of coastal wetlands is affected by Land use/land cover (LULC) types of conversion. To address this issue, in the current study, we used the Boreal Ecosystem Productivity Simulator (BEPS) model to simulate the AGB of the Yellow River Delta during 2000–2015. Based on the LULC types transform, we analyzed the spatiotemporal dynamics of the AGB simulation results and their relationship with the human-nature driving process. At the same time, combined with the actual situation of LULC transformation in the Yellow River Delta, a new driving process (Replace) is introduced. The results show that from 2000 to 2015, 755 km2 of natural wetlands in the Yellow River Delta were converted into constructed wetlands, and AGB increased by 386,121 Mg. Both single and multiple driving processes contributed to the decrease in AGB, with 72.6% of the increase in AGB associated with single artificial (such as Restore) or natural (such as Accretion) driving processes and 27.4% of the increase in AGB associated with multiple driving processes. Naturally driven processes bring much more AGB gain than loss, and human-driven processes bring the largest AGB gain. LULC conversion brought on by anthropogenic and natural driving processes has a large impact on AGB in coastal wetlands, and exploring this impact has a significant role in planning coastal wetland land use and protecting blue carbon ecosystems

Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years' winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security. However, using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions. Thus, we proposed a new approach to approximating irrigations of winter wheat over the North China Plain (NCP), where irrigation occurs extensively during the winter wheat growing season. This approach used irrigation pattern parameters (IPPs) to define the irrigation frequency and timing. Then, they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat (PRYM–Wheat), to improve the regional estimates of winter wheat over the NCP. The IPPs were determined using statistical yield data of reference years (2010–2015) over the NCP. Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield, with an increase and decrease in the correlation coefficient (R) and root mean square error (RMSE) of 0.15 (about 37%) and 0.90 t ha–1 (about 41%), respectively. The data in validation years (2001–2009 and 2016–2019) were used to validate PRYM–Wheat. In addition, our findings also showed R (RMSE) of 0.80 (0.62 t ha–1) on a site level, 0.61 (0.91 t ha–1) for Hebei Province on a county level, 0.73 (0.97 t ha–1) for Henan Province on a county level, and 0.55 (0.75 t ha–1) for Shandong Province on a city level. Overall, PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years, providing a scientific basis for ensuring regional food security

Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms

Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms

Ridge-furrow mulched with plastic film improves the anti-oxidative defence system and photosynthesis in leaves of winter wheat under deficit irrigation

<div><p>In semi-arid areas of China, the ridge-furrow mulched with plastic film (RF) cultivation system is a common water-saving agricultural technique where the shortage of water resources has become a serious problem. Therefore, we aimed to explore whether this cultivation is actually an improvement over the traditional flat planting (TF) method while testing two deficit irrigation (150, 75 mm) levels to grow winter wheat. Furthermore, we examined the responses of the anti-oxidative defence system and photosynthetic capacity of winter wheat flag leaves under three simulated rainfall (275, 200 and 125 mm) conditions. The results showed that the RF system with 150 mm deficit irrigation and 200 mm simulated rainfall condition (RF2₁₅₀) treatment raised soil water content (%) at the jointing and flowering stages and achieved higher net photosynthesis rates (Pn) in flag leaves. Furthermore, such improvements were due to the reduction of malondialdehyde (MDA) content and oxidative damage during different growth stages of winter wheat. The RF2₁₅₀ treatment significantly increased the activities of superoxide dismutase (SOD); peroxidise (POD), catalase (CAT) and ascorbate peroxidase (APX) and the content of soluble protein (SP) during different growth stages of winter wheat. Furthermore, RF2₁₅₀ treatment attained the highest value at the flowering stage, while also exhibiting significant declines in contents of proline, MDA, H₂O₂ and O₂ in flag leaves. The higher free H₂O₂ and O₂ scavenging capacity and better anti-oxidative enzyme activities under the RF2₁₅₀ treatment were due to the lower level of lipid peroxidation, which effectively protected the photosynthetic machinery. The net photosynthetic rate of flag leaves was positively correlated with SOD, POD, CAT, APX and SP activities, and negatively correlated with proline, MDA, H₂O₂ and O₂ contents. We concluded that the RF2₁₅₀ treatment was the better water-saving management strategy because it significantly delayed flag leaf senescence and caused the increases in SWC, WUE, Pn, antioxidant enzyme activities and grain yield of winter wheat grown in semi-arid regions of China.</p></div