Mapping Land Use/Cover Dynamics of the Yellow River Basin from 1986 to 2018 Supported by Google Earth Engine

Assessing the impact of agriculture, coal mining, and ecological restoration on water sustainability in the Mu Us Sandyland

Balancing water demand for socio-economic development and ecosystem stability presents a challenge for regional sustainable management, especially in drylands. Previous studies have indicated that large-scale ecological restoration projects (ERPs) lead to a decline in terrestrial water storage (TWS) in the Mu Us Sandyland (MUS). However, the effects of other human activities (e.g., cropland reclamation, coal mining) on water resources remain unclear, raising concerns regarding water crisis and human-natural system sustainability. Through the utilization of coal mine location data, we found that the impact of coal mass loss on the Gravity Recovery and Climate Experiment (GRACE) products cannot be ignored in MUS, especially in the coal-rich northeastern part. Combining these data with auxiliary datasets, we observed a significant (p < 0.05) decrease in TWS (-0.85 cm yr-1) and groundwater storage (GWS, -0.95 cm yr-1) in the MUS, with human activities accounting for 79.23 % of TWS and 90.45 % of GWS reductions, primarily due to increased agricultural and industrial water consumption. Agricultural water consumption increased 2.23 times from 2001 to 2020, attributed to enhanced water use intensity (62.6 %) and cropland expansion (37.4 %). Industrial water consumption in Shenmu, a representative coal county, experienced a 4.16-fold rise between 2001 and 2020. Despite these challenges, local governments have alleviated water stress, ensured food security, and increased household income by comprehensive management strategies, such as enhancing water-saving technology and enforcing stringent policies. Previous studies have overestimated the amount of water resources consumed by ERPs. However, ERPs has played a critical role in stabilizing the regional ecological environment and ensuring the region as a vital food and energy supplier. Our findings can guide for socio-economic development and water management policies in similar regions.

Discriminating the impacts of vegetation greening and climate change on the changes in evapotranspiration and transpiration fraction over the Yellow River Basin

Evapotranspiration (ET) is a vital parameter in terrestrial water-energy cycles. The transpiration fraction (TF) is defined as the ratio of transpiration (T) to evapotranspiration (ET), representing the contribution rate of vegetation transpiration to ecosystem ET. Quantifying the relative contributions of vegetation and climate change on the ET and TF dynamic is of great significance to better understand the water budget between the land and atmosphere. Here, we chose Yellow River Basin (YRB) as the study area and analyzed the spatiotemporal changes of ET, T, and TF from 1982 to 2015 using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. Meanwhile, the relative contributions of vegetation and climate change to ET, T and TF change were quantified. Model evaluation showed that the PT-JPL model performs well in the simulation of ET and T. During 1982-2015, the average annual ET, T, and TF increased at a rate of 3.20 mm/a, 0.77 mm/a and 0.003/a over the YRB during 1982-2015, respectively. The regions with significant increases in ET, T and TF almost covered the whole study area except for the upper reaches of the YRB. Vegetation greening was the main factor for the increase of ET and TF in the YRB and enhanced ET and TF at a rate of 0.72 mm/a and 0.57/a, respectively, which mainly observed in the entire Loess Plateau region (over 50 % of the study area). Precipitation (PRE) was also the dominated factor contributing to the increase in ET and TF, and temperature (TEM) showed a positive correlation with the changes in ET and TF in the most areas of YRB, which jointly dominated ET changes in the upper reaches of the YRB and TF changes in the southern part of the basin. Except for the total effects, leaf area index (LAI) also indirectly promoted ET changes by affecting PRE, TEM and relative humidity (RH). While wind speed (WS) and radiation (RAD) had a relatively weak regulatory effect on the changes in ET and TF. These findings were helpful for regional water resources management and formulating water resources-sustainable vegetation restoration strategies for local government.

Eco-environmental response to land cover change in ASEAN countries from 2001 to 2020 based on spatial granular association

As one of the major forms of terrestrial ecosystem change, land cover change (LCC) alters the structure of surface landscape patterns, thereby causing regional eco-environmental responses. Due to limitations in research methods, existing studies have focused more on the overall response between LCC and the eco-environment, and cannot calculate the level change response of eco-environmental quality caused by LCC. Based on the method of spatial data information granulation, this study used a remote sensing ecological index to represent the eco-environmental system and divided the complex eco-environmental system and land system into a simple system composed of spatial information granules, thus simplifying the spatial data calculation. The main contributions of this study are as follows: (1) A computing method of eco-environmental response to LCC based on spatial granular association was proposed, which can spatially identify the main response types of regional LCC; (2) three measures, namely, spatial association support degree, spatial association confidence degree, and spatial association cover degree, were proposed to measure the eco-environmental response of regional LCC from different perspectives; and (3) during 2001-2020, the eco-environmental response to l LCC, namely, the response to degradation caused by shrinking forest area, was not very dramatic in ASEAN (Association of Southeast Asian Nations).

The evolution of ecological security and its drivers in the Yellow River Basin

Ecological security is the state achieved once an ecosystem maintains its stability under external stress. The Yellow River Basin (YRB) is the largest river basin in northwest and north China and an important area for grain and energy production. The assessment and attribution of ecological security in the YRB are important for protecting the natural environment and ensuring sustainable development. Here, the ecological security of the YRB was assessed by the ecological security index (ESI), a comprehensive index based on the oxygen cycle, and its drivers were attributed to climate change, human activities, vegetation, and soil factors. The spatial pattern of ecological security in the YRB showed high heterogeneity. Ecological insecurity occurred mainly in the middle reaches and regions where the major stream of the Yellow River passes through. The ESI decreased at a rate of - 0.82/year since 2000, which indicated the natural environment continued to be improved in the YRB. Climate change dominated the evolution of ecological security in the upper reaches. The level of ecological security has been improved in the middle reaches after a series of ecological restoration projects conducted. With higher intensity of industrial activity, human activities played a more critical role in ecological security in the lower reaches. Our results suggested that government and local people need to adopt different strategies and actions based on the dominant drivers in the upper, middle, and lower reaches to ensure protection of the natural environment and achieve sustainable development targets.

Degradation or Restoration? The Temporal-Spatial Evolution of Ecosystem Services and Its Determinants in the Yellow River Basin, China

Ecosystem services (ESs) are irreplaceable natural resources, and their value is closely related to global change and to human well-being. Research on ecosystem services value (ESV) and its influencing factors can help rationalize ecological regulatory policies, and is especially relevant in such an ecologically significant region as the Yellow River Basin (YRB). In this study, the ecological contribution model was used to measure the contribution of intrinsic land use change to ESV, the bivariate spatial autocorrelation model was applied to investigate the relationship between land use degree and ESV, and the geographical detector model (GDM) and geographically weighted regression (GWR) were applied to reveal the impact of natural and socio-economic factors on ESV. Results showed that: (1) The total ESV increased slightly, but there were notable changes in spatial patterns of ESV in the YRB. (2) Land use changes can directly lead to ESV restoration or degradation, among which, conversion from grassland to forest land and conversion from unused land to grassland are vital for ESV restoration in the YRB, while degradation of grassland is the key factor for ESV deterioration. (3) According to GDM, NDVI is the most influential factor affecting ESV spatial heterogeneity, and the combined effect of multiple factors can exacerbate ESV spatial heterogeneity. (4) GWR reveals that NDVI is always positively correlated with ESV, GDP is mainly positively correlated with ESV, and population density is mainly negatively correlated with ESV, while positive and negative correlation areas for other factors are roughly equal. The findings can provide theoretical support and scientific guidance for ecological regulation in the YRB.

Land Use/Land Cover Change and Their Driving Factors in the Yellow River Basin of Shandong Province Based on Google Earth Engine from 2000 to 2020

As the convenient outlet to the Bo Sea and the major region of economic development in the Yellow River Basin, Shandong Province in China has undergone large changes in land use/land cover (LULC) in the past two decades with rapid urbanization and population growth. The analysis of the LULC change patterns and its driving factors in the Shandong section of the Yellow River Basin can provide a scientific basis for rational planning and ecological protection of land resources in the Shandong section of the Yellow River Basin. In this manuscript, we analyzed the spatial pattern of LULC and its spatial and temporal changes in the Shandong section of the Yellow River Basin in 2000, 2010, and 2020 by using the random forest classification algorithm with the Google Earth Engine platform and multi-temporal Landsat TM/OLI data. The driving factors of LULC changes were also quantified by the factor detector and interaction detector in the geodetector. Results show that in the past two decades, the LULC types in the study area are mainly farmland and construction land, among which the proportion of farmland area has decreased and the proportion of construction land area has increased from 19.4% to 29.7%. Based on the results of factor detector, it can be concluded that elevation, slope, and soil type are the key factors affecting LULC change in the study area. The interaction between elevation and slope, slope and soil type, and temperature and precipitation has strong explanatory power for the spatial variation of LULC change in the study area. The research results can provide data support for ecological environmental protection, sustainable, and high-quality development of the Shandong section of the Yellow River Basin, and help local governments take corresponding measures to achieve coordinated and sustainable socioeconomic and environmental development.

A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region

Planted forests provide a variety of meaningful ecological functions and services, which is a major approach for ecological restoration, especially in arid areas. However, mapping planted forests with remote-sensed data remains challenging due to the similarities in canopy spectral and structure characteristics and associated phenology features between planted forests and other vegetation types. In this study, taking advantage of the Google Earth Engine (GEE) platform and taking the Ningxia Hui Autonomous Region in northwestern China as an example, we developed an approach to map planted forests in an arid region by applying long-term features of the NDVI derived from dense Landsat time series. Our land cover map achieved a satisfactory accuracy and relatively low uncertainty, with an overall accuracy of 93.65% and a kappa value of 0.92. Specifically, the producer (PA) and user accuracies (UA) were 92.48% and 91.79% for the planted forest class, and 93.88% and 95.83% for the natural forest class, respectively. The total planted forest area was estimated as 3608.72 km2 in 2020, accounting for 20.60% of the study area. The proposed mapping approach can facilitate assessment of the restoration effects of ecological engineering and research on ecosystem services and stability of planted forests.

River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data

River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R2, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R2 value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R2 values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology.