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In 2015, the Sustainable Development Goals (SDG) replaced the Millennium Development Goals (MDG), providing guidance and direction for the future development of countries around the world. Both MDG and SDG have established multiple indicators to achieve environmental sustainability. However, few studies have compared the changes in the ecological environment during the two periods. Therefore, this study selected the seven major river basins in China as the study area and comprehensively assessed the changes in ecosystem service importance (ESI) within different basins in the MDG and SDG periods based on six ecosystem services, including habitat quality, soil conservation, water yield, carbon storage, grain production, and aesthetic landscape. Specifically, the changes from 2000 to 2015 and 2015 to 2020 were used to represent MDG and SDG periods, respectively. The results showed that the overall ESI of the seven major river basins showed an increasing trend in both periods, and the proportion of extremely important basins reached 20.39% in 2020, mostly distributed in the Yangtze River basin and the Pearl River basin. Except for carbon storage, other ecosystem services were restored in the SDG period. However, COVID-19 and a series of sudden disasters in 2020 have greatly affected grain production and the aesthetic landscape. The fluctuation of habitat quality also indicated that although the ecological environment has a trend of recovery, there was still some degradation compared to 20 years ago. National protection policies and restoration projects have gradually improved the ecological environment of the seven river basins and are striving to reach the SDG standards step by step. However, many problems still need to be solved urgently, such as the reduction of ecological land, the increase in carbon emissions, and the weak resilience to disasters. China has made significant positive progress in MDG, and more efforts are needed to achieve the SDG goals in the future. [Display omitted] • The changes in ecosystem services and their importance of the seven major river basins in China were explored. • The ecosystem service importance has steadily increased in the MDG and SDG periods. • China ecological policies and restoration projects have been essential to mitigate the environment degradation. [ABSTRACT FROM AUTHOR]

To understand the variation and patterns of vegetation coverage in the Yellow River Basin, as well as to promote regional ecological protection and maintain ecological construction achievements, MOD13Q1 data at a resolution of 250 m were used to calculate the annual average normalised difference vegetation index (NDVI) in a time series from 2000 to 2010. Using a variation coefficient, a Theil–Sen Median trend analysis, the Mann–Kendall test, and the Hurst index method, this study investigated the temporal and spatial variations of vegetation coverage characteristics of the Yellow River Basin. The results showed that (1) the vegetation coverage of the Yellow River appeared to have an overall trend of high in the southeast and west and low in the northwest; (2) the averaged NDVI of the whole basin fluctuated in a range of 0.3 to 0.4 from 2000 to 2010 (from 2000 to 2004 there were larger variations and these have been growing rapidly since 2005); (3) the NDVI was stable, 73.4% of the vegetation-coverage area fluctuated with a low-to-medium amplitude, while 27.6% of the area varied by a large amplitude; (4) the regions with improved vegetation coverage (62.9%) were far greater than the degraded regions (27.7%), while the sustained invariant area accounted for 9.4% of the total vegetation coverage regions; and (5) 86% of the vegetation-covered area was positively sustainable. The areas with sustainable improvement accounted for 53.7% of the total vegetation coverage area; the invariant area accounted for 7.8%. The area with sustainable degradation was 24.5%; the future variation in trends of the residual (14%) could not be determined. Therefore, continuous attention must be given to the variation in trends of vegetation in the sustainably degraded and underdetermined regions. [ABSTRACT FROM AUTHOR]

The spatiotemporal changes of open-surface water bodies in the Yangtze River Basin (YRB) have profound influences on sustainable economic development, and are also closely relevant to water scarcity in China. However, long-term changes of open-surface water bodies in the YRB have remained poorly characterized. Taking advantage of the Google Earth Engine (GEE) cloud platform, this study processed 75,593 scenes of Landsat images to investigate the long-term changes of open-surface water bodies in the YRB from 1984 to 2018. In this study, we adopted the percentile-based image composite method to collect training samples and proposed a multiple index water detection rule (MIWDR) to quickly extract the open-surface water bodies. The results indicated that (1) the MIWDR is suitable for the long-term and large-scale Landsat water bodies mapping, especially in the urban regions. (2) The areas of permanent water bodies and seasonal water bodies were 29,076.70 km2 and 21,526.24 km2, accounting for 57.46% and 42.54% of the total open-surface water bodies in the YRB, respectively. (3) The permanent water bodies in the YRB increased along with the decreases in the seasonal water bodies from 1984 to 2018. In general, the total open-surface surface water bodies in the YRB experienced an increasing trend, with an obvious spatial heterogeneity. (4) The changes of open-surface water bodies were associated with the climate changes and intense human activities in the YRB, however, the influences varied among different regions and need to be further investigated in the future. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area. [ABSTRACT FROM AUTHOR]

The digital elevation model data from traditional stereo photogrammetric methods are inadequate in providing accurate vertical parameters to feed hydrologic models for low-lying, extremely flat areas. High-resolution light detection and ranging (Li DAR) data provide the robust capability of capturing small variations in low-relief playa wetlands. The Rainwater Basin in south-central Nebraska includes a complex of seasonally shallow playa wetlands that attract millions of migratory waterfowl every spring and fall. This research focuses on the development of a procedure with applicable protocols to produce Li DAR-derived three-dimensional wetland maps and to extract the critical surface parameters (i.e., watershed boundaries, flow direction, flow accumulation, and drainage lines) for playa wetlands. The topo-hydrologic conditions of playa wetlands were evaluated at the watershed level. The results show that in the Rainwater Basin, 70.7% of the historic hydric soil footprints identified in the Soil Survey Geographic ( SSURGO) database were not functioning as topographically depressional wetlands. This finding was confirmed by a recent five-year Annual Habit Survey showing that 69.8% of the historic hydric soil footprints did not function during the spring migratory bird seasons between 2004 and 2009. The majority of playa wetlands' topographic conditions have been substantially changed and the SSURGO data cannot fully reflect current topographic reality in the Rainwater Basin. [ABSTRACT FROM AUTHOR]