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

2021 ◽  
Vol 13 (7) ◽  
pp. 1299
Author(s):  
Qiulei Ji ◽  
Wei Liang ◽  
Bojie Fu ◽  
Weibin Zhang ◽  
Jianwu Yan ◽  
...  
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Yellow River ◽  
Landsat Imagery ◽  
Urban Expansion ◽  
Google Earth ◽  
The Yellow River ◽  
Entire Study ◽  
Land System ◽  
Google Earth Engine

Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

scholarly journals Changes in Vegetation Greenness in the Upper and Middle Reaches of the Yellow River Basin over 2000–2015

2019 ◽  
Vol 11 (7) ◽  
pp. 2176 ◽  
Author(s):  
Wei Wang ◽  
Lin Sun ◽  
Yi Luo
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Yellow River ◽  
The Yellow River ◽  
Fluctuation Analysis ◽  
Vegetation Development ◽  
Vegetation Dynamic ◽  
Study Region ◽  
Entire Study ◽  
Land Use Types

The Grain to Green Project (GTGP), a large ecological restoration project aiming to control soil erosion and improve the ecological environment, has been implemented since 1999 and has led to great land use changes with decreased farmland and increased forest and grass, and significant vegetation variations. Understanding vegetation variations for different land use types is important for accessing the present vegetation development and providing scientific guidance for future ecological restoration design and regional sustainable development. With two land use maps and MODIS LAI data, trend analysis, fluctuation analysis, and R/S methods were applied to analyze the vegetation dynamic changes and sustainability for converted land use types from cropland and unconverted types over 2000–2015 in the upper and middle reaches of the Yellow River. The results obtained were as follows: (1) Vegetation greening was remarkable in the entire study region (0.036 yr−1). The increasing rate was higher in wetter conditions with AI < 3 (0.036–0.053 yr−1) than arid regions with AI > 3 (0.012–0.024 yr−1). (2) Vegetation improved faster for converted forestland, shrubland, and grassland than unconverted types under similar drying conditions. Converted shrubland and grassland had a larger relative change than converted forestland. (3) Converted land use types generally exhibited stronger fluctuation than unconverted types with small differences among types. (4) Vegetation exhibited a sustainable increasing trend in the future, which accounted for more than 73.1% of the region, mainly distributed in the middle reach of the Yellow River. Vegetation restoration exerted important influences on vegetation greening and the effect was stronger for converted types than unconverted types.

scholarly journals Variation of alpine lakes from 1986 to 2019 in the Headwater Area of the Yellow River, Tibetan Plateau using Google Earth Engine

2020 ◽  
Vol 11 (1) ◽  
pp. 11-21
Author(s):  
Dong-Liang LUO ◽  
Hui-Jun JIN ◽  
He-Qiang DU ◽  
Chao LI ◽  
Qiang MA ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Alpine Lakes ◽  
Google Earth ◽  
The Yellow River ◽  
Google Earth Engine

scholarly journals Urban Sprawl and Changes in Land-Use Efficiency in the Beijing–Tianjin–Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data

2021 ◽  
Vol 13 (15) ◽  
pp. 2850
Author(s):  
Meiling Zhou ◽  
Linlin Lu ◽  
Huadong Guo ◽  
Qihao Weng ◽  
Shisong Cao ◽  
...  
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Areas ◽  
Landsat Imagery ◽  
Spatiotemporal Analysis ◽  
Google Earth ◽  
Observation Data ◽  
Land Use Efficiency ◽  
Use Efficiency ◽  
Bth Region

Sustainable development in urban areas is at the core of the implementation of the UN 2030 Agenda and the Sustainable Development Goals (SDG). Analysis of SDG indicator 11.3.1—Land-use efficiency based on functional urban boundaries—provides a globally harmonized avenue for tracking changes in urban settlements in different areas. In this study, a methodology was developed to map built-up areas using time-series of Landsat imagery on the Google Earth Engine cloud platform. By fusing the mapping results with four available land-cover products—GlobeLand30, GHS-Built, GAIA and GLC_FCS-2020—a new built-up area product (BTH_BU) was generated for the Beijing–Tianjin–Hebei (BTH) region, China for the time period 2000–2020. Using the BTH_BU product, functional urban boundaries were created, and changes in the size of the urban areas and their form were analyzed for the 13 cities in the BTH region from 2000 to 2020. Finally, the spatiotemporal dynamics of SDG 11.3.1 indicators were analyzed for these cities. The results showed that the urban built-up area could be extracted effectively using the BTH_BU method, giving an overall accuracy and kappa coefficient of 0.93 and 0.85, respectively. The overall ratio of the land consumption rate to population growth rate (LCRPGR) in the BTH region fluctuated from 1.142 in 2000–2005 to 0.946 in 2005–2010, 2.232 in 2010–2015 and 1.538 in 2015–2020. Diverged changing trends of LCRPGR values in cities with different population sizes in the study area. Apart from the megacities of Beijing and Tianjin, after 2010, the LCRPGR values were greater than 2 in all the cities in the region. The cities classed as either small or very small had the highest LCRPGR values; however, some of these cities, such as Chengde and Hengshui, experienced population loss in 2005–2010. To mitigate the negative impacts of low-density sprawl on environment and resources, local decision makers should optimize the utilization of land resources and improve land-use efficiency in cities, especially in the small cities in the BTH region.

scholarly journals Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems

2018 ◽  
Vol 10 (9) ◽  
pp. 1488 ◽  
Author(s):  
Roberta Ravanelli ◽  
Andrea Nascetti ◽  
Raffaella Cirigliano ◽  
Clarissa Di Rico ◽  
Giovanni Leuzzi ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Urban Heat Island ◽  
Landsat Imagery ◽  
Google Earth ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Google Earth Engine ◽  
Spatio Temporal

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

scholarly journals Multi-Scenario Analysis of Habitat Quality in the Yellow River Delta by Coupling FLUS with InVEST Model

2021 ◽  
Vol 18 (5) ◽  
pp. 2389
Author(s):  
Qinglong Ding ◽  
Yang Chen ◽  
Lingtong Bu ◽  
Yanmei Ye
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Habitat Quality ◽  
Scenario Analysis ◽  
Yellow River ◽  
The Yellow River ◽  
Cultivated Land ◽  
Invest Model ◽  
Unused Land ◽  
Dongying City

The past decades were witnessing unprecedented habitat degradation across the globe. It thus is of great significance to investigate the impacts of land use change on habitat quality in the context of rapid urbanization, particularly in developing countries. However, rare studies were conducted to predict the spatiotemporal distribution of habitat quality under multiple future land use scenarios. In this paper, we established a framework by coupling the future land use simulation (FLUS) model with the Intergrated Valuation of Environmental Services and Tradeoffs (InVEST) model. We then analyzed the habitat quality change in Dongying City in 2030 under four scenarios: business as usual (BAU), fast cultivated land expansion scenario (FCLE), ecological security scenario (ES) and sustainable development scenario (SD). We found that the land use change in Dongying City, driven by urbanization and agricultural reclamation, was mainly characterized by the transfer of cultivated land, construction land and unused land; the area of unused land was significantly reduced. While the habitat quality in Dongying City showed a degradative trend from 2009 to 2017, it will be improved from 2017 to 2030 under four scenarios. The high-quality habitat will be mainly distributed in the Yellow River Estuary and coastal areas, and the areas with low-quality habitat will be concentrated in the central and southern regions. Multi-scenario analysis shows that the SD will have the highest habitat quality, while the BAU scenario will have the lowest. It is interesting that the ES scenario fails to have the highest capacity to protect habitat quality, which may be related to the excessive saline alkali land. Appropriate reclamation of the unused land is conducive to cultivated land protection and food security, but also improving the habitat quality and giving play to the versatility and multidimensional value of the agricultural landscape. This shows that the SD of comprehensive coordination of urban development, agricultural development and ecological protection is an effective way to maintain the habitat quality and biodiversity.

scholarly journals Extension of the Upper Yellow River into the Tibet Plateau: Review and New Data

Quaternary ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 14
Author(s):  
Zhengchen Li ◽  
Xianyan Wang ◽  
Jef Vandenberghe ◽  
Huayu Lu
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Google Earth ◽  
Tibet Plateau ◽  
The Yellow River ◽  
Published Data ◽  
The Tibetan Plateau ◽  
Large Lakes ◽  
River Terraces ◽  
The Tibet Plateau

The Wufo Basin at the margin of the northeastern Tibet Plateau connects the upstream reaches of the Yellow River with the lowland catchment downstream, and the fluvial terrace sequence in this basin provides crucial clues to understand the evolution history of the Yellow River drainage system in relation to the uplift and outgrowth of the Tibetan Plateau. Using field survey and analysis of Digital Elevation Model/Google Earth imagery, we found at least eight Yellow River terraces in this area. The overlying loess of the highest terrace was dated at 1.2 Ma based on paleomagnetic stratigraphy (two normal and two reversal polarities) and the loess-paleosol sequence (12 loess-paleosol cycles). This terrace shows the connections of drainage parts in and outside the Tibetan Plateau through its NE margin. In addition, we review the previously published data on the Yellow River terraces and ancient large lakes in the basins. Based on our new data and previous researches, we conclude that the modern Yellow River, with headwaters in the Tibet Plateau and debouching in the Bohai Sea, should date from at least 1.2 Ma. Ancient large lakes (such as the Hetao and Sanmen Lakes) developed as exorheic systems and flowed through the modern Yellow River at that time.

scholarly journals Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 138
Author(s):  
Zijie Jiang ◽  
Weiguo Jiang ◽  
Ziyan Ling ◽  
Xiaoya Wang ◽  
Kaifeng Peng ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Surface Water ◽  
Essential Element ◽  
Water Area ◽  
Google Earth ◽  
Water Extraction ◽  
Natural Ecosystem ◽  
The Sustainable Development ◽  
Baiyangdian Lake ◽  
Google Earth Engine

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

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