Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 May 7;19(9):2118.
doi: 10.3390/s19092118.

Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine

Binfei Hao  1   2   3 , Mingguo Ma  4   5   6 , Shiwei Li  7 , Qiuping Li  8   9   10 , Dalei Hao  11 , Jing Huang  12   13   14 , Zhongxi Ge  15   16   17 , Hong Yang  18   19   20 , Xujun Han  21   22   23
Affiliations

Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine

Binfei Hao et al. Sensors (Basel). .

Abstract

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 °C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000-2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 °C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment.

Keywords: Climate change; Google Earth Engine; Land use and land cover change (LULCC); Three Gorges Reservoir Catchment (TGRC); land surface temperature (LST); seasonally integrated normalized difference vegetation index (SINDVI).

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The study area and its location in Southwest China.
Figure 2
Figure 2
Workflow of the study. GLDAS: Global Land Data Assimilation System; ROI: region of interest; LULCC: land use and land cover change; SINDVI: seasonally integrated normalized difference vegetation index; LST: land surface temperature.
Figure 3
Figure 3
Training and validation sample sites in the TGRC.
Figure 4
Figure 4
Workflow of the satellite image treatment.
Figure 5
Figure 5
Land use maps in the TGRC in 2000 (a), 2010 (b) and 2015 (c).
Figure 5
Figure 5
Land use maps in the TGRC in 2000 (a), 2010 (b) and 2015 (c).
Figure 6
Figure 6
Change of seasonally integrated normalized difference vegetation index (SINDVI) in the TGRC from 2000 to 2015.
Figure 7
Figure 7
Change of land surface temperature (LST) (°C) in the TGRC in the period of 2000-2015.
Figure 8
Figure 8
The change of LST (°C) in Chongqing City. (a), (b,c) represent the highest, lowest and average LST changes, respectively, from 2001 to 2015. (d) represents the changes of highest LST (the red line) and average LST (the orange line) from 2010 to 2015.
Figure 9
Figure 9
Changes in the air temperature, precipitation and evapotranspiration in the TGRC from 2000 to 2015.
Figure 10
Figure 10
Variation of annual mean (a) air temperature, (b) precipitation, and (c) evapotranspiration in the TGRC and Southwest China from 2000 to 2015. The green line represents the variation in the TGRC, and the red line represents the variation in Southwest China.
Figure 10
Figure 10
Variation of annual mean (a) air temperature, (b) precipitation, and (c) evapotranspiration in the TGRC and Southwest China from 2000 to 2015. The green line represents the variation in the TGRC, and the red line represents the variation in Southwest China.
Figure 11
Figure 11
Spatial distribution of correlation coefficients between Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and land surface temperature (LST). The colored regions are characterized by a 95% confidence interval, while the areas are shown in white with P values higher than 0.05. Positive values mean positive correlation, while negative values mean negative correlation.
Figure 12
Figure 12
Time series change of the SINDVI (the green line) and LST (the red line) in four regions in the TGRC. The solid lines in green and red are the linear regressions of the time series data. The P values between the two specified variables are displayed in the upper right corner of each diagram box. The middle diagram is the land use map from 2015. The time series changes are the regional average values of grid cells area (1 km × 1 km). (a) Forest (30.48 °N, 110.18 °E). (b) Forest (30.96 °N, 111.38 °E). (c) Artificial surface (29.76 °N, 106.65 °E). (d) Artificial surface (29.73 °N, 107.24 °E).
See this image and copyright information in PMC

References

    1. Foley J.A., Defries R., Asner G.P., Carol B., Gordon B., Carpenter S.R., Chapin F.S., Coe M.T., Daily G.C., Gibbs H.K., et al. Global consequences of land use. Science. 2005;309:570–574. doi: 10.1126/science.1111772. - DOI - PubMed
    1. Huang C., Teng M., Zeng L., Zhou Z., Xiao W., Zhu J., Wang P. Long-term changes of land use/cover in the Three Gorges Reservoir Area of the Yangtze River, China. Chin. J. Appl. Ecol. 2018;29:1585–1596. - PubMed
    1. Rodríguez-Echeverry J., Echeverría C., Oyarzún C., Morales L. Impact of land-use change on biodiversity and ecosystem services in the Chilean temperate forests. Landsc. Ecol. 2018;33:1–15. doi: 10.1007/s10980-018-0612-5. - DOI
    1. Pappas C., Fatichi S., Burlando P. On the impact of land use changes in terrestrial water cycle and the role of plant trait variability; Proceedings of EGU General Assembly; Vienna, Austria. 12–17 April 2015.
    1. Liu J., Kuang W., Zhang Z., Xu X., Qin Y., Ning J., Zhou W., Zhang S., Li R., Yan C., et al. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s. Acta Geogr. Sin. 2014;69:3–14.

LinkOut - more resources