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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (2): 246-265    DOI: 10.1007/s40333-024-0071-6     CSTR: 32276.14.s40333-024-0071-6
Research article     
Land use and cover change and influencing factor analysis in the Shiyang River Basin, China
ZHAO Yaxuan1, CAO Bo1,2,*(), SHA Linwei1, CHENG Jinquan1, ZHAO Xuanru1, GUAN Weijin1, PAN Baotian1,2
1Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730030, China
2Shiyang River Basin Scientific Observing Station, Lanzhou University, Lanzhou 730030, China
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Abstract  

Land use and cover change (LUCC) is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface, with significant impacts on the environment and social economy. Rapid economic development and climate change have resulted in significant changes in land use and cover. The Shiyang River Basin, located in the eastern part of the Hexi Corridor in China, has undergone significant climate change and LUCC over the past few decades. In this study, we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991, 1995, 2000, 2005, 2010, 2015, and 2020 based on Landsat images. We validated the land use and cover data in 2015 from the random forest classification results (this study), the high-resolution dataset of annual global land cover from 2000 to 2015 (AGLC-2000-2015), the global 30 m land cover classification with a fine classification system (GLC_FCS30), and the first Landsat-derived annual China Land Cover Dataset (CLCD) against ground-truth classification results to evaluate the accuracy of the classification results in this study. Furthermore, we explored and compared the spatiotemporal patterns of LUCC in the upper, middle, and lower reaches of the Shiyang River Basin over the past 30 years, and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural (evapotranspiration, precipitation, temperature, and surface soil moisture) and anthropogenic (nighttime light, gross domestic product (GDP), and population) factors. The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015, GLC_FCS30, and CLCD datasets in both overall and partial validations. Moreover, the classification results in this study exhibited a high level of agreement with the ground truth features. From 1991 to 2020, the area of bare land exhibited a decreasing trend, with changes primarily occurring in the middle and lower reaches of the basin. The area of grassland initially decreased and then increased, with changes occurring mainly in the upper and middle reaches of the basin. In contrast, the area of cropland initially increased and then decreased, with changes occurring in the middle and lower reaches. The LUCC was influenced by both natural and anthropogenic factors. Climatic factors and population contributed significantly to LUCC, and the importance values of evapotranspiration, precipitation, temperature, and population were 22.12%, 32.41%, 21.89%, and 19.65%, respectively. Moreover, policy interventions also played an important role. Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years, with the ecological environment improving in the last 10 years. This suggests that governance efforts in the study area have had some effects, and the government can continue to move in this direction in the future. The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Key words： land use and cover classification      land use and cover change (LUCC)      climate change      random forest      accuracy assessment      three-dimensional sampling method      Shiyang River Basin     
Received: 08 October 2023      Published: 29 February 2024
Corresponding Authors: *CAO Bo (E-mail: caobo@lzu.edu.cn)
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ZHAO Yaxuan
CAO Bo
SHA Linwei
CHENG Jinquan
ZHAO Xuanru
GUAN Weijin
PAN Baotian
Cite this article:

ZHAO Yaxuan, CAO Bo, SHA Linwei, CHENG Jinquan, ZHAO Xuanru, GUAN Weijin, PAN Baotian. Land use and cover change and influencing factor analysis in the Shiyang River Basin, China. Journal of Arid Land, 2024, 16(2): 246-265.

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http://jal.xjegi.com/10.1007/s40333-024-0071-6     OR     http://jal.xjegi.com/Y2024/V16/I2/246

Fig. 1 Overview of the upper, middle, and lower reaches of the Shiyang River Basin and the spatial distribution of verification points and regions. The base map is derived from Gaofen-1 (GF-1) satellite images in 2020.
Year Sensor type Path/row Acquisition time Cloud cover (%) Year Sensor type Path/row Acquisition time Cloud cover (%)
1991 TM 131/33 25 Jun 1991 0.00 1995 TM 131/33 19 Jul 1994 0.00
131/34 25 Jun 1991 0.00 131/34 19 Jul 1994 0.00
132/33 16 Jun 1991 2.00 132/33 27 Jun 1995 0.00
132/34 03 Aug 1991 2.00 132/34 16 Aug 1996 0.00
2000 TM 131/33 08 Sep 2001 0.00 2005 TM 131/33 02 Aug 2005 0.00
131/34 04 Jun 2001 2.00 131/34 30 Jul 2004 0.00
132/33 08 Jun 2000 0.00 132/33 06 Jun 2005 0.00
132/34 14 Aug 2001 0.00 132/34 06 Jun 2005 2.00
2010 TM 131/33 29 Aug 2009 0.00 2015 OLI 131/33 14 Aug 2015 0.00
131/34 13 Aug 2009 2.00 131/34 13 Jul 2015 1.76
132/33 22 Jul 2010 2.00 132/33 18 Aug 2014 0.00
132/34 08 Sep 2010 2.00 132/34 17 Jul 2014 1.30
2020 OLI 131/33 10 Jul 2020 0.27
131/34 26 Jul 2020 0.61
132/33 18 Aug 2020 0.08
132/34 19 Sep 2020 2.03
Table 1 Landsat images used in the study
Influencing factor Unit Time span Spatial resolution Dataset Source
Evapotranspiration mm 1991-2020 1 km 1 km monthly potential evapotranspiration dataset for China from 1901 to 2022 National Earth System Science Data Center (http://loess.geodata.cn)
Precipitation mm 1991-2020 1 km 1 km monthly temperature and precipitation dataset for China from 1901 to 2022
Temperature °C 1901-2020 1 km
Surface soil moisture m3/m3 2003-2020 1 km Daily all weather surface soil moisture data set with 1 km resolution in China (2003-2022) National Tibetan Plateau Scientific Data Center (http://data.tpdc.ac.cn)
Nighttime light - 1991-2020 1 km A prolonged artificial nighttime-light dataset of China (1984-2020)
GDP ×106 USD 1992-2019 1 km Global 1 km×1 km gridded revised real GDP during 1992-2019 Figshare (https://figshare.com)
Population persons/
km2		 2000, 2005, 2010,
2015, and 2020		 1 km Gridded Population of the World (GPW), v4 (2000, 2005, 2010, 2015, and 2020) Socioeconomic Data and Applications Center (https://sedac.ciesin.
columbia.edu)
Table 2 Detailed description of influencing factors used in the study
Land use and cover type Description
Bare land Land with vegetation fractional coverage lower than 10%, including deserts, sandy areas, bare rocks, and saline-alkali land.
Grassland Herbaceous plants, all kinds of grassland with vegetation fractional coverage more than 10%.
Cropland Land where crops are grown.
Forest Forest land for growing trees, shrubs, etc.
Wetland Located at the junction of land and water of low-relief areas, including perched bogs and potholes.
Impervious surface Land use and cover types formed by human activities, including residential areas, transportation facilities, and industrial and mining facilities.
Water body Natural water area and land for water conservancy facilities.
Glacier Land covered by snow and glaciers.
Table 3 Land use and cover classification system used in this study
Fig. 2 Flowchart for selecting region of interest (ROI) samples using a 3D sampling method by combining different band combinations, vegetation fractional coverage, and Google Earth view. 3D, three-dimensional; 2D, two-dimensional.
Fig. 3 Spatial distribution of land use and cover classification results (2020) in this study and field verification points in the Shiyang River Basin
Fig. 4 Spatial distribution of land use and cover classification results in the Shiyang River Basin from 1991 to 2020
Shiyang River Basin Indicator AGLC-2000-2015 GLC_FCS30 CLCD Landsat_RFC
Whole basin Accuracy rate (%) 83.69 83.03 86.52 92.01
Upper reaches Overall accuracy (%) 47.11 69.98 47.83 76.05
Kappa coefficient 0.33 0.54 0.33 0.62
Middle reaches Overall accuracy (%) 48.84 60.18 51.56 67.66
Kappa coefficient 0.24 0.42 0.34 0.53
Lower reaches Overall accuracy (%) 47.21 84.01 44.11 84.33
Kappa coefficient 0.32 0.74 0.32 0.75
Table 4 Quantitative analysis of the four land use and cover datasets in the whole basin, and in the upper, middle, and lower reaches of the Shiyang River Basin in 2015
Fig. 5 Area proportion of each land use and cover type in the whole basin (a), and in the upper (b), middle (c), and lower (d) reaches of the Shiyang River Basin in 2020
Fig. 6 Area distribution of each land use and cover type in the upper, middle, and lower reaches of the Shiyang River Basin in 2020
Fig. 7 Trends of Normalized Difference Vegetation Index (NDVI) in the whole basin (a) and three sub-regions (b) of the Shiyang River Basin from 1998 to 2019
Fig. 8 Temporal variations in area proportion of each land use and cover type in the Shiyang River Basin from 1991 to 2020. (a), bare land; (b), grassland; (c), cropland; (d), forest; (e), impervious surface; (f), glacier; (g), wetland; (h), water body. The values represent the maximum and minimum values of the area proportion for each land use and cover type.
Fig. 9 Temporal variations in area of each land use and cover type in the upper (a), middle (b), and lower (c) reaches of the Shiyang River Basin from 1991 to 2020
Fig. 10 Transfer between different land use and cover types in different periods in the Shiyang River Basin. The width of the curve represents the amount of transfer between different land use and cover types.
Fig. 11 Importance of the influencing factors of LUCC in the whole basin (a), and in the upper (b), middle (c), and lower (d) reaches of the Shiyang River Basin. Eva, evapotranspiration; Pre, precipitation; Tem, temperature; SSM, surface soil moisture; NL, nighttime light; Pop, population. According to the Pearson's correlation coefficients, we identified the first four important factors as significant factors, and the last three as insignificant factors.
Fig. 12 Trends in annual precipitation and annual average temperature from 1991 to 2020 in the Shiyang River Basin. The blue and red segments represent the average values of annual precipitation and annual average temperature over a specific 5-year period, respectively. The grey area indicates the transition period.
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