Abstract：Satellite remote sensing products MODIS-EVI from 2001 to 2017, wetness index (WI), desertification index (DI) and modified normalized difference water index (MNDWI) extracted from Landsat data in 2001, 2009, and 2017, meteorological data and other social economic data sets were used to reveal the characteristics of ecological environment changes and driving factors in Heihe River Basin. The results showed that, the areas of low, medium-high, and high vegetation coverage increased at the rates of 36,29 and 132km2/a, respectively. While the areas of non-vegetation and medium vegetation coverage decreased at the rates of 185 and 11km2/a, respectively. The highest vegetation coverage was in upper reaches, and during summer times. The total water area in the basin first increased to 3854.5km2 from 2001 to 2009, then decreased to 2628.9km2 from 2009 to 2017. More water bodies were found in the upper reaches, the decreased water volume from mountains to foothills, and the increased water volume from west to east. The wetness index in the research area increased from 0.55 in 2001 to 0.65 in 2017, with the higher value were mainly distributed in the upper reaches and several counties (Minle, Shandan and Jinta) of middle reaches. The desertified areas were mainly distributed in Gaotai, Linze and Jinta counties of middle reaches and Ejina Banner of lower reaches, and the degree of desertification firstly reversed and then expanded. Meteorological factors of temperature, precipitation, potential evaporation, socio-economic factors like gross domestic production (GDP), primary industry, developed area and cultivated land area were found to be the significant driving factors contributing to these ecological environment evolution in Heihe River Basin.
乔雪梅, 刘普幸, 任媛, 司文洋, 花亚萍. 基于遥感的黑河流域生态环境变化特征及成因分析[J]. 中国环境科学, 2020, 40(9): 3962-3971.
QIAO Xue-mei, LIU Pu-xing, REN Yuan, SI Wen-yang, HUA Ya-ping. Analysis of the characteristics and driving factors of ecological environment changes in Heihe river basin based on remote sensing data. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(9): 3962-3971.
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