| 1991~2020年贵阳市生态质量演变及空间分异定量归因 |
| 摘要点击 1151 全文点击 158 投稿时间：2024-01-25 修订日期：2024-04-05 |
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| 中文关键词 生态质量 遥感生态指数（RSEI） 谷歌地球引擎（GEE） 地理探测器 Hurst指数 贵阳市 |
| 英文关键词 ecological quality remote sensing ecological index（RSEI） Google earth engine（GEE） geodetector Hurst index Guiyang |
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| 中文摘要 |
| 探究贵阳市长时序生态质量演变趋势与空间分异定量归因,对于实现区域生态保护与高质量发展战略具有重要意义. 基于1991~2020年7期Landsat遥感影像,利用谷歌地球引擎 （GEE）计算贵阳市1991~2020年遥感生态指数（RSEI）,采用不同随机采样数量和分类策略的地理探测器、Hurst指数和变异系数方法,分析生态质量演变趋势和空间分异影响因素. 结果表明：①近30 a贵阳市生态质量主要以中等和良好为主,呈波浪式变化规律,最高值和最低值分别出现在2020年（均值0.58）和2010年（均值0.47）,近10 a RSEI整体趋势向好,空间上呈现东北向西南递减分布趋势；②贵阳市RSEI变异系数值域范围为（0~2）,其中46.61%区域处于高波动变化,RSEI整体波动性较大；Hurst指数均值为0.59,大于0.5的面积占比为73.98%,多数区域表现为弱持续性,未来变化趋势与过去30 a相同. ③不同分类方法与随机样本影响地理探测器的q值结果,但不同因子解释力的大小排序趋势总体一致. 土地利用、夜间灯光指数、坡度和人口密度指标对于RSEI空间分异的解释力较强,因子交互探测均属双因子增强与非线性增强,土地利用与其他因子交互最有利于解释RSEI空间分异. |
| 英文摘要 |
| Exploring the trend of long time-series ecological quality evolution and spatial differentiation of influencing factors in Guiyang is of great significance for realizing regional ecological protection and high-quality development strategies. Based on the 7-period Landsat remote sensing images from 1991 to 2020, the remote sensing ecological index （RSEI） of Guiyang from 1991 to 2020 was calculated using the GEE remote sensing big data platform, and a geodetector, Hurst index, and coefficient of variation with different random sampling quantities and classification strategies were used to analyze the evolutionary pattern of ecological quality, change trends, and spatial differentiation influencing factors. The results showed that ① The ecological quality of Guiyang in the past 30 years was mainly medium and good, showing a wave-like pattern of change, with the highest and lowest values occurring in 2020 （mean value 0.58） and 2010 （mean value 0.47）, respectively, and the overall trend of the RSEI in the past ten years was good, with a spatial trend of decreasing distribution from the northeast to the southwest. ② The domain of values of the coefficient of variation for the RSEI in Guiyang was in the range of （0-2）, of which 46.61% of the area was in high fluctuation change, and the overall volatility of the RSEI was large； the mean value of the Hurst index was 0.59, and the area greater than 0.5 accounted for 73.98%, most of which showed weak persistence, and the future trend of change was the same as that of the past 30 years. ③ Different classification methods and random samples affect the q-value results of the geodetector, but the trend of the size ordering of the explanatory power of different factors was generally consistent. Land use, nighttime lighting index, slope, and population density indicators had stronger explanatory power for RSEI spatial differentiation； factor interaction detection was two-factor enhancement and nonlinear enhancement； and the interaction between land use and other factors was most favorable for explaining RSEI spatial differentiation. |
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