| 基于AWRSEI的岱海流域生态环境质量时空演变及驱动因子分析 |
| 摘要点击 739 全文点击 150 投稿时间：2023-05-07 修订日期：2023-06-19 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 生态环境质量 遥感生态指数(RSEI) 岱海流域 空间自相关 地理探测器 |
| 英文关键词 ecological environment quality remote sensing ecological index (RSEI) Daihai Basin spatial autocorrelation geographic detector |
|
| 中文摘要 |
| 流域是重要的生态安全屏障和社会经济发展区域，为了更加客观准确地评价干旱和半干旱区流域生态环境质量，基于遥感生态指数（RSEI）引入盐度指标构建适用于干旱和半干旱区流域的遥感生态指数（AWRSEI）并以岱海流域为例分析其适用性.基于AWRSEI模型，采用4期Landsat TM/OLI合成影像，对2001~2020年岱海流域的生态环境质量进行定量评价，通过变异系数和空间自相关分析岱海流域生态环境质量时空演变规律，并通过地理探测器进行成因分析和驱动因子解释力分析.结果表明：①AWRSEI与各生态因子的平均相关系数为0.860，比单个指标分量更具代表性；第一主成分的荷载正负值和排序与RSEI一致，特征值贡献率比RSEI高3.69%，评价结果与真实地表情况更为接近，适合用于干旱区流域的生态环境质量评价.②岱海流域2001~2020年AWRSEI指数的年均值为0.427，生态环境质量基础较差，期间AWRSEI的均值呈现波动上升的趋势，并且在2020年均值达到最高0.502.整体生态环境质量明显好转，恶化区域减少了20.51%，改善区域增加了12.71%.空间分布上，岱海流域南部和西北部高海拔地区的林地生态环境质量优，北部和南部中海拔地区生态环境质量较差，湖区北侧生态环境质量优于南侧.③岱海流域AWRSEI变异系数平均值为0.280，生态环境质量状况稳定，整体变化波动较小，高波动主要集中在湖区南侧和居民点区域.岱海流域生态环境质量存在显著的空间自相关性，高-高集聚区主要分布在高海拔的林地区域和低海拔耕地区域；低-低集聚区零星分布在中海拔区域.④2001~2020年岱海流域生态环境的提升主要由于NDVI提高，NDBSI和NDSI降低.NDVI和NDBSI是交互作用最强的组合，对生态环境的解释力最强.土地利用是AWRSEI的主导因子，解释力最强.土地利用和气象因子的组合是交互作用最强，且各驱动因子之间均为增强关系. |
| 英文摘要 |
| Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced. |
|
|
|
