白龙江流域生态环境质量动态监测与评价

曹源; 武江民

doi:10.13961/j.cnki.stbctb.2023.03.014

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白龙江流域生态环境质量动态监测与评价

试验研究

- 白龙江流域生态环境质量动态监测与评价
- Dynamic Monitoring and Evaluation of Ecological Environmental Quality in Bailong River Basin
- 水土保持通报 2023年43卷第3期页码：105-112
- 作者机构：
  
  西北师范大学地理与环境科学学院,甘肃,兰州,730070
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目“房屋空置空间识别与空间分异机制研究”(42071216);甘肃省自然科学资助项目“基于多源数据的甘肃省潜在植被NPP与潜在生态承载力空间模拟及其空间分布研究”(21JR7RA145)
- DOI：10.13961/j.cnki.stbctb.2023.03.014
  中图分类号： X87;X821
- 纸质出版：2023
- 稿件说明：
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曹源, 武江民. 白龙江流域生态环境质量动态监测与评价[J]. 水土保持通报, 2023,43(3):105-112.

Cao Yuan, Wu Jiangmin. Dynamic Monitoring and Evaluation of Ecological Environmental Quality in Bailong River Basin[J]. Bulletin of Soiland Water Conservation, 2023, 43(3): 105-112.
曹源, 武江民. 白龙江流域生态环境质量动态监测与评价[J]. 水土保持通报, 2023,43(3):105-112. DOI： 10.13961/j.cnki.stbctb.2023.03.014.

Cao Yuan, Wu Jiangmin. Dynamic Monitoring and Evaluation of Ecological Environmental Quality in Bailong River Basin[J]. Bulletin of Soiland Water Conservation, 2023, 43(3): 105-112. DOI： 10.13961/j.cnki.stbctb.2023.03.014.

摘要

[目的

]

分析1990—2020年白龙江流域生态环境变化及驱动因素，为流域可持续发展提供科学依据和决策支持。[方法

]

逐年筛选植被生长季6—9月的Landsat TM/OLI影像数据，计算绿度(NDVI)、湿度(WET)、热度(LST)、干度(NDSI)4个生态指标，采用主成分分析法(PCA)构建遥感生态指数(RSEI)，对白龙江流域生态环境进行评价。[结果

]

1990—2020年白龙江流域RSEI均值从0.531上升至0.675，生态环境质量总体好转；生态环境质量改善区域主要位于舟曲—武都段的白龙江两岸、宕昌县西北及岷江东岸，改善面积达8 393.97 km

，占总面积45.55%；各生态指标对于生态环境质量影响程度在1990年干度＞湿度＞热度＞绿度；2006年干度＞绿度＞湿度＞热度；2020年绿度＞湿度＞干度＞热度。[结论

]

利用GEE平台实现RSEI模型扩展了在大范围尺度、长时间序列下对区域生态环境质量监测与评价，近年来白龙江流域生态环境质量整体呈改善态势，对于流域的保护治理工作仍需继续。

Abstract

[Objective] The driving factors of ecological environmental changes in the Bailong River basin from 1990 to 2020 were determined in order to provide a scientific basis and decision support for the sustainable development of the Bailong River basin.[Methods] Landsat TM/OLI image data from the vegetation growing season (June to September) were obtained from the Google Earth Engine (GEE) platform and screened year by year. From these data

the four ecological indicators of greenness (NDVI)

humidity (WET)

heat (LST)

and dryness (NDSI) were calculated. Principal component analysis (PCA) was used to construct the remote sensing ecological index (RSEI)

and the ecological environment of the Bailong River basin was evaluated.[Results] From 1990 to 2020

the mean RSEI value in the Bailong River basin increased from 0.531 to 0.675

indicating that the ecological and environmental quality had generally improved. The area of ecological and environmental quality improvement was mainly located along the two banks of the Bailong River in the Zhouqu-Wudu section

Northwest Tanchang County

and the east bank of the Minjiang River

with an area of 8 393.97 km2

comprising 45.55% of the total area. The influence degree of each ecological index on the ecological environmental quality followed the order of NDSI>WET>LST>NDVI in 1990; NDSI>NDVI>WET>LST in 2006; NDVI>WET>NDSI>LST in 2020.[Conclusion] Using the GEE platform to implement the RSEI model expanded the ability to monitor and evaluate the regional ecological environmental quality over a large area and for a long time period. In recent years

the ecological environmental quality of the Bailong River basin has generally improved

but protection and management of the basin will need to continue.

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