Dynamic Monitoring and Evaluation of Ecological Environmental Quality in Bailong River Basin
Author:
Clc Number:

X821,X87

  • Article
  • | |
  • Metrics
  • |
  • Reference [31]
  • |
  • Related [20]
  • | | |
  • Comments
    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.

    Reference
    [1] 王高峰,杨强,陈宗良,等.白龙江流域甘家沟泥石流风险评估研究[J].泥沙研究,2020,45(4):66-73.
    [2] 国家环保局.生态环境状况评价技术规范(试行):HJ/T192-2006[S]. 北京:中国标准出版社,2006.
    [3] 岳昂,张赞.基于EI值的生态状况变化分析研究[J].绿色科技,2018(14):182.
    [4] Leveau L M, Isla F I. Predicting bird species presence in urban areas with NDVI:An assessment within and between cities[J]. Urban Forestry & Urban Greening, 2021,63:127199.
    [5] Guo Beibei, Fang Yelin, Jin Xiaobin, et al. Monitoring the effects of land consolidation on the ecological environmental quality based on remote sensing:A case study of Chaohu Lake basin, China[J]. Land Use Policy, 2020,95:104569.
    [6] 高吉喜,赵少华,侯鹏.中国生态环境遥感四十年[J].地球信息科学学报,2020,22(4):705-719.
    [7] 王甜,闫金凤,乔海燕.马来西亚吉隆坡市土地利用变化特征分析与预测[J].水土保持通报,2020,40(5):268-275.
    [8] 刘硕,李小光,宋建伟,等.长山沟露天矿集中区土地利用时空变化的遥感监测与分析[J].水土保持通报,2021,41(4):121-127.
    [9] 陈桃,包安明,何大明.基于MODIS NDVI的攀枝花市植被覆盖变化及其驱动力[J].长江流域资源与环境,2018,27(8):1847-1857.
    [10] 熊巧利,何云玲,李同艳,等.西南地区生长季植被覆盖时空变化特征及其对气候与地形因子的响应[J].水土保持研究,2019,26(6):259-266.
    [11] Sagris V, Sepp M. Landsat-8 TIRS data for assessing urban heat island effect and its impact on human health[J]. IEEE Geoscience and Remote Sensing Letters, 2017,14(12):2385-2389.
    [12] Singh P, Kikon N, Verma P. Impact of land use change and urbanization on urban heat island in Lucknow City, Central India. A remote sensing based estimate[J]. Sustainable Cities and Society, 2017,32:100-114.
    [13] 徐涵秋.城市遥感生态指数的创建及其应用[J].生态学报,2013,33(24):7853-7862.
    [14] 徐涵秋.区域生态环境变化的遥感评价指数[J].中国环境科学,2013,33(5):889-897.
    [15] 农兰萍,王金亮.基于RSEI模型的昆明市生态环境质量动态监测[J].生态学杂志,2020,39(6):2042-2050.
    [16] 魏雨涵,钱建平,范伟伟,等.基于RSEI的漓江流域生态环境质量动态监测[J].中国水土保持科学(中英文),2021,19(1):122-131.
    [17] 杨泽康,田佳,李万源,等.黄河流域生态环境质量时空格局与演变趋势[J].生态学报,2021,41(19):7627-7636.
    [18] 张华,宋金岳,李明,等.基于GEE的祁连山国家公园生态环境质量评价及成因分析[J].生态学杂志,2021,40(6):1883-1894.
    [19] 周侃.白龙江[J].甘肃水利水电技术,2014,50(4):60-65.
    [20] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.
    [21] Goward S N, Xue Yongkang, Czajkowski K P. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements[J]. Remote Sensing of Environment, 2002,79(2/3):225-242.
    [22] 梁治华.基于时空融合算法的缨帽变换分量时序数据集构建方法研究[D].甘肃兰州:兰州大学,2015.
    [23] Crist E P. A TM Tasseled Cap equivalent transformation for reflectance factor data[J]. Remote Sensing of Environment, 1985,17(3):301-306.
    [24] Nichol J. Remote sensing of urban heat islands by day and night[J]. Photogrammetric Engineering & Remote Sensing, 2005,71(5):613-621.
    [25] Chander G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors[J]. Remote Sensing of Environment, 2009,113(5):893-903.
    [26]Xu H. A new index for delineating built-up land features in satellite imagery[J]. International Journal of Remote Sensing, 2008,29(14):4269-4276.
    [27] Rikimaru A, Roy P, Miyatake S. Tropical forest cover density mapping[J]. Tropical Ecology, 2002,43:39-47.
    [28] Hu Xisheng, Xu Hanqiu. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality:A case from Fuzhou City, China[J]. Ecological Indicators, 2018,89:11-21.
    [29] 王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
    [30] 谢余初,巩杰,张玲玲.基于PSR模型的白龙江流域景观生态安全时空变化[J].地理科学,2015,35(6):790-797.
    [31] 齐姗姗,巩杰,钱彩云,等.基于SRP模型的甘肃省白龙江流域生态环境脆弱性评价[J].水土保持通报,2017,37(1):224-228.
    Cited by
Get Citation

曹源,武江民.白龙江流域生态环境质量动态监测与评价[J].水土保持通报英文版,2023,43(3):105-112,122

Copy
Share
Article Metrics
  • Abstract:271
  • PDF: 613
  • HTML: 0
  • Cited by: 0
History
  • Received:September 29,2022
  • Revised:November 16,2022
  • Online: August 16,2023