基于遥感生态指数的陕西省东庄水库流域生态环境变化监测与评价

权文婷; 张树誉; 刘艳; 王卫东

doi:10.13961/j.cnki.stbctb.2022.05.013

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基于遥感生态指数的陕西省东庄水库流域生态环境变化监测与评价

试验研究

- 基于遥感生态指数的陕西省东庄水库流域生态环境变化监测与评价
- Monitoring and Evaluation of Ecological Environment Changes in Dongzhuang Reservoir Basin in Shaanxi Province Based on Remote Sensing Ecological Index
- 水土保持通报 2022年42卷第5期页码：96-104
- 作者机构：
  
  1. 西安交通大学人居环境与建筑工程学院,陕西,西安,710049
  2. 陕西省农业遥感与经济作物气象服务中心,陕西,西安,710016
  3. 陕西省气象局,陕西,西安,710014
  4. 咸阳市气象局,陕西,咸阳,712000
- 作者简介：
- 基金信息：
  
  陕西省重点产业创新链项目“黄河流域(陕西段)河湖生态安全遥感监测与预警关键技术研究”(2022ZDLSF06-09);国家重点研发计划项目“空间辐射基准传递定标及地基验证技术”(2018YFB0504800);咸阳市重大科技专项计划项目“东庄水库区域生态环境变化与种植布局调整气象分析研究”(2020k01-33)
- DOI：10.13961/j.cnki.stbctb.2022.05.013
  中图分类号： S127;TP79
- 纸质出版：2022
- 稿件说明：
移动端阅览
权文婷, 张树誉, 刘艳, 等. 基于遥感生态指数的陕西省东庄水库流域生态环境变化监测与评价[J]. 水土保持通报, 2022,42(5):96-104.

Quan Wenting, Zhang Shuyu, Liu Yan, et al. Monitoring and Evaluation of Ecological Environment Changes in Dongzhuang Reservoir Basin in Shaanxi Province Based on Remote Sensing Ecological Index[J]. Bulletin of Soiland Water Conservation, 2022, 42(5): 96-104.
权文婷, 张树誉, 刘艳, 等. 基于遥感生态指数的陕西省东庄水库流域生态环境变化监测与评价[J]. 水土保持通报, 2022,42(5):96-104. DOI： 10.13961/j.cnki.stbctb.2022.05.013.

Quan Wenting, Zhang Shuyu, Liu Yan, et al. Monitoring and Evaluation of Ecological Environment Changes in Dongzhuang Reservoir Basin in Shaanxi Province Based on Remote Sensing Ecological Index[J]. Bulletin of Soiland Water Conservation, 2022, 42(5): 96-104. DOI： 10.13961/j.cnki.stbctb.2022.05.013.

摘要

[目的] 对陕西省东庄水库流域生态环境开展实时准确的监测，为流域开发与保护、城镇经济发展提供数据和理论支撑。[方法] 选取2000

2010

2020年Landsat遥感影像，基于谷歌地球引擎(GEE)平台构建湿度、绿度、干度、热度4个指标，通过主成分分析法计算遥感生态指数，对东庄水库流域20 a间生态环境变化进行监测与评价，基于IDRISI软件的CA-Markov模型对2030年东庄水库流域生态环境情况进行模拟。[结果] ①20 a间东庄水库流域生态环境整体维持在一个水平，RSEI均值由0.499上升到0.500，生态环境状况略微有上升； ②从RSEI各等级变化来看，20 a间东庄水库流域生态环境状况较为复杂，生态环境的改善与恶化长期持续并存，渭北地区1999年以来实施的退耕还林政策对流域生态环境改善起到正向作用，且效用开始凸显。[结论] 东庄水库生态环境恶化的区域主要集中在城镇及其周边区域，改善的区域主要集中在林区和丘陵地区。今后仍需继续关注经济高速发展与生态环境治理二者间的平衡问题。

Abstract

[Objective] Real-time and accurate monitoring of the ecological environment of the Dongzhuang reservoir basin in Shaanxi Province was carried out to provide data and theoretical support for the development and protection of the watershed and urban economic development. [Methods] Landsat remote sensing images in 2000

2010

and 2020 were selected

and four indexes (humidity

greenness

dryness

and heat) were constructed based on the Google Earth Gngine (GEE) platform. The remote sensing ecological index (RSEI) was calculated by the principal component analysis method

and the ecological environment changes in the Dongzhuang reservoir basin over the past 20 years were monitored and evaluated. The CA-Markov model based on IDRISI software was used to simulate the ecological environment of the Dongzhuang reservoir basin in 2030. [Results] ① During the past 20 years

the ecological environment of the Dongzhuang reservoir basin has been maintained at the same level

and average RSEI increased from 0.499 to 0.500

indicating a slight increase in the ecological environment. ② According to changes in RSEI values

the ecological environment of the Dongzhuang reservoir basin over the past 20 years was relatively complex

and both improvement and deterioration of the ecological environment continued to coexist for a long time. The policy of returning farmland to forest instituted in 1999 in the Weibei region played a positive role in the improvement of the ecological environment

and its effect has begun to be prominent. [Conclusion] The ecological environment of the Dongzhuang reservoir basin has deteriorated mainly in the towns and their surrounding areas

while the improved areas have mainly occurred in the forest and hilly areas. It is still necessary to give more attention to the balance between rapid economic development and ecological environment management.

关键词

Keywords

references

李彬,张金良,白玉川.基于Landsat8影像的泾河峡谷段高含沙河流信息提取方法[J].人民黄河,2022,44(1):75-79.

张金良.泾河东庄水利枢纽工程双泥沙侵蚀基准面排沙研究[J].人民黄河,2021,43(10):35-39.

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.

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.

李晓明,孙从建,孙九林,等.基于遥感信息的黄土高原主要灌溉农业分布区生态安全特征[J].应用生态学报,2021,32(9):3177-3184.

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.

缪鑫辉,梁勤欧.基于遥感生态指数的甬江流域生态环境变化分析[J].长江流域资源与环境,2021,30(2):427-438.

薛陈利,张会琼,邹滔,等.中老铁路经济廊带生态质量及其与人类活动的关系[J].应用生态学报,2021,32(2):638-648.

钟欣呈,许泉立.基于RSEI模型的玉溪市生态环境变化监测与评价[J].水土保持研究,2021,28(4):350-357.

陈炜,黄慧萍,田亦陈,等.基于Google Earth Engine平台的三江源地区生态环境质量动态监测与分析[J].地球信息科学学报,2019,21(9):1382-1391.

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.

Dong Jinwei, Xiao Xiangming, Menarguez M A, et al. Mapping paddy rice planting area in Northeastern Asia with Landsat 8 images,phenology-based algorithm and Google Earth Engine [J]. Remote Sensing of Environment, 2016,185:142-154.

Shelestov A, Lavreniuk M, Kussul N, et al. Exploring google earth engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping [J]. Frontiers in Earth Science, 2017,5:17.

Teluguntla P, Thenkabail P S, Oliphant A, et al. A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,144:325-340.

Souza C M Jr, Shimbo J Z, Rosa M R, et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with landsat archive and earth engine [J]. Remote Sensing, 2020,12(17):2735.

Wang Xinxin, Xiao Xiangming, Zou Zhenhua, et al. Tracking annual changes of coastal tidal flats in China during 1986—2016 through analyses of Landsat images with Google Earth Engine [J]. Remote Sensing of Environment, 2020,238:110987.

Goldblatt R, You Wei, Hanson G, et al. Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in google earth engine [J]. Remote Sensing, 2016,8(8):634.

周伟东,黄海真,田惠娟,等.东庄水库工程对泾渭湿地生态影响浅析[J].环境生态学,2020,2(10):50-54.

雷春荣,宋宪琳,王泉伟.泾河东庄水库地下水环境影响效应研究[J].人民黄河,2018,40(12):97-99.

李鹏峰.东庄水库水环境数值模拟及水质评价研究[D].陕西西安:西安理工大学,2020.

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.

徐涵秋.水土流失区生态变化的遥感评估[J].农业工程学报,2013,29(7):91-97.

Gao Bocai. NDWI： A normalized difference water index for remote sensing of vegetation liquid water from space [J]. Remote Sensing of Environment, 1996,58(3):257-266.

刘玉琴.基于RS和GIS的哈尔滨市生态环境质量评价与分析[D].黑龙江哈尔滨:哈尔滨师范大学,2021.

Jin Suming, Sader S A. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances [J]. Remote Sensing of Environment, 2005,94(3):364-372.

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.

Roy P, Rikimaru A, Miyatake S. Tropical forest cover density mapping [J]. Tropical Ecology, 2002,43(1):39-47.

傅湘,纪昌明.区域水资源承载能力综合评价:主成分分析法的应用[J].长江流域资源与环境,1999,8(2):168-173.

吴可人,高祺,王让会,等.基于RSEI模型的石家庄生态环境质量评价[J].地球物理学进展,2021,36(3):968-976.

朱增云,阿里木江·卡斯木.基于CA-Markov模型的呼图壁县土地利用景观格局预测研究[J].生态科学,2020,39(1):136-145.

王丽霞,张珈玮,孟妮娜,等.基于CA-Markov的渭河流域NDVI时空变化模拟及预测[J].水土保持研究,2020,27(4):206-212.

成超男,胡杨,冯尧,等.基于CA-Markov模型的城市生态分区构建研究: 以晋中主城区为例[J].生态学报,2020,40(4):1455-1462.

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