Please wait a minute...
 
自然资源遥感  2023, Vol. 35 Issue (3): 201-211    DOI: 10.6046/zrzyyg.2022194
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
干旱区绿洲城市生态环境时空格局变化及影响因子研究
排日海·合力力1(), 昝梅1,2()
1.新疆师范大学地理科学与旅游学院,乌鲁木齐 830054
2.新疆干旱区湖泊环境与资源重点实验室,乌鲁木齐 830054
Spatio-temporal changes and influencing factors of ecological environments in oasis cities of arid regions
PARIHA Helili1(), ZAN Mei1,2()
1. College of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China
2. Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi 830054, China
全文: PDF(6217 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

城市作为人类生活和生产的核心区域,其生态环境质量越来越受到人们关注,特别是生态环境脆弱的干旱区城市。在新疆的南疆和北疆2个典型绿洲城市(乌鲁木齐市和喀什市)分别选取研究区,基于Google Earth Engine(GEE)构建2个城市遥感生态指数(remote sensing ecological index, RSEI),比较2000年、2010年和2020年2个研究区生态环境质量的时空变化特征,并利用随机森林模型定量分析2个研究区RSEI的影响因素。结果表明: ①近20 a间研究区1生态环境质量变差,研究区2生态环境质量变好,研究区1生态环境改善的区域集中在城市中心的老城区,而城区外围的新建区生态环境变差,研究区2东北部生态环境明显改善,而城市中心周围的新建区生态环境质量变差; ②植被覆盖度是影响2个研究区RSEI最重要的因子,气温和降水量是影响2个研究区RSEI次重要的因子,影响因子对2个研究区RSEI的影响范围有所差异; ③近20 a间研究区1城市规模的扩大、不透水面的增加和植被覆盖度的减小是生态环境变差的主要原因,而研究区2城市化与绿色健康城市发展模式共同推进的举措对生态环境质量的改善发挥了重要作用。研究结果可以为研究区城市健康发展提供科学依据。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
排日海·合力力
昝梅
关键词 乌鲁木齐市喀什市RSEIGEE随机森林    
Abstract

Cities are core areas for human life and production. The ecological environment quality is a growing concern in cities, especially cities with fragile ecological environments in arid regions. This study selected 2 study areas from two typical oasis cities, namely Urumqi City in northern Xinjiang and Kashgar City in southern Xinjiang. It compared the spatio-temporal changes in the ecological environment quality of the two study areas in 2000, 2010, and 2020 using two urban remote sensing-based ecological indices (RSEIs) constructed based on the Google Earth Engine (GEE). Furthermore, it quantitatively analyzed the factors influencing the RESIs of the two cities using the random forest model. The results are as follows: ① Over the past 20 years, the ecological environment quality in study area 1 worsened but that in study area 2 improved overall. In study area 1, the ecological environment improved mainly in the old urban area and deteriorated in the newly built area at the periphery of the urban area. In study area 2, the ecological environment significantly improved in the northeastern part and deteriorated in the newly built area around the city center. ② The fractional vegetation cover is the most critical factor influencing RESIs of both study areas, followed by temperature and precipitation. These influencing factors had different influences on the RSEIs of the two study areas. ③ The primary reasons for the deterioration of the ecological environment in study area 1 included the expanded urban scale, the increased impervious surfaces, and the decreased fractional vegetation cover in the past 20 years are. In contrast, urbanization and green and healthy urban development pattern jointly played a significant role in improving the ecological environment quality in study area 2. The results of this study can provide a scientific basis for healthy urban development in both study areas.

Key wordsUrumqi City    Kashgar City    RSEI    GEE    random forest
收稿日期: 2022-05-16      出版日期: 2023-09-19
ZTFLH:  TP79  
基金资助:新疆干旱区湖泊环境与资源重点实验室开放课题“玛纳斯湖湖滨植被水分利用效率对干旱胁迫的影响研究”(XJDX0909-2021-01);新疆师范大学博士科研启动基金项目“新疆森林碳储量影响因子研究”(XJNUBS2003)
通讯作者: 昝 梅(1979-),女,副教授,主要从事干旱区生态环境与遥感应用研究。Email: zanmei1102@163.com
作者简介: 排日海·合力力(1998-),女,硕士研究生,主要从事干旱区生态环境研究。Email: 1601250145@qq.com
引用本文:   
排日海·合力力, 昝梅. 干旱区绿洲城市生态环境时空格局变化及影响因子研究[J]. 自然资源遥感, 2023, 35(3): 201-211.
PARIHA Helili, ZAN Mei. Spatio-temporal changes and influencing factors of ecological environments in oasis cities of arid regions. Remote Sensing for Natural Resources, 2023, 35(3): 201-211.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022194      或      https://www.gtzyyg.com/CN/Y2023/V35/I3/201
Fig.1  研究区位置示意图
Fig.2  2020年RSEI,WET,NDBSI,NDVI和LST之间的相关系数及相关性检验
研究区 年份 NDVI WET LST NDBSI RSEI
平均值 标准差 平均值 标准差 平均值 标准差 平均值 标准差
研究区1 2000年 0.32 0.16 0.62 0.19 0.70 0.14 0.79 0.13 0.38
2010年 0.36 0.19 0.57 0.21 0.70 0.18 0.74 0.16 0.36
2020年 0.24 0.18 0.48 0.18 0.74 0.14 0.73 0.15 0.34
研究区2 2000年 0.37 0.31 0.48 0.27 0.48 0.25 0.69 0.30 0.37
2010年 0.36 0.29 0.53 0.25 0.39 0.22 0.66 0.27 0.40
2020年 0.27 0.20 0.53 0.25 0.53 0.26 0.60 0.28 0.41
Tab.1  2个研究区各年份指标和RSEI的变化
研究区 年份 较差 中等 良好
研究
区1
2000年 14.75 44.09 30.41 7.63 1.84
2010年 20.53 41.55 24.76 9.86 3.29
2020年 19.51 46.92 22.64 8.12 2.81
研究
区2
2000年 44.30 11.21 15.99 16.36 12.13
2010年 36.63 15.57 16.48 23.44 7.88
2020年 33.27 19.74 15.72 16.64 14.63
Tab.2  各年份生态等级和面积比例统计
Fig.3  2个研究区RSEI的等级分布
研究区 类别 级差 2000—2010年 2010—2020年 2000—2020年
面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/%
研究区1 变差 -3,-2,-1 1 399.19 10.14 2 694.28 20.36 2 584.41 19.53
不变 0 10 547.88 76.47 9 933.21 75.06 8 992.78 67.96
变好 1,2,3 1 845.92 13.38 605.46 4.58 1 655.81 12.51
研究区2 变差 -3,-2,-1 93.07 16.92 90.37 16.49 112.26 20.49
不变 0 257.78 46.86 272.22 49.69 213.48 38.97
变好 1,2,3 199.20 36.21 185.21 33.81 222.10 40.54
Tab.3  2000—2020年2个研究区RSEI等级变化检测
Fig.4  2000—2020年2个研究区RSEI变化监测
Fig.5  影响因子重要性排序
Fig.6  研究区1影响因子对RSEI的偏依赖
Fig.7  研究区2影响因子对RSEI的偏依赖
Fig.8  2000年和2020年不同土地利用类型的RSEI等级面积比列
[1] 王东升, 王小磊, 雷泽勇. 基于遥感生态指数的阜新市生态质量评估[J]. 生态科学, 2020, 39(3):88-94.
Wang D S, Wang X L, Lei Z Y. Ecological change assessment of Fuxin based on remote sensing ecological index[J]. Ecological Science, 2020, 39(3):88-94.
[2] Ochoa-Gaona S, Kampichler C, De Jong B H J, et al. Amulti-criteri on index for the evaluation of local tropical forest conditions in Mexico[J]. Forest Ecology Management, 2010, 260:618-627.
doi: 10.1016/j.foreco.2010.05.018
[3] 张瑞钢, 莫兴国, 林忠辉. 滹沱河上游山区近50年蒸散变化及主要影响因子分析[J]. 地理科学, 2012, 32(5):628-634.
doi: 10.13249/j.cnki.sgs.2012.05.628
Zhang R G, Mo X G, Lin Z H. The trend and the principal influence factors of evapotranspiration in Hutuo River basin during last 50 years[J]. Journal of Geophysical Research, 2012, 32(5):628-634.
[4] 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.
Xu H Q. A remote sensing urban ecological index and its application[J]. Acta Ecologica Sinica, 2013, 33(24):7853-7862.
[5] 朱泓, 王金亮, 程峰, 等. 滇中湖泊流域生态环境质量监测与评价[J]. 应用生态学报, 2020, 31(4):1289-1297.
doi: 10.13287/j.1001-9332.202004.011
Zhu H, Wang J L, Cheng F, et al. Monitoring and evaluation of eco-environmental quality of lake basin regions in central Yunnan Province,China[J]. Chinese Journal of Applied Ecology, 2020, 31(4):1289-1297.
[6] 蒋超亮, 吴玲, 刘丹, 等. 干旱荒漠区生态环境质量遥感动态监测——以古尔班通古特沙漠为例[J]. 应用生态学报, 2019, 30(3):877-883.
doi: 10.13287/j.1001-9332.201903.008
Jiang C L, Wu L, Liu D, et al. Dynamic monitoring of eco-environmental quality in arid desert area by remote sensing:Taking the Gurbantunggut Desert China as an example[J]. Chinese Journal of Applied Ecology, 2019, 30(3):877-883.
[7] Karbalaei S S, Amoushahi S, Gholipour M. Spatiotemporal ecological quality assessment of metropolitan cities:A case study of central Iran[J]. Environmental Monitoring and Assessment, 2021, 193(5):305-305.
doi: 10.1007/s10661-021-09082-2 pmid: 33900465
[8] 张亚球, 姜放, 纪梦达, 等. 基于遥感指数的区县级生态环境评价[J]. 干旱区研究, 2020, 37(6):1598-1605.
Zhang Y Q, Jiang F, Ji M D, et al. Assessment of the ecological environment at district and county level based on remote sensing index[J]. Arid Zone Research, 2020, 37(6):1598-1605.
[9] 刘立冰, 熊康宁, 任晓冬. 基于遥感生态指数的龙溪—虹口国家级自然保护区生态环境状况评估[J]. 生态与农村环境学报, 2020, 36(2):202-210.
Liu L B, Xiong K N, Ren X D. Assessment of ecological environment status in the Longxi-Hongkou National Nature Reserve based on remote sensing ecological index[J]. Journal of Ecology and Rural Environment, 2020, 36(2):202-210.
[10] 万虹麟, 霍飞, 牛玉芬, 等. 顾及PM2.5浓度遥感生态指数模型的沧州市区生态环境质量动态监测分析[J]. 地球物理学进展, 2021, 36(3):953-960.
Wan H L, Huo F, Niu Y F, et al. Dynamic monitoring and analysis of ecological environment change in Cangzhou City based on RSEI model considering PM2.5 concentration[J]. Progress in Geophysics, 2021, 36(3):953-960.
[11] 郑子豪, 吴志峰, 陈颖彪, 等. 基于Google Earth Engine的长三角城市群生态环境变化与城市化特征分析[J]. 生态学报, 2021, 41(2):717-729.
Zheng Z H, Wu Z F, Chen Y B, et al. Analyzing the ecological environment and urbanization characteristics of the Yangtze River Delta Urban Agglomeration based on Google Earth Engine[J]. Acta Ecologica Sinica, 2021, 41(2):717-729.
[12] 杭鑫, 罗晓春, 曹云, 等. 基于RSEI模型的生态质量评估及城镇化影响——以南京市为例[J]. 应用生态学报, 2020, 31(1):219-229.
doi: 10.13287/j.1001-9332.202001.030
Hang X, Luo X C, Cao Y, et al. Ecological quality assessment and the impact of urbanization based on RSEI model for Nanjing,Jiangsu Province,China[J]. Chinese Journal of Applied Ecology, 2020, 31(1):219-229.
[13] 李婷婷, 马超, 郭增长. 基于RSEI模型的贺兰山长时序生态质量评价及影响因素分析[J]. 生态学杂志, 2021, 40(4):1154-1165.
Li T T, Ma C, Guo Z C. Ecological quality evaluation and influencing factors analysis of Helan Mountain based on RSEI[J]. Chinese Journal of Ecology, 2021, 40(4):1154-1165.
[14] 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.
Zhang H, Song J Y, Li M, et al. Eco-environmental quality assessment of Qilian Mountain National Park based on GEE[J]. Chinese Journal of Ecology, 2021, 40(6):1883-1894.
[15] 约日古丽·卡斯木, 孜比布拉·司马义, 王蕾, 等. 新疆博乐市生态环境变化对城市建设用地扩张的响应[J]. 农业工程学报, 2019, 35(1):252-259.
Yueriguli K, Zibibula S, Wang L, et al. Response of ecological environment change to urban construction land expansion in Bole City of Xinjiang[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(1):252-259.
[16] Xiong Y, Xu W, Lu N, et al. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE:A case study in Erhai Lake basin,Yunnan Province,China[J]. Ecological Indicators, 2021, 125:107518.
doi: 10.1016/j.ecolind.2021.107518
[17] 韩芹芹, 张克潭. 乌鲁木齐市环境应急监测体系存在的问题及对策[J]. 中国环境监测, 2013, 29(2):86-90.
Han Q Q, Zhang K T. The Study on problems and countermeasures of environmental emergency monitoring system in Urumqi[J]. Environmental Monitoring in China, 2013, 29(2):86-90.
[18] 阿力木江·塔依尔, 木合塔尔·艾买提. 城市化与干旱区环境耦合度分析——以喀什市为例[J]. 环境影响评价, 2016, 38(4):92-96.
Alimjan T, Muhetaer A. Analysis on coupling degree between urbanization and arid area:Taking Kashi City as an example[J]. Environmental Impact Assessment, 2016, 38(4):92- 96.
[19] 哈孜亚·包浪提将, 毋兆鹏, 陈学刚, 等. 乌鲁木齐市景观格局变化及驱动力分析[J]. 生态科学, 2018, 37(1):62-70.
Haziya B, Wu Z P, Chen X G, et al. Analysis of landscape pattern change and driving force in Urumqi City[J]. Ecological Science, 2018, 37(1):62-70.
[20] 王志杰, 苏嫄. 南水北调中线汉中市水源地生态脆弱性评价与特征分析[J]. 生态学报, 2018, 38(2):432-442.
Wang Z J, Su Y. Analysis of eco-environmental vulnerability characteristics of Hanzhong City,near the water source midway along the route of the South-to-North Water Transfer Project,China[J]. Acta Ecological Sinica, 2018, 38(2):432-442.
[21] 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.
doi: 10.1080/01431160802039957
[22] 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):91-97,294.
Xu H Q. Remote sensing assessment of ecological changes in soil and water loss areas[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(7):91- 97,294.
[23] Jiménez-Muoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research Atmospheres, 2003, 108(22):4688.
[24] Breiman L. Random forests[J]. Machine Learning, 2001, 45 (1):5-32.
doi: 10.1023/A:1010933404324
[25] 陈兵红, 靳全锋, 柴红玲, 等. 浙江省大气PM2.5时空分布及相关因子分析[J]. 环境科学学报, 2021, 41(3):817-829.
Chen B H, Jin Q F, Chai H L, et al. Spatiotemporal distribution and correlation factors of PM2.5 concentrations in Zhejiang Province[J]. Acta Scientiae Circumstantiae, 2021, 41(3):817-829.
[26] 张文强, 罗格平, 郑宏伟, 等. 基于随机森林模型的内陆干旱区植被指数变化与驱动力分析:以北天山北坡中段为例[J]. 植物生态学报, 2020, 44(11):1113-1126.
Zhang W Q, Luo G P, Zheng H W, et al. Analysis of vegetation index changes and driving forces in inland arid areas based on random forest model:A case study of the middle part of northern slope of the north Tianshan Mountains[J]. Chinese Journal of Plant Ecology, 2020, 44(11):1113-1126.
doi: 10.17521/cjpe.2020.0111
[27] 赖自力, 向杰, 陈建平, 等. 基于随机森林模型的云南元阳梯田地形因子分析[J]. 地质学刊, 2016, 40(3):518-525.
Lai Z L, Xiang J, Chen J P, et al. Analysis on topographic factors of the Yuanyang terrace in Yunnan Province based on random forest model[J]. Journal of Geology, 2016, 40(3):518-525.
[28] 杨保华, 杨清华, 陈剑虹. 关于《生态环境状况评价技术规范(试行)》中土地退化指数的权重及计算方法的探讨[J]. 生态与农村环境学报, 2011, 27(3):103-107.
Yang B H, Yang Q H, Chen J H. Weight of land (soil) degradation indeces and optimization of their calculation in “echnical criteria or evaluation of ecological environment (Trial)”[J]. Journal of Ecology and Rural Environment, 2011, 27(3):103-107.
[29] 刘盼, 任春颖, 王宗明, 等. 南瓮河自然保护区生态环境质量遥感评价[J]. 应用生态学报, 2018, 29(10):3347-3356.
Liu P, Ren C Y, Wang Z M, et al. Assessment of the eco-environmental quality in the Nanweng River Nature Reserve,Northeast China by remote sensing[J]. Chinese Journal of Applied Ecology, 2018, 29(10):3347-3356.
[30] Guidotti R, Monreale A, Ruggieri S, et al. A survey of methods for explaining black box models[J]. ACM Computing Surveys, 2018, 51(5):1-42.
[31] 哈尚辰, 阿里木江·卡斯木. 近20年来喀什市乡村转型发展评价[J]. 水土保持通报, 2016, 36(6):282-287.
Ha S C, Alimujiang K. Evaluation of rural transformation development in Kashgar City during last 20 years[J]. Bulletin of Soil and Water Conservation, 2016, 36(6):282-287.
[32] Liu C, Yang M H, Hou Y T, et al. Spatiotemporal evolution of island ecological quality under different urban densities:A comparative analysis of Xiamen and Kinmen Islands,Southeast China[J]. Ecological Indicators, 2021, 124:107438.
doi: 10.1016/j.ecolind.2021.107438
[1] 伍炜超, 叶发旺. 面向多背景环境的Sentinel-2云检测[J]. 自然资源遥感, 2023, 35(3): 124-133.
[2] 席磊, 舒清态, 孙杨, 黄金君, 宋涵玥. 基于ICESat2的西南山地森林LAI遥感估测模型优化[J]. 自然资源遥感, 2023, 35(3): 160-169.
[3] 梁锦涛, 陈超, 张自力, 刘志松. 一种融合指数与主成分分量的随机森林遥感图像分类方法[J]. 自然资源遥感, 2023, 35(3): 35-42.
[4] 于森, 贾明明, 陈高, 鲁莹莹, 李毅, 张博淳, 路春燕, 李慧颖. 基于LandTrendr算法海南东寨港红树林扰动研究[J]. 自然资源遥感, 2023, 35(2): 42-49.
[5] 石敏, 李慧颖, 贾明明. 基于GEE云平台与Landsat数据的山口自然保护区红树林时空变化分析[J]. 自然资源遥感, 2023, 35(2): 61-69.
[6] 吴玉鑫, 王卷乐, 韩保民, 严欣荣. 基于时空谱特征的墨脱县森林分类方法与实现[J]. 自然资源遥感, 2023, 35(1): 180-188.
[7] 陈行, 刘汉湖, 李金豪, 范诗铃, 葛宗旭. 基于夜光遥感的城市化与生态环境耦合协调分析[J]. 自然资源遥感, 2022, 34(4): 280-285.
[8] 李叶繁, 王琳, 张冬珠. 光谱特征和空间卷积相协同的近岸海域养殖塘遥感信息提取[J]. 自然资源遥感, 2022, 34(4): 42-52.
[9] 李毅, 程丽娜, 鲁莹莹, 张博淳, 于森, 贾明明. 基于最大值合成和最大类间方差法莱州湾滨海滩涂变化研究[J]. 自然资源遥感, 2022, 34(4): 68-75.
[10] 张昊, 高小红, 史飞飞, 李润祥. 基于Sentinel-2 MSI与Sentinel-1 SAR相结合的黄土高原西部撂荒地提取——以青海民和县为例[J]. 自然资源遥感, 2022, 34(4): 144-154.
[11] 王春霞, 张俊, 李屹旭, Phoumilay. 复杂环境下GF-2影像水体指数的构建及验证[J]. 自然资源遥感, 2022, 34(3): 50-58.
[12] 邓静雯, 田义超, 张强, 陶进, 张亚丽, 黄升光. 机载LiDAR在红树林林分平均高估算中的应用[J]. 自然资源遥感, 2022, 34(3): 129-137.
[13] 王雪洁, 施国萍, 周子钦, 甄洋. 基于随机森林算法对ERA5太阳辐射产品的订正[J]. 自然资源遥感, 2022, 34(2): 105-111.
[14] 吴琳琳, 李晓燕, 毛德华, 王宗明. 基于遥感和多源地理数据的城市土地利用分类[J]. 自然资源遥感, 2022, 34(1): 127-134.
[15] 柳明星, 刘建红, 马敏飞, 蒋娅, 曾靖超. 基于GF-2 PMS影像和随机森林的甘肃临夏花椒树种植监测[J]. 自然资源遥感, 2022, 34(1): 218-229.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发