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自然资源遥感  2022, Vol. 34 Issue (2): 203-214    DOI: 10.6046/zrzyyg.2021224
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
基于MODIS的京津冀地区生态质量综合评价及其时空变化监测
左璐1,2(), 孙雷刚1,2,3(), 鲁军景1,2, 徐全洪1,2, 刘剑锋1,2, 马晓倩1,2
1.河北省科学院地理科学研究所,石家庄 050021
2.河北省地理信息开发应用工程技术研究中心,石家庄 050021
3.巨鹿县应用技术研究院,邢台 055250
MODIS-based comprehensive assessment and spatial-temporal change monitoring of ecological quality in Beijing-Tianjin-Hebei region
ZUO Lu1,2(), SUN Leigang1,2,3(), LU Junjing1,2, XU Quanhong1,2, LIU Jianfeng1,2, MA Xiaoqian1,2
1. Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050021, China
2. Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang 050021, China
3. Julu Institute of Applied Technology, Xingtai 055250, China
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摘要 

生态质量评价是保障人类生产生活与生态环境协调稳定、实现区域社会经济可持续发展的重要前提,利用遥感技术快速、准确、客观地定量评价区域生态质量已成为一种新的趋势。选取2001年、2010年、2019年京津冀地区的MODIS遥感数据产品,提取出反映绿度、湿度、热度和改进的干度4个重要指标,利用主成分分析法得到MODIS遥感生态指数(MODIS remote sensing ecological index,RSEIM),对京津冀地区2001—2019年近20 a间的生态质量状况进行综合评价及变化监测。结果表明: ①京津冀地区生态质量区域差异明显,北部燕山及西部太行山一带生态质量好,冀西北张家口及冀东南城市中心区生态质量差; ②2001年、2010年和2019年,京津冀地区的RSEIM均值分别为0.556,0.583和0.527,生态质量整体呈下降趋势; ③2001—2019年,京津冀地区生态质量改善和退化的面积占比分别为20.18%和35.69%,空间分布上呈现出西北改善、东南退化的格局。西北部生态改善的主要原因在于降水量增加、温度升高等水热条件变化及一系列的人为保护措施; 东南部生态退化的主要原因在于城镇化的快速推进及社会经济活动的增强。基于MODIS数据可有效实现区域大范围的生态质量综合评价,为区域社会经济绿色高质量发展提供参考。
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左璐
孙雷刚
鲁军景
徐全洪
刘剑锋
马晓倩
关键词 ： 生态质量,  综合评价,  遥感监测,  MODIS,  时空变化    
Abstract：

Ecological quality assessment is an important prerequisite for guaranteeing the harmony and stability of the production and life of human beings and the ecological environment and for achieving the sustainable development of regional social economy. It has become a new trend to quickly, accurately, and objectively assess the regional ecological quality using use remote sensing technology. This study used the MODIS data of the Beijing-Tianjin-Hebei region in 2001, 2010, and 2019 to extract four important indices, namely, NDVI (greenness), LSM (humidity), NDBSI (dryness), and LST (heat). Then, this study obtained the MODIS remote sensing ecological index (RSEIM) using the principal component analysis method to conduct a comprehensive assessment and change monitoring of the ecological quality in the Beijing-Tianjin-Hebei region over the past 20 years. The results are as follows. ① The ecological quality of the Beijing-Tianjin-Hebei region shows distinct regional differences. The Yanshan Mountain in the north and the Taihang Mountain in the west have high ecological quality, while the Zhangjiakou area in the northwestern part of Hebei Province and the urban center in the southeastern part of Hebei Province suffer low ecological quality. ② In 2001, 2010, and 2019, the average RSEIM of the Beijing-Tianjin-Hebei region was 0.556, 0.583, and 0.527, respectively, with the overall ecological quality showing a downward trend. ③ From 2001 to 2019, the area with improved and degraded ecological quality in the Beijing-Tianjin-Hebei region accounted for 20.18% and 35.69% respectively, and the ecological quality in this region showed a pattern of improvement in the northwest and degradation in the southeast. The main reasons for the ecological improvement in the northwestern part of the region are the changes in water and heat conditions, such as an increase in precipitation and temperature, and a series of man-made protection measures. The reasons for ecological degradation in the southeastern part of the Beijing-Tianjin-Hebei region mainly include the rapid advancement of urbanization and the enhancement of social and economic activities. The comprehensive assessment of regional ecological quality can be effectively achieved based on MODIS data, thus providing a reference for the green and high-quality development of regional social economy.
Key words： ecological quality    comprehensive evaluation    remote sensing monitoring    MODIS    spatial-temporal changes
收稿日期: 2021-07-01      出版日期: 2022-06-20
ZTFLH:  TP79  
基金资助:河北省科学院科技计划项目“河北省区域生态质量动态监测评价及提升研究”(20104);河北省科学院高层次人才培养与资助项目“河北省生态环境质量遥感评价及其时空变化”(2019G16);“基于遥感植被指数时间序列的人工林演变研究”(2020G03)
通讯作者: 孙雷刚
作者简介: 左 璐(1991-),女,博士,助理研究员,研究方向为生态遥感及资源环境监测。Email: zuol.14b@igsnrr.ac.cn。
引用本文:   
左璐, 孙雷刚, 鲁军景, 徐全洪, 刘剑锋, 马晓倩. 基于MODIS的京津冀地区生态质量综合评价及其时空变化监测[J]. 自然资源遥感, 2022, 34(2): 203-214.
ZUO Lu, SUN Leigang, LU Junjing, XU Quanhong, LIU Jianfeng, MA Xiaoqian. MODIS-based comprehensive assessment and spatial-temporal change monitoring of ecological quality in Beijing-Tianjin-Hebei region. Remote Sensing for Natural Resources, 2022, 34(2): 203-214.
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https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021224      或      https://www.gtzyyg.com/CN/Y2022/V34/I2/203
指标 2001年 2010年 2019年
PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4
LST -0.353 0.923 0.139 -0.063 -0.333 0.933 0.122 -0.059 -0.392 0.914 0.068 -0.085
NDSIM -0.543 -0.327 0.538 -0.556 -0.530 -0.300 0.605 -0.513 -0.512 -0.304 0.735 -0.325
SWCI 0.552 0.180 -0.158 -0.799 0.560 0.163 -0.110 -0.804 0.561 0.158 0.099 -0.806
NDVI 0.525 0.093 0.816 0.222 0.543 0.111 0.779 0.294 0.519 0.218 0.667 0.487
特征值 0.217 0.020 0.004 0.002 0.195 0.019 0.005 0.002 0.176 0.029 0.005 0.003
特征值贡献率/% 89.31 8.25 1.77 0.67 88.33 8.76 2.03 0.88 82.81 13.74 2.19 1.26
Tab.1  指标主成分分析结果
指标 2001年 2010年 2019年
均值 标准差 均值 标准差 均值 标准差
LST 0.622 0.210 0.605 0.196 0.646 0.227
NDSIM 0.439 0.260 0.424 0.242 0.466 0.227
SWCI 0.549 0.260 0.573 0.251 0.525 0.241
NDVI 0.607 0.251 0.635 0.246 0.618 0.227
RSEIM 0.556 0.257 0.583 0.244 0.527 0.239
Tab.2  各指标和RSEIM统计值
指标 2001年 2010年 2019年
LST NDSIM SWCI NDVI RSEIM LST NDSIM SWCI NDVI RSEIM LST NDSIM SWCI NDVI RSEIM
LST 1.000 -0.779 1.000 -0.745 1.000 -0.722
NDSIM 0.656 1.000 -0.971 0.618 1.000 -0.966 0.533 1.000 -0.945
SWCI -0.711 -0.970 1.000 0.987 -0.678 -0.959 1.000 0.986 -0.627 -0.931 1.000 0.979
NDVI -0.721 -0.930 0.954 1.000 0.975 -0.680 -0.921 0.952 1.000 0.974 -0.580 -0.909 0.943 1.000 0.959
平均相关度① 0.696 0.852 0.879 0.868 0.928 0.658 0.833 0.863 0.851 0.918 0.580 0.791 0.834 0.811 0.901
3 a均值 LST=0.645,NDSIM=0.825,SWCI=0.858,NDVI=0.843,RSEIM=0.916
Tab.3  各指标和RSEIM相关系数统计表
Fig.1  研究区RSEIM空间分布图
Fig.2  2001—2019年研究区各等级生态质量面积和占比
Fig.3  研究区RSEIM变化空间分布
变化类别 极差 2001—2010年 2010—2019年 2001—2019年
面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/%
明显变差 -4,-3 225.72 0.10 712.40 0.33 1502.91 0.69
变差 -2,-1 32 838.31 15.13 76 358.30 35.18 75 967.23 35.00
不变 0 128 285.30 59.11 119 116.43 54.88 95 766.28 44.13
变好 1,2 55 369.04 25.51 20 841.91 9.60 42 920.70 19.78
明显变好 3,4 314.73 0.15 4.06 0.00 875.98 0.40
Tab.4  2001—2019年研究区生态质量变化面积和占比
Fig.4  2001—2019年研究区各市RSEIM统计
Fig.5  2001—2019年气候因子变化趋势
Fig.6  2001—2019年研究区各市社会经济因素变化对比
指标 2001年 2010年 2019年
RSEIM LST NDVI RSEIM LST NDVI RSEIM LST NDVI
干度 NDSIM -0.971 0.656 -0.930 -0.966 0.618 -0.921 -0.945 0.533 -0.909
NDSI -0.916 0.561 -0.876 -0.908 0.523 -0.864 -0.857 0.403 -0.850
湿度 SWCI 0.987 -0.711 0.954 0.986 -0.678 0.952 0.979 -0.627 0.943
Wet 0.775 -0.549 0.685 0.736 -0.486 0.651 0.620 -0.307 0.559
Tab.5  改进前后的干度、湿度指标与RSEIM、热度、绿度指标的相关性
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