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遥感技术与应用  2023, Vol. 38 Issue (3): 729-738    DOI: 10.11873/j.issn.1004-0323.2023.3.0729
遥感应用     
非洲Munyaka地区铁皮屋顶时空变化规律研究
张文志(),杜梦豪(),丁来中,杨森
河南理工大学测绘与国土信息工程学院,河南 焦作 454003
Temporal and Spatial Variation of Tin Roof in Munyaka Area of Africa
Wenzhi ZHANG(),Menghao DU(),Laizhong DING,Sen YANG
School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China
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摘要:

在非洲,铁皮屋顶的分布能够反映居民的生活水平,对其时空变化分析能够反映当地经济发展状况。采用Sentinel-2研究非洲肯尼亚埃尔多雷特市Munyaka地区铁皮屋顶的光谱特征、遥感指数特征以及纹理特征,利用归一化植被指数和归一化建筑指数与铁皮屋顶的差异剔除农田,构建归一化表面指数且分析纹理特征分别剔除荒地和裸地;建立了多特征决策树提取模型,通过混淆矩阵进行精度评定得到Kappa系数为0.922 3,用户精度和制图精度分别为97.79%和91.10%。同时结合埃尔多雷特市政道路工程,对2016~2020年工程前后铁皮屋顶变化进行研究,2018~2020年阶段比2016~2018年阶段,铁皮屋顶面积增加一倍,平均年增长率提升近3%。研究表明该方法能够实现对铁皮屋顶的动态监测,同时说明了“一带一路”建设为解决非洲贫困问题起到推动作用。

关键词: 哨兵2号铁皮屋顶光谱特征纹理特征遥感提取    
Abstract:

In Africa, the distribution of tin roof can reflect the living standard of residents, and the analysis of its temporal and spatial changes can reflect the local economic development. Sentinel-2 was used to study the spectral characteristics, remote sensing index characteristics and texture features of iron roof in Munyaka area, Eldoret, Kenya, Africa.The Normalized Difference Vegetation Index(NDVI) and Normalized Difference Building Index(NDBI) were used to remove farmland from the difference of the iron roof. The normalized surface index was constructed and the texture features were analyzed to eliminate wasteland and bare land respectively. The model of multi feature decision tree extraction is established. The Kappa coefficient is 0.922 3, and the user precision and mapping precision are 97.79% and 91.10% respectively. At the same time, combined with the erdoret municipal road project, the changes of iron roof before and after the project in 2016~2020 are studied. Compared with 2016~2018, the area of iron roof in 2018~2020 is doubled, and the average annual growth rate is nearly 3%. Research shows that this method can achieve dynamic monitoring of tin roofs, and illustrates that "The Belt and Road Initiative" construction plays a driving role in solving poverty problems in Africa.

Key words: Sentinel-2    Tin roof    Spectral characteristics    Texture features    Remote sensing extraction
收稿日期: 2021-07-26 出版日期: 2023-07-11
ZTFLH:  P237  
基金资助: 国家自然科学基金项目“沁水煤田采空区场地高速铁路路基灾变机制与防控”(U1810203);河南省科技攻关项目“高速铁路循环动荷载作用下采空区场地活化变形灾变风险评价”(212102310404)
通讯作者: 杜梦豪     E-mail: zhangwenzhi@hpu.edu.cn;1301056287@qq.com
作者简介: 张文志(1976-),男,河南沈丘人,博士后,讲师,主要从事形变监测与防治研究。E?mail:zhangwenzhi@hpu.edu.cn
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引用本文:

张文志,杜梦豪,丁来中,杨森. 非洲Munyaka地区铁皮屋顶时空变化规律研究[J]. 遥感技术与应用, 2023, 38(3): 729-738.

Wenzhi ZHANG,Menghao DU,Laizhong DING,Sen YANG. Temporal and Spatial Variation of Tin Roof in Munyaka Area of Africa. Remote Sensing Technology and Application, 2023, 38(3): 729-738.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.3.0729        http://www.rsta.ac.cn/CN/Y2023/V38/I3/729

图1  研究区样本点分布
波段中心波长/μm分辨率/m
蓝光-Band20.49010
绿光-Band30.56010
红光-Band40.66510
近红外-Band80.84210
短波红外1-Band111.61020
短波红外2-Band122.20220
表1  Sentinel-2影像所用波段信息简介
图2  地物多光谱曲线
图3  各地物归一化植被指数
图4  各地物归一化建筑指数
图5  地物归一化表面指数
指标铁皮屋顶裸地相对差异 %
均值方差变异系数 %均值方差变异系数 %
蓝色协同性 (Homogeneity)0.799 70.026 13.269 80.158 40.010 06.311 6404.75
蓝色二阶矩 (Second moment)0.479 40.059 612.438 80.115 90.000 10.125 1313.52
红色协同性 (Homogeneity HdsdaHomogeneity)0.705 00.039 05.534 30.190 70.010 15.306 6269.65
绿色协同性 (Homogeneity)0.774 50.023 33.013 70.222 70.026 111.732 6247.71
绿色二阶矩 (Second moment)0.439 30.045 710.396 80.135 20.002 50.863 3225.05
红色二阶矩 (Second moment)0.361 00.033 29.190 40.118 20.000 20.201 7205.51
短波红外2相关性 (Correlation)0.337 20.111 232.980 70.116 20.096 182.718 9190.10
近红外二阶矩 (Second moment)0.318 40.030 99.705 60.124 90.000 70.571 5154.95
短波红外2二阶矩 (Second moment)0.346 10.042 812.365 60.138 00.002 92.117 3150.72
短波红外1协同性 (Homogeneity)0.319 10.088 427.705 70.133 30.089 867.371 8139.44
表2  地物纹理特征统计
图6  铁皮屋顶和裸地间的纹理特征值
干扰地物特征阈值
农田NDVI>0.42 NDBI<0.1
荒地V>0.11 Swir1>3 000
裸地R Second moment<0.16 G Second moment<0.22 B Second moment<0.18
表4  特征阈值设定
图7  铁皮屋顶遥感识别流程图
图8  研究区分类结果图
图9  铁皮屋顶识别结果对比图
图10  主要道路分布
图11  铁皮屋顶时空变化
图12  铁皮屋顶阶段增长图
阶段起始面积 /km2结束面积 /km2AI/km2AGR/%
2016~20180.561 00.600 60.019 83.469 2
2018~20200.600 60.691 40.045 47.293 1
表5  2016~2020年铁皮屋顶年增长与年增长率
1 XIAN Zude, BA Yunhong, CHENG Jinjing. Study on the relationship between the indicators and policies of the UN 2030 sustainable development goals[J]. Statistical Research,2021,38(1):4-14.
1 鲜祖德, 巴运红, 成金璟. 联合国2030年可持续发展目标指标及其政策关联研究[J]. 统计研究, 2021, 38(1):4–14.
2 YAN Lei. History and current situation of poverty in Africa[J]. Journal of Xinzhou Normal University, 2012,28(1): 93-98.
2 严磊. 非洲贫困问题的历史与现状[J]. 忻州师范学院学报, 2012, 28(1): 93–98.
3 YU Yongfa, WANG Siyuan, WANG Bin, et al. Hierarchical extraction of buildings from high resolution remote sensing images[J]. Journal of Remote Sensing, 2019,23(1): 125-136.
3 游永发, 王思远, 王斌, 等. 高分辨率遥感影像建筑物分级提取[J]. 遥感学报, 2019,23(1): 125–136.
4 WANG Xiaolong, YAN Haowen, ZHOU Liang, et al. Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea[J].Remote Sensing for Land & Resources,2020,32(4):163-171.
4 王小龙,闫浩文,周亮,等.利用SVM分类Landsat影像的朝鲜主要城市建设用地时空特征分析[J].国土资源遥感,2020,32(4):163-171.
5 SHACKELFORD, AARON K. A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas[J]. IEEE Transactions on Geoscience & Remote Sensing,2003,41:2354-2363.
6 WANG Xüe, LI Peijun, JIANG Shasha, et al. Building extraction using airborne LiDAR data and very high resolution imagery over a complex urban area[J].Remote Sensing for Land & Resources, 2016,28(2): 106-111.
6 王雪, 李培军, 姜莎莎, 等. 利用机载LiDAR数据和高分辨率图像提取复杂城区建筑物[J]. 国土资源遥感, 2016, 28(2): 106–111.
7 HOFMANN P. Detecting urban features from IKONOS data using an object-oriented approach[C/OL]∥1st Annual Conf RSPSoc 2001.[2021-07-16].
8 LI Pengyuan, YANG Shuwen, YAO Huaqin,et al. Extraction of urban color steel shed based on high resolution remote sensing image[J]. Geospatial Information,2017,15(9):13-15,18,7.
8 李鹏元, 杨树文, 姚花琴, 等. 基于高分辨率遥感影像的城区彩钢棚提取研究[J]. 地理空间信息, 2017,15(9):13-15,18,7.
9 MA Jijing, YANG Shuwen, JIA Xin, et al. Temporal and spatial change of color steel sheds in anning district of Lanzhou city[J]. Science of Surveying and Mapping, 2018,43 (12): 34-37 + 71.
9 马吉晶, 杨树文, 贾鑫, 等. 兰州市安宁区彩钢棚时空变化[J]. 测绘科学, 2018, 43(12): 34-37,71.
10 DRUSCH M, BELLO U D, CARLIER S,et al. Sentinel-2 optical high resolution mission for GMES land operational services[J]. Remote Sensing of Environment,2012,120:25-36. DOI:10. 1016/j.rse.2011.11.026
doi: 10. 1016/j.rse.2011.11.026
11 ZHANG Weichun, LIU Hongbin, WU Wei.Classification of land use in low mountain and hilly area based on random forest and Sentinel-2 satellite data:A case study of Lishi Town,Jiangjin, Chongqing[J]. Resources and Environment in the Yangtze Basin, 2019,28(6):1334-1343.
11 张卫春, 刘洪斌, 武伟. 基于随机森林和Sentinel-2影像数据的低山丘陵区土地利用分类——以重庆市江津区李市镇为例[J]. 长江流域资源与环境, 2019, 28(6): 1334-1343.
12 BAILLARIN S J, MEYGRET A, DECHOZ C, et al. Sentinel-2 level 1 products and image processing performances[C/OL]∥Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International.[2021-07-16]. . DOI:10.1109/IGARSS. 2012. 6351959
doi: 10.1109/IGARSS. 2012. 6351959
13 SU Wei, ZHANG Mingzheng, JIANG kunping, et al. Atmospheric correction method for Sentinel-2 satellite imagery[J]. Acta optica Sinica, 2018,38(1): 322-331.
13 苏伟, 张明政, 蒋坤萍, 等. Sentinel-2卫星影像的大气校正方法[J]. 光学学报, 2018,38(1): 322–331.
14 ZHENG Changchun, WANG Xiuzhen, HUANG Jingfeng. Mapping paddy rice planting area in Zhejiang Province using multi-temporal MODIS images[J]. Journal of Zhejiang University(Agriculture and Life Sciences Edition),2009,35(1):98-104.
14 郑长春, 王秀珍, 黄敬峰. 多时相MODIS影像的浙江省水稻种植面积信息提取方法研究[J]. 浙江大学学报(农业与生命科学版),2009,35(1):98-104.
15 FRIEDL M A, BRODLEY C E. Decision tree classification of land cover from remotely sensed data[J]. Remote Sensing of Environment, 1997,61(3):399-409.
16 CONRAD C, COLDITZ R R, DECH S, et al. Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems[J]. International Journal of Remote Sensing, 2011, 32(23): 8763–8778. DOI:10.1080/01431161.2010.550647
doi: 10.1080/01431161.2010.550647
17 ROUSE J W. Monitoring vegetation systems in the great plains with Erts[C]∥Third ERTS Symposium, 1973.
18 ZHEN Jiguo, CHEN Yawei. Vegetation indices and the applications in monitoring tillage reverting to woodland or grassland ecosystems[J]. Remote Sensing Technology and Application, 2006,21(1):41-48.
18 甄计国, 陈亚伟. 植被指数与退耕还林(草)初期的遥感监测应用[J]. 遥感技术与应用,2006,21(1):41-48.
19 CHEN Xianchun, ZHAO Junsan, CHEN Leishi, et al. Land cover classification and change analysis of Hongta district in Yuxi city based on landsat image[J]. Forest engineering,2019,35(3):5-12.
19 陈仙春, 赵俊三, 陈磊士, 等. 基于Landsat影像的玉溪市红塔区土地覆盖分类及变化分析[J]. 森林工程, 2019, 35(3): 1–8.
20 Yong CHA, NI Shaoxiang, YANG Shan. An effective approach to automatically extract urban land-use from TM imagery[J]. Journal of Remote Sensing,2003,7(1):37-40,82.
20 查勇, 倪绍祥, 杨山. 一种利用TM图像自动提取城镇用地信息的有效方法[J]. 遥感学报, 2003,7(1):37-40,82.
21 Jinwei YÜ. Research on information extraction of agricultural greenhouse based on sentinel cart decision tree[D]. Dalian: Liaoning Normal University, 2020.
21 于津伟. 基于Sentinel的CART决策树农业大棚信息提取研究[D]. 大连:辽宁师范大学, 2020.
22 LU Zhou, Feifei XÜ, LUO Ming, et al.Characteristic analysis of lodging rice and study of the multi-spectral remote sensing extraction method[J].Chinese Journal of Eco-Agriculture, 2021,29(4):751-761.
22 陆洲, 徐飞飞, 罗明, 等. 倒伏水稻特征分析及其多光谱遥感提取方法研究[J]. 中国生态农业学报,2021,29(4):751-761.
23 LI Zongnan, CHEN Zhongxin, WANG Limin, et al. Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle[J]. Chinese Journal of Eco-Agriculture, 2014,30 (19): 207-213.
23 李宗南, 陈仲新, 王利民, 等. 基于小型无人机遥感的玉米倒伏面积提取[J]. 农业工程学报, 2014, 30(19): 207-213.
24 GUAN Xingliang, FANG Chuanglin, ZHOU Min, et al.Spatial and temporal characteristics of spatial expansion of urban land in Wuhan urban agglomeration[J]. Journal of Natural Resources, 2012,27(9): 1447-1459.
24 关兴良, 方创琳, 周敏, 等. 武汉城市群城镇用地空间扩展时空特征分析[J]. 自然资源学报, 2012, 27(9): 1447-1459.
25 Sisi YÜ, SUN Zhongchang, GUO Huadong, et al. Monitoring and analyzing the spatial dynamics and patterns of megacities along the Maritime Silk Road[J]. Journal of Remote Sensing, 2017,21(2): 169-181.
25 禹丝思, 孙中昶, 郭华东, 等. 海上丝绸之路超大城市空间扩展遥感监测与分析[J]. 遥感学报, 2017, 21(2):169-181.
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