Please wait a minute...
img

官方微信

遥感技术与应用  2020, Vol. 35 Issue (4): 882-892    DOI: 10.11873/j.issn.1004-0323.2020.4.0882
甘肃遥感学会专栏     
有云Landsat TM/OLI影像结合DEM提取青藏高原湖泊边界的自动算法研究
王鑫蕊1,2(),晋锐1,3(),林剑4,曾祥飞4,赵泽斌1,2
1.中国科学院西北生态环境资源研究院,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.中国科学院青藏高原地球科学卓越创新中心,北京 100101
4.湖南科技大学 知识处理与网络化制造实验室, 湖南 湘潭 411201
Automatic Algorithm for Extracting Lake Boundaries in Qinghai-Tibet Plateau based on Cloudy Landsat TM/OLI Image and DEM
Xinrui Wang1,2(),Rui Jin1,3(),Jian Lin4,Xiangfei Zeng4,Zebin Zhao1,2
1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
4.Key Laboratory Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan 411201, China
 全文: PDF(6724 KB)   HTML
摘要:

青藏高原的湖泊数量众多且分布广泛,约占全国湖泊总数量和总面积的41%和57%,对于全国甚至全球的湖泊研究十分重要。遥感监测湖泊分布历来已久,但光学遥感影像时常被云遮蔽,难以自动化提取得到完整的湖泊边界。提出了一种有云的Landsat TM/OLI影像结合航天飞机雷达地形测绘数据(Shuttle Radar Topography Mission, SRTM) 30 m分辨率的数字高程模型(DEM)的湖泊完整边界的自动插值生成算法。首先,在Google Earth Engine平台上,利用Landsat TM/OLI影像的地表反射率Tier1数据,根据其中的像元质量评价(Pixel Quality Assessment, pixel_qa)属性,结合SRTM 30 m DEM数据,先剔除云、云阴影、积雪和山体区域的影响;计算改进的归一化差异水体指数(Modified Normalized Difference Water Index,MNDWI),采用Canny边缘检测算法,得到无云覆盖区域的已知部分湖泊边界(L)。在本地对DEM进行极差滤波,得到可能的湖泊区域;同时,利用DEM生成等高距间隔为1 m的等高线,将包围着可能湖泊区域的一系列等高线自动筛选出来,根据等高线间的包含关系建立树结构。叶子节点为最内部等高线,记为内等高线(C1)。由于Landsat和DEM的获取时间不同,随着湖泊扩张或萎缩,湖泊水位会相对于内等高线上升或下降,对此采用不同的外等高线(C2)确定方法;随后,建立内等高线C1、外等高线C2与已知部分湖泊边界L之间对应点的坡度坡向关系,插值得到未知的湖泊边界点;最后利用最近邻法连接已知的湖泊边界点与插值得到的湖泊边界点形成完整的湖泊边界。利用相近日期的资源三号影像或无云Landsat影像的手工数字化湖泊边界对提取的湖泊边界进行验证,发现两者基本重合,且长度差百分比为-6.81%~9.4%,面积差百分比为-2.11%~2.7%。表明该方法对于有云Landsat TM/OLI影像的湖泊边界自动化提取十分有效,并为在GEE等大数据平台中自动化提取长时间序列、高时间分辨率的青藏高原湖泊边界及其时空变化分析提供了新方法。

关键词: 青藏高原湖泊边界Landsat影像有云DEM    
Abstract:

Lakes in the Qinghai-Tibet Plateau are numerous and widely distributed, accounting for 41% and 57% of the total number and area of lakes in China, which are very important for the study of lakes in the whole country and even in the whole world. Remote sensing has been used to monitor the lake distribution for a long time, but optical remote sensing images are often obscured by clouds, from which it’s impossible to automatically extract complete lake boundaries. An automatic interpolation algorithm for lake boundary generation based on cloudy Landsat TM/OLI image and Shuttle Radar Topography Mission (SRTM) 30 m resolution Digital Elevation Model (DEM) is proposed. Firstly, supported by the platform of Google Earth Engine, the tier1 data of Landsat TM/OLI images are used to eliminate the effects of cloud, cloud shadow, snow and mountain area, based on the Pixel Quality Assessment (pixel_qa) attribute and SRTM 30 m DEM. Then, the Modified Normalized Difference Water Index (MNDWI) is calculated, and the Canny edge detection algorithm are used to obtain the known part of the lake boundary (L) in cloud-free areas. The possible lake areas are obtained by range filtering of DEM locally. At the same time, DEM is used to generate contours with an isometric interval of 1 m, and a series of contours surrounding the possible lake area are automatically screened out. The tree structure is established according to the inclusion relationship between contours. The leaf nodes are the innermost contours, which are recorded as inner contours (C1). Because the acquisition time of Landsat and DEM is different, with the lake expanding or shrinking, the lake water surface will rise or fall relative to the inner contour. Different methods of determining the outer contour (C2) are adopted. Subsequently, the slope-aspect relationship between the inner contour C1 and the outer contour C2 and the known part of the lake boundary L is established, and the unknown lake boundary points are interpolated. Finally, the nearest neighbor method is used to connect the known lake boundary points with the interpolated Lake boundary points to form a complete lake boundary. The extracted lake boundaries were validated by visual digitized lake boundaries from ZiYuan-3 image or cloud-free Landsat image on the near date. It is found that they are basically coincided, and the percentage of differences in length and area are -6.81%~9.4% and -2.11%~2.7% respectively. It shows that this method is very effective for automatic extraction of Lake boundary from cloudy Landsat TM/OLI images, and provides a new method for automatic extraction of long time series Lake boundary and its temporal and spatial variation analysis in the Qinghai-Tibet Plateau on GEE and other big data platforms.

Key words: Qinghai-Tibet plateau    Lake boundary    Landsat    Cloud    DEM
收稿日期: 2019-09-24 出版日期: 2020-09-15
ZTFLH:  P941.78  
基金资助: 中国科学院战略性先导科技专项(A类)“时空三极环境”项目(XDA19070104);NSFC项目(41531174)
通讯作者: 晋锐     E-mail: wangxinrui@lzb.ac.cn;jinrui@lzb.ac.cn
作者简介: 王鑫蕊(1995-),女,山西晋中人,硕士研究生,主要从事湖泊遥感、湖泊变化分析。E?mail: wangxinrui@lzb.ac.cn
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  
王鑫蕊
晋锐
林剑
曾祥飞
赵泽斌

引用本文:

王鑫蕊, 晋锐, 林剑, 曾祥飞, 赵泽斌. 有云Landsat TM/OLI影像结合DEM提取青藏高原湖泊边界的自动算法研究[J]. 遥感技术与应用, 2020, 35(4): 882-892.

Xinrui Wang, Rui Jin, Jian Lin, Xiangfei Zeng, Zebin Zhao. Automatic Algorithm for Extracting Lake Boundaries in Qinghai-Tibet Plateau based on Cloudy Landsat TM/OLI Image and DEM. Remote Sensing Technology and Application, 2020, 35(4): 882-892.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0882        http://www.rsta.ac.cn/CN/Y2020/V35/I4/882

图1  研究区
数据种类数据来源空间分辨率时间是否有云
光学遥感数据Landsat-830 m2015年9月16日有云
2015年10月2日无云
ZY-32.1 m2015年9月28日无云
Landsat-530 m1989年9月24日有云
1989年10月26日无云
DEM数据SRTM 30 m DEM30 m2000年2月不受云的影响
表1  数据源信息
图2  技术流程图
图3  Canny边缘检测算法结果
图4  DEM极差滤波示意图(单位:m)
图5  等高线示意图
图6  部分等高线树示意图
图7  湖泊水位相对于内等高线上升时,未知湖泊边界点的插值示意图
图8  2015年9月16日有云Landsat-8影像提取完整湖泊边界的验证
湖泊名称Landsat-8+DEM确定长度/kmZY-3号影像确定长度/km长度差/km长度差百分比/%Landsat-8+DEM确定面积/km2

ZY-3号影像

确定面积/km2

面积差/km2面积差百分比/%
达如错59.7255.184.548.2371.8769.981.892.70
切里错19.1618.980.180.9513.0512.960.090.69
懂错82.4681.451.011.24152.06151.110.950.63
江错29.4531.40-1.95-6.2140.6540.430.220.54
蓬错78.5074.813.694.93176.45176.55-0.10-0.06
表2  本算法自动提取的2015年湖泊边界与手工数字化边界的长度、面积差异分析
图9  湖泊水面相对于内等高线面下降时,未知湖泊边界点插值示意图
图10  1989年9月24日有云Landsat-5影像提取完整湖泊边界的验证
湖泊名称Landsat-5+DEM确定长度/km无云影像确定长度/km长度差/km长度差百分比/%Landsat-5+DEM确定面积/km2

无云影像确定

面积/km2

面积差/km2面积差百分比/%
达如错56.7251.844.889.4061.4459.941.502.50
切里错19.4820.55-1.07-5.2110.6610.86-0.20-1.84
懂错72.1471.770.370.52131.37133.03-1.66-1.25
江错26.6828.63-1.95-6.8136.1736.95-0.78-2.11
蓬错67.4369.10-1.67-2.42142.31140.761.551.10
表3  本算法自动提取的1989年湖泊边界与手工数字化边界的长度、面积差异分析
1 Zhang G, Yao T, Chen W, et al. Regional Differences of Lake Evolution Across China during 1960s-2015 and Its Natural and Anthropogenic Causes[J]. Remote sensing of Environment, 2019, 221: 386-404.
2 Madsen D B. Conceptualizing the Tibetan Plateau: Environmental Constraints on the Peopling of the “Third Pole”[J]. Archaeological Research in Asia 5, 2016, 5: 24-32.
3 Ma R, Yang G, Duan H,et al. China’s Lakes at Present: Number, Area and Spatial Distribution[J]. Science China Earth Sciences, 2011, 54(2): 283-289.
3 马荣华, 杨桂山, 段洪涛, 等.中国湖泊的数量, 面积与空间分布[J]. 中国科学: 地球科学, 2011, 41(3): 394-401.
4 Zhang G, Luo W, Chen W, et al. A Fobust but Variable Lake Expansion on the Tibetan Plateau[J]. Science Bulletin, 2019, 64(18): 1306-1309.
5 Crétaux J F, Jelinski W, Calmant S, et al. SOLS: A Lake Database to Monitor in the Near Real Time Water Level and Storage Variations from Remote Sensing Data[J]. Advances in Space Research, 2011, 47(9): 1497-1507.
6 Zhang G, Xie H, Kang S, et al. Monitoring Lake Level Changes on the Tibetan Plateau Using ICESat Altimetry Data (2003~2009)[J]. Remote Sensing of Environment, 2011, 115(7): 1733-1742.
7 Zhang G, Yao T, Shum C K, et al. Lake Volume and Groundwater Storage Variations in Tibetan Plateau's Endorheic basin: Water Mass Balance in the TP[J]. Geophysical Research Letters, 2017, 44(11): 5550-5560.
8 Koponen S, Pulliainen J, Kallio K, et al. Lake Water Quality Classification with Airborne Hyperspectral Spectrometer and Simulated MERIS Data[J]. Remote Sensing of Environment, 2002, 79(1): 51-59.
9 Jupp D L B, Mayo K K, Kucher D A, et al. Landsat based interpretation of the Cairns section of the Great Barrier Reef Marine Park[M]. Canberra, ACT, CSIRO Division of Water & Land Resources, 1985.
10 Zhou C H, Du Y Y, Luo J C. A Description Model based on Knowledge for Automatically Recognizing Water from NOAA/AVHRR[J]. Journal of Natural Disasters, 1996,5(3):100-108.
10 周成虎, 杜云艳, 骆剑承. 基于知识的AVHRR影像的水体自动识别方法与模型研究[J]. 自然灾害学报, 1996, 5(3): 100-108.
11 Mcfeeters S K. The Use of the Normalized Difference Water Index(NDWI) in the Delineation of Open Water Features[J]. International Journal of Remote Sensing,1996,17(7):1425-1432.
12 Du Jinkang, Huang Yongsheng, Feng Xuezhi, et al. Study on Water Bodies Extraction and Classification from SPOT Image[J]. Journal of Remote Sensing, 2001, 5(3): 214-219.
12 都金康, 黄永胜, 冯学智,等. SPOT卫星影像的水体提取方法及分类研究[J]. 遥感学报, 2001, 5(3): 214-219.
13 Shen Jinxiang, Yang Liao, Chen Xi, et al. A Method for Object-oriented Automatic Extraction of Lakes in the Mountain Area from Remote Sensing Image[J]. Remote Sensing for Land & Resources, 2012, 24(3): 84-91.
13 沈金祥, 杨辽, 陈曦,等.面向对象的山区湖泊信息自动提取方法[J]. 国土资源遥感, 2012, 24(3) :84-91.
14 Xu H Q. Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery[J]. International Journal of Remote Sensing, 2006, 27(14): 3025-3033.
15 Singh K V, Setia R, Sahoo S, et al. Evaluation of NDWI and MNDWI for Assessment of Waterlogging by Integrating Digital Elevation Model and Groundwater Level[J]. Geocarto International, 2015, 30(6): 650-661.
16 Luo Jianchen, Sheng Yongwei, Shen Zhanfeng, et al. Automatic and High-precise Extraction for Water Information from Multispectral Images with the Step-by-Step Iterative Transformation Mechanism[J]. Journal of Remote Sensing, 2009, 13(4): 604-615.
16 骆剑承, 盛永伟, 沈占锋,等. 分步迭代的多光谱遥感水体信息高精度自动提取[J]. 遥感学报, 2009, 13(4):604-615
17 Li Junli, Sheng Yongwei, Luo Jiancheng. Automatic Extraction of Himalayan Glacial Lakes with Remote Sensing[J]. Journal of Remote Sensing, 2011, 15(1):29-43.
17 李均力, 盛永伟, 骆剑承. 喜马拉雅山地区冰湖信息的遥感自动化提取[J]. 遥感学报, 2011, 15(1):29-43.
18 Liu H, Jezek K. Automated Extraction of Coastline from Satellite Imagery by Integrating Canny Edge Detection and Locally Adaptive Thresholding Mthods[J]. International journal of remote sensing, 2004, 25(5): 937-958.
19 Setiawan B D, Rusydi A N, Pradityo K. Lake edge detection using Canny algorithm and Otsu thresholding[C]∥ 2017 International Symposium on Geoinformatics (ISyG), IEEE, 2017: 72-76, doi: 10.1109/ISYG.2017.8280676.
doi: 10.1109/ISYG.2017.8280676
20 Kumar R. Snakes: Active Contour Models[J]. International Journal of Computer Vision, 1988, 1(4): 321-331.
21 Zhu Shulong, Meng Weican, Zhu Baoshan. Irregular Water Boundary Extraction Using GVF Snake[J]. Journal of Remote Sensing, 2013, 17(4):742-758.
21 朱述龙, 孟伟灿, 朱宝山. 运用GVF Snake算法提取水域的不规则边界[J]. 遥感学报, 2013, 17(4): 742-758.
22 Shen H, Li H, Yan Q, et al. An Effective Thin Cloud Removal Procedure for Visible Remote Sensing Images[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2014, 96(11): 224-235.
23 Wang Hui, Tan Bing, Shen Zhiyun. The Processing Technology of Removing Clouds Image based on the Multi-resource RS Image[J]. Journal of the Pla Institute of Surveying & Mapping, 2001,18(3):195-198.
23 王惠, 谭兵, 沈志云. 多源遥感影像的去云层处理[J]. 测绘学院学报, 2001, 18(3):195-198.
24 Eckardt R, Berger C, Thiel C, et al. Removal of Optically Thick Clouds from Multi-spectral Satellite Images Using Multi-frequency SAR Data[J]. Remote Sensing, 2013, 5(6): 2973-3006.
25 Roerink G J, Menenti M, Verhoef W. Reconstructing Cloudfree NDVI Composites Using Fourier Analysis of Time Series[J]. International Journal of Remote Sensing, 2000, 21(9): 1911-1917.
26 Wang Dan, Jiang Xiaoguang, Tang Lingli, et al. The Application of Time-series Fourier Analysis to Reconstructing Cloud-free NDVI Images[J]. Remote Sensing for Land & Resources, 2005, 17(2): 29-32.
26 王丹,姜小光,唐伶俐,等.利用时间序列傅立叶分析重构无云NDVI图像[J]. 国土资源遥感, 2005, 17(2): 29-32.
27 Farr T G, Rosen P A, Caro E, et al. The Shuttle Radar Topography Mission[J]. Reviews of Geophysics, 2007, 45(2): RG2004, doi:10.1029/2005RG000183.
doi: 10.1029/2005RG000183
28 Foga S, Scaramuzza P L, Guo S, et al. Cloud Detection Algorithm Comparison and Validation for Operational Landsat Data Products[J]. Remote Sensing of Environment, 2017, 194:379-390.
29 Canny J. A Computation Approach to Edge Detection[J]. IEEE Trans Pattern Anal Mach Intell, 1986, 8(6): 670-700.
30 Ding L, Goshtasby A. On the Canny Edge Detector[J]. Pattern Recognition, 2001, 34(3): 721-725.
31 Mcilhagga W. The Canny Edge Detector Revisited[J]. In-ternational Journal of Computer Vision, 2011, 91(3): 251-261.
32 Douglas D H, Peucker T K. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature[J]. Cartographica: the International Journal for Geographic Information and Geovisualization, 1973, 10(2): 112-122.
[1] 焦雪敏,张赫林,徐富宝,王岩,彭代亮,李存军,徐希燕,范海生,黄运新. 青藏高原1982~2015年FPAR时空变化分析[J]. 遥感技术与应用, 2020, 35(4): 950-961.
[2] 张永宏,杨晨阳,陶润喆,王剑庚,田伟. 基于FY-4A数据的青藏高原多时相云检测方法[J]. 遥感技术与应用, 2020, 35(2): 389-398.
[3] 陈家利,郑东海,庞国锦,李新. 基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水[J]. 遥感技术与应用, 2020, 35(1): 48-57.
[4] 张正,肖鹏峰,张学良,冯学智,杨永可,胡瑞,盛光伟,刘豪. 青藏高原融雪期积雪反照率特性分析[J]. 遥感技术与应用, 2019, 34(6): 1146-1154.
[5] 刘培,余志远,马威,韩瑞梅,陈正超,王涵,杨磊库. 基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究[J]. 遥感技术与应用, 2019, 34(6): 1269-1275.
[6] 王润科, 王建, 李弘毅, 郝晓华, 马佳培. 基于Landsat ETM+遥感数据的组分温度反演方法研究[J]. 遥感技术与应用, 2019, 34(3): 571-582.
[7] 许文鑫, 周玉科, 梁娟珠. 基于变化点的青藏高原植被时空动态变化研究 [J]. 遥感技术与应用, 2019, 34(3): 667-676.
[8] 张博, 吴立宗. 基于Spark的分布式青藏高原MODIS LST插值方法实现研究[J]. 遥感技术与应用, 2018, 33(6): 1178-1185.
[9] 张玉伦, 王叶堂. 低山丘陵区多源数字高程模型误差分析[J]. 遥感技术与应用, 2018, 33(6): 1112-1121.
[10] 李晨伟,张瑞丝,张竹桐,曾敏 . 基于多源遥感数据的构造解译与分析—以西藏察隅吉太曲流域为例[J]. 遥感技术与应用, 2018, 33(4): 657-665.
[11] 周玉科,刘建文. 基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析[J]. 遥感技术与应用, 2018, 33(3): 486-498.
[12] 陈思宇,巩垠熙,梁天刚. 星载激光雷达在青藏高原湖泊变迁中的应用研究[J]. 遥感技术与应用, 2018, 33(2): 351-359.
[13] 曹晓晨,尤红建,刘佳音,王峰. 基于误差建模的SRTM高程精度提升方法研究[J]. 遥感技术与应用, 2017, 32(5): 801-808.
[14] 马敏娜,袁文平. 青藏高原总初级生产力估算的模型差异[J]. 遥感技术与应用, 2017, 32(3): 406-418.
[15] 王丽娟,郭铌,王玮,芦亚玲,沙莎. 基于TESEBS模型估算高原地区地表蒸散发[J]. 遥感技术与应用, 2017, 32(3): 507-513.