基于Landsat 8 OLI数据与面向对象分类的昆嵛山地区土地覆盖信息提取
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摘要
利用2015年Landsat 8 OLI遥感影像和DEM作为分类数据源,结合野外调查数据,采用面向对象的分类方法对昆嵛山地区土地覆盖信息进行提取,并对分类结果进行精度评价与比较分析。研究表明:面向对象分类方法提取的各地类连续且边界清晰,分类效果与实际情况基本吻合。昆嵛山地区占主导地位的土地覆盖类型是针叶林,面积为1 546.81 km2。研究区土地覆盖分类的总体精度和Kappa系数分别为91.5%和0.88,其中针叶林、草地、水体和建设用地的生产者精度均达到87%以上。相对于监督分类方法,本研究提出的土地覆盖信息提取方法的总体分类精度和Kappa系数分别提高14.7%和0.17。基于面向对象的中分辨率遥感影像,能够获取较高精度的土地覆盖信息,为大范围土地覆盖分类研究提供方法参考。
Land cover classification is the basis for geoscience and global change studies. It can provide essential information for modelling and understanding the complex interactions between human activities and global change. Remote sensing has been widely recognized as the most economic and feasible approach to derive land cover information on a large regional scale. Landsat satellite data are commonly used remote sensing data for land cover classification. The object-oriented classification method, which takes full advantage of the spectral, geometrical and textural information of remote sensing images and considers the spatial distribution characteristics and correlations of geographical objects, can mitigate the deficiency associated with the pixel-based approach. The purpose of this study is to deepen the application of object-oriented classification method that is utilized to extract land cover information automatically and quickly from the satellite imagery. Taking the Kunyu Mountain of Jiaodong peninsula in Shandong province as the study area, land cover classification was conducted by using the object-oriented classification method on eCognition software platform, with Landsat 8 OLI satellite image in 2015 and digital elevation model(DEM) as data sources. Firstly, Landsat 8 OLI data of high quality was selected, and preprocessed by radiometric calibration, atmospheric correction, accurate geometric correction, image registration and fusion. Feature parameters including spectral(normalized difference vegetation index(NDVI), band brightness), shape(area, roundness, rectangular fit), and topographic(DEM,slope) characteristics were calculated. Then, the land cover information was classified into cropland, grassland,needleleaf forest, broadleaf forest, built-up land, water bodies, and barren land by the object-oriented method following the steps of multi-resolution image segmentation, object feature extraction, and classification rule set construction. Finally, the accuracy of this method was evaluated and compared with that of the pixel-based supervised classification method and ground validation sampling points. The results indicate that land cover information extracted by the object-oriented classification method using Landsat 8 OLI data is well consistent with the true condition on distribution and range of each land cover type in the Kunyu Mountain. The dominant type of land cover is needleleaf forests, with the area of 1546.81 km2. The overall accuracy and Kappa coefficient of the method are 91.5% and 0.88, respectively. The production accuracy is higher than 87% for needleleaf forests, grassland, water bodies, and built-up land. By comparison with the maximum likelihood supervised classification method, the overall classification accuracy and Kappa coefficient of the proposed method in this study are increased by 14.7% and 0.17, respectively. This means the moderate resolution Landsat 8 OLI image, combined with the object-oriented classification method can effectively improve the accuracy of land cover information extraction in the typical vegetation areas. This study will provide a credible approach and valuable example for extracting and monitoring regional land cover type, and broaden the application vision and the scope of ecological remote sensing investigation in terrestrial ecosystem.
Land cover classification is the basis for geoscience and global change studies. It can provide essential information for modelling and understanding the complex interactions between human activities and global change. Remote sensing has been widely recognized as the most economic and feasible approach to derive land cover information on a large regional scale. Landsat satellite data are commonly used remote sensing data for land cover classification. The object-oriented classification method, which takes full advantage of the spectral, geometrical and textural information of remote sensing images and considers the spatial distribution characteristics and correlations of geographical objects, can mitigate the deficiency associated with the pixel-based approach. The purpose of this study is to deepen the application of object-oriented classification method that is utilized to extract land cover information automatically and quickly from the satellite imagery. Taking the Kunyu Mountain of Jiaodong peninsula in Shandong province as the study area, land cover classification was conducted by using the object-oriented classification method on eCognition software platform, with Landsat 8 OLI satellite image in 2015 and digital elevation model(DEM) as data sources. Firstly, Landsat 8 OLI data of high quality was selected, and preprocessed by radiometric calibration, atmospheric correction, accurate geometric correction, image registration and fusion. Feature parameters including spectral(normalized difference vegetation index(NDVI), band brightness), shape(area, roundness, rectangular fit), and topographic(DEM,slope) characteristics were calculated. Then, the land cover information was classified into cropland, grassland,needleleaf forest, broadleaf forest, built-up land, water bodies, and barren land by the object-oriented method following the steps of multi-resolution image segmentation, object feature extraction, and classification rule set construction. Finally, the accuracy of this method was evaluated and compared with that of the pixel-based supervised classification method and ground validation sampling points. The results indicate that land cover information extracted by the object-oriented classification method using Landsat 8 OLI data is well consistent with the true condition on distribution and range of each land cover type in the Kunyu Mountain. The dominant type of land cover is needleleaf forests, with the area of 1546.81 km2. The overall accuracy and Kappa coefficient of the method are 91.5% and 0.88, respectively. The production accuracy is higher than 87% for needleleaf forests, grassland, water bodies, and built-up land. By comparison with the maximum likelihood supervised classification method, the overall classification accuracy and Kappa coefficient of the proposed method in this study are increased by 14.7% and 0.17, respectively. This means the moderate resolution Landsat 8 OLI image, combined with the object-oriented classification method can effectively improve the accuracy of land cover information extraction in the typical vegetation areas. This study will provide a credible approach and valuable example for extracting and monitoring regional land cover type, and broaden the application vision and the scope of ecological remote sensing investigation in terrestrial ecosystem.
引文
[1]朱永森,曾永年,张猛.基于HJ卫星数据与面向对象分类的土地利用/覆盖信息提取[J].农业工程学报, 2017, 33:258-265.[Zhu Yongsen, Zeng Yongnian, Zhang Meng. Object-oriented classification of land use/cover based on HJ satellites data.Transactions of the Chinese Society of Agricultural Engineering, 2017, 33:258-265.]
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[22] Hadavand A, Saadatseresht M, Homayouni S. Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data[J]. International Journal of Remote Sensing, 2017, 38(12):3586-3607.
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[25] Jia K, Wei X Q, Gu X F et al. Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China[J].Geocarto International, 2014, 29(8):941-951.
[26] Zhu Z, Woodcock C E. Continuous change detection and classification of land cover using all available Landsat data[J]. Remote Sensing of Environment, 2014, 144:152-171.
[27] Coulter L L, Stow D A, Tsai Y H et al. Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+imagery[J]. Remote Sensing of Environment, 2016, 184:396-409.
[28] Henits L, Jürgens C, Mucsi L. Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data[J]. International Journal of Remote Sensing, 2016, 37(15):3439-3454.
[29]徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义[J].生态学报, 2013, 33(11):3249-3257.[Xu Hanqiu, Tang Fei. Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance. Acta Ecologica Sinica, 2013, 33(11):3249-3257.]
[30] Zhu Z, Fu Y C, Woodcock C E et al. Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images:A case study from Guangzhou, China(2000–2014)[J]. Remote Sensing of Environment, 2016, 185:243-257.
[31]陈齐,李新通. Landsat 8 OLI影像新增特征对土地覆盖遥感分类的影响分析[J].亚热带资源与环境学报, 2015, 10(3):79-86.[Chen Qi, Li Xintong. Effects of new characteristics of Landsat 8 operational land imager(OLI)data on land-cover remote sensing classification. Journal of Subtropical Resources and Environment, 2015, 10(3):79-86.]
[32] Hackman K O, Gong P, Wang J. New land-cover maps of Ghana for 2015 using Landsat 8 and three popular classifiers for biodiversity assessment[J]. International Journal of Remote Sensing, 2017, 38(14):4008-4021.
[33] Li C C, Gong P, Wang J et al. The first all-season sample set for mapping global land cover with Landsat-8 data[J]. Science Bulletin, 2017, 62:508-515.
[34] Goodin D G, Anibas K L, Bezymennyi M. Mapping land cover and land use from object-based classification:an example from a complex agricultural landscape[J]. International Journal of Remote Sensing, 2015, 36(18):4702-4723.
[35]张森,陈健飞,龚建周.面向对象分类的决策树方法探讨—以Landsata-8 OLI为例[J].测绘科学, 2016, 41(6):117-125.[Zhang Sen, Chen Jianfei, Gong Jianzhou. Object-oriented classification based on C5.0 algorithm. Science of Surveying and Mapping, 2016, 41(6):117-125.]
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[2]黄亚博,廖顺宝.首套全球30 m分辨率土地覆被产品区域尺度精度评价——以河南省为例[J].地理研究, 2016, 35(8):1433-1446.[Huang Yabo, Liao Shunbao. Regional accuracy assessments of the first global land cover dataset at 30-meter resolution:A case study of Henan province. Geographical Research,2016, 35(8):1433-1446.]
[3] Gómez C, White J C, Wulder M A. Optical remotely sensed time series data for land cover classification:A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,116:55-72.
[4] Jin S M, Yang L M, Zhu Z et al. A land cover change detection and classification protocol for updating Alaska NLCD 2001 to2011[J]. Remote Sensing of Environment, 2017, 195:44-55.
[5] Andrew M E, Wulder M A, Nelson T A. Potential contributions of remote sensing to ecosystem service assessments[J]. Progress in Physical Geography, 2014, 38(3):328-353.
[6] Huang H B, Chen Y L, Clinton N et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine[J]. Remote Sensing of Environment, 2017, 202:166-176.
[7] Gong P, Wang J, Yu L et al. Finer resolution observation and monitoring of global land cover:first mapping results with Landsat TM and ETM+data[J]. International Journal of Remote Sensing, 2013, 34(7):2607-2654.
[8] Yu L, Liang L, Wang J et al. Meta-discoveries from a synthesis of satellite-based land-cover mapping research[J]. International Journal of Remote Sensing, 2014, 35(13):4573-4588.
[9] Khatami R, Mountrakis G, Stehman S V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification process:General guidelines for practitioners and future research[J]. Remote Sensing of Environment, 2016,177:89-100.
[10] Zhang H K, Roy D P. Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification[J]. Remote Sensing of Environment, 2017,197:15-34.
[11]张昊然.遥感影像信息提取的方式[J].测绘与空间地理信息,2014, 37(2):156-158.[Zhang Haoran. Remote sensing information extraction method. Geomatics&Spatial Information Technology, 2014, 37(2):156-158.]
[12] Estoque R C, Murayama Y. Classification and change detection of built-up lands from Landsat-7 ETM+and Landsat-8 OLI/TIRS imageries:A comparative assessment of various spectral indices[J]. Ecological Indicators, 2015, 56:205-217.
[13] Shao Y, Lunetta R S, Wheeler B et al. An evaluation of time-series smoothing algorithms for land-cover classification using MODIS-NDVI multi-temporal data[J]. Remote Sensing of Environment, 2016, 174:258-265.
[14]李亮,周亚光,梁彬,等.融合时间特征的遥感影像分类[J].国土资源遥感, 2017, 29(1):36-42.[Li Liang, Zhou Yaguang, Liang Bin et al. Remote sensing image classification based on fusion of temporal features. Remote Sensing for Land and Resources, 2017, 29(1):36-42.]
[15] Mack B, Leinenkugel P, Kuenzer C et al. A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data[J]. Remote Sensing Letters, 2017, 8(3):244-253.
[16] Jia K, Liang S L, Wei X Q et al. Land cover classification of Landsat data with phenological features extracted from time series MODIS NDVI data[J]. Remote Sensing, 2014, 6:11518-11532.
[17]王宇航,范文义,刘超逸.基于面向对象的QUICKBIRD数据和SAR数据融合的地物分类[J].东北林业大学学报, 2016, 44(9):44-49.[Wang Yuhang, Fan Wenyi, Liu Chaoyi et al. An object-based fusion of QUICKBIRD data and RADARSAT SAR data for classification analysis. Journal of Northeast Forestry University, 2016, 44(9):44-49.]
[18] Chen B, Huang B, Xu B. Multi-source remotely sensed data fusion for improving land cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 124:27-39.
[19]王志慧,李世明,刘良云,等.基于MODIS NDVI时间序列的土地覆盖分层分类方法研究[J].遥感技术与应用, 2013, 28(5):910-919.[Wang Zhihui, Li Shiming, Liu Liangyun et al. Hierarchical land cover classification based on MODIS NDVI time-series. Remote Sensing Technology and Application, 2013,28(5):910-919.]
[20] Zhang W W, Chen J, Liao A P et al. Geospatial knowledgebased verification and improvement of GlobeLand30[J]. Science China Earth Sciences, 2016, 59(9):1709-1719.
[21]王娟,廖静娟,沈国状,等.基于面向对象技术的鄱阳湖湿地地物分类研究[J].遥感技术与应用, 2016, 31(3):543-550.[Wang Juan, Liao Jingjuan, Shen Guozhuang et al. Study on classification of Poyang Lake wetland using object-oriented technology. Remote Sensing Technology and Application, 2016,31(3):543-550.]
[22] Hadavand A, Saadatseresht M, Homayouni S. Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data[J]. International Journal of Remote Sensing, 2017, 38(12):3586-3607.
[23] Ma L, Li M C, Ma X X et al. A review of supervised objectbased land-cover image classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:277-293.
[24] Roy D P, Wulder M A, Loveland T R et al. Landsat-8:Science and product vision for terrestrial global change research[J]. Remote Sensing of Environment, 2014, 145:154-172.
[25] Jia K, Wei X Q, Gu X F et al. Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China[J].Geocarto International, 2014, 29(8):941-951.
[26] Zhu Z, Woodcock C E. Continuous change detection and classification of land cover using all available Landsat data[J]. Remote Sensing of Environment, 2014, 144:152-171.
[27] Coulter L L, Stow D A, Tsai Y H et al. Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+imagery[J]. Remote Sensing of Environment, 2016, 184:396-409.
[28] Henits L, Jürgens C, Mucsi L. Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data[J]. International Journal of Remote Sensing, 2016, 37(15):3439-3454.
[29]徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义[J].生态学报, 2013, 33(11):3249-3257.[Xu Hanqiu, Tang Fei. Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance. Acta Ecologica Sinica, 2013, 33(11):3249-3257.]
[30] Zhu Z, Fu Y C, Woodcock C E et al. Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images:A case study from Guangzhou, China(2000–2014)[J]. Remote Sensing of Environment, 2016, 185:243-257.
[31]陈齐,李新通. Landsat 8 OLI影像新增特征对土地覆盖遥感分类的影响分析[J].亚热带资源与环境学报, 2015, 10(3):79-86.[Chen Qi, Li Xintong. Effects of new characteristics of Landsat 8 operational land imager(OLI)data on land-cover remote sensing classification. Journal of Subtropical Resources and Environment, 2015, 10(3):79-86.]
[32] Hackman K O, Gong P, Wang J. New land-cover maps of Ghana for 2015 using Landsat 8 and three popular classifiers for biodiversity assessment[J]. International Journal of Remote Sensing, 2017, 38(14):4008-4021.
[33] Li C C, Gong P, Wang J et al. The first all-season sample set for mapping global land cover with Landsat-8 data[J]. Science Bulletin, 2017, 62:508-515.
[34] Goodin D G, Anibas K L, Bezymennyi M. Mapping land cover and land use from object-based classification:an example from a complex agricultural landscape[J]. International Journal of Remote Sensing, 2015, 36(18):4702-4723.
[35]张森,陈健飞,龚建周.面向对象分类的决策树方法探讨—以Landsata-8 OLI为例[J].测绘科学, 2016, 41(6):117-125.[Zhang Sen, Chen Jianfei, Gong Jianzhou. Object-oriented classification based on C5.0 algorithm. Science of Surveying and Mapping, 2016, 41(6):117-125.]
[36]牟进鹏,陈妮娜,董章凯.昆嵛山国家级自然保护区物种水平指标的分析与研究[J].吉林业科技, 2014, 43(2):22-24.[Mu Jinpeng, Chen Nina, Dong Zhangkai. Analysis and research of species level indicator in Kunyu Mountain national nature reserve. Journal of Jilin Forestry Science and Technology, 2014,43(2):22-24.]
[37]杜宁,王琦,郭卫华,等.昆嵛山典型植物群落生态学特性[J].生态学杂志, 2007, 26(2):151-158.[Du Ning, Wang Qi, Guo Weihua, et al. Ecological characteristics of typical plant communities in Kunyu Mountain. Chinese Journal of Ecology, 2007, 26(2):151-158.]
[38]杜华.昆嵛山赤松林不同林型结构特征与生产力的研究[J].北京林业大学学报, 2012, 34(1):19-24.[Du Hua. Community structure characteristics and productivity of varied Pinus densiflora forest types in Kunyu Mountain. Journal of Beijing Forestry University, 2012, 34(1):19-24.]
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