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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 157-164     DOI: 10.6046/gtzyyg.2020.03.21
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Accurate monitoring of ecological redline areas in Nanjing City using high resolution satellite imagery
ZHANG Peng1,2,3(), LIN Cong1,2,3, DU Peijun1,2,3(), WANG Xin1,2,3, TANG Pengfei1,2,3
1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
3. Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China
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Abstract  

The rapid development of China’s industrialization and urbanization has brought about a series of ecological and environmental problems. China has proposed a new ecological redline policy (ERP), which plays an important role in protecting natural ecosystems and guaranteeing the national ecological safety. For accurate monitoring of ecological redline areas (ERAs), the high temporal-and-spatial resolution BJ-2 satellite imagery was used for land cover classification of the ERAs of Nanjing. Given the characteristics of BJ-2 satellite imagery, a workflow from data preprocessing to object-based land cover classification was established. The overall accuracy of the classification can reach to 91.65%. It is shown that the ERAs of Nanjing is mainly composed of three kinds of land cover types: forest, cultivated land and water, which occupy 33%, 21% and 25% of the study area respectively. In addition, buildings and artificial pile digging account for 6% and 2%, which can represent human influence to a certain extent. The experimental results show that the multi-temporal BJ-2 imagery can be used to detect the detailed changes of land cover that are difficult to identify in low- and medium-resolution images, and achieve the purpose of dynamic and accurate monitoring of ERAs.
Keywords ecological redline areas (ERAs)      BJ-2      accurate monitoring      object-based methods     
:  TP79  
Corresponding Authors: DU Peijun     E-mail: pzhangrs@smail.nju.edu.cn;dupjrs@126.com
Issue Date: 09 October 2020
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Peng ZHANG
Cong LIN
Peijun DU
Xin WANG
Pengfei TANG
Cite this article:   
Peng ZHANG,Cong LIN,Peijun DU, et al. Accurate monitoring of ecological redline areas in Nanjing City using high resolution satellite imagery[J]. Remote Sensing for Land & Resources, 2020, 32(3): 157-164.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.21     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/157
Fig.1  Location of the ecological redline areas of Nanjing City
类型 参数
空间分辨率 全色: 0.8 m; 多光谱: 3.2 m
重复观测周期 1 d
波段范围 蓝: 440~510 nm; 绿: 510~590 nm; 红: 600~670 nm; 近红外: 760~910 nm; 全色: 450~650 nm
Tab.1  BJ-2 satellite parameters
类别
序号		 主要地表覆盖类型 分割
尺度		 光谱因
子权重		 紧凑度
权重
1 林地、草地为主的植被覆盖地表 85 0.1 0.5
2 建筑物、道路、人工堆掘地为主的人工地表 85 0.2 0.5
3 水体为主的地表 90 0.2 0.5
4 耕地为主的地表 75 0.1 0.5
Tab.2  Optimal segmentation parameters for each land-cover type
Fig.2  Image segmentation results using the optimal parameters of each land-cover type
地表类型 草地 林地 耕地 建筑物 道路 水体 人工堆掘地 合计 用户精度/%
草地 1 013 292 119 0 0 0 1 1 425 71.09
林地 40 6 792 213 4 9 2 0 7 060 92.28
耕地 43 37 2 290 6 7 20 9 2 412 89.04
建筑物 6 1 48 1 273 29 11 3 1 371 92.85
道路 4 0 10 70 916 0 0 1 000 91.60
水体 0 0 3 4 62 5 495 20 5 584 97.88
人工堆掘地 6 7 10 68 0 8 471 5 70 82.63
合计 1 112 7 129 2 693 1 425 1 023 5 536 504 19 912
生产者精度/% 67.00 95.27 84.41 86.31 89.54 98.90 93.45
总体精度: 91.65%; Kappa系数: 0.889 8
Tab.3  Accuracy assessment of the proposed land-cover classification method
Fig.3  Land-cover classification results in the ecological redline areas of Nanjing City
Fig.4  Land cover changes in the experimental area
Fig.5  Xinjizhou wetland park and its land cover types
地表覆盖类型 面积/km2 占比/%
水体 142.366 7 62.76
耕地 35.912 2 15.83
林地 19.354 8 8.53
草地 16.706 7 7.36
建筑物 6.704 3 2.95
人工堆掘地 3.964 5 1.75
道路 1.848 9 0.82
Tab.4  Statistics on the land cover of wetland parks and important wetlands
[1] 林勇, 樊景凤, 温泉, 等. 生态红线划分的理论和技术[J]. 生态学报, 2016,36(5):1244-1252.
doi: 10.5846/stxb201407091405 url: http://www.ecologica.cn/stxb/ch/reader/view_abstract.aspx?file_no=stxb201407091405&flag=1
[1] Lin Y, Fan J F, Wen Q, et al. Primary exploration of ecological theories and technologies for delineation of ecological redline zones[J]. Acta Ecologica Sinica, 2016,36(5):1244-1252.
[2] Kennedy R E, Yang Z Q, Cohen W B. Detecting trends in forest disturbance and recovery using yearly landsat time series:1.LandTrendr-temporal segmentation algorithms[J]. Remote Sensing of Environment, 2010,114(12):2897-2910.
doi: 10.1016/j.rse.2010.07.008 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425710002245
[3] Cohen W B, Yang Z Q, Kennedy R E, et al. A LandTrendr multispectral ensemble for forest disturbance detection[J]. Remote Sensing of Environment, 2018,205:131-140.
doi: 10.1016/j.rse.2017.11.015 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717305576
[4] Verbesselt J, Zeileis A, Herold M. Near real-time disturbance detection using satellite image time series[J]. Remote Sensing of Environment, 2012,123(123):98-108.
doi: 10.1016/j.rse.2012.02.022 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425712001150
[5] Huang C Q, Goward S N, Masek J G, et al. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks[J]. Remote Sensing of Environment, 2010,114(1):183-198.
doi: 10.1016/j.rse.2009.08.017 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425709002685
[6] 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(1):152-171.
doi: 10.1016/j.rse.2014.01.011 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425714000248
[7] Goetz S, Gross J, Hansen A, et al. Landscape conditions and trends in and around delaware water gap and upper delaware national recreation areas:Summary of findings[R].Report,NASA Applications Program,NPS I&M Program, 2011.
[8] 严恩萍, 林辉, 党永峰, 等. 2000—2012年京津风沙源治理区植被覆盖时空演变特征[J]. 生态学报, 2014,34(17):5007-5020.
doi: 10.5846/stxb201305251179 url: http://www.ecologica.cn/stxb/ch/reader/view_abstract.aspx?file_no=stxb201305251179&flag=1
[8] Yan E P, Lin H, Dang Y F, et al. The spatiotemporal changes of vegetation cover in Beijing-Tianjin sandstorm source control region during 2000—2012[J]. Acta Ecologica Sinica, 2014,34(17):5007-5020.
[9] Chéret V, Denux J P. Analysis of MODIS NDVI time series to calculate indicators of mediterranean forest fire susceptibility[J]. Mapping Sciences & Remote Sensing, 2011,48(2):171-194.
[10] 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,(7):91-97.
url: http://www.tcsae.org/nygcxb/ch/reader/view_abstract.aspx?file_no=20130712&flag=1
[10] Xu H Q. Assessment of ecological change in soil loss area using remote sensing technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(7):91-97.
url: http://www.tcsae.org/nygcxb/ch/reader/view_abstract.aspx?file_no=20130712&flag=1
[11] 满苏尔·沙比提, 娜斯曼·那斯尔丁, 阿尔斯朗·马木提. 托木尔峰国家级自然保护区土地利用/覆被生态服务价值变化分析[J]. 地理研究, 2016,35(11):2116-2124.
doi: 10.11821/dlyj201611009 url: http://www.dlyj.ac.cn/CN/abstract/abstract38707.shtml
[11] Sabit M, Nasirdin N, Mamut A. Analysis on the change of land use/cover ecological service value in Tomur National Nature Reserve[J]. Geographical Research, 2016,35(11):2116-2124.
[12] 国务院.国务院关于加强环境保护重点工作的意见[EB/OL].( 2011-10-20)[2019-10-17]. http://www.gov.cn/zhuanti/2015-06/13/content_2878991.htm.
url: http://www.gov.cn/zhuanti/2015-06/13/content_2878991.htm
[12] State Council of China.Opinions of the state council on strengthening the key work of environmental protection[EB/OL].(2011-10-20)[2019-10-17]. http://www.gov.cn/zhuanti/2015-06/13/content_2878991.htm.
url: http://www.gov.cn/zhuanti/2015-06/13/content_2878991.htm
[13] Ma L, Li M C, Ma X X, et al. A review of supervised object-based land-cover image classification[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2017,130:277-293.
[14] 陈玲, 梁树能, 周艳, 等. 国产高分卫星数据在高海拔地区地质调查中的应用潜力分析[J]. 国土资源遥感, 2015,27(1):140-145.doi: 10.6046/gtzyyg.2015.01.22.
doi: 10.6046/gtzyyg.2015.01.22 url: http://www.gtzyyg.com/CN/abstract/abstract1841.shtml
[14] Chen L, Liang S N, Zhou Y, et al. Potential of applying domestic high-resolution remote sensing data to geological survey in high altitudes[J]. Remote Sensing for Land and Resources, 2015,27(1):140-145.doi: 10.6046/gtzyyg.2015.01.22.
[15] 王永立, 董卫宏. 资源一号02C卫星数据在地质公园调查中的应用——以天津市蓟县为例[J]. 国土资源遥感, 2017,29(s1):95-100.doi: 10.6046/gtzyyg.2017.s1.16.
[15] Wang Y L, Dong W H. Application of ZY1-02C remote sensing data to the investigation of geoparks:Taking Jixian County,Tianjin City for example[J]. Remote Sensing for Land and Resources, 2017,29(s1):95-100.doi: 10.6046/gtzyyg.2017.s1.16.
[16] 杜培军, 单丹丹, 夏俊士, 等. 北京一号小卫星数据的城市景观格局监测分析——以徐州市城区为例[J]. 地球信息科学学报, 2010,12(6):855-862.
url: http://www.dqxxkx.cn/CN/abstract/abstract8450.shtml
[16] Du P J, Shan D D, Xia J S, et al. Monitoring and analyzing urban landscape pattern change using Beijing-1 small satellite remote sensing data:Taking the urban area of Xuzhou City as an example[J]. Journal of Geo-Information Science, 2010,12(6):855-862.
[17] 郭海会, 王卷乐, 周玉洁, 等. 面向对象的鄱阳湖南矶湿地国家级自然保护区烧荒地信息提取[J]. 地球信息科学学报, 2014,16(3):499-506.
doi: 10.3724/SP.J.1047.2014.00499 url: http://www.dqxxkx.cn/CN/abstract/abstract10259.shtml
[17] Guo H H, Wang J L, Zhou Y J, et al. The moorburn information extraction on Poyang Lake Nanji Wetland Nature Reserve area based on the object-oriented classification method[J]. Journal of Geo-Information Science, 2014,16(3):499-506.
[18] 苏伟, 李京, 陈云浩, 等. 基于多尺度影像分割的面向对象城市土地覆被分类研究——以马来西亚吉隆坡市城市中心区为例[J]. 遥感学报, 2007,11(4):521-530.
doi: 10.11834/jrs.20070472 url: http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=20070472&flag=1
[18] Su W, Li J, Chen Y H, et al. Object-oriented urban land-cover classification of multi-scale image segmentation method:A case study in Kuala Lumpur City Center,Malaysia[J]. Journal of Remote Sensing, 2007,11(4):521-530.
doi: 10.11834/jrs.20070472 url: http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=20070472&flag=1
[19] Blaschke T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010,65(1):2-16.
doi: 10.1016/j.isprsjprs.2009.06.004 url: http://www.sciencedirect.com/science/article/pii/S0924271609000884
[20] Pande-Chhetri R, Abd-Elrahman A, Liu T, et al. Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery[J]. European Journal of Remote Sensing, 2017,50(1):564-576.
doi: 10.1080/22797254.2017.1373602 url: https://www.tandfonline.com/doi/full/10.1080/22797254.2017.1373602
[21] 方朝阳, 邬浩, 陶长华, 等. 鄱阳湖南矶湿地景观信息高分辨率遥感提取[J]. 地球信息科学学报, 2016,18(6):847-856.
doi: 10.3724/SP.J.1047.2016.00847 url: http://www.dqxxkx.cn/CN/abstract/abstract38246.shtml
[21] Fang C Y, Wu H, Tao C H, et al. The wetland information extraction research of Nanji Wetland in Poyang Lake based on high resolution remote sensing image[J]. Journal of Geo-Information Science, 2016,18(6):847-856.
[22] Myint S W, Gober P, Brazel A, et al. Per-pixel vs.object-based classification of urban land cover extraction using high spatial resolution imagery[J]. Remote Sensing of Environment, 2011,115(5):1145-1161.
doi: 10.1016/j.rse.2010.12.017 url: http://dx.doi.org/10.1016/j.rse.2010.12.017
[23] 陶超, 谭毅华, 蔡华杰, 等. 面向对象的高分辨率遥感影像城区建筑物分级提取方法[J]. 测绘学报, 2010,39(1):39-45.
[23] Tao C, Tan Y H, Cai H J, et al. Object-oriented method of hierarchical urban building extraction from high-resolution remote-sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2010,39(1):39-45.
[24] Li X X, Myint S W, Zhang Y J, et al. Object-based land-cover classification for metropolitan Phoenix,Arizona,using aerial photography[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,33(1):321-330.
doi: 10.1016/j.jag.2014.04.018 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243414001081
[25] Duro D C, Franklin S E, Dubé M G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery[J]. Remote Sensing of Environment, 2012,118:259-272.
doi: 10.1016/j.rse.2011.11.020 url: http://dx.doi.org/10.1016/j.rse.2011.11.020
[26] 程乾, 陈金凤. 基于高分1号杭州湾南岸滨海陆地土地覆盖信息提取方法研究[J]. 自然资源学报, 2015,30(2):350-360.
[26] Chen Q, Chen J F. Research on the extraction method of landcover information in southern coastal land of Hangzhou Bay based on GF-1 image[J]. Journal of Natural Resources, 2015,30(2):350-360.
[27] Dronova I, Gong P, Clinton N E, et al. Landscape analysis of wetland plant functional types:The effects of image segmentation scale,vegetation classes and classification methods[J]. Remote Sensing of Environment, 2012,12:357-369.
[28] 薄树奎, 韩新超, 丁琳. 面向对象影像分类中分割参数的选择[J]. 武汉大学学报(信息科学版), 2009,34(5):514-517.
url: http://ch.whu.edu.cn/CN/abstract/abstract1246.shtml
[28] Bo S K, Han X C, Ding L. Automatic selection of segmentation parameters for object oriented image classification[J]. Geomatics and Information Science of Wuhan University, 2009,34(5):514-517.
url: http://ch.whu.edu.cn/CN/abstract/abstract1246.shtml
[29] Pu R, Landry S. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species[J]. Remote Sensing of Environment, 2012,124:516-533.
doi: 10.1016/j.rse.2012.06.011 url: http://dx.doi.org/10.1016/j.rse.2012.06.011
[30] 杨凯歌, 冯学智, 肖鹏峰, 等. 优化子空间SVM集成的高光谱图像分类[J]. 遥感学报, 2016,20(3):409-419.
[30] Yang K G, Feng X Z, Xiao P F, et al. Optimal subspace ensemble with SVM for hyperspectral image classification[J]. Journal of Remote Sensing, 2016,20(3):409-419.
[31] 何灵敏, 沈掌泉, 孔繁胜, 等. SVM在多源遥感图像分类中的应用研究[J]. 中国图象图形学报, 2007,12(4):648-654.
doi: 10.11834/jig.20070410 url: http://www.cjig.cn/jig/ch/reader/view_abstract.aspx?file_no=200704109&flag=1
[31] He L M, Shen Z Q, Kong F S, et al. Study on multi-source remote sensing images classification with SVM[J]. Journal of Image and Graphics, 2007,12(4):648-654.
doi: 10.11834/jig.20070410 url: http://www.cjig.cn/jig/ch/reader/view_abstract.aspx?file_no=200704109&flag=1
[32] 燕守广, 林乃峰, 沈渭寿. 江苏省生态红线区域划分与保护[J]. 生态与农村环境学报, 2014,30(3):294-299.
[32] Yan S G, Lin N F, Shen W S. Delineation and protection of ecological red lines in Jiangsu Province[J]. Rural Eco-Environment, 2014,30(3):294-299.
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