Object-oriented multi-index integrated vegetation change analysis based on WorldView2 and GF-2

Zhu Fangyan; Shen Wenjuan; Li Mingshi

doi:10.13332/j.1000-1522.20180435

Journal of Beijing Forestry University > 2019 > 41(11): 54-65. > DOI: 10.13332/j.1000-1522.20180435

Zhu Fangyan, Shen Wenjuan, Li Mingshi. Object-oriented multi-index integrated vegetation change analysis based on WorldView2 and GF-2[J]. Journal of Beijing Forestry University, 2019, 41(11): 54-65. DOI: 10.13332/j.1000-1522.20180435

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Object-oriented multi-index integrated vegetation change analysis based on WorldView2 and GF-2

1.
College of Forestry, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
2.
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

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Received Date: December 24, 2018
Revised Date: June 22, 2019
Available Online: September 28, 2019
Published Date: October 31, 2019

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Abstract

Abstract

ObjectiveUsing high spatial resolution satellite images to capture accurate information on vegetation change is of great significance for the rational use of vegetation resources and sustainable management. Traditional pixel-based direct change detection methods are easy to cause salt-and-pepper noises and the results of object-oriented classification methods depend heavily on the classification accuracy. After investigating the advantages and weaknesses of the existing algorithms for vegetation change analysis, the major objective of this study was to develop a relatively objective algorithm for vegetation change detection by high spatial resolution remote sensing data, and to verify its effectiveness.
MethodAn object-oriented multi-index integrated change analysis (MIICA) algorithm was proposed in the analysis based on a existing MIICA. First, bi-temporal cross-sensor high spatial resolution images were segmented uniformly with the optimal segmentation parameters, which were determined by examining the precision (P) and recall (R) indices, followed by the extraction of feature parameters of the segmented objects. Then the appropriate thresholds objectively determined by ROC (receiver operating characteristic) curves were integrated to derive vegetation change positions and directions (vegetation gain or loss) finally.
ResultResults showed that compared with the pixel-based MIICA and the object-oriented classification method, the producer’s accuracy of our method was higher than that of the pixel-based MIICA, meanwhile the user’s accuracy was higher than that of the object-oriented classification method. And our method had higher overall accuracy and Kappa coefficient, estimated at 0.880 and 0.805, respectively. The detection results of our method could better reflect the positions and shapes of the vegetation change areas, with some subtle vegetation changes clearly detected.
ConclusionObject-oriented MIICA can improve the shortcomings of pixel-based MIICA and object-oriented classification method, and improve the detection accuracy. The proposed method is essential for the analysis of vegetation changes, the reasonable utilization and sustainable management of vegetation resources in forest parks or natural reserves, where heavy anthropogenic disturbances frequently exist.
- high spatial resolution image,
- object-oriented,
- multi-index integrated change analysis,
- vegetation change detection

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[1]	王琰, 舒宁, 龚龑. 高分辨率遥感影像土地利用变化检测方法研究[J]. 国土资源遥感, 2012, 24(1):43−47. doi: 10.6046/gtzyyg.2012.01.08 Wang Y, Shu N, Gong Y. A study of land use change detection based on high resolution remote sensing images[J]. Remote Sensing for Land & resources, 2012, 24(1): 43−47. doi: 10.6046/gtzyyg.2012.01.08
[2]	Deng J S, Wang K, Deng Y H, et al. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data[J]. International Journal of Remote Sensing, 2008, 29(16): 4823−4838. doi: 10.1080/01431160801950162
[3]	赖祖龙, 申邵洪, 程新文, 等. 基于图斑的高分辨率遥感影像变化检测[J]. 测绘通报, 2009, 16(3):16−19. Lai Z L, Shen S H, Cheng X W, et al. Change detection of high-resolution remote sensing image based on map spot[J]. Bulletin of Surveying and Mapping, 2009, 16(3): 16−19.
[4]	Weismiller R A, Kristof S J, Scholz D K, et al. Change detection in coastal zone environments[J]. Photogrammetric Engineering & Remote Sensing, 1997, 43(12): 1533−1539.
[5]	杨存建, 欧晓昆, 党承林, 等. 森林植被动态变化信息的遥感检测[J]. 地球信息科学学报, 2000, 2(4):71−74. doi: 10.3969/j.issn.1560-8999.2000.04.016 Yang C J, Ou X K, Dang C L, et al. Detecting the change information of forest vegetation in Lushui County of Yunnan Province[J]. Geo-Information Science, 2000, 2(4): 71−74. doi: 10.3969/j.issn.1560-8999.2000.04.016
[6]	Chen J, Lu M, Chen X, et al. A spectral gradient difference based approach for land cover change detection[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2013, 85(2): 1−12.
[7]	Jin S M, Yang L M, Danielson P, et al. A comprehensive change detection method for updating the National Land Cover Database to circa 2011[J]. Remote Sensing of Environment, 2013, 132(10): 159−175.
[8]	韩飞. 光谱梯度差分与面向对象方法相结合的高分辨率遥感影像变化检测[D]. 成都: 西南交通大学, 2016. Han F. The change detection combining spectral gradient difference with object-oriented method for high resolution remote sensing images[D]. Chengdu: Southwest Jiaotong University, 2016.
[9]	Baatz M, Schäpe A. Object-oriented and multi-scale image analysis in semantic networks[C]//Proceeding of 2nd International Symposium: Operationalization of Remote Sensing, Enschede: ITC, 1999: 87−109.
[10]	Chen G, Hay G J, Carvalho L M T, et al. Object-based change detection[J]. International Journal of Remote Sensing, 2012, 33(14): 4434−4457. doi: 10.1080/01431161.2011.648285
[11]	Howell R G, Petersen S L. A comparison of change detection measurements using object-based and pixel-based classification methods on western juniper dominated woodlands in eastern Oregon[J]. Aims Environmental Science, 2017, 4(2): 348−357. doi: 10.3934/environsci.2017.2.348
[12]	Gamanya R, Maeyer P D, Dapper M D. Object-oriented change detection for the city of Harare, Zimbabwe[J]. Expert Systems with Applications, 2009, 36(1): 571−588. doi: 10.1016/j.eswa.2007.09.067
[13]	Desclée B, Bogaert P, Defourny P. Forest change detection by statistical object-based method[J]. Remote Sensing of Environment, 2006, 102(1): 1−11.
[14]	李亮, 王蕾, 孙晓鹏, 等. 面向对象变化向量分析的遥感影像变化检测[J]. 遥感信息, 2017, 32(6):71−77. doi: 10.3969/j.issn.1000-3177.2017.06.012 Li L, Wang L, Sun X P, et al. Remote sensing change detection method based on object-oriented change vector analysis[J]. Remote Sensing Information, 2017, 32(6): 71−77. doi: 10.3969/j.issn.1000-3177.2017.06.012
[15]	刘璐. 南京紫金山植被变化及其对环境质量的影响[J]. 福建农业, 2015(6):209−211. Liu L. Changes of vegetation and its effect on environmental quality in Purple Mountain, Nanjing[J]. Fujian Agric, 2015(6): 209−211.
[16]	董丽娜, 徐海兵, 居峰, 等. 南京紫金山国家森林公园植物多样性现状及保护对策[J]. 江苏林业科技, 2011, 38(1):30−35. doi: 10.3969/j.issn.1001-7380.2011.01.008 Dong L N, Xu H B, Ju F, et al. Plant diversity and its conservation strategies in Zijin Mountain National Forest Park in Nanjing[J]. Journal of Jiangsu Forestry Science & Technology, 2011, 38(1): 30−35. doi: 10.3969/j.issn.1001-7380.2011.01.008
[17]	Qi C, Lalasia B M. Assessment of Vegetation Response to Ungulate Removal Utilizing High Resolution Remotely Sensed Data[R/OL]. Status Report for the Makua and Oahu Implementation Plans, 2014 [2018−09−16]. http://manoa.hawaii.edu/hpicesu/DPW/2014_YER/ES5.pdf.
[18]	胡茂莹. 基于高分二号遥感影像面向对象的城市房屋信息提取方法研究[D]. 长春: 吉林大学, 2016. Hu M Y. Research on the object-oriented method of urban building extraction with GF-2 remote-sensing imagery[D]. Changchun: Jilin University, 2016.
[19]	李明诗, 梅昭容. 土地覆盖变化检测中不同相对辐射归一化方法的评价[J]. 南京林业大学学报(自然科学版), 2017, 41(4):108−114. Li M S, Mei Z R. Performance assessment of different relative radiometric normalization approaches applied to land cover change detection[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2017, 41(4): 108−114.
[20]	彭士纯, 柳健. 基于高通滤波的多光谱图像融合方法[J]. 计算机应用研究, 2007, 24(8):218−219. doi: 10.3969/j.issn.1001-3695.2007.08.070 Peng S C, Liu J. Multi-spectral image fusion method based on high pass filter[J]. Application Research of Computers, 2007, 24(8): 218−219. doi: 10.3969/j.issn.1001-3695.2007.08.070
[21]	Witharana C, Larue M A, Lynch H J. Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 113: 124−143. doi: 10.1016/j.isprsjprs.2015.12.009
[22]	龚建周, 刘彦随, 夏北成, 等. 小波基及其参数对遥感影像融合图像质量的影响[J]. 地理与地理信息科学, 2010, 26(2):6−10. Gong J Z, Liu Y S, Xia B C, et al. Effect of wavelet basis and decomposition levels on performance of fusion images from remotely sensed data[J]. Geography and Geo-Information Science, 2010, 26(2): 6−10.
[23]	刘春燕. 图像分割评价方法研究[D]. 西安: 西安电子科技大学, 2011. Liu C Y. Survey on evaluation methods of image segmentation algorithms[D]. Xi’an: Xidian University, 2011.
[24]	朱成杰, 杨世植, 崔生成, 等. 面向对象的高分辨率遥感影像分割精度评价方法[J/OL]. 强激光与粒子束, 2015, 27(6): 27061007[2018−06−16]. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qjgylzs201506007. Zhu C J, Yang S Z, Cui S C, et al. Accuracy evaluating method for object-based segmentation of high resolution remote sensing image[J/OL]. High Power Laser and Particle Beams, 2015, 27(6): 27061007 [2018−06−16]. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qjgylzs201506007.
[25]	LópezGarcía M J, Caselles V. Mapping burns and natural reforestation using thematic Mapper data[J]. Geocarto International, 1991, 6(1): 31−37.
[26]	Hanley J A. The meaning and use of the areas under a receiver operating characteristic (ROC) curve[J]. Radiology, 1982, 143(1): 29−36. doi: 10.1148/radiology.143.1.7063747
[27]	Leshowitz B. Comparison of ROC curves from one- and two-interval rating-scale procedures[J]. The Journal of the Acoustical Society of America, 1969, 46(2B): 399−402. doi: 10.1121/1.1911703
[28]	Banko G. A review of assessing the accuracy of classifications of remotely sensed data and of methods including remote sensing data in forest inventory[R]. Vienna: International Institute for Applied Systems Analysis Interim Report, 1998: IR-98-081.
[29]	Congalton R G. A review of assessing the accuracy of classifications of remotely sensed data[J]. Remote Sensing of Environment, 1991, 37(1): 35−46.

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