基于面向对象分类的延庆区公益林变化检测
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摘要
以北京市延庆区2004年Spot-5影像和2015年GF-1影像数据为研究对象,应用面向对象分类变化检测算法,通过选择最优尺度和结合典型地物光谱特征、纹理特征建立规则集来对两期影像进行分类,然后提取十年间延庆区公益林的变化地块,最后进行精度评价,旨在对延庆区公益林的变化及其驱动因素进行探索分析。结果表明:Spot-5影像的分类精度为87.1%,加入FC特征值规则的GF-1影像的分类精度为89.1%,高于未加入FC特征值规则的GF分类精度(84.8%),说明在规则集中加入FC特征值能提高森林分类精度;变化信息提取的结果总体精度为87.3%,漏判率、错判率都在20%以内,提取效果较佳;2004—2015年间,公益林面积呈上升趋势,且主要集中在有林地面积增加,农田、灌木地和其他土地面积减少,这与国家对林业及公益林日益增加的重视度、各项工程项目密不可分。
Based on Spot-5 and GF-1 image data, object-oriented classification method was used to detect the dynamic change in Yanqing District of Beijing. To extract the changeable plots, the two images were classified by selecting the optimal multi-segmentation scale and combining typical spectral features and texture features to establish rule sets system, afterwards the accuracy evaluation and analysis of the changes were carried out. This research aims to explore and analyze the changes and driving factors of non-commercial forest. The results indicate that the classification accuracy of Spot-5 image is 87.1%, the classification accuracy of GF-1 image with FC eigenvalue rule is 89.1%, which is higher than that of GF-1 image without FC eigenvalue rule, 84.8%, which means that adding FC eigenvalue in rule sets system can improve forest classification accuracy; the overall accuracy of the results of change information extraction is 87.3%, missed rates and error rates were less than 20%; From 2004 to 2015, the area of non-commercial forest showed an upward trend, which mainly reflected on increasing forest land area and decreasing farmland, shrubs and other lands. These changes were closely related to the increasing importance of forestry and non-commercial forests and various engineering projects.
Based on Spot-5 and GF-1 image data, object-oriented classification method was used to detect the dynamic change in Yanqing District of Beijing. To extract the changeable plots, the two images were classified by selecting the optimal multi-segmentation scale and combining typical spectral features and texture features to establish rule sets system, afterwards the accuracy evaluation and analysis of the changes were carried out. This research aims to explore and analyze the changes and driving factors of non-commercial forest. The results indicate that the classification accuracy of Spot-5 image is 87.1%, the classification accuracy of GF-1 image with FC eigenvalue rule is 89.1%, which is higher than that of GF-1 image without FC eigenvalue rule, 84.8%, which means that adding FC eigenvalue in rule sets system can improve forest classification accuracy; the overall accuracy of the results of change information extraction is 87.3%, missed rates and error rates were less than 20%; From 2004 to 2015, the area of non-commercial forest showed an upward trend, which mainly reflected on increasing forest land area and decreasing farmland, shrubs and other lands. These changes were closely related to the increasing importance of forestry and non-commercial forests and various engineering projects.
引文
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[19]王凯.基于ZY-1-02C和OLI影像的林地地类变化信息提取研究[D].南京:南京林业大学,2015.
[2]温伟庆.基于GIS的漳平市生态公益林建设监测与评价[J].森林与环境学报,2010,30(2):187-192.
[3]罗號,孙华,胡满,等.基于Landsat影像的黄丰桥林场森林变化检测研究[J].中南林业科技大学学报,2017,37(12):65-71.
[4]蔡银桥,毛政元.基于多特征对象的高分辨率遥感影像分类方法及其应用[J].国土资源遥感,2007,19(1):77-81.
[5]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.
[6]吴田军,胡晓东,夏列钢,等.基于对象级分类的土地覆盖动态变化及趋势分析[J].遥感技术与应用,2014,29(4):600-606.
[7]余坤勇.基于3S技术闽江流域生态公益林森林资源动态监测[D].福州:福建农林大学,2006.
[8]张桂林.卫星遥感图像判读在重点公益林监测中的应用[J].防护林科技,2014(4):109-110.
[9]HANSEN M C,ROY D P,LINDQUIST E,et al.A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin[J].Remote Sensing of Environment,2008,112(5):2495-2513.
[10]逄锦娇,孙睿,王汶.高分辨率影像配准误差对土地覆盖分类和变化检测的影响[J].遥感技术与应用,2014,29(3):498-505.
[11]朱俊杰,范湘涛,杜小平.面向对象的高分辨率遥感图像分析[M].北京:科学出版社,2014.
[12]张博,何彬彬.改进的分水岭变换算法在高分辨率遥感影像多尺度分割中的应用[J].地球信息科学学报,2014,16(1):142-150.
[13]JOHNSON B,XIE Z.Unsupervised image segmentation evaluation and refinement using a multi-scale approach[J].Isprs Journal of Photogrammetry&Remote Sensing,2011,66(4):473-483.
[14]陈春雷,武刚.多源遥感影像的最优尺度选择[J].浙江农林大学学报,2011,28(1):164-172.
[15]DRǎGU?L,TIEDE D,LEVICK S R.ESP:A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[J].International Journal of Geographical Information Science,2010,24(6):859-871.
[16]卢涛.基于均值方差法的遥感图像多尺度表达算法[J].科技创新与应用,2013(29):80.
[17]李朝奎,方文,董小姣.面向对象和规则的高分辨率影像分类研究[J].测绘通报,2015(9):9-13.
[18]刘玉安,黄波,程涛,等.基于像元二分模型的淮河上游植被覆盖度遥感研究[J].水土保持通报,2012,32(1):93-97.
[19]王凯.基于ZY-1-02C和OLI影像的林地地类变化信息提取研究[D].南京:南京林业大学,2015.
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