基于低空无人机的草原灌丛遥感辨识方法
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摘要
以位于中国科学院内蒙古草原生态研究定位站灌丛化样地实验平台为研究区,基于低空无人机遥感影像,结合实地调查,开展草原灌丛遥感辨识方法研究。通过对灌丛、草地和裸地归一化植被指数(NDVI)的方差统计分析,确定了裸地与植被的分割阈值为-0.08,并使用该阈值提取植被覆盖区,然后分别利用面向对象的决策树(DT)、贝叶斯(Bayes)、K最邻近(KNN)、支持向量机(SVM)机器学习分类器进行灌丛辨识。研究表明:借助Estimation of Scale Parameter(ESP)最优分割尺度评价工具可以快速确定分割参数,获取灌丛、草地影像对象;利用特征空间优化工具选取了18个的对象特征,可以有效避免盲目选择而导致的计算量增大;通过对不同分类器分类结果的对比和样本数量敏感性实验得出:Bayes分类器精度稳定、无需设置参数,灌丛分类精度最高,总体精度和Kappa系数分别达到92%和0.83,结果与影像地物嵌合最好,能够精确识别单株灌丛;根据Bayes分类器分类结果统计得研究区灌丛盖度为14.74%,平均冠幅为0.6 m2,与样方调查结果基本一致。由于4种分类器的算法特征以及对训练样本数量的敏感性各不相同,因此选择合适的分类器还需根据具体影像的地物特征、空间分辨率和研究区范围来确定。
Satellite remote sensing imagery,when used in shrub monitoring,cannot accurately identify individual plants or young shrubs due to limitations in spatial resolution.However,using low-altitude unmanned aerial vehicles(UAVs)for remote sensing has the potential to identify such plants while yielding ultrahigh-resolution images.Therefore,we carried out shrub identification based on remote sensing with a low-altitude UAV,combined with a field investigation,at a shrub encroachment in Xilinhot,Inner Mongolia.An analysis of variance of the normalized differential vegetation index for shrubland,grassland,and bare land determined that the segmentation threshold of the bare and vegetated lands was-0.08.The vegetation covered area was extracted using this threshold and four object-oriented machine learning classification classifiers,i.e.,Decision Tree(DT),Bayes,k-nearest neighbor(KNN),and Support Vector Machine(SVM),were used to identify the shrubs.Results showed that the tool used to evaluate the optimal segmentation scale,i.e.,estimate the scale parameter,could rapidly determine the segmentation parameters,obtain shrub and grass images,and provide the basis for studying the former.We effectively avoided blind selection and increased the number of calculations by using the feature selection and optimization tool to select 18 object features with the highest degree of discrimination.A comparison of results from the different classifiers revealed that the Bayes classifier had the highest classification accuracy,with an overall accuracy and Kappa coefficient of 92% and 0.83,respectively.Its classification results matched the image features well and identified individual shrubs precisely.According to Bayes classification,shrubs covered 14.74% of the study area,and the average crown was 0.6 m~2.The results of the remote sensing experiments were almost the same as those from ground-based measurements in the field.A sensitivity experiment on the sample size showed that:1)SVM obtained a high classification accuracy with relatively few counts in the training sample but was sensitive to parameter settings,which may lead to abnormal classification results.2)the Bayes classifier had a high precision with stable number of samples,if sufficient training samples were provided.3)the overall classification accuracy of the DT classifier varied with the number of samples,and 4)the overall accuracy of the KNN classifier was the lowest among the four classifiers.Due to their different algorithmic features and different sensitivities to the number of training samples,the selection of a suitable classifier depends on the surface object being observed and spatial resolution of the image,along with the scope of the study area.
Satellite remote sensing imagery,when used in shrub monitoring,cannot accurately identify individual plants or young shrubs due to limitations in spatial resolution.However,using low-altitude unmanned aerial vehicles(UAVs)for remote sensing has the potential to identify such plants while yielding ultrahigh-resolution images.Therefore,we carried out shrub identification based on remote sensing with a low-altitude UAV,combined with a field investigation,at a shrub encroachment in Xilinhot,Inner Mongolia.An analysis of variance of the normalized differential vegetation index for shrubland,grassland,and bare land determined that the segmentation threshold of the bare and vegetated lands was-0.08.The vegetation covered area was extracted using this threshold and four object-oriented machine learning classification classifiers,i.e.,Decision Tree(DT),Bayes,k-nearest neighbor(KNN),and Support Vector Machine(SVM),were used to identify the shrubs.Results showed that the tool used to evaluate the optimal segmentation scale,i.e.,estimate the scale parameter,could rapidly determine the segmentation parameters,obtain shrub and grass images,and provide the basis for studying the former.We effectively avoided blind selection and increased the number of calculations by using the feature selection and optimization tool to select 18 object features with the highest degree of discrimination.A comparison of results from the different classifiers revealed that the Bayes classifier had the highest classification accuracy,with an overall accuracy and Kappa coefficient of 92% and 0.83,respectively.Its classification results matched the image features well and identified individual shrubs precisely.According to Bayes classification,shrubs covered 14.74% of the study area,and the average crown was 0.6 m~2.The results of the remote sensing experiments were almost the same as those from ground-based measurements in the field.A sensitivity experiment on the sample size showed that:1)SVM obtained a high classification accuracy with relatively few counts in the training sample but was sensitive to parameter settings,which may lead to abnormal classification results.2)the Bayes classifier had a high precision with stable number of samples,if sufficient training samples were provided.3)the overall classification accuracy of the DT classifier varied with the number of samples,and 4)the overall accuracy of the KNN classifier was the lowest among the four classifiers.Due to their different algorithmic features and different sensitivities to the number of training samples,the selection of a suitable classifier depends on the surface object being observed and spatial resolution of the image,along with the scope of the study area.
引文
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Breiman L,Friedman J H,Olshen R A and Stone C J.1984.Classification and Regression Trees.CA,USA:Wadsworth&Brooks:Monterey.
陈蕾伊,沈海花,方精云.2014.灌丛化草原:一种新的植被景观.自然杂志,36(6):391-396.[Chen Leiyi,Shen Haihua and Fang Jingyun.2014.Shrub-Encroached Grassland:a New Velgetation Type.Chinese Journal of Nature,36(6):391-396.]
Dragut L,Tiede D and Levick S.2010.ESP:a Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data.International Journal of Geographical Information Science,24(6):859-871.
董洲,赵霞,梁栋,黄文江,彭代亮,黄林生.2014.内蒙古灌丛化草原分布特征的遥感辨识.农业工程学报,30(11):152-158.[Dong Zhou,Zhao Xia,Liang Dong,Huang Wenjiang,Peng Dailiang and Huang Linsheng.2014.Remote Sensing Identification of Shrub Encroachment in Grassland in Inner Mongolia.Transactions of the Chinese Society of Agricultural Engineering,30(11):152-158.]
Foody G M,Mathur A,Sanchez-Hernandez C and Boyd D S.2006.Training Set Size Requirements for the Classification of a Specific Class.Remote Sensing of Environment.104(1):1-14.
管珍,曹广超,易俊柱.2010.面向对象的遥感影像分类研究.科技创新导报,(34):8-10.,12.[Guan Zhen,Cao Anchao and Yi Junzhu.2010.Research on Object-Oriented Remote Sensing Image Classification.Science and Technology Consulting Herald,(34):8-10,12.]
胡健波,张健.2018.无人机遥感在生态学中的应用进展.生态学报,38(1):20-30.[Hu Jianbo and Zhang Jian.2018.Unmanned Aerial Vehicle Remote Sensing in Ecology:Advances and Prospects.Acta Ecologica Sinica,38(1):20-30.]
Laliberte A S,Rango A,Havstad K M,Paris J F,Beck R F and Mcneely R.2004.Object-Oriented Image Analysis for Mapping Shrub Encroachment from 1937 to 2003 in Southern New Mexico.Remote Sensing of Environment,93(1):198-210.
Lett M S and Knapp A K.2010.Consequences of Shrub Expansion in Mesic Grassland:Resource Alterations and Graminoid Responses.Journal of Vegetation Science.14(4):487-496.
Lorent H,Evangelou C,Stellmes M.,Hill J,Papanastasis V,Tsiourlis G,Roeder A and Lambin E F.2008.Land Degradation and Economic Conditions of Agricultural Households in a Marginal Region of Northern Greece.Global Planet Change,64(S3/4):198-209.
李毓堂.2008.中国草原政策的变迁.草业科学,25(6):1-7.[Li Yutang.2008.Changes for Chinese Grassland Policy.Pratacultural Science,25(6):1-7.]
廖小罕,周成虎,苏奋振,卢海英,岳焕印,缑吉平.2016.无人机遥感众创时代.地球信息科学学报,18(11):1439-1447.[Liao Xiaohan,Zhou Chenghu,Su Fenzhen,Lu Haiying,Yue Huanyin and Gou Jiping.2016.The Mass Innovation Era of UAV Remote Sensing.Journal of Geo-Information Science,18(11):1439-1447.]
Morgan J L,Gerlgel S E and Coops N C.2010.Aerial Photography:A Rapidly Evolving Tool for Ecolotgical Manatgement.BioScience,60:47-59.
彭海英,李小雁,童绍玉.2014.干旱半干旱区草原灌丛化研究进展.草业学报,23(2):313-322.[Peng Haiying,Li Xiaoyan and Tong Shaoyu.2014.Advance in Shrub Encroachment in Arid and Semiarid Region.Pratacultural Science,23(2):313-322.]
Qian Yuguo,Zhou Weiqi,Yan Jingli,Li Weifeng and Han Lijian.2014.Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery.Remote Sensing,7(1):153-168.
Stehman S V.1997.Selecting and Interpreting Measures of Thematic Classification Accuracy.Remote Sensing of Environment,62:77-89.
孙中宇,陈燕乔,杨龙,唐光良,袁少雄,林志文.2017.轻小型无人机低空遥感及其在生态学中的应用进展.应用生态学报,28(2):528-536.[Sun Zhongyu,Chen Yanqiao,Yang Long,Tang Guangliang,Yuan Shaoxiong and Lin Zhiwen.2017.Small Unmanned Arial Vehicles for Low-Altitude Remote Sensing and Its Application Process in Ecology.Chinese Journal of Applied Ecology,28(2):528-536.]
田新光.2007.面向对象高分辨率遥感影像信息提取.北京:中国测绘科学研究院.[Tian Xinguang.2007.Object-Oriented Information Extraction from High Resolution Remote Sensing Imagery.Beijing:Chinese Academy of Surveying&Mapping.]
Xing Yuanyuan,Wang Yongdong,You Yuan,Song Qin,Hare M L,Jorro Z B and Jirma G H.2018.Dynamic Changes of the Bush Encroachment in Low Altitude Area of Ethiopia.Journal of Resources and Ecology,9(3):281-289.
薛牡丹.2018.基于面向对象分析的无人机影像梯田田面提取研究.咸阳:西北农林科技大学.[Xue Mudan.2018.Surface Extraction of Terraced Field from UAV Imagery Based on Object-Oriented Analysis.Xianyang:Northwest A&F University.]
周道玮.1990.内蒙古小叶锦鸡儿灌丛化草地.内蒙古草业,(3):17-19.[Zhou Daowei.1990.Encroachment Grassland of Cara:gana Microphalla in Inner Mongolia.Inner Mongolia Pratacultural,(3):17-19.]
Breiman L,Friedman J H,Olshen R A and Stone C J.1984.Classification and Regression Trees.CA,USA:Wadsworth&Brooks:Monterey.
陈蕾伊,沈海花,方精云.2014.灌丛化草原:一种新的植被景观.自然杂志,36(6):391-396.[Chen Leiyi,Shen Haihua and Fang Jingyun.2014.Shrub-Encroached Grassland:a New Velgetation Type.Chinese Journal of Nature,36(6):391-396.]
Dragut L,Tiede D and Levick S.2010.ESP:a Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data.International Journal of Geographical Information Science,24(6):859-871.
董洲,赵霞,梁栋,黄文江,彭代亮,黄林生.2014.内蒙古灌丛化草原分布特征的遥感辨识.农业工程学报,30(11):152-158.[Dong Zhou,Zhao Xia,Liang Dong,Huang Wenjiang,Peng Dailiang and Huang Linsheng.2014.Remote Sensing Identification of Shrub Encroachment in Grassland in Inner Mongolia.Transactions of the Chinese Society of Agricultural Engineering,30(11):152-158.]
Foody G M,Mathur A,Sanchez-Hernandez C and Boyd D S.2006.Training Set Size Requirements for the Classification of a Specific Class.Remote Sensing of Environment.104(1):1-14.
管珍,曹广超,易俊柱.2010.面向对象的遥感影像分类研究.科技创新导报,(34):8-10.,12.[Guan Zhen,Cao Anchao and Yi Junzhu.2010.Research on Object-Oriented Remote Sensing Image Classification.Science and Technology Consulting Herald,(34):8-10,12.]
胡健波,张健.2018.无人机遥感在生态学中的应用进展.生态学报,38(1):20-30.[Hu Jianbo and Zhang Jian.2018.Unmanned Aerial Vehicle Remote Sensing in Ecology:Advances and Prospects.Acta Ecologica Sinica,38(1):20-30.]
Laliberte A S,Rango A,Havstad K M,Paris J F,Beck R F and Mcneely R.2004.Object-Oriented Image Analysis for Mapping Shrub Encroachment from 1937 to 2003 in Southern New Mexico.Remote Sensing of Environment,93(1):198-210.
Lett M S and Knapp A K.2010.Consequences of Shrub Expansion in Mesic Grassland:Resource Alterations and Graminoid Responses.Journal of Vegetation Science.14(4):487-496.
Lorent H,Evangelou C,Stellmes M.,Hill J,Papanastasis V,Tsiourlis G,Roeder A and Lambin E F.2008.Land Degradation and Economic Conditions of Agricultural Households in a Marginal Region of Northern Greece.Global Planet Change,64(S3/4):198-209.
李毓堂.2008.中国草原政策的变迁.草业科学,25(6):1-7.[Li Yutang.2008.Changes for Chinese Grassland Policy.Pratacultural Science,25(6):1-7.]
廖小罕,周成虎,苏奋振,卢海英,岳焕印,缑吉平.2016.无人机遥感众创时代.地球信息科学学报,18(11):1439-1447.[Liao Xiaohan,Zhou Chenghu,Su Fenzhen,Lu Haiying,Yue Huanyin and Gou Jiping.2016.The Mass Innovation Era of UAV Remote Sensing.Journal of Geo-Information Science,18(11):1439-1447.]
Morgan J L,Gerlgel S E and Coops N C.2010.Aerial Photography:A Rapidly Evolving Tool for Ecolotgical Manatgement.BioScience,60:47-59.
彭海英,李小雁,童绍玉.2014.干旱半干旱区草原灌丛化研究进展.草业学报,23(2):313-322.[Peng Haiying,Li Xiaoyan and Tong Shaoyu.2014.Advance in Shrub Encroachment in Arid and Semiarid Region.Pratacultural Science,23(2):313-322.]
Qian Yuguo,Zhou Weiqi,Yan Jingli,Li Weifeng and Han Lijian.2014.Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery.Remote Sensing,7(1):153-168.
Stehman S V.1997.Selecting and Interpreting Measures of Thematic Classification Accuracy.Remote Sensing of Environment,62:77-89.
孙中宇,陈燕乔,杨龙,唐光良,袁少雄,林志文.2017.轻小型无人机低空遥感及其在生态学中的应用进展.应用生态学报,28(2):528-536.[Sun Zhongyu,Chen Yanqiao,Yang Long,Tang Guangliang,Yuan Shaoxiong and Lin Zhiwen.2017.Small Unmanned Arial Vehicles for Low-Altitude Remote Sensing and Its Application Process in Ecology.Chinese Journal of Applied Ecology,28(2):528-536.]
田新光.2007.面向对象高分辨率遥感影像信息提取.北京:中国测绘科学研究院.[Tian Xinguang.2007.Object-Oriented Information Extraction from High Resolution Remote Sensing Imagery.Beijing:Chinese Academy of Surveying&Mapping.]
Xing Yuanyuan,Wang Yongdong,You Yuan,Song Qin,Hare M L,Jorro Z B and Jirma G H.2018.Dynamic Changes of the Bush Encroachment in Low Altitude Area of Ethiopia.Journal of Resources and Ecology,9(3):281-289.
薛牡丹.2018.基于面向对象分析的无人机影像梯田田面提取研究.咸阳:西北农林科技大学.[Xue Mudan.2018.Surface Extraction of Terraced Field from UAV Imagery Based on Object-Oriented Analysis.Xianyang:Northwest A&F University.]
周道玮.1990.内蒙古小叶锦鸡儿灌丛化草地.内蒙古草业,(3):17-19.[Zhou Daowei.1990.Encroachment Grassland of Cara:gana Microphalla in Inner Mongolia.Inner Mongolia Pratacultural,(3):17-19.]
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