Please wait a minute...
 
Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 118-124     DOI: 10.6046/gtzyyg.2019.02.17
|
Tree species and age groups classification based on GF-2 image
Feng FU1, Xinjie WANG1(), Jin WANG1, Na WANG2, Jihong TONG1
1.College of Forestry, Beijing Forestry University, Beijing 100083, China
2.College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
Download: PDF(2043 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

With the Jiangle state-owned forest farm of Fujian Province as the study area, the potential of classification in tree species and age groups through GF-2 image were explored. First, the canopy spectral curve of main tree species were measured and the reflectance differences between them were analyzed. After image preprocessing and in combination with normalized difference vegetation index (NDVI) and topographic factors, multi band remote sensing images were constructed. Object-oriented multi-scale segmentation technology was applied to extracting the spectral and texture attributes, followed by attributes filter. On the basis of 7 kinds of schemes, Cunninghamia lanceolata (3 age groups),Pinus massoniana and Phyllostachys edulis were classified by random forest classifier. The role of spectrum, texture and auxiliary data in classification was quantitatively analyzed. The results show that the scheme of spectra combined with 4 directions of texture attributes has overall accuracy of 87.4% with Kappa coefficient being 0.85, and age groups in Cunninghamia lanceolate were effectively classified. Random forest classifier can achieve better classification results based on the optimal attribute set. GF-2 has great potential in tree species and age group classification and provides reliable data source for forest resources investigation and management.

Keywords GF-2      tree species classification      object-oriented      random forest     
:  TP701  
Corresponding Authors: Xinjie WANG     E-mail: xinjiew@bjfu.edu.cn
Issue Date: 23 May 2019
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Feng FU
Xinjie WANG
Jin WANG
Na WANG
Jihong TONG
Cite this article:   
Feng FU,Xinjie WANG,Jin WANG, et al. Tree species and age groups classification based on GF-2 image[J]. Remote Sensing for Land & Resources, 2019, 31(2): 118-124.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.17     OR     https://www.gtzyyg.com/EN/Y2019/V31/I2/118
类型 属性 参与
波段
纹理
方向
属性
数量
光谱 最小像元值(minimum pixel value,MIN) Blue,Green,Red,NIR,NDVI 25
最大像元值(maximum pixel value)
像元均值(mean)
像元标准差(standard deviation,SD)
像元偏度(skewness)
纹理 同质性(homogeneity,HOM) Blue,Green,Red,NIR 全方向 32
对比度(contrast,CON)
非相似性(dissimilarity,DIS)
熵(entropy,ENT)
4方向 128
纹理均值(mean,MEA)
纹理标准差(standard deviation,STD)
相关性(correlation,COR)
角二阶矩(angular second moment,ASM)
辅助数据 最小像元值(minimum pixel value,MIN) DEM,坡向(Aspect),坡度(Slope) 15
最大像元值(maximum pixel value)
像元均值(mean)
像元标准差(standard deviation,SD)
像元偏度(skewness)
Tab.1  Object-oriented image attributes extraction
分类方案 属性类型 属性数量
方案1 光谱+4方向纹理+辅助数据 25+128+15=168
方案2 光谱+4方向纹理 25+128=153
方案3 光谱+全方向纹理 25+32=57
方案4 光谱+辅助数据 25+15=40
方案5 光谱 5×5=25
方案6 4方向纹理 8×4×4=128
方案7 全方向纹理 8×4=32
Tab.2  Attributes number of different classification schemes
Fig.1  Canopy spectral curves of five tree species in Jiangle forest farm
分类方案 最优属性子集包含的属性 最优属性数/
属性总数
随机森林参数优化
K I
1 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green; mean_Aspect 10/168 5 1 200
2 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green 9/153 4 100
3 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Blue; CON_All_Red; CON_All_Blue; DIS_All_NIR; DIS_All_Blue; COR_All_NIR; COR_All_Red 11/57 3 1 100
4 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; skewness_Blue; mean_Aspect; SD_DEM 8/40 3 1 600
5 MIN_Green; mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; skewness_Blue 7/25 3 3 000
6 HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green; COR_135_Red; COR_135_NIR; COR_135_Blue 7/128 3 3 000
7 CON_All_Red; CON_All_Blue; DIS_All_NIR; DIS_All_Green; DIS_All_Blue; COR_All_NIR; COR_All_Red 7/32 3 100
Tab.3  Attributes screening and parameter optimization under classification schemes
Fig.2  F accuracy of classification under 7 schemes
树种 马尾松 毛竹 杉木
幼龄林
杉木
中龄林
杉木
成熟林
马尾松 92 4 0 0 3
毛竹 2 54 9 4 0
杉木幼龄林 0 4 86 5 0
杉木中龄林 0 2 4 49 7
杉木成熟林 3 0 0 5 52
制图精度/% 94.8 84.4 86.9 77.8 83.9
用户精度/% 92.8 77.0 90.8 79.2 86.8
Tab.4  Confusion matrix of GF-2 image in tree species classification
Fig.3  Results of object-oriented random forest classification
[1] Lka D, Maier B, Seijmonsbergen A C . Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification[J]. Forest Ecology and Management, 2003,183(1):31-46.
doi: 10.1016/S0378-1127(03)00113-0 url: https://linkinghub.elsevier.com/retrieve/pii/S0378112703001130
[2] Hall R J, Skakun R S, Arsenault E J , et al. Modeling forest stand structure attributes using Landsat ETM+ data:Application to mapping of aboveground biomass and stand volume[J]. Forest Ecology and Management, 2006,225(1-3):378-390.
doi: 10.1016/j.foreco.2006.01.014 url: https://linkinghub.elsevier.com/retrieve/pii/S0378112706000235
[3] Castillo-Santiago M A, Ricker M , Jong B H J D . Estimation of tropical forest structure from SPOT-5 satellite images[J]. International Journal of Remote Sensing, 2010,31(10):2767-2782.
doi: 10.1080/01431160903095460 url: https://www.tandfonline.com/doi/full/10.1080/01431160903095460
[4] Wolter P T, Townsend P A, Sturtevant B R . Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data[J]. Remote Sensing of Environment, 2009,113(9):2019-2036.
doi: 10.1016/j.rse.2009.05.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425709001722
[5] Immitzer M, Atzberger C, Koukal T . Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data[J]. Remote Sensing, 2012,4(9):2661-2693.
doi: 10.3390/rs4092661 url: http://www.mdpi.com/2072-4292/4/9/2661
[6] 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(9):516-533.
doi: 10.1016/j.rse.2012.06.011 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425712002477
[7] 刘怀鹏, 安慧君, 王冰 , 等. 基于递归纹理特征消除的WorldView-2树种分类[J]. 北京林业大学学报, 2015,37(8):53-59.
doi: 10.13332/j.1000--1522.20140311 url: http://www.cnki.com.cn/Article/CJFDTotal-BJLY201508008.htm
[7] Liu H P, An H J, Wang B , et al. Tree species classification using WorldView-2 images based on recursive texture feature elimination[J]. Journal of Beijing Forestry University, 2015,37(8):53-59.
[8] 王妮, 彭世揆, 李明诗 . 基于树种分类的高分辨率遥感数据纹理特征分析[J]. 浙江农林大学学报, 2012,29(2):210-217.
doi: 10.3969/j.issn.2095-0756.2012.02.010 url: http://www.cqvip.com/QK/97079A/201202/41610869.html
[8] Wang N, Peng S K, Li M S . High-resolution remote sensing of textural images for tree species classification[J]. Journal of Zhejiang Agricultural and Forestry University, 2012,29(2):210-217.
[9] Franklin S E, Wulder M A, Gerylo G R . Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia[J]. International Journal of Remote Sensing, 2001,22(13):2627-2632.
doi: 10.1080/01431160120769 url: https://www.tandfonline.com/doi/full/10.1080/01431160120769
[10] Kim S R, Lee W K, Kwak D A , et al. Forest cover classification by optimal segmentation of high resolution satellite imagery[J]. Sensors, 2011,11(2):1943.
doi: 10.3390/s110201943 pmid: 22319391 url: http://europepmc.org/articles/PMC3274007/
[11] Rodriguez-Galiano V F, Chica-Olmo M, Abarca-Hernandez F , et al. Random forest classification of mediterra -nean land cover using multi-seasonal imagery and multi-seasonal texture[J]. Remote Sensing of Environment, 2012,121(138):93-107.
doi: 10.1016/j.rse.2011.12.003 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425711004408
[12] Carleer A, Wolff E . Exploitation of very high resolution satellite data for tree species identification[J]. Photogrammetric Engineering and Remote Sensing, 2004,70(1):135-140.
doi: 10.14358/PERS.70.1.135 url: http://openurl.ingenta.com/content/xref?genre=article&issn=0099-1112&volume=70&issue=1&spage=135
[13] 陈旭, 徐佐荣, 余世孝 . 基于对象的QuickBird遥感图像多层次森林分类[J]. 遥感技术与应用, 2009,24(1):22-26.
[13] Chen X, Xu Z R, Yu S X . Multi-level forest classification of QuickBird remote sensing image based on objects[J]. Remote Sensing Technology and Application, 2009,24(1):22-26.
[14] 潘腾 . 高分二号卫星的技术特点[J].中国航天, 2015(1):3-9.
[14] Pan T . The technical features of the GF-2 satellite[J].Aerospace China, 2015(1):3-9.
[15] 张过, 李扬, 祝小勇 , 等. 有理函数模型在光学卫星影像几何纠正中的应用[J]. 航天返回与遥感, 2010,31(4):51-57.
[15] Zhang G, Li Y, Zhu X Y , et al. Application of RFM in geometric rectification of optical satellite image[J]. Spaceraft Recovery and Remote Sensing, 2010,31(4):51-57.
[16] 孙攀, 董玉森, 陈伟涛 , 等. 高分二号卫星影像融合及质量评价[J]. 国土资源遥感, 2016,28(4):108-113.doi: 10.6046/gtzyyg.2016.04.17.
doi: 10.6046/gtzyyg.2016.04.17
[16] Sun P, Dong Y S, Chen W T , et al. Research on fusion of GF-2 imagery and quality evaluation[J]. Remote Sensing for Land and Resources, 2016,28(4):108-113.doi: 10.6046/gtzyyg.2016.04.17.
[17] 张莹, 张晓丽, 王书涵 , 等. 福建将乐林场主要树种冠层光谱反射特征分析[J]. 西北农林科技大学学报(自然科学版), 2016,44(2):83-89.
[17] Zhang Y, Zhang X L, Wang S H , et al. Spectral reflectance characteristics of canopies of main tree species in Jiangle forest farm in Fujian[J]. Journal of Northwest Agricultural and Forestry University(Natural Science Edition), 2016,44(2):83-89.
[18] 白金婷 . 结合高分辨率遥感影像多维特征的森林分类[D]. 北京:北京林业大学, 2016.
[18] Bai J T . The Forest Classification Combining Multidimensional Features Based on High-resolution Remote Sensing Images[D]. Beijing:Beijing Forestry University, 2016.
[19] 李光, 姜春雪, 刘争战 , 等. Laws纹理能量结合灰度共生矩阵的遥感影像面状地物提取[J]. 测绘与空间地理信息, 2017,40(7):179-181.
[19] Li G, Jiang C X, Liu Z Z , et al. Polygon feature extraction of remote sensing image based on Laws texture energy and gray level co-occurrence matrix[J]. Geomatics and Spatial Information Technology, 2017,40(7):179-181.
[20] Shahshahani B M, Landgrebe D . The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994,32(5):1087-1095.
doi: 10.1109/36.312897 url: http://ieeexplore.ieee.org/document/312897/
[21] 袁梅宇 . 数据挖掘与机器学习[M]. 北京: 清华大学出版社, 2014.
[21] Yuan M Y. Data Mining and Machine Learning[M]. Beijing: Tsinghua University Press, 2014.
[22] 方匡南, 吴见彬, 朱建平 , 等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011,26(3):32-38.
[22] Fang K N, Wu J B, Zhu J P , et al. A review of technologies on random forests[J]. Statistics and Information Forum, 2011,26(3):32-38.
[23] 曹正凤 . 随机森林算法优化研究[D]. 北京:首都经济贸易大学, 2014.
[23] Cao Z F . Study on Optimization of Random Forests Algorithm[D]. Beijing:Capital University of Economics and Business, 2014.
[24] Rodriguez-Galiano V F, Ghimire B, Rogan J , et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012,67(1):93-104.
doi: 10.1016/j.isprsjprs.2011.11.002 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271611001304
[25] Gislason P O, Benediktsson J A, Sveinsson J R . Random forests for land cover classification[J]. Pattern Recognition, 2006,27(4):294-300.
doi: 10.1016/j.patrec.2005.08.011 url: https://linkinghub.elsevier.com/retrieve/pii/S0167865505002242
[26] 吕杰, 汪康宁, 李崇贵 , 等. 基于小波变换和随机森林的森林类型分类研究[J]. 西北林学院学报, 2016,31(6):264-267.
[26] Lyu J, Wang K N, Li C G , et al. Classification of forest types based on discrete wavelet transform and random forests from GF-1 images[J]. Journal of Northwest Forestry University, 2016,31(6):264-267.
[27] 张晓羽, 李凤日, 甄贞 , 等. 基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J]. 东北林业大学学报, 2016,44(6):53-57.
[27] Zhang X Y, Li F R, Zhen Z , et al. Forest vegetation classification of Landsat-8 remote sensing image based on random forests model[J]. Journal of Northeast Forestry University, 2016,44(6):53-57.
[28] 米爱中, 张盼 . 一种基于混淆矩阵的分类器选择方法[J]. 河南理工大学学报(自然科学版), 2017,36(2):116-121.
[28] Mi A Z, Zhang P . A method of classifier selection based on confusion matrix[J]. Journal of Henan Polytechnic University (Natural Science), 2017,36(2):116-121.
[29] Dalponte M, Bruzzone L, Gianelle D. Tree species classification in the Southern Alps with very high geometrical resolution multispectral and hyperspectral data [C]//2011 3rd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing(WHISPERS), 2011: 1-4.
[30] 陈玲, 郝文乾, 高德亮 . 光学影像纹理信息在林业领域的最新应用进展[J]. 北京林业大学学报, 2015,37(3):1-12.
[30] Chen L, Hao W Q, Gao D L . The latest applications of optical image texture in forestry[J]. Journal of Beijing Forestry University, 2015,37(3):1-12.
[1] WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[2] LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[3] GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[4] FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.
[5] LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[6] HU Guoqing, CHEN Donghua, LIU Congfang, XIE Yimei, LIU Saisai, LI Hu. Dynamic monitoring of urban black-odor water bodies based on GF-2 image[J]. Remote Sensing for Land & Resources, 2021, 33(1): 30-37.
[7] CAI Xiang, LI Qi, LUO Yan, QI Jiandong. Surface features extraction of mining area image based on object-oriented and deep-learning method[J]. Remote Sensing for Land & Resources, 2021, 33(1): 63-71.
[8] XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[9] WU Qian, JIANG Qigang, SHI Pengfei, ZHANG Lili. The estimation of soil calcium carbonate content based on Hyperspectral data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 138-144.
[10] YANG Lijuan. Estimating PM2.5 concentrations in eastern coastal area of China using a two-stage random forest model[J]. Remote Sensing for Land & Resources, 2020, 32(4): 137-144.
[11] WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[12] LI Guoqing, HUANG Jinghua, LIU Guan, LI Jie, ZHAI Bochao, DU Sheng. A study of the landscape fragmentations of land cover structure based on Landsat8 remote sensing image: A case study of Mata watershed in Yan’an, Shaanxi Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 121-128.
[13] Wenya LIU, Anzhi YUE, Jue JI, Weihua SHI, Ruru DENG, Yeheng LIANG, Longhai XIONG. Urban green space extraction from GF-2 remote sensing image based on DeepLabv3+ semantic segmentation model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 120-129.
[14] Yuting YANG, Hailan CHEN, Jiaqi ZUO. Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017[J]. Remote Sensing for Land & Resources, 2020, 32(2): 241-250.
[15] Jisheng XIA, Mengying MA, Zhongren FU. Extraction of mechanical damage surface using GF-2 remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 26-32.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech