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
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.
傅锋, 王新杰, 汪锦, 王娜, 佟济宏. 高分二号影像树种识别及龄组划分[J]. 国土资源遥感, 2019, 31(2): 118-124.
Feng FU, Xinjie WANG, Jin WANG, Na WANG, Jihong TONG. Tree species and age groups classification based on GF-2 image. Remote Sensing for Land & Resources, 2019, 31(2): 118-124.
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
[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
[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
[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
[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
[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
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.
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
[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
[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
[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
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.
Pan T . The technical features of the GF-2 satellite[J].Aerospace China, 2015(1):3-9.
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.
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.
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.
Bai J T . The Forest Classification Combining Multidimensional Features Based on High-resolution Remote Sensing Images[D]. Beijing:Beijing Forestry University, 2016.
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
[21]
袁梅宇 . 数据挖掘与机器学习[M]. 北京: 清华大学出版社, 2014.
Yuan M Y. Data Mining and Machine Learning[M]. Beijing: Tsinghua University Press, 2014.
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.
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
[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
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.
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.
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.