Recognition of red-attack pine trees using WorldView-2 imagery.

QIAO Rui; TANG Ping; SHI Jin; JIANG Li-ya; LI Shuang; .

doi:10.13332/j.1000-1522.20150112

Journal of Beijing Forestry University > 2015 > 37(11): 33-40. > DOI: 10.13332/j.1000-1522.20150112

QIAO Rui, TANG Ping, SHI Jin, JIANG Li-ya, LI Shuang, .. Recognition of red-attack pine trees using WorldView-2 imagery.[J]. Journal of Beijing Forestry University, 2015, 37(11): 33-40. DOI: 10.13332/j.1000-1522.20150112

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Recognition of red-attack pine trees using WorldView-2 imagery.

1 School for the Gifted Young, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China;

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Received Date: April 08, 2015
Published Date: November 29, 2015

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Abstract

Abstract

Dangerous pests are severe forest disasters, and they occur every year in large areas and cause great damage to forest ecology and tremendous loss to economy. It has great significance to detect the occurrence of the hazard in time and to monitor its spreading process. The purpose of this study is to probe into the possibility to recognize red-attack pine trees with high resolution imagery, so as to provide a quick and efficient methodology for monitoring, prevention and control of fatal forest pests. The recognition of red-attack pine trees was studied based on the WorldView-2 multi-spectral data using the analysis method of the quadratic classifier. Through the Fisher determination analysis, we found that the red, green and blue bands have rich information of red-attack pine trees. Based on this discovery, we first conducted classification with all the eight bands, then with only the red, green and blue bands, and did comprehensive analysis. Results show that the accuracies of the two classifications are nearly the same, which means that the red, green and blue bands are enough to recognize the red-attack pine trees, so that the dimension of raw imagery can be reduced. This also contributes to the new application areas of GF-2 imagery. Because of the complexity of forest phases, the selection of image resolution is very important in the recognition of individual trees. Researches show that the 2-m resolution remote sensing data can well be used in extracting the red-attack pine trees. Moreover, it is better to choose data in the vegetation growing season, so that the discoloration mixture phenomenon can be reduced and the accuracy and efficiency of monitoring can be increased.
- WorldView-2 multispectral imagery,
- red-attack pine trees,
- discolored individual trees,
- remote sensing monitoring

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References

[1]	GB/T 23478—2009 Technical regulation on the surveying and monitoring of pine wood nematode[S].Beijing:China Standard Press,2009.
[1]	GB/T 23478—2009松材线虫普查监测技术规程 [S]. 北京:中国标准出版社,2009.
[2]	“高分二号”卫星发射成功:中国开启遥感亚米级时代[EB/OL]. (2014-08-19) [2015-04-17] . http:∥news.xinhuanet.com/2014-08/19/c_1112143967.htm.
[2]	The successful launch of high spatial resolution satellite GF-2: China is stepping into a new era of sub-meter remote sensing[EB/OL]. (2014-08-19) [2015-04-17] . http:∥news.xinhuanet.com/2014-08/19/c_1112143967.htm.
[3]	PU R L,LANGRY 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: 516-533.
[3]	WU H G, SHI J, JIANG L Y.Growth trend monitoring of individual trees with WorldView-2 Data [J]. Remote Sensing Information, 2013, 28(5):64-68.
[4]	SKURIKHIN A N, GARRITY S R, MCDOWELL N G, et al . Automated tree crown detection and size estimation using multiscale analysis of high-resolution satellite imagery[J]. Remote Sensing Letters, 2013,4 (5):465-474.
[5]	BAO Y, TIAN Q J, CHEN M , et al. An automatic extraction method for individual tree crowns based on self-adaptive mutual information and tile computing[J/OL]. International Journal of Digital Earth , 2014[2015-04-17]. http:∥dx.doi.org/10.1080/17538947.2014.912683.
[6]	OZDEMIR I, KARNIELI A. Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel[J]. International Journal of Applied Earth Observation and Geoinformation , 2011, 13:701-711.
[7]	LATIF Z A, ZAMRI I, OMAR H. Determination of tree species using Worldview-2 data[C]∥ Proceedings of IEEE 8th International Colloquium on Signal Processing and its Applications. Hongkong: IEEE, 2012: 383-387.
[8]	HEENKENDA M K, JOYCE K E, MAIER S W, et al. Mangrove species identification-comparing WorldView-2 with aerial photographs[J]. Remote Sensing, 2014,6:6064-6088.
[9]	COMES M F, MAILLARD P. Identification of urban tree crown in a tropical environment using WorldView-2 data: problems and perspectives[C/OL]. Proceedings of SPIE[2015-04-17]. http:∥proceedings.spiedigitallibrary.org/on 09/13/2014 .
[10]	GHOSH A, JOSHI P K. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,26:298-311.
[11]	KARLSON M, REESE H, OSTWALD M. Tree crown mapping in managed woodlands (Parklands) of semi-arid west africa using WorldView-2 imagery and geographic object based image analysis[J]. Sensors, 2014,14 :22643-22669.
[12]	CHEMURA A, DUREN I V, LEEUWEN L M. Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: the case of Ejisu-Juaben district, Ghana[J]. Journal of Photogrammertry and Remote Sensing, 2015, 100: 118-127.
[13]	MUTANGA O, ADAM E, ADJORLOLO C, et al. Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 34:178-187.
[14]	WUlDER M A, DYMOND C C, WHITE J C, et al. Surveying mountain pine beetle damage of forests:a review of remote sensing opportunities[J]. Forest Ecology and Management, 2006,221:27-41.
[15]	PONTIUS J, MARTIN M, PLOURDE L, et al. Ash decline assessment in emerald ash borer-infested regions: a test of tree-level, hyperspectral technologies[J]. Remote Sensing of Environment, 2008, 112:2665-2676.
[16]	FASSNACHT F E, LATIFI H, GHOSH A, et al. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality[J]. Remote Sensing of Environment, 2014,140: 533-548.
[17]	WHITE J C, WULDER M A, BROOKS D, et al. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery[J]. Remote Sensing of Environment, 2005,96:340-352.
[18]	COOPS N C, JOHNSON M, WULDER M A, et al. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation[J]. Remote Sensing of Environment, 2006,103:67-80.
[19]	HICKE J A, LOGAN J. Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery[J]. International Journal of Remote Sensing, 2009, 30(17): 4427-4441.
[20]	WULDER M A, WHITE J C, OOPS N C, et al. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring[J]. Remote Sensing of Environment, 2008, 112: 2729-2740.
[21]	EITEL J U H, VIERLING L A, LITVAK M E, et al. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland[J]. Remote Sensing of Environment, 2011,115:340-351.
[22]	GARRITY S R, ALLEN C D, BRUMBY S P, et al. Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery[J]. Remote Sensing of Environment, 2013,129:54-65.
[23]	MEDDENS A J H, HICKE J A, VIERLING L A. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality[J]. Remote Sensing of Environment, 2011, 115: 1632-1642.
[24]	OUMAR Z, MUTANGA O. Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests[J]. International Journal of Remote Sensing, 2013,34(6): 2236-2249.
[25]	OUMAR Z, MUTANGA O. Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests[J]. Journal of Photogrammertry ang Remote Sensing, 2014,87:39-46.
[26]	WASER L T, KUCHLER M, JUTTE K, et al. Evaluating the potential of WorldView-2 data to classify tree species and different levels of ash mortality[J]. Remote Sensing, 2014,6:4515-4545.
[27]	武红敢,石进,蒋丽雅. 基于WorldView-2数据的单木长势监测[J].遥感信息,2013,28(5):64-68.
[28]	VENABLES W N, RIPLEY B D. Modern applied statistics with S [M]. New York: Springer-Verlag, 2002:331-332.

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Recognition of red-attack pine trees using WorldView-2 imagery.

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