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2017 | 161 | 01 |

Tytuł artykułu

Wykorzystanie lotniczej teledetekcji hiperspektralnej w klasyfikacji gatunkowej lasów strefy umiarkowanej

Treść / Zawartość

Warianty tytułu

EN
Airborne hyperspectral data for the classification of tree species a temperate forests

Języki publikacji

PL

Abstrakty

EN
The review focuses on use of airborne hyperspectral imagery in forest species classification. Studies mentioned in the review concern hyperspectral image classification with use of various methods. Only research, where study area is located in Europe or North America were selected. Articles were reviewed with respect to used pre−processing methods, methods of feature selection or feature extraction, algorithms of image classification and trees species which were classified. The whole process of acquiring and working with hyperspectral data is described. Different approaches (e.g. use or skip atmospheric corrections) were compared. In each article, various deciduous and conifer species were classified. Studies comparing several classification algorithms (Spectral Angle Mapper, Support Vector Machine, Random Forest) were mentioned. In most cases SVM gives the best results. Species, which are classified with the highest accuracy, include Scots pine (Pinus sylvestris) and Norway spruce (Picea abies). Broadleaved species are, in general, classified with lower accuracy than conifer ones. Within broadleaved trees, European beech (Fagus sylvatica) and oaks (Quercus sp.) are classified with the highest accuracy.

Wydawca

-

Czasopismo

Rocznik

Tom

161

Numer

01

Opis fizyczny

s.3-17,tab.,bibliogr.

Twórcy

autor
  • Instytut Badawczy Leśnictwa, Sękocin Stary, ul.Braci Leśnej 3, 05-090 Raszyn
  • Instytut Badawczy Leśnictwa, Sękocin Stary, ul.Braci Leśnej 3, 05-090 Raszyn
  • Instytut Badawczy Leśnictwa, Sękocin Stary, ul.Braci Leśnej 3, 05-090 Raszyn

Bibliografia

  • van Aardt J. A. N., Wynne R. H. 2007. Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field-based results. International Journal of Remote Sensing 28 (2): 431-436.
  • Adams J. B., Sabol D. E., Kapos V., Filho R. A., Roberts D. A., Smith M. O., Gillespie A. R. 1995. Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment 52 (2): 137-154.
  • Adler-Golden S. M., Matthew M. W., Bernstein L. S., Levine R. Y., Berk A., Richtsmeier S. C., Acharya P. K., Anderson G. P., Felde G., Gardner J., Hike M., Jeong L. S., Pukall B., Mello J., Ratkowski A., Burke H. H. 1999. Atmospheric correction for short-wave spectral imagery based on MODTRAN4. SPIE Proc. Imaging Spectrometry 3753: 61-69.
  • Alonso M. C., Malpica J. A., de Agirre A. M. 2011. Consequences of the hughes phenomenon on some classification techniques. ASPRS 2011 Annual Conference. Milwaukee, Wisconsin May 1-5, 2011.
  • Bernstein L. S., Sundberg R. L., Levine R. Y., Perkins T. C., Berk A. 2005. A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick Atmospheric Correction). IEEE IGARSS 00: 3549-3552.
  • Boschetti M., Boschetti L., Oliveri S., Casati L., Canova I. 2007. Tree species mapping with airborne hyper-spectral MIVIS data: the Ticino Park study case. International Journal of Remote Sensing 28 (6): 1251-1261.
  • Buddenbaum H., Schlerf M., Hill J. 2005. Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. International Journal of Remote Sensing 26 (24): 5453-5465.
  • Dadon A., Ben-Dor E., Karnieli A. 2010. Use of derivative calculations and minimum noise fractiontransform for detecting and correcting the spectral curvature effect (smile) in Hyperion images. IEEE Transactions on Geoscience and Remote Sensing 48 (6): 2603-2612.
  • Dalponte M., Bruzzone L., Gianelle D. 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment 123: 258-270.
  • Dalponte M., Bruzzone L., Vescovo L., Gianelle D. 2009. The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. Remote Sensing of Environment 113: 2345-2355.
  • Dalponte M., Orka H. O., Ene L. T., Gobakken T., Naesset E. 2014. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment 140: 306-317.
  • Forzieri G., Moser G., Catani F. 2012. Assessment of hyperspectral MIVIS sensor capability for heterogeneous landscape classification. ISPRS Journal of Photogrammetry and Remote Sensing 74: 175-184.
  • Ghiyamat A., Shafri H. Z. M., Mahdirajic G. A., Shariff A. R. M., Mansor S. 2013. Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember. International Journal of Applied Earth Observation and Geoinformation 23: 177-191.
  • Ghosh A., Fassnacht F. E., Joshia P. K., Koch B. 2014. A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation 26: 49-63.
  • Głowienka E. 2008. Porównanie metod korekcji atmosferycznej dla danych z sensorów hiperspektralnych. Archiwum Fotogrametrii, Kartografii i Teledetekcji 18: 121-130.
  • Green A., Berman M., Switzer B., Craig M. 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing 26 (1): 65-74.
  • Heinzel J., Koch B. 2012. Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. International Journal of Applied Earth Observation and Geoinformation 18: 101--110.
  • Hill J., Mehl W. 2003. Geo- und radiometrische Aufbereitung multi- und hyperspektraler Daten zur Erzeugung langjahriger kalibrierter Zeitreihen. Photogrammetrie, Fernerkundung, Geoinformation 1: 7-14.
  • Hill J., Mehl W., Radeloff V. 1995. Improved forest mapping by combining corrections of atmospheric and topographic effects. W: Asken J. [red.]. Sensors and environmental applications of remote sensing. Proceedings 14th EARSeL Symposium. Göteborg, Sweden, 6-8 June 1994. 143-151.
  • Hughes G. F. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory IT-14: 55-63.
  • Jones T. G., Coops N. C., Sharma T. 2010. Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sensing of Environment 114: 2841-2852.
  • Kurczyński Z., Wolniewicz W. 2002. Korekcja geometryczna wysokorozdzielczych obrazów satelitarnych. Geodeta 11: 90.
  • Latifi H., Fassnacht F., Koch B. 2012. Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sensing of Environment 121: 10-25.
  • Martin M. E., Newman S. D., Aber J. D., Congalton R. G. 1998. Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data. Remote Sensing of Environment 65 (3): 249-254.
  • Melgani F., Bruzzone L. 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778-1790.
  • Raczko E., Zagajewski B., Ochtyra A., Jarocińska A., Marcinkowska-Ochtyra A., Dobrowolski M. 2015. Określenie składu gatunkowego lasów Góry Chojnik (Karkonoski Park Narodowy) z wykorzystaniem lotniczych danych hiperspektralnych APEX. Sylwan 159 (7): 593-599.
  • Richter R. 1996. Atmospheric correction of DAIS hyperspectral image data. Computers & Geosciences 22: 785-793.
  • Schläpfer D., Schaepman M., Itten K. I. 1998. Level II pre-processing concept for the AIRBORNE PRISM Experiment (APEX). Proceeding of 1st EARSEL Workshop on Imaging Spectroscopy EARSeL/RSL. Zurich.
  • Serpico S., Moser G. 2007. Extraction of Spectral Channels from Hyperspectral Images for Classification Purposes. IEEE Transactions On Geoscience And Remote Sensing 45 (2): 484-495.
  • Stereńczak K., Hycza T., Ciesielski M., Bałazy R., Sławik Ł. 2014. Jednoczesna rejestracja lotnicza zobrazowań hiperspektralnych i ALS – możliwości wykorzystania w leśnictwie. VII Konferencja Geomatyka w Lasach Państwowych. Rogów, 16-18 września 2014.
  • Tanre´ D., Deroo C., Duhaut O., Herman M., Morcrette J. J., Perbos J., Deschamps P. Y. 1990. Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code. International Journal of Remote Sensing 11: 659-668.
  • Tompalski P., Coops N. C., White J. C., Wulder M. A. 2014. Simulating the impacts of error in species and height upon tree volume derived from airborne laser scanning data. Forest Ecology and Management 327: 167-177.
  • Vane G., Goetz A. F. H. 1993. Terrestrial imaging spectrometry: Current status, future trends. Remote Sensing of Environment 44: 117-126.
  • Vapnick V. N. 1998. Statistical learning theory. John Wiley and Sons Inc.
  • Wulder M. A., Dymond C. C., White J. C., Leckie D. G., Caroll A. L. 2006. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. For. Ecol. Manage. 221: 27-41.
  • Zagajewski B. 2010. Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich. Teledetekcja Środowiska 43: 38-44.

Typ dokumentu

Bibliografia

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