Detekcja posuszu kornikowego z wykorzystaniem zobrazowań BlackBridge na przykładzie drzewostanów Sudetów i Beskidów

• Autorzy: Kycko M. , Sterenczak K. , Balazy R.

Warianty tytułu

EN

Detection of bark beetle infected trees with BlackBridge image on the example of the Sudety and the Beskidy mountains

• Języki publikacji: PL

Abstrakty

EN

BlackBridge imagery is one of the new means of information used in forest condition analysis. Rapid Eye satellite data with a 5 m spatial resolution register spectral information from 440 to 850 nm through 5 spectral bands. This range of electromagnetic spectrum provides information on plant chlorophyll content as well as cell structure. Such data allows to monitor vegetation condition. This paper focuses on a research conducted in the Sudety and Western Beskidy mountains (southern Poland). The aim of the research was to verify whether high resolution satellite imagery is applicable in detection of the damages caused by Ips typographus and acid rain in Norway spruce dominated stands through supervised classification. BlackBridge Rapid Eye satellite images from 2012 and 2013 were analysed. Various modifications of classification methods were tested, including change in combination of spectral bands. Each method resulted in different classification accuracy. Best results were observed in case of the Maximum Likelihood classification method applied on all spectral bands. The analysis showed that the time of the image registration has a significant impact on classification results. The average classification accuracy for 2012 images was 0.53, whereas for 2013 – 0.69. Moreover, information gathered from 5 m pixels is too general to classify individual dead trees in a precise manner. Tested methods are applicable only in detection of clusters of dead trees.

Słowa kluczowe

PL

lesnictwo; Beskidy Zachodnie; Sudety; lasy gorskie; drzewostany swierkowe; drzewa martwe; posusz; teledetekcja; zdjecia satelitarne; zobrazowania BlackBridge

• Wydawca: -
• Czasopismo: Sylwan
• Rocznik: 2016
• Tom: 160
• Numer: 09
• Opis fizyczny: s.707-719,rys.,tab.,bibliogr.

Twórcy

autor

Kycko M.

• Zakład Geoinformatyki, Kartografii i Teledetekcji, Uniwersytet Warszawski, ul.Krakowskie Przedmieście 24/26, 00-927 Warszawa

autor

Sterenczak K.

• Zakład Zarządzania Zasobami Leśnymi, Instytut Badawczy Leśnictwa, Sękocin Stary, ul.Braci Leśnej 3, 05-090 Raszyn

autor

Balazy R.

• Zakład Zarządzania Zasobami Leśnymi, Instytut Badawczy Leśnictwa, Sękocin Stary, ul.Braci Leśnej 3, 05-090 Raszyn

Bibliografia

• Abd L. Z. 2012. Determination of Tree Species Using Worldview-2 Data. Paper presented at the 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.
• Adelabu S., Mutanga O., Adam E. 2014. Evaluating the impact of red-edge band from Rapidehe image for classifying insect defoliation levels. ISPRS Journal of Photogrammetry and Remote Sensing 95: 34-41.
• Arnett J. T. T. R., Coops N. C., Gergel S. E., Falls R. W., Baker R. H. 2014. Detecting Stand-Replacing Disturbance using RapidEye Imagery: a Tasseled Cap Transformation and Modified Disturbance Index. Canadian Journal of Remote Sensing 40: 1-14.
• Baniya N., Zawiła-Niedźwiecki T., Majunke C., Hauswirth M. 2006. Zdjęcia satelitarne Landsat TM w ocenie gradacji brudnicy mniszki. Leśn. Pr. Bad. 3: 33-44.
• Beguet B., Chehata N., Boukir S., Guyon D. 2015. Classification of forest structure using very high resolution Pleiades image texture. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International. 2324--2327.
• Błaś M., Dore J. 1998. Znaczenie bariery orograficznej w determinowaniu roli efektu „seeder-feeder” na przykładzie Gór Izerskich i masywu Ślęży. Problemy klimatyczno-botaniczne Gór Izerskich. Materiały z konferencji naukowej 21-23.IX.1998, Świeradów Zdrój.
• Boyd D., Danson F. 2005. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography 29 (1): 1-26.
• Ciołkosz A., Kęsik A. 1989. Teledetekcja satelitarna. PWN, Warszawa.
• Dash J., Watt M., Bhandari S., Watt P. 2015. Characterising forest structure using combinations of airborne laser scanning data, RapidEye satellite imagery and environmental variables. Forestry 89 (2): cpv048.
• Davranche A., Lefebvre G., Poulin B. 2010. Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sensing of Environment 114 (3): 552-562.
• Dorren L. K. A., Maier B., Seijmonsbergen A. C. 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management 183 (1-3): 31-46.
• Elatawneh A., Kalaitzidis C., Petropoulos G. P., Schneider T. 2012. Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data. International Journal of Digital Earth. International Journal of Digital Earth 1-23.
• Elatawneh A., Rappl A., Rehush N., Schneider T., Knoke T. 2013. Forest tree species communities identification using multiphenological stages RapidEye data: case study in the forest of Freising. 5 RESA Workshop 4/2015, Berlin.
• Elatawneh A., Wallner A., Manakos I., Schneider T., Knoke T. 2014. Forest Cover Database Updates Using Multi-Seasonal RapidEye Data – Storm Event Assessment in the Bavarian Forest National Park. Forests 5 (6): 1284-1303.
• Franco-Lopez H., Ek A. R., Bauer M. E. 2001. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment 77 (3): 251-274.
• Ghioca-Robrecht D. M., Johnston C. A., Tulbure M. G. 2008. Assessing the Use of Multiseason Quickbird Imagery for Mapping Invasive Species in a Lake Erie Coastal Marsh. Wetlands 28 (4): 1028-1039.
• Goodwin N. R., Coops N. C., Wulder M. A., Gillanders S., Schroeder T. A., Nelson T. 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment 112: 3680-3689.
• Grodzki W. 1995. Wpływ osłabienia świerka przez zanieczyszczenia przemysłowe w Sudetach Zachodnich na zagro-żenie ze strony szkodników wtórnych. Pr. Inst. Bad. Leś. B 25 (1): 145-162.
• Grodzki W. 2004. Some reactions of Ips typographus (L.) (Col.: Scolytidae) to changing breeding conditions in a forest decline area in the Sudeten Mountains, Poland. Journal of Pest Science 77: 43-48.
• Grodzki W. 2009. Forest decline in mountain spruce stands affected by bark beetle outbreaks in Poland. Attempt to spatial characteristics. Forest, Wildlife and Wood Sciences for Society Development Prague.
• Grodzki W., Guzik M. 2009. Wiatro- i śniegołomy oraz gradacje kornika drukarza w Tatrzańskim Parku Narodowym na przestrzeni ostatnich 100 lat. Próba charakterystyki przestrzennej. W: Guzik M. [red.]. Długookresowe zmiany w przyrodzie użytkowaniu TPN. Wydawnictwa Tatrzańskiego Parku Narodowego, Zakopane. 33-46.
• Grodzki W., Starzyk J. R., Kosibowicz M. 2014. Wpływ wybranych elementów charakterystyki drzewostanów na intensywność występowania kornika drukarza Ips typographus (L.) w Beskidzie Żywieckim. Leśn. Pr. Bad. 75 (2): 159-169.
• Grodzki W., Turčáni M., Jakuš R., Hlásny T., Raši R., McManus M. L. 2010. Bark beetles in the Tatra Mountains. International research 1998-2005 – an overview. Folia Forestalia Polonica A 52 (2): 114-130.
• Halperin J., Lemay W., Coops N., Verchot L., Marshall P., Lochhead K. 2016. Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods. Remote Sensing of Environment 179: 170-182.
• Holmgren P., Thuresson T. 1998. Satellite remote sensing for forestry planning – A review. Scandinavian Journal of Forest Research 13 (1-4): 90-110.
• Immitzer M., Atzberger C., Koukal T. 2012. Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data. Remote Sensing 4 (9): 2661-2693.
• Jadczyk P. 1995. Przyczyny zniszczenia lasów w Górach Izerskich i Karkonoszach Cz. II. Przemiany kwaśnych gazów w środowisku i ich wpływ na lasy. Sylwan 139 (1): 63-72.
• Key T., Warner T. A., McGraw J. B., Fajvan M. A. 2001. A Comparison of Multispectral and Multitemporal Information in High Spatial Resolution Imagery for Classification of Individual Tree Species in a Temperate Hardwood Forest. Remote Sensing of Environment 75 (1): 100-112.
• Kokaly R. F., Despain D. G., Clark R. N., Livo K. E. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sensing of Environment 84: 437-456.
• Kongwen Z., Baoxin H. 2012. Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi--Spectral Imagery Using Longitudinal Profiles. Remote Sens. 4 (6): 1741-1757.
• Kozioł K., Wężyk P. 2005. Rola klasyfikacji nadzorowanej obrazów satelitarnych QuickBird w nowej koncepcji wyzna-czania przemysłowych stref uszkodzeń drzewostanów na przykładzie Miasteczka Śląskiego. Roczniki Geomatyki 3 (2): 87-96.
• Kycko M., Zagajewski B., Kozłowska A. 2014. Variability in spectral characteristics of trampled high-mountain grasslands. Miscellanea Geographica 18 (2): 10-14.
• Manion P. 1981. Tree Disease Concepts. Prentice Hall Inc., Englewood Cliffs. New Jersey.
• Marx A. 2010. Detection and Classification of Bark Beetle Infestation in Pure Norway Spruce Stands with Multi-temporal RapidEye Imagery and Data Mining Techniques. PFG Photogrammetrie, Fernerkundung, Geoinformation Jahrgang Heft 4 : 243-252.
• Mickelson Jr. J., Civco D., Silander Jr. J. 1998. Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery. Photogrammetric Engineering & Remote Sensing 64 (9): 891-904.
• Ochtyra A., Zagajewski B., Kozłowska A., Marcinkowska-Ochtyra A., Jarocińska A. 2016. Ocena kondycji drzewostanów Tatrzańskiego Parku Narodowego za pomocą metody drzewa decyzyjnego oraz wielospektralnych obrazów satelitarnych Landsat 5 TM. Sylwan 160 (3): 256-264.
• 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.
• Rajee G., Hitendra P., Kushwaha S. P. S. 2014. Forest tree species discrimination in western Himalaya using EO-1 Hyperion. International Journal of Applied Earth Observation and Geoinformation 28: 140-149.
• Reese H. M., Lillesand T. M., Nagel D. E., Stewart J. S., Goldmann R. A., Tessar P. 2002. Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project. Remote Sensing of Environment 82 (2-3): 224-237.
• Roslani M. A., Mustapha M. A., Lihan T., Wan Julaina W. A. 2014. Applicability of RapidEye Satellite Imagery in Mapping Mangrove Vegetation Species at Matang Mangrove Forest Reserve, Perak, Malaysia. Journal of Environmental Science and Technology 7: 123-136.
• Rougier S., Puissant A. 2014. Improvements of Urban vegetation segmentation and classification using multi-temporal Pleiades images. South-Eastern European Journal of Earth Observation and Geomatics 3 (2S).
• Rouse J., Haas R., Schell J., Deering D. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA. 309-317.
• Shen G., Sakai K., Hoshino Y. 2010. High Spatial Resolution Hyperspectral Mapping for Forest Ecosystem at Tree Species Level. Agricultural Information Research 19: 71-78.
• Sims D., Gamon J. 2002. Relationships Between Leaf Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment 81: 337-354.
• Stoffels J., Mader S., Hill J., Werner W., Ontrup G. 2012. Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach. Eur. J. Forest. Res. 131 (4): 1071-1089.
• Tavares de Carvalho L. M. 2013. Evaluating segmentation and Classification approaches using RapidEye data for vegetation Mapping in minas gerais, brazil. Célio helder resende de souse.
• Townsend P., Walsh S. 2001. Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA. Plant Ecology 157 (2): 129-149.
• Tyc G., Tulip J., Schulten D., Krischke M., Oxfort M. 2005. The RapidEye mission Design. Acta Astronaut. 56: 213-219.
• Vohland M., Stoffels J., Hau C., Schüler G. 2007. Remote sensing techniques for forest parameter assessment: multispectral classification and linear spectral mixture analysis. Silva Fennica 41 (3): 441-456.
• Walczykowski P., Orych A., Łysenko J. 2012. Przykład wykorzystania zobrazowań Landsat TM do oceny stanu zagrożenia pożarowego lasów. Archiwum Fotogrametrii, Kartografii i Teledetekcji 24: 393-402.
• Wallner A., Elatawneh A., Schneider T., Knoke T. 2015. Estimation of forest structural information using RapidEye satellite data. Forestry 88 (1): 96-107.
• Walsh S. J. 1980. Coniferous tree species mapping using LANDSAT data. Remote Sensing of Environment 9 (1): 11-26.
• Waser L. T., Ginzler C., Kuechler M., Baltsavias E., Hurni L. 2011. Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sensing of Environment 115 (1): 76-85.
• Wolter P. 1995. Improved forest classification in the northern Lake States using multi-temporal Landsat imagery. Photogrammetric Engineering & Remote Sensing 61 (9): 1129-1143.
• Zagajewski B. 2010. Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich. Teledetekcja Środowiska 43: 113.
• Zawiła-Niedźwiecki T., Wiśniewska E. 2004. Satellite based inventory of insects outbreaks as a tool of forest landscape monitoring. Proceedings of the Lviv University, Ser. Geogr. 31: 285-291.