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Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ

Year 2013, Volume: 14 Issue: 1, 24 - 30, 19.02.2013

Abstract

Abstract: In forestry, inventory data is needed for ecological, economical and social values of forest and in preparing forest management planning. The planning process starts with forest inventory. In forestry, inventory data is obtained from both remotely sensed (aerial photo interpretation or satellite image) data and field survey with temporary sample plots. In the preparation of forest management plans, stand volume, basal area, number of trees stand as an important inventory data for the required parameters. Obtaining measurements of these parameters is costly and time consuming. This study were carried out to examine the Quickbird and Landsat 7 ETM+ satellite images in estimating stand volume in pure stands of oriental beech (Fagus orientalis Lipsky). The stand volume was determined by field measurements at total 70 temporary sampling plots. Reflectance values were calculated based on the Quickbird and Landsat 7 ETM+ satellite data points that correspond to the sampling plots. Regression analyses were conducted to examine the relationships between the reflectance values and stand volume. The results demonstrated that regression model with band 1, band 2, band 3 and band 4 as independent variables for Quickbird and ETM 2, ETM 3 and ETM 4 as independent variables for Landsat 7 ETM+ were used for a better estimation of stand volume (R2=0.70, RMSE=28.56 m3/ha-1; R2=0.545, RMSE=53.13 m3 ha-1), respectively.
Keywords: Stand volume, Quickbird satellite data, Landsat 7 ETM+ satellite data

References

  • Armston J.D., Danaher T.J., Goulevitch B.M., Byrne M.I. 2002. Geometric correction of Landsat MSS, TM, and ETM+ Imagery for mapping of woody vegetation cover and change detection in Queenlands, http://www.nrm.gld.gov.au/slats/pdf/0078anav.pdf.
  • Astola, H., Bounsaythip,C., Ahola, J., Häme, T., Parmes, E.,Sirro, L., Veikkanen, B., 2004. Highforest-forest parameter estimation from high resolution remote sensing data. Proceedings of the International Society for Photogrammetry and Remote Sensing Twentieth Congress, 12–23 July, , pp. 355–340, Istanbul, Turkey
  • Avery, T.E., Burkhart H.E. 1994. Forest measurements. McGraw-Hill Inc., New York.
  • Beal, D.J., 2007. Information criteria methods in SAS® for multiple linear regression models. SAS Note, Paper SA05, 10 s.
  • Çakır, G., 2006. Orman Amenajman Planlamasında Gerekli Bilişimin Sağlanması İçin Uzaktan Algılama ve Coğrafi Bilgi Sistemlerinden Yararlanılması. Doktora Tezi, KTÜ Fen Bilimleri Enstitüsü, Trabzon.
  • Carus, S., 1998. Aynı Yaslı Doğu Kayını (Fagus orientalis Lipsky) Ormanlarında Artım ve Büyüme. Doktora Tezi, İstanbul Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Chubey, M.S., Franklin, S.E., Wulder, M.A., 2006. Object- basedanalysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering & Remote Sensing, 72 (4): 383-394.
  • Günlü, A., Sivrikaya, F., Başkent, E.Z., Keleş, S., Çakır, G., Kadıoğulları, A.İ., 2008. Estimation of stand type parameters and land cover using Landsat-7 ETM image: A Case Study from Turkey. Sensors, 8: 2509-2525.
  • Günlü, A., 2009. Yetişme Ortamı Envanterinin Doğrudan, Dolaylı ve Uzaktan Algılama Yöntemleri ile Belirlenmesi ve Karşılaştırılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • Günlü, A., 2012. Landsat TM Uydu Görüntüsü Yardımıyla Bazı Meşcere Parametreleri (Gelişim Çağı ve Kapalılık) ve Arazi Kullanım Sınıflarının Belirlenmesi. Kastamonu Üniversitesi, Orman Fakültesi Dergisi, 12(1):71-79.
  • Hall, R.J., Skakun, R.S., Arsenault, E.J., 2006. Modeling forest stand structure attributes using Landsat ETMş data: application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225:378–390.
  • Hyyppa, J., Hyyppa, H., Inkinen, M., Engdahl, M., Linko, S., Zhu, Y., 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128:109-120.
  • Jensen, J.R., 1996. Introductory Dijital Image Processing: A Remote Sensing Perspective 2d. Ed. Engle wood Cliffs, New Jersey: Prentice-Hall.
  • Kayitakire, F., Hamel, C., Defourny, P., 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 102:390–401.
  • Kilpelainen, P., Tokola, T., 1999. Gain to be achieved from stand delineation in Landsat TM image-based estimates of stand volume. Forest Ecology and Management, 124:105–111.
  • Leckie, D.G.,Gillis, M.D., 1995. Forest inventory in Canada with emphasis on map production. The Forestry Chronicle,71:74-88.
  • Lund, H.G., Thomas, C.E., 1989. A primer on stand and forest inventory designs. General Technical Report WO-54. USDA Forest Service, Washington, DC.
  • Mallows , C.L., 1973. Some comments on Cp. Technometrics, 15:661- 675.
  • Mohammadi, J., Joibary, S.S., Yaghmaee, F., Mahiny, A.S., 2010. Modelling forest stand volume and tree density using Landsat ETM data. International Journal of Remote Sensing, 31: 2959–2975.
  • Özdemir, İ., 2004. Orman Envanterinden Uydu verilerinden Yararlanma Olanakları, SDÜ Orman Fakültesi Dergisi, Seri A, Sayı: 1, 84-96.
  • Özdemir, İ., Karnieli, A., 2011. Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel, International Journal of Applied Earth Observation and Geoinformation, 13(5):701-710.
  • Peuhkurinen, J,, Maltamo, M., Vesa, L., Packalén, P., 2008. Estimation of forest stand characteristics using spectral histograms derived from an Ikonos Satellite Image. Photogrammetric Engineering & Remote Sensing , 74:1335–1341.
  • Poulain, M., Peña, M., Schmidt, A., Schmidt, H., Schulte, A., 2010. Relationships between forest variables and remote sensing data in a Nothofagus pumilio forest. Geocarto International, 25:25-43.
  • SAS Institute Inc., 2004. SAS/STAT 9.1 User's Guide: statistics, Version 9.1, SAS Institute Inc., Cary, NC., 816 s.
  • Sawa, T., 1978. Information criteria for discriminating among alternative regression models. Econometrica, 46:1273-1282.
  • Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics, 6:461-464.
  • Sivanpillai, R., Smith, C.T., Srinivasan, R., Messina, M.G., Wu, X.B., 2006. Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management, 223:247–254.
  • Sivrikaya, F., 2011. The importance of spatial accuracy in characterizing stand types using remotely sensed data. African Journal of Biotechnology, 10(66):14891-14906.
  • Yeşil, A., Musaoğlu, N., Kaya, Ş., Coşkun, G., Asan, Ü., Örmeci, C., 2002. Statistical modelling and stand type forest mapping selected area around Istanbul using Landsat-TM and Spot data. Proceeding, International Symposium on Remote Sensing and Integrated Technologies, 291-300, Istanbul, Turkey,
  • Zimble, D.A., Evans, D.L., Carison, G.C., Parker, R.C., Grado, S.C., Gerard, P.D., 2003. Characterizing vertical forest structure using small-footprint airborne lidar. Remote Sensing of Environment, 87(2-3):171-182.

Quickbird ve Landsat 7 ETM+ uydu görüntüleri kullanılarak Ayancık-Göldağ kayın (Fagus orientalis Lipsky) meşcerelerinde hacim tahmini

Year 2013, Volume: 14 Issue: 1, 24 - 30, 19.02.2013

Abstract

Orman amenajman planlarının hazırlanmasında ve ormanların ekolojik, ekonomik sosyokültürel değerlerinin belirlenmesinde envanter verisine ihtiyaç duyulmaktadır. Bilindiği gibi planlama süreci envanter çalışmaları ile başlamaktadır. Ormancılıkta envanter verisi, yersel ölçümler veya uzaktan algılama verileri (hava fotoğrafı veya uydu görüntüsü) ya da bu iki tekniğin birlikte kullanılmasıyla elde edilmektedir. Orman amenajman planlarının hazırlanmasında meşcere hacmi, göğüs yüzeyi, ağaç sayısı gibi meşcere parametreleri ihtiyaç duyulan önemli envanter verileridir. Bu parametrelerin yersel ölçümlerle elde edilmesi pahalı ve zaman alıcı bir aşamayı içermektedir. Bu çalışma, saf kayın meşcerelerinde Quickbird ve Landsat 7 ETM+ uydu görüntüleri yardımıyla meşcere hacminin tahmin edilmesi amacıyla yapılmıştır. Toplam geçici 70 örnek alanda, yersel ölçümlerle meşcere hacmi belirlenmiştir. Aynı örnek alanların koordinat değerlerinden yararlanarak Quickbird ve Landsat 7 ETM+ uydu görüntüleri üzerinde parlaklık değerleri hesaplanmıştır. Uydu görüntülerinden elde edilen parlaklık değerleri ile meşcere hacmi arasındaki ilişkiler regresyon analiziyle ortaya konulmuştur. Analizler sonucunda, Quickbird uydu görüntüsünün Bant 1, Bant 2, Bant 3 ve Bant 4 bağımsız değişkenleri ile elde edilen regresyon denklemi ile meşcere hacmi arasında en iyi ilişki (R2=0.70, RMSE=28.56 m3/ha) bulunurken, Landsat 7 ETM+ uydu görüntüsünde ise ETM 2, ETM 3 ve ETM 4 bağımsız değişkenlerinde (R2=0.545, RMSE=53.13 m3/ha) iyi ilişki olduğu bulunmuştur.
Anahtar kelimeler: Meşcere hacmi, Quickbird uydu görüntüsü, Landsat 7 ETM+ uydu görüntüsü

References

  • Armston J.D., Danaher T.J., Goulevitch B.M., Byrne M.I. 2002. Geometric correction of Landsat MSS, TM, and ETM+ Imagery for mapping of woody vegetation cover and change detection in Queenlands, http://www.nrm.gld.gov.au/slats/pdf/0078anav.pdf.
  • Astola, H., Bounsaythip,C., Ahola, J., Häme, T., Parmes, E.,Sirro, L., Veikkanen, B., 2004. Highforest-forest parameter estimation from high resolution remote sensing data. Proceedings of the International Society for Photogrammetry and Remote Sensing Twentieth Congress, 12–23 July, , pp. 355–340, Istanbul, Turkey
  • Avery, T.E., Burkhart H.E. 1994. Forest measurements. McGraw-Hill Inc., New York.
  • Beal, D.J., 2007. Information criteria methods in SAS® for multiple linear regression models. SAS Note, Paper SA05, 10 s.
  • Çakır, G., 2006. Orman Amenajman Planlamasında Gerekli Bilişimin Sağlanması İçin Uzaktan Algılama ve Coğrafi Bilgi Sistemlerinden Yararlanılması. Doktora Tezi, KTÜ Fen Bilimleri Enstitüsü, Trabzon.
  • Carus, S., 1998. Aynı Yaslı Doğu Kayını (Fagus orientalis Lipsky) Ormanlarında Artım ve Büyüme. Doktora Tezi, İstanbul Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Chubey, M.S., Franklin, S.E., Wulder, M.A., 2006. Object- basedanalysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering & Remote Sensing, 72 (4): 383-394.
  • Günlü, A., Sivrikaya, F., Başkent, E.Z., Keleş, S., Çakır, G., Kadıoğulları, A.İ., 2008. Estimation of stand type parameters and land cover using Landsat-7 ETM image: A Case Study from Turkey. Sensors, 8: 2509-2525.
  • Günlü, A., 2009. Yetişme Ortamı Envanterinin Doğrudan, Dolaylı ve Uzaktan Algılama Yöntemleri ile Belirlenmesi ve Karşılaştırılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • Günlü, A., 2012. Landsat TM Uydu Görüntüsü Yardımıyla Bazı Meşcere Parametreleri (Gelişim Çağı ve Kapalılık) ve Arazi Kullanım Sınıflarının Belirlenmesi. Kastamonu Üniversitesi, Orman Fakültesi Dergisi, 12(1):71-79.
  • Hall, R.J., Skakun, R.S., Arsenault, E.J., 2006. Modeling forest stand structure attributes using Landsat ETMş data: application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225:378–390.
  • Hyyppa, J., Hyyppa, H., Inkinen, M., Engdahl, M., Linko, S., Zhu, Y., 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128:109-120.
  • Jensen, J.R., 1996. Introductory Dijital Image Processing: A Remote Sensing Perspective 2d. Ed. Engle wood Cliffs, New Jersey: Prentice-Hall.
  • Kayitakire, F., Hamel, C., Defourny, P., 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 102:390–401.
  • Kilpelainen, P., Tokola, T., 1999. Gain to be achieved from stand delineation in Landsat TM image-based estimates of stand volume. Forest Ecology and Management, 124:105–111.
  • Leckie, D.G.,Gillis, M.D., 1995. Forest inventory in Canada with emphasis on map production. The Forestry Chronicle,71:74-88.
  • Lund, H.G., Thomas, C.E., 1989. A primer on stand and forest inventory designs. General Technical Report WO-54. USDA Forest Service, Washington, DC.
  • Mallows , C.L., 1973. Some comments on Cp. Technometrics, 15:661- 675.
  • Mohammadi, J., Joibary, S.S., Yaghmaee, F., Mahiny, A.S., 2010. Modelling forest stand volume and tree density using Landsat ETM data. International Journal of Remote Sensing, 31: 2959–2975.
  • Özdemir, İ., 2004. Orman Envanterinden Uydu verilerinden Yararlanma Olanakları, SDÜ Orman Fakültesi Dergisi, Seri A, Sayı: 1, 84-96.
  • Özdemir, İ., Karnieli, A., 2011. Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel, International Journal of Applied Earth Observation and Geoinformation, 13(5):701-710.
  • Peuhkurinen, J,, Maltamo, M., Vesa, L., Packalén, P., 2008. Estimation of forest stand characteristics using spectral histograms derived from an Ikonos Satellite Image. Photogrammetric Engineering & Remote Sensing , 74:1335–1341.
  • Poulain, M., Peña, M., Schmidt, A., Schmidt, H., Schulte, A., 2010. Relationships between forest variables and remote sensing data in a Nothofagus pumilio forest. Geocarto International, 25:25-43.
  • SAS Institute Inc., 2004. SAS/STAT 9.1 User's Guide: statistics, Version 9.1, SAS Institute Inc., Cary, NC., 816 s.
  • Sawa, T., 1978. Information criteria for discriminating among alternative regression models. Econometrica, 46:1273-1282.
  • Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics, 6:461-464.
  • Sivanpillai, R., Smith, C.T., Srinivasan, R., Messina, M.G., Wu, X.B., 2006. Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management, 223:247–254.
  • Sivrikaya, F., 2011. The importance of spatial accuracy in characterizing stand types using remotely sensed data. African Journal of Biotechnology, 10(66):14891-14906.
  • Yeşil, A., Musaoğlu, N., Kaya, Ş., Coşkun, G., Asan, Ü., Örmeci, C., 2002. Statistical modelling and stand type forest mapping selected area around Istanbul using Landsat-TM and Spot data. Proceeding, International Symposium on Remote Sensing and Integrated Technologies, 291-300, Istanbul, Turkey,
  • Zimble, D.A., Evans, D.L., Carison, G.C., Parker, R.C., Grado, S.C., Gerard, P.D., 2003. Characterizing vertical forest structure using small-footprint airborne lidar. Remote Sensing of Environment, 87(2-3):171-182.
There are 30 citations in total.

Details

Primary Language English
Journal Section Orijinal Araştırma Makalesi
Authors

Alkan Günlü

İlker Ercanlı

Emin Başkent This is me

Muammer Şenyurt

Publication Date February 19, 2013
Published in Issue Year 2013 Volume: 14 Issue: 1

Cite

APA Günlü, A., Ercanlı, İ., Başkent, E., Şenyurt, M. (2013). Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ. Turkish Journal of Forestry, 14(1), 24-30. https://doi.org/10.18182/tjf.75745
AMA Günlü A, Ercanlı İ, Başkent E, Şenyurt M. Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ. Turkish Journal of Forestry. February 2013;14(1):24-30. doi:10.18182/tjf.75745
Chicago Günlü, Alkan, İlker Ercanlı, Emin Başkent, and Muammer Şenyurt. “Predicting Stand Volume Using Quickbird and Landsat 7 ETM+ Satellite Images for Stands of Oriental Beech (Fagus Orientalis Lipsky): A Case Study inAyancık-Göldağ”. Turkish Journal of Forestry 14, no. 1 (February 2013): 24-30. https://doi.org/10.18182/tjf.75745.
EndNote Günlü A, Ercanlı İ, Başkent E, Şenyurt M (February 1, 2013) Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ. Turkish Journal of Forestry 14 1 24–30.
IEEE A. Günlü, İ. Ercanlı, E. Başkent, and M. Şenyurt, “Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ”, Turkish Journal of Forestry, vol. 14, no. 1, pp. 24–30, 2013, doi: 10.18182/tjf.75745.
ISNAD Günlü, Alkan et al. “Predicting Stand Volume Using Quickbird and Landsat 7 ETM+ Satellite Images for Stands of Oriental Beech (Fagus Orientalis Lipsky): A Case Study inAyancık-Göldağ”. Turkish Journal of Forestry 14/1 (February 2013), 24-30. https://doi.org/10.18182/tjf.75745.
JAMA Günlü A, Ercanlı İ, Başkent E, Şenyurt M. Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ. Turkish Journal of Forestry. 2013;14:24–30.
MLA Günlü, Alkan et al. “Predicting Stand Volume Using Quickbird and Landsat 7 ETM+ Satellite Images for Stands of Oriental Beech (Fagus Orientalis Lipsky): A Case Study inAyancık-Göldağ”. Turkish Journal of Forestry, vol. 14, no. 1, 2013, pp. 24-30, doi:10.18182/tjf.75745.
Vancouver Günlü A, Ercanlı İ, Başkent E, Şenyurt M. Predicting stand volume using Quickbird and Landsat 7 ETM+ satellite images for stands of oriental beech (Fagus orientalis Lipsky): a case study inAyancık-Göldağ. Turkish Journal of Forestry. 2013;14(1):24-30.