BibTex RIS Kaynak Göster

Forest stand delineation using Ikonos image and object based image analysis

Yıl 2016, Cilt: 66 Sayı: 2, 600 - 612, 01.07.2016
https://doi.org/10.17099/jffiu.95674

Öz

Forest stand delineation using Ikonos image and object based image analysis

Abstract: Together with the developments in satellite technology, it is considered that high resolution satellite data may be used as an alternative source of information to aerial photos in delineation of stand types. The study aims to reveal how detailed one could work to generate the map of stand types which form the basis of forest management plans using IKONOS satellite data. For this purpose, object based classification was applied to satellite image. Firstly, image segments which represent target objects were generated applying image segmentation algorithm to the satellite image. The image segments generated at three different levels according to different scale parameters and homogeneity criteria were classified according to standard nearest-neighbor approach. Classification accuracy was determined using both the stand maps of study area and ground control points. Overall accuracy was calculated as 58% (Kappa=0.54). Accordingly, it was understood that it was not possible to generate a stand map with sufficient accuracy from the IKONOS satellite image using automatic classification.

Keywords: Ikonos, forest inventory, image segmentation, object based classification, stand map

Ikonos görüntüsü ve obje bazlı görüntü analizi kullanılarak meşcere tiplerinin ayrılması

Özet: Uydu teknolojisindeki gelişmelerle birlikte yüksek çözünürlüklü uydu verilerinin, meşcere tipleri ayrımında hava fotoğraflarının yerine alternatif bir bilgi kaynağı olarak kullanılabileceği düşünülmektedir. Çalışmada, IKONOS uydu verisinden amenajman planlarının temelini oluşturan meşcere tipleri haritasını düzenlemek için ne kadar ayrıntıya gidilebileceğinin ortaya konulması amaçlanmıştır. Bunun için uydu görüntüsüne obje bazlı sınıflandırma işlemi uygulanmıştır. Uydu görüntüsüne öncelikle görüntü dilimleme işlmei uygulanarak, hedef objeleri temsil edecek görüntü dilimleri oluşturulmuştur. Farklı ölçek parametreleri ve homojenlik kriterlerine göre üç farklı seviyede oluşturulan görüntü dilimleri, standart en yakın komşu yaklaşımına göre sınıflandırılmıştır. Sınıflandırma sonuçlarının doğruluk değerlendirmesi çalışma alanına ait meşcere tipleri haritasından ve arazi çalışmaları sırasında alınan denetim noktalarından faydalanılarak yapılmıştır. Meşcere tipleri düzeyinde yapılan sınıflandırma sonuçlarının toplam doğruluk değeri %55 (Kappa=0.52) olarak hesaplanmıştır. Buna göre, IKONOS uydu görüntüsünden otomatik sınıflandırma ile yeterli doğrulukta meşcere tipleri haritasının üretilmesinin mümkün olmadığı anlaşılmıştır.

Anahtar Kelimeler: Ikonos, orman envanteri, görüntü dilimleme, obje bazlı sınıflandırma, meşcere haritası

Received (Geliş): 11.01.2016 - Revised (Düzeltme): 18.01.2016 - Accepted (Kabul): 22.01.2016

Cite (Atıf): Ozkan, U.Y., Yesil, A., 2016. Forest stand delineation using Ikonos image and object based image analysis. Journal of the Faculty of Forestry Istanbul University 66(2): xxx-xxx. DOI: 10.17099/jffiu.xxxxx

Kaynakça

  • Antunes, A.F.B., Lingnau C., Centeno, J.A.S., 2003. Object Oriented Analysis and Semantic Network for High Resolution Image Classification. Boletim de Ciências Geodésicas, 9(2): 233-242,
  • Arockiaraj, S., Kumar, A., Hoda, N., Jeyaseelan, A.T., 2015. Identification and Quantification of Tree Species in Open Mixed Forests using High Resolution QuickBird Satellite Imagery. Journal of Tropical Forestry and Environment 5(2): 40-53.
  • Asan, Ü., 1999. Using Possibilities of Satellite Images in Forestry And The Applications In Turkey. International Symposium on Remote Sensing and Integrated Technologies, Istanbul, 20-22 October 1999, pp. 113-126.
  • Asan, Ü., Başkent, E.Z., Özçelik, R., 2001. Gelişmiş Ülkelerdeki Ulusal Orman Envanteri Sistemleri ve Türkiye İçin Öneriler. I. Ulusal Ormancılık Kongresi, Ankara, 19-20 Mart 2001, pp. 30-51.
  • Asan, Ü., Yeşil, A., 2005. Ulusal Orman Envanterinin Türkiye için Önemi ve Model seçiminde Gözetilecek Genel Kriterler. Türkiye Ulusal Orman Envanteri Sempozyumu, İstanbul, 24-28 Eylül 2002, pp. 112-131.
  • Asan, Ü., Yeşil, A., Özdemir, İ., Özkan, U.Y., Ercan, M., Baş, N., Ün, C., Kündük, H.E., Başaran, M.A., 2007. Türkiye Ulusal Orman Envanteri Konseptine Uydu Görüntülerinin Entegrasyonu, Proje No: TOVAG-JULICH 2002-1, TUBİTAK.
  • Birth, G.S., McVey, G.R., 1968. Measuring color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60: 640-649.
  • Blaschke, T., Burnett, C., Pekkarinen, A., 2004. Image Segmentation Methods for Object based Analysis and Classification. In: De Jong, S. M., & van der Meer, F. (Eds.), Remote sensing image analysis: including the spatial domain, Kluwer Academic Publishers, USA, pp.211-236.
  • Bock, M., Xofis, P., Mitchley, J., Rossner, G., Wissen, M. 2005. Object-oriented methods for habitat mapping at multiple scales– Case studies from Northern Germany and Wye Downs, UK. Journal for Nature Conservation 13 (2): 75–89, doi:10.1016/j.jnc.2004.12.002.
  • Chen, Y., Su, W., Li, J., Sun, Z., 2009. Hierarchical object classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research 43 (7): 1101-1110, doi:10.1016/j.asr.2008.11.008.
  • Corona, P., Köhl, M., Marchetti, M., 2003. Advances in forest inventory for sustainable forest management and biodiversity monitoring. Kluwert Academic Publishers, Dordrecht.
  • Dalponte, M., Ørka, H. O., Ene, L. T., Gobakken, T., Næsset, E., 2014. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote sensing of environment 140: 306-317, doi:10.1016/j.rse.2013.09.006.
  • Definiens, A.G. 2006. Definiens Professional 5 Reference Book. Definiens AG, Munich.
  • Deering, D. W., Rouse, J. V., Haas, R. H., Schell, J. A., 1975. Measuring forage production of grazing units from Landsat MSS data. In Proceedings of 10th International Symposium on Remote Sensing of Environment, Michıgan, pp. 1169-1178.
  • Drǎguţ, L., Tiede, D., & Levick, S.R., 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871, doi: 10.1080/13658810903174803.
  • Eler, Ü., 2001. Orman Amenajmanı, Süleyman Demirel Üniversitesi Yayınları, Isparta.
  • Furuya, N., Saito, P., Tith B., MEAS, M., 2007. Object-oriented land cover classification based on two satellite images obtained in one dry season in Cambodia. In Forest Environment in the Mekond River Basin, Springer Japan, pp 149-158.
  • Gong, P., Biging, G.S., Lee, S.M., Mei, X., Sheng, Y., Pu, R., Xu, B., 1999. Photo Ecometrics for Forest Inventory. Geographic Informayion Sciences 5(1): 9-14, doi: 10.1080/10824009909480508.
  • Günlü, A., Sivrikaya, F., Baskent, E. Z., Keles, S., Çakir, G., Kadiogullari, A.I., 2008. Estimation of stand type parameters and land cover using Landsat-7 ETM image: A case study from Turkey. Sensors, 8(4): 2509-2525, doi:10.3390/s8042509.
  • Günlü, A., Başkent E.Z., Kadiogullari, A.I., Altun, L., 2009. Forest site classification using Landsat 7 ETM data: a case study of Maçka-Ormanüstü forest, Turkey. Environmental Monitoring and Assessment 151: 93-104, doi: 10.1007/s10661-008-0252-3.
  • Hajek, F., 2006. Object analysis of IKONOS xs and PAN-SHARPENED imagery in comparision for purpose of tree species estimation. http://www.commission4.isprs.org/obia06/Papers/13_Automated%20classification%20IC%20I%20-%20Forest/OBIA2006_Hajek.pdf (Ziyaret tarihi: 18 Şubat 2008).
  • Herold, M., Scepan, J., Muller, A., Gunther, S., 2002. Object-oriented mapping and analysis of urban land use/cover using IKONOS data. In 22nd Earsel Symposium Geoinformation for European-Wide Integration, Prague, June 2002, pp. 4-6.
  • Holopainen, M., Kalliovirta, J., 2006. Modern Data Acquisition for Forest Inventories, Forest Inventory – Methodology and Applications. Springer, Netherlands.
  • Huang, W., Li, H., & Lin, G., 2015. Classifying forest stands based on multi-scale structure features using Quickbird image. In Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2nd IEEE International Conference, Gdynia, 24-26 June 2015, pp. 202-208.
  • Hung, M.C., 2002. Urban land cover analysis from satellite images. In:Proceedings of Pecora, pp. 10-15.
  • 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, doi:10.3390/rs4092661.
  • Jensen, R.J., 1996. Introductory Digital Image Processing, a Remote Sensing Perspective, Prentice Hall. Upper Saddle River, New Jersey.
  • Kamal, M., Phinn, S., Johansen, K., 2015. Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets. Remote Sensing 7(4): 4753-4783, doi:10.3390/rs70404753.
  • Kim, S.R., Lee, W.K., Kwak, D.A., Biging, G.S., Gong, P., Lee, J.H., Cho, H.K., 2011. Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors 11(2): 1943-1958, doi:10.3390/s110201943.
  • Koç, A., Yener, H., 2006. Landsat ETM verilerinde topografik normalizasyonun sınıflandırma doğruluğu üzerindeki etkisi. İ.Ü. Orman Fakültesi Dergisi A, 56(2): 57-76.
  • Köhl, M., 1993. Forest Inventory. In: Pancel, L. (ed), Tropical Forestry Handbook, Springer Verlag, Heidelberg, pp. 273-276.
  • Köse, S., Çakır, G., Sönmez, T., Sivrikaya, F., 2002. Uzaktan Algılamanın Orman Amenajman Planlamasında ve Bilgi Sistemleri Kurulmasındaki Önemi. Orman Amenajmanı’nda Kavramsal Açılımlar ve Yeni Hedefler Sempozyumu, İstanbul, 18-19 Nisan 2002, pp. 148-157.
  • Laliberte, A.S., Rango, A., Havstad, K.M., Paris, J.F., Beck, R.F., McNeely R., Gonzalez, A.L., 2004. Object-oriented image for mapping shrup encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment 93(1): 198-210, doi:10.1016/j.rse.2004.07.011.
  • Leboeuf, A., Fournier, R.A., 2013. Estimating stand attributes of boreal forests using digital aerial photography and a shadow fraction method.Canadian Journal of Remote Sensing 39(3): 217-231, doi: 10.5589/m13-030.
  • Marcal, A.R.S., Borges, J.S., Gomes J.A., Pinto Da Costa J.F., 2005. Land Cover Update by Supervised Classification of Segmented ASTER Images. International Journal of Remote Sensing 26(7): 1347-1362, doi: 10.1080/01431160412331291233.
  • Mathieu, R., Aryal, J., 2005. Object-oriented classification and IKONOS multispectral imagery for mapping vegetation communities in urban areas. The 17th Annual Colloquium of Spatial Information Research Centre Unniversity of Otago, Dunedin, 24-25 November 2005.
  • Mathieu, R., Aryal, J., Chong, A., 2007. Object-Based classification of IKONOS imagery for mapping large-scale vegetation communities in urban areas. Sensors 7(2007): 2860-2880, doi:10.3390/s7112860.
  • Maxwell, T., 2005. Object-oriented classification: Classification of PAN-SHARPENED QuickBird images and a fuzzy approach to improving image segmentation efficiency, Thesis (MD), The University of New Brunswick.
  • Musaoğlu, N., 1999. Elektro-Optik ve Aktif Mikrodalga Algılayıcılarından Elde Edilen Uydu Verilerinden Orman Alanlarında Meşcere Tiplerinin ve Yetişme Ortamı Birimlerinin Belirlenme Olanakları, Doktora Tezi, İ.T.Ü.Fen Bilimleri Enstitüsü.
  • Özdemir, İ., 2004. Orman envanterinde uydu verilerinden yararlanma olanakları, S.D.Ü. Orman Fakültesi Dergisi, A, 1: 84-96.
  • Ozdemir, I., 2008. Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing 29(19): 5643-5655, doi: 10.1080/01431160802082155.
  • Ozdemir, I., 2014. Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using RapidEye data. GIScience & Remote Sensing 51(3): 288-300, doi: 10.1080/15481603.2014.912876.
  • Ozdemir, I., Norton, D.A., Ozkan, U.Y., Mert, A., Senturk, O., 2008. Estimation of tree size diversity using object oriented texture analysis and aster imagery. Sensors 8(8): 4709-4724, doi:10.3390/s8084709.
  • Ozdemir, I., 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, doi:10.1016/j.jag.2011.05.006.
  • Özkan, U.Y. 2006. Uydu Görüntüleri Yardımıyla Meşcere Parametrelerinin Kestirilmesi ve Orman Amenajmanında Kullanılması Olanakları. İ.Ü. Orman Fakültesi Dergisi 56: 191-218.
  • Plattier, T., Loureiro, M., Marques, P., Caetano, M., 2006. Spectral analyses and classification of IKONOS images for forest cover characterisation,. Proceedings of the 2nd Workshop of the EARSeL SIG on Land Use and Land Cover, Center for Remote Sensing of Land surfaces, Bonn, 28-30 September 2006, pp. 260-268.
  • Rego, L.F.G., 2003. Automatic land-cover classification derived from high-resolution IKONOS satellite image in the urban Atlantic forest in Rio de janerio, Brasil by means of an object-oriented approach, Thesis (PhD), Albert-Ludwigs-Universitat.
  • Rego, L.F.G., Ueffing, C., Vianna, S.B., 2007. Automatic land-cover classification derived from high-resolution IKONOS satellite imagery in the urban atlantic forets of Rio de Janerio, Brazil, by means of an Object-Oriented Approach. In: Netzband, M., Stefanov, W.L., Redman, C. (Eds), Applied Remote Sensing for Urban planning, Governance and Sustainability, Springer Berlin Heidelberg, pp. 25-36.
  • Renaud, M., Aryal, J., Chong, A.K., 2007. Object-based classification of IKONOS imagery for mapping large-scale vegetation communities in urban areas. Sensors 7(11): 2860-2880, doi:10.3390/s7112860.
  • Rouse, J. W., Haas, R. H., Jr., Schell, J. A., Deering, D. W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of Third ERTS-1 Symposium, Washington DC, pp. 309–317.
  • 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. European Journal of Forest Research, 131(4): 1071-1089, doi: 10.1007/s10342-011-0577-2.
  • Tuominen, S., Pekkarinen, A., 2005. Performance of Different Spectral and Textural Aerial Photograph Features in Multi-Source Forest Inventory. Remote Sensing and Environment 94(2): 256-268, doi:10.1016/j.rse.2004.10.001.
  • Yener, H., 2005. Ulusal Orman Envanterinde Uzaktan Algılama Verilerinden Yararlanma Olanakları. Türkiye Ulusal Orman Envanteri Sempozyumu, İstanbul, 24-28 Eylül 2002, pp. 424-446.
  • Yeşil, A., Asan, Ü., Coşkun, G., Örmeci, C., Kaya, Ş., 1999. Statical Modelling and Stand Type Forest Mapping Selected Area Around Istanbul Using Landsat-TM and SPOT Data. Proceedings of the Internatinal Symposium on Remote Sensing & Integrated Technologies, İstanbul, 20-22 October 1999, pp. 151-162.

Ikonos görüntüsü ve obje bazlı görüntü analizi kullanılarak meşcere tiplerinin ayrılması

Yıl 2016, Cilt: 66 Sayı: 2, 600 - 612, 01.07.2016
https://doi.org/10.17099/jffiu.95674

Öz

Uydu teknolojisindeki gelişmelerle birlikte yüksek çözünürlüklü uydu verilerinin, meşcere tipleri ayrımında hava fotoğraflarının yerine alternatif bir bilgi kaynağı olarak kullanılabileceği düşünülmektedir. Çalışmada, IKONOS uydu verisinden amenajman planlarının temelini oluşturan meşcere tipleri haritasını düzenlemek için ne kadar ayrıntıya gidilebileceğinin ortaya konulması amaçlanmıştır. Bunun için uydu görüntüsüne obje bazlı sınıflandırma işlemi uygulanmıştır. Uydu görüntüsüne öncelikle görüntü dilimleme işlemi uygulanarak, hedef objeleri temsil edecek görüntü dilimleri oluşturulmuştur. Farklı ölçek parametreleri ve homojenlik kriterlerine göre üç farklı seviyede oluşturulan görüntü dilimleri, standart en yakın komşu yaklaşımına göre sınıflandırılmıştır. Sınıflandırma sonuçlarının doğruluk değerlendirmesi çalışma alanına ait meşcere tipleri haritasından ve arazi çalışmaları sırasında alınan denetim noktalarından faydalanılarak yapılmıştır. Meşcere tipleri düzeyinde yapılan sınıflandırma sonuçlarının toplam doğruluk değeri %55 (Kappa=0.52) olarak hesaplanmıştır. Buna göre, IKONOS uydu görüntüsünden otomatik sınıflandırma ile yeterli doğrulukta meşcere tipleri haritasının üretilmesinin mümkün olmadığı anlaşılmıştır.

Kaynakça

  • Antunes, A.F.B., Lingnau C., Centeno, J.A.S., 2003. Object Oriented Analysis and Semantic Network for High Resolution Image Classification. Boletim de Ciências Geodésicas, 9(2): 233-242,
  • Arockiaraj, S., Kumar, A., Hoda, N., Jeyaseelan, A.T., 2015. Identification and Quantification of Tree Species in Open Mixed Forests using High Resolution QuickBird Satellite Imagery. Journal of Tropical Forestry and Environment 5(2): 40-53.
  • Asan, Ü., 1999. Using Possibilities of Satellite Images in Forestry And The Applications In Turkey. International Symposium on Remote Sensing and Integrated Technologies, Istanbul, 20-22 October 1999, pp. 113-126.
  • Asan, Ü., Başkent, E.Z., Özçelik, R., 2001. Gelişmiş Ülkelerdeki Ulusal Orman Envanteri Sistemleri ve Türkiye İçin Öneriler. I. Ulusal Ormancılık Kongresi, Ankara, 19-20 Mart 2001, pp. 30-51.
  • Asan, Ü., Yeşil, A., 2005. Ulusal Orman Envanterinin Türkiye için Önemi ve Model seçiminde Gözetilecek Genel Kriterler. Türkiye Ulusal Orman Envanteri Sempozyumu, İstanbul, 24-28 Eylül 2002, pp. 112-131.
  • Asan, Ü., Yeşil, A., Özdemir, İ., Özkan, U.Y., Ercan, M., Baş, N., Ün, C., Kündük, H.E., Başaran, M.A., 2007. Türkiye Ulusal Orman Envanteri Konseptine Uydu Görüntülerinin Entegrasyonu, Proje No: TOVAG-JULICH 2002-1, TUBİTAK.
  • Birth, G.S., McVey, G.R., 1968. Measuring color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60: 640-649.
  • Blaschke, T., Burnett, C., Pekkarinen, A., 2004. Image Segmentation Methods for Object based Analysis and Classification. In: De Jong, S. M., & van der Meer, F. (Eds.), Remote sensing image analysis: including the spatial domain, Kluwer Academic Publishers, USA, pp.211-236.
  • Bock, M., Xofis, P., Mitchley, J., Rossner, G., Wissen, M. 2005. Object-oriented methods for habitat mapping at multiple scales– Case studies from Northern Germany and Wye Downs, UK. Journal for Nature Conservation 13 (2): 75–89, doi:10.1016/j.jnc.2004.12.002.
  • Chen, Y., Su, W., Li, J., Sun, Z., 2009. Hierarchical object classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research 43 (7): 1101-1110, doi:10.1016/j.asr.2008.11.008.
  • Corona, P., Köhl, M., Marchetti, M., 2003. Advances in forest inventory for sustainable forest management and biodiversity monitoring. Kluwert Academic Publishers, Dordrecht.
  • Dalponte, M., Ørka, H. O., Ene, L. T., Gobakken, T., Næsset, E., 2014. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote sensing of environment 140: 306-317, doi:10.1016/j.rse.2013.09.006.
  • Definiens, A.G. 2006. Definiens Professional 5 Reference Book. Definiens AG, Munich.
  • Deering, D. W., Rouse, J. V., Haas, R. H., Schell, J. A., 1975. Measuring forage production of grazing units from Landsat MSS data. In Proceedings of 10th International Symposium on Remote Sensing of Environment, Michıgan, pp. 1169-1178.
  • Drǎguţ, L., Tiede, D., & Levick, S.R., 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871, doi: 10.1080/13658810903174803.
  • Eler, Ü., 2001. Orman Amenajmanı, Süleyman Demirel Üniversitesi Yayınları, Isparta.
  • Furuya, N., Saito, P., Tith B., MEAS, M., 2007. Object-oriented land cover classification based on two satellite images obtained in one dry season in Cambodia. In Forest Environment in the Mekond River Basin, Springer Japan, pp 149-158.
  • Gong, P., Biging, G.S., Lee, S.M., Mei, X., Sheng, Y., Pu, R., Xu, B., 1999. Photo Ecometrics for Forest Inventory. Geographic Informayion Sciences 5(1): 9-14, doi: 10.1080/10824009909480508.
  • Günlü, A., Sivrikaya, F., Baskent, E. Z., Keles, S., Çakir, G., Kadiogullari, A.I., 2008. Estimation of stand type parameters and land cover using Landsat-7 ETM image: A case study from Turkey. Sensors, 8(4): 2509-2525, doi:10.3390/s8042509.
  • Günlü, A., Başkent E.Z., Kadiogullari, A.I., Altun, L., 2009. Forest site classification using Landsat 7 ETM data: a case study of Maçka-Ormanüstü forest, Turkey. Environmental Monitoring and Assessment 151: 93-104, doi: 10.1007/s10661-008-0252-3.
  • Hajek, F., 2006. Object analysis of IKONOS xs and PAN-SHARPENED imagery in comparision for purpose of tree species estimation. http://www.commission4.isprs.org/obia06/Papers/13_Automated%20classification%20IC%20I%20-%20Forest/OBIA2006_Hajek.pdf (Ziyaret tarihi: 18 Şubat 2008).
  • Herold, M., Scepan, J., Muller, A., Gunther, S., 2002. Object-oriented mapping and analysis of urban land use/cover using IKONOS data. In 22nd Earsel Symposium Geoinformation for European-Wide Integration, Prague, June 2002, pp. 4-6.
  • Holopainen, M., Kalliovirta, J., 2006. Modern Data Acquisition for Forest Inventories, Forest Inventory – Methodology and Applications. Springer, Netherlands.
  • Huang, W., Li, H., & Lin, G., 2015. Classifying forest stands based on multi-scale structure features using Quickbird image. In Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2nd IEEE International Conference, Gdynia, 24-26 June 2015, pp. 202-208.
  • Hung, M.C., 2002. Urban land cover analysis from satellite images. In:Proceedings of Pecora, pp. 10-15.
  • 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, doi:10.3390/rs4092661.
  • Jensen, R.J., 1996. Introductory Digital Image Processing, a Remote Sensing Perspective, Prentice Hall. Upper Saddle River, New Jersey.
  • Kamal, M., Phinn, S., Johansen, K., 2015. Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets. Remote Sensing 7(4): 4753-4783, doi:10.3390/rs70404753.
  • Kim, S.R., Lee, W.K., Kwak, D.A., Biging, G.S., Gong, P., Lee, J.H., Cho, H.K., 2011. Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors 11(2): 1943-1958, doi:10.3390/s110201943.
  • Koç, A., Yener, H., 2006. Landsat ETM verilerinde topografik normalizasyonun sınıflandırma doğruluğu üzerindeki etkisi. İ.Ü. Orman Fakültesi Dergisi A, 56(2): 57-76.
  • Köhl, M., 1993. Forest Inventory. In: Pancel, L. (ed), Tropical Forestry Handbook, Springer Verlag, Heidelberg, pp. 273-276.
  • Köse, S., Çakır, G., Sönmez, T., Sivrikaya, F., 2002. Uzaktan Algılamanın Orman Amenajman Planlamasında ve Bilgi Sistemleri Kurulmasındaki Önemi. Orman Amenajmanı’nda Kavramsal Açılımlar ve Yeni Hedefler Sempozyumu, İstanbul, 18-19 Nisan 2002, pp. 148-157.
  • Laliberte, A.S., Rango, A., Havstad, K.M., Paris, J.F., Beck, R.F., McNeely R., Gonzalez, A.L., 2004. Object-oriented image for mapping shrup encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment 93(1): 198-210, doi:10.1016/j.rse.2004.07.011.
  • Leboeuf, A., Fournier, R.A., 2013. Estimating stand attributes of boreal forests using digital aerial photography and a shadow fraction method.Canadian Journal of Remote Sensing 39(3): 217-231, doi: 10.5589/m13-030.
  • Marcal, A.R.S., Borges, J.S., Gomes J.A., Pinto Da Costa J.F., 2005. Land Cover Update by Supervised Classification of Segmented ASTER Images. International Journal of Remote Sensing 26(7): 1347-1362, doi: 10.1080/01431160412331291233.
  • Mathieu, R., Aryal, J., 2005. Object-oriented classification and IKONOS multispectral imagery for mapping vegetation communities in urban areas. The 17th Annual Colloquium of Spatial Information Research Centre Unniversity of Otago, Dunedin, 24-25 November 2005.
  • Mathieu, R., Aryal, J., Chong, A., 2007. Object-Based classification of IKONOS imagery for mapping large-scale vegetation communities in urban areas. Sensors 7(2007): 2860-2880, doi:10.3390/s7112860.
  • Maxwell, T., 2005. Object-oriented classification: Classification of PAN-SHARPENED QuickBird images and a fuzzy approach to improving image segmentation efficiency, Thesis (MD), The University of New Brunswick.
  • Musaoğlu, N., 1999. Elektro-Optik ve Aktif Mikrodalga Algılayıcılarından Elde Edilen Uydu Verilerinden Orman Alanlarında Meşcere Tiplerinin ve Yetişme Ortamı Birimlerinin Belirlenme Olanakları, Doktora Tezi, İ.T.Ü.Fen Bilimleri Enstitüsü.
  • Özdemir, İ., 2004. Orman envanterinde uydu verilerinden yararlanma olanakları, S.D.Ü. Orman Fakültesi Dergisi, A, 1: 84-96.
  • Ozdemir, I., 2008. Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing 29(19): 5643-5655, doi: 10.1080/01431160802082155.
  • Ozdemir, I., 2014. Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using RapidEye data. GIScience & Remote Sensing 51(3): 288-300, doi: 10.1080/15481603.2014.912876.
  • Ozdemir, I., Norton, D.A., Ozkan, U.Y., Mert, A., Senturk, O., 2008. Estimation of tree size diversity using object oriented texture analysis and aster imagery. Sensors 8(8): 4709-4724, doi:10.3390/s8084709.
  • Ozdemir, I., 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, doi:10.1016/j.jag.2011.05.006.
  • Özkan, U.Y. 2006. Uydu Görüntüleri Yardımıyla Meşcere Parametrelerinin Kestirilmesi ve Orman Amenajmanında Kullanılması Olanakları. İ.Ü. Orman Fakültesi Dergisi 56: 191-218.
  • Plattier, T., Loureiro, M., Marques, P., Caetano, M., 2006. Spectral analyses and classification of IKONOS images for forest cover characterisation,. Proceedings of the 2nd Workshop of the EARSeL SIG on Land Use and Land Cover, Center for Remote Sensing of Land surfaces, Bonn, 28-30 September 2006, pp. 260-268.
  • Rego, L.F.G., 2003. Automatic land-cover classification derived from high-resolution IKONOS satellite image in the urban Atlantic forest in Rio de janerio, Brasil by means of an object-oriented approach, Thesis (PhD), Albert-Ludwigs-Universitat.
  • Rego, L.F.G., Ueffing, C., Vianna, S.B., 2007. Automatic land-cover classification derived from high-resolution IKONOS satellite imagery in the urban atlantic forets of Rio de Janerio, Brazil, by means of an Object-Oriented Approach. In: Netzband, M., Stefanov, W.L., Redman, C. (Eds), Applied Remote Sensing for Urban planning, Governance and Sustainability, Springer Berlin Heidelberg, pp. 25-36.
  • Renaud, M., Aryal, J., Chong, A.K., 2007. Object-based classification of IKONOS imagery for mapping large-scale vegetation communities in urban areas. Sensors 7(11): 2860-2880, doi:10.3390/s7112860.
  • Rouse, J. W., Haas, R. H., Jr., Schell, J. A., Deering, D. W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of Third ERTS-1 Symposium, Washington DC, pp. 309–317.
  • 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. European Journal of Forest Research, 131(4): 1071-1089, doi: 10.1007/s10342-011-0577-2.
  • Tuominen, S., Pekkarinen, A., 2005. Performance of Different Spectral and Textural Aerial Photograph Features in Multi-Source Forest Inventory. Remote Sensing and Environment 94(2): 256-268, doi:10.1016/j.rse.2004.10.001.
  • Yener, H., 2005. Ulusal Orman Envanterinde Uzaktan Algılama Verilerinden Yararlanma Olanakları. Türkiye Ulusal Orman Envanteri Sempozyumu, İstanbul, 24-28 Eylül 2002, pp. 424-446.
  • Yeşil, A., Asan, Ü., Coşkun, G., Örmeci, C., Kaya, Ş., 1999. Statical Modelling and Stand Type Forest Mapping Selected Area Around Istanbul Using Landsat-TM and SPOT Data. Proceedings of the Internatinal Symposium on Remote Sensing & Integrated Technologies, İstanbul, 20-22 October 1999, pp. 151-162.
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi (Research Article)
Yazarlar

Ulaş Özkan

Ahmet Yeşil

Yayımlanma Tarihi 1 Temmuz 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 66 Sayı: 2

Kaynak Göster

APA Özkan, U., & Yeşil, A. (2016). Forest stand delineation using Ikonos image and object based image analysis. Journal of the Faculty of Forestry Istanbul University, 66(2), 600-612. https://doi.org/10.17099/jffiu.95674
AMA Özkan U, Yeşil A. Forest stand delineation using Ikonos image and object based image analysis. J FAC FOR ISTANBUL U. Temmuz 2016;66(2):600-612. doi:10.17099/jffiu.95674
Chicago Özkan, Ulaş, ve Ahmet Yeşil. “Forest Stand Delineation Using Ikonos Image and Object Based Image Analysis”. Journal of the Faculty of Forestry Istanbul University 66, sy. 2 (Temmuz 2016): 600-612. https://doi.org/10.17099/jffiu.95674.
EndNote Özkan U, Yeşil A (01 Temmuz 2016) Forest stand delineation using Ikonos image and object based image analysis. Journal of the Faculty of Forestry Istanbul University 66 2 600–612.
IEEE U. Özkan ve A. Yeşil, “Forest stand delineation using Ikonos image and object based image analysis”, J FAC FOR ISTANBUL U, c. 66, sy. 2, ss. 600–612, 2016, doi: 10.17099/jffiu.95674.
ISNAD Özkan, Ulaş - Yeşil, Ahmet. “Forest Stand Delineation Using Ikonos Image and Object Based Image Analysis”. Journal of the Faculty of Forestry Istanbul University 66/2 (Temmuz 2016), 600-612. https://doi.org/10.17099/jffiu.95674.
JAMA Özkan U, Yeşil A. Forest stand delineation using Ikonos image and object based image analysis. J FAC FOR ISTANBUL U. 2016;66:600–612.
MLA Özkan, Ulaş ve Ahmet Yeşil. “Forest Stand Delineation Using Ikonos Image and Object Based Image Analysis”. Journal of the Faculty of Forestry Istanbul University, c. 66, sy. 2, 2016, ss. 600-12, doi:10.17099/jffiu.95674.
Vancouver Özkan U, Yeşil A. Forest stand delineation using Ikonos image and object based image analysis. J FAC FOR ISTANBUL U. 2016;66(2):600-12.