Research Article
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Year 2018, , 50 - 55, 28.12.2018
https://doi.org/10.33904/ejfe.495088

Abstract

References

  • Akay. A.E.. Erdas. O.. Reis. M.. Yuksel. A., 2008. Estimating sediment yield from a forest road network by using a sediment prediction model and GIS techniques. Building and Environment. 43(5):687-695.
  • Akay. A.E.. Sivrikaya. F.. Gulci. S., 2014. Analyzing riparian forest cover changes along the Firniz River in the Mediterranean City of Kahramanmaras in Turkey. Environmental Monitoring and Assessment. 186(5):2741-2747.
  • Akay, A.E., Gencal, B., Taş, İ., 2017. Spatiotemporal change detection using Landsat imagery: the case study of Karacabey flooded forest, Bursa, Turkey. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October, Safranbolu, Karabuk, Turkey.
  • Chupezi. T.J.. Ndoye. O.. Tchatat. M.. Chikamai. B.. 2009. Processing and marketing of non-wood forest products: potential impacts and challenges in Africa. Discov. Innov.. 21(1):60-65.
  • Çakır. G.. Sivrikaya. F.. Terzioğlu. S.. Keleş. S.. Başkent. E.Z.. 2007. Monitoring 30-year Changes in Secondary Forest Succession with Land Cover in Artvin Forest Planning Unit of Northeastern Turkey. Scottish Geographical Journal. 123(3): 209 – 226.
  • Dembner. S.A.. Perlis. A., 1999. Non-wood Forest Products and Income Generation. Unasylva. 50(198).
  • Desai. C.G.. Patil. M.B.. Mahale. V.D.. Umrikar. B.. 2009. Application of remote sensing and geographic information system to study land use/land cover changes: a case study of Pune Metropolis. Advances in Computational Research. 1(2):10-13.
  • Dewan. A.M.. Yamaguchi. Y.. 2009. Land use and land cover change in Greater Dhaka. Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography. 29: 390-401. ERDAS Field Guide, 1999. Fifth Edition, Revised and Expanded. Atlanta, Georgia. 698 p. http://web.pdx.edu/~emch/ip1/FieldGuide.pdf
  • Jong. S.M... Meer. F.D... Clevers. J.G., 2004. Basics of Remote Sensing. In: Jong S.M.D.. Meer F.D.V. (eds) Remote Sensing Image Analysis: Including the Spatial Domain. Remote Sensing and Digital Image Processing. vol 5. Springer. Dordrecht.
  • Lillesand. T.M.. Kiefer. R.W.. 2000. Remote Sensing and Image Interpretation. 4th ed. New York: Wiley.
  • Moller-Jensen. L.. 1997. Classification of Urban land cover based on expert systems. object models and texture. Computers. Environment and Urban Systems. 21(3/4):291-302.
  • Ruelland. D.. Levavasseur. F.. Tribotte. A.. 2010. Patterns and dynamics of land-cover changes since the 1960s over three experimental areas in Mali. International Journal of Applied Earth Observation and Geoinformation. 12(1):11–17.
  • Sivrikaya. F.. Çakır. G.. Keleş. S.. Başkent. E.Z.. 2009. Spatiotemporal Dynamics of Land Use/Land Cover and Timber Carbon Storage: A Case Study from Bulanikdere.Turkey Chapter. Pages:215-247. Geoinformatics for Natural Resource Management. Editors: Joshi. P.K.. Pani. P.. Mohapartra. S.N.. Singh. T.P.. Nova Science Publishers. 634 pp. ISBN: 978-160692-211-8.
  • Tuttu. G.. Ursavaş. S.. Söyler. R.. 2017. Harvest amounts and ethnobotanical uses of the linden flowers in turkey. Anatolian Journal of Forest Research. 3(1): 60-66.
  • Yomralıoğlu. T., 2005. Geographical Information System Basic Terms and Applications. Akademi Publishing. 480 p. ISBN: 975973690x.

Spatiotemporal Change Detection of the Linden Forests in Bursa, Turkey

Year 2018, , 50 - 55, 28.12.2018
https://doi.org/10.33904/ejfe.495088

Abstract

Nonwood
forest products potentially provide many economic, social and environmental
benefits in Turkey. Increasing public demand for nonwood forest products has
led to the development of spatial planning and updating of existing plans. In
order to ensure the sustainable management of nonwood forest products, their
trends and spatial distributions by time can be estimated by using land use/land
cover change detection approach. The Linden is one of the most important
nonwood forest products in Turkey and the most widespread distribution of
linden is located in the province of Bursa. In this study, it was aimed to
determine the spatiotemporal changes of one of the world's largest linden
forests in Yeniköy Forestry Enterprise Chief within the border of Bursa
Forestry Regional Directorate. Change detection analysis was applied to Landsat
5 TM image and Landsat 8 OLI/TIRS image captured in August 2008 and in July
2017, respectively. The spatiotemporal change detection was implemented on
these two images by using various digital image processing techniques
(pre-processing, classification, post-processing, and change detection) through
ERDAS Imagine 2015, ArcGIS 10.5, and ENVI 5.3 program. The supervised
classification, applied on both images using ERDAS Imagine 2015 program,
revealed that there were six significant land use/land cover types in the study
area; linden, other deciduous trees, wetlands, swamp, sand, and other lands
(settlements, agriculture, open areas). The results indicated that there was
increase in the areas of wetlands, sand, and other lands, while the area of
linden forest, other deciduous trees, and swamp decreased from 2008 to 2017.
According to the accuracy assessment results, the classification processes
applied on 2008 and 2017 images provided overall accuracy of 84.38% and 82.81%,
respectively. It is determined that some of the linden forests have been
converted into residential areas and farmlands to grow crops.

References

  • Akay. A.E.. Erdas. O.. Reis. M.. Yuksel. A., 2008. Estimating sediment yield from a forest road network by using a sediment prediction model and GIS techniques. Building and Environment. 43(5):687-695.
  • Akay. A.E.. Sivrikaya. F.. Gulci. S., 2014. Analyzing riparian forest cover changes along the Firniz River in the Mediterranean City of Kahramanmaras in Turkey. Environmental Monitoring and Assessment. 186(5):2741-2747.
  • Akay, A.E., Gencal, B., Taş, İ., 2017. Spatiotemporal change detection using Landsat imagery: the case study of Karacabey flooded forest, Bursa, Turkey. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October, Safranbolu, Karabuk, Turkey.
  • Chupezi. T.J.. Ndoye. O.. Tchatat. M.. Chikamai. B.. 2009. Processing and marketing of non-wood forest products: potential impacts and challenges in Africa. Discov. Innov.. 21(1):60-65.
  • Çakır. G.. Sivrikaya. F.. Terzioğlu. S.. Keleş. S.. Başkent. E.Z.. 2007. Monitoring 30-year Changes in Secondary Forest Succession with Land Cover in Artvin Forest Planning Unit of Northeastern Turkey. Scottish Geographical Journal. 123(3): 209 – 226.
  • Dembner. S.A.. Perlis. A., 1999. Non-wood Forest Products and Income Generation. Unasylva. 50(198).
  • Desai. C.G.. Patil. M.B.. Mahale. V.D.. Umrikar. B.. 2009. Application of remote sensing and geographic information system to study land use/land cover changes: a case study of Pune Metropolis. Advances in Computational Research. 1(2):10-13.
  • Dewan. A.M.. Yamaguchi. Y.. 2009. Land use and land cover change in Greater Dhaka. Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography. 29: 390-401. ERDAS Field Guide, 1999. Fifth Edition, Revised and Expanded. Atlanta, Georgia. 698 p. http://web.pdx.edu/~emch/ip1/FieldGuide.pdf
  • Jong. S.M... Meer. F.D... Clevers. J.G., 2004. Basics of Remote Sensing. In: Jong S.M.D.. Meer F.D.V. (eds) Remote Sensing Image Analysis: Including the Spatial Domain. Remote Sensing and Digital Image Processing. vol 5. Springer. Dordrecht.
  • Lillesand. T.M.. Kiefer. R.W.. 2000. Remote Sensing and Image Interpretation. 4th ed. New York: Wiley.
  • Moller-Jensen. L.. 1997. Classification of Urban land cover based on expert systems. object models and texture. Computers. Environment and Urban Systems. 21(3/4):291-302.
  • Ruelland. D.. Levavasseur. F.. Tribotte. A.. 2010. Patterns and dynamics of land-cover changes since the 1960s over three experimental areas in Mali. International Journal of Applied Earth Observation and Geoinformation. 12(1):11–17.
  • Sivrikaya. F.. Çakır. G.. Keleş. S.. Başkent. E.Z.. 2009. Spatiotemporal Dynamics of Land Use/Land Cover and Timber Carbon Storage: A Case Study from Bulanikdere.Turkey Chapter. Pages:215-247. Geoinformatics for Natural Resource Management. Editors: Joshi. P.K.. Pani. P.. Mohapartra. S.N.. Singh. T.P.. Nova Science Publishers. 634 pp. ISBN: 978-160692-211-8.
  • Tuttu. G.. Ursavaş. S.. Söyler. R.. 2017. Harvest amounts and ethnobotanical uses of the linden flowers in turkey. Anatolian Journal of Forest Research. 3(1): 60-66.
  • Yomralıoğlu. T., 2005. Geographical Information System Basic Terms and Applications. Akademi Publishing. 480 p. ISBN: 975973690x.
There are 15 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Burhan Gencal

İnanç Taş

Abdullah Emin Akay

Publication Date December 28, 2018
Published in Issue Year 2018

Cite

APA Gencal, B., Taş, İ., & Akay, A. E. (2018). Spatiotemporal Change Detection of the Linden Forests in Bursa, Turkey. European Journal of Forest Engineering, 4(2), 50-55. https://doi.org/10.33904/ejfe.495088

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