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Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample
Öz
Currently, it is very important to identify and
use the most appropriate methods in the management of limited resources and to
reach a conclusion in a short time period by using the technology in an
effective manner to fastly obtain information in high quality. Remote sensing (RS)
techniques are used as a very effective tool for this purpose. Obtaining
information about various parameters without direct contact with the objects
provides advantages in terms of both time and cost. RS technologies are used in
various different disciplines. One of the most important application areas
where these technologies are used is to monitor urban development by the help
of the satellite images. Determination of urban land use in detail is important
for decision-makers, planners, practitioners and researchers to conduct
effective planning activities. In this study the change in land cover and land
use between the years of 1999 and 2016 in the central district of Kastamonu was
investigated; land use and exchange groups were formed. First, satellite images
of the study area were classified by controlled classification method and their
accuracy was calculated. The classified satellite images are used to model the
probable land area, its usage and changes in 2033 by using Artificial Neural
Networks (ANN) approach. According to this, changes in the field between the
years of 1999 and 2016 are given as follows; 7.8% decrease for forest areas,
10.8% increase for water areas, 13.9% decrease for agricultural areas and 10.9%
increase for construction areas. Based on the results, it was thought that it
is a feasible and practical tool to determine the change of land cover and land
use to predict the course of the future. The ANN approach used in this study is
predicted to become an important decision support system for planners and
decision makers.
Anahtar Kelimeler
Kaynakça
- Blackwell W J, Chen F W (2009). Neural Networks in Atmospheric Remote Sensing. [Boston]: Artech House, Inc.
- Blumenthal R L (2013). Remote Sensing. Salem Press Encyclopedia Of Science
- Zhang Y (2006) Land Surface Temperature Retrieval from CBERS-02 IRMSS Thermal İnfrared Data and its Applications in Quantitative Analysis of Urban Heat Island Effect. J. Remote Sens., 10: 789-797.
- Veldkamp A, Verburg P H (2004). Modelling Land Use Change And Environmental Impact. Journal of Environmental Management, Volume 72, Issues 1–2, Pages 1-3, https://doi.org/ 10.1016/j.jenvman.2004.04.004.
- Watson R T, Noble I R, Bolin B, Ravindranath N H, Verardo D J, Dokken D J (2000). Land use, land-use change and forestry. A Special Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge: Cambridge University.
- Pocewicz A, Nielsen-Pincus M, Goldberg C S, Johnson M H, Morgan P, Force J E, ... & Vierling L (2008). Predicting Land Use Change: Comparison of Models Based on Landowner Surveys and Historical Land Cover Trends. Landscape Ecology, 23(2), 195-210.
- Almeida C M, Gleriani J M, Castejon E F, Soares‐Filho B S (2008). Using Neural Networks and Cellular Automata for Modelling Intra‐Urban Land‐Use Dynamics, International Journal of Geographical Information Science, 22:9, 943-963, DOI: 10.1080/13658810701731168.
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Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
15 Aralık 2018
Gönderilme Tarihi
6 Ekim 2018
Kabul Tarihi
15 Aralık 2018
Yayımlandığı Sayı
Yıl 2018 Cilt: 20 Sayı: 3
APA
Doğan, S., & Buğday, E. (2018). Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample. Bartın Orman Fakültesi Dergisi, 20(3), 653-663. https://izlik.org/JA25TB24YS
AMA
1.Doğan S, Buğday E. Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample. Bartın Orman Fakültesi Dergisi. 2018;20(3):653-663. https://izlik.org/JA25TB24YS
Chicago
Doğan, Samet, ve Ender Buğday. 2018. “Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample”. Bartın Orman Fakültesi Dergisi 20 (3): 653-63. https://izlik.org/JA25TB24YS.
EndNote
Doğan S, Buğday E (01 Aralık 2018) Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample. Bartın Orman Fakültesi Dergisi 20 3 653–663.
IEEE
[1]S. Doğan ve E. Buğday, “Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample”, Bartın Orman Fakültesi Dergisi, c. 20, sy 3, ss. 653–663, Ara. 2018, [çevrimiçi]. Erişim adresi: https://izlik.org/JA25TB24YS
ISNAD
Doğan, Samet - Buğday, Ender. “Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample”. Bartın Orman Fakültesi Dergisi 20/3 (01 Aralık 2018): 653-663. https://izlik.org/JA25TB24YS.
JAMA
1.Doğan S, Buğday E. Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample. Bartın Orman Fakültesi Dergisi. 2018;20:653–663.
MLA
Doğan, Samet, ve Ender Buğday. “Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample”. Bartın Orman Fakültesi Dergisi, c. 20, sy 3, Aralık 2018, ss. 653-6, https://izlik.org/JA25TB24YS.
Vancouver
1.Samet Doğan, Ender Buğday. Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample. Bartın Orman Fakültesi Dergisi [Internet]. 01 Aralık 2018;20(3):653-6. Erişim adresi: https://izlik.org/JA25TB24YS