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Land Use Change Detection Between Tarsus - Karataş in Lower Seyhan Plain with Spectral Angle Mapper Technique

Yıl 2020, Cilt: 6 Sayı: 2, 415 - 430, 01.07.2020

Mamadou Traore , Senem Tekin , Tolga Çan

https://doi.org/10.21324/dacd.660148

Öz

The idea of protecting the nature and natural resources in order to prevent the damages caused by unconscious and uncontrolled use of the land pieces constituting the habitats increases the importance of land use planning. The region between Tarsus (Mersin) and Karataş (Adana) located in the Ramsar protected areas, including the Akyatan wildlife development area, which is one of the ecologically important natural conservation areas in the southern part of Çukurova is selected to be examined the land use changes between 1985, 2000 and 2019 in this study. Landsat-5TM 1985, Landsat-5TM 2000 and Landsat-8 OLI 2019 satellite images with a spatial resolution of 30 m were used in the analyzes. Pretreatment studies, which consist primarily of geometric, radiometric calibration and atmospheric arrangements, have been performed with satellite images. Spectral Angle mapping method was used for land use change detection. According to the results obtained, growth of 192%, 37%, 7% and 8% growth in settlement, non-cultivated agriculture, forest and semi-natural and lagoon / lakes areas between 1985-2019, and 43% and 21% in bare and cultivated agricultural areas Decreases in rates of 21 have occurred. It was determined that the settlements around the Karataş fault increased by 192% between 1985 and 2019. The accuracy of the controlled classification studies was evaluated with kappa statistics and calculated as 0,80, 0,84, 0,87 for 1985, 2000 and 2019, respectively.

Anahtar Kelimeler

Land Use Planning Remote Sensing Landsat Spectral Angle Mapping

Kaynakça

  • Adep R.N., Shetty A., Ramesh H., (2017), EXhype: A tool for mineral classification using hyperspectral data, ISPRS Journal of Photogrammetry and Remote Sensing, 124, 106-118.
  • Agapiou A., Alexakis D.D., Sarris A., Themistocleous K., Papoutsa C., Hadjimitsis D.G., (2014), Satellite-derived land use changes along the Xin'an river watershed for supporting water quality investigation for potential fishing grounds in Qiandao Lake, China Second International Conference on Remote Sensing and Geoinformation of the Environment (Rscy2014) Volume 9229 içinde, (Hadjimitsis D.F., Themistocleous K., Michaelides S., Papadavid G., Ed.), doi: 10.1117/12.2066314.
  • Ahmad N., Ahsan N., Said S., (2019), Land use mapping of Yamuna river flood plain in Delhi using K-Mean and spectral angle image classification algorithms, Water and Energy International, 62(5), 63-68.
  • Anggraeni A., Lin C.S., (2011), Application of SAM and SVM Techniques to Burned Area Detection for Landsat TM Images in Forests of South Sumatra, 2nd International Conference on Environmental Science and Technology, volüme 6 içinde, Singapore, ss.160-164.
  • Aswatha S.M., Mukhopadhyay J., Biswas P.K., (2017), Semi-supervised classification of land cover in multi-spectral images using spectral slopes, Proceedings Ninth International Conference on Advances in Pattern Recognition (Icapr) içinde, Bangalore, India, ss.338-343.
  • Awad M.M., (2018), Forest mapping: a comparison between hyperspectral and multispectral images and technologies, Journal of Forestry Research, 29, 1395-1405.
  • Borges E.F., Sano E.E., (2014), Temporal series of EVI from MODIS sensor for land use and land cover mapping of western Bahia, BCG - Boletim de Ciências Geodésicas, doi:10.1590/S1982-21702014000200030.
  • Carvalho L.M.T., Fonseca L.M.G., Murtagh F., Cleves J.G.P.W., (2001), Digital change detection with the aid of multiresolution wavelet analysis. International Journal of Remote Sensing, 22, 3871–3876.
  • Cohen J.A., (1960), Coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37-46.
  • Dennison P.E., Halligan K.Q., Roberts D.A., (2004), A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper, Remote Sensing of Environment, 93(3), 359-367.
  • Elatawneh A., Manakos I., Kalaitzidis C., Schneider T., (2010), Land-cover classification and unmixing of hyperion image in area of Anopoli, Imagin [E,G] Europe, Proceedings of the 29th Symposium of the European Association of Remote Sensing Laboratories içinde, (Manakos I, Kalaitzidis C., Ed.), Greece, ss. 111-121.
  • Elmahdy S.I., Mohamed M.M., (2016), Land use/land cover change impact on groundwater quantity and quality: a case study of Ajman Emirate, the United Arab Emirates, using remote sensing and GIS, Arabian Journal of Geoscience, doi: 10.1007/s12517-016-2725-y.
  • Emre Ö., Duman T.Y., Özalp S., Şaroğlu F., Olgun Ş., Elmacı H., Çan T., (2018), Active fault database of Turkey, Bulletin of Earthquake Engineering, 16(8), 3229-3275.
  • Fan C., Zhang P., Wang S., Hu B., (2018), A study on classification of mineral pigments based on spectral angle mapper and decision tree, Tenth International Conference on Digital Image Processing (ICDIP 2018) Proceedings Volume 10806 içinde, Shanghai, China, doi:10.1117/12.2503088.
  • Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.
  • Forzieri G., Tanteri L., Moser G., Catani F., (2013), Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies, International Journal of Applied Earth Observation and Geoinformation, 23:313-323.
  • Gannouni S., Rebai N., (2020), Comparative Analysis of the Classification of Maximum Reality (MVS) and the Spectral Angle Mapper (SAM) of an Aster Image. Case Study: Soil Occupancy in the Main Area (Tunisia), In Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development, Springer, Cham, doi:10.1007/978-3-030-21166-0_5.
  • Gopinath G., Sasidharan N., Surendran U., (2020), Landuse classification of hyperspectral data by spectral angle mapper and support vector machine in humid tropical region of India, Earth Science Informatics, doi:10.1007/s12145-019-00438-4 1-8.
  • Gündoğan A.C., Aydın C.İ., Voyvoda E., Turhan E., Özen İ.C., (2017), İklim değişikliği hedeflerine ulaşılamamasının Türkiye’ye maliyeti üzerine ortak sosyoekonomik patikalar perspektifinden bir değerlendirme, WWF-Türkiye, (Berke M.Ö. Ed.), Yeryüzü Derneği Yayınları – 7, 84 ss.
  • Gürsoy, Ö., (2016). Determining the most appropriate classification methods for water quality. In IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 44, No. 2, p. 022038.
  • Helmer E.H., Brown S., Cohen W.B., (2000), Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery, International Journal of Remote Sensing, 21(11), 2163-2183.
  • Huang B., Zhang H.K., Yu L., (2012), Improving Landsat ETM plus urban area mapping via spatial and angular fusion with MISR multi-angle observations, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 5(1), 101–109.
  • Irish R.R., Barker J.L., Goward S.N., Arvidson T., (2006), Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm, Photogrammetric Engineering & Remote Sensing, 72(10), 1179-1188.
  • Islam K., Jashimuddin M., Nath B., Nath T. K., (2017), Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh, The Egyptian Journal of Remote Sensing and Space Sciences, 21(1), 37–47.
  • Karabulut A., Elbaşı F., Ustaoğlu S., Yatman D., (2011), Türkiye büyük toprak grubu haritası, Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü Toprak Gübre ve Su Kaynakları Merkez Araştırma Enstitüsü Müdürlüğü, Mekanizasyon ve Bilişim Teknolojileri Bölümü, Ankara.
  • Karakus P., Karabork H., Kaya S., (2017), A comparison of the classification accuracies in determining the land cover of Kadirli Region of Turkey by using the pixel based and object based classification algorithms, International Journal of Engineering and Geosciences (IJEG), 2(2), 52-60.
  • Kruse F.A., Lefkoff A.B., Boardman J.B., Heidebrecht K.B., Shapiro A.T., Barloon P.J., Goetz A.F.H., (1993), The spectral image processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data, Remote Sensing of Environment 44(2-3), 145-163.
  • Landis J.R., Koch G.G., (1977), The measurement of observer agreement for categorical data, Biometrics, 33(1), 159–174.
  • Luc B., Deronde B., Kempeneers P., Debruyn W., Provoost S., (2005), Optimized spectral angle mapper classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastline, 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS), Beijing, China.
  • Lunetta R. S., and Elvidge C.D., (1998), Remote Sensing change détection, environmental monitoring methods and applications. Ann Arbor Press,Ann Arbor(Michigan) 318 p.
  • Massironi M., Bertoldi L., Calafa P., Visonà D., Bistacchi A., Giardino C., Schiavo A., (2008), Interpretation and processing of ASTER data for geological mapping and granitoids detection in the Saghro massif (eastern AntiAtlas, Morocco). Geosphere, 4, 736−759.
  • Nigussie T.A., Altunkaynak A., (2019), Modeling the effect of urbanization on flood risk in Ayamama Watershed, Istanbul, Turkey, using the MIKE 21 FM model, Natural Hazards, 99, 1031-1047.
  • Olayiwola A.M., Fakayode O., (2019), Landscape metrics analysis of land use patterns and changes in suburban local government areas of Ibadan, Nigeria, South African Journal of Geomatics, 8(2), 209-220.
  • Padmanaban R., Bhowmik A.K., Cabral P., (2019), Satellite image fusion to detect changing surface permeability and emerging urban heat islands in a fast-growing city, Plos One, doi: 10.1371/journal.pone.0208949.
  • Paul B.K., Rashid H., (2017), Land use change and coastal management, Climatic Hazards in Coastal Bangladesh, Non-Structural and Structural Solutions içinde, ss. 183-207, https://doi.org/10.1016/B978-0-12-805276-1.00006-5.
  • Putro S.T., Prasetiyowati S.H., (2019), Challenges in collecting primary data for environmental research purposes: a case study in Parangtritis sand dune, Yogyakarta, First International Conference on Environmental Geography and Geography Education (Icege) 243, doi:10.1088/1755-1315/243/1/012004.
  • Rahman M.K., Schmidlin T.W., Munro-Stasiuk M.J., Curtis A., (2019), Geospatial analysis of land loss, land cover change, and landuse patterns of Kutubdia Island, Bangladesh, Environmental Information Systems: Concepts, Methodologies, Tools, and Applications içinde, doi:10.4018/978-1-5225-7033-2.ch048.
  • Rowan, L. C., Mars, J. C., & Simpson, C. J., (2005). Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote sensing of Environment, 99 (1-2), 105-126.
  • Stow D.A., (1999), Reducing the effects of misregistration on pixel-level change detection, International Journal of Remote Sensing, 20, 2477–2483.
  • Thanikachalam M., Nimalan K., (2019), Investigation and prediction of urban-sprawl and land-use changes for Chennai city using geo-spatial technologies, Indian Journal of Geo-Marine Sciences (IJMS), 48(09), 1443-1451.
  • Tornaghi C., Dehaene M., (2019), The prefigurative power of urban political agroecology: rethinking the urbanisms of agroecological transitions for food system transformation, Agroecology and Sustainable Food Systems, doi:10.1080/21683565.2019.1680593.
  • Ulu Ü., (2009a). 1/100000 Ölçekli Türkiye Jeoloji Haritaları Mersin-O33 Paftası, Maden Tetkik ve Arama Jeoloji Etütleri Dairesi, Ankara.
  • Ulu Ü., (2009b). 1/100000 Ölçekli Türkiye Jeoloji Haritaları Mersin-O34 Paftası, Maden Tetkik ve Arama Jeoloji Etütleri Dairesi, Ankara.
  • URL-1, (2019), CORINE, (Coordination of Information on the Environment), (2018), CORINE Land Cover, https://land.copernicus.eu/pan-european/corine-land-cover, [Erişim 14 Aralık 2019].
  • URL-2, (2019), CORINE (Çevresel Bilginin Koordinasyonu), https://corinecbs.tarimorman.gov.tr/corine, [Erişim 14 Aralık 2019].
  • URL-3, (2020), WorldClim Maps, graphs, tables, and data of the global climate, http://www.worldclim.org/version1, [Erişim 15 Mart 2020].
  • URL-4, (2014), Mekânsal Planlar Yapım Yönetmeliği, Resmî Gazete Tarihi: 14.06.2014 Resmî Gazete Sayısı: 29030, https://www.mevzuat.gov.tr/Metin.Aspx?MevzuatKod=7.5.19788&MevzuatIliski=0&sourceXmlSearcmekan, [Erişim 30 Mart 2020].
  • Uysal M, Turgut B, Polat N, Mehmet A. D , Yalçın M., (2017), Uzaktan algılama teknikleri ile açık maden ocaklarında bor minerallerinin tespiti, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, Özel Sayı, 270-276.
  • Verbyla D.L., Boles S.H., (2000), Bias in land cover change estimates due to misregistration, International Journal of Remote Sensing, 21, 3553–3560.
  • Wiafe E.D, Asamoah G., (2018), Land-cover change assessment in Ejisu –Juabeng district in Ghana, Eurasian Journal of Forest Science, 6(1), 1-8.
  • Yoon E., Thorne J., Park C., Lee D.K., Kim K., Yoon H., Seo C., Lim C.H., Kim H., Song Y., (2019), Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk, Environmental Research Letter, 14(2), 1-10.

Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi

Yıl 2020, Cilt: 6 Sayı: 2, 415 - 430, 01.07.2020

Mamadou Traore , Senem Tekin , Tolga Çan

https://doi.org/10.21324/dacd.660148

Öz

Yaşam alanlarını oluşturan kara parçalarının bilinçsiz ve kontrolsüz kullanımı nedeni ile ortaya çıkan olumsuzlukların, doğada neden olduğu tahribatı engellemeye yönelik doğayı ve doğal kaynakları koruma düşüncesi arazi kullanım planlamalarının önemini artırmaktadır. Bu çalışmada, Çukurova’nın güney kesiminde ekolojik olarak öneme sahip olan doğal koruma alanlarından biri olan Akyatan yaban hayatı geliştirme sahasını da içeren ve Ramsar koruma alanlarının da yer aldığı, bir kısmı ile Seyhan ovası içerisinde bulunan Tarsus (Mersin) ile Karataş (Adana) arasındaki bölgede, 1985, 2000 ve 2019 yılları arasındaki arazi kullanımında meydana gelen değişimler incelenmiştir. Analizlerde 30 m mekânsal çözünürlüğe sahip Landsat-5TM 1985, Landsat-5TM 2000 ve Landsat-8 OLI 2019 uydu görüntüleri kullanılmıştır. Uydu görüntüleriyle öncelikli olarak geometrik, radyometrik kalibrasyon ve atmosferik düzenlemelerden oluşan ön işleme çalışmaları gerçekleştirilmiştir. Arazi kullanım değişim tespitinde Spektral Açı haritalama yöntemi kullanılmıştır. Elde edilen sonuçlara göre 1985-2019 yılları arasında yerleşim, ekili olmayan tarım, orman ve yarı doğal ve lagün/göller alanlarında %192, %37, %7 ve %8’lik büyüme gelişirken, çıplak ve ekili tarım alanlarda ise %43 ve %21’lik oranlarda azalmalar meydana gelmiştir. Aynı zamanda ülkemizde bulunan aktif fay hatlarından biri olan Karataş fayı civarında 500, 1000 ve 2000 m’lik tampon bölgeler içerisinde yerleşim birimlerinde meydana gelen değişimler değerlendirilmiştir. Karataş fayı civarında ise yerleşimin 1985 ile 2019 yılları arasında yapılaşmaların %192 oranında arttığı belirlenmiştir. Yapılan kontrollü sınıflama çalışmalarının doğruluğu kappa istatistiği ile değerlendirilmiş olup 1985, 2000 ve 2019 yılları için sırasıyla 0.80, 0.84, 0.87 olarak hesaplanmıştır.

Anahtar Kelimeler

Arazi Kullanım Planlamaları Uzaktan Algılama Landsat Spektral Açı Haritalama

Kaynakça

  • Adep R.N., Shetty A., Ramesh H., (2017), EXhype: A tool for mineral classification using hyperspectral data, ISPRS Journal of Photogrammetry and Remote Sensing, 124, 106-118.
  • Agapiou A., Alexakis D.D., Sarris A., Themistocleous K., Papoutsa C., Hadjimitsis D.G., (2014), Satellite-derived land use changes along the Xin'an river watershed for supporting water quality investigation for potential fishing grounds in Qiandao Lake, China Second International Conference on Remote Sensing and Geoinformation of the Environment (Rscy2014) Volume 9229 içinde, (Hadjimitsis D.F., Themistocleous K., Michaelides S., Papadavid G., Ed.), doi: 10.1117/12.2066314.
  • Ahmad N., Ahsan N., Said S., (2019), Land use mapping of Yamuna river flood plain in Delhi using K-Mean and spectral angle image classification algorithms, Water and Energy International, 62(5), 63-68.
  • Anggraeni A., Lin C.S., (2011), Application of SAM and SVM Techniques to Burned Area Detection for Landsat TM Images in Forests of South Sumatra, 2nd International Conference on Environmental Science and Technology, volüme 6 içinde, Singapore, ss.160-164.
  • Aswatha S.M., Mukhopadhyay J., Biswas P.K., (2017), Semi-supervised classification of land cover in multi-spectral images using spectral slopes, Proceedings Ninth International Conference on Advances in Pattern Recognition (Icapr) içinde, Bangalore, India, ss.338-343.
  • Awad M.M., (2018), Forest mapping: a comparison between hyperspectral and multispectral images and technologies, Journal of Forestry Research, 29, 1395-1405.
  • Borges E.F., Sano E.E., (2014), Temporal series of EVI from MODIS sensor for land use and land cover mapping of western Bahia, BCG - Boletim de Ciências Geodésicas, doi:10.1590/S1982-21702014000200030.
  • Carvalho L.M.T., Fonseca L.M.G., Murtagh F., Cleves J.G.P.W., (2001), Digital change detection with the aid of multiresolution wavelet analysis. International Journal of Remote Sensing, 22, 3871–3876.
  • Cohen J.A., (1960), Coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37-46.
  • Dennison P.E., Halligan K.Q., Roberts D.A., (2004), A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper, Remote Sensing of Environment, 93(3), 359-367.
  • Elatawneh A., Manakos I., Kalaitzidis C., Schneider T., (2010), Land-cover classification and unmixing of hyperion image in area of Anopoli, Imagin [E,G] Europe, Proceedings of the 29th Symposium of the European Association of Remote Sensing Laboratories içinde, (Manakos I, Kalaitzidis C., Ed.), Greece, ss. 111-121.
  • Elmahdy S.I., Mohamed M.M., (2016), Land use/land cover change impact on groundwater quantity and quality: a case study of Ajman Emirate, the United Arab Emirates, using remote sensing and GIS, Arabian Journal of Geoscience, doi: 10.1007/s12517-016-2725-y.
  • Emre Ö., Duman T.Y., Özalp S., Şaroğlu F., Olgun Ş., Elmacı H., Çan T., (2018), Active fault database of Turkey, Bulletin of Earthquake Engineering, 16(8), 3229-3275.
  • Fan C., Zhang P., Wang S., Hu B., (2018), A study on classification of mineral pigments based on spectral angle mapper and decision tree, Tenth International Conference on Digital Image Processing (ICDIP 2018) Proceedings Volume 10806 içinde, Shanghai, China, doi:10.1117/12.2503088.
  • Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.
  • Forzieri G., Tanteri L., Moser G., Catani F., (2013), Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies, International Journal of Applied Earth Observation and Geoinformation, 23:313-323.
  • Gannouni S., Rebai N., (2020), Comparative Analysis of the Classification of Maximum Reality (MVS) and the Spectral Angle Mapper (SAM) of an Aster Image. Case Study: Soil Occupancy in the Main Area (Tunisia), In Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development, Springer, Cham, doi:10.1007/978-3-030-21166-0_5.
  • Gopinath G., Sasidharan N., Surendran U., (2020), Landuse classification of hyperspectral data by spectral angle mapper and support vector machine in humid tropical region of India, Earth Science Informatics, doi:10.1007/s12145-019-00438-4 1-8.
  • Gündoğan A.C., Aydın C.İ., Voyvoda E., Turhan E., Özen İ.C., (2017), İklim değişikliği hedeflerine ulaşılamamasının Türkiye’ye maliyeti üzerine ortak sosyoekonomik patikalar perspektifinden bir değerlendirme, WWF-Türkiye, (Berke M.Ö. Ed.), Yeryüzü Derneği Yayınları – 7, 84 ss.
  • Gürsoy, Ö., (2016). Determining the most appropriate classification methods for water quality. In IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 44, No. 2, p. 022038.
  • Helmer E.H., Brown S., Cohen W.B., (2000), Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery, International Journal of Remote Sensing, 21(11), 2163-2183.
  • Huang B., Zhang H.K., Yu L., (2012), Improving Landsat ETM plus urban area mapping via spatial and angular fusion with MISR multi-angle observations, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 5(1), 101–109.
  • Irish R.R., Barker J.L., Goward S.N., Arvidson T., (2006), Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm, Photogrammetric Engineering & Remote Sensing, 72(10), 1179-1188.
  • Islam K., Jashimuddin M., Nath B., Nath T. K., (2017), Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh, The Egyptian Journal of Remote Sensing and Space Sciences, 21(1), 37–47.
  • Karabulut A., Elbaşı F., Ustaoğlu S., Yatman D., (2011), Türkiye büyük toprak grubu haritası, Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü Toprak Gübre ve Su Kaynakları Merkez Araştırma Enstitüsü Müdürlüğü, Mekanizasyon ve Bilişim Teknolojileri Bölümü, Ankara.
  • Karakus P., Karabork H., Kaya S., (2017), A comparison of the classification accuracies in determining the land cover of Kadirli Region of Turkey by using the pixel based and object based classification algorithms, International Journal of Engineering and Geosciences (IJEG), 2(2), 52-60.
  • Kruse F.A., Lefkoff A.B., Boardman J.B., Heidebrecht K.B., Shapiro A.T., Barloon P.J., Goetz A.F.H., (1993), The spectral image processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data, Remote Sensing of Environment 44(2-3), 145-163.
  • Landis J.R., Koch G.G., (1977), The measurement of observer agreement for categorical data, Biometrics, 33(1), 159–174.
  • Luc B., Deronde B., Kempeneers P., Debruyn W., Provoost S., (2005), Optimized spectral angle mapper classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastline, 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS), Beijing, China.
  • Lunetta R. S., and Elvidge C.D., (1998), Remote Sensing change détection, environmental monitoring methods and applications. Ann Arbor Press,Ann Arbor(Michigan) 318 p.
  • Massironi M., Bertoldi L., Calafa P., Visonà D., Bistacchi A., Giardino C., Schiavo A., (2008), Interpretation and processing of ASTER data for geological mapping and granitoids detection in the Saghro massif (eastern AntiAtlas, Morocco). Geosphere, 4, 736−759.
  • Nigussie T.A., Altunkaynak A., (2019), Modeling the effect of urbanization on flood risk in Ayamama Watershed, Istanbul, Turkey, using the MIKE 21 FM model, Natural Hazards, 99, 1031-1047.
  • Olayiwola A.M., Fakayode O., (2019), Landscape metrics analysis of land use patterns and changes in suburban local government areas of Ibadan, Nigeria, South African Journal of Geomatics, 8(2), 209-220.
  • Padmanaban R., Bhowmik A.K., Cabral P., (2019), Satellite image fusion to detect changing surface permeability and emerging urban heat islands in a fast-growing city, Plos One, doi: 10.1371/journal.pone.0208949.
  • Paul B.K., Rashid H., (2017), Land use change and coastal management, Climatic Hazards in Coastal Bangladesh, Non-Structural and Structural Solutions içinde, ss. 183-207, https://doi.org/10.1016/B978-0-12-805276-1.00006-5.
  • Putro S.T., Prasetiyowati S.H., (2019), Challenges in collecting primary data for environmental research purposes: a case study in Parangtritis sand dune, Yogyakarta, First International Conference on Environmental Geography and Geography Education (Icege) 243, doi:10.1088/1755-1315/243/1/012004.
  • Rahman M.K., Schmidlin T.W., Munro-Stasiuk M.J., Curtis A., (2019), Geospatial analysis of land loss, land cover change, and landuse patterns of Kutubdia Island, Bangladesh, Environmental Information Systems: Concepts, Methodologies, Tools, and Applications içinde, doi:10.4018/978-1-5225-7033-2.ch048.
  • Rowan, L. C., Mars, J. C., & Simpson, C. J., (2005). Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote sensing of Environment, 99 (1-2), 105-126.
  • Stow D.A., (1999), Reducing the effects of misregistration on pixel-level change detection, International Journal of Remote Sensing, 20, 2477–2483.
  • Thanikachalam M., Nimalan K., (2019), Investigation and prediction of urban-sprawl and land-use changes for Chennai city using geo-spatial technologies, Indian Journal of Geo-Marine Sciences (IJMS), 48(09), 1443-1451.
  • Tornaghi C., Dehaene M., (2019), The prefigurative power of urban political agroecology: rethinking the urbanisms of agroecological transitions for food system transformation, Agroecology and Sustainable Food Systems, doi:10.1080/21683565.2019.1680593.
  • Ulu Ü., (2009a). 1/100000 Ölçekli Türkiye Jeoloji Haritaları Mersin-O33 Paftası, Maden Tetkik ve Arama Jeoloji Etütleri Dairesi, Ankara.
  • Ulu Ü., (2009b). 1/100000 Ölçekli Türkiye Jeoloji Haritaları Mersin-O34 Paftası, Maden Tetkik ve Arama Jeoloji Etütleri Dairesi, Ankara.
  • URL-1, (2019), CORINE, (Coordination of Information on the Environment), (2018), CORINE Land Cover, https://land.copernicus.eu/pan-european/corine-land-cover, [Erişim 14 Aralık 2019].
  • URL-2, (2019), CORINE (Çevresel Bilginin Koordinasyonu), https://corinecbs.tarimorman.gov.tr/corine, [Erişim 14 Aralık 2019].
  • URL-3, (2020), WorldClim Maps, graphs, tables, and data of the global climate, http://www.worldclim.org/version1, [Erişim 15 Mart 2020].
  • URL-4, (2014), Mekânsal Planlar Yapım Yönetmeliği, Resmî Gazete Tarihi: 14.06.2014 Resmî Gazete Sayısı: 29030, https://www.mevzuat.gov.tr/Metin.Aspx?MevzuatKod=7.5.19788&MevzuatIliski=0&sourceXmlSearcmekan, [Erişim 30 Mart 2020].
  • Uysal M, Turgut B, Polat N, Mehmet A. D , Yalçın M., (2017), Uzaktan algılama teknikleri ile açık maden ocaklarında bor minerallerinin tespiti, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, Özel Sayı, 270-276.
  • Verbyla D.L., Boles S.H., (2000), Bias in land cover change estimates due to misregistration, International Journal of Remote Sensing, 21, 3553–3560.
  • Wiafe E.D, Asamoah G., (2018), Land-cover change assessment in Ejisu –Juabeng district in Ghana, Eurasian Journal of Forest Science, 6(1), 1-8.
  • Yoon E., Thorne J., Park C., Lee D.K., Kim K., Yoon H., Seo C., Lim C.H., Kim H., Song Y., (2019), Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk, Environmental Research Letter, 14(2), 1-10.
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makalesi
Yazarlar

Mamadou Traore 0000-0003-0558-1724

Senem Tekin 0000-0001-7734-9700

Tolga Çan 0000-0001-9940-2832

Yayımlanma Tarihi 1 Temmuz 2020
Gönderilme Tarihi 16 Aralık 2019
Kabul Tarihi 29 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 6 Sayı: 2

Kaynak Göster

APA Traore, M., Tekin, S., & Çan, T. (2020). Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi. Doğal Afetler Ve Çevre Dergisi, 6(2), 415-430. https://doi.org/10.21324/dacd.660148
AMA Traore M, Tekin S, Çan T. Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi. Doğ Afet Çev Derg. Temmuz 2020;6(2):415-430. doi:10.21324/dacd.660148
Chicago Traore, Mamadou, Senem Tekin, ve Tolga Çan. “Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi”. Doğal Afetler Ve Çevre Dergisi 6, sy. 2 (Temmuz 2020): 415-30. https://doi.org/10.21324/dacd.660148.
EndNote Traore M, Tekin S, Çan T (01 Temmuz 2020) Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi. Doğal Afetler ve Çevre Dergisi 6 2 415–430.
IEEE M. Traore, S. Tekin, ve T. Çan, “Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi”, Doğ Afet Çev Derg, c. 6, sy. 2, ss. 415–430, 2020, doi: 10.21324/dacd.660148.
ISNAD Traore, Mamadou vd. “Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi”. Doğal Afetler ve Çevre Dergisi 6/2 (Temmuz 2020), 415-430. https://doi.org/10.21324/dacd.660148.
JAMA Traore M, Tekin S, Çan T. Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi. Doğ Afet Çev Derg. 2020;6:415–430.
MLA Traore, Mamadou vd. “Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi”. Doğal Afetler Ve Çevre Dergisi, c. 6, sy. 2, 2020, ss. 415-30, doi:10.21324/dacd.660148.
Vancouver Traore M, Tekin S, Çan T. Spektral Açı Haritalama Tekniği İle Aşağı Seyhan Ovası Tarsus - Karataş Arasının Arazi Değişiminin Belirlenmesi. Doğ Afet Çev Derg. 2020;6(2):415-30.
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