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Short-Term Change Detection and Markov Chain Prediction of Greenhouse Areas in Alanya, Turkey Using Sentinel-2 Imageries

Yıl 2021, Sayı: 31, 776 - 782, 31.12.2021
https://doi.org/10.31590/ejosat.1019033

Öz

Greenhouses provide controlled growth conditions and possibility off-season production for various agricultural products while there are some reported adverse effects on the environment due to particularly increased plastic waste, changed soil properties, and ecosystem degradation in their extensive use. Monitoring recent status and forecasting future probabilities of greenhouse coverage (ha, %) comprise influential tool for researchers and planners to reach more sustainable and environmental-friendly situations. Present paper deals with detection of short-term changes in greenhouse areas using high resolution Sentinel-2 imageries, and prediction of probable future status via markov chain model within Alanya, Turkey. The changes in greenhouse coverages were evaluated considering initial acquisition year of imageries, and change analyses were conducted between 2015 and 2021 years. Use of a Landsat-derived plastic greenhouse index to discriminate between greenhouse and other surrounding land cover land use (LCLU) types was tested for Sentinel-2. The LCLU2015 and LCLU2021 maps were consisted of five main classes including natural vegetation, open agricultural field, water surface, concrete structure, and greenhouse. Classification accuracies were assessed by checking the actual statuses of 200 equalized random control points using Google Earth application. The changes in LCLU within the major greenhouse located zone were evaluated through post-classification comparison technique. Future greenhouse areas, as well as other LCLU types, were predicted through markov chains for 2027 year by considering the same time interval. Findings have revealed that greenhouse areas have remarkably increased in the last seven years, and have great potential to continue expanding in the near future. Utilization of the index imageries to increase the classification accuracy of greenhouses is recommended.

Teşekkür

The satellite data have been obtained from United States Geological Survey (USGS). The Future Land Use Simulation (FLUS) model was downloaded from Geographical Simulation and Optimization Systems (GeoSOS) website.

Kaynakça

  • Aguilar, M.A., Vallario, A., Aguilar, F.J., Lorca, A.G., Parente, C. (2015). Object-based greenhouse horticultural crop identification from multi-temporal satellite imagery: A case study in Almeria, Spain. Remote Sening, 7, 7378-7401.
  • Burnham, B.O. (1973). Markov intertemporal land use simulation model. Southern Journal of Agricultural Economics., 5, 253-258.
  • Cemek, B., Guler, M., Arslan, H. (2017). Spatial analysis of climate factors used to determine suitability of greenhouse production in Turkey. Theorotical and Applied Climatology, 128, 1-11.
  • Congalton, RG., Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Ed. Lewis Publishers, Boca Raton.
  • Garnaud, J.C. (2000). Plasticulture: Bulletin du comité international des plastiques en agriculture. Plasticulture, 119, 30–43.
  • Hamad, R., Balzter, H., Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10, 3421. https://doi:10.3390/su10103421
  • Jiang, W.J., Yu, H.J. (2008). Present situation and future development for protected horticulture in mainland China. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science, 770, 29-35.
  • Jimenez-Lao, R., Aguilar F.J., Nammaoui, A., Aguilar, M.A. (2020). Remote sensing of agricultural greenhouses and plastic-mulched farmland: An Analysis of Worldwide Research. Remote Sensing, 12, 2649. https://doi:10.3390/rs12162649
  • Koc-San, D. (2013). Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery. Journal of Applied Remote Sensing, 7(1), 073553. https://doi.org/10.1117/1.JRS.7.073553
  • Levin, N., Lugassi, R., Ramon, U., Braun, O., Ben‐Dor, E. (2007). Remote sensing as a tool for monitoring plasticulture in agricultural landscapes. International Journal of Remote Sensing, 28(1), 183-202. https://doi:10.1080/01431160600658156
  • Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., Zhang, C. (2020). A review of remote sensing for environmental monitoring in China. Remote Sensing, 12, 1130.
  • Lu, L., Di, L., Ye, Y.A. (2014). Decision-tree classifier for extracting transparent plastic-mulched land cover from Landsat-5 TM images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, 7, 4548-4558.
  • Ma, A., Chen, D., Zhong, Y., Zheng, Z., Zhang, L. (2021). National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: A case study of China. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 279-294.
  • Novelli, A., Aguilar, M.A., Nemmaoui A., Aguilar, F.J., Tarantino, E. (2016). Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain). International Journal of Applied Earth Observation and Geoinformation, 52, 403-411.
  • Parra, S., Aguilar, F.J., Calatrava, J. (2008). Decision modelling for environmental protection: The contingent valuation method applied to greenhouse waste management. Biosystems Engineering, 99, 469-477.
  • Rezaeiniya, N., Ghadikolaei, A.S., Mehri-Tekmeh, J., Rezaeiniya, H. (2014). Fuzzy ANP approach for new application: greenhouse location selection; a case in Iran. Journal of Mathematics and Computer Science, 8, 1- 20.
  • Saltuk, B. (2019). Determination of greenhouse potential in Siirt Province and districts by using GIS and recommendations to producers. European Journal of Science and Technology, 15, 343-350.
  • Sang, L., Zhang, C., Yang, J., Zhu, D., Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54, 938-943.
  • Sonmez, N.K., Sari, M. (2006). Use of remote sensing and geographic information system technologies for developing greenhouse databases. Turkish Journal Of Agriculture and Forestry, 30, 413-420.
  • Sun, H., Wang, L., Lin, R., Zhang, Z., Zhang, B. (2021). Mapping plastic greenhouses with two-temporal Sentinel-2 images and 1D-CNN deep learning. Remote Sensing, 13, 2820. https://doi.org/10.3390/rs13142820
  • Tarantino, E., Figorito, B. (2012). Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote Sensing, 4(7), 1913-1928. https://doi.org/10.3390/rs4071913
  • Themistocleous, K., Papoutsa, C., Michaelides, S., Hadjimitsis, D. (2020). Investigating detection of floating plastic litter from space using Sentinel-2 Imagery. Remote Sensing, 12, 2648.
  • Thompson, R.B., Padilla, F.M., Peña-Fleitas, M.T., Gallardo, M. (2020). Reducing nitrate leaching losses from vegetable production in Mediterranean greenhouses. Acta Horticulture, 1268, 105-117.
  • Wu, C.F., Deng, J.S., Wang, K., Ma, L.G., Tahmassebi, A.R.S. (2016). Object-based classification approach for greenhouse mapping using Landsat-8 imagery. International Journal of Agricultural and Biological Engineering, 9(1), 79-88.
  • Yang D., Chen, Z., Zhou, Y., Chen, X., Chen, X., Cao, X., (2017). Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 47-60.

Sentinel-2 Görüntüleri Kullanılarak Alanya, Türkiye’ deki Örtüaltı Alanlarının Kısa Dönem Değişiminin Belirlenmesi ve Markov Zinciri Tahminlemesi

Yıl 2021, Sayı: 31, 776 - 782, 31.12.2021
https://doi.org/10.31590/ejosat.1019033

Öz

Örtüaltı yetiştiriciliği birçok tarımsal ürün için kontrollü büyüme şartları ve mevsimden bağımsız üretim olanağı sağlarken, yoğun kullanılmaları durumunda artan plastik atıklar, değişen toprak özellikleri ve çevresel bozunumdan dolayı rapor edilmiş bazı olumsuz etkileri de bulunmaktadır. Daha sürdürülebilir ve çevre dostu koşullara ulaşmak için örtüaltı yetiştiriciliği alanlarının (ha, %) hâlihazırdaki durumunun izlenmesi ve gelecekteki olası alanların tahminlenmesi, araştırmacılar ve planlayıcılar için etkili araçlardır. Bu çalışma Alanya’ da yer alan örtüaltı yetiştiriciliği yapılan alanlarda kısa dönemde meydana gelen değişimlerin yüksek çözünürlüklü Sentinel-2 görüntüleri kullanılarak belirlenmesi ve gelecekteki olası durumun markov zinciri modeli aracılığı ile tahminlenmesine odaklanmıştır. Örtüaltı alanlarındaki değişimler, görüntülerin ilk alındığı tarih gözetilerek değerlendirilmiş ve değişim analizleri 2015 ile 2021 yılları arasında yürütülmüştür. Landsattan türetilmiş bir plastik örtüaltı indeksinin, örtüaltı alanları ve etrafındaki diğer arazi örtüsü ve arazi kullanımı (AÖAK) tiplerinin birbirinden ayrılabilmesinde kullanımı Sentinel-2 için test edilmiştir. AÖAK2015 ve AÖAK2021 haritaları doğal vejetasyon, açık tarım alanı, su yüzeyi, betonarme yapılar ve örtüaltı sınıfları olmak üzere beş ana sınıftan oluşmuştur. Sınıflama doğrulukları sınıflardan rastgele ve eşit miktarda atanan 200 kontrol noktasının doğruluğu Google Earth uygulaması ile kontrol edilerek değerlendirilmiştir. Örtüaltı alanlarının bulunduğu ana zonda meydana gelen AÖAK değişimleri sınıflama sonrası karşılaştırma tekniği ile değerlendirilmiştir. Gelecekteki örtüaltı alanları, diğer AÖAK sınıflarında olduğu üzere, markov zinciri modeli ile ayrı yıl aralığı göz önünde bulundurularak 2027 yılı için tahminlenmiştir. Bulgular, geçtiğimiz yedi yılda sera alanlarının fark edilebilir biçimde arttığını ve yakın gelecekte de gelecekte de bu alanların büyük bir genişleme potansiyelinin bulunduğunu göstermiştir. Örtüaltı alanların sınıflama doğruluğunun artırılması için türetilen indeks görüntülerinin kullanımı önerilmiştir.

Kaynakça

  • Aguilar, M.A., Vallario, A., Aguilar, F.J., Lorca, A.G., Parente, C. (2015). Object-based greenhouse horticultural crop identification from multi-temporal satellite imagery: A case study in Almeria, Spain. Remote Sening, 7, 7378-7401.
  • Burnham, B.O. (1973). Markov intertemporal land use simulation model. Southern Journal of Agricultural Economics., 5, 253-258.
  • Cemek, B., Guler, M., Arslan, H. (2017). Spatial analysis of climate factors used to determine suitability of greenhouse production in Turkey. Theorotical and Applied Climatology, 128, 1-11.
  • Congalton, RG., Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Ed. Lewis Publishers, Boca Raton.
  • Garnaud, J.C. (2000). Plasticulture: Bulletin du comité international des plastiques en agriculture. Plasticulture, 119, 30–43.
  • Hamad, R., Balzter, H., Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10, 3421. https://doi:10.3390/su10103421
  • Jiang, W.J., Yu, H.J. (2008). Present situation and future development for protected horticulture in mainland China. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science, 770, 29-35.
  • Jimenez-Lao, R., Aguilar F.J., Nammaoui, A., Aguilar, M.A. (2020). Remote sensing of agricultural greenhouses and plastic-mulched farmland: An Analysis of Worldwide Research. Remote Sensing, 12, 2649. https://doi:10.3390/rs12162649
  • Koc-San, D. (2013). Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery. Journal of Applied Remote Sensing, 7(1), 073553. https://doi.org/10.1117/1.JRS.7.073553
  • Levin, N., Lugassi, R., Ramon, U., Braun, O., Ben‐Dor, E. (2007). Remote sensing as a tool for monitoring plasticulture in agricultural landscapes. International Journal of Remote Sensing, 28(1), 183-202. https://doi:10.1080/01431160600658156
  • Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., Zhang, C. (2020). A review of remote sensing for environmental monitoring in China. Remote Sensing, 12, 1130.
  • Lu, L., Di, L., Ye, Y.A. (2014). Decision-tree classifier for extracting transparent plastic-mulched land cover from Landsat-5 TM images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, 7, 4548-4558.
  • Ma, A., Chen, D., Zhong, Y., Zheng, Z., Zhang, L. (2021). National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: A case study of China. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 279-294.
  • Novelli, A., Aguilar, M.A., Nemmaoui A., Aguilar, F.J., Tarantino, E. (2016). Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain). International Journal of Applied Earth Observation and Geoinformation, 52, 403-411.
  • Parra, S., Aguilar, F.J., Calatrava, J. (2008). Decision modelling for environmental protection: The contingent valuation method applied to greenhouse waste management. Biosystems Engineering, 99, 469-477.
  • Rezaeiniya, N., Ghadikolaei, A.S., Mehri-Tekmeh, J., Rezaeiniya, H. (2014). Fuzzy ANP approach for new application: greenhouse location selection; a case in Iran. Journal of Mathematics and Computer Science, 8, 1- 20.
  • Saltuk, B. (2019). Determination of greenhouse potential in Siirt Province and districts by using GIS and recommendations to producers. European Journal of Science and Technology, 15, 343-350.
  • Sang, L., Zhang, C., Yang, J., Zhu, D., Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54, 938-943.
  • Sonmez, N.K., Sari, M. (2006). Use of remote sensing and geographic information system technologies for developing greenhouse databases. Turkish Journal Of Agriculture and Forestry, 30, 413-420.
  • Sun, H., Wang, L., Lin, R., Zhang, Z., Zhang, B. (2021). Mapping plastic greenhouses with two-temporal Sentinel-2 images and 1D-CNN deep learning. Remote Sensing, 13, 2820. https://doi.org/10.3390/rs13142820
  • Tarantino, E., Figorito, B. (2012). Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote Sensing, 4(7), 1913-1928. https://doi.org/10.3390/rs4071913
  • Themistocleous, K., Papoutsa, C., Michaelides, S., Hadjimitsis, D. (2020). Investigating detection of floating plastic litter from space using Sentinel-2 Imagery. Remote Sensing, 12, 2648.
  • Thompson, R.B., Padilla, F.M., Peña-Fleitas, M.T., Gallardo, M. (2020). Reducing nitrate leaching losses from vegetable production in Mediterranean greenhouses. Acta Horticulture, 1268, 105-117.
  • Wu, C.F., Deng, J.S., Wang, K., Ma, L.G., Tahmassebi, A.R.S. (2016). Object-based classification approach for greenhouse mapping using Landsat-8 imagery. International Journal of Agricultural and Biological Engineering, 9(1), 79-88.
  • Yang D., Chen, Z., Zhou, Y., Chen, X., Chen, X., Cao, X., (2017). Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 47-60.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Melis İnalpulat 0000-0001-7418-1666

Levent Genç 0000-0002-0074-0987

Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 31

Kaynak Göster

APA İnalpulat, M., & Genç, L. (2021). Short-Term Change Detection and Markov Chain Prediction of Greenhouse Areas in Alanya, Turkey Using Sentinel-2 Imageries. Avrupa Bilim Ve Teknoloji Dergisi(31), 776-782. https://doi.org/10.31590/ejosat.1019033