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Agricultural Land Suitability Analysis for Suğla Lake from Multispectral PlanetScope Satellite Data Between 2017–2023

Yıl 2025, Cilt: 7 Sayı: 1, 28 - 36, 30.06.2025
https://doi.org/10.53030/tufod.1711754

Öz

This study aims to evaluate the sustainability of agricultural areas surrounding Suğla Lake, located in Konya Province, by integrating the Agricultural Land Suitability Analysis (ALSA) approach with remote sensing techniques. Spectral indices such as NDVI, NDWI, EVI, GCI, and SAVI were calculated using PlanetScope satellite imagery from 2017 to 2023. Spatial changes were assessed through a comprehensive approach involving Principal Component Analysis (PCA) and Object-Based Image Analysis (OBIA). Temporal analyses supported by imagery from Google Earth revealed significant annual fluctuations in vegetation density over and around the lake surface. In this context, the data from 2020 and 2023 indicate increased vegetation stress, higher proportions of bare soil, and notable changes in agricultural production patterns. The observed changes around the lake appear to have considerable impacts on both agricultural activities and the ecological balance of surrounding wetlands. The multiyear evaluation of spectral index data using the ALSA method in the Suğla Lake area addresses a notable gap in the literature and stands out as one of the pioneering comprehensive studies in this context. The findings demonstrate that the ALSA approach can be utilized as an effective decision support tool in developing sustainable land use scenarios and in the early detection of environmental risks.

Kaynakça

  • Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
  • Akosman, E. N., & Makineci, H. B. (2023). Sentinel-2A Verileriyle Trabzon İli 2019-2020 Yılları Arasında Ortaya Çıkan Sınıflandırma Farklarının Çeşitli Algoritmalarla Değerlendirilmesi. Türkiye Uzaktan Algılama Dergisi, 5(2), 78-88.
  • Alawathugoda, C., Hinge, G., Elkollaly, M., & Hamouda, M. A. (2024). Impact of Utilizing High-Resolution PlanetScope Imagery on the Accuracy of LULC Mapping and Hydrological Modeling in an Arid Region. Water, 16(16), 2356.
  • Blaschke, T. (2010). Object-based image analysis for remote sensing. International Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
  • Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical methods, 6(9), 2812-2831.
  • Chanda, M., & Hossain, A. A. (2024). Application of PlanetScope Imagery for Flood Mapping: A Case Study in South Chickamauga Creek, Chattanooga, Tennessee. Remote Sensing, 16(23), 4437.
  • Choudhary, K., et al. (2023). Agricultural land suitability assessment for sustainable development using remote sensing techniques with analytic hierarchy process. Environmental Modelling & Software, 32, 101051.
  • Cilbiz, M., et al. (2022). Suğla Gölü (Konya-Türkiye) Sudak Balığı (Sander lucioperca Linnaeus, 1758) avcılığında sade uzatma ağı seçiciliği. Journal of Fisheries Science, 6(1), 110–115.
  • Coşkun, M., Minaz, D. J., & G. Education. (2024). Suğla Gölü (Konya) alansal değişiminin (1984/2022) uzaktan algılama ve CBS teknikleriyle analizleri. Journal of Geography Education, 52, 141–158.
  • Dadon, A., et al. (2019). Sequential PCA-based classification of Mediterranean forest plants using airborne hyperspectral remote sensing. Remote Sensing, 11(23), 2800.
  • Darwish, K., & Smith, S. (2021). A comparison of Landsat-8 OLI, Sentinel-2 MSI and PlanetScope satellite imagery for assessing coastline change in El-Alamein, Egypt. Earth Perspectives, 10(1), 23.
  • Demšar, U., Harris, P., Brunsdon, C., Fotheringham, A. S., & McLoone, S. (2013). Principal component analysis on spatial data: an overview. Annals of the Association of American Geographers, 103(1), 106-128.
  • Dong, J., Qin, Y., Wang, J., Zhou, Y., Chen, Y., & Cui, Y. (2018). Accuracy assessment of land cover maps based on remote sensing images using the support vector machine classifier. IEEE Access, 6, 52384–52396.
  • Duru, S. (2025). Sürdürülebilir tarım ve iklim değişikliği. Muş Alparslan Üniversitesi Tarım ve Doğa Dergisi, 5(1), 21–32.
  • Gao, M., Liu, J. L., & Li, J. (2013). Change detection using principal component analysis and support vector machines in remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 51, 3794–3803.
  • Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International journal of remote sensing, 18(12), 2691-2697.
  • Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100.
  • Hasbek, M., & Yiğit, A. (2025). “Gölümüz var ama suyumuz yok”: Suğla Gölü’nün (Konya) kültürel ekolojisi. Uludağ Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, 26(48), 149–177.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213.
  • Kuzucuoğlu, C. (2002). Internal developments and external relations during the 9th–6th millennia cal BC. In The Environmental Frame in Central Anatolia from the 9th to the 6th Millennia cal BC (pp. 33–58). Ege Yayınları.
  • Maki̇neci̇, H. B., & Arıkan, D. (2024). Seyfe lake seasonal drought analysis for the winter and summer periods between 2017 and 2022. Remote Sensing Applications: Society and Environment, 34, 101172.
  • Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of high resolution Google Earth satellite imagery in landuse map preparation for urban related applications. Procedia Technology, 24, 1835–1842.
  • McFeeters, S. K. (2013). Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: a practical approach. Remote Sensing, 5(7), 3544-3561.
  • Orhan, O., & Makineci, H. B. (2022). Agricultural land suitability analysis. In Encyclopedia of Smart Agriculture Technologies (pp. 1-9). Cham: Springer International Publishing.
  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222.
  • Rao, P., Zhou, W., Bhattarai, N., Srivastava, A. K., Singh, B., Poonia, S., ... & Jain, M. (2021). Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms. Remote Sensing, 13(10), 1870.
  • Rijal, S. S., Pham, T. D., Noer’Aulia, S., Putera, M. I., & Saintilan, N. (2023). Mapping mangrove above-ground carbon using multi-source remote sensing data and machine learning approach in Loh Buaya, Komodo National Park, Indonesia. Forests, 14(1), 94.
  • Rodarmel, C., Shan, J., & Science, L. I. (2002). Principal component analysis for hyperspectral image classification. Photogrammetric Engineering & Remote Sensing, 62(2), 115–122.
  • Rouse Jr, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. NASA-CR-132982).
  • Singh, R. P., Roy, S., & Kogan, F. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing, 24(22), 4393–4402.
  • Szostak, M., Likus-Cieślik, J., & Pietrzykowski, M. (2021). PlanetScope imageries and LiDAR point clouds processing for automation land cover mapping and vegetation assessment of a reclaimed sulfur mine. Remote Sensing, 13(14), 2717.
  • Thenkabail, P. S., et al. (2016). Hyperspectral remote sensing for terrestrial applications. In Remote Sensing Handbook, Volume III (pp. 285–358). CRC Press.
  • Tsai, F., Lin, E. K., & Yoshino, K. (2007). Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species. International Journal of Remote Sensing, 28(5), 1023–1039.
  • Wang, Y., Liu, Y., Wang, X., & Wei, Y. (2019). Accuracy assessment of land cover mapping using remote sensing data and machine learning methods. ISPRS International Journal of Geo-Information, 8(10), 457.
  • Wibowo, A., et al. (2016). Spatial temporal land use change detection using Google Earth data. In IOP Conference Series: Earth and Environmental Science. IOP Publishing.
  • Yan, G., Fan, W., & Huang, H. (2016). Land use/cover classification using remote sensing images based on a hybrid method of fuzzy C-means clustering and object-based approach. Arabian Journal of Geosciences, 9(4), 301.
  • Zhang, M., Liu, J., Wang, Y., & Zhang, F. (2020). Evaluation of different vegetation indices for mapping vegetation cover in grassland ecosystems. Remote Sensing, 12(16), 2611.
  • Zhao, Y., Qiu, S., Yang, X., Gao, T., & Lin, H. (2017). A study on remote sensing classification of land use/cover in arid mountain areas based on spectral index. International Journal of Applied Earth Observation and Geoinformation, 62, 145–157.

Suğla Gölü için 2017-2023 Yılları Arasında Multispektral PlanetScope Uydu Verileri ile Tarımsal Arazi Uygunluk Analizi (ALSA)

Yıl 2025, Cilt: 7 Sayı: 1, 28 - 36, 30.06.2025
https://doi.org/10.53030/tufod.1711754

Öz

Bu çalışma, Konya ilinde yer alan Suğla Gölü çevresindeki tarım alanlarının sürdürülebilirliğini değerlendirmek amacıyla, Tarımsal Arazi Uygunluk Analizi (ALSA) yaklaşımının uzaktan algılama teknikleriyle entegrasyonunu hedeflemektedir. 2017–2023 yılları arasına ait PlanetScope uydu görüntüleri kullanılarak NDVI, NDWI, EVI, GCI ve SAVI gibi spektral indeksler hesaplanmış; mekânsal değişimler, Temel Bileşen Analizi (PCA) ve Nesneye Dayalı Görüntü Analizi (OBIA) yöntemleriyle bütüncül bir yaklaşımla değerlendirilmiştir. Google Earth üzerinden elde edilen görüntülerle desteklenen zamansal analizler, göl yüzeyi ve çevresindeki bitki örtüsü yoğunluğunda yıllık bazda önemli dalgalanmalar yaşandığını ortaya koymaktadır. Bu bağlamda, 2020 ve 2023 yıllarına ait veriler, bitkisel stresin arttığını, çıplak toprak oranının yükseldiğini ve tarımsal üretim deseninde belirgin değişiklikler yaşandığını göstermektedir. Göl çevresinde gözlemlenen bu değişimlerin hem tarımsal faaliyetler hem de sulak alanların ekolojik dengesi üzerinde dikkate değer etkiler yarattığı görülmektedir. Suğla Gölü çevresinde çok yıllı spektral indeks verilerinin ALSA yöntemiyle kapsamlı biçimde değerlendirilmesi, literatürdeki önemli bir boşluğu doldurmakta ve bu kapsamda detaylı olarak ele alınan öncü çalışmalardan biri olarak öne çıkmaktadır. Elde edilen bulgular, ALSA yaklaşımının sürdürülebilir arazi kullanım senaryolarının geliştirilmesinde ve çevresel risklerin erken tespitinde etkili bir karar destek aracı olarak kullanılabileceğini göstermektedir.

Kaynakça

  • Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
  • Akosman, E. N., & Makineci, H. B. (2023). Sentinel-2A Verileriyle Trabzon İli 2019-2020 Yılları Arasında Ortaya Çıkan Sınıflandırma Farklarının Çeşitli Algoritmalarla Değerlendirilmesi. Türkiye Uzaktan Algılama Dergisi, 5(2), 78-88.
  • Alawathugoda, C., Hinge, G., Elkollaly, M., & Hamouda, M. A. (2024). Impact of Utilizing High-Resolution PlanetScope Imagery on the Accuracy of LULC Mapping and Hydrological Modeling in an Arid Region. Water, 16(16), 2356.
  • Blaschke, T. (2010). Object-based image analysis for remote sensing. International Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
  • Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical methods, 6(9), 2812-2831.
  • Chanda, M., & Hossain, A. A. (2024). Application of PlanetScope Imagery for Flood Mapping: A Case Study in South Chickamauga Creek, Chattanooga, Tennessee. Remote Sensing, 16(23), 4437.
  • Choudhary, K., et al. (2023). Agricultural land suitability assessment for sustainable development using remote sensing techniques with analytic hierarchy process. Environmental Modelling & Software, 32, 101051.
  • Cilbiz, M., et al. (2022). Suğla Gölü (Konya-Türkiye) Sudak Balığı (Sander lucioperca Linnaeus, 1758) avcılığında sade uzatma ağı seçiciliği. Journal of Fisheries Science, 6(1), 110–115.
  • Coşkun, M., Minaz, D. J., & G. Education. (2024). Suğla Gölü (Konya) alansal değişiminin (1984/2022) uzaktan algılama ve CBS teknikleriyle analizleri. Journal of Geography Education, 52, 141–158.
  • Dadon, A., et al. (2019). Sequential PCA-based classification of Mediterranean forest plants using airborne hyperspectral remote sensing. Remote Sensing, 11(23), 2800.
  • Darwish, K., & Smith, S. (2021). A comparison of Landsat-8 OLI, Sentinel-2 MSI and PlanetScope satellite imagery for assessing coastline change in El-Alamein, Egypt. Earth Perspectives, 10(1), 23.
  • Demšar, U., Harris, P., Brunsdon, C., Fotheringham, A. S., & McLoone, S. (2013). Principal component analysis on spatial data: an overview. Annals of the Association of American Geographers, 103(1), 106-128.
  • Dong, J., Qin, Y., Wang, J., Zhou, Y., Chen, Y., & Cui, Y. (2018). Accuracy assessment of land cover maps based on remote sensing images using the support vector machine classifier. IEEE Access, 6, 52384–52396.
  • Duru, S. (2025). Sürdürülebilir tarım ve iklim değişikliği. Muş Alparslan Üniversitesi Tarım ve Doğa Dergisi, 5(1), 21–32.
  • Gao, M., Liu, J. L., & Li, J. (2013). Change detection using principal component analysis and support vector machines in remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 51, 3794–3803.
  • Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International journal of remote sensing, 18(12), 2691-2697.
  • Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100.
  • Hasbek, M., & Yiğit, A. (2025). “Gölümüz var ama suyumuz yok”: Suğla Gölü’nün (Konya) kültürel ekolojisi. Uludağ Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, 26(48), 149–177.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213.
  • Kuzucuoğlu, C. (2002). Internal developments and external relations during the 9th–6th millennia cal BC. In The Environmental Frame in Central Anatolia from the 9th to the 6th Millennia cal BC (pp. 33–58). Ege Yayınları.
  • Maki̇neci̇, H. B., & Arıkan, D. (2024). Seyfe lake seasonal drought analysis for the winter and summer periods between 2017 and 2022. Remote Sensing Applications: Society and Environment, 34, 101172.
  • Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of high resolution Google Earth satellite imagery in landuse map preparation for urban related applications. Procedia Technology, 24, 1835–1842.
  • McFeeters, S. K. (2013). Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: a practical approach. Remote Sensing, 5(7), 3544-3561.
  • Orhan, O., & Makineci, H. B. (2022). Agricultural land suitability analysis. In Encyclopedia of Smart Agriculture Technologies (pp. 1-9). Cham: Springer International Publishing.
  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222.
  • Rao, P., Zhou, W., Bhattarai, N., Srivastava, A. K., Singh, B., Poonia, S., ... & Jain, M. (2021). Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms. Remote Sensing, 13(10), 1870.
  • Rijal, S. S., Pham, T. D., Noer’Aulia, S., Putera, M. I., & Saintilan, N. (2023). Mapping mangrove above-ground carbon using multi-source remote sensing data and machine learning approach in Loh Buaya, Komodo National Park, Indonesia. Forests, 14(1), 94.
  • Rodarmel, C., Shan, J., & Science, L. I. (2002). Principal component analysis for hyperspectral image classification. Photogrammetric Engineering & Remote Sensing, 62(2), 115–122.
  • Rouse Jr, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. NASA-CR-132982).
  • Singh, R. P., Roy, S., & Kogan, F. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing, 24(22), 4393–4402.
  • Szostak, M., Likus-Cieślik, J., & Pietrzykowski, M. (2021). PlanetScope imageries and LiDAR point clouds processing for automation land cover mapping and vegetation assessment of a reclaimed sulfur mine. Remote Sensing, 13(14), 2717.
  • Thenkabail, P. S., et al. (2016). Hyperspectral remote sensing for terrestrial applications. In Remote Sensing Handbook, Volume III (pp. 285–358). CRC Press.
  • Tsai, F., Lin, E. K., & Yoshino, K. (2007). Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species. International Journal of Remote Sensing, 28(5), 1023–1039.
  • Wang, Y., Liu, Y., Wang, X., & Wei, Y. (2019). Accuracy assessment of land cover mapping using remote sensing data and machine learning methods. ISPRS International Journal of Geo-Information, 8(10), 457.
  • Wibowo, A., et al. (2016). Spatial temporal land use change detection using Google Earth data. In IOP Conference Series: Earth and Environmental Science. IOP Publishing.
  • Yan, G., Fan, W., & Huang, H. (2016). Land use/cover classification using remote sensing images based on a hybrid method of fuzzy C-means clustering and object-based approach. Arabian Journal of Geosciences, 9(4), 301.
  • Zhang, M., Liu, J., Wang, Y., & Zhang, F. (2020). Evaluation of different vegetation indices for mapping vegetation cover in grassland ecosystems. Remote Sensing, 12(16), 2611.
  • Zhao, Y., Qiu, S., Yang, X., Gao, T., & Lin, H. (2017). A study on remote sensing classification of land use/cover in arid mountain areas based on spectral index. International Journal of Applied Earth Observation and Geoinformation, 62, 145–157.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

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

Büşranur Güvercin 0009-0007-1451-5573

Hasan Bilgehan Makineci 0000-0003-3627-5826

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 1 Haziran 2025
Kabul Tarihi 30 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 1

Kaynak Göster

APA Güvercin, B., & Makineci, H. B. (2025). Suğla Gölü için 2017-2023 Yılları Arasında Multispektral PlanetScope Uydu Verileri ile Tarımsal Arazi Uygunluk Analizi (ALSA). Türkiye Fotogrametri Dergisi, 7(1), 28-36. https://doi.org/10.53030/tufod.1711754