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Evaluation Of The Relationship Between Spatial-Temporal Changes Of Land Use/Land Cover (Lulc) And Land Surface Temperature (Lst): A Case Study Of Nilüfer, Bursa

Yıl 2023, Cilt: 6 Sayı: 1, 56 - 74, 30.08.2023

Tuğba Üstün Topal

https://doi.org/10.51552/peyad.1346845

Öz

This study was carried out in Nilüfer district of Bursa in order to reveal the extent of urbanization, to monitor the changes in landscape elements such as water, vegetation and agricultural lands, and to examine the effects of this on Land Surface Temperature (LST). For this purpose, images taken by Sentinel-2 satellites in 2017 and 2022 were used. With these images, NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), mNDWI (Modified Normalized Difference Water Index) and NDBI (Normalized Difference Built-up Index), which are widely used in understanding terrain changes, were calculated. Time series analyzes were made between the relevant years The relationship between the changes in the field and the surface temperature was questioned by calculating the LST value with Landsat 8 OLI_TIRS images, and the relations between the indexes and the LST were evaluated by correlation analysis. The results show that NDVI, SAVI, and mNDWI are on a decreasing trend between 2017-2022, while NDBI is on an increasing trend. In other words, the results showed that the vegetation areas and water-covered surfaces decreased, while the built-up areas increased. It has been observed that the changes in Land Use/Land Cover (LULC) increase the LST in the west and south regions of the district.

Anahtar Kelimeler

NDVI SAVI mNDWI NDBI GEE Bursa

Kaynakça

  • Aksoy, E., & Özsoy, G. (2002, June). Investigation of multi-temporal land use/cover and shoreline changes of the Uluabat Lake Ramsar Site using RS and GIS. In Proceedings of the International Conference on Sustainable Land Use and Management. 73-79.
  • Akyürek, Ö. (2020). Termal Uzaktan Algılama Görüntüleri İle Yüzey Sıcaklıklarının Belirlenmesi: Kocaeli Örneği. Doğal Afetler ve Çevre Dergisi, 6(2), 377–390.
  • Alademomi, A. S., Okolie, C. J., Daramola, O. E., Akinnusi, S. A., Adediran, E., Olanrewaju, H. O., Alabi, A. O., Salami, T. J., & Odumosu, J. (2022). The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria. Applied Geomatics, 14(2), 299–314.
  • Alex, E., Ramesh, K., & Hari, S. (2017). Quantification and understanding the observed changes in land cover patterns in Bangalore. International Journal of Civil Engineering and Technology, 8, 597–603.
  • Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350.
  • Anonymous. (2023). Nüfus, Konum, İklim ve Coğrafya https://www.bursa.com.tr/tr/sayfa/nufus-konum-iklim-ve-cografya-47/ Access date: 31.03.2023
  • Ashok, A., Rani, H. P., & Jayakumar, K. V. (2021). Monitoring of dynamic wetland changes using NDVI and NDWI based landsat imagery. Remote Sensing Applications: Society and Environment, 23, 100547.
  • Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 1-8. e1480307.
  • Bouhennache, R., Bouden, T., Taleb-Ahmed, A., & Cheddad, A. (2018). A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. 34(14), 1531–1551.
  • Bramhe, V. S., Ghosh, S. K., & Garg, P. K. (2018). Extraction of Built-Up Area By Combining Textural Features and Spectral Indices From Landsat-8 Multispectral Image. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–5, 727–733.
  • Chen, X., & Zhang, Y. (2017). Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society, 32, 87–99.
  • Chen, X.L., Zhao, H.M., Li, P.X., & Yin, Z.Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133–146.
  • Copernicus Open Access Hub. (2023). Sentinel-2 images. https://scihub.copernicus.eu/dhus/#/home Access date: 31.03.2023
  • Değerli, B., & Çetin, M. (2022). Evaluation from rural to urban scale for the effect of NDVI-NDBI indices on land surface temperature, in Samsun, Türkiye. Turkish Journal of Agriculture-Food Science and Technology, 10(12), 2446-2452.
  • Ekumah, B., Armah, F. A., Afrifa, E. K. A., Aheto, D. W., Odoi, J. O., & Afitiri, A. R. (2020). Geospatial assessment of ecosystem health of coastal urban wetlands in Ghana. Ocean & Coastal Management, 193. 105226.
  • Estoque, R. C., & Murayama, Y. (2015). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205–217.
  • Floreano, I. X., & de Moraes, L. A. F. (2021). Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, 193(4), 239.
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
  • Google Earth Engine (GEE). (2023). https://code.earthengine.google.com/ Sentinel-2 MSI: MultiSpectral Instrument. Access date: 30.03.2023
  • Güneş, C., Pekkan, E., & Tün, M. (2021). Eskişehir Kent Merkezinde Yer Alan Üniversite Kampüslerindeki Kentsel Isı Adası Etkilerinin LANDSAT-8 Uydu Görüntüleri Üzerinden Araştırılması. Ulusal Çevre Bilimleri Araştırma Dergisi, 4(1), 22–32.
  • Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Monitoring the effect of urban development on urban heat island based on remote sensing and geo-spatial approach in Kolkata and adjacent areas, India. Sustainable Cities and Society, 74, 103186.
  • Hay Chung, L. C., Xie, J., & Ren, C. (2021). Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine. Building and Environment, 199, 107879.
  • He, C., Shi, P., Xie, D., & Zhao, Y. (2010). Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters, 1(4), 213–221.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
  • Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H. M., Ali, M., Masood, N., Amin, A., Farid, H. U., Sultana, S. R., Fahad, S., Wang, D., & Nasim, W. (2020). Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan. Environmental Science and Pollution Research, 27(32), 39676–39692.
  • Jamei, Y., Rajagopalan, P., & Sun, Q. (Chayn). (2019). Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of The Total Environment, 659, 1335–1351.
  • Kaimaris, D., & Patias, P. (2016). Identification and Area Measurement of the Built-up Area with the Built-up Index (BUI). International Journal of Advanced Remote Sensing and GIS, 5(1), 1844–1858.
  • Keerthi Naidu, B. N., & Chundeli, F. A. (2023). Assessing LULC changes and LST through NDVI and NDBI spatial indicators: A case of Bengaluru, India. GeoJournal, 88(4), 4335–4350.
  • Korkut, A., Gültürk, P., & Üstün Topal, T. (2016). Kentsel Peyzaj Yapılarında Zemin Geçirimliliği Üzerine Bir Araştırma: Tekirdağ Örneği. Kastamonu Üniversitesi Orman Fakültesi Dergisi, 16(2), 412-422.
  • Kumari, B., Tayyab, M., Shahfahad, Salman, Mallick, J., Khan, M. F., & Rahman, A. (2018). Satellite-Driven Land Surface Temperature (LST) Using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) Data and Its Association with Built-Up and Green Cover Over Urban Delhi, India. Remote Sensing in Earth Systems Sciences, 1(3), 63–78.
  • Li, K., & Chen, Y. (2018). A Genetic Algorithm-Based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization. Remote Sensing, 10(2), 277.
  • Liu, L., & Zhang, Y. (2011). Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sensing, 3(7), 1535-1552.
  • Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13(24), 13758.
  • Majeed, M., Tariq, A., Anwar, M. M., Khan, A. M., Arshad, F., Mumtaz, F., Farhan, M., Zhang, L., Zafar, A., Aziz, M., Abbasi, S., Rahman, G., Hussain, S., Waheed, M., Fatima, K., & Shaukat, S. (2021). Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data. Land, 10(10), 1026.
  • Malik, M. S., Shukla, J. P., & Mishra, S. (2019). Relationship of LST, NDBI and NDVI using landsat-8 data in Kandaihimmat watershed, Hoshangabad, India. Indian Journal of Geo Marine Sciences, 48 (01), 25-31.
  • Mutanga, O., & Kumar, L. (2019). Google Earth Engine Applications. Remote Sensing, 11(5), 591. Nilüfer Municipality. (2023a). 2022-2024 Strategic Plan. https://www.nilufer.bel.tr/i/pdf/83.pdf, Access date: 26.08.2023.
  • Nilüfer Municipality. (2023b). Nilüfer Municipality new neighborhood boundaries. https://www.nilufer.bel.tr/ Access date: 26.08.2023
  • OpenStreetMap (2023). Available online: https://www.openstreetmap.org/, Access date: 26.08.2023
  • Ranagalage, M., Estoque, R. C., & Murayama, Y. (2017). An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017). ISPRS International Journal of Geo-Information, 6(7), 189.
  • Rhyma, P. P., Norizah, K., Hamdan, O., Faridah-Hanum, I., & Zulfa, A. W. (2020). Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sensing Applications: Society and Environment, 17, 100280.
  • Rouse, Jr. J. W., Haas, R. H., Schell, J. A., & Deering, W., D. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. (No. E75-10354).
  • Saçın Y. (2010). Investigation of The Kocacay Delta and Uluabat Lake By Using Remote Sensing Methods. Master's thesis, Balıkesir University, Institute of Science, Department of Civil Engineering, Balıkesir, Turkey.
  • Saini, V. (2021). Mapping Environmental Impacts of Rapid Urbanisation and Deriving Relationship between NDVI, NDBI and Surface Temperature: A Case Study. IOP Conference Series: Earth and Environmental Science, 940(1), 012005.
  • Sarp, G., & Erener, A. (2017). Barajların Çevresel Etkilerinin Zamansal ve Mekansal Olarak Uzaktan Algılama İle Değerlendirilmesi: Atatürk Barajı Örneği. Geomatik Dergisi Journal of Geomatics, 2(1), 1–11.
  • Shah, S. A., Kiran, M., Nazir, A., & Ashrafani, S. H. (2022). Exploring NDVI and NDBI Relationship Using Landsat 8 OLI/TIRS in Khangarh Taluka, Ghotki. Malaysian Journal of Geosciences, 6(1), 08–11.
  • Shahfahad, Kumari, B., Tayyab, M., Ahmed, I. A., Baig, M. R. I., Khan, M. F., & Rahman, A. (2020). Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences, 13(19), 1040.
  • Singh, K. V., Setia, R., Sahoo, S., Prasad, A., & Pateriya, B. (2015). Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level. Geocarto International, 30(6), 650–661.
  • Sun, Q., Wu, Z., & Tan, J. (2012). The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65(6), 1687–1694.
  • Tağıl, Ş. (2004, September). Landuse & Landcover Change of Uluabat Wetland Using Remore Sensing and GIS. In Turkey 9th ESRI and ERDAS Users Group Meeting, 21-22.
  • Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170.
  • Tassi, A., & Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sensing, 12(22), Article 22.
  • Tonyaloğlu, E. E. (2019). Kentleşmenin kentsel termal çevre üzerindeki etkisinin değerlendirilmesi, efeler ve İncirliova (Aydın) örneği. Türkiye Peyzaj Araştırmaları Dergisi, 2(1), 1-13.
  • Topal, T. U., & Baykal, T.M. (2023). Monitoring the changes of Lake Uluabat Ramsar site and its surroundings in the 1985-2021 period using RS and GIS methods. Global Nest Journal, 25(3), 103-114.
  • Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11), 1395–1416.
  • Türkiye İstatistik Kurumu (TÜİK). (2023). İstatistik Göstergeler. İl Göstergeleri, Toplam Nüfus. 2017 yılı İl ve İlçe Nüfusları. https://biruni.tuik.gov.tr/ilgosterge/?locale=tr Access date: 04.04.2023.
  • United Nations. (2014). World urbanization prospects: The 2014 revision, highlights. Department of Economic and Social Affairs.
  • United States Geological Survey (USGS). (2023). EarthExplorer – Home. https://earthexplorer.usgs.gov/ Access date: 31.03.2023
  • Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, 112002.
  • Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 225–244.
  • Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033.
  • Yamak, B., Yağci, Z., Bilgilioğlu, B. B., & Çömert, R. (2021). Investigation of the effect of urbanization on land surface temperature example of Bursa. International Journal of Engineering and Geosciences, 6(1), 1-8.
  • Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022). Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sensing 2022, Vol. 14, Page 3253, 14(14), 3253.
  • Zha, Y., Gao, J., & Ni, S. (2010). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International journal of remote sensing, 24(3), 583-594.
  • Zhang, Y., Odeh, I. O. A., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264.
  • Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of google earth and google earth engine. Remote Sensing, 13(18), 3778.
  • Zheng, Y., Tang, L., & Wang, H. (2021). An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328, 129488.
  • Zuhairi, A., Nur Syahira Azlyn, A., Nur Suhaila, M. R., & Mohd Zaini, M. (2020). Land Use Classification and Mapping Using Landsat Imagery for GIS Database in Langkawi Island. Science Heritage Journal, 4(2), 59–63.

Arazi Kullanımı/Arazi Örtüsü (AK/AÖ)’nün Mekansal-Zamansal Değişimleri İle Yer Yüzey Sıcaklığı (YYS) Arasındaki İlişkinin Değerlendirilmesi: Nilüfer, Bursa Örneği

Yıl 2023, Cilt: 6 Sayı: 1, 56 - 74, 30.08.2023

Tuğba Üstün Topal

https://doi.org/10.51552/peyad.1346845

Öz

Bursa’nın Nilüfer ilçesinde gerçekleştirilen bu çalışma, kentleşmenin boyutlarını ortaya koymak, su, vejetasyon, tarım arazileri gibi peyzaj öğelerinin değişimlerini izlemek, ve bunun Yer Yüzey Sıcaklığı (YYS) üzerindeki etkilerini incelemek amacıyla gerçekleştirilmiştir. Bunun için Google Earth Engine (GEE) ve Coğrafi Bilgi Sistemleri (CBS)’den yararlanılmıştır. Çalışmada Sentinel-2 uyduları tarafından çekilen 2017 ve 2022 yıllarındaki görüntüler kullanılmıştır. Bu görüntüler ile arazi değişimlerini anlamada yaygın olarak kullanılan spektral indekslerden NDVI (Normalize Edilmiş Fark Bitki Örtüsü İndeksi), SAVI (Toprakla Düzeltilmiş Bitki Örtüsü İndeksi), mNDWI (Modifiye Edilmiş Normalize Fark Su İndeksi) ve NDBI (Normalize Edilmiş Fark Yerleşim Alanı Indeksi) hesaplanmıştır. İlgili yıllar arası zaman serisi analizleri yapılmıştır. Arazideki değişimlerin yüzey sıcaklığı ile nasıl bir ilişkisi olduğu Landsat 8 OLI_TIRS görüntüleri ile YYS değeri hesaplanarak sorgulanmış ve indeksler ile YYS arasındaki ilişkiler korelasyon analizi ile değerlendirilmiştir. Sonuçlar 2017-2022 yılları arasında NDVI, SAVI ve mNDWI’nin azalma trendinde olduğunu göstermekte, buna karşılık NDBI’nin ise artma trendinde olduğunu göstermektedir. Yani sonuçlar, vejetasyon alanlarının ve su ile kaplı yüzeylerin azaldığını, yapılaşmış alanların ise arttığını göstermiştir. Arazi Kullanımı/Arazi Örtüsü (AKAÖ)’ndeki değişimlerin ilçenin batı ve güney bölgelerinde YYS’yi arttırdığı görülmüştür.

Anahtar Kelimeler

NDVI SAVI mNDWI NDBI GEE Bursa

Kaynakça

  • Aksoy, E., & Özsoy, G. (2002, June). Investigation of multi-temporal land use/cover and shoreline changes of the Uluabat Lake Ramsar Site using RS and GIS. In Proceedings of the International Conference on Sustainable Land Use and Management. 73-79.
  • Akyürek, Ö. (2020). Termal Uzaktan Algılama Görüntüleri İle Yüzey Sıcaklıklarının Belirlenmesi: Kocaeli Örneği. Doğal Afetler ve Çevre Dergisi, 6(2), 377–390.
  • Alademomi, A. S., Okolie, C. J., Daramola, O. E., Akinnusi, S. A., Adediran, E., Olanrewaju, H. O., Alabi, A. O., Salami, T. J., & Odumosu, J. (2022). The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria. Applied Geomatics, 14(2), 299–314.
  • Alex, E., Ramesh, K., & Hari, S. (2017). Quantification and understanding the observed changes in land cover patterns in Bangalore. International Journal of Civil Engineering and Technology, 8, 597–603.
  • Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350.
  • Anonymous. (2023). Nüfus, Konum, İklim ve Coğrafya https://www.bursa.com.tr/tr/sayfa/nufus-konum-iklim-ve-cografya-47/ Access date: 31.03.2023
  • Ashok, A., Rani, H. P., & Jayakumar, K. V. (2021). Monitoring of dynamic wetland changes using NDVI and NDWI based landsat imagery. Remote Sensing Applications: Society and Environment, 23, 100547.
  • Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 1-8. e1480307.
  • Bouhennache, R., Bouden, T., Taleb-Ahmed, A., & Cheddad, A. (2018). A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. 34(14), 1531–1551.
  • Bramhe, V. S., Ghosh, S. K., & Garg, P. K. (2018). Extraction of Built-Up Area By Combining Textural Features and Spectral Indices From Landsat-8 Multispectral Image. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–5, 727–733.
  • Chen, X., & Zhang, Y. (2017). Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society, 32, 87–99.
  • Chen, X.L., Zhao, H.M., Li, P.X., & Yin, Z.Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133–146.
  • Copernicus Open Access Hub. (2023). Sentinel-2 images. https://scihub.copernicus.eu/dhus/#/home Access date: 31.03.2023
  • Değerli, B., & Çetin, M. (2022). Evaluation from rural to urban scale for the effect of NDVI-NDBI indices on land surface temperature, in Samsun, Türkiye. Turkish Journal of Agriculture-Food Science and Technology, 10(12), 2446-2452.
  • Ekumah, B., Armah, F. A., Afrifa, E. K. A., Aheto, D. W., Odoi, J. O., & Afitiri, A. R. (2020). Geospatial assessment of ecosystem health of coastal urban wetlands in Ghana. Ocean & Coastal Management, 193. 105226.
  • Estoque, R. C., & Murayama, Y. (2015). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205–217.
  • Floreano, I. X., & de Moraes, L. A. F. (2021). Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, 193(4), 239.
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
  • Google Earth Engine (GEE). (2023). https://code.earthengine.google.com/ Sentinel-2 MSI: MultiSpectral Instrument. Access date: 30.03.2023
  • Güneş, C., Pekkan, E., & Tün, M. (2021). Eskişehir Kent Merkezinde Yer Alan Üniversite Kampüslerindeki Kentsel Isı Adası Etkilerinin LANDSAT-8 Uydu Görüntüleri Üzerinden Araştırılması. Ulusal Çevre Bilimleri Araştırma Dergisi, 4(1), 22–32.
  • Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Monitoring the effect of urban development on urban heat island based on remote sensing and geo-spatial approach in Kolkata and adjacent areas, India. Sustainable Cities and Society, 74, 103186.
  • Hay Chung, L. C., Xie, J., & Ren, C. (2021). Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine. Building and Environment, 199, 107879.
  • He, C., Shi, P., Xie, D., & Zhao, Y. (2010). Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters, 1(4), 213–221.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
  • Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H. M., Ali, M., Masood, N., Amin, A., Farid, H. U., Sultana, S. R., Fahad, S., Wang, D., & Nasim, W. (2020). Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan. Environmental Science and Pollution Research, 27(32), 39676–39692.
  • Jamei, Y., Rajagopalan, P., & Sun, Q. (Chayn). (2019). Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of The Total Environment, 659, 1335–1351.
  • Kaimaris, D., & Patias, P. (2016). Identification and Area Measurement of the Built-up Area with the Built-up Index (BUI). International Journal of Advanced Remote Sensing and GIS, 5(1), 1844–1858.
  • Keerthi Naidu, B. N., & Chundeli, F. A. (2023). Assessing LULC changes and LST through NDVI and NDBI spatial indicators: A case of Bengaluru, India. GeoJournal, 88(4), 4335–4350.
  • Korkut, A., Gültürk, P., & Üstün Topal, T. (2016). Kentsel Peyzaj Yapılarında Zemin Geçirimliliği Üzerine Bir Araştırma: Tekirdağ Örneği. Kastamonu Üniversitesi Orman Fakültesi Dergisi, 16(2), 412-422.
  • Kumari, B., Tayyab, M., Shahfahad, Salman, Mallick, J., Khan, M. F., & Rahman, A. (2018). Satellite-Driven Land Surface Temperature (LST) Using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) Data and Its Association with Built-Up and Green Cover Over Urban Delhi, India. Remote Sensing in Earth Systems Sciences, 1(3), 63–78.
  • Li, K., & Chen, Y. (2018). A Genetic Algorithm-Based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization. Remote Sensing, 10(2), 277.
  • Liu, L., & Zhang, Y. (2011). Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sensing, 3(7), 1535-1552.
  • Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13(24), 13758.
  • Majeed, M., Tariq, A., Anwar, M. M., Khan, A. M., Arshad, F., Mumtaz, F., Farhan, M., Zhang, L., Zafar, A., Aziz, M., Abbasi, S., Rahman, G., Hussain, S., Waheed, M., Fatima, K., & Shaukat, S. (2021). Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data. Land, 10(10), 1026.
  • Malik, M. S., Shukla, J. P., & Mishra, S. (2019). Relationship of LST, NDBI and NDVI using landsat-8 data in Kandaihimmat watershed, Hoshangabad, India. Indian Journal of Geo Marine Sciences, 48 (01), 25-31.
  • Mutanga, O., & Kumar, L. (2019). Google Earth Engine Applications. Remote Sensing, 11(5), 591. Nilüfer Municipality. (2023a). 2022-2024 Strategic Plan. https://www.nilufer.bel.tr/i/pdf/83.pdf, Access date: 26.08.2023.
  • Nilüfer Municipality. (2023b). Nilüfer Municipality new neighborhood boundaries. https://www.nilufer.bel.tr/ Access date: 26.08.2023
  • OpenStreetMap (2023). Available online: https://www.openstreetmap.org/, Access date: 26.08.2023
  • Ranagalage, M., Estoque, R. C., & Murayama, Y. (2017). An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017). ISPRS International Journal of Geo-Information, 6(7), 189.
  • Rhyma, P. P., Norizah, K., Hamdan, O., Faridah-Hanum, I., & Zulfa, A. W. (2020). Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sensing Applications: Society and Environment, 17, 100280.
  • Rouse, Jr. J. W., Haas, R. H., Schell, J. A., & Deering, W., D. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. (No. E75-10354).
  • Saçın Y. (2010). Investigation of The Kocacay Delta and Uluabat Lake By Using Remote Sensing Methods. Master's thesis, Balıkesir University, Institute of Science, Department of Civil Engineering, Balıkesir, Turkey.
  • Saini, V. (2021). Mapping Environmental Impacts of Rapid Urbanisation and Deriving Relationship between NDVI, NDBI and Surface Temperature: A Case Study. IOP Conference Series: Earth and Environmental Science, 940(1), 012005.
  • Sarp, G., & Erener, A. (2017). Barajların Çevresel Etkilerinin Zamansal ve Mekansal Olarak Uzaktan Algılama İle Değerlendirilmesi: Atatürk Barajı Örneği. Geomatik Dergisi Journal of Geomatics, 2(1), 1–11.
  • Shah, S. A., Kiran, M., Nazir, A., & Ashrafani, S. H. (2022). Exploring NDVI and NDBI Relationship Using Landsat 8 OLI/TIRS in Khangarh Taluka, Ghotki. Malaysian Journal of Geosciences, 6(1), 08–11.
  • Shahfahad, Kumari, B., Tayyab, M., Ahmed, I. A., Baig, M. R. I., Khan, M. F., & Rahman, A. (2020). Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences, 13(19), 1040.
  • Singh, K. V., Setia, R., Sahoo, S., Prasad, A., & Pateriya, B. (2015). Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level. Geocarto International, 30(6), 650–661.
  • Sun, Q., Wu, Z., & Tan, J. (2012). The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65(6), 1687–1694.
  • Tağıl, Ş. (2004, September). Landuse & Landcover Change of Uluabat Wetland Using Remore Sensing and GIS. In Turkey 9th ESRI and ERDAS Users Group Meeting, 21-22.
  • Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170.
  • Tassi, A., & Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sensing, 12(22), Article 22.
  • Tonyaloğlu, E. E. (2019). Kentleşmenin kentsel termal çevre üzerindeki etkisinin değerlendirilmesi, efeler ve İncirliova (Aydın) örneği. Türkiye Peyzaj Araştırmaları Dergisi, 2(1), 1-13.
  • Topal, T. U., & Baykal, T.M. (2023). Monitoring the changes of Lake Uluabat Ramsar site and its surroundings in the 1985-2021 period using RS and GIS methods. Global Nest Journal, 25(3), 103-114.
  • Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11), 1395–1416.
  • Türkiye İstatistik Kurumu (TÜİK). (2023). İstatistik Göstergeler. İl Göstergeleri, Toplam Nüfus. 2017 yılı İl ve İlçe Nüfusları. https://biruni.tuik.gov.tr/ilgosterge/?locale=tr Access date: 04.04.2023.
  • United Nations. (2014). World urbanization prospects: The 2014 revision, highlights. Department of Economic and Social Affairs.
  • United States Geological Survey (USGS). (2023). EarthExplorer – Home. https://earthexplorer.usgs.gov/ Access date: 31.03.2023
  • Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, 112002.
  • Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 225–244.
  • Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033.
  • Yamak, B., Yağci, Z., Bilgilioğlu, B. B., & Çömert, R. (2021). Investigation of the effect of urbanization on land surface temperature example of Bursa. International Journal of Engineering and Geosciences, 6(1), 1-8.
  • Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022). Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sensing 2022, Vol. 14, Page 3253, 14(14), 3253.
  • Zha, Y., Gao, J., & Ni, S. (2010). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International journal of remote sensing, 24(3), 583-594.
  • Zhang, Y., Odeh, I. O. A., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264.
  • Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of google earth and google earth engine. Remote Sensing, 13(18), 3778.
  • Zheng, Y., Tang, L., & Wang, H. (2021). An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328, 129488.
  • Zuhairi, A., Nur Syahira Azlyn, A., Nur Suhaila, M. R., & Mohd Zaini, M. (2020). Land Use Classification and Mapping Using Landsat Imagery for GIS Database in Langkawi Island. Science Heritage Journal, 4(2), 59–63.
Toplam 67 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Peyzaj Mimarlığında Arazi ve Su Kaynakları, Peyzaj Mimarlığında Bilgisayar Teknolojileri, Peyzaj Planlama
Bölüm Makaleler
Yazarlar

Tuğba Üstün Topal 0000-0002-9687-927X

Erken Görünüm Tarihi 30 Ağustos 2023
Yayımlanma Tarihi 30 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 6 Sayı: 1

Kaynak Göster

APA Üstün Topal, T. (2023). Evaluation Of The Relationship Between Spatial-Temporal Changes Of Land Use/Land Cover (Lulc) And Land Surface Temperature (Lst): A Case Study Of Nilüfer, Bursa. Türkiye Peyzaj Araştırmaları Dergisi, 6(1), 56-74. https://doi.org/10.51552/peyad.1346845
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