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Year 2023, Volume: 8 Issue: 3, 277 - 289, 15.10.2023

Çağan Alevkayalı Onur Yayla Yıldırım Atayeter

https://doi.org/10.26833/ijeg.1149738
Cited By: 2

Abstract

References

  • Parise, M., Gabrovsek, F., Kaufmann, G., & Ravbar, N. (2018). Recent advances in karst research: from theory to fieldwork and applications. Geological Society, London, Special Publications, 466(1), 1-24.
  • Erinc, S. (2002). Jeomorfoloji II. İstanbul: DER Yayınları.
  • Theilen-Willige, B., Ait Malek, H., Charif, A., El Bchari, F., & Chaïbi, M. (2014). Remote sensing and GIS contribution to the investigation of karst landscapes in NW-Morocco. Geosciences, 4(2), 50-72.
  • Jiang, Z., Lian, Y., & Qin, X. (2014). Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Science Reviews, 132, 1-12.
  • Zhang, X., Shang, K., Cen, Y., Shuai, T., & Sun, Y. (2014). Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. International Journal of Applied Earth Observation and Geoinformation, 31, 86-94.
  • Ekmekçi, M. (2005). Karst in Turkish Thrace: compatibility between geological history and karst type. Turkish Journal of Earth Sciences, 14(1), 73-90.
  • Qi, X., Zhang, C., & Wang, K. (2019). Comparing remote sensing methods for monitoring karst rocky desertification at sub-pixel scales in a highly heterogeneous karst region. Scientific reports, 9(1), 1-12.
  • Yue, Y. M., Wang, K. L., Liu, B., Li, R., Zhang, B., Chen, H. S., & Zhang, M. Y. (2013). Development of new remote sensing methods for mapping green vegetation and exposed bedrock fractions within heterogeneous landscapes. International journal of remote sensing, 34(14), 5136-5153.
  • Yue, Y. M., Wang, K. L., Zhang, B., Jiao, Q. J., Liu, B., & Zhang, M. Y. (2012). Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment. Procedia Environmental Sciences, 13, 847-853.
  • Zhang, C., Qi, X., Wang, K., Zhang, M., & Yue, Y. (2017). The application of geospatial techniques in monitoring karst vegetation recovery in southwest China: A review. Progress in Physical Geography, 41(4), 450-477.
  • Xiong, Y. J., Qiu, G. Y., Mo, D. K., Lin, H., Sun, H., Wang, Q. X., ... & Yin, J. (2009). Rocky desertification and its causes in karst areas: a case study in Yongshun County, Hunan Province, China. Environmental Geology, 57, 1481-1488.
  • Liu, Y., Wang, J., & Deng, X. (2008). Rocky land desertification and its driving forces in the karst areas of rural Guangxi, Southwest China. Journal of Mountain Science, 5, 350-357.
  • Yang, W., Chu, W., & Zhou, L. (2019). Evaluating the impact of karst rocky desertification on regional climate in Southwest China with WRF. Theoretical and Applied Climatology, 137, 481-492.
  • Pei, J., Wang, L., Huang, N., Geng, J., Cao, J., & Niu, Z. (2018). Analysis of Landsat-8 OLI imagery for estimating exposed bedrock fractions in typical karst regions of Southwest China using a karst bare-rock index. Remote Sensing, 10(9), 1321.
  • Bai, X. Y., Wang, S. J., & Xiong, K. N. (2013). Assessing spatial‐temporal evolution processes of karst rocky desertification land: indications for restoration strategies. Land Degradation & Development, 24(1), 47-56.
  • Li, S., & Wu, H. (2015). Mapping karst rocky desertification using Landsat 8 images. Remote sensing letters, 6(9), 657-666.
  • Guha, S., & Govil, H. (2020). Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series. International Journal of Engineering and Geosciences, 7(1), 9-16.
  • Wang, H., Li, Q., Du, X., & Zhao, L. (2018). Quantitative extraction of the bedrock exposure rate based on unmanned aerial vehicle data and Landsat-8 OLI image in a karst environment. Frontiers of Earth Science, 12, 481-490.
  • Xu, E. Q., Zhang, H. Q., & Li, M. X. (2015). Object‐based mapping of karst rocky desertification using a support vector machine. Land Degradation & Development, 26(2), 158-167.
  • Kaynarca, M., Demir, N., & San, B. T. (2020). Yeraltı Suyu Kaynaklarının Uzaktan Algılama ve CBS Teknikleri Kullanarak Modellenmesine Yönelik bir Yaklaşım: Kırkgöz Havzası (Antalya). Geomatik, 5(3), 241-245.
  • Reis, H. Ç., & YılancI, G. (2020). Destek vektör makineleri ve NDVI kullanarak pamuk ekili alanların tespiti: Harran ovası örneği. Türkiye Uzaktan Algılama Dergisi, 2(1), 29-41.
  • Şimşek, M., Utlu, M., Poyraz, M., & Öztürk, M. Z. (2019). Geyik Dağı kütlesinin yüzey karstı jeomorfolojisi ve kütle üzerindeki karst-buzul jeomorfolojisi ilişkisi. Ege Coğrafya Dergisi, 28(2), 97-110.
  • Tokgözlü, A., & Özkan, E. (2018). Taşkın risk haritalarında AHP yönteminin uygulanması: Aksu Çayı Havzası örneği. Süleyman Demirel Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, (44), 151-176.
  • Atayeter, Y. (2005). Aksu Çayı havzası'nın jeomorfolojisi. Fakülte Kitabevi.
  • Karatepe, Y., Özçelik, R., Gürlevik, N. E. V. Z. A. T., Yavuz, H., & Kiriş, R. (2014). Batı Akdeniz’de farklı yetişme ortamı bölgelerindeki kızılçam (Pinus brutia Ten.) ormanlarının vejetasyon yapısının ekolojik değerlendirilmesi. Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi, 15(1), 1-8.
  • Atayeter, Y. (2011). Eğirdir Gölü Depresyonu ve Yakın Çevresinin Fiziki Coğrafya Özellikleri. Isparta. Fakülte Yayınları
  • Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172.
  • Sefercik, U. G., Ateşoğlu, A., & Atalay, C. (2021). Orman meşcere yükseklik haritası üretiminde hava kaynaklı lazer tarama performans analizi. Geomatik, 6(3), 179-188.
  • Genç, M., Kasarcı, E., & Kaya, C. (2012). Meşcere Kuruluşu Araştırmaları Üzerine Silvikültürel Bir Değerlendirme. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 13(2), 291-303.
  • Huang, Q., & Cai, Y. (2009). Mapping karst rock in Southwest China. Mountain Research and Development, 29(1), 14-20.
  • Xie, X., Du, P., Xia, J., & Luo, J. (2015). Spectral indices for estimating exposed carbonate rock fraction in karst areas of southwest China. IEEE Geoscience and Remote Sensing Letters, 12(9), 1988-1992.
  • Sabuncu, A., & Ozener, H. (2019). Detection of burned areas by remote sensing techniques: İzmir Seferihisar Forest fire case study. Journal of Natural Hazards and Environment, 5(2), 317-326.
  • Xu, D., Kang, X., Qiu, D., Zhuang, D., & Pan, J. (2009). Quantitative assessment of desertification using Landsat data on a regional scale–a case study in the Ordos Plateau, China. Sensors, 9(3), 1738-1753.
  • Somers, B., Asner, G. P., Tits, L., & Coppin, P. (2011). Endmember variability in spectral mixture analysis: A review. Remote Sensing of Environment, 115(7), 1603-1616.
  • Bedini, E., Van Der Meer, F., & Van Ruitenbeek, F. (2009). Use of HyMap imaging spectrometer data to map mineralogy in the Rodalquilar caldera, southeast Spain. International Journal of Remote Sensing, 30(2), 327-348.
  • Lippitt, C. L., Stow, D. A., Roberts, D. A., & Coulter, L. L. (2018). Multidate MESMA for monitoring vegetation growth forms in southern California shrublands. International journal of remote sensing, 39(3), 655-683.
  • Powell, R. L., & Roberts, D. A. (2008). Characterizing variability of the urban physical environment for a suite of cities in Rondonia, Brazil. Earth Interactions, 12(13), 1-32.
  • Moughal, T. A. (2013, June). Hyperspectral image classification using support vector machine. In journal of physics: conference series (Vol. 439, No. 1, p. 012042). IOP Publishing.
  • Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis. Object-based image analysis: Spatial concepts for knowledge-driven remote sensing applications, 663-677.
  • Benbahria, Z., Sebari, I., Hajji, H., & Smiej, M. F. (2021). Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning. International Journal of Engineering and Geosciences, 6(1), 40-50.
  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on geoscience and remote sensing, 42(8), 1778-1790.
  • Pu, J., Zhao, X., Dong, P., Wang, Q., & Yue, Q. (2021). Extracting information on rocky desertification from satellite images: A comparative study. Remote Sensing, 13(13), 2497.
  • Wang, S. J., Liu, Q. M., & Zhang, D. F. (2004). Karst rocky desertification in southwestern China: geomorphology, landuse, impact and rehabilitation. Land degradation & development, 15(2), 115-121.
  • Xu, E., Zhang, H., & Li, M. (2013). Mining spatial information to investigate the evolution of karst rocky desertification and its human driving forces in Changshun, China. Science of the Total Environment, 458, 419-426.
  • Dai, G., Sun, H., Wang, B., Huang, C., Wang, W., Yao, Y., ... & Zhang, Z. (2021). Assessment of karst rocky desertification from the local to regional scale based on unmanned aerial vehicle images: A case‐study of Shilin County, Yunnan Province, China. Land Degradation & Development, 32(18), 5253-5266.
  • Zhang, Y., Tian, Y., Li, Y., Wang, D., Tao, J., Yang, Y., ... & Wu, L. (2022). Machine learning algorithm for estimating karst rocky desertification in a peak-cluster depression basin in southwest Guangxi, China. Scientific Reports, 12(1), 19121.
  • Dindaroğlu, T., & Çelik, H. (2019). Yeşil kuşak orman ekosistemlerindeki orman parçalılığının bazı toprak özellikleri üzerindeki etkilerinin araştırılması (Kahramanmaraş Ahir dağı örneği). Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 22(2), 322-332.
  • Chong, G., Hai, Y., Zheng, H., Xu, W., & Ouyang, Z. (2021). Characteristics of changes in karst rocky desertification in southtern and western china and driving mechanisms. Chinese Geographical Science, 31, 1082-1096.
  • Haktanir, K., Karaca, A., & Omar, S. M. (2004). The prospects of the impact of desertification on Turkey, Lebanon, Syria and Iraq. In Environmental Challenges in the Mediterranean 2000–2050: Proceedings of the NATO Advanced Research Workshop on Environmental Challenges in the Mediterranean 2000–2050 Madrid, Spain 2–5 October 2002 (pp. 139-154). Springer Netherlands.
  • Ballut, C., & Faivre, S. (2012). New data on the dolines of Velebit Mountain: An evaluation of their sedimentary archive potential in the reconstruction of landscape evolution. Acta Carsologica, 41(1).
  • Şimşek, M., Öztürk, M. Z., Doğan, U., & Mustafa, U. T. L. U. (2021). Toros polyelerinin morfometrik özellikleri. Coğrafya Dergisi, (42), 101-119.

Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine

Year 2023, Volume: 8 Issue: 3, 277 - 289, 15.10.2023

Çağan Alevkayalı Onur Yayla Yıldırım Atayeter

https://doi.org/10.26833/ijeg.1149738
Cited By: 2

Abstract

Karst Rocky Desertification (KRD) is the reduction of vegetative productivity of this land with the release of bedrock as a result of the full or partial transportation of the fertile soil through natural processes and human activities in karst landscapes. The purpose of this study is to reveal the effectiveness of Remote Sensing methods in monitoring, mapping and evaluating KRD. Landsat 8 OLI images were used to carry out these procedures. In monitoring this process, Karst Bare Rock Index (KBRI), Normalized Difference Rock Index (NDRI), Carbonate Rock Index 2 (CRI2), Normalized Difference Build-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Dimidiate Pixel Model (DPM), Multi Endmember Spectral Mixture Analysis (MESMA) and Support Vector Machine (SVM) were used from the spectral indices. In order to determine KRD with spectral indexes, a strong linear relationship was tested between some indices such as DPM (R2=0,79), KBRI (R2=0,66), and NDBI (R2=0,64) and field measurements. In order to evaluate the results obtained, KRD was divided into 4 basic classes such as none, mild, moderate, and severe. According to these classification levels, it was determined that the SVM method had the highest accuracy (Kappa=0.88). According to the classification results, which have the highest accuracy in the study area, the rate of areas undergoing severe karst desertification is 40%, moderate desertification process is 17%, mild desertification is 14% and non-desertification is 29%. In the study, it was concluded that the KRD strengthens as one goes from south to north and from west to east in the research area. This study points out KRD is one of the effective ecosystem problems in the Mediterranean region, Türkiye.

Keywords

Remote Sensing Karst Rocky Desertification Spectral Indices Spectral Mixture Analysis Machine Learning

References

  • Parise, M., Gabrovsek, F., Kaufmann, G., & Ravbar, N. (2018). Recent advances in karst research: from theory to fieldwork and applications. Geological Society, London, Special Publications, 466(1), 1-24.
  • Erinc, S. (2002). Jeomorfoloji II. İstanbul: DER Yayınları.
  • Theilen-Willige, B., Ait Malek, H., Charif, A., El Bchari, F., & Chaïbi, M. (2014). Remote sensing and GIS contribution to the investigation of karst landscapes in NW-Morocco. Geosciences, 4(2), 50-72.
  • Jiang, Z., Lian, Y., & Qin, X. (2014). Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Science Reviews, 132, 1-12.
  • Zhang, X., Shang, K., Cen, Y., Shuai, T., & Sun, Y. (2014). Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. International Journal of Applied Earth Observation and Geoinformation, 31, 86-94.
  • Ekmekçi, M. (2005). Karst in Turkish Thrace: compatibility between geological history and karst type. Turkish Journal of Earth Sciences, 14(1), 73-90.
  • Qi, X., Zhang, C., & Wang, K. (2019). Comparing remote sensing methods for monitoring karst rocky desertification at sub-pixel scales in a highly heterogeneous karst region. Scientific reports, 9(1), 1-12.
  • Yue, Y. M., Wang, K. L., Liu, B., Li, R., Zhang, B., Chen, H. S., & Zhang, M. Y. (2013). Development of new remote sensing methods for mapping green vegetation and exposed bedrock fractions within heterogeneous landscapes. International journal of remote sensing, 34(14), 5136-5153.
  • Yue, Y. M., Wang, K. L., Zhang, B., Jiao, Q. J., Liu, B., & Zhang, M. Y. (2012). Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment. Procedia Environmental Sciences, 13, 847-853.
  • Zhang, C., Qi, X., Wang, K., Zhang, M., & Yue, Y. (2017). The application of geospatial techniques in monitoring karst vegetation recovery in southwest China: A review. Progress in Physical Geography, 41(4), 450-477.
  • Xiong, Y. J., Qiu, G. Y., Mo, D. K., Lin, H., Sun, H., Wang, Q. X., ... & Yin, J. (2009). Rocky desertification and its causes in karst areas: a case study in Yongshun County, Hunan Province, China. Environmental Geology, 57, 1481-1488.
  • Liu, Y., Wang, J., & Deng, X. (2008). Rocky land desertification and its driving forces in the karst areas of rural Guangxi, Southwest China. Journal of Mountain Science, 5, 350-357.
  • Yang, W., Chu, W., & Zhou, L. (2019). Evaluating the impact of karst rocky desertification on regional climate in Southwest China with WRF. Theoretical and Applied Climatology, 137, 481-492.
  • Pei, J., Wang, L., Huang, N., Geng, J., Cao, J., & Niu, Z. (2018). Analysis of Landsat-8 OLI imagery for estimating exposed bedrock fractions in typical karst regions of Southwest China using a karst bare-rock index. Remote Sensing, 10(9), 1321.
  • Bai, X. Y., Wang, S. J., & Xiong, K. N. (2013). Assessing spatial‐temporal evolution processes of karst rocky desertification land: indications for restoration strategies. Land Degradation & Development, 24(1), 47-56.
  • Li, S., & Wu, H. (2015). Mapping karst rocky desertification using Landsat 8 images. Remote sensing letters, 6(9), 657-666.
  • Guha, S., & Govil, H. (2020). Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series. International Journal of Engineering and Geosciences, 7(1), 9-16.
  • Wang, H., Li, Q., Du, X., & Zhao, L. (2018). Quantitative extraction of the bedrock exposure rate based on unmanned aerial vehicle data and Landsat-8 OLI image in a karst environment. Frontiers of Earth Science, 12, 481-490.
  • Xu, E. Q., Zhang, H. Q., & Li, M. X. (2015). Object‐based mapping of karst rocky desertification using a support vector machine. Land Degradation & Development, 26(2), 158-167.
  • Kaynarca, M., Demir, N., & San, B. T. (2020). Yeraltı Suyu Kaynaklarının Uzaktan Algılama ve CBS Teknikleri Kullanarak Modellenmesine Yönelik bir Yaklaşım: Kırkgöz Havzası (Antalya). Geomatik, 5(3), 241-245.
  • Reis, H. Ç., & YılancI, G. (2020). Destek vektör makineleri ve NDVI kullanarak pamuk ekili alanların tespiti: Harran ovası örneği. Türkiye Uzaktan Algılama Dergisi, 2(1), 29-41.
  • Şimşek, M., Utlu, M., Poyraz, M., & Öztürk, M. Z. (2019). Geyik Dağı kütlesinin yüzey karstı jeomorfolojisi ve kütle üzerindeki karst-buzul jeomorfolojisi ilişkisi. Ege Coğrafya Dergisi, 28(2), 97-110.
  • Tokgözlü, A., & Özkan, E. (2018). Taşkın risk haritalarında AHP yönteminin uygulanması: Aksu Çayı Havzası örneği. Süleyman Demirel Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, (44), 151-176.
  • Atayeter, Y. (2005). Aksu Çayı havzası'nın jeomorfolojisi. Fakülte Kitabevi.
  • Karatepe, Y., Özçelik, R., Gürlevik, N. E. V. Z. A. T., Yavuz, H., & Kiriş, R. (2014). Batı Akdeniz’de farklı yetişme ortamı bölgelerindeki kızılçam (Pinus brutia Ten.) ormanlarının vejetasyon yapısının ekolojik değerlendirilmesi. Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi, 15(1), 1-8.
  • Atayeter, Y. (2011). Eğirdir Gölü Depresyonu ve Yakın Çevresinin Fiziki Coğrafya Özellikleri. Isparta. Fakülte Yayınları
  • Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172.
  • Sefercik, U. G., Ateşoğlu, A., & Atalay, C. (2021). Orman meşcere yükseklik haritası üretiminde hava kaynaklı lazer tarama performans analizi. Geomatik, 6(3), 179-188.
  • Genç, M., Kasarcı, E., & Kaya, C. (2012). Meşcere Kuruluşu Araştırmaları Üzerine Silvikültürel Bir Değerlendirme. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 13(2), 291-303.
  • Huang, Q., & Cai, Y. (2009). Mapping karst rock in Southwest China. Mountain Research and Development, 29(1), 14-20.
  • Xie, X., Du, P., Xia, J., & Luo, J. (2015). Spectral indices for estimating exposed carbonate rock fraction in karst areas of southwest China. IEEE Geoscience and Remote Sensing Letters, 12(9), 1988-1992.
  • Sabuncu, A., & Ozener, H. (2019). Detection of burned areas by remote sensing techniques: İzmir Seferihisar Forest fire case study. Journal of Natural Hazards and Environment, 5(2), 317-326.
  • Xu, D., Kang, X., Qiu, D., Zhuang, D., & Pan, J. (2009). Quantitative assessment of desertification using Landsat data on a regional scale–a case study in the Ordos Plateau, China. Sensors, 9(3), 1738-1753.
  • Somers, B., Asner, G. P., Tits, L., & Coppin, P. (2011). Endmember variability in spectral mixture analysis: A review. Remote Sensing of Environment, 115(7), 1603-1616.
  • Bedini, E., Van Der Meer, F., & Van Ruitenbeek, F. (2009). Use of HyMap imaging spectrometer data to map mineralogy in the Rodalquilar caldera, southeast Spain. International Journal of Remote Sensing, 30(2), 327-348.
  • Lippitt, C. L., Stow, D. A., Roberts, D. A., & Coulter, L. L. (2018). Multidate MESMA for monitoring vegetation growth forms in southern California shrublands. International journal of remote sensing, 39(3), 655-683.
  • Powell, R. L., & Roberts, D. A. (2008). Characterizing variability of the urban physical environment for a suite of cities in Rondonia, Brazil. Earth Interactions, 12(13), 1-32.
  • Moughal, T. A. (2013, June). Hyperspectral image classification using support vector machine. In journal of physics: conference series (Vol. 439, No. 1, p. 012042). IOP Publishing.
  • Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis. Object-based image analysis: Spatial concepts for knowledge-driven remote sensing applications, 663-677.
  • Benbahria, Z., Sebari, I., Hajji, H., & Smiej, M. F. (2021). Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning. International Journal of Engineering and Geosciences, 6(1), 40-50.
  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on geoscience and remote sensing, 42(8), 1778-1790.
  • Pu, J., Zhao, X., Dong, P., Wang, Q., & Yue, Q. (2021). Extracting information on rocky desertification from satellite images: A comparative study. Remote Sensing, 13(13), 2497.
  • Wang, S. J., Liu, Q. M., & Zhang, D. F. (2004). Karst rocky desertification in southwestern China: geomorphology, landuse, impact and rehabilitation. Land degradation & development, 15(2), 115-121.
  • Xu, E., Zhang, H., & Li, M. (2013). Mining spatial information to investigate the evolution of karst rocky desertification and its human driving forces in Changshun, China. Science of the Total Environment, 458, 419-426.
  • Dai, G., Sun, H., Wang, B., Huang, C., Wang, W., Yao, Y., ... & Zhang, Z. (2021). Assessment of karst rocky desertification from the local to regional scale based on unmanned aerial vehicle images: A case‐study of Shilin County, Yunnan Province, China. Land Degradation & Development, 32(18), 5253-5266.
  • Zhang, Y., Tian, Y., Li, Y., Wang, D., Tao, J., Yang, Y., ... & Wu, L. (2022). Machine learning algorithm for estimating karst rocky desertification in a peak-cluster depression basin in southwest Guangxi, China. Scientific Reports, 12(1), 19121.
  • Dindaroğlu, T., & Çelik, H. (2019). Yeşil kuşak orman ekosistemlerindeki orman parçalılığının bazı toprak özellikleri üzerindeki etkilerinin araştırılması (Kahramanmaraş Ahir dağı örneği). Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 22(2), 322-332.
  • Chong, G., Hai, Y., Zheng, H., Xu, W., & Ouyang, Z. (2021). Characteristics of changes in karst rocky desertification in southtern and western china and driving mechanisms. Chinese Geographical Science, 31, 1082-1096.
  • Haktanir, K., Karaca, A., & Omar, S. M. (2004). The prospects of the impact of desertification on Turkey, Lebanon, Syria and Iraq. In Environmental Challenges in the Mediterranean 2000–2050: Proceedings of the NATO Advanced Research Workshop on Environmental Challenges in the Mediterranean 2000–2050 Madrid, Spain 2–5 October 2002 (pp. 139-154). Springer Netherlands.
  • Ballut, C., & Faivre, S. (2012). New data on the dolines of Velebit Mountain: An evaluation of their sedimentary archive potential in the reconstruction of landscape evolution. Acta Carsologica, 41(1).
  • Şimşek, M., Öztürk, M. Z., Doğan, U., & Mustafa, U. T. L. U. (2021). Toros polyelerinin morfometrik özellikleri. Coğrafya Dergisi, (42), 101-119.
There are 51 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Çağan Alevkayalı 0000-0001-7044-8183

Onur Yayla 0000-0002-8710-3701

Yıldırım Atayeter 0000-0002-7570-2993

Early Pub Date May 8, 2023
Publication Date October 15, 2023
Published in Issue Year 2023 Volume: 8 Issue: 3

Cite

APA Alevkayalı, Ç., Yayla, O., & Atayeter, Y. (2023). Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine. International Journal of Engineering and Geosciences, 8(3), 277-289. https://doi.org/10.26833/ijeg.1149738
AMA Alevkayalı Ç, Yayla O, Atayeter Y. Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine. IJEG. October 2023;8(3):277-289. doi:10.26833/ijeg.1149738
Chicago Alevkayalı, Çağan, Onur Yayla, and Yıldırım Atayeter. “Monitoring and Classification of Karst Rocky Desertification With Landsat 8 OLI Images Using Spectral Indices, Multi-Endmember Spectral Mixture Analysis and Support Vector Machine”. International Journal of Engineering and Geosciences 8, no. 3 (October 2023): 277-89. https://doi.org/10.26833/ijeg.1149738.
EndNote Alevkayalı Ç, Yayla O, Atayeter Y (October 1, 2023) Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine. International Journal of Engineering and Geosciences 8 3 277–289.
IEEE Ç. Alevkayalı, O. Yayla, and Y. Atayeter, “Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine”, IJEG, vol. 8, no. 3, pp. 277–289, 2023, doi: 10.26833/ijeg.1149738.
ISNAD Alevkayalı, Çağan et al. “Monitoring and Classification of Karst Rocky Desertification With Landsat 8 OLI Images Using Spectral Indices, Multi-Endmember Spectral Mixture Analysis and Support Vector Machine”. International Journal of Engineering and Geosciences 8/3 (October 2023), 277-289. https://doi.org/10.26833/ijeg.1149738.
JAMA Alevkayalı Ç, Yayla O, Atayeter Y. Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine. IJEG. 2023;8:277–289.
MLA Alevkayalı, Çağan et al. “Monitoring and Classification of Karst Rocky Desertification With Landsat 8 OLI Images Using Spectral Indices, Multi-Endmember Spectral Mixture Analysis and Support Vector Machine”. International Journal of Engineering and Geosciences, vol. 8, no. 3, 2023, pp. 277-89, doi:10.26833/ijeg.1149738.
Vancouver Alevkayalı Ç, Yayla O, Atayeter Y. Monitoring and classification of karst rocky desertification with Landsat 8 OLI images using spectral indices, multi-endmember spectral mixture analysis and support vector machine. IJEG. 2023;8(3):277-89.

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