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Landsat ETM+ Uydu Görüntüleri ile Metal Kaynaklı Bitki Stresinin Araştırılması

Year 2022, Volume: 3 Issue: 2, 183 - 190, 18.09.2022
https://doi.org/10.48123/rsgis.1126649

Abstract

Uzaktan algılama teknolojisi günümüzde birçok alanda kullanılmakta, zor şartlar altındaki konumsal analiz işlemlerini kolaylaştırmaktadır. Bu teknoloji; hastalıklı bitkilerin tespiti, arazi sınıflandırması, değişim analizi, afet ve orman yönetimi ve maden tespiti gibi birçok alanda analiz imkânı sunmaktadır. Özellikle maden tespiti, ülkelerin ekonomisi için büyük önem arz etmektedir. Yersel ölçüm teknikleriyle bitki kaplı arazilerde, maden tespiti yapmak uğraştırıcı bir durumken, uydu görüntüleriyle daha kısa sürede maden sahaları tespit edilebilmektedir. Bitkiler ağır metallere maruz kaldıklarında klorofil miktarlarında, dolayısıyla yansıma değerlerinde anomaliler açığa çıkmaktadır. Diğer bir deyişle, toprak altındaki madenler bitkilerde strese neden olup, bitki anomalilerine yol açmaktadır. Bitki stresi tespitinde anomali miktarlarındaki değişimler baz alınmıştır. Bitkilerin maruz kaldığı stres ile anomali miktarı doğru orantılıdır. Bu çalışmada, Çukuralan (İzmir) ve Kışladağ (Uşak) bölgelerinde toprak altında altın madeni bulunan alanlara ait Landsat Enhanced Thematic Mapper Plus (ETM+) uydu görüntülerini kullanarak, bitki örtüsünün maruz kaldığı stres VIGS indeksiyle bulunmaya çalışılmıştır. İlave olarak test alanı için bitki örtüsüyle kaplı iki bölgenin madenli ve madensiz olmak üzere toplamda dört farklı arazi türünde literatürde mevcut diğer bitki indeksleriyle (NDVI, GNDVI, BNDVI) karşılaştırması yapılmıştır. Değerlendirme sonuçlarına göre, madenli alanlardaki VIGS ve NDVI indekslerinin, madensiz alanlara ait değerleri arasındaki farkın yüksek olduğu görülmüş, bundan dolayı ağaçlık alanlara ait anomali miktarı tespitinde kullanılabileceği önerilmiştir.

References

  • AMD. (2022, Ağustos 8). Türkiye’deki altın madenleri. Altın Madencileri Derneği (AMD). Retrieved from https://altinmadencileri.org.tr/aktif-olan-altin-madenleri/
  • Bannari, A., Morin, D., Bonn, F., & Huete, A. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1-2), 95-120.
  • Baran, H. A. (2021). Hakkâri ili baz metal yataklarının uzaktan algılama ile belirlenmesi. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 11(2), 339-347.
  • Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711-722.
  • Carranza, E. J. M., & Hale, M. (2001, July). Remote detection of vegetation stress for mineral exploration. In IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217) (Vol. 3, pp. 1324-1326). IEEE.
  • Chatterjee, S., & Hadi, A. S. (1986). Influential observations, high leverage points, and outliers in linear regression. Statistical Science, 1(3), 379-393.
  • Fu, P., Zhang, W., Yang, K., & Meng, F. (2020). A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD. Ecotoxicology and Environmental Safety, 206, 111211. doi: 10.1016/j.ecoenv.2020.111211.
  • Hede, A. N. H., Kashiwaya, K., Koike, K., & Sakurai, S. (2015). A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area. Remote Sensing of Environment, 171, 83-97.
  • Hede, A. N. H., Koike, K., Kashiwaya, K., Sakurai, S., Yamada, R., & Singer, D. A. (2017). How can satellite imagery be used for mineral exploration in thick vegetation areas?. Geochemistry, Geophysics, Geosystems, 18(2), 584-596.
  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3-4), 185-200.
  • Jiang, H., Yao, M., Guo, J., Zhang, Z., Wu, W., & Mao, Z. (2022). Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI). Remote Sensing, 14(4), 882-899.
  • Jin, M., Liu, X., & Zhang, B. (2017). Evaluating heavy-metal stress levels in rice using a theoretical model of canopy-air temperature and leaf area index based on remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), 3232-3242.
  • Lee, G., Hwang, J., & Cho, S. (2021). A novel index to detect vegetation in urban areas using UAV-based multispectral images. Applied Sciences, 11(8), 3472-3490.
  • Ren, H. Y., Zhuang, D. F., Pan, J. J., Shi, X. Z., & Wang, H. J. (2008). Hyper-spectral remote sensing to monitor vegetation stress. Journal of Soils and Sediments, 8(5), 323-326.
  • Sun, G., Huang, H., Weng, Q., Zhang, A., Jia, X., Ren, J., ... & Chen, X. (2019). Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 78, 53-65.
  • TÜPRAG. (2022, Ağustos 8). Kışladağ altın madeni - Uşak. Retrieved from https://web.archive.org/web/ 20200211071400/http://www.tuprag.com.tr/tr/projelerimiz/kisladag-altin-madeni/7/projenin-tanitimi-ve-amaci/24
  • USGS. (2022, Ağustos 8). USGS Earth Explorer. Retrieved from https://earthexplorer.usgs.gov/
  • Yang, C., Everitt, J. H., Bradford, J. M., & Murden, D. (2004). Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability. Precision Agriculture, 5(5), 445-461.
  • Zhang, C., Yang, K., Wang, M., Gao, P., Cheng, F., Li, Y., & Xia, T. (2019). A new vegetation heavy metal pollution index for detecting the pollution degree of different varieties of maize under copper stress. Remote Sensing Letters, 10(5), 469-477.

Investigating Metal-Induced Vegetation Stress with Landsat ETM+ Satellite Images

Year 2022, Volume: 3 Issue: 2, 183 - 190, 18.09.2022
https://doi.org/10.48123/rsgis.1126649

Abstract

Remote sensing technology is used in many areas today, facilitating spatial analysis operations under difficult conditions. This technology offers solutions in different fields such as forest degradation, land classification, change analysis and mine detection. In particular, mine detection is of great importance for the economies of countries. Detecting mines with terrestrial measurement techniques in vegetated areas is a challenging situation, but mine sites can be easily detected, by using satellite images in a shorter time. When plants are exposed to heavy metals, anomalies occur and they cause reduction in the amount of chlorophyll. This can be observed as decrease in the reflectance values. In this study, it is aimed to detect the stress of the plants due to heavy metal by measuring the amount of the change in the reflectance values of plants in mining area and non-mining area. In this context, VIGS index is exploited on Landsat ETM+ satellite images belong to Cukuralan/Izmir and Kisladag/Usak regions for both gold mine and non-gold mine areas. Other vegetation indices (NDVI, GNDVI, BNDVI) were also computed on these sites when they were covered with vegetation. According to the evaluation results, it was seen that the difference between the values of the VIGS and NDVI indexes in the mine and not mine areas were higher, and therefore it was suggested that they could be used to determine the amount of anomaly in the wooded areas.

References

  • AMD. (2022, Ağustos 8). Türkiye’deki altın madenleri. Altın Madencileri Derneği (AMD). Retrieved from https://altinmadencileri.org.tr/aktif-olan-altin-madenleri/
  • Bannari, A., Morin, D., Bonn, F., & Huete, A. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1-2), 95-120.
  • Baran, H. A. (2021). Hakkâri ili baz metal yataklarının uzaktan algılama ile belirlenmesi. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 11(2), 339-347.
  • Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711-722.
  • Carranza, E. J. M., & Hale, M. (2001, July). Remote detection of vegetation stress for mineral exploration. In IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217) (Vol. 3, pp. 1324-1326). IEEE.
  • Chatterjee, S., & Hadi, A. S. (1986). Influential observations, high leverage points, and outliers in linear regression. Statistical Science, 1(3), 379-393.
  • Fu, P., Zhang, W., Yang, K., & Meng, F. (2020). A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD. Ecotoxicology and Environmental Safety, 206, 111211. doi: 10.1016/j.ecoenv.2020.111211.
  • Hede, A. N. H., Kashiwaya, K., Koike, K., & Sakurai, S. (2015). A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area. Remote Sensing of Environment, 171, 83-97.
  • Hede, A. N. H., Koike, K., Kashiwaya, K., Sakurai, S., Yamada, R., & Singer, D. A. (2017). How can satellite imagery be used for mineral exploration in thick vegetation areas?. Geochemistry, Geophysics, Geosystems, 18(2), 584-596.
  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3-4), 185-200.
  • Jiang, H., Yao, M., Guo, J., Zhang, Z., Wu, W., & Mao, Z. (2022). Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI). Remote Sensing, 14(4), 882-899.
  • Jin, M., Liu, X., & Zhang, B. (2017). Evaluating heavy-metal stress levels in rice using a theoretical model of canopy-air temperature and leaf area index based on remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), 3232-3242.
  • Lee, G., Hwang, J., & Cho, S. (2021). A novel index to detect vegetation in urban areas using UAV-based multispectral images. Applied Sciences, 11(8), 3472-3490.
  • Ren, H. Y., Zhuang, D. F., Pan, J. J., Shi, X. Z., & Wang, H. J. (2008). Hyper-spectral remote sensing to monitor vegetation stress. Journal of Soils and Sediments, 8(5), 323-326.
  • Sun, G., Huang, H., Weng, Q., Zhang, A., Jia, X., Ren, J., ... & Chen, X. (2019). Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 78, 53-65.
  • TÜPRAG. (2022, Ağustos 8). Kışladağ altın madeni - Uşak. Retrieved from https://web.archive.org/web/ 20200211071400/http://www.tuprag.com.tr/tr/projelerimiz/kisladag-altin-madeni/7/projenin-tanitimi-ve-amaci/24
  • USGS. (2022, Ağustos 8). USGS Earth Explorer. Retrieved from https://earthexplorer.usgs.gov/
  • Yang, C., Everitt, J. H., Bradford, J. M., & Murden, D. (2004). Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability. Precision Agriculture, 5(5), 445-461.
  • Zhang, C., Yang, K., Wang, M., Gao, P., Cheng, F., Li, Y., & Xia, T. (2019). A new vegetation heavy metal pollution index for detecting the pollution degree of different varieties of maize under copper stress. Remote Sensing Letters, 10(5), 469-477.
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Feride Seçil Yıldırım 0000-0003-1091-1791

Esra Tunç Görmüş 0000-0002-3334-2061

Publication Date September 18, 2022
Submission Date June 17, 2022
Acceptance Date September 1, 2022
Published in Issue Year 2022 Volume: 3 Issue: 2

Cite

APA Yıldırım, F. S., & Tunç Görmüş, E. (2022). Landsat ETM+ Uydu Görüntüleri ile Metal Kaynaklı Bitki Stresinin Araştırılması. Türk Uzaktan Algılama Ve CBS Dergisi, 3(2), 183-190. https://doi.org/10.48123/rsgis.1126649