Öz
The
distinction between the types of vegetation on earth is used extensively in
remote sensing studies. Remote sensing studies using satellite imagery are
inadequate in the studies requiring high accuracy and precise in temporal and
spatial resolutions caused by frequency of revisit time and ground sample
distance. In this study, an application was performed with the opportunities
offering flexible mobility of unmanned aerial vehicle (UAV) technology which is
rapidly increasing and widening recently. UAVs have flexible mobility thanks to
the ability to fly at an altitude proper for the desired time and ground
sampling distance. The multispectral
imagery acquired from a camera mounted on a UAV. Determining the land uses and
researching the product efficiency by classifying the species of vegetation on
earth can give a new perspective to the remote sensing studies by using the
images obtained from the multispectral camera mounted on the UAV. Detectability
of clover and soybean was investigated using green, red, red-edge and
near-infrared imagery obtained from multispectral camera. Vegetation
classification was carried out by composite imagery created from multispectral
imagery.
Anahtar Kelimeler
Remote Sensing UAV Vegetation Multispectral Camera Clover Soybean
Kaynakça
- Adam, E. ve Mutanga, O. (2009). Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry, ISPRS Journal of Photogrammetry and Remote Sensing, 64 (6), 612-620.
- Ahmad, A., Tahar, K. N., Udin, W. S., Hashim, K. A., Darwin, N., Hafis, M., Room, M., Hamid, N. F. A., Azhar, N. A. M. ve Azmi, S. M. (2013). Digital aerial imagery of unmanned aerial vehicle for various applications, Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on, 535-540.
- Anchang, J. Y., Ananga, E. O. ve Pu, R. (2016). An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery, International Journal of Applied Earth Observation and Geoinformation, 50, 211-220.
- Dudley, K. L., Dennison, P. E., Roth, K. L., Roberts, D. A. ve Coates, A. R. (2015). A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients, Remote Sensing of Environment, 167, 121-134.
- Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T., Straub, C. ve Ghosh, A. (2016). Review of studies on tree species classification from remotely sensed data, Remote Sensing of Environment, 186, 64-87.
- Ishida, T., Kurihara, J., Viray, F. A., Namuco, S. B., Paringit, E. C., Perez, G. J., Takahashi, Y. ve Marciano, J. J. (2018). A novel approach for vegetation classification using UAV-based hyperspectral imaging, Computers and Electronics in Agriculture, 144, 80-85.
- Jia, K., Wu, B., Tian, Y., Li, Q. ve Du, X. (2011). Spectral discrimination of opium poppy using field spectrometry, IEEE Transactions on Geoscience and Remote Sensing, 49 (9), 3414-3422.
- Martínez-Verduzco, G. C., Galeana-Pizaña, J. M. ve Cruz-Bello, G. M. (2012). Coupling Community Mapping and supervised classification to discriminate Shade coffee from Natural vegetation, Applied Geography, 34, 1-9.
- Peña, J., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. ve López-Granados, F. (2015). Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution, Sensors, 15 (3), 5609.
- Salamí, E., Barrado, C. ve Pastor, E. (2014). UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas, Remote Sensing, 6 (11), 11051.
- Somers, B. ve Asner, G. P. (2014). Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis, International Journal of Applied Earth Observation and Geoinformation, 31, 57-66.
İHA İLE MULTİSPEKTRAL KAMERALARDAN SAĞLANAN GÖRÜNTÜLER YARDIMIYLA BİTKİ TÜRLERİNİN SINIFLANDIRILMASI
Öz
Yeryüzündeki bitki örtüsü
türlerinin ayırt edilmesi uzaktan algılama çalışmalarında yoğun olarak
kullanılmaktadır. Uydu görüntüleri kullanılarak yapılan bu uzaktan algılama
çalışmaları, uyduların gerek her zaman aynı yerden görüntü elde edememesi
gerekse yer örnekleme aralıklarından kaynaklanan sebeplerle zamansal ve
mekânsal çözünürlükleri yüksek hassasiyet ve doğruluk istenen çalışmalarda
yetersiz kalmaktadır. Bu çalışmada, son zamanlarda hızla artan ve yaygınlaşan
İnsansız Hava Aracı (İHA) teknolojisinin esnek hareket kabiliyetine sahip
olması nedeniyle sunmuş olduğu olanaklar üzerinde bir uygulama
gerçekleştirilmiştir. İHA’ların istenilen zaman ve yer örnekleme aralığına
uygun yükseklikte uçurulabilmesi esnek hareket kabiliyetine sahip olmasını
sağlamaktadır. İHA üzerine yerleştirilen multispektral kameradan elde edilen
görüntüler ile yeryüzündeki bitki örtüsü türlerinin tespiti yapılıp sınıflandırılarak
arazi kullanımlarının belirlenmesi ve ürün verimliliğinin araştırılması gibi
konular uzaktan algılama çalışmalarına yeni bir bakış açısı kazandırabilir. İHA
üzerine monte edilen kameranın elde ettiği yeşil, kırmızı, kırmızı-kenar ve
yakın kızıl ötesi görüntüler kullanılarak yonca ve soya bitkilerinin tespit
edilebilirliği incelenmiştir. Multispektral görüntülerden oluşturulan kompozit
görüntüler yardımıyla tür sınıflandırması yapılmıştır.
Anahtar Kelimeler
Uzaktan Algılama İHA Bitki Türü Multispektral Kamera Yonca Soya
Kaynakça
- Adam, E. ve Mutanga, O. (2009). Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry, ISPRS Journal of Photogrammetry and Remote Sensing, 64 (6), 612-620.
- Ahmad, A., Tahar, K. N., Udin, W. S., Hashim, K. A., Darwin, N., Hafis, M., Room, M., Hamid, N. F. A., Azhar, N. A. M. ve Azmi, S. M. (2013). Digital aerial imagery of unmanned aerial vehicle for various applications, Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on, 535-540.
- Anchang, J. Y., Ananga, E. O. ve Pu, R. (2016). An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery, International Journal of Applied Earth Observation and Geoinformation, 50, 211-220.
- Dudley, K. L., Dennison, P. E., Roth, K. L., Roberts, D. A. ve Coates, A. R. (2015). A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients, Remote Sensing of Environment, 167, 121-134.
- Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T., Straub, C. ve Ghosh, A. (2016). Review of studies on tree species classification from remotely sensed data, Remote Sensing of Environment, 186, 64-87.
- Ishida, T., Kurihara, J., Viray, F. A., Namuco, S. B., Paringit, E. C., Perez, G. J., Takahashi, Y. ve Marciano, J. J. (2018). A novel approach for vegetation classification using UAV-based hyperspectral imaging, Computers and Electronics in Agriculture, 144, 80-85.
- Jia, K., Wu, B., Tian, Y., Li, Q. ve Du, X. (2011). Spectral discrimination of opium poppy using field spectrometry, IEEE Transactions on Geoscience and Remote Sensing, 49 (9), 3414-3422.
- Martínez-Verduzco, G. C., Galeana-Pizaña, J. M. ve Cruz-Bello, G. M. (2012). Coupling Community Mapping and supervised classification to discriminate Shade coffee from Natural vegetation, Applied Geography, 34, 1-9.
- Peña, J., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. ve López-Granados, F. (2015). Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution, Sensors, 15 (3), 5609.
- Salamí, E., Barrado, C. ve Pastor, E. (2014). UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas, Remote Sensing, 6 (11), 11051.
- Somers, B. ve Asner, G. P. (2014). Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis, International Journal of Applied Earth Observation and Geoinformation, 31, 57-66.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Araştırma Makaleleri [tr] Research Articles [en] |
| Yazarlar | |
| Yayımlanma Tarihi | 18 Aralık 2019 |
| Gönderilme Tarihi | 20 Kasım 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 1 Sayı: 1 |