PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Characterization and Estimation of Dates Palm Trees in an Urban Area Using GIS-Based Least-Squares Model and Minimum Noise Fraction Images

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Date palm is the major food source and possesses an important role in the economic aspects, environmental parts, and society. These crops were subjected to degradation due to the financial and numerous military conflicts. Because of the expensive cost of monitoring and managing date palm in field measurements, and limited studies using satellite images, the authors proposed a method to estimate and map date palm using the Landsat-8 satellite images. The authors applied the least-squares multiple regression and GIS techniques to find suitable predictors from the set of variables such as original bands of Landsat-8, Minimum Noise Fraction (MNF) transformation, tasseled cap component transformation, and spectral index. In order to validate the proposed method, the field measurement data were utilized to assess the estimated date palm from the Landsat-8 images. A linear combination of MNF Landsat-8 band 4 (red, 0.636–0.673 µm), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI) were the best date palm predictor (R2adj= 0.988, root-mean-squared error (RMSE) = 0.013). The results demonstrate that the MNF Landsat-8 images in the least square regression help improve the date palm estimation and mapping for the practical use in the study area with high accuracy.
Rocznik
Strony
78--85
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
  • Department of Surveying Engineering Techniques, Technical College of Kirkuk, Northern Technical University, Baghdad Road, 36001 Kirkuk, Iraq
  • Department of Surveying Engineering Techniques, Technical College of Kirkuk, Northern Technical University, Baghdad Road, 36001 Kirkuk, Iraq
Bibliografia
  • 1. Al-Khayri J.M., S.M. Jain, and D.V. Johnson. 2015. Date palm genetic resources and utilization. Africa and the Americas 1.
  • 2. Al Shidi R.H., L. Kumar, S.A. Al-Khatri, M.M. Albahri, and M.S. Alaufi. 2018. Relationship of Date Palm Tree Density to Dubas Bug Ommatissus lybicus Infestation in Omani Orchards. Agriculture 8, 64.
  • 3. Alhammadi M. and E. Glenn. 2008. Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI. International Journal of Remote Sensing 29, 1745–1765.
  • 4. Chao C.T. and R.R. Krueger. 2007. The date palm (Phoenix dactylifera L.): overview of biology, uses, and cultivation. HortScience 42, 1077–1082.
  • 5. Chong K.L., K.D. Kanniah, C. Pohl, and K.P. Tan. 2017. A review of remote sensing applications for oil palm studies. Geo-spatial Information Science 20, 184–200.
  • 6. El-Juhany L.I. 2010. Degradation of date palm trees and date production in Arab countries: causes and potential rehabilitation. Australian Journal of Basic and Applied Sciences 4, 3998–4010.
  • 7. Hansen S.B., R. Padfield, K. Syayuti, S. Evers, Z. Zakariah, and S. Mastura. 2015. Trends in global palm oil sustainability research. Journal of Cleaner Production 100, 140–149.
  • 8. Hardisky M., V. Klemas, and M. Smart. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of. Spartina alterniflora 49, 77–83.
  • 9. Johnson D. 2010. Worldwide dispersal of the date palm from its homeland. Pages 369–375 in IV International Date Palm Conference 882.
  • 10. Joseph W. 1994. Automated spectral analysis: A geologic example using AVIRIS data, north Grapevine Mountains, Nevada. In: Proc. Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, 1407–1418.
  • 11. Khierallah H.S., S.M. Bader, K.M. Ibrahim and I.J. Al-Jboory. 2015. Date palm status and perspective in Iraq. Pages 97–152 Date palm genetic resources and utilization. Springer.
  • 12. Lazecky M., S. Lhota, T. Penaz, and D. Klushina. 2018. Application of Sentinel-1 satellite to identify oil palm plantations in Balikpapan Bay. Page 012064 in IOP Conference Series: Earth and Environmental Science. IOP Publishing.
  • 13. Li W., H. Fu, L. Yu, and A. Cracknell. 2016. Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing 9, 22.
  • 14. Liao Z., B. He, and X. Quan. 2015. Modified enhanced vegetation index for reducing topographic effects. Journal of Applied Remote Sensing 9, 096068.
  • 15. Lykhovyd P.V. 2020. Sweet corn yield simulation using normalized difference vegetation index and leaf area index. Journal of Ecological Engineering, 21(3), 228–236.
  • 16. Malek S., Y. Bazi, N. Alajlan, H. AlHichri, and F. Melgani. 2014. Efficient framework for palm tree detection in UAV images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4692–4703.
  • 17. Manandhar A., L. Hoegner, and U. Stilla. 2016. Palm Tree Detection Using Circular Autocorrelation of Polar Shape Matrix. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3, 465.
  • 18. Morel A.C., J.B. Fisher, and Y. Malhi. 2012. Evaluating the potential to monitor aboveground biomass in forest and oil palm in Sabah, Malaysia, for 2000–2008 with Landsat ETM+ and ALOS-PALSAR. International Journal of Remote Sensing 33, 3614–3639.
  • 19. Oishi Y., H. Ishida, and R. Nakamura. 2018. A new Landsat 8 cloud discrimination algorithm using thresholding tests. International Journal of Remote Sensing 39, 9113–9133.
  • 20. Rouse Jr, J. W., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS.
  • 21. Santoso, H., H. Tani, and X. Wang. 2016. A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery. International Journal of Remote Sensing 37, 5122–5134.
  • 22. Sayan M.S. 2001. Landscaping with palms in the Mediterranean. Palms-Lawrence 45, 171–176.
  • 23. Shareef, H., Hasan, Noori. 2019. Integrating of GIS and fuzzy multi-criteria method to evaluate land degradation and their impact on the urban growth of Kirkuk city, Iraq. International Journal of Advanced Science and Technology 28, 800–815.
  • 24. Shareef M.A., A. Toumi, and A. Khenchaf. 2014a. Estimation of water quality parameters using the regression model with fuzzy k-means clustering. international journal of advanced computer science and applications 5, xx.
  • 25. Shareef M.A., A. Toumi, and A. Khenchaf. 2014b. Prediction of water quality parameters from SAR images by using multivariate and texture analysis models. Page 924319 in SAR Image Analysis, Modeling, and Techniques XIV. International Society for Optics and Photonics.
  • 26. Shi J., J. Wang, A.Y. Hsu, P.E. O’Neill, and E.T. Engman. 1997. Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Transactions on Geoscience and Remote Sensing 35, 1254–1266.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-43a37c66-73ac-4371-bdff-09bf445f502b
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.