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Water Requirement for Irrigation of Complicated Agricultural Land by Using Classified Airborne Digital Sensor Images

  • Li ChenEmail author
  • Ta-Wei Chien
  • Chia-Sheng Hsu
  • Chih-Hung Tan
  • Hsiang-Yi Hsu
  • Chang-Huan Kou
Research Article
  • 11 Downloads

Abstract

Land use of irrigated areas nearby the metropolitan is complex. On fields, crop growth may differ with variations in the water demand. Among the image classification methods, combined object-oriented classification is currently preferred over conventional pixel-based classification. Compared with the traditional pixel-based method, which generally exhibits a spot-like salt-and-pepper effect, object-based classification can significantly reduce the salt-and-pepper effect and amount of data required for analysis. To obtain improved spectral recognition, maximum image information is described using color, texture, and shape to enhance image recognition. In this study, image information extraction and crop interpretation were performed using the airborne digital sensor ADS40 to obtain experimental data, and the traditional supervised image and image object classification methods were compared. The results indicate that both the image classification methods could yield an overall accuracy of more than 80%, and the accuracy of object-based classification (88.68%) was higher than that of the other classification. The daily water requirement of crops in the study area, calculated using a high-precision image object classification method, was approximately 2585 m3. The current results may aid in the effective estimation of agricultural irrigation water consumption.

Keywords

Land use Irrigated area Spectral recognition Object-based classification ADS40 

Notes

References

  1. Arcidiacono, C., & Porto, S. M. C. (2010). Classification of crop-shelter coverage by RGB aerial images: a compendium of experiences and findings. Journal of Agricultural Engineering, 3, 1–11.CrossRefGoogle Scholar
  2. Fathizad, H., Ardakani, M. A. H., Mehrjardi, R. T., & Hamid Sodaiezadeh, H. (2018). Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian Central Desert. African Earth Sciences, 145(9), 115–130.CrossRefGoogle Scholar
  3. Hofmann, P. (2001). Detecting urban features from IKONOS data using an object-oriented approach. In Proceedings of the first annual conference of the remote sensing & photogrammetry society, 12–14 September (pp. 79–91).Google Scholar
  4. Hsieh, Y.-T., Wu, S.-T., Chen, C.-T., Chen, J.-C. (2016). Analyzing spectral characteristics of shadow area from ADS-40 high radiometric resolution aerial images. In International archives of the photogrammetry, remote sensing and spatial information sciences 2016, XLI-B7 (pp. 223–227).Google Scholar
  5. Huang, F. (2018). Object oriented change detection and damage assessment using high resolution remote sensing images, Tangjiao Landslide, Three Gorges Reservoir, China. Environmental Earth Sciences, 77, 183.CrossRefGoogle Scholar
  6. Lin, G.-R. (2004). A study of normalization of knowledge of category-guided segmentation of airphotos. Master’s Thesis, National Taiwan University Institute of Geography.Google Scholar
  7. Ma, L.-R. (2018). Application of texture-assisted analysis of object-oriented classification to crop interpretation. Master’s Thesis, National Taipei University of Technology Institute of Civil Engineering.Google Scholar
  8. Water Resources Agency. (1999). General report on the scope of reasonable water use for business use (p. 49).Google Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringChung Hua UniversityHsinchu CityTaiwan, ROC
  2. 2.Taiwan HsinChu Irrigation Association Engineering DivisionZhubei CityTaiwan, ROC
  3. 3.Agricultural Engineering Research CenterTaoyuan CityTaiwan, ROC

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