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Water Resource Surveillance for the Salton Sea in California By Adaptive Sequential Monitoring of Its Landsat Images

Author

Listed:
  • Fan Yi

    (University of Florida)

  • Peihua Qiu

    (University of Florida)

Abstract

Gradual loss of water resource in the Salton Sea has got much attention from researchers recently for its damage to the local environment and ecosystems for human beings, animals and plants. To monitor the water resource of the lake, researchers usually obtain certain water resource indices manually from databases such as the satellite images of the region. In this paper, we develop a new method to monitor the area of the Salton Sea automatically. By this method, the lake boundary is first segmented from each satellite image by an image segmentation procedure, and then its area is computed by a numerical algorithm. The sequence of lake areas computed from satellite images taken at different time points is then monitored by a control chart from the statistical process control literature. Because the lake area changes gradually over time, the control chart designed for detecting process mean drifts is used here for the water resource surveillance application.

Suggested Citation

  • Fan Yi & Peihua Qiu, 2023. "Water Resource Surveillance for the Salton Sea in California By Adaptive Sequential Monitoring of Its Landsat Images," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(3), pages 549-563, September.
    • Handle: RePEc:spr:jagbes:v:28:y:2023:i:3:d:10.1007_s13253-023-00545-2
    DOI: 10.1007/s13253-023-00545-2
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    References listed on IDEAS

    as
    1. Qiu, Peihua & Sun, Jingran, 2007. "Local Smoothing Image Segmentation for Spotted Microarray Images," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1129-1144, December.
    2. Zhou, Qin & Luo, Yunzhao & Wang, Zhaojun, 2010. "A control chart based on likelihood ratio test for detecting patterned mean and variance shifts," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1634-1645, June.
    3. Changliang Zou & Yukun Liu & Zhaojun Wang, 2009. "Comparisons of control schemes for monitoring the means of processes subject to drifts," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(2), pages 141-163, September.
    Full references (including those not matched with items on IDEAS)

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