Modeling the Long-Term Variability in the Surfaces of Three Lakes in Morocco with Limited Remote Sensing Image Sources

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 17; p. 3133
• Main Authors: Haidu, Ionel, El Orfi, Tarik, Magyari-Sáska, Zsolt, Lebaut, Sébastien, El Gachi, Mohamed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.08.2024; MDPI
• Subjects: Acceptable noise levels; autocorrelation; Environmental Sciences; Fluctuations; Humanities and Social Sciences; Hydrology; Image acquisition; Image reconstruction; interpolation; Kalman filter; Kalman filters; Lakes; Landsat; Landsat satellites; limited images; Methods; Missing data; Modelling; Morocco; Opacity; philosophy; Remote monitoring; Remote sensing; RS application; Satellite imagery; Satellites; Statistical methods; Time series; Variability; Water balance; Water purification; Water resources management; White noise; Morocco; interpolation; limited images; water resources; Morocco; Kalman filter; Landsat; RS application Landsat limited images lakes interpolation Kalman filter ratio method water resources Morocco; lakes; ratio method; RS application
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16173133

Satellite imagery has become a widespread resource for modeling variability in lake surfaces. However, the extended monitoring of a lake’s perimeter faces significant challenges due to atmospheric obstacles that cannot be rectified. Due to the atmosphere’s everchanging opacity, only half of the acquired satellite images have reliable qualitative accuracy making it possible to identify a lake’s contour. Consequently, approximately 50% of the monthly lake outline values can be determined using remote sensing methods, leaving the remaining 50% unknown. This situation is applicable to three lakes in Morocco (Abakhan, Ouiouan, and Tiglmanine), the subjects of the current research for the period between 1984 and 2022. What can we do if, during a period of time in which we monitored the evolution of the surface of a lake by satellite means, we obtain only about 50% of the possible images? Shall we just settle for this and stop the analysis? Although it may be challenging to believe, the present study introduces two statistical methods for interpolating and validating the monthly values of the lake outline: the iterative ratio method based on the autocorrelation of the monthly water balance and the Kalman filter. We estimated the reconstruction errors of the missing values and validated the methodology using an inverse philosophy, reconstructing the initial data from the table of the simulation results. Given that the difference between the initial values and the reconstructed initial values resembles white noise or an AR (1) process with a low coefficient, we deemed the methodological approach acceptable. Several comparison criteria between the two interpolation methods were employed, yet determining the more appropriate one remains challenging. Based on our surface reconstruction method, Lake Abakhan, with an average area of 22 hectares, experienced significant fluctuations, ranging from a maximum of 34 hectares in 2010 to a minimum of 0.8 hectares in 2022. Lake Ouiouan, with an average area of 14 hectares, displayed much lower variation, with a maximum of 17 hectares in 2020 and a minimum of 6.5 hectares in 1988. Lake Tiglmanine showed a pattern similar to that of Lake Abakhan but with less pronounced fluctuations. With an average area of 6.1 hectares, its maximum was 9.2 hectares in 2011 and its minimum was 4.1 hectares in 1984.