Empirical satellite imagery-based models to enable and assess the current state of surface freshwaters Yuli Sudriani(a*), Ridho Ismanto(a), Intan Nuni Wahyuni(a), Meti Yulianti(b), Arie Vatresia(c), Arnida Lailatul Latifah(a,d)
a) Research Center for Computing, National Research and Innovation Agency (BRIN), Indonesia
*yuli038[at]brin.go.id
b) Research Center for Water Resource and Limnology, National Research and Innovation Agency (BRIN), Indonesia
c) University of Bengkulu, Indonesia
d) School of Computing, Telkom University, Indonesia
Abstract
This study proposes an integrated analysis for evaluating the potential of eutrophication enrichment in Maninjau Lake as a case study based on a single trophic state index (TSI) of surface freshwater, namely carlson^s TSI chlorophyll-a (Chl-a). The spatio-temporal concentration of Chl-a is estimated from remote sensing data, specifically, Landsat-8 Operational Land Imager (OLI), and the technique is not trivial. There is no unique model that can quantify Chl-a in any surface freshwater. Therefore, we investigate various models, consisting of seventy-six existing equations, that represent empirical relationships between in-situ Chl-a measurements and the remote sensing reflectance ratio of the Landsat-8 bands. The structure of the equations includes linear, polynomial, exponential, and logarithmic relationships. Jenn-Fisher classification is used to cluster the result from carlson^s TSI Chl-a which provides more comprehensive analysis in each zone. The model evaluation is based on the reliability of its range and correlation with in-situ data. By combining remote sensing monitoring and an integrated algorithms, the presented research has identified the optimal model and the spectral band combinations to estimate the Chl-a concentration patterns, which promisingly enables spatially and temporally automated assessment detection on a current-state basis. This finding is expected to enhance the effectiveness of the eutrophication monitoring system in multiple-locations, typical seasonality, and hydrogeomorphology, ultimately supporting the sustainability of watershed management.
