Long-Term Change of the Secchi Disk Depth in Lake Maninjau, Indonesia Shown by Landsat TM and ETM+ Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2019-12, Vol.11 (23), p.2875
• Main Authors: Setiawan, Fajar, Matsushita, Bunkei, Hamzah, Rossi, Jiang, Dalin, Fukushima, Takehiko
• Format: Article
• Language: English
• Subjects: Algae; Aquaculture; Atmospheric effects; Data collection; Datasets; Depletion; Dissolved oxygen; Eutrophication; Fish culture; Global positioning systems; GPS; Image filters; Lake management; Lakes; Landsat; Landsat satellites; Noise; Remote sensing; Satellite imagery; Sea level; Sensors; Water depth; Water pollution; Water purification; Water quality; Watersheds
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11232875

Most of the lakes in Indonesia are facing environmental problems such as eutrophication, sedimentation, and depletion of dissolved oxygen. The water quality data for supporting lake management in Indonesia are very limited due to financial constraints. To address this issue, satellite data are often used to retrieve water quality data. Here, we developed an empirical model for estimating the Secchi disk depth (SD) from Landsat TM/ETM+ data by using data collected from nine Indonesian lakes/reservoirs (SD values 0.5–18.6 m). We made two efforts to improve the robustness of the developed model. First, we carried out an image preprocessing series of steps (i.e., removing contaminated water pixels, filtering images, and mitigating atmospheric effects) before the Landsat data were used. Second, we selected two band ratios (blue/green and red/green) as SD predictors; these differ from previous studies’ recommendation. The validation results demonstrated that the developed model can retrieve SD values with an R2 of 0.60 and the root mean square error of 1.01 m in Lake Maninjau, Indonesia (SD values ranged from 0.5 to 5.8 m, n = 74). We then applied the developed model to 230 scenes of preprocessed Landsat TM/ETM+ images to generate a long-term SD database for Lake Maninjau during 1987–2018. The visual comparison of the in situ-measured and satellite estimated SD values, as well as several events (e.g., algal bloom, water gate open, and fish culture), showed that the Landsat-based SD estimations well captured the change tendency of water transparency in Lake Maninjau, and these estimations will thus provide useful data for lake managers and policy-makers.