Local Algorithm for Monitoring Total Suspended Sediments in Micro-Watersheds Usin Drones and Remote Sensing Applications. Case Study: Teusacá River, La Calera, Colombia

Abstract

ABSTRACT:An empirical relationship of Total Suspended Sediments (TSS) concentrations and reflectance values obtained with Drones' aerial photos and processed using remote sensing tools was set up as the main objective of this research. A local mathematic algorithm for the micro-watershed of the Teusacá River at La Calera, Colombia, was developed based on the computing of four component of bands from consumed-grade cameras obtaining from each their corresponding reflectance values from procedures for correcting digital camera imagery and using statistical analysis for study the fit and RMSE of 25 regressions. The assessment was characterized by the comparison of reflectance values and 34 in-situ data measurements concentrations between 1.6 and 33 −1 taken from the superficial layer of the river in two campaigns. A large data set of empirical and referenced algorithm from literature were used to evaluate the accuracy and precision of the relationship. For estimation of TSS, a higher accuracy was achieved using the Tassan's algorithm with the BAND X/ BANDX ratio. The correlation coefficient with 2 = demonstrate the feasibility of use remote sensed data with consumed-grade cameras as an effective tool for a frequent monitoring and controlling of water quality parameters such as Total Suspended Solids of watersheds, these being the most vulnerable and less compliance with environmental regulations.

Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images

“…Thus, the generated TSS maps for Lake Unisinos are shown in Figure 6. Although several studies show good results using regression methods to predict TSS [2,7,11,[15][16][17][18][19], others, such as Song et al [6], Amanollahi et al [10], Moridnejad et al [12], and Wu et al [25], compared the two methodologies (RA and ANN) and obtained results indicating better quality in the prediction of the data through an ANN, signaling the capacity of the neural networks to model more complex and non-linear relations between the parameters. Only Kong et al [8] reported that an ANN did not present better results than regression methods for TSS predictions in their area of study.…”

“…Although some applications of UAVs for water quality parameters monitoring, such as chlorophyll-a [1,[15][16][17], organic matter [18], and suspended solids [1,[18][19][20], have been demonstrated in the literature, there are still few studies focused on this application. For suspended solids monitoring, for example, Veronez et al [18] and Saénz et al [19] used regression analyses between TSS values measured in the laboratory and the UAV responses in the visible and near infrared (NIR) regions to generate their prediction models.…”

“…Although some applications of UAVs for water quality parameters monitoring, such as chlorophyll-a [1,[15][16][17], organic matter [18], and suspended solids [1,[18][19][20], have been demonstrated in the literature, there are still few studies focused on this application. For suspended solids monitoring, for example, Veronez et al [18] and Saénz et al [19] used regression analyses between TSS values measured in the laboratory and the UAV responses in the visible and near infrared (NIR) regions to generate their prediction models. Saenz et al [19] explored relations between individual bands and combinations between them (as NIR-red, for example), and Veronez et al [18] chose to relate to vegetation indexes such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI).…”

“…For suspended solids monitoring, for example, Veronez et al [18] and Saénz et al [19] used regression analyses between TSS values measured in the laboratory and the UAV responses in the visible and near infrared (NIR) regions to generate their prediction models. Saenz et al [19] explored relations between individual bands and combinations between them (as NIR-red, for example), and Veronez et al [18] chose to relate to vegetation indexes such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). Although both mentioned studies have shown positive results, the modeling of these parameters in complex environments is not always possible through regression analysis.…”

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

“…A variety of conventional cameras and narrow-band imaging sensors (for example, Micasense Altum with RBG, Red Edge, NIR, and Thermal IR bands; Headwall Hyperspec sensors) have been adapted to drones and aircraft, enabling measurements across the solar reflective spectrum, as well as emissive thermal infrared. The following studies have used drones for a variety of water quality measurements: primary production and trophic state [ 88 ], harmful algal blooms [ 89 , 90 ], macrophytes [ 91 , 92 ], chlorophyll-a [ 93 ], total suspended matter (TSM) [ 94 ], and turbidity [ 95 ]. It is safe to assume that emerging technologies will provide more capabilities in monitoring water quality parameters.…”