README.md

In RSAlgaeR: Builds Empirical Remote Sensing Models of Water Quality Variables and Analyzes Long-Term Trends

Purpose

This package was developed for processing, modeling, cross-validation, and analysis of water quality conditions (specifically chl-a).

Example analysis

One useful analysis that can be done with this package is evaluating trends in the timing of maximum chl-a conditions.

library(RSAlgaeR)
#Load a record of water quality data
wqrecord <- readRDS('EstimatedRecord.rds')

#Use doy.max.trend function to find the location, value, and day of year when the maximum value occured

wqdoytrend <- doy.max.trend(data=wqrecord,date="ImageDate",value="Chla",location="StationID")

This function calculates the DOY when the maximum value occurs, where this occured, and returns a list containing a dataframe of the annual maxima information, summary of the model fit (DOY vs year) and a plot of the DOY of maximum vs year.

Installation instructions

The package can be installed and loaded using the following commands:

install.packages("RSAlgaeR")
library(RSAlgaeR)

An overview that describes the main components of the package.

A suggested workflow for using the RSAlgaeR Package for formatting/modeling/analyzing data:

1. Format data.

This includes formatting dates, removing negative values, cloud pixels, etc. (See sampleformatreflectancedata.R script for example which uses formatSRdata function)

2. Create model variables.

Use the create.model.vars function

3. Parameterize model.

Use glm() to develop the final model, based on an appropriate season, timewindow, and parameters.

(Optional) Use the step.model function (stepwise regression based on a user-specified timewindow) to explore performance for various parameters, definitions of near coincident data and seasons. Examine model performance using k-fold cross validation and exploring the goodness of fit (cv.model and modresults functions)

4. Apply model.

Apply the model to remotely sensed imagery for a user-specified season using apply.mod.seasonal

5. Plot estimates.

• With error bars: plotrecord.errors
• With data used in calibration: plotrecord.cal
• With field-sampled data: plotrecord

6. Explore trends.

Long term, linear changes in values/year can be explored using the Theil-Sen Estimator, which is more robust than a simple OLS regression. Annually: annualtrend.ts Monthly: monthlytrend.ts

7. Explore connections to local climate conditions.

• Immediate connections Overall, monthly, or by location: climate.factor.effect
• Seasonal summaries of water quality and climate conditions: annual.summary.wq and annual.summary.climate

RSAlgaeR documentation built on May 2, 2019, 3:48 p.m.