matchup_satellite_track_data.Rmd

---
title: Matchups satellite data to animal tracks
author: NOAA CoastWatch
date: May 20, 2021
output:
  md_document:
    variant: gfm
---

# Matchups to ship or animal tracks
>notebook filename | matchup_satellite_track_data.Rmd    
history | Created May 2021 - converted to R notebook from xyt_matchup.R  

This exercise you will extract satellite data around a set of points defined by longitude, latitude, and time coordinates like that produced by an animal telemetry tag, and ship track, or a glider tract. 

The exercise demonstrates the following techniques:  

* Using the **rxtracto** function to extract satellite data along a track  
* Using **rerddap** to retrieve information about a dataset from ERDDAP  
* Using **plotTrack** to plot the satellite data onto a map as well as to make an animation 
* Loading data from a tab separated file  
* Plotting the satellite data onto a map

This data is taken from the ERDDAP server at [http://coastwatch.pfeg.noaa.gov/erddap/](http://coastwatch.pfeg.noaa.gov/erddap/)

## Install required packages and load libraries
```{r install,message=FALSE,warning=FALSE}

# Function to check if pkgs are installed, and install any missing pkgs

pkgTest <- function(x)
{
  if (!require(x,character.only = TRUE))
  {
    install.packages(x,dep=TRUE,repos='http://cran.us.r-project.org')
    if(!require(x,character.only = TRUE)) stop(x, " :Package not found")
  }
}

# create list of required packages
list.of.packages <- c("ncdf4", "rerddap", "plotdap", "parsedate",
                      "graphics", "maps", "mapdata", "RColorBrewer", "ggplot2", "gifski",
                      "png", "rerddapXtracto")

# create list of installed packages
pkges = installed.packages()[,"Package"]

# Install and load all required pkgs
for (pk in list.of.packages) {
  pkgTest(pk)
}

```
## Get XYZ coordinates
In this exercise we willuse in the XYZ coordinates that have been brought in from a file. Installation of the "rerddapXtracto" package comes with the "Marlintag38606" dataset which we will use for this exercise. It is the track of a tagged marlin in the Pacific Ocean (courtesy of Dr. Mike Musyl of the Pelagic Research Group LLC). 

The "Marlintag38606" file has this structure:  

```{r tag}
str(Marlintag38606)
```

We will use the "date", "lon" and "lat" variables to get the matching satellite data. 
Here the time variable is already in a date format.  Often when reading in your own data you will have to convert the date into a date format (Remember R syntax is Y for a 4 digit year and y for a 2 digit year)

```{r rename}

## For convenience make shorter names for the variables  
xcoord <- Marlintag38606$lon  
ycoord <- Marlintag38606$lat
tcoord <- Marlintag38606$date

```

## Select the dataset and download its metadata  
For this example we will use the SeaWiFS 8-day composite chlorophyll dataset (ID erdSW2018chla8day)  

**The script below:**  

* Gathers information about the dataset (metadata) using **rerddap**  
* Displays the information  

**Set the following arguments for rerddap**  

* Set the dataset ID: dataset <- 'erdSW2018chla8day'

* The default source ERDDAP for **rerddap** is "https://upwell.pfeg.noaa.gov/erddap". Since we are pulling the data from the ERDDAP at "http://coastwatch.pfeg.noaa.gov/erddap/", change the url to url = "http://coastwatch.pfeg.noaa.gov/erddap/"

```{r dataInfo}
dataset <- 'erdSW2018chla8day'
# Use rerddap to get dataset metadata 
# if you encouter an error reading the nc file clear the rerrdap cache: 
rerddap::cache_delete_all(force = TRUE)
dataInfo <- rerddap::info(dataset, url= "https://coastwatch.pfeg.noaa.gov/erddap/")
# Display the metadata
dataInfo
```
## Extract the satellite data  

* Double check dataInfo to make sure the dataset covers the time, longitude, and latitude ranges in your XYT data.  

* Use the name of the chlorophyll parameter that was displayed above in dataInfo: **parameter <- "chlorophyll"**  

* Use the xcoord, ycoord, and tcoord vectors you extracted from the marlin tag file. 

* Some datasets have an altitude dimension. If so, then zcood must be included in the rxtracto call. The "erdSW2018chla8day" dataset does not include an altitude dimension.  

* Define the search "radius" for the gridded data. The **rxtracto** function allow you to set the size of the box used to collect data around the track points using the xlen and ylen arguments. The values for xlen and ylen are in degrees. For our example we 0.2 degrees for both arguments. Note: You can also submit vectors for xlen and ylen, as long as the are the same length as xcoord, ycoord, and tcoord

* Run the rxtracto function to extract the data from ERDDAP.  

```{r rxtracto, message=FALSE}

parameter <- 'chlorophyll'

xlen <- 0.2 
ylen <- 0.2

# Some datasets have an altitude dimension. If so, then zcood must be included in the rxtracto call.  
# If the dataInfo shows an altitude dimension, uncomment "zcoord <- 0" and include zcoord=zcoord in the rxtracto call.
# zcoord <- 0.

swchl <- rxtracto(dataInfo, 
                  parameter=parameter, 
                  xcoord=xcoord, ycoord=ycoord, 
                  tcoord=tcoord, xlen=xlen, ylen=ylen)

```

After the extraction is complete, "swchl" will contain the following columns.

```{r swchl}
str(swchl)
```


## Plotting the results

We will use the "plotTrack" function to plot the results.  
* "plotTrack" is a function of the "rerddapXtracto" package designed specifically to plot the results from "rxtracto".  

* The example below uses a color palette specifically designed for chlorophyll. 
  
```{r plot}
# Uncomment the png line and the dev.off() line to save the image
# png(file="xyt_matchup.png")

plotTrack(swchl, xcoord, ycoord, tcoord, plotColor = 'algae')

#dev.off()
```

## Animating the track

Make a cumulative animation of the track. This will take a minute to run. It creates an animated gif that will display in the Rstudio viewer window once the encoding to gif is done.

```{r track}
plotTrack(swchl, xcoord, ycoord, tcoord, plotColor = 'algae',
                    animate = TRUE, cumulative = TRUE)
```

## Try this on your own
This match up was done using weekly (8-day) data. Try rerunning the example using the daily (erdSW2018chla1day) or the monthly (erdSW2018chlamday) satellite data product and see how the results differ 

## Crossing the Dateline
In July 2019 version 0.4.1 of "reddapXtracto"" was updated allowing "rxtracto"" to work on data that crosses the dateline. In this example we will extract chlorophyll data for a grid of stations along the Aleutian Islands. 

__Create a station array__   
For crossing the dateline the longitudes for that animal/ship track must be in 0-360 format.  
* Create a grid of stations from 172E to 170W (190°) and 50-54N, spaced every 2°.
* Then, set up vectors with these values, and then make arrays of the station longitudes and latitudes

```{r stn}
lat <- seq(50,54,2)
lon <- seq(173,189,2)

stax <- matrix(lon,nrow=length(lat),ncol=length(lon),byrow=TRUE)
stay <- matrix(lat,nrow=length(lat),ncol=length(lon),byrow=FALSE)
```

To input values into "rxtracto" the longitudes and latitudes need to be in vector format
```{r addvals}
xcoord <- as.vector(stax) 
ycoord <- as.vector(stay) 
```

Define the search "radius" in the x any y directions, in units of degrees
```{r}
xlen <- 0.2 
ylen <- 0.2 
```

Create an array of dates. For this exercise we are going to assume all stations were sampled in the same month, so we are going to make all the values the same, but they don't have to be.   

```{r dates}
tcoord <- rep('2019-04-15',length(xcoord))
```

Selects the dataset and parameter for the extraction  
In this example the dataset chosen is the monthly OC-CCI chlorophyll data 
```{r}
url <- "https://coastwatch.pfeg.noaa.gov/erddap/"
dataset <- 'pmlEsaCCI50OceanColorMonthly'
dataInfo <- rerddap::info(dataset,url=url)
parameter <- 'chlor_a'
```

## Look at DataInfo to see if dataset has an altitude dimension. 

```{r dinfo}
dataInfo
```


This dataset does not have an altitude dimension, so we do not need to supply an altitude parameter in the following "rxtracto" call. 

Note that in both rxtracto() and rxtracto_3D() the zcoord can be a range. 
* For rxtracto_3D() if the zCoord needs to be given, it must be of length two. 
* For rxtracto() if the zCoord needs to be given, it must be of the same length as the other coordinates, and can also have a “zlen”“, like ”xlen" and “ylen”, that defines a bounding box within which to make the extract. 
* The advantage of this is it allows rxtracto() to make extracts moving in (x, y, z, t) space.

## Matchup satelite data and station locations using rxtracto()
Now we will make the rxtracto call to match up satellite data with station locations.  
```{r matchup}
chl <- rxtracto(dataInfo, 
                  parameter=parameter, 
                  xcoord=xcoord, ycoord=ycoord, 
                  tcoord=tcoord, xlen=xlen, ylen=ylen)
```


Next we will map the data. We will do this two different ways, using base graphics and using "ggplot".  

Note that "plotTrack", the routine used in the example above, is part of the "rerddapXtracto" package, and is designed to easily plot the output from "rxtracto", but currently it can not handle crossing the dateline, so we can't use it for this example. 

## Map Method 1: Make a map using base graphics  

First set up the color palette. 
This will use a yellow-green palette from the Brewer package  


```{r m1}
cols <- brewer.pal(n = 9, name = "YlGn")
chlcol <- cols[as.numeric(cut(chl$'mean chlor_a',breaks = 9))]
```

Identify stations which have a satellite values

```{r stid}
gooddata <- !is.na(chl$'mean chlor_a')
```

Set-up the layout to have a map and a color bar 

```{r layout}
oldmar <- par("mar")
layout(t(1:2),widths=c(6,1))
par(mar=c(4,4,1,.5))
```

Create the base map, and then overlay stations with data, and then overlay empty circles around all statons 
```{r basmap}
ww2 <- map('world', wrap=c(0,360), plot=FALSE, fill=TRUE)
map(ww2, xlim = c(140, 240),ylim=c(45,70), fill=TRUE,col="gray80",lforce="e")
map.axes(las=1)

points(xcoord[gooddata],ycoord[gooddata],col=chlcol, pch=19, cex=.9)
points(xcoord,ycoord, pch=1, cex=.9)
```

Add the colorbar 
```{r colorbar}
par(mar=c(4,.5,5,3))
chlv <- min(chl$'mean chlor_a'[gooddata])+(0:9)*(max(chl$'mean chlor_a'[gooddata])-min(chl$'mean chlor_a'[gooddata]))/10
image(y=chlv,z=t(1:9), col=cols, axes=FALSE, main="Chl", cex.main=.8)
axis(4,mgp=c(0,.5,0),las=1)
```


## Map Method 2: ggplot graphics  
ggplot handles colorbars much easier than base graphics! 

Put station lat, long and chl values into a dataframe for passing to ggplot 
 
```{r adddf}
chlsta <- data.frame(x=xcoord,y=ycoord,chl=chl$'mean chlor_a')
```

Get land boundary data in 0-360 units of longitude. 

```{r land}
mapWorld <- map_data("world", wrap=c(0,360))
```

Make the map 
```{r map1}
ggplot(chlsta) +
  geom_point(aes(x,y,color=chl)) +
  geom_polygon(data = mapWorld, aes(x=long, y = lat, group = group)) + 
  coord_cartesian(xlim = c(140,240),ylim = c(47,70)) +
  scale_color_gradientn(colours=brewer.pal(n = 8, name = "YlGn")) +
  labs(x="", y="")
```