# H Exercise 06

Last updated: 2024-06-24 11:11:15

## H.1 Materials

The materials for this exercise are:

• Processing spatio-temporal data (Chapter 11)

## H.2 Question 1

• Read the MOD13A3_2000_2019.tif raster to a multi-band raster object. This raster is a series of NDVI images from southern Israel. The date each layer refers to is recorded in the corresponding MOD13A3_2000_2019_dates.csv file.
• Calculate a 12-band raster that will contain the mean NDVI per pixel (excluding NA) for each month. The first band will contain the mean values per pixel in all images taken in January, the second band will contain the mean values per pixel in all images taken in February, and so on. Use corresponding the MOD13A3_2000_2019_dates.csv table to determine the layers corresponding to each month.
• Plot the resulting raster as shown in Figure H.1 (including month names).

(50 points)

## H.3 Question 2

• The following code creates a data.frame named elk_data:
library(moveHMM)
data(elk_data)
elk_data$ID = as.character(elk_data$ID)
##        ID Easting Northing dist_water
## 1 elk-115  769928  4992847     200.00
## 2 elk-115  766875  4997444     600.52
## 3 elk-115  765949  4998516     561.81
## 4 elk-115  765938  4998276     550.00
## 5 elk-115  766275  4998005     302.08
## 6 elk-115  766368  4998051     213.60
• The elk_data table contains the locations (x=Easting, y=Northing, crs=32617) of four different animals (ID) (elks in Canada), as shown in the following map.

• Add the code section to load elk_data in your exercise.
• Create an sf line layer with the trajectory of each animal (four features) and one attribute (the ID).
• Calculate the convex hull polygon of each animal trajectory.
• Plot the trajectories and the convex hull polygons, using a different color for each animal (Figure H.2). You can use col=hcl.colors(4, "Dark 2") to plot the geometries with four different colors and lty=2 to draw the convex hull polygons using dashed lines.

(50 points)