Lesson 1 of6
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Useful Gutenberg Blocks

Heading:
Use H2 to H6 for headline size

Headline 2

Headline 3

Headline 4

Headline 5
Headline 6

Paragraph:
to write texts.


List:
use as bullets or numbered list

  • Bullet 1
  • Bullet 2
    • Bullet 2a
  • Bullet 3
  1. Bullet 1
  2. Bullet 2
    1. Bullet 2a
  3. Bullet 3

Quote:

Quote get’s in here

Author can go here

Pullquote:
Give special visual emphasis to a quote from your text.

Quote get’s in here

Auhtor can get in here

Seperator:
Create a break between ideas or sections with a horizontal separator.




Spacer:
Add white space between blocks and customize its height.


Vers:

Insert poetry. Use special spacing formats. Or quote song lyrics ;-)

Buttons:
multiple buttons can be onside one row.


Image:

With caption

Image Gallery:
Display multiple images in a rich gallery. Options for columns avaibable


Media & Text
Set media and words side-by-side. Options for image position and alignment available.

Heading

other Content


Accordion Item

Title 1

Content within

Title 2

Content

With image

Title 3
# check for raster package and install if missing
if(!"raster" %in% rownames(installed.packages())){install.packages("raster")}

# check for sf package and install if missing
if(!"sf" %in% rownames(installed.packages())){install.packages("sf")}

## Libraries
library(raster)
library(sf)
## Download, unzip and load the data
tempdir = "temp"
if (!dir.exists(tempdir)) dir.create(tempdir)
download.file(url = 'https://github.com/GeoScripting-WUR/AdvancedRasterAnalysis/archive/gh-pages.zip', destfile = file.path(tempdir, 'data.zip'), method = 'auto')
unzip(file.path(tempdir, 'data.zip'), exdir=tempdir)
# Move from the temp dir to your working directory and clean up
file.rename(file.path(tempdir, 'AdvancedRasterAnalysis-gh-pages', 'data'), 'data')
unlink(file.path(tempdir, 'AdvancedRasterAnalysis-gh-pages'), TRUE)
file.remove(file.path(tempdir, 'data.zip'))
file.remove(tempdir)

## Load data
load("data/GewataB2.rda")
load("data/GewataB3.rda")
load("data/GewataB4.rda")

## Check out the attributes
GewataB2
## Some basic statistics using cellStats() from the raster package
cellStats(GewataB2, stat=max)
cellStats(GewataB2, stat=mean)
# This is equivalent to:
maxValue(GewataB2)
## What is the maximum value of all three bands?
max(c(maxValue(GewataB2), maxValue(GewataB3), maxValue(GewataB4)))
## summary() is useful function for a quick overview
summary(GewataB2)

## Put the 3 bands into a RasterBrick object to summarize together
gewata <- brick(GewataB2, GewataB3, GewataB4)
# 3 histograms in one window (automatic, if a RasterBrick is supplied)
hist(gewata, maxpixels=1000)

Learning objectives of this topic

  • Understand the current challenges of urban monitoring
  • Overview of data and methods of urban monitoring based on remote sensing
  • Visualization of Global Urban Settlement Layers in Earth Engine and assessment of the changes of urban area for a specific region
  • Land surface temperature (LST) with MODIS data
  • Classification of urban areas with Sentinel 2

Dr. Gohar Ghazaryan

Gohar Ghazaryan is a Tenure track scientist at Leibniz Centre for Agricultural Landscape Research (ZALF). Her research focuses on developing and applying remote sensing methods for environmental monitoring at different scales. She holds a Ph.D. from the University of Bonn, where the core project was focused on crop mapping and drought monitoring and contributed to several projects at the Ruhr University of Bochum, with the focus on land use classification and climate adaptation strategies in cities.