Basics of

Remote Sensing

 

Britta Ricker, PhD

GEO3-2419

Land Use Change Science

Objectives of this course

 

https://osiris.uu.nl/osiris_student_uuprd/OnderwijsCatalogusZoekCursus.do

  • Use the basic functionalities of GIS for spatial data processing;
  • Develop land use change scenarios;

  • Identify and assess global and local historical, current and future drivers of land use change;

Today in Class

  1. GIS & Remote Sensing 
  2. Color Composites
  3. CyberGIS and Big Data
  4. Review: How do these concepts relate to land use change?

Disaster and Land Use

Maps can help reduce complexity

Vanacker (2016) Impact of deforestation on slope stability

 

Geographic Information
Science

Geographic Information Systems(GIS)

Geographic Information Systems (GIS)

Tegucigalpa

Capital of Honduras

Population 850,848

landslides and flooding

 

 

Online "slippy" maps

Remote Sensing 

 

Sentinel 2:

S2A (launched 23 June 2015)  and S2B (launched 7 March 2017)

Land use monitoring

Temporal Resolution: Revisit Every 5 days

True Color

Details

From Sentinel 2

Date of image: 22 Feb 2017

Spatial Resolution:

Each pixel represents 20 m

2325 columns

2960 rows of pixels

Sentinel 2

 

Active Sensor

Passive Sensor

Lidar

1 m resolution! 

Electromagnetic Energy

Features on the Earth reflect, absorb, transmit, and emit electromagnetic energy from the sun.

Properties of electromagnetic energy

Multispectral imagery 

wavelength, fequency, amplitude

The visible spectrum is small! 

What else is collected?

Bands and spectral ranges

Spectral signature

How are images rendered on our screen?

3 bands projected light  

The primary colors are Red, Green, Blue

True Color

 

 

= Band 4 Red

 

= Band 3 Green

 

= Band 2 Blue

False Color Composites

Urban areas

 

Urban=Purple​

= Band 12 SWIR II

 

= Band 11 SWIR I

 

= Band 4 Red

Vegetation

 

 

= Band 8 NIR

 

= Band 4 Blue

 

= Band 3 Red

Spectral signature

Bands and spectral signatures

Healthy vegetation

Normalized Difference Vegetation Index

NDVI=NIR-IR/NIR+IR

Animation

Lab Assignment:

Can landslides be correlated to land use change?

Future Lessons:

Quantify Change over time between two different images

 

(atmospheric correction)

 

Future Lessons:

Classification

Supervised & Unsupervised

Animation

Slider

Sentinel-1 images track the major flooding in Kerala during the 2018 monsoon season

Slider

Land use change:

Malakal refugee camp in South Sudan

Digital Globe

Big Data and

CyberGIS

 

Cloud Computing

  • valuable for information dissemination

  • faster and more data processing that was not previously possible

  • data access

Google Earth Engine

  • Cloud-based platform for geospatial analysis

  • Access over 40 years of satellite imagery

  • Upload own data sets to integrate with publicly available data

  • Apply range of common algorithms to data

  • Export images, tables, charts, map outputs

Hansen et al (2013)

High-Resolution Global Maps of 21st-Century Forest Cover Change, Science  

15 Nov. Vol. 342, Issue 6160, pp. 850-853 DOI: 10.1126/science.1244693.

McSweeney (2017) Cocaine trafficking is destroying Central Americ's forests

Title Text

// Load Sentinel-2 TOA reflectance data.
var dataset = ee.ImageCollection('COPERNICUS/S2')
                  .filterDate('2018-01-01', '2018-12-30')
                  // Pre-filter to get less cloudy granules.
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
                  

//true color
var rgbVis = {
  min: 0.0,
  max: 2500,
  bands: ['B4', 'B3', 'B2'],
};
//urban visualization
var urbanVis = {
  min: 0.0,
  max: 2500,
  bands: ['B12', 'B11', 'B4'],
};

//Agriculture visualization
var agVis = {
  min: 0.0,
  max: 2500,
  bands: ['B11', 'B8', 'B2'],
};
//Healthy vegitation visualization
var VegVis = {
  min: 0.0,
  max: 2500,
  bands: ['B8', 'B11', 'B2'],
};

//Land Water visualization
var WaterVis = {
  min: 0.0,
  max: 2500,
  bands: ['B8', 'B11', 'B4'],
};

//Utrecht
Map.setCenter(5.104480, 52.092876, 11);

//different layers 
Map.addLayer(dataset.median(), rgbVis, 'RGB');
Map.addLayer(dataset.median(), urbanVis, 'Urban');
Map.addLayer(dataset.median(), agVis, 'Agriculture');
Map.addLayer(dataset.median(), VegVis, 'Vegitation');
Map.addLayer(dataset.median(), WaterVis, 'Water');

//END

Summary

Benefits of CyberGIS

  • Data access
  • Distributed data processing
  • Information dissemination through interactive visualizations

Review!

In your own words...

What is the difference between an active and passive sensor? 

What is a spectral signature?

What is a false-color composite?

How are they useful for land use change monitoring?

Spatial and

temporal resolution

What do these terms mean and how are they different?

Name two strengths of CyberGIS and cloud computing? 

Name two different Visualization techniques to show change over time.

Thank you!

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