Basics of

Remote Sensing

 

Britta Ricker, PhD

 

 

Today in Class

  1. GIS & Remote Sensing 
  2. Color Composites
  3. CyberGIS and Big Data
  4. Supervised and Unsupervised Classification
  5. How can remote sensing be used to monitor the SDGs?
  6. 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

 

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

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

image source - and read more about NDVI here

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

Healthy vegetation

Normalized Difference Vegetation Index

NDVI=NIR-IR/NIR+IR

Healthy vegetation

Normalized Difference Vegetation Index

NDVI=NIR-IR/NIR+IR

Other indices

  • Leave Area Index (LAI)
  • Net Primary Production (NPP)
  • Vegetation Fraction (VF)
  • Chlorophyll Index Green
  • Chlorophyll Index Rededge 
  • this list goes on...

Animation

Animation

Slider

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

Supervised & Unsupervised

Classification

Digital image classification 

Assigning pixels to classes

 

tip: Classes should be homogenous (the same)

Digital image classification 

Digital image classification 

Unsupervised vs supervised classification 

Supervised Classification

Human guide the classification by identifying areas on the image that are known to belong to each category

 

Process of using samples of known identify
(pixels assigned to classes) to classify
pixels of an unknown identity

benefits 

  • Analyst has control over selecting categories to specific purpose
  • specific areas of known identify - local knowledge
  • errors can be detected early (hopefully)
  • no need to match spectral categories on the final map 

drawbacks

  • imposed classification structure
  • training areas often vague - secondarily to spectral properties 
  • training areas may not be representative of conditions on the ground
  • training data is time consuming 

Unsupervised Classification

Identify natural groups or structures based on the multispectral data alone

Minimal human input

Unsupervised Classification

benefits

  • no prior knowledge of region required
  • reduce likelihood of human error biased 
  • unique classes as distinct units

drawbacks

  • spectrally homogeneous classes may not correspond to informational categories of interest
  • analysis has limited control over menu of classes
  • spectral properties of specific classes may change over time (seasons)

Big Data and

CyberGIS

 

Cloud Computing

  • valuable for information dissemination

  • faster and more data processing that was not previously possible

  • data access

  • data storage

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

  • Export images, tables, charts, map outputs

  • All in your internet browser!

Integrated Development Environment (IDE) Code Editor features are designed to make developing complex geospatial workflows fast and easy. The Code Editor has the following elements:

  • JavaScript code editor
  • Map display for visualizing geospatial datasets
  • API reference documentation (Docs tab)
  • Git-based Script Manager (Scripts tab)
  • Console output (Console tab)
  • Task Manager (Tasks tab) to handle long-running queries
  • Interactive map query (Inspector tab)
  • Search of the data archive or saved scripts
  • Geometry drawing tools

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

Remote Sensing to Monitor the SDGs

 

Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space. Foody GM, Ling F, Boyd DS, et al. 2019 Remote Sensing . DOI: 10.3390/rs11030266.

Subtitle

Ferreira, B., Iten, M., & Silva, R. G. (2020). Monitoring sustainable development by means of earth observation data and machine learning: a review. Environmental Sciences Europe, 32(1). https://doi.org/10.1186/s12302-020-00397-4

Examples from the web

 

GitHub - LeonidShumilo/Vlab_241: Vlab workflow for SDG indicator 2.4.1 in Vlabd

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.

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? 

What is the difference between supervised and unsupervised classification? 

Name two different Visualization techniques to show change over time.

Thank you!

RS_UCU

By Britta Ricker

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