Land Cover ChangeLand Cover Change

Monitoring change over timeMonitoring change over time

Ned HorningNed HorningDirector of Applied Biodiversity InformaticsDirector of Applied Biodiversity Informatics

horning@amnh.orghorning@amnh.org

http://biodiversityinformatics.amnh.orghttp://biodiversityinformatics.amnh.org

Land Cover ChangeLand Cover Change

change image

early date

late date

Why monitor land cover Why monitor land cover change?change?

Identify areas of deforestation/reforestationIdentify areas of deforestation/reforestation Monitor growth of urban or rural populationsMonitor growth of urban or rural populations Predict future change based on past changePredict future change based on past change Provide data for climate or carbon budget Provide data for climate or carbon budget

modelsmodels Monitor changes in species habitatMonitor changes in species habitat Monitor changes in agriculture patterns Monitor changes in agriculture patterns

What are the options for output What are the options for output products?products?

Classified mapsClassified maps StatisticsStatistics Image mapsImage maps

Classified mapsClassified maps

The most The most familiar type of familiar type of land cover land cover change productchange product

Provides “wall-Provides “wall-to-wall” to-wall” mapped outputmapped output

Typically costly Typically costly and time and time consumingconsuming

StatisticsStatistics

Common during the early years of remote sensingCommon during the early years of remote sensing Relies on sampling statisticsRelies on sampling statistics Primary disadvantage is that accuracy is lower and Primary disadvantage is that accuracy is lower and

mapped output is not createdmapped output is not created

Forest unchangedForest unchanged 6271 6271 Hectares Hectares

67.4% 67.4%

Non-forest Non-forest unchangedunchanged

2823 2823 Hectares Hectares

30.3% 30.3%

DeforestationDeforestation 212 Hectares 212 Hectares 2.3% 2.3%

Total areaTotal area 9306 9306 Hectares Hectares

100% 100%

Visual change imageVisual change image Very quick and easy method for illustrating changeVery quick and easy method for illustrating change Requires minimal skill to create the visualizationRequires minimal skill to create the visualization

Red = Band 5 most recent imageRed = Band 5 most recent image Green = Band 5 older imageGreen = Band 5 older image Blue = Band 5 older imageBlue = Band 5 older image

Interpretation requires familiarity of the landscapeInterpretation requires familiarity of the landscape No quantitative/classified product is produced No quantitative/classified product is produced

Classification approachesClassification approaches

Post classificationPost classification Multi-date compositesMulti-date composites Image mathImage math Spectral change vectorsSpectral change vectors On-screen digitizing/editingOn-screen digitizing/editing On-screen swipe or flickerOn-screen swipe or flicker Multi-temporal RGB imageMulti-temporal RGB image Hybrid approachesHybrid approaches

Comparing two classified Comparing two classified images (post-classification)images (post-classification)

Very intuitiveVery intuitive Rarely the most accurate because errors from Rarely the most accurate because errors from

each land cover classification are added togethereach land cover classification are added together

Early date Late date Change image

Multi-date composite Multi-date composite classificationclassification

Combines imagery from Combines imagery from two dates into a single two dates into a single multi-date imagemulti-date image

Multi-date image is Multi-date image is classified using the classified using the automated classification automated classification method of choicemethod of choice

Advantage is that change Advantage is that change classes are directly outputclasses are directly output

Often the method of choiceOften the method of choice

Image Image mathmath

Uses single-band Uses single-band products (i.e., products (i.e., image bands or image bands or NDVI) from each NDVI) from each datedate

Easy and fast to Easy and fast to computecompute

Output shows Output shows areas that have areas that have changed from changed from one date to the one date to the nextnext

Often used to Often used to create a mask create a mask highlighting highlighting areas that have areas that have undergone some undergone some sort of land sort of land cover changecover change

TM band 5 early date TM band 5 late date

Difference image Image mask white = change

Spectral Spectral change change vectorsvectors

Produces a Produces a magnitude of magnitude of change image change image (similar to (similar to image math) image math) and a direction and a direction of change of change imageimage

On-screen swipe or flickerOn-screen swipe or flicker Visual assessment onlyVisual assessment only Often used to help with on-screen digitizingOften used to help with on-screen digitizing

Multi-temporal Multi-temporal RGB imageRGB image

Visual assessment onlyVisual assessment only Often used to help with Often used to help with

on-screen digitizingon-screen digitizing

Red=band 5 late dateRed=band 5 late date Green=band 5 early dateGreen=band 5 early date Red=band 5 early dateRed=band 5 early date

On-screen digitizing / editingOn-screen digitizing / editing Sometimes called heads-up digitizingSometimes called heads-up digitizing Visual methods are used to manually outline areas that have Visual methods are used to manually outline areas that have

been visually identified as changing from one cover type to been visually identified as changing from one cover type to anotheranother

Editing/updating previous land cover maps with more recent Editing/updating previous land cover maps with more recent imagery can provide a reliable land cover change mapimagery can provide a reliable land cover change map

Requires familiarity of landscapeRequires familiarity of landscape

Hybrid approachHybrid approach

Uses a combination of manual and Uses a combination of manual and automated classification methodsautomated classification methods

Use automated methods to classify the Use automated methods to classify the image and then manual methods to edit image and then manual methods to edit the classificationthe classification

Use automated methods to classify the Use automated methods to classify the “easy” classes and manual methods for “easy” classes and manual methods for the restthe rest

Use automated methods to create land Use automated methods to create land cover for one date then edit the land cover cover for one date then edit the land cover map to determine changemap to determine change

Dealing with different data Dealing with different data sourcessources

Difficult/impossible to use similar imagery Difficult/impossible to use similar imagery when conducting land cover change over a when conducting land cover change over a long time periodlong time period

On-screen digitizing works well since the On-screen digitizing works well since the human brain is pretty good and sorting human brain is pretty good and sorting through the different image qualities when through the different image qualities when using multiple image typesusing multiple image types

Post-classification is an alternative if Post-classification is an alternative if automated methods are preferredautomated methods are preferred

What about data normalizationWhat about data normalization

Goal is to make the two images similar with Goal is to make the two images similar with respect to radiometric and geometric qualitiesrespect to radiometric and geometric qualities

Accurate image-to-image registration is very Accurate image-to-image registration is very important when using automated methods to important when using automated methods to avoid false change due to offset pixels between avoid false change due to offset pixels between datesdates

Image-to-image registration is more important Image-to-image registration is more important than absolution image registrationthan absolution image registration

Radiometric normalization reduces the change Radiometric normalization reduces the change in pixel value between two dates caused by in pixel value between two dates caused by factors other than changes in land coverfactors other than changes in land cover

Issues to considerIssues to consider

Sensor Sensor characteristics characteristics (resolution, (resolution, radiometric)radiometric)

Solar illumination / Solar illumination / seasonalityseasonality

Soil moistureSoil moisture Acquisition date and Acquisition date and

frequencyfrequency Water levels (tide, Water levels (tide,

river and lake level)river and lake level)

Vietnam case studyVietnam case study

Change detection in central VietnamChange detection in central Vietnam Wanted to monitor changes in land Wanted to monitor changes in land

cover from the early 1960’s to the cover from the early 1960’s to the presentpresent

Wanted to use four or five time periods Wanted to use four or five time periods Decided to use ASTER, Landsat ETM+, Decided to use ASTER, Landsat ETM+,

Landsat TM, Landsat MSS, Corona, and Landsat TM, Landsat MSS, Corona, and aerial photography. aerial photography.

Use visual methods primarily Use visual methods primarily

Historical land cover change in Central Vietnam

Red-shanked Douc Langur

http://www.szgdocent.org/pp/p-douc.htm

Saola

http://coombs.anu.edu.au/~vern/species/schaller.htmlhttp://www.wwfindochina.org/conservation/species/saola.htm

• Understand critical biodiversity needs

• Determine how the landscape has taken shape

• Support the development of protected areas

Giant Muntjac

Vietnam’s Central Truong Son

Digital color infraredAcquired: April 21, 2003Spatial resolution: 30 meters

Landsat ETM+

Landsat TMDigital color infraredAcquired: February 17, 1989Spatial resolution: 30 meters

Digital color infraredAcquired: March 14, 1975Spatial resolution: 57 meters

Landsat MSS

Panchromatic (b/w) filmAcquired: March 2, 1969Spatial Resolution: 3 meters

Corona