GIS & Earth Observation

Stuart Green stuart.green@teagasc.ie

How satellites, drones and digital mapping can help with environmental resource management

Land-cover mapping

• Manual Digitisation

• Photomorphic Labelling

• Vegetation Detection

• Unsupervised Classification

• Supervised Classification

– Maxiumum Likelihood

– Random Forest

– Support Vector Machine

• Object Orientated

• Hybrid Approaches

http://conservationmaven.com/frontpage/first-detailed-national-map-of-us-land-cover-vegetation-rele.html

Land-cover mapping – what is land cover

Habitats are described by Fossitt

GA1: Improved Agricultural Grasslands

GS4: Wet Grassland

GS3: Dry-humid acid grassland

Land-cover mapping – what is land cover

• http://gis.epa.ie

Corine 2018

land cover – Land use - Habitat

Land Use is fundamental to GHG reporting- its an ECV

• Forest land

• Cropland

• Grassland

• Wetlands

• Settlements

• Other lands (e.g. bare soil, rock, ice, etc.)

• http://oceanservice.noaa.gov/facts/lclu.html

• Land cover indicates the physical land type such as forest or open water whereas land use documents how people are using the land

Or landcover is what is under your feet- landuse is what you might do with that

https://ghginstitute.org/wp-content/uploads/2015/04/Understanding_Land_Use_in_the_UNFCCC.pdf

land cover – Land use - Habitat

We Often have Levels of classication- the higher the level-the higher the detail

https://www.epa.ie/researchandeducation/research/researchpublications/researchre

ports/Research_Report_254.pdf

Land-cover mapping – what is land cover

Why map Land cover?

Sensors recording optical

wavelengths have long been used

to distinguish land cover classes

based on reflectance patterns

Red (0.6-0.7µm) and NIR (0.9-

1.2µm) to create vegetation indices

During the growing season spectral

signature changes requiring

multiple images to capture

vegetation dynamics and

phenology – cloud can preclude

acquisition of optical images at

optimal times

Growing interest globally to support policy decisions (e.g. European environmental directives and ensure effective land management

One driver is need to provide carbon budget and greenhouse gas inventories – legally binding targets may be enforced in second commitment

period of Kyoto Protocol

Manual

Clicking lines around objects- just like last weeks practical

Automation

Turn an Image into a map- automatically

Replacing the digital numbers in each pixel (that tell us the average spectral

properties of everything in the pixel), with a single number code that represents:

• The majority land cover type in the pixel

• or a biophysical property in the pixel (e.g. amount of biomass)

• or a relative value for a Landover (percentage of pixel that is forestry).

https://landmapping.wordpress.com/

Mapping “Green”

Vegetation Indices are an indictor of the presesnce and abundance of

vegeation

• Biomass: The mass per unit area of vegetation.

• Cover: The vertical projection of the plant parts on

the ground surface per unit area

of ground. Usually expressed as a percent. No

species can have more than 100% cover.

• Leaf Area Index: The ratio of the area of leaves and

green vegetation in theplant canopy per unit area of

ground surface. LAI can exceed 1.The only way to

get true leaf area is to strip all the leaves off the

plants and measure their area. All other methods

provide an “index” of this value

• Normalized Difference Vegetation Index (NDVI):

An index of vegetation greenness derived from

remote sensing methods. Often used as an index

of biomass.

We can measure with the satellite sensors the ratio of red to NIR light- this ratio is called a

Vegetation Index

Mapping “Green”

• http://earthobservatory.nasa.gov/Features/MeasuringVegetation/

• http://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:net_primary_productivity

• ftp://ftp.biosfera.dea.ufv.br/users/francisca/Franciz/papers/Running%20et%20al.%20Bioscience%202004.pdf

Vegetation Indices

Normalized Difference Vegetation Index- NDVI

• The gigantic chlorophyll “absorption well” distinguishes vegetation from non-vegetation.

• Its size tells us chlorophyll concentration in the leaf and the canopy.

• Many vegetation indices are a simplistic attempt to estimate the size of this absorption well.

VI= NIR/R

Vegetation Indices

wavelength

400 600 800 1000 1200

reflecta

nce(%

)0.0

0.1

0.2

0.3

0.4

0.5

density 1

density 2

density 3

density 4

density 5

density 6

sunlit soil

https://www.youtube.com/watch?v=h3gjnD1QqUU

Vegetation Indices

A pixel by pixel mathematical process

NDVI=(B8-B4)/(B8+B4)

Sentinel 2

The answer is between

-1 and +1.

On land only concerned with

Between 0 and 1.

0.2= No vegetation

0.5= some vegetation

Irish grasslands range between

0.5 and 0.8

Simple Vegation maps with NDVI

Simply set thresholds and colour

Less than 0.2 – Water

Less than 0.45- Natural Vegetation

GT>0.45-Farmland

Time Series

Relief Cloud is always a problem…..

Watching how things change

Over the season is very powerful.

Using more than one observation

Will improve your classification

Phenology!!

UNSUPERVISED CLASSIFICATION

Using statistical clustering- the computer allocates every pixel into a

“class”

Class 1

Forest

Class 3

Agriculture

Class 2

Developed

The computer performs a clustering exercise on the image: The

user tells the computer how many clusters to look for and the

computer then analysis the image to \produce this number of

statistically sound clusters.

Most commonly the ISODATA algorithm is used

Images are Just Numbers

We can also plot these numbers on a graph….

Remember these

images are just arrays

of numbers and we

can do maths on

these numbers!

23

40

55

Plot pixels on a graph

Band 1

Band 2

• Two bands of data.

• Each pixel marks a

location in this 2d spectral

space

• Our eye’s can split the

data into clusters.

• Some points do not fit

clusters.

Rory Hutson

Remote Sensing Group

Plymouth Marine Laboratory

Isodata

Where in the landscape?

Unsupervised Classification

This is simple k-means clustering- all the computer is doing is finding

pixels with similar “colours”

2. Minimum Distance calculations:

Each pixel is associated with

closest mean

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Band 1

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Cluster Means 1. Means are initialized along diagonal

3. New mean calculated for each

cluster and means migrate to new

locations

1

2

4. Iterations continue until

convergence or maximum iterations

is reached 5. Each cluster associated with a

value. Each pixel given this value

2

• Unsupervised classification- the objective is to group multi-band spectral response patterns into clusters that are statistically separable

• Our example uses 3 bands – More bands can be used, but it can’t be shown in this 3-D plot

• A = Agriculture; D= Desert; M = Mountains; W = Water

Classify Data into Groups Unsupervised classification using

20 different categories was carried

out. Now, the task will be to group

these categories into some kind of

smaller grouping. In our case we

have been using 5 classes:

Agriculture, Developed, Natural,

Forest, and Water.

Obviously, the red is water, we

can see the Lake. Also, the

purple looks like a city, so we

would call that developed.

The rest of the colors are

anyone’s guess. So, the laborious

process of assigning a category to

the different classes (colors) will

now begin.

Assign a name to each group

Simple Map

Developed

Water

Agriculture

Forest

After about 20 minutes, I was able to assign

the classes with four of the categories to

create a final land use map.

But where is Natural? This is sometimes a

problem in digital image processing. Natural

can look like the other classes

And, based on the digital numbers, we were

unable to discriminate the spectral

differences

This is known as spectral confusion. We

may be able to discriminate between the

extractive and developed if we chose more

classes, but even then it might not be

enough. So, that is part of the struggle we

will have as image processors.

MERMS12 L6 NDVI

Image Pixels

Enlarged 10 Times

Thematic Mapper Image

The limit to zooming – the image has been captured at a max

Resolution- you cannot go beyond this- and in fact well before

This resolution is reached its hard for us to recognise objects-

We loose all that other context infromation we use to interpr

Images

MERMS12 L6 NDVI

• This image of the dying Aral Sea was taken by Envisat's Medium Resolution Imaging Spectrometer (MERIS) instrument on July 9. The whitish land surrounding the remaining waters of the evaporating Aral Sea is a salt-covered dry waterbed known as the Aralkum Desert.

MERMS12 L6 NDVI

Enhancements are used to make it easier for visual

interpretation and understanding of imagery.

Remote sensing devices must be designed to cope with

levels of target/background energy which are typical of all

conditions likely to be encountered in routine use.

With large variations in spectral response from a diverse

range of targets (e.g. forest, deserts, snowfields, water, etc.)

no generic radiometric correction could optimally account for

and display the optimum brightness range and contrast for

all targets. Thus, for each application and each image, a

custom adjustment of the range and distribution of

brightness values is usually necessary.

MERMS12 L6 NDVI

In raw imagery, the useful data often populates only a small portion of the available

range of digital values (commonly 8 bits or 256 levels).

Contrast enhancement involves changing the original values so that more of the

available range is used, thereby increasing the contrast between targets and their

backgrounds. The key to understanding contrast enhancements is to understand the

concept of an image histogram.

A histogram is a graphical representation of the brightness values that comprise an

image. The brightness values (i.e. 0-255) are displayed along the x-axis of the graph.

The frequency of occurrence of each of these values in the image is shown on the y-

axis.

MERMS12 L6 NDVI

By manipulating the range of digital values in an image, graphically represented

by its histogram, we can apply various enhancements to the data. The simplest

type of enhancement is a linear contrast stretch. This involves identifying lower

and upper bounds from the histogram (usually the minimum and maximum

brightness values in the image) and applying a transformation to stretch this range

to fill the full range.

MERMS12 L6 NDVI

A uniform distribution of the input range of values across the full range may not

always be an appropriate enhancement, particularly if the input range is not

uniformly distributed. In this case, a histogram-equalized stretch may be better.

This stretch assigns more display values (range) to the frequently occurring

portions of the histogram.