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Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

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Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao
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Page 1: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Remote Sensing Theory & Background IIIGEOG370Instructor: Yang Shao

Page 2: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Vegetation InformationNormalized Difference Vegetation Index

dNIR

dNIR

RRRRNDVI

Re

Re

NDVI: [-1.0, 1.0]

Often, the more the leaves of vegetation present, the bigger theContrast in reflectance in the red and near-infrared spectra.

Page 3: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
Page 4: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

2. Feature space and image classification Imagine you have available image data from a multi-spectral scanner that has two narrow spectral bands. One is centered on 0.65 and the other on 1.0 wavelength. Suppose the corresponding region on the earth’s surface consists of water, vegetation and soil.

Construct a graph with two axes, one representing the brightness of a pixel in the 0.65 band and the other representing the brightness of the pixel in the 1.0 band. In this show where you would expect to find vegetation pixels, soil pixels and water pixels.

Page 5: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

An Example

“Sorting incoming Fish on a conveyor according to species using optical sensing”

Sea bassSpecies

Salmon

Page 6: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Problem Analysis

Set up a camera and take some sample images to extract features

• Length• Lightness• Width• Number and shape of fins• Position of the mouth, etc…

Page 7: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
Page 8: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Classification

Select the length of the fish as a possible feature for discrimination

Page 9: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
Page 10: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

The length is a poor feature alone!

Select the lightness as a possible feature.

Page 11: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
Page 12: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
Page 13: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Image classification

“Labeling image pixels according to land use/cover classes using spectral signals”

vegetationLand use/cover classes urban water soil

Page 14: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

1 1 1 1 1 1 1 11 1 1 1 1 1 1 11 1 2 2 2 2 2 21 1 2 2 2 2 2 21 1 1 2 2 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 1

Forest: 1Non-forest: 2

Page 15: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

1 1 1 1 1 1 1 11 1 1 1 1 1 1 11 1 2 2 2 2 2 21 1 2 2 2 2 2 21 1 1 2 2 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 1

1 1 1 1 1 1 1 11 1 1 1 1 1 1 12 2 2 2 2 2 2 22 2 2 2 2 2 2 22 2 2 2 2 1 1 12 2 2 2 1 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 1

Forest: 1Non-forest: 2

1990 image 2000 image

Page 16: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

1. The rate of land use/cover change

2. The pattern of land use/cover change (e.g., large/small patch, along road/stream)

3. What are the drivers of land use/cover change?

4. What are the environmental, social, economic, and human health consequences of current and potential land-use and land-cover change

Page 17: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Fragmentation Statistics

Landscape CompositionProportional Abundance of each Class

Landscape ConfigurationPatch size distribution and densityPatch shape complexityIsolation/Proximity

See Fragstats website: http://www.umass.edu/landeco/research/fragstats/fragstats.html

Page 18: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Remote sensing applications Deforestation

Urban growth mapping

Coastal wetlands vegetation

Geology (mineral identification)

Precision Agriculture

Sea surface temperature

Identify invasive species

Page 19: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Wrapping up: You should know

What is remote sensing?How it works?Remote sensing data characteristicsNDVIHow image classification worksSome applications (e.g., biodiversity and conservation)

Page 20: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Remote sensing for biodiversity

1. Two approaches - direct and indirect approaches 2. Challenges - spatial/spectral resolution - data analysis

Page 21: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Elementary Spatial Analysis

Page 22: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

OverviewSpatial Analysis

Flowcharting

Query

Defining spatial characteristics

Page 23: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Spatial Analysis

Spatial analysis: Way in which we turn raw data into useful information

A set of techniques whose results are dependent on the locations of the objects being analyzed

Variety of methods

Powerful computers

Intelligent users

Page 24: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Preparing a Spatial Analysis: Flowcharting

Flowchart tools provided by: ESRI’s Model Builder, ERDAS’s GIS Modeler, etc.)

Objective – systematizing thinking and documenting procedures about a GIS application/project

Input OutputOperation

(Plus conditions)

General form of most GIS flowcharts:

From Fundamentals of Geographic Information Systems, Demers (2005)

Page 25: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

GIS Data Query

ImportantWhy?

Narrowing down informationBetter understanding of map

What might you want to know?Which features occur most oftenHow often they occurWhere are they located?

Page 26: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Vector dataSelect by attributesSelect by location

Raster dataRaster calculator

GIS Data Query: Vector and rater data

Page 27: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

GIS Data QueryWhat is it?

Using tools to find records meeting specific criteriaHow?

Select criteriaUse operators to

define expression• Simple • Complex

And: Intersection of setsEx.: ([area] > 1500) and ( [b_room] > 3)

Or: Union of setsEx: ([age] < 18 or [age] > 65)

Not: Subtracts one set from another setEx.: ([sub_region] = "N Eng") and ( not ( [state_name] = "Maine"))

Page 28: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Raster calculator

Page 29: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

Examining vector entities’ attributes

Check spatial objects’ properties•Using identify tool•Using find tool •Performing queries

GIS Data Query: Vector

Page 30: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

GIS Data Query: Raster

Examining raster attributes

Unique colors assigned to attribute values

Tabulating results # of grid cells in each category• For those interested in landscape ecology

fragmentation statistics

Page 31: Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.

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