Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao

Remote Sensing Theory & Background IIIGEOG370Instructor: 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.

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.

An Example

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

Sea bassSpecies

Salmon

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…

Classification

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

The length is a poor feature alone!

Select the lightness as a possible feature.

Image classification

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

vegetationLand use/cover classes urban water soil

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

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

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

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

Remote sensing applications Deforestation

Urban growth mapping

Coastal wetlands vegetation

Geology (mineral identification)

Precision Agriculture

Sea surface temperature

Identify invasive species

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)

Remote sensing for biodiversity

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

Elementary Spatial Analysis

OverviewSpatial Analysis

Flowcharting

Query

Defining spatial characteristics

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

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)

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?

Vector dataSelect by attributesSelect by location

Raster dataRaster calculator

GIS Data Query: Vector and rater data

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"))

Raster calculator

Examining vector entities’ attributes

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

GIS Data Query: Vector

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