indices for information extraction from satellite imagery

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INDICES FOR INFORMATION EXTRACTION FROM SATELLITE IMAGERY Course: Introduction to RS & DIP Mirza Muhammad Waqar Contact: [email protected] +92-21-34650765-79 EXT:2257 RG610

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Indices for information extraction from satellite imagery. Mirza Muhammad Waqar Contact: [email protected] +92-21-34650765-79 EXT:2257. RG610. Course: Introduction to RS & DIP. Outlines. Introduction Band Ratio Normalized Difference Vegetation Index (NDVI) - PowerPoint PPT Presentation

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Page 1: Indices for information extraction from satellite imagery

INDICES FOR INFORMATION EXTRACTION FROM SATELLITE IMAGERY

Course: Introduction to RS & DIP

Mirza Muhammad WaqarContact:

[email protected]+92-21-34650765-79 EXT:2257

RG610

Page 2: Indices for information extraction from satellite imagery

Outlines

Introduction Band Ratio Normalized Difference Vegetation Index (NDVI) Normalized Difference Water Index (NDWI) Normalized Difference Snow Index (NDSI) Normalized Difference Built-up Area Index (NDBI) Vegetation Condition Index (VCI) Mean Referenced Vegetation Condition Index (MVCI) NDVI Change Ratio to Previous Year

Page 3: Indices for information extraction from satellite imagery

Introduction

Indices are used to enhance a particular single feature on image.

Different indices are used to enhance different features on image, like enhancement of vegetation, water , snow etc.

Indices discussed in my lecture are1. NDVI (Normalized Difference Vegetation Index)2. NDWI (Normalized Difference Water Index)3. NBI (Normalized Built-up Index)4. NDSI (Normalized Difference Snow Index)

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Band ratio

Aerial images commonly exhibit illumination differences produced by Shadows Differing surface slope angles Slope directions.

Because of these effects, the brightness of each surface material can vary from place to place in the image. Although these variations help us to visualize the three-dimensional shape of the landscape, they hamper our ability to recognize materials with similar spectral properties.

We can remove these effects, and accentuate the spectral differences between materials, by computing a ratio image using two spectral bands.

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Example of Band ratio

A ratio of near infrared (NIR) and red bands (TM4 / TM3)

This ratio is useful in mapping vegetation and vegetation condition.

Note:- Simple band ratio have some problems like sensor noise amplification and range/distortion of the calculated values.

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NDVI (Normalized Difference Vegetation Index)

A variant method of the simple ratio calculation that avoids problems in band ratio.

Corresponding cell values in the two bands are first subtracted, and this difference is then “normalized” by dividing by the sum of two brightness values.

The normalization tends to reduce artifacts related to sensor noise, and most illumination effects still are removed.

The most widely used example is the Normalized Difference Vegetation Index (NDVI), which is

(NIR – RED)/(NIR + RED). Or (Band4 – Band3)/(Band4 + Band3)

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NDVI (Normalized Difference Vegetation Index)

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NDWI (Normalized Difference Water Index)

NDWI is used to enhance water index in a given image.

Formula for normalized difference water index is

(GREEN – NIR)/(GREEN + NIR). Or

(Band4 – Band5)/(Band4 + Band5).

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NDWI (Normalized Difference Water Index)

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NDSI (Normalized Difference Snow Index)

NDSI is used to enhance snow index in a given image.

Formula for normalized difference water index is

(BLUE – NIR)/(BLUE + NIR). Or

(Band2 – Band5)/(Band2 + Band5).

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NDBI (Normalized Difference built-up Index)

NDBI is used to enhance built-up index in a given image.

Formula for normalized difference built-up index is

(Band5 – Band4)/(Band5 + Band4).

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NDBI (Normalized Difference built-up Index)

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Vegetation Condition Index

VCIj is the image of vegetation condition index values for date j;

NDVIj is the image of NDVI values for date j;

NDVImax and NDVImin are images of maximum and minimum NDVI values from all images within the data set;

VCI assesses changes in the NDVI signal through time due to weather conditions, reducing the influence of ‘geographic’ (Kogan, 1990) or ‘ecosystem’ (Kogan, 1995c) variables i.e. climate, soils, vegetation type and topography.

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Mean Referenced Vegetation Condition Index (MVCI)

Let NDVIm(x, y), NDVImax(x, y) and NDVImin(x, y) be the mean, maximum and minimum of the time series NDVI at location (x, y) across entire time span.

Let NDVIi(x, y) be the current NDVI. Then a measure of vegetation condition can be defined by the NDVI percent change ratio to the historical NDVI time series mean NDVIm(x, y) as following:

Page 15: Indices for information extraction from satellite imagery

NDVI Change Ratio to Previous Year

Let NDVIi(x, y) be the current year NDVI value at location (x, y), and NDVIi-1(x, y) be the previous year NDVI. The current year NDVI ratio to the previous year value is given by

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Questions & Discussion