Colour composites Dr Nigel Trodd Coventry University.

Slides:



Advertisements
Similar presentations
Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Satellite Remote Sensing for Land-Use/Land-Cover.
Advertisements

Microwave remote sensing applications and it’s use in Vietnam
Interpreting land surface features SWAC module 3.
Example Applications visible / NIR / MIR - day only, no cloud cover vegetation presence geological mapping (structure, mineral / petroleum exploration)
SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.
Landsat-based thermal change of Nisyros Island (volcanic)
Application Of Remote Sensing & GIS for Effective Agricultural Management By Dr Jibanananda Roy Consultant, SkyMap Global.
Remote Sensing Systems. Early Satellite Sensing Spy satellites gave exquisite but very local views and were classified Even before satellites were launched,
NEAR INFRA RED and Thermal Radiation Dr. M. M. Yagoub Dep. Of Geography, UAEU URL:
“ I consider that a man's brain originally is like a little empty attic, and you have to stock it with such furniture as you choose. A fool takes in all.
Dr. Garver GEO 420. Radiation that reaches that surface interacts with targets in 3 ways: Absorption(A), transmission(T), reflection (R).
Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.
Data Merging and GIS Integration
A class delivered at the 3rd IUCN World Conservation Congress
Remote Sensing Part 1.
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
Principals of Remote Sensing
More Remote Sensing Today- - announcements - Review of few concepts - Measurements from imagery - Satellites and Scanners.
Remote Sensing Applications. Signatures – a unique identifier…
Course: Introduction to RS & DIP
DROUGHT MONITORING THROUGH THE USE OF MODIS SATELLITE Amy Anderson, Curt Johnson, Dave Prevedel, & Russ Reading.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Copyright © 2003 Leica Geosystems GIS & Mapping, LLC Turning Imagery into Information Suzie Noble, Product Specialist Leica Geosystems Denver, CO.
Remote Sensing on the Nez Perce Reservation Nez Perce Tribe -Land Services Laurie Ames Remote Sensing Analyst By asking for the impossible we obtain the.
earthobs.nr.no Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images Arnt-Børre Salberg and.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Spectral Characteristics
Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.
Dr. Garver GEO 420 Sensors. So far we have discussed the nature and properties of electromagnetic radiation Sensors - gather and process information detect.
Remote Sensing with Multispectral Scanners. Multispectral scanners First developed in early 1970’s Why use? Concept: Gather data from very specific wavelengths.
Karnieli: Introduction to Remote Sensing
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Introduction to Remote Sensing Images By:
Remote Sensing Introduction to light and color. What is remote sensing? Introduction to satellite imagery. 5 resolutions of satellite imagery. Satellite.
Infoday Environment Alicia Palacios Orueta ETSI Montes. Universidad Politécnica de Madrid. Spain School of forestry. Department of Silvopasciculture
What is an image? What is an image and which image bands are “best” for visual interpretation?
VQ3a: How do changes in climate and atmospheric processes affect the physiology and biogeochemistry of ecosystems? [DS 194, 201] Science Issue: Changes.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
4.3 Digital Image Processing
AGRAR THEMATIC MAPPING - ThemAMap This Project is funded by the Austria Research and Promotion Agency (FFG) Intermediate Project Results.
Hyperspectral remote sensing
earthobs.nr.no Retraining maximum likelihood classifiers using a low-rank model Arnt-Børre Salberg Norwegian Computing Center Oslo, Norway IGARSS.
ERDAS 1: INTRODUCTION TO ERDAS IMAGINE
Satellite Band Combinations.. Bands 3, 2, 1 in red, green, blue. This is considered the natural colour composition. It is usually used primarily for display.
Beyond Spectral and Spatial data: Exploring other domains of information: 3 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
U N I V E R S I T À D E G L I S T U D I D I M I L A N O C17 SC for Environmental Applications and Remote Sensing I M S C I A Soft Computing for Environmental.
M. Shah Alam Khan Associate Professor Institute of Water and Flood Management, Bangladesh University of Engineering and Technology Hydro-ecological Investigation.
Interactions of EMR with the Earth’s Surface
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
Orbits and Sensors Multispectral Sensors. Satellite Orbits Orbital parameters can be tuned to produce particular, useful orbits Geostationary Sun synchronous.
Copernicus's contribution to land cover mapping in Africa Andreas Brink Senior Scientist Joint Research Centre – European Commission AfriGEOSS April.
Satellite based Sensors for Agricultural Applications
Sensors Dr. Garver GEO 420.
Dr. Pinliang Dong Associate Professor Department of Geography University of North Texas USA.
Dr. P Shanmugam Associate Professor
Week Fourteen Remote sensing of vegetation Remote sensing of water
Using vegetation indices (NDVI) to study vegetation
Copernicus - contribution to land cover mapping in Africa Andreas Brink Senior Scientist Joint Research Centre – European Commission AfriGEOSS
Colour air photo: 15th / University Way
HIERARCHICAL CLASSIFICATION OF DIFFERENT CROPS USING
Landsat-based thermal change of Nisyros Island (volcanic)
ERT 247 SENSOR & PLATFORM.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Land Cover and Soil Properties of the San Marcos Basin
Green Revolution 2.0 Remote Sensing.
Image Information Extraction
DECISION SUPPORT TOOLS Draft For Discussion Purposes
Unit 3: Life Supports SUMMARY Vegetation & Climate Composition of soil
REMOTE SENSING.
Remote Sensing Landscape Changes Before and After King Fire 2014
Agricultural Intelligence From Satellite Imagery
Presentation transcript:

Colour composites Dr Nigel Trodd Coventry University

Single band of data l grey levels correspond to digital numbers (DN)

Multispectral colour composite l 3 gun monitor l 1 band of data displayed thru’ 1 colour gun

Multiband selection

Landsat ETM+ spectral regionrole 1 Bluemap shallow water bodies, identify forest type 2 Greendiscriminate vegetation & assess vigour, detect man-made objects 3 Redidentify plant species & phenological status, detect man- made objects 4 Very Near IRIdentify plant species, assess vigour & estimate biomass, delineate water bodies, estimate soil moisture 5 Short Wave IRdelineate water bodies, estimate vegetation & soil moisture, differentiate snow from cloud cover 6 Thermal IRestimate soil moisture, analyse vegetation stress 7 Short Wave IRIdentify rock type & mineralisation, estimate vegetation moisture Panchromaticdetect objects, locate boundaries

Mapping coastal waters spectral regionrole 1 Bluemap shallow water bodies, identify forest type 2 Greendiscriminate vegetation & assess vigour, detect man-made objects 3 Redidentify plant species & phenological status, detect man- made objects 4 Very Near IRIdentify plant species, assess vigour & estimate biomass, delineate water bodies, estimate soil moisture 5 Short Wave IRdelineate water bodies, estimate vegetation & soil moisture, differentiate snow from cloud cover 6 Thermal IRestimate soil moisture, analyse vegetation stress 7 Short Wave IRIdentify rock type & mineralisation, estimate vegetation moisture Panchromaticdetect objects, locate boundaries

Monitoring crops spectral regionrole 1 Bluemap shallow water bodies, identify forest type 2 Greendiscriminate vegetation & assess vigour, detect man-made objects 3 Redidentify plant species & phenological status, detect man- made objects 4 Very Near IRIdentify plant species, assess vigour & estimate biomass, delineate water bodies, estimate soil moisture 5 Short Wave IRdelineate water bodies, estimate vegetation & soil moisture, differentiate snow from cloud cover 6 Thermal IRestimate soil moisture, analyse vegetation stress 7 Short Wave IRIdentify rock type & mineralisation, estimate vegetation moisture Panchromaticdetect objects, locate boundaries

Mapping urban areas spectral regionrole 1 Bluemap shallow water bodies, identify forest type 2 Greendiscriminate vegetation & assess vigour, detect man-made objects 3 Redidentify plant species & phenological status, detect man- made objects 4 Very Near IRIdentify plant species, assess vigour & estimate biomass, delineate water bodies, estimate soil moisture 5 Short Wave IRdelineate water bodies, estimate vegetation & soil moisture, differentiate snow from cloud cover 6 Thermal IRestimate soil moisture, analyse vegetation stress, thermal mapping 7 Short Wave IRIdentify rock type & mineralisation, estimate vegetation moisture Panchromaticdetect objects, locate boundaries

Summary: 3 steps 1. Identify individual waveband(s) 2. Combine (upto) 3 bands 3. Allocate one band of data to one colour gun