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.

Slides:



Advertisements
Similar presentations
LANDSAT Program Update Tim Newman Coordinator, USGS Land Remote Sensing Program National Geospatial Advisory Committee December 3, 2014.
Advertisements

Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
VEGETATION MAPPING FOR LANDFIRE National Implementation.
Resolution.
Remote Sensing What can we do with it?. The early years.
Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.
Department of Geography, University of California, Santa Barbara
The image characteristics are usually referred to as:
JIBRAN KHAN 1* &TAHREEM OMAR 2 JIBRAN KHAN&TAHREEM OMAR IMPACTS OF URBANIZATION ON LAND SURFACE TEMPERATURE OF KARACHI.
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
More Remote Sensing Today- - announcements - Review of few concepts - Measurements from imagery - Satellites and Scanners.
Forestry Department, Faculty of Natural Resources
Introduction to Digital Data and Imagery
Measuring Vegetation Health Investigation Using Remote Sensing Data : Spatial Aspects MODIS satellite image of New England.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Segmenting multi bands images by color and texture Eldman O. Nunes - Aura Conci IC - UFF.
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.
Classification & Vegetation Indices
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
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.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
Mapping lichen in a caribou habitat of Northern Quebec, Canada, using an enhancement-classification method and spectral mixture analysis J.Théau, D.R.
Remote Sensing with Multispectral Scanners. Multispectral scanners First developed in early 1970’s Why use? Concept: Gather data from very specific wavelengths.
The role of remote sensing in Climate Change Mitigation and Adaptation.
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Chuvieco and Huete (2009): Fundamentals of Satellite Remote Sensing, Taylor and Francis Emilio Chuvieco and Alfredo Huete Fundamentals of Satellite Remote.
Resolution Resolution. Landsat ETM+ image Learning Objectives Be able to name and define the four types of data resolution. Be able to calculate the.
Winter precipitation and snow water equivalent estimation and reconstruction for the Salt-Verde-Tonto River Basin for the Salt-Verde-Tonto River Basin.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.
Imagery Data  Landsat imagery Apr May 2000 Apr  RapidEye Dec  Fisheye ground survey images Oct Spring 2013 Spring 2014.
MODIS Workshop An Introduction to NASA’s Earth Observing System (EOS), Terra, and the MODIS Instrument Michele Thornton
CHAPTER 9 Band Algebra and Vegetation Indices BAND TRANSFORMATIONS A. Dermanis.
Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.
What is an image? What is an image and which image bands are “best” for visual interpretation?
Multi-bands image analysis using local fractal dimension Aura Conci and Eldman O. Nunes IC - UFF.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
A 30-m approach to cropland extent Cristina Milesi Fourth Workshop On Global Food Security-Support Analysis 30 m (GFSAD30) 6/25/14.
Digital Image Processing, 2nd ed. © 2002 R. C. Gonzalez & R. E. Woods Image Processing Example.
Terra Launched December 18, 1999
Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations G.P.Asner and K.B.Heidebrecht.
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
LIDAR & CIR Airborne 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.
Remote Sensing of Macrocystis with SPOT Imagery
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
Canopy Forest Cover Change in Fanjingshan National Nature Reserve: “What measurable environmental changes have taken place after implementing payment for.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
A Difficult Region for Remote Sensing Studies Probability of imaging the Brazilian Legal Amazon Once per year… Asner (2001) Int’l J. of Remote Sensing.
Citation: Moskal, L. M., D. M. Styers, J. Richardson and M. Halabisky, Seattle Hyperspatial Land use/land cover (LULC) from LiDAR and Near Infrared.
Performance Performance is fundamentally limited by: –Size of data –Where the data is stored –Type of processing –Processing software –Hardware available.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Date of download: 9/17/2016 Copyright © 2016 SPIE. All rights reserved. Location and Landsat TM images of the study area: Wuhan, China. Figure Legend:
Using vegetation indices (NDVI) to study vegetation
Term Project Presentation
Hyperspectral Sensing – Imaging Spectroscopy
Performance Performance is fundamentally limited by: Size of data
LANDSAT OBSERVATİON SATELITE SYSTEM
Two Models Representing World Features
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.
Satellite Sensors – Historical Perspectives
NASA alert as Russian and US satellites crash in space
Hyperspectral remote sensing ('Image spectroscopy')
Planning a Remote Sensing Project
Resolution.
Worldview-1 The DigitalGlobe constellation of very high resolution
Satellite Eyes Slide Show Assessment.
Summary - end of term Lab Monday: 4 April return of assignments Lecture exam: 5 April, projects by end of term: April 6 Lecture evaluation.
Igor Appel Alexander Kokhanovsky
Remote Sensing Section 3.
Presentation transcript:

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 o ) 1 Large Regional Study Area (3.5 o x 4.5 o ) 9 1 Local-Scale Observation Site (LSOS) (1 ha) Landsat Satellite Data

35/32 34/32

Landsat 7 – ETM Data Specifications Image Dimensions for a Landsat 7 Product Band Number Resolution (meters) Samples (columns) Data Lines (rows) Bits per Sample 1-5, 7306, ,3003, ,20012,0008

Landsat – Snow Cover Area Landsat data provides a high spatial and spectral resolution imagery set useful for: Estimation of snow cover extent expressed as fraction per pixel, Estimation of tree canopy cover, Other geophysical and vegetation parameters.

Landsat Data Acquisition Landsat TM 34/ /11/19 34/ /02/07 34/ /10/21 34/ /11/06 34/ /01/09 35/ /11/10 35/ /03/02 Landsat ETM 34/ /02/15 34/ /03/03 34/ /03/19 34/ /04/04 34/ /05/06 34/ /10/13 34/ /11/30 34/ /03/22 35/ /11/02 35/ /03/10 35/ /05/13 35/ /01/08 35/ /03/13

Landsat Satellite Data

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.