Forest classification using Landsat 8 satellite images and open source-software

Similar documents
Review for Introduction to Remote Sensing: Science Concepts and Technology

Finding and Downloading Landsat Data from the U.S. Geological Survey s Global Visualization Viewer Website

Resolutions of Remote Sensing

Selecting the appropriate band combination for an RGB image using Landsat imagery

SAMPLE MIDTERM QUESTIONS

A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW ABSTRACT

HANDBOOK for detecting land cover changes with Landsat data archive

Landsat Monitoring our Earth s Condition for over 40 years

ANALYSIS OF FOREST CHANGE IN FIRE DAMAGE AREA USING SATELLITE IMAGES

Cloud Detection for Sentinel 2. Curtis Woodcock, Zhe Zhu, Shixiong Wang and Chris Holden

and satellite image download with the USGS GloVis portal

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS

Lesson 1: How to Download and Decompress USGS GloVis Landsat Data

Spectral Response for DigitalGlobe Earth Imaging Instruments

Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images

Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery

Methods for Monitoring Forest and Land Cover Changes and Unchanged Areas from Long Time Series

Remote Sensing Method in Implementing REDD+

Accuracy Assessment of Land Use Land Cover Classification using Google Earth

Introduction to Imagery and Raster Data in ArcGIS

TerraColor White Paper

Generation of Cloud-free Imagery Using Landsat-8

Software requirements * :

The USGS Landsat Big Data Challenge

Lab #8: Introduction to ENVI (Environment for Visualizing Images) Image Processing

Using Remote Sensing to Monitor Soil Carbon Sequestration

Understanding Raster Data

How Landsat Images are Made

COASTAL MONITORING & OBSERVATIONS LESSON PLAN Do You Have Change?

Lake Monitoring in Wisconsin using Satellite Remote Sensing

Calculation of Minimum Distances. Minimum Distance to Means. Σi i = 1

The Idiots Guide to GIS and Remote Sensing

RESOLUTION MERGE OF 1: SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY

ArcGIS Agricultural Land Use Maps from the Mississippi Cropland Data Layer

TerraAmazon - The Amazon Deforestation Monitoring System - Karine Reis Ferreira

Cloud-based Geospatial Data services and analysis

Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California

Land Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed

SEMI-AUTOMATED CLOUD/SHADOW REMOVAL AND LAND COVER CHANGE DETECTION USING SATELLITE IMAGERY

How To Update A Vegetation And Land Cover Map For Florida

A remote sensing instrument collects information about an object or phenomenon within the

ENVI Classic Tutorial: Atmospherically Correcting Multispectral Data Using FLAASH 2

Forest Service Southern Region Jess Clark & Kevin Megown USFS Remote Sensing Applications Center (RSAC)

Digital image processing

SLC-off Gap-Filled Products Gap-Fill Algorithm Methodology

Data Processing Flow Chart

Remote Sensing and Land Use Classification: Supervised vs. Unsupervised Classification Glen Busch

Preface. Ko Ko Lwin Division of Spatial Information Science University of Tsukuba 2008

INVESTIGA I+D+i 2013/2014

ENVI THE PREMIER SOFTWARE FOR EXTRACTING INFORMATION FROM GEOSPATIAL IMAGERY.

SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007

SMEX04 Land Use Classification Data

Environmental Remote Sensing GEOG 2021

APPLICATION OF MULTITEMPORAL LANDSAT DATA TO MAP AND MONITOR LAND COVER AND LAND USE CHANGE IN THE CHESAPEAKE BAY WATERSHED

MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA

Moderate- and high-resolution Earth Observation data based forest and agriculture monitoring in Russia using VEGA Web-Service

Extraction of Satellite Image using Particle Swarm Optimization

Remote Sensing Satellite Information Sheets Geophysical Institute University of Alaska Fairbanks

RESULTS. that remain following use of the 3x3 and 5x5 homogeneity filters is also reported.

Object-Oriented Approach of Information Extraction from High Resolution Satellite Imagery

Tutorial. VISUALIZATION OF TERRA-i DETECTIONS

III THE CLASSIFICATION OF URBAN LAND COVER USING REMOTE SENSING

Remote sensing is the collection of data without directly measuring the object it relies on the

Landuse and Land Cover Mapping of the Simiyu Catchment (Tanzania) Using Remote Sensing Techniques

LANDSAT 8 Level 1 Product Performance

A Brief Explanation of Basic Web Services

Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite

Obtaining and Processing MODIS Data

The premier software for extracting information from geospatial imagery.

The extraction of digital vector data from historic land use maps of Great Britain using image processing techniques

Files Used in this Tutorial

UTM: Universal Transverse Mercator Coordinate System

ASSESSMENT OF FOREST RECOVERY AFTER FIRE USING LANDSAT TM IMAGES AND GIS TECHNIQUES: A CASE STUDY OF MAE WONG NATIONAL PARK, THAILAND

Cloud Masking and Cloud Products

Improving global data on forest area & change Global Forest Remote Sensing Survey

ENVI Classic Tutorial: Atmospherically Correcting Hyperspectral Data using FLAASH 2

Big data and Earth observation New challenges in remote sensing images interpretation

APPLICATION OF TERRA/ASTER DATA ON AGRICULTURE LAND MAPPING. Genya SAITO*, Naoki ISHITSUKA*, Yoneharu MATANO**, and Masatane KATO***

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features

Land Cover Mapping of the Comoros Islands: Methods and Results. February ECDD, BCSF & Durrell Lead author: Katie Green

Coastline change detection using remote sensing

Analysis of Landsat ETM+ Image Enhancement for Lithological Classification Improvement in Eagle Plain Area, Northern Yukon

Demystifying CLOUD COMPUTING

DETECTING LANDUSE/LANDCOVER CHANGES ALONG THE RING ROAD IN PESHAWAR CITY USING SATELLITE REMOTE SENSING AND GIS TECHNIQUES

MOD09 (Surface Reflectance) User s Guide

Remote Sensing in Natural Resources Mapping

PIXEL-LEVEL IMAGE FUSION USING BROVEY TRANSFORME AND WAVELET TRANSFORM

Myths and misconceptions about remote sensing

Information Contents of High Resolution Satellite Images

2.3 Spatial Resolution, Pixel Size, and Scale

LEOworks - a freeware to teach Remote Sensing in Schools

Pipeline Routing using GIS and Remote Sensing Tobenna Opara Ocean Engineering Department University of Rhode Island

Transcription:

Forest classification using Landsat 8 satellite images and open source-software

Data and programs we are using for the classification Landsat 8 satellite images (maybe Landsat 7) QGIS (Geographic Information System) Semi automatic classification plugin

Landsat history February 2013 Landsat 8

Important developments and facts Landsat 5 Introduced the TM (thematic mapper) sensor Very useful for land cover classification at 30m resolution Landsat 7 Introduced the ETM+ (enhanced thematic mapper plus) sensor Includes a panchromatic band with 15m resolution Data available from 1999 till present Data available for download free of charge since December 2005 In 2003 an error occured (Scan Line Corrector failed)

Scan line corrector error Gaps (stripes) in the images

Scan line corrector error Tools for gap filling PANCROMA ENVI/IDL ERDAS Imagine

Why use Landsat 7 instead of Landsat 8? Landsat 8 is on duty only within the past few months (since February 2013) Only few images are available (the satellite returns to the same place only every 16 days There are less cloud-free images to choose

Why use Landsat 7 instead of Landsat 8 If you find cloud-free images from Landsat 8 The best images for landuse analysis are images taken at the end of the growing season Take Landsat 8 The chance to get a cloud-free image exactly for this period is not high given that Landsat 8 returns to the same location only every 16 days

Landsat 8

Landsat 5 (1984) Landsat 8 (2013)

Landsat 8 Technical facts Two new spectral bands A deep-blue band for coastal water (band 1) A band for cirrus cloud detection (band 9) Pixel size: 15 meters/30 meters/100 meters (panchromatic/multispectral/thermal) Captures approximately 400 scenes a day (Landsat 7: 250) Landsat 8 data are sufficiently consistent with data from the earlier Landsat missions Main instrument: Operational Land Imager (OLI) Improved land surface information

Download Landsat 8 satellite images Glovis http://glovis.usgs.gov Earthexplorer http://earthexplorer.usgs.gov

Select satellite image type

Landsat Path/Row-System Mongolia Path Row Mongolia Paths: 123-143 Rows: 24-31

Select Landsat 8 satellite images Bogd Khan

Select Landsat 8 satellite images Cloud cover The less the better Depending on clouds nature and where the clouds are located Clouds above non forest area is not a problem Clouds above forest area is a problem

Select Landsat 8 satellite images OK

Register for download

Bulk Download Application Right-click and extract with 7-Zip Choose a destination folder for the image(s) Landsat 8 images Consisting of 12 single bands

QGIS Geographic Information System Functions are similar to ArcGIS Open software Changeable and extendable Free software No costs A lot of extensions for free Free download

Semi automatic classification plugin Tool for supervised classification of remote sensing images

QGIS Semi automatic classification plugin

Load Landsat 8 images into QGIS

Land/water Combination of Landsat 8 bands The single bands of Landsat 8 can be combined depending on the purpose of the analysis 5-6-4 Bogd Khan Agriculture 6-5-2 Deep red = forest Vegetation 5-4-3 Color infraret

Create color composite image A color composite image is created with the QGISmerge tool

Check with google or bing maps Select forest pixels

Select forest pixels Pixels around the selected pixel having a similar spectral value are selected With these pixels an average value for the forest classification is calculated All pixels in the satellite image being similar to the average value are classified as forest.

Result: Forest mask Black = Forest White = Non-forest The forest mask can be compared with older forest masks to determine changes Bogd Khan

Forest mask 2013 The forest mask 2013 using Landsat 8 images is currently in process Results are expected by the end of November 2013