Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure /

Slides:



Advertisements
Similar presentations
Remote Sensing. Readings: and lecture notes Figures to Examine: to Examine the Image from IKONOS, and compare it with the others.
Advertisements

Optical Imaging and Field Spectroscopy: CLPX 2002 and 2003 Thomas H. Painter.
Aerial Photography Aerial platforms are primarily stable wing aircraft. Aircraft are often used to collect very detailed images and facilitate the collection.
Oil spill off NW coast of Spain IKONOS image Oil reaching shore.
Remote Sensing Media Aircraft BasedAircraft Based –photography (BW, Color), infrared (BW, Color) –RADAR (SLAR, SAR) –LIDAR (light detection and ranging)
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
Remote Sensing Hyperspectral Imaging AUTO3160 – Optics Staffan Järn.
Radar, Lidar and Vegetation Structure. Greg Asner TED Talk.
Airborne Laser Scanning: Remote Sensing with LiDAR.
Brian S. Keiling Program Head – Forest Management Dabney S.Lancaster Community College.
Digital Elevation Models GLY 560: GIS and Remote Sensing for Earth Scientists Class Home Page:
FOR 474: Forest Inventory Plot Level Metrics from Lidar Heights Other Plot Measures Sources of Error Readings: See Website.
Hyperspectral Imagery
Remote Sensing What can we do with it?. The early years.
Active Microwave and LIDAR. Three models for remote sensing 1. Passive-Reflective: Sensors that rely on EM energy emitted by the sun to illuminate the.
Modern Remote Sensing: Imagery, Capabilities, Possibilities Paul F. Hopkins Workshop on Advanced Technologies.
Lecture 17 – Forest remote sensing  Reading assignment:  Ch 4.7, 8.23,  Kane et al., Interpretation and topographic correction of conifer forest.
Akira Kato 1, Manabu Watanabe 2, Tatsuaki, Kobayashi 1, Yoshio Yamaguchi 3,and Joji Iisaka 4 1 Graduate School of Horticulture, Chiba University, Japan.
What is RADAR? What is RADAR? Active detecting and ranging sensor operating in the microwave portion of the EM spectrum Active detecting and ranging sensor.
Mapping Fire Scars in Global Boreal Forests Using Imaging Radar Data Written By: L.L. Bourgeau-Chavez, E.S. Kasischke, S. Brunzell, J.P. Mudd, and M. Tukman.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry.
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Using Remote Sensing Imagery By: J.Verplanke,
Remote Sensing Microwave Remote Sensing. 1. Passive Microwave Sensors ► Microwave emission is related to temperature and emissivity ► Microwave radiometers.
U.S. Department of the Interior U.S. Geological Survey Multispectral Remote Sensing of Benthic Environments Christopher Moses, Ph.D. Jacobs Technology.
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.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.
Robert E. Crippen, Ph.D. NASA Jet Propulsion Laboratory California Institute of Technology Pasadena, California USA
Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.
Active Microwave and LIDAR. Three models for remote sensing 1. Passive-Reflective: Sensors that rely on EM energy emitted by the sun to illuminate the.
Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.
Károly Róbert College The GREEN College. Remote sensing applications in disaster management Tibor Bíró dean Károly Róbert College Faculty of Natural Resources.
RASTERTIN. What is LiDAR? LiDAR = Light Detection And Ranging Active form of remote sensing measuring distance to target surfaces using narrow beams of.
Beyond Spectral and Spatial data: Exploring other domains of information GEOG3010 Remote Sensing and Image Processing Lewis RSU.
LiDAR Remote Sensing of Forest Vegetation Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota.
Beyond Spectral and Spatial data: Exploring other domains of information: 4 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Commercial High Resolution Sensors SatelliteResolutionSwath Width Pricing (%$USD)Applications Quickbird-20.61m Pan 2.44m MS 16.5 kmNew:$22/km 2 (min 64km.
RSSJ.
Remote Sensing of Forest Structure Van R. Kane College of Forest Resources.
SGM as an Affordable Alternative to LiDAR
FOR 274: From Photos to Lidar Introduction to LiDAR What is it? How does it work? LiDAR Jargon and Terms Natural Resource Applications Data Acquisition.
Citation: Kato, A.., L. M. Moskal., P. Schiess, M. Swanson, D. Calhoun and W. Stuetzel, LiDAR based tree crown surface reconstruction. Factsheet.
Active Remote Sensing for Elevation Mapping
RADAR.  Go through intro part of LeToan.pdfhttp://earth.esa.int/landtraining07/D1LA1- LeToan.pdf.
UNIT 2 – MODULE 7: Microwave & LIDAR Sensing. MICROWAVES & RADIO WAVES In this section, it is important to understand that radio waves and microwaves.
SCM x330 Ocean Discovery through Technology Area F GE.
U NIVERSITY OF J OENSUU F ACULTY OF F ORESTRY Introduction to Lidar and Airborne Laser Scanning Petteri Packalén Kärkihankkeen ”Multi-scale Geospatial.
Puulajeittainen estimointi ja ei-parametriset menetelmät Multi-scale Geospatial Analysis of Forest Ecosystems Tahko Petteri Packalén Faculty.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Layover Layover occurs when the incidence angle (  ) is smaller than the foreslope (  + ) i.e.,  <  +. i.e.,  <  +. This distortion cannot be corrected!
Lidar Point Clouds for Developing Canopy Height Models (CHM) for Bankhead National Forest Plots By: Soraya Jean-Pierre REU Program at Alabama A & M University.
Light detection and ranging technology Seminar By: Md Hyder Hussain Pasha.
Class tutorial Measuring Earthquake and volcano activity from space Shimon Wdowinski University of Miami.
Factsheet # 27 Canopy Structure From Aerial and Terrestrial LiDAR
Active Microwave Remote Sensing
Factsheet # 17 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Estimating Tree Species Diversity.
Using vegetation indices (NDVI) to study vegetation
Active Remote Sensing for Elevation Mapping
Hyperspectral Sensing – Imaging Spectroscopy
Colour air photo: 15th / University Way
Factsheet # 19 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Hyperspectral Remote Sensing of Urban.
Sea ice remote sensing from space
Remote Sensing of Forest Structure
Remote Sensing What is Remote Sensing? Sample Images
Why LiDAR makes hyperspectral imagery more valuable for forest species mapping OLI 2018 Andrew Brenner, Scott Nowicki & Zack Raymer.
Introduction to Remote-Sensing
Presentation transcript:

Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure / functionality estimation Monitoring – To trace temporal changes of forest Expectable changes: growths, management activities Unexpected changes: disasters, illegal activities

Landuse classification by Landsat TM km Plate 2

Clearcuts in Russian Far East Plate 5

Modern sensors Optical (excl. thermal) – High spatial resolution – Hyperspectral – Aerial photo Radar (SAR; Synthetic Aperture Radar) LiDAR (Light Detection And Ranging) – Large footprint – Small footprint

Optical – High spatial resolution Able to identify individual tree crowns – Count trees – Measure sizes (from above!!!) – Interpret forest structure – Accurate locating by GPS

Stand density estimation by high resolution images LANDSAT TM IKONOS Cedar plantations white dots: tree tops Tree densities DenseSparse Courtesy: N. Furua, FFPRI 160m3km Stand densities

Optical – Hyperspectral Very large number of narrow wavelength bands Spectral Reflectance Band 1 Band 2Band 7Band 5 Band 4 Band 3 VISNIR Landsat TM/ETM+ 224 Bands AVIRIS

Optical – Hyperspectral Forest applications – Species identification – Accurate classification – Chemical contents of leaves – Stress detection (red edge shift) Water/Disease/etc. Available sensors – Space-borne Hyperion (EO-1) – Air-borne AVIRIS/CASI/AISA/etc.

Optical – Aerial photo Surface model from stereo pairs of aerial photo – Automatic calculation – Canopy height (change) detection Retrospectively monitor by old photos Cheaper than LiDAR Mori (2004)

SAR Active sensor using microwaves Responses of objects depend on their permittivity, shapes and sizes Waves penetrate much smaller objects than their wavelengths – Waves can penetrate rain, clouds – Shorter waves cannot penetrate the surface of forest canopies X band: 3.24 cm C band: 5.66 cm L band: 24.0 cm P band: 68.1 cm For forests, – Canopy detection – Volume estimation

SAR

SAR Available sensors – Space-borne ALOS PALSAR (L) JERS1 SAR (L) RADARSAT (C) TerraSARX (X) (to be launched)

LiDAR Light Detection and Ranging Distance and direction to object is measured by the traveling time and direction of laser pulses By scanning the pulses, the height distribution of the ground surface can be estimated in 3D Canopy height is derived by subtracting the ground height from the surface height – No other mean can directly measure the tree/canopy height – Strong correlations with tree dimensions, e.g. volume High accuracy and detailed data – ~10cm error

Courtesy: Y. Hirata, FFPRI LiDAR Small footprint 木木 One shot of laser GPS IMU Time First pulse last pulse LiDAR: Light Detection And Ranging Laser pulse density: 22.5pts/m 2 Detected tree tops: 551 trees out of 657 trees = 83.9 % The technology has been adopted to the National Forest Inventories in North European coutnries