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

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

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

Transcription

1 Methods for Monitoring Forest and Land Cover Changes and Unchanged Areas from Long Time Series Project using historical satellite data from SACCESS (Swedish National Satellite Data Archive) for developing time-series methods in preparation for mapping and monitoring of forest and landcover with Sentinel-2 and Landsat-8 Mats Rosengren, Metria Greger Lindeberg, Metria Prof Håkan Olsson, Swedish University of Agricultural Sciences Anders Persson, Swedish Forest Agency Erik Willén, Metria Sentinel-2 for Science Workshop May 2014 ESA ESRIN Frascati

2 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Content Project background National operational use of SPOT/IRS/Landsat 10-30m Saccess, Swedish National Satellite Data Archive Previous development (change detection and time series for forest applications) Project work Time series stack data preparation and calibration Cloud / shadow mask preparation Time series methods for change detection (CUSUM) Integrated statistical measures from image stacks using full history for mapping Results Change mapping / Clear cuts, thinning, burned areas Land Cover mapping / forest, non-forest, agriculture Unchanged areas / unproductive forest impediments (rocks, mires) Method implementation in other projects

3 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Current operational use of SPOT/IRS/Landsat in Sweden using yearly national satellite data coverages Yearly national clear cut mapping for legal monitoring (Swedish Forest Agency) [started National Forest Inventory based knn timber volume estimates (NFI -Swedish University of Agricultural Sciences) KNAS - Recurrent mapping updates for all nature reserves, national parks, Natura2000 and other protected areas (appr km 2 ) (Swedish EPA/Metria) Based on SACCESS Swedish national satellite data archive yearly coverages, free, jointly funded and sponsored by: Swedish National Space Board Lantmäteriet (National Land Survey of Sweden) Swedish Environmental Protection Agency Swedish Forest Agency Swedish University of Agricultural Sciences Metria AB 4 Forest Companies Holmen AB SCA Svenska Cellulosa Aktiebolaget Sveaskog Bergvik Skog

4 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 SACCESS Swedish National Satellite Data Archive (saccess.lantmateriet.se) SPOT5/ SPOT4/ IRS / Landsat Yearly national coverages > One best cloud free coverage per year from vegetation season Historical coverages 1970ies (MSS), 1980ies (TM), 1990ies, 2000 Used for Land Cover change mapping and monitoring Free access to residents/companies registered in the Nordic Countries

5 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Time Series Project Areas Sjuhärad Långsele Långsele (lat 63deg N) 25x25 km 17 scenes common bands Spot4 Spot5 Landsat5 Landsat7 IRSP6 Sjuhärad (lat 57 deg N) 50x50km 25 scenes common bands Landsat 1 (MSS) [3 bands] Spot4 Spot5 Landsat5 Landsat7 IRSP6 Landsat-8

6 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Data preparation and calibration of time stacks Manual cloud mask Resampling to 10m pixels Saccess data used with no additional geometric modelling Landsat data thorugh USGS, reprojected without additional geometric refinement Calibration o o Långsele Reference scene TOA reflectance calibrated (SPOT) All scenes relative calibrated (normalized) using mean/ standarddev under forest mask from map Sjuhärad Reference scene atmospheric corrected into surface reflectance (SPOT5) All scenes relative calibrated (normalized) using mean/ standarddev under forest mask from map This task has been the major (manual) effort within the project. Standardised user products from Sentinel-2 and Landsat-8 of Atmospheric corrected surface reflectance Orthocorrected with good DEM Cloud/shadow masks with automatic methods are needed to achieve operational monitoring applications.

7 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Långsele Scene list (from Saccess) No Comment Scene id MISSION PATHROW DATE 0 L5_194016_ LANDSAT-5 194/ S4_053220_ SPOT-4 053/ L7_195016_ LANDSAT-7 195/ clouds L7_196016_ LANDSAT-7 196/ S4_053220_ SPOT-4 053/ Same date S5_053220_ SPOT-5 053/ Same date S4_050220_ SPOT-4 050/ P6_024020_ IRS-P6 024/ S5_053220_ SPOT-5 053/ S5_053220_ SPOT-5 053/ S5_050220_ SPOT-5 050/ S4_053220_ SPOT-4 053/ Clouds, haze S5_053220_ SPOT-5 053/ L5_194016_ LANDSAT-5 194/ Same date L5_195016_ LANDSAT-5 195/ Same date, clouds S5_053220_ SPOT-5 053/ S5_053220_ SPOT-5 053/ Dates between 14 May 14 Sept Selected one scene per year for best cloud free time series Seasonal differences, cloudy data are the major problems.

8 Sjuhärad Scene list (from Saccess + USGS) 1972 (Landsat 1 MSS) 2013 (Landsat-8) Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 No Comment Scene id MISSION PATHROW DATE Time Sensor MODE Look angle Sun elevation 1 L1_210020_ LANDSAT :46:00 MSS L5_194020_ LANDSAT :37:49 TM L5_195020_ LANDSAT :41:04 TM L5_195020_ LANDSAT :33:37 TM L5_194020_ LANDSAT TM 46.0 Prod date 6 s4_053232_990519_1i0 SPOT / :15:16 HRV-1 XS Same date s4_053232_990911_1i0 SPOT / :02:30 HRV-1 XS Same date L7_195020_ LANDSAT s5_054232_030603_1j0 SPOT / :05:37 HRG-1 XS s5_050232_030914_2j0 SPOT / :24:40 HRG-2 XS L5_194020_ LANDSAT :02:55 TM MS s4_050232_040908_1i0 SPOT :28:18 HRVIR1 XS s4_053232_040929_1i0 SPOT :24:31 HRVIR1 XS s5_053232_050901_2j0 SPOT :13:25 HRG2 XS s4_053232_050906_1i0 SPOT :47:19 HRVIR1 XS s5_053232_060715_1j0 SPOT :16:36 HRG1 XS p6_025026_ IRS-P / :29:43 LISS-3 MS s5_053232_070913_2j0 SPOT / :39:51 HRG-2 XS p6_025026_ IRS-P / :28:58 LISS-3 MS s5_050232_090530_1j0 SPOT / :19:06 HRG-1 XS SR reference s5_053232_090626_2j0 SPOT / :00:25 HRG-2 XS s5_053232_100604_2j0 SPOT / HRG s5_053232_120727_1j0 SPOT / HRG L8_195020_ LANDSAT L8_195020_ LANDSAT NA For cloud detection test L8_195020_ LANDSAT Dates between 5 May 29 Sept. Sun elevation between 29 deg 55 deg Seasonal differences, cloudy data are the major problems.

9 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Långsele - 17 scenes refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img refl_12sub_s5_053220_ img refl_13sub_l5_194016_ img refl_14sub_l5_195016_ img refl_15sub_s5_053220_ img refl_16sub_s5_053220_ img

10 refl_0sub_l5_194016_ img

11 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img

12 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img

13 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img

14 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img

15 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img

16 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img

17 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img

18 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img

19 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img

20 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img

21 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img

22 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img refl_12sub_s5_053220_ img

23 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img refl_12sub_s5_053220_ img refl_13sub_l5_194016_ img

24 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img refl_12sub_s5_053220_ img refl_13sub_l5_194016_ img refl_14sub_l5_195016_ img

25 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img refl_12sub_s5_053220_ img refl_13sub_l5_194016_ img refl_14sub_l5_195016_ img refl_15sub_s5_053220_ img

26 refl_0sub_l5_194016_ img refl_1sub_s4_053220_ img refl_2sub_l7_195016_ img refl_3sub_l7_196016_ img refl_4sub_s4_053220_ img refl_5sub_s5_053220_ img refl_6sub_s4_050220_ img refl_7sub_i6_024020_ img refl_8sub_s5_053220_ img refl_9sub_s5_053220_ img refl_10sub_s5_050220_ img refl_11sub_s4_053220_ img refl_12sub_s5_053220_ img refl_13sub_l5_194016_ img refl_14sub_l5_195016_ img refl_15sub_s5_053220_ img refl_16sub_s5_053220_ img

27 Project requirements Robust method regarding phenological differences between scenes Able to use a combination of available datasets (SPOT, Landsat, IRS) Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Develop method for automatic mapping of permanent radiometric changes (Clear cuts, thinnings etc.) => CUSUM Extract time stamp date - on changes (dates of images before and after change) Change magnitude (per spectral band) Measure of confidence Test the feasibility of integrated statistical measures for the full historical time series for land cover classification (5-6 major classes) the description of the present state is a function of the history of the pixel Forest Water Agriculture Urban Other open areas Test mapping of unchanged areas within map forest mask = unproductive forest

28 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Methods CUSUM (Cumulative sum control chart) used for change mapping cumulative sum of pixel value over time All time-scrambled combinations used for calculation of point in time with maximum confidence of change Change magnitude at mapped time of change Timeseries CUSUM

29 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Change types within forest clear cuts / thinnings Examples based on only B4 (MIR) B4_refl_stack_change.img B4_refl_stack_conf_level.img B4_refl_stack_10scener_magn.img B4_refl_stack_10scener_trend.img Time of most significant change (CUSUM) Confidence measure from CUSUM Change Magnitude Trend slope after change Clear cut mapping rule B4_change (not first or last image) B4_magn > +600 ; =reflectance change of +6% in MIR B4_conf_level > 0.9 Last image: No confidence threshold Thinnings mapping rules B4_change (not first or last image) B4_magn > +50 ; =reflectance change of +0.5% in MIR B4_conf_level > 0.9

30 B4 Year of change per pixel from CUSUM Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

31 Mapped Year of change Combination of time of change, change magnitude and confidence Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

32 Changed/ unchanged forest Combination of time of change, change magnitude and confidence Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

33 Integrated statistical measures of historical time series (examples) Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Single band image stack (each image date = one layer) computed separately for each band Standard function in ERDAS Stack Statistics STACK MAX ( <arg> ) STACK MIN ( <arg> ) STACK MEAN ( <arg> ) STACK MEDIAN ( <arg> ) STACK STANDARD DEVIATION ( <arg> ) RANGE = STACK MAX ( <arg> ) - STACK MIN ( <arg> )

34 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Stack mean Gradually changing with year of change

35 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Stack median Sensitive to majority before/after change

36 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Stack max Brightest sample of each pixel. Dark forest= untouched forest

37 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Stack min Darkest sample of each pixel. Light areas within forest = impediments Shows maximum forest area

38 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Stack standarddev Spectral variability of each pixel

39 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Range = Stack max Stack min range

40 Simple classification 25 clusters from Stack MAX (4 bands) Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

41 Simple classification 25 clusters from Stack MIN (4 bands) Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

42 Characterization of unchanged areas (B4) Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

43 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Some field and aerial image results Always difficulties of evaluating historical changes due to lack of reference data from same points in time Unchanged areas (not sensitive to time differences) Class Correct Total % Correct Impediment % Old Broadleaf % Old Conifer %

44 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Comparison of Clear Cut mapping result with operational interactive yearly mapping No of objects CUSUM results Reference data Sum Sum %

45 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Applications The CUSUM method have been implemented with additional features Handling of varying timeseries between pixels (with no data gaps of no data ) Calculation of trends before and after change Used in two other projects: Landsat change mapping of forest in Mocambique CadasterENV (ESA project)

46 CadasterENV a multi-scale and multi-purpose Land Cover monitoring system Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Project run by Metria Financed by ESA Two CadasterENV projects (Sweden and Austria) Nov Dec 2014 Users and participating organisations

47 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Objective The primary objective of the CadasterENV Sweden project is to implement a multi-scale and multi-purpose Land Cover (LC) monitoring system in Sweden, according to the national user specifications. The system have two components: 1. HR/VHR (Very High Resolution) Land Cover mapping component, with a priority to high dynamic regions and; 2. HR (High Resolution) Land Cover Change (LCC) monitoring component. LC Mapping: Prototyping Phase (12 m) LC Mapping: Demonstration Phase (12 m) Experimental Analysis and Validation Requirement Consolidation Development and Implemen -tation Production and Verification LCC Monitoring: Prototyping Phase (10 m) LCC Monitoring: Demonstration Phase (14 m)

48 -----> to Forest Forest not on wetland/on wetlands Pine forest Spruce forest Mixed coniferous forest Mixed forest Decidous forest Hardwood decidous forest Mixed decidous and hardwood decidous forest Disturbed forest (clear-felled, young forest...) Open wetland Arable land Other open land Non-vegetated Vegetated Artificial non-vegetated surfaces Built-up Non built-up Water Inland water Marine water Changes between major class groups and prioritized changes within major classes Fast changes Slow changes Forest Forest not on wetland/on wetlands Pine forest Spruce forest Mixed coniferous forest Mixed forest Decidous forest Hardwood decidous forest Mixed decidous and hardwood decidous forest Disturbed forest (clear-felled, young forest...) Open wetland Arable land From -----> Other open land Non-vegetated Vegetated Artificial non-vegetated surfaces Built-up Non built-up Water Inland water Marine water 23/05/ Sentinel-2 for Science Workshop ESRIN Frascati; May 2014

49 Sentinel-2 for Science Workshop ESRIN Frascati; May 2014 Prioritized Slow changes LC class or mask Arable land / Other open land Forest Clear cut Change Increased cover of trees/bushes Decreased cover of trees/bushes Increased coniferous percentage (spruce) Increased deciduous percentage Regrowth No regrowth 50 23/05/2014

The USGS Landsat Big Data Challenge

The USGS Landsat Big Data Challenge The USGS Landsat Big Data Challenge Brian Sauer Engineering and Development USGS EROS bsauer@usgs.gov U.S. Department of the Interior U.S. Geological Survey USGS EROS and Landsat 2 Data Utility and Exploitation

More information

Remote Sensing Method in Implementing REDD+

Remote Sensing Method in Implementing REDD+ Remote Sensing Method in Implementing REDD+ FRIM-FFPRI Research on Development of Carbon Monitoring Methodology for REDD+ in Malaysia Remote Sensing Component Mohd Azahari Faidi, Hamdan Omar, Khali Aziz

More information

GIO land. Copernicus Initial Operations Land: Services, current status and ideas for validation

GIO land. Copernicus Initial Operations Land: Services, current status and ideas for validation GIO land + Copernicus Initial Operations Land: Services, current status and ideas for validation Gyorgy.Buttner@eea.europa.eu Land Products Validation and Evolution Workshop 28 30 January 2014 ESRIN, Frascati,

More information

Virtual constellations, time series, and cloud screening opportunities for Sentinel 2 and Landsat

Virtual constellations, time series, and cloud screening opportunities for Sentinel 2 and Landsat Virtual constellations, time series, and cloud screening opportunities for Sentinel 2 and Landsat Sentinel 2 for Science Workshop 20 22 May 2014 ESA ESRIN, Frascati (Rome), Italy 1 Part 1: Title: Towards

More information

Data Processing Flow Chart

Data Processing Flow Chart Legend Start V1 V2 V3 Completed Version 2 Completion date Data Processing Flow Chart Data: Download a) AVHRR: 1981-1999 b) MODIS:2000-2010 c) SPOT : 1998-2002 No Progressing Started Did not start 03/12/12

More information

Swedish National Earth Observation Activities. Göran Boberg Swedish National Space Board

Swedish National Earth Observation Activities. Göran Boberg Swedish National Space Board Swedish National Earth Observation Activities Göran Boberg Swedish National Space Board boberg@snsb.se 1 Swedish National Space Board SNSB is a central governmental agency under Ministry of Enterprise,

More information

CadasterENV Sweden. ESRIN/Contract No /12/I-LG. Requirements Baseline Document. Document No: CadasterENV-DEL-4-RB-10

CadasterENV Sweden. ESRIN/Contract No /12/I-LG. Requirements Baseline Document. Document No: CadasterENV-DEL-4-RB-10 Metr ia I D : MS2 012 /03 47 2. 10 Page: 1(14 7) Document No: CadasterENV-DEL-4-RB-10 CadasterENV Sweden ESRIN/Contract No. 4000107180/12/I-LG Requirements Baseline Document Metr ia I D : MS2 012 /03 47

More information

Liberia Forest Mapping. World Bank January 2012

Liberia Forest Mapping. World Bank January 2012 Liberia Forest Mapping World Bank January 2012 Scope of presentation 1. Overview (5 min) 2. Service presentation (20 min) 3. Operational scenario (10min) 4. Service Utility Review (45 min) 5. Wrap-up and

More information

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

SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007 SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007 Topics Presented Quick summary of system characteristics Formosat-2 Satellite Archive

More information

Report 2005 EUR 21579 EN

Report 2005 EUR 21579 EN Feasibility study on the use of medium resolution satellite data for the detection of forest cover change caused by clear cutting of coniferous forests in the northwest of Russia Report 2005 EUR 21579

More information

Application of Remotely Sensed Data and Technology to Monitor Land Change in Massachusetts

Application of Remotely Sensed Data and Technology to Monitor Land Change in Massachusetts Application of Remotely Sensed Data and Technology to Monitor Land Change in Massachusetts Sam Blanchard, Nick Bumbarger, Joe Fortier, and Alina Taus Advisor: John Rogan Geography Department, Clark University

More information

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

Moderate- and high-resolution Earth Observation data based forest and agriculture monitoring in Russia using VEGA Web-Service Moderate- and high-resolution Earth Observation data based forest and agriculture monitoring in Russia using VEGA Web-Service Sergey BARTALEV and Evgeny LOUPIAN Space Research Institute, Russian Academy

More information

Developments toward a European Land Monitoring Framework. Geoff Smith. Seminar 2 nd December, 2015 Department of Geography, University of Cambridge

Developments toward a European Land Monitoring Framework. Geoff Smith. Seminar 2 nd December, 2015 Department of Geography, University of Cambridge Developments toward a European Land Monitoring Framework Geoff Smith Specto Natura Limited Enable clients to deliver useful, accurate and reliable environmental information from EO. Positioned at the interface

More information

Information Contents of High Resolution Satellite Images

Information Contents of High Resolution Satellite Images Information Contents of High Resolution Satellite Images H. Topan, G. Büyüksalih Zonguldak Karelmas University K. Jacobsen University of Hannover, Germany Keywords: satellite images, mapping, resolution,

More information

LANDSAT 8 Level 1 Product Performance

LANDSAT 8 Level 1 Product Performance Réf: IDEAS-TN-10-QualityReport LANDSAT 8 Level 1 Product Performance Quality Report Month/Year: January 2016 Date: 26/01/2016 Issue/Rev:1/9 1. Scope of this document On May 30, 2013, data from the Landsat

More information

Potential of RS/GIS data for GHG inventory in forest sector. Forestry and Forest Products Research Institute. Yasumsa Hirata F F P R I

Potential of RS/GIS data for GHG inventory in forest sector. Forestry and Forest Products Research Institute. Yasumsa Hirata F F P R I Potential of RS/GIS data for GHG inventory in forest sector Forestry and Forest Products Research Institute Yasumsa Hirata Forest monitoring using remote sensing Unique technique of forest monitoring widely

More information

The USGS Landsat Big Data Experience

The USGS Landsat Big Data Experience The USGS Landsat Big Data Experience Mr. Steven Covington Systems Director, Aerospace Corporation USGS Landsat Flight Systems Manager sjcovington@usgs.gov U.S. Department of the Interior U.S. Geological

More information

High Resolution Information from Seven Years of ASTER Data

High Resolution Information from Seven Years of ASTER Data High Resolution Information from Seven Years of ASTER Data Anna Colvin Michigan Technological University Department of Geological and Mining Engineering and Sciences Outline Part I ASTER mission Terra

More information

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

SEMI-AUTOMATED CLOUD/SHADOW REMOVAL AND LAND COVER CHANGE DETECTION USING SATELLITE IMAGERY SEMI-AUTOMATED CLOUD/SHADOW REMOVAL AND LAND COVER CHANGE DETECTION USING SATELLITE IMAGERY A. K. Sah a, *, B. P. Sah a, K. Honji a, N. Kubo a, S. Senthil a a PASCO Corporation, 1-1-2 Higashiyama, Meguro-ku,

More information

Automatic land-cover map production of agricultural areas using supervised classification of SPOT4(Take5) and Landsat-8 image time series.

Automatic land-cover map production of agricultural areas using supervised classification of SPOT4(Take5) and Landsat-8 image time series. Automatic land-cover map production of agricultural areas using supervised classification of SPOT4(Take5) and Landsat-8 image time series. Jordi Inglada 2014/11/18 SPOT4/Take5 User Workshop 2014/11/18

More information

Argentina Teodora NERTAN, Gheorghe STANCALIE, Denis MIHAILESCU

Argentina Teodora NERTAN, Gheorghe STANCALIE, Denis MIHAILESCU Argentina Teodora NERTAN, Gheorghe STANCALIE, Denis MIHAILESCU International Conference on current knowledge of Climate Change Impacts on Agriculture and Forestry in EuropeCOST-WMO Topolcianky, SK, 3-6

More information

European Forest Data Centre Biosoil/Biodiversity Forest Type Map 2006 P. Kempeneers, JRC T. Durant F. Sedano, JRC L. Seebach, JRC J. San-Miguel-Ayanz, JRC EC -, IES Land Management and Natural Hazard Unit

More information

Preprocessing in Remote Sensing. Introduction Geo Information (GRS 10306)

Preprocessing in Remote Sensing. Introduction Geo Information (GRS 10306) Preprocessing in Remote Sensing Lammert Kooistra Contact: Lammert.Kooistra@wur.nl Introduction Geo Information (GRS 10306) The art of remote sensing source: ASTER satellite (earthobservatory.nasa.gov)

More information

Emerging remote environmental monitoring techniques. Remote Sensing

Emerging remote environmental monitoring techniques. Remote Sensing Emerging remote environmental monitoring techniques Remote Sensing Satellite and airborne Remote Sensing techniques Emerging trends in remote sensing are occurring largely in four broad areas: 1. advances

More information

A HIERARCHICAL APPROACH TO LAND USE AND LAND COVER MAPPING USING MULTIPLE IMAGE TYPES ABSTRACT INTRODUCTION

A HIERARCHICAL APPROACH TO LAND USE AND LAND COVER MAPPING USING MULTIPLE IMAGE TYPES ABSTRACT INTRODUCTION A HIERARCHICAL APPROACH TO LAND USE AND LAND COVER MAPPING USING MULTIPLE IMAGE TYPES Daniel L. Civco 1, Associate Professor James D. Hurd 2, Research Assistant III Laboratory for Earth Resources Information

More information

Combining automated satellite based flood mapping with exposure mapping for flood risk assessment and management

Combining automated satellite based flood mapping with exposure mapping for flood risk assessment and management DLR.de Chart 1 > United Nations/Germany Expert Meeting > A. Twele and F. Hummel > 05 06 2014 Combining automated satellite based flood mapping with exposure mapping for flood risk assessment and management

More information

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

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 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 S. E. Báez Cazull Pre-Service Teacher Program University

More information

IMAGINES_VALIDATIONSITESNETWORK ISSUE 1.00. EC Proposal Reference N FP7-311766. Name of lead partner for this deliverable: EOLAB

IMAGINES_VALIDATIONSITESNETWORK ISSUE 1.00. EC Proposal Reference N FP7-311766. Name of lead partner for this deliverable: EOLAB Date Issued: 26.03.2014 Issue: I1.00 IMPLEMENTING MULTI-SCALE AGRICULTURAL INDICATORS EXPLOITING SENTINELS RECOMMENDATIONS FOR SETTING-UP A NETWORK OF SITES FOR THE VALIDATION OF COPERNICUS GLOBAL LAND

More information

Testing CHRIS images for a Land Use Monitoring Service

Testing CHRIS images for a Land Use Monitoring Service Testing CHRIS images for a Land Use Monitoring Service Olga Renda, Michele Bocci Intecs S.p.A. Informatica e Tecnologia del Software KYOTO INVENTORY PROJECT DUP2 project Large Implementation Wall-to-wall

More information

SAMPLE MIDTERM QUESTIONS

SAMPLE MIDTERM QUESTIONS Geography 309 Sample MidTerm Questions Page 1 SAMPLE MIDTERM QUESTIONS Textbook Questions Chapter 1 Questions 4, 5, 6, Chapter 2 Questions 4, 7, 10 Chapter 4 Questions 8, 9 Chapter 10 Questions 1, 4, 7

More information

A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW ABSTRACT

A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW ABSTRACT A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW Mingjun Song, Graduate Research Assistant Daniel L. Civco, Director Laboratory for Earth Resources Information Systems Department of Natural Resources

More information

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

Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California Graham Emde GEOG 3230 Advanced Remote Sensing February 22, 2013 Lab #1 Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California Introduction Wildfires are a common disturbance

More information

Operational Space- Based Crop Mapping Protocols at AAFC A. Davidson, H. McNairn and T. Fisette.

Operational Space- Based Crop Mapping Protocols at AAFC A. Davidson, H. McNairn and T. Fisette. Operational Space- Based Crop Mapping Protocols at AAFC A. Davidson, H. McNairn and T. Fisette. Science & Technology Branch. Agriculture and Agri-Food Canada. 1. Introduction Space-Based Crop Mapping at

More information

Application of airborne remote sensing for forest data collection

Application of airborne remote sensing for forest data collection Application of airborne remote sensing for forest data collection Gatis Erins, Foran Baltic The Foran SingleTree method based on a laser system developed by the Swedish Defense Research Agency is the first

More information

Landsat color composite image draped on a Digital Elevation Model

Landsat color composite image draped on a Digital Elevation Model SFR406 Spring 2015 Formation and Interpretation of Color Composite Images Introduction One sees color images collected by earth orbiting satellites in popular magazines, in movies and television shows;

More information

Supporting Online Material for Achard (RE 1070656) scheduled for 8/9/02 issue of Science

Supporting Online Material for Achard (RE 1070656) scheduled for 8/9/02 issue of Science Supporting Online Material for Achard (RE 1070656) scheduled for 8/9/02 issue of Science Materials and Methods Overview Forest cover change is calculated using a sample of 102 observations distributed

More information

Forest Fire Information System (EFFIS): Rapid Damage Assessment

Forest Fire Information System (EFFIS): Rapid Damage Assessment Forest Fire Information System (EFFIS): Fire Danger D Rating Rapid Damage Assessment G. Amatulli, A. Camia, P. Barbosa, J. San-Miguel-Ayanz OUTLINE 1. Introduction: what is the JRC 2. What is EFFIS 3.

More information

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

Improving global data on forest area & change Global Forest Remote Sensing Survey Improving global data on forest area & change Global Forest Remote Sensing Survey work by FAO and partners - Adam Gerrand, E. Lindquist, R. D Annunzio, M. Wilkie, FAO, - F. Achard et al. TREES team at

More information

The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories

The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories Dr. Farrag Ali FARRAG Assistant Prof. at Civil Engineering Dept. Faculty of Engineering Assiut University Assiut, Egypt.

More information

Generation of Cloud-free Imagery Using Landsat-8

Generation of Cloud-free Imagery Using Landsat-8 Generation of Cloud-free Imagery Using Landsat-8 Byeonghee Kim 1, Youkyung Han 2, Yonghyun Kim 3, Yongil Kim 4 Department of Civil and Environmental Engineering, Seoul National University (SNU), Seoul,

More information

Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia

Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia Remote Sens. 2012, 4, 1856-1886; doi:10.3390/rs4061856 Article OPEN ACCESS Remote Sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Preparing Landsat Image Time Series (LITS) for Monitoring Changes

More information

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

Land Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed Land Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed Kansas Biological Survey Kansas Applied Remote Sensing Program April 2008 Previous Kansas LULC Projects Kansas LULC Map

More information

Time and Trees on the Map Land Cover Database 4

Time and Trees on the Map Land Cover Database 4 Time and Trees on the Map Land Cover Database 4 Key steps producing LCDB v3.0, v3.3 & v4.0 What s planned in v4.1 Applications using LCDB Data quality feedback Research results Satellite data processing

More information

River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models

River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models Steven M. de Jong & Raymond Sluiter Utrecht University Corné van der Sande Netherlands Earth Observation

More information

EO based glacier monitoring

EO based glacier monitoring EO based glacier monitoring THEMES 1. WGMS & GLIMS within GTN G: strategic set up 2. GlobGlacier & Glaciers_cci: EO based products 3. LDCM & Sentinel 2: future monitoring perspectives Frank Paul* Department

More information

The Idiots Guide to GIS and Remote Sensing

The Idiots Guide to GIS and Remote Sensing The Idiots Guide to GIS and Remote Sensing 1. Picking the right imagery 1 2. Accessing imagery 1 3. Processing steps 1 a. Geocorrection 2 b. Processing Landsat images layerstacking 4 4. Landcover classification

More information

New Methods and Satellites: A Program Update on the NASS Cropland Data Layer Acreage Program. Rick Mueller Claire Boryan Bob Seffrin

New Methods and Satellites: A Program Update on the NASS Cropland Data Layer Acreage Program. Rick Mueller Claire Boryan Bob Seffrin New Methods and Satellites: A Program Update on the NASS Cropland Data Layer Acreage Program Rick Mueller Claire Boryan Bob Seffrin 01/12/2006 Agenda Acreage background Program scope/cooperators Program

More information

Satellite Orbits. Satellites & Orbits. Satellite Orbits. Geo-stationary Orbit. Satellite Orbits. Prof. D. Nagesh Kumar.

Satellite Orbits. Satellites & Orbits. Satellite Orbits. Geo-stationary Orbit. Satellite Orbits. Prof. D. Nagesh Kumar. Satellites & Orbits Prof. D. Nagesh Kumar Dept. of Civil Engg. IISc, Bangalore 560 012, India URL: http://www.civil.iisc.ernet.in/~nagesh Orbit will be elliptical or near circular Time taken by a satellite

More information

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS Nguyen Dinh Duong Department of Environmental Information Study and Analysis, Institute of Geography, 18 Hoang Quoc Viet Rd.,

More information

INTRODUCTION REMOTE SENSING

INTRODUCTION REMOTE SENSING INTRODUCTION REMOTE SENSING dr.ir. Jan Clevers Centre for Geo-Information Dept. Environmental Sciences Wageningen UR Remote Sensing --> RS Sensor at a distance EARTH OBSERVATION EM energy Earth RS is a

More information

Nature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data

Nature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data Nature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data Aleksi Räsänen*, Anssi Lensu, Markku Kuitunen Environmental Science and Technology Dept. of Biological

More information

Introduction to Imagery and Raster Data in ArcGIS

Introduction to Imagery and Raster Data in ArcGIS Esri International User Conference San Diego, California Technical Workshops July 25, 2012 Introduction to Imagery and Raster Data in ArcGIS Simon Woo slides Cody Benkelman - demos Overview of Presentation

More information

Advanced Image Management using the Mosaic Dataset

Advanced Image Management using the Mosaic Dataset Esri International User Conference San Diego, California Technical Workshops July 25, 2012 Advanced Image Management using the Mosaic Dataset Vinay Viswambharan, Mike Muller Agenda ArcGIS Image Management

More information

Resolutions of Remote Sensing

Resolutions of Remote Sensing Resolutions of Remote Sensing 1. Spatial (what area and how detailed) 2. Spectral (what colors bands) 3. Temporal (time of day/season/year) 4. Radiometric (color depth) Spatial Resolution describes how

More information

Sentinel Collaborative Ground Segment Sweden Final Report

Sentinel Collaborative Ground Segment Sweden Final Report Sentinel Collaborative Ground Segment Sweden Final Report Prepared for the Swedish National Space Board Document ID Version SM-CGSS-FREP-10 1.0 Change record Version Date Author Final delivery to SNSB

More information

TREES 3 Forest monitoring from satellite remote sensing

TREES 3 Forest monitoring from satellite remote sensing TREES 3 Forest monitoring from satellite remote sensing TREES-III 2007-2013 Rationale: 1. to reduce uncertainties in global estimates of forest cover change and related biosphere-atmosphere processes with

More information

GMES-DSL GMES - Downstream Service Land: Austria-Slovenia-Andalusia. Concept for a Harmonized Cross-border Land Information System.

GMES-DSL GMES - Downstream Service Land: Austria-Slovenia-Andalusia. Concept for a Harmonized Cross-border Land Information System. ERA-STAR Regions Project GMES-DSL GMES - Downstream Service Land: Austria-Slovenia-Andalusia. Concept for a Harmonized Cross-border Land Information System Executive Summary submitted by JOANNEUM RESEARCH,

More information

Estimation of surface variables at the sub-pixel level for use as input to climate and hydrological models

Estimation of surface variables at the sub-pixel level for use as input to climate and hydrological models Estimation of surface variables at the sub-pixel level for use as input to climate and hydrological models Jean-Pierre Fortin Monique Bernier Danielle DeSève Stéphane Lapointe Institut national de la recherche

More information

VCS REDD Methodology Module. Methods for monitoring forest cover changes in REDD project activities

VCS REDD Methodology Module. Methods for monitoring forest cover changes in REDD project activities 1 VCS REDD Methodology Module Methods for monitoring forest cover changes in REDD project activities Version 1.0 May 2009 I. SCOPE, APPLICABILITY, DATA REQUIREMENT AND OUTPUT PARAMETERS Scope This module

More information

AN OPENGIS WEB MAP SERVER FOR THE ESA MULTI-MISSION CATALOGUE

AN OPENGIS WEB MAP SERVER FOR THE ESA MULTI-MISSION CATALOGUE AN OPENGIS WEB MAP SERVER FOR THE ESA MULTI-MISSION CATALOGUE T. Westin a, *, C. Caspar b, L. Edgardh a, L. Schylberg c a Spacemetric AB, Tingsv 19, 19161 Sollentuna, Sweden - tw@spacemetric.se b ESA Esrin,

More information

Satellite imagery to map degradation: techniques and challenges

Satellite imagery to map degradation: techniques and challenges Place for a photo (no lines around photo) Satellite imagery to map degradation: techniques and challenges GFOI/GOFC-GOLD workshop on Degradation, Wageningen, October 1-3, 2014 Tuomas Häme, Laura Sirro,

More information

Objectives. Raster Data Discrete Classes. Spatial Information in Natural Resources FANR 3800. Review the raster data model

Objectives. Raster Data Discrete Classes. Spatial Information in Natural Resources FANR 3800. Review the raster data model Spatial Information in Natural Resources FANR 3800 Raster Analysis Objectives Review the raster data model Understand how raster analysis fundamentally differs from vector analysis Become familiar with

More information

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

Forest Service Southern Region Jess Clark & Kevin Megown USFS Remote Sensing Applications Center (RSAC) Hurricane Katrina Damage Assessment on Lands Managed by the Desoto National Forest using Multi-Temporal Landsat TM Imagery and High Resolution Aerial Photography Renee Jacokes-Mancini Forest Service Southern

More information

A tiered reconnaissance approach toward flood monitoring utilising multi-source radar and optical data

A tiered reconnaissance approach toward flood monitoring utilising multi-source radar and optical data 5 th International Workshop on Remote Sensing for Disaster Response A tiered reconnaissance approach toward flood monitoring utilising multi-source radar and optical data Anneley McMillan Dr. Beverley

More information

Imagery. 1:50,000 Basemap Generation From Satellite. 1 Introduction. 2 Input Data

Imagery. 1:50,000 Basemap Generation From Satellite. 1 Introduction. 2 Input Data 1:50,000 Basemap Generation From Satellite Imagery Lisbeth Heuse, Product Engineer, Image Applications Dave Hawkins, Product Manager, Image Applications MacDonald Dettwiler, 3751 Shell Road, Richmond B.C.

More information

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

Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite R.Manonmani, G.Mary Divya Suganya Institute of Remote Sensing, Anna University, Chennai 600 025

More information

Review for Introduction to Remote Sensing: Science Concepts and Technology

Review for Introduction to Remote Sensing: Science Concepts and Technology Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director ann@baremt.com Funded by National Science Foundation Advanced Technological Education program [DUE

More information

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

APPLICATION OF TERRA/ASTER DATA ON AGRICULTURE LAND MAPPING. Genya SAITO*, Naoki ISHITSUKA*, Yoneharu MATANO**, and Masatane KATO*** APPLICATION OF TERRA/ASTER DATA ON AGRICULTURE LAND MAPPING Genya SAITO*, Naoki ISHITSUKA*, Yoneharu MATANO**, and Masatane KATO*** *National Institute for Agro-Environmental Sciences 3-1-3 Kannondai Tsukuba

More information

EO Information Services in support of West Africa Coastal vulnerability - Service Utility Review -

EO Information Services in support of West Africa Coastal vulnerability - Service Utility Review - EO Information Services in support of West Africa Coastal vulnerability - Service Utility Review - Christian Hoffmann, GeoVille World Bank HQ, Washington DC Date : 24 February 2012 Content - Project background

More information

Image Classification II

Image Classification II Image Classification II Supervised Classification Using pixels of known classes to identify pixels of unknown classes Advantages Generates information classes Self-assessment using training sites Training

More information

Availability and Potential Use of Low Resolution Satellite Imagery for Peacekeeping and Disaster Management. Mryka Hall-Beyer

Availability and Potential Use of Low Resolution Satellite Imagery for Peacekeeping and Disaster Management. Mryka Hall-Beyer Availability and Potential Use of Low Resolution Satellite Imagery for Peacekeeping and Disaster Management Mryka Hall-Beyer Spatial resolution: The ability to see detail Expectations people have: to be

More information

Some elements of photo. interpretation

Some elements of photo. interpretation Some elements of photo Shape Size Pattern Color (tone, hue) Texture Shadows Site Association interpretation Olson, C. E., Jr. 1960. Elements of photographic interpretation common to several sensors. Photogrammetric

More information

Detection of Forest Fires Using Remotely Sensed Data

Detection of Forest Fires Using Remotely Sensed Data Detection of Forest Fires Using Remotely Sensed Data Dr.Jaruntorn Boonyanuphap Faculty of Agriculture Natural Resources and Environment, Naresuan University The Impacts of the forest fires Disturbance

More information

RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY

RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY M. Erdogan, H.H. Maras, A. Yilmaz, Ö.T. Özerbil General Command of Mapping 06100 Dikimevi, Ankara, TURKEY - (mustafa.erdogan;

More information

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

DETECTING LANDUSE/LANDCOVER CHANGES ALONG THE RING ROAD IN PESHAWAR CITY USING SATELLITE REMOTE SENSING AND GIS TECHNIQUES ------------------------------------------------------------------------------------------------------------------------------- Full length Research Paper -------------------------------------------------------------------------------------------------------------------------------

More information

MASS PROCESSING OF REMOTE SENSING DATA FOR ENVIRONMENTAL EVALUATION IN EUROPE

MASS PROCESSING OF REMOTE SENSING DATA FOR ENVIRONMENTAL EVALUATION IN EUROPE MASS PROCESSING OF REMOTE SENSING DATA FOR ENVIRONMENTAL EVALUATION IN EUROPE Lic. Adrián González Applications Research Earth Science Conference 2014 29.07.2014 Earth Science San Conference Francisco

More information

Remote Sensing in an

Remote Sensing in an Chapter 14: Radiometric Enhancement of Landsat Imagery Remote Sensing in an ArcMap Environment Remote Sensing Analysis in an ArcMap Environment Tammy E. Parece Image source: landsat.usgs.gov Tammy Parece

More information

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

APPLICATION OF MULTITEMPORAL LANDSAT DATA TO MAP AND MONITOR LAND COVER AND LAND USE CHANGE IN THE CHESAPEAKE BAY WATERSHED APPLICATION OF MULTITEMPORAL LANDSAT DATA TO MAP AND MONITOR LAND COVER AND LAND USE CHANGE IN THE CHESAPEAKE BAY WATERSHED S. J. GOETZ Woods Hole Research Center Woods Hole, Massachusetts 054-096 USA

More information

The New ImageDB Server Direct Access to Selected ERS SAR Data Products

The New ImageDB Server Direct Access to Selected ERS SAR Data Products the new imagedb server The New ImageDB Server Direct Access to Selected ERS SAR Data Products S. D Elia Earth Remote Sensing Exploitation Division, ESA Directorate of Application Programmes, ESRIN, Frascati,

More information

Radiometric Calibration of a Modified DSLR for NDVI

Radiometric Calibration of a Modified DSLR for NDVI Radiometric Calibration of a Modified DSLR for NDVI Christian Taylor Carlson Center for Imaging Science, Rochester Institute of Technology ABSTRACT Silicon CCD detectors found in commercial DSLR cameras

More information

STAR Algorithm and Data Products (ADP) Beta Review. Suomi NPP Surface Reflectance IP ARP Product

STAR Algorithm and Data Products (ADP) Beta Review. Suomi NPP Surface Reflectance IP ARP Product STAR Algorithm and Data Products (ADP) Beta Review Suomi NPP Surface Reflectance IP ARP Product Alexei Lyapustin Surface Reflectance Cal Val Team 11/26/2012 STAR ADP Surface Reflectance ARP Team Member

More information

Monitoring Wetland Changes Using Multitemporal Landsat Change Detection, Web Mapping Services, and Crowd Sourcing

Monitoring Wetland Changes Using Multitemporal Landsat Change Detection, Web Mapping Services, and Crowd Sourcing Monitoring Wetland Changes Using Multitemporal Landsat Change Detection, Web Mapping Services, and Crowd Sourcing Presented By: Mary Latiolais Outline The Vision: Crowd sourcing to document and reduce

More information

Landsat 8: Greater than 1 Reflectance Values. Right: 2013May05_GreaterOne_Mask

Landsat 8: Greater than 1 Reflectance Values. Right: 2013May05_GreaterOne_Mask Landsat 8: Greater than 1 Reflectance Values When the DN values were converted to the TOA reflectance values, there were pixels with reflectivity greater than 1. Unlike negative reflectivity, objects that

More information

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

Calculation of Minimum Distances. Minimum Distance to Means. Σi i = 1 Minimum Distance to Means Similar to Parallelepiped classifier, but instead of bounding areas, the user supplies spectral class means in n-dimensional space and the algorithm calculates the distance between

More information

China s Global Land Cover Mapping at 30 M Resolution

China s Global Land Cover Mapping at 30 M Resolution Geospatial World Forum 2015 China s Global Land Cover Mapping at 30 M Resolution Jun Chen1,2 1 National Geomatics Center, 2ISPRS Lisbon, Portugal, May 28,2015 GlobeLand30 Video Contents Introduction Introduction

More information

landusemonitoring bysatelliteimages

landusemonitoring bysatelliteimages landusemonitoring bysatelliteimages --- urban growth descripted by satellite images --- statistical comparison of the results by Ingrid Christ and Dr. Rolf Lessing agenda. methodology of satellite image

More information

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

Remote sensing is the collection of data without directly measuring the object it relies on the Chapter 8 Remote Sensing Chapter Overview Remote sensing is the collection of data without directly measuring the object it relies on the reflectance of natural or emitted electromagnetic radiation (EMR).

More information

Pixel-based and object-oriented change detection analysis using high-resolution imagery

Pixel-based and object-oriented change detection analysis using high-resolution imagery Pixel-based and object-oriented change detection analysis using high-resolution imagery Institute for Mine-Surveying and Geodesy TU Bergakademie Freiberg D-09599 Freiberg, Germany imgard.niemeyer@tu-freiberg.de

More information

Received in revised form 24 March 2004; accepted 30 March 2004

Received in revised form 24 March 2004; accepted 30 March 2004 Remote Sensing of Environment 91 (2004) 237 242 www.elsevier.com/locate/rse Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index

More information

2. VIIRS SDR Tuple and 2Dhistogram MIIC Server-side Filtering. 3. L2 CERES SSF OPeNDAP dds structure (dim_alias and fixed_dim)

2. VIIRS SDR Tuple and 2Dhistogram MIIC Server-side Filtering. 3. L2 CERES SSF OPeNDAP dds structure (dim_alias and fixed_dim) MIIC Server-side Filtering Outline 1. DEMO Web User Interface (leftover from last meeting) 2. VIIRS SDR Tuple and 2Dhistogram MIIC Server-side Filtering 3. L2 CERES SSF OPeNDAP dds structure (dim_alias

More information

Damage Detection from Landsat-7 Satellite Images for the 2001 Gujarat, India Earthquake

Damage Detection from Landsat-7 Satellite Images for the 2001 Gujarat, India Earthquake Damage Detection from Landsat-7 Satellite Images for the 2001 Gujarat, India Earthquake Yalkun YUSUF 1), Masashi MATSUOKA 1), Fumio YAMAZAKI 1) Earthquake Disaster Mitigation Research Center National Research

More information

Lake Monitoring in Wisconsin using Satellite Remote Sensing

Lake Monitoring in Wisconsin using Satellite Remote Sensing Lake Monitoring in Wisconsin using Satellite Remote Sensing D. Gurlin and S. Greb Wisconsin Department of Natural Resources 2015 Wisconsin Lakes Partnership Convention April 23 25, 2105 Holiday Inn Convention

More information

The use of Earth Observation technology to support the implementation of the Ramsar Convention

The use of Earth Observation technology to support the implementation of the Ramsar Convention Wetlands: water, life, and culture 8th Meeting of the Conference of the Contracting Parties to the Convention on Wetlands (Ramsar, Iran, 1971) Valencia, Spain, 18-26 November 2002 COP8 DOC. 35 Information

More information

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

Cloud Detection for Sentinel 2. Curtis Woodcock, Zhe Zhu, Shixiong Wang and Chris Holden Cloud Detection for Sentinel 2 Curtis Woodcock, Zhe Zhu, Shixiong Wang and Chris Holden Background 3 primary spectral regions useful for cloud detection Optical Thermal Cirrus bands Legacy Landsats have

More information

Lectures Remote Sensing

Lectures Remote Sensing Lectures Remote Sensing OPTICAL REMOTE SENSING dr.ir. Jan Clevers Centre of Geo-Information Environmental Sciences Wageningen UR EM Spectrum and Windows reflection emission 0.3 0.6 1.0 5.0 10 50 100 200

More information

SPOT4 (Take 5) first validation and application results

SPOT4 (Take 5) first validation and application results SPOT4 (Take 5) first validation and application results O.Hagolle CESBIO/CNES, M.Huc CESBIO/CNRS, M.Kadiri CESBIO/THEIA ; J.Inglada CESBIO/CNES, C. Marais-Sicre CESBIO/CNRS, J.Osman CESBIO/CNES (PhD),

More information

Vegetation Classification and Mapping. Developed by Montana Natural Heritage Program 1515 East 6 th Avenue Helena, MT

Vegetation Classification and Mapping. Developed by Montana Natural Heritage Program 1515 East 6 th Avenue Helena, MT Vegetation Classification and Mapping Developed by Montana Natural Heritage Program 1515 East 6 th Avenue Helena, MT 59620-1800 http://mtnhp.org/ Vegetation classification Vegetation classification is

More information

Global environmental information Examples of EIS Data sets and applications

Global environmental information Examples of EIS Data sets and applications METIER Graduate Training Course n 2 Montpellier - february 2007 Information Management in Environmental Sciences Global environmental information Examples of EIS Data sets and applications Global datasets

More information

Land Cover Mapping of the Comoros Islands: Methods and Results. February 2014. ECDD, BCSF & Durrell Lead author: Katie Green

Land Cover Mapping of the Comoros Islands: Methods and Results. February 2014. ECDD, BCSF & Durrell Lead author: Katie Green Land Cover Mapping of the Comoros Islands: Methods and Results February 2014 ECDD, BCSF & Durrell Lead author: Katie Green About the ECDD project The ECDD project was run by Bristol Conservation & Science

More information

KOMPSAT Workshop. II. Satellites Overview

KOMPSAT Workshop. II. Satellites Overview KOMPSAT Workshop - Discover the Earth at Sub-meter Resolution - II. Satellites Overview March, 2015 SI Imaging Services Distribution Limitation, SI Imaging Services Proprietary Data : The data contained

More information