REDD+ FOR THE GUIANA SHIELD Technical Cooperation Project

Similar documents
REDD+ FOR THE GUIANA SHIELD Technical Cooperation Project

Discussions around the different methodologies and consensus for a regional monitoring

Figure 1: Workflow of object-based classification

Automated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Cadastre - Preliminary Results

Traffic Sign Recognition Senior Project Proposal

Classification (or thematic) accuracy assessment. Lecture 8 March 11, 2005

Classifying Depositional Environments in Satellite Images

Glacier Mapping and Monitoring

QUESTIONS & ANSWERS FOR. ORTHOPHOTO & LiDAR AOT

VALIDATION OF THE CLOUD AND CLOUD SHADOW ASSESSMENT SYSTEM FOR IKONOS IMAGERY (CASA-I2 VERSION 1.3)

Aerial photography: Principles. Visual interpretation of aerial imagery

Example. Section: PS 709 Examples of Calculations of Reduced Hours of Work Last Revised: February 2017 Last Reviewed: February 2017 Next Review:

GLS Analysis. Dave Skole, Walter Chomentowski, and Jay Samek Michigan State University April 2010

QWG5 PAYLOAD DATA GROUND SEGMENT STATUS 09-10/05/2017 D. CLARIJS

First Review of COP 21 and Potential impacts on Space Agencies

DIGITAL IMAGE ANALYSIS. Image Classification: Object-based Classification

Guidelines for Metadata and Data Directory

Global Forest Observations Initiative (GFOI) Overview. Frank Martin Seifert, ESA SDCG-8 Commercial Providers Day, Bonn, 23 September 2015

hereby recognizes that Timotej Verbovsek has successfully completed the web course 3D Analysis of Surfaces and Features Using ArcGIS 10

TELEDYNE GEOSPATIAL SOLUTIONS

Space Data Coordination Group Meeting (SDCG-13) 15 th -16 th September 2018, Bogotá, Colombia Meeting Objectives

Remote Sensing Introduction to the course

EVALUATION OF WORLDVIEW-1 STEREO SCENES AND RELATED 3D PRODUCTS

GEOBIA for ArcGIS (presentation) Jacek Urbanski

CROP MAPPING WITH SENTINEL-2 JULY 2017, SPAIN

COMBINING HIGH SPATIAL RESOLUTION OPTICAL AND LIDAR DATA FOR OBJECT-BASED IMAGE CLASSIFICATION

TARGET Instant Payment Settlement

Measuring Ground Deformation using Optical Imagery

OPERATIONAL SHIP DETECTION & RAPID URBAN MAPPING : EXPLORING DIVERSE METHODOLOGICAL APPROACHES IN OBJECT RECOGNTION AND SATELLITE IMAGE CLASSIFICATION

Using ArcGIS for Landcover Classification. Presented by CORE GIS May 8, 2012

Quality assessment of RS data. Remote Sensing (GRS-20306)

Introduction to the Image Analyst Extension. Mike Muller, Vinay Viswambharan

Short Flip Calendar Custom Photos Template

Iowa Assessments TM Planning Guide

GEOGRAPHICAL RESEARCH

CLC2018 methodology. Barbara Kosztra, György Büttner ETC/ULS. CORINE Land Cover Technical Team. Eionet NRC Land Cover meeting

Raster Classification with ArcGIS Desktop. Rebecca Richman Andy Shoemaker

Global Data Flows Study. Prepared by the CEOS Space Data Coordination Group (SDCG) for the Global Forest Observations Initiative (GFOI)

FedRAMP: Understanding Agency and Cloud Provider Responsibilities

By Colin Childs, ESRI Education Services. Catalog

INTEGRATION OF TREE DATABASE DERIVED FROM SATELLITE IMAGERY AND LIDAR POINT CLOUD DATA

VTC FY19 CO-OP GOOGLE QUALIFICATIONS PARAMETERS & REIMBURSEMENT DOCUMENTATION HOW-TO

Land Cover Classification Techniques

CHANGE DETECTION OF LINEAR MAN-MADE OBJECTS USING FEATURE EXTRACTION TECHNIQUE

Introduction to digital image classification

/Internet Random Moment Sampling. STATE OF ALASKA Department of Health and Social Services Division of Public Assistance

Classification of vegetation types with Sentinel-1 radar data C

LIDAR MAPPING FACT SHEET

AUTOMATIC CLASSIFICATION METHODS OF HIGH-RESOLUTION SATELLITE IMAGES: THE PRINCIPAL COMPONENT ANALYSIS APPLIED TO THE SAMPLE TRAINING SET

Review of the WMO DRR Programme activities in the area of MHEWS as a contribution to the WMO DRR Priority (2005 to present)

ii. From the Tools menu choose Multi-Extract

2010 LiDAR Project. GIS User Group Meeting June 30, 2010

Website Operators Manual

This is the general guide for landuse mapping using mid-resolution remote sensing data

NORTH CAROLINA NC MRITE. Nominating Category: Enterprise IT Management Initiatives

Physical Security Reliability Standard Implementation

Digital Services. Supported Browser categories. 1 P a g e

Maryland Soybeans: Historical Basis and Price Information

This section is maintained by the drafting team during the development of the standard and will be removed when the standard becomes effective.

Obtaining Submerged Aquatic Vegetation Coverage from Satellite Imagery and Confusion Matrix Analysis

Sendai Framework for Disaster Risk Reduction & 2030 Agenda for Sustainable Development

Roberto Cardoso Ilacqua. QGis Handbook for Supervised Classification of Areas. Santo André

General Digital Image Utilities in ERDAS

VEGETATION Geometrical Image Quality

Digital Image Classification Geography 4354 Remote Sensing

2018 Biometric Technology Rally

Lab 9. Julia Janicki. Introduction

VALERI 2003 : Concepcion site (Mixed Forest) GROUND DATA PROCESSING & PRODUCTION OF THE LEVEL 1 HIGH RESOLUTION MAPS

Oracle Managed Cloud Services for Software as a Service - Service Descriptions. February 2018

SAM and ANN classification of hyperspectral data of seminatural agriculture used areas

CHRIS PROBA instrument

INFORMATION ASSURANCE DIRECTORATE

Development of Country Integrated NTD Database System in the African Region

ArcGIS Pro: Image Segmentation, Classification, and Machine Learning. Jeff Liedtke and Han Hu

Geometric Accuracy Evaluation, DEM Generation and Validation for SPOT-5 Level 1B Stereo Scene

Strategic Action Plan. for Web Accessibility at Brown University

The United Nations Crime Trends Survey (UN-CTS) Michael Jandl Statistics and Surveys Section UNODC

Using Imagery for Intelligence Analysis

DeVry University Houston

Classify Multi-Spectral Data Classify Geologic Terrains on Venus Apply Multi-Variate Statistics

Nov 20, 2017 Page 1. Tripwire, Inc. Product Support and Discontinuation Policy November 2017

The IDN Variant TLD Program: Updated Program Plan 23 August 2012

DETECTION OF CHANGES IN ISTANBUL AREA WITH MEDIUM AND HIGH RESOLUTION SPACE IMAGES

Design and Use of. Earth Observation Image Content Tools

Managing Image Data on the ArcGIS Platform Options and Recommended Approaches

Atras Network Communications

Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey

Overview of support provided by the LEG

CEF ehealth DSI 2018 Technical Boot Camp State of Play

NATIONAL TECHNICAL UNIVERSITY OF ATHENS PERMANENT COMMITTEE FOR BASIC RESEARCH NTUA BASIC RESEARCH SUPPORT PROGRAMME THALES 2001

Google Earth Engine. Introduction to satellite data analysis in cloud-based environment. Written by: Petr Lukeš

CANADIAN PAYMENTS ASSOCIATION ASSOCIATION CANADIENNE DES PAIEMENTS STANDARD 005 STANDARDS FOR THE EXCHANGE OF FINANCIAL DATA ON AFT FILES

LEED Interpretations and Addenda Database Help Document

Training of BRs/NCs reviewers and experts for Biennial Update Reports technical analysis. 5 th BRs and NCs lead reviewers meeting

MODULE 3 LECTURE NOTES 3 ATMOSPHERIC CORRECTIONS

VTC CO-OP GOOGLE QUALIFICATIONS PARAMETERS & REIMBURSEMENT DOCUMENTATION HOW-TO

Joining efforts. Colocation of CRP research activities within Burkina Faso. Michael Balinga and Julien Colomer

The Revised Design Examination Guidelines regarding graphic image on a screen designs entered into force on April 1, , in Japan

Grid Code Planner EU Code Modifications GC0100/101/102/104

Year 10 OCR GCSE Computer Science (9-1)

Transcription:

REDD+ FOR THE GUIANA SHIELD Technical Cooperation Project REDD+ for the Guiana Shield Mathieu Rahm, ONF international Impact of Gold mining training session, 24-28 November 2014 Cayenne French Guiana

Methodology Building MONITORING THE IMPACT OF GOLD MINING ON THE FOREST COVER AND FRESHWATER AT THE GUIANA SHIELD REGIONAL SCALE

Why and how establish a clear methodology/protocol? 1. Why? The results of data processed by several operators will have to be compiled need of comparability of results To ensure comparability of results: Operators need to do the exact same steps of process A clear decision tool need to be developed for ensuring similar responses/decisions by all operators when they encounter a specific situation A detailed methodology ensure the quality of data produced 2. How? Operators follow a protocol which guides them through the process. This will ensure that nothing has been omitted By defining the exact steps that the operator will follow By providing guidelines and precise rules to the operator for all type of situation

Choices to be made - Based on: - Results of the last study - Experience and knowledge of each forest services - Methods used by the different forest services - Available data in each country (roads, settlements, deforestation maps, )?? - Need of: - Update last results - Regional methodology - Comparability of results with the last study (2000-2007) - Use of open source software - Use of SPOT images (SPOT 5 10 m resolution) and additional data

Chronology of the methodology steps 1. Image ordering and acquisition 2. Quality check of all acquired images 3. Archive of data and production of raster catalogue 4. Image pre-processing 5. Quality check of pre-processed images 6. Image processing 7. Quality check of the processing results (internal national control) 8. Delivery of results 9. Compilation, validation and accuracy assessment of results (external regional control) 10. Final delivery with assessed uncertainty

Flowchart of the steps Image acquisition and ordering NOK Quality check OK Archive Image pre-processing NOK Quality check OK Archive Image processing NOK Quality check OK Archive Results delivery

Decisions to take: 1. Image ordering 1. What level of pre-processing? 1A; 2A; 2B; 2. Maximum cloud cover allowed for selection of image? < 20%; <30%; Is the presence of Haze an issue? 3. Priority given to the most recent image for SPOT selection 4. If quality of recent images is bad, do we go back in time or do we prefer to use other satellite data like Landsat? In order terms, do we give priority to the resolution of data or to the time of acquisition? 5. If we fill gaps with Landsat image, a clear methodology need to be established. Eg: Landsat image is clipped to fill the gap of one SPOT

Decisions to take: 2. Quality check of acquired images Criteria _ images & orthorectification Coordina Is the cloud cover mentioned te in the Zero metadata correct or is it underestimated, filterin QC All bands system in Missing Cloud g Passed? making the available image Data type not Pixel useful size WGS84_U for the image purpose image cover. of the study? Histo Pixel neede Overlapp Tile_id Acq_date (5) (16 bit) (5 m) TM_33S data lines (20%) Artefacts quality noise d ing area Is the radiometric correction correct? Is the pixel resolution correct? 3338125 14/08/2011 y y y y y y y y y y y 3338124 14/08/2011 Are all spectral y ybands ypresent? y y y y y y y y 3338123 14/08/2011 y y y y y y y y y? y y 3338123 12/08/2011 Is the georeferencing y y ywithin ythe limits y of y quality y mentioned y y by the image y y 3338123 11/08/2011 y y y y y y y y y y y provider?. 3338122 12/08/2011 y y y y y y y y y n N 3338121 12/08/2011 y y y y y y y y y n N 3338120 02/05/2011 y y y y n y y y y y n y N 3338025 14/08/2011 y y y y y y y y y y y 3338024 14/08/2011 y y y y y y y y y y y 3338023 14/08/2011 y y y y y y y y y y y 3338023 11/08/2011 y y y y y y y y y n y y y 3338022 20/07/2011 y y y y y y y y y y y 3338021 12/08/2011 y y y y y y y y y n N 1. The points of control need to be established, eg:

Decisions to take: 4. Image preprocessing Based on the level of pre-processed images ordered, specify the preprocessing steps: 1. Define the projection system 2. Georeferencing needed? 3. Coregistration with last images probably needed in any case because a potential shift could lead to digitalization and surfaces errors 4. Index calculation and/or filtering for later photo-interpretation? 5. Contrast, histogram adjustment? 6. If use of other data like Landsat, other pre-processing steps needed? Eg: Layer stack

Decisions to take: 5. Quality check of pre-processed images Based on the steps identified for pre-processing, the points of control need to be established, eg:

Decisions to take: 6. Image processing (1) 1. What is the digitalization methodology to use? Automatic, semi-automatic, manual? Given the cloudy areas and the specificity of this study (update of polygons), manual classification seems the best option 2. What features do we digitalize? Mining activities impacting forest areas and freshwater Roads and settlements 3. How do we digitalize these features? What is the minimum mapping unit for each feature? Which combination of bands and/or index do we use to digitalize each feature? Infrared false color, NDVI, NDWI, SARVI, etc. How to detect each feature based on the band combinations and/or index proposed? What color, shape

Decisions to take: 6. Image processing (2) How do we digitalize it? Based on which band combination/index and what are the boundaries? How do we deal with misclassified objects of the previous study? How do we deal with clouds and cloud shadows/ no data areas? How do we report it? Based on the metadata rate of cloud or do we need to precise the location which means to localize them? How do we deal with roads and / or settlements integrated in or near mining sites? What would be the definition of human settlements? Does it integrate all infrastructures built by human? How could we integrate existing data in each country Which type of ancillary data do we use and for what purpose?

Decisions to take: 6. Quality check of the processing results (internal national control) 1. What control do we want to make? If we produce random points that need to be verified, it should: Be verified by another operator Be defined how many points we create in each class? Be define if we generate a confusion matrix? 2. Completeness control of the processing. Was all images processed? Within each image, has the complete area been analyzed? 3. What controls are commonly realized in each forest services?

Calendar of activities As planned, the methodological choices should be established by the end of this week. October 2014 November 2014 December 2014 January 2015 February 2015 March 2015 April 2015 May 2015 June 2016 July 2017 Activities Responsability W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16 W17 W18 W19 W20 W21 W22 W23 W24 W25 W26 W27 W28 W29 W30 W31 W32 W33 W34 W35 W36 W37 W38 1. Work Preparation Team constitution: prepare a job description and facilitate selection in countries ONFG Images' identification and purchase, organization of a complete file ONFG Data preprocessing Core team Preparatory work to update and detail the methodology based on existing data in the countries Literature review and exchanges with countries ONFG Methodology preparation ONFG 2. Training on data preparation and discussion on the methodology Preparation of training and training ONFG 5 days mission of the team in French Guiana Core team X Final description of the updated methodology to be submitted to partners validation ONFG Expected validation by partners Partners X 3. Data Processing by the team Preparation of the training and training ONFG 10 days mission of the team in French Guiana Core team X Processing for French Guiana territory Core team Processing for Suriname Core team Processing for Guyana Core team Processing for Amapa Core team Delivery of intermediate processing results Core team X Delivery of final processing results Core team X Support mission to countries (1 mission in Suriname/Guyana and 1 mission in Amapa) ONFG/ONFI X X 4. Data validation Identification of existing ground-truthing points ONFI Development of a validation protocole ONFI Implementation of the validation protocole and uncertainty estimates ONFI 5. Data compilation, report and analysis Compilation of data ONFG Report writing ONFG/ONFI Preparation of data files ONFG Provision of draft report ONFG/ONFI X Expected comments Partners X Provision of final report ONFG/ONFI X 6. Coordination Coordination ONFI Technical support ONFG

Photo credit: ONF Guyane Let s discuss and find the best options