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Forest and Vegetation Stratification the HCS Approach Toolkit V2.0 May 2017

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Forest and Vegetation Stratification the HCS Approach Toolkit V2.0 May 2017 Photo: Remote Sensing Solutions GmbH © THE HCS APPROACH TOOLKIT MODULE 4 VERSION 2.0 MAY 2017 THE HCS APPROACH PUTTING NO DEFORESTATION INTO PRACTICE Forest and vegetation stratification THE HCS AppROACH TOOLKIT V2.0 MAY 2017 Published by the HCS Approach Steering Group Copyright © 2017 High Carbon Stock Approach Steering Group This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 MEMBERS OF THE HCS APPROACH International License. To view a copy of this license, visit STEERING GROUP AS OF AUGUST 2017 http://creativecommons.org/licenses/by-nc-nd/4.0/. • Asian Agri All or portions of this report may be used, reprinted • Asia Pulp & Paper (Executive Committee) or distributed, provided the source is acknowledged. • BASF No use of this publication may be made for resale or • Conservation International other commercial purposes. • Daemeter Bibliographic reference: • EcoNusantara Rosoman, G., Sheun, S.S., Opal, C., Anderson, P., and • Forest Carbon Trapshah, R., editors. (2017) The HCS Approach Toolkit. • Forest Peoples Programme (Executive Committee) Singapore: HCS Approach Steering Group. • Golden Agri-Resources (Executive Committee) • Golden Veroleum (Liberia) Inc. Authors: • Greenbury & Associates Zakaria Adriani, Patrick Anderson, Sahat Aritonang, • Greenpeace (Executive Committee) Uwe Ballhorn, Bill Barclay, Sophie Chao, • IOI Group Marcus Colchester, Jules Crawshaw, Gabriel Eickhoff, • Mighty Robert Ewers, Jaboury Ghazoul, David Hoyle, George Kuru, • Musim Mas Paul Lemaistre, Pi Li Lim, Jennifer Lucey, Rob McWilliam, • National Wildlife Federation Peter Navratil, Jana Nejedlá, Ginny Ng, Annette Olsson, • New Britain Palm Oil Ltd. Charlotte Opal, Meri Persch-Orth, Sebastian Persch, • Proforest Michael Pescott, Sapta Ananda Proklamasi, Ihwan Rafina, • P&G Grant Rosoman, Mike Senior, Matthew Struebig, • Rainforest Action Network (Executive Committee) Tri A. Sugiyanto, Achmad Saleh Suhada, Alex Thorp, • Rainforest Alliance Sander van den Ende, Paulina Villalpando, and Michal Zrust. • TFT (Executive Committee) • Unilever (Executive Committee) Copy Editor: • Union of Concerned Scientists Sean Merrigan (Merrigan Communications) • Wilmar International Ltd. (Executive Committee) • WWF (Executive Committee) Production and Design Management: Helikonia The production of this toolkit has been funded by the members Design: of the HCS Approach Steering Group and the UK government Peter Duifhuizen (Sneldruk & Ontwerp) through the Partnerships for Forests programme. Diagram Design: • Open Air Design • Proforest MODULE 4 Forest and vegetation stratification Photo: Remote Sensing Solutions GmbH © MODULE CONTENTS P4: Section A: Outline of the process for making the indicative HCS forest map P10: Section B: Land cover and carbon stock classification using airborne LiDAR and/or satellite imagery P24: Appendix: An overview of optical multispectral satellite image options P26: Section C: Using field plots to estimate carbon stock and finalise delineation of land cover classes: LiDAR AGB calibration plots and forest inventory plots P41: Appendix 1: Inventory field form and inventory team equipment list P42: Appendix 2: Additional biodiveristy analysis tools 3 Photo: Ulet Ifansasti © SECTION A Outline of the process for making the indicative HCS forest map By Uwe Ballhorn and Peter Navratil (Remote Sensing Solutions GmbH). INTRODUCTION might be necessary to address issues relating to image quality and types of land cover and land use in different regions. The goal of Phase 1 in a HCS assessment is to create an indicative map of potential This module is intended for technical experts with HCS forest areas in a development area and experience in remote sensing analysis and forest its surrounding landscape. This is achieved inventory that can use this document to guide their using a combination of airborne Light work and create an indicative map of potential HCS Detection and Ranging (LiDAR) data (if forest areas, without need for further guidance. We thus assume that the reader has an advanced level available), satellite images and field data. of knowledge in these analysis techniques, but have The sections in Module 4 focus on Phase 1: provided references to more detailed guidance where Making the first indicative HCS forest map. it may be helpful. They take the reader through the required steps, including pre-requirements, metho- dology and expected output. METHODOLOGICAL AppROACH The methodology presented in this module has been tested and refined through pilot tests in development The methodological approach for the identification areas in Indonesia, Liberia and Papua New Guinea. of potential HCS forests is based on three options, The methodology is intended to be applicable for any where each option represents a level of accuracy moist tropical forest on mineral soils, but we have and detail, but also of methodological complexity and included details of variations to the methodology which cost (Figure 1). Progressing from Option 1 to Option 3 MODULE 4 Forest and Vegetation Stratification 4 SECTION A: Outline of the process for Making the indicative HCS forest map Version 2.0: May 2017 represents a decrease of accuracy/detail and an increase in uncertainty, but on the other hand, a decrease of methodological complexity and costs. The decision on which option to apply should be based on the desired accuracy and detail of the output that is required. Cost should not be the only or the primary consideration. There are also broader benefits that may be gained through choosing Option 1, such as accurate Digital Terrain Models (DTMs) that can be used for plantation planning if LiDAR is used. Further, if it is foreseen that accurate calculations of carbon will be required in the future, Option 1 might be the best since it provides the most accurate calculations of carbon. Alternatively, if the objective of the HCS assessment is simply to identify forest areas for land use planning decisions, then Option 3 may be Photo: Remote Sensing Solutions GmbH © satisfactory. As Option 3 has the highest level of uncertainty, this option is the Methodological approach least recommended, but is still HCS Stratification and allowed in the HCS Approach. Carbon Assessment Decision based on: desired detail, budget, and data availability Option 1: Option 2: Option 3: + Full-coverage LiDAR + LiDAR transects + Satellite-based land cover + LiDAR calibration plots + LiDAR calibration plots classification + Satellite-based land cover + Satellite-based land cover + Forest inventory plots classification classification Relevant HCS Toolkit Relevant HCS Toolkit Relevant HCS Toolkit Modules/Sections: Modules/Sections: Modules/Sections: Module 4b: Module 4b: Module 4b: • Airborne LiDAR data (p. 11) • Airborne LiDAR data (p. 11) • Optical satellite data (p. 15) • Optical satellite data (p. 15) • Optical satellite data (p. 15) • Satellite-based land cover • Satellite-based land cover • Satellite-based land cover classification (p. 15) classification (p. 15) classification (p. 15) • Assigning the land cover • Assigning the land cover • Assigning the land cover classes to the carbon stock classes to the carbon stock classes to the carbon stock classes (p. 20) classes (p. 20) classes (p. 20) Module 4c: Module 4c: Module 4c: • LiDAR calibration plots (p. 27) • LiDAR calibration plots (p. 27) • Forest inventory plots (p. 27) • LiDAR calibration plots (p. 29) • LiDAR calibration plots (p. 29) • Forest inventory plots (p. 31) • LiDAR calibration and LiDAR • LiDAR calibration and LiDAR • Forest inventory estimation (p. 38) AGB model development (p. 37) AGB model development (p. 37) Figure 1: Overview of the methodological approach for HCS Stratification and Carbon Assessment. Progressing from Option 1 to Option 3 represents a decrease of accuracy/detail and an increase in uncertainty, but on the other hand, a decrease of methodological complexity and costs. Also shown are the relevant toolkit sections describing the necessary tasks for these approaches. MODULE 4 Forest and Vegetation Stratification SECTION A: Outline of the process for Making the indicative HCS forest map Version 2.0: May 2017 5 Option 1 The first and most accurate/detailed option is the use of a full-coverage airborne LiDAR data set of the development area, which is calibrated through LiDAR AGB (above-ground biomass) calibration plots collected in the field in order to create an AGB model for the development area. This model is then reclassified into the different HCS classes. In this Photo: Ardiles Rante © option, the satellite-based land cover classification is primarily used for the sampling design of the Whereas the use of full-coverage LiDAR, LiDAR LiDAR AGB calibration plots. Figure 2 describes the transects, or no LiDAR, will depend on project-specific workflow of Option 1. The reasons for choosing this considerations (e.g. desired accuracy/detail, budget, option should be explained in the HCS summary data availability) a satellite-based high resolution report. land cover classification (including ground truthing and subsequent accuracy assessment) is mandatory for all three options. The Area of Interest (AOI) to be mapped by satellites must include the development area and also the broader landscape adjacent to the development area, and is further described in the next section. Imagery Sampling LiDAR calibration Full-coverage Design plots LiDAR Pre-processing Calibrate Object-based Preliminary land classification cover classification Accuracy Field ground assessment truth Final land
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