Approved VCS Methodology VM0027

Transcription

1 Approved VCS Mehodology VM0027 Version 1.0, 10 July 2014 Mehodology for Reweing Drained Tropical Pealands Page 1

2 Documen prepared by: WWF Indonesia and WWF Germany Winrock Inernaional Remoe Sensing Soluions GmbH TerraCarbon, LLC Alerra Wageningen, UR Page 2

3 Table of Conens 1 Sources Summary Descripion of he Mehodology Definiions Applicabiliy Condiions Projec Boundary Geographic boundary Temporal boundary Carbon pools Sources of greenhouse gases Procedure for Deermining he Baseline Scenario Procedure for Demonsraing Addiionaliy Quanificaion of GHG Emission Reducions and Removals Baseline Emissions Projec Emissions Leakage Summary of GHG Emission Reducion and/or Removals Uncerainy Analysis Calculaion of VCS Buffer Calculaion of Verified Carbon Unis Monioring Daa and Parameers Available a Validaion Daa and Parameers Moniored Descripion of he Monioring Plan References...64 Annex I: Design of projec measures...67 Page 3

4 1 SOURCES This mehodology uses he laes versions of he following ools: VCS Tool for he Demonsraion and Assessmen of Addiionaliy in VCS Agriculure, Foresry and Oher Land Use (AFOLU) Projec Aciviies CDM Tool for esing significance of GHG emissions in A/R CDM projec aciviies Sraificaion by pea depleion ime is based on VCS mehodology, VM0004 Mehodology for Conservaion Projecs ha Avoid Planned Land Use Conversion in Pea Swamp Foress. 2 SUMMARY DESCRIPTION OF THE METHODOLOGY Addiionaliy and Crediing Mehod Addiionaliy Crediing Baseline Projec Mehod Projec Mehod This mehodology applies o projec aciviies in which drained ropical pealands are rewe hrough he consrucion of permanen and/or emporary srucures (eg, dams) which hold back waer in drainage waerways. As such, his mehodology is caegorized as a Resoring Weland Ecosysems (RWE) mehodology. This mehodology quanifies he reducion in carbon dioxide (CO2) emissions due o decreased oxidaion of soil organic maerial ha occurs as a resul of projec aciviies. Annex I provides a recommended approach for deermining he number and locaion of dams ha are included in he projec. Emissions from nirous oxide (N2O) are conservaively excluded from his mehodology since projec aciviies increase he waer able in comparison o he baseline, and hus such emissions will be equal or lower as a resul of projec aciviies. The quanificaion of emission reducions is based primarily on oupus from he Simulaion of Groundwaer (SIMGRO) model which esimaes he waer able deph based on a range of inpu parameers such as errain characerisics, pea hickness and climae variables. This mehodology is only applicable o projecs in Souheas Asia; specifically, Malaysia, Indonesia, Brunei and Papua New Guinea. The main mehodological seps are provided below: Definiion of he projec area: Various geographic areas mus be specified for he pea reweing projec. The projec area is specified for all eligible discree areas of pealand o be subjeced o reweing projec aciviies. The area of he waershed(s) of ineres ha is modeled o esimae he impac of projec aciviies on waer levels in he area of hydrological influence is also specified. Under he applicabiliy condiions of his mehodology, he projec area is no required o coincide wih he area of he waershed(s) of ineres. However, he waershed(s) of ineres mus consiue one or Page 4

5 more complee hydrological unis or waersheds and he enire projec area mus be conained wihin he waershed(s) of ineres. A spaially explici digial errain model (DTM), which characerizes elevaion and slope, is used o deermine he spaial exen of he waershed(s) of ineres for his sudy. Topographic condiions (elevaion, slope) deermine he direcion of waer flow in a region and hus he waershed area. If here are areas wihin he waershed(s) of ineres, bu ouside he projec area, his excluded area of he waershed(s) mus also be delineaed. Discree land areas wihin he waershed(s) of ineres and he projec area are recorded in spaially explici polygons. Sraificaion: Iniial projec condiions are esablished by modeling pea deph and waer levels relaive o he pea surface across he waershed(s) of ineres using remoe sensing and field daa in combinaion wih a hydrological model. The projec area is sraified by drainage deph. The applicaion of his mehodology requires he ex-ane sraificaion of he projec area by pea deph. Idenifying he baseline scenario: The laes version of he VCS Tool for he Demonsraion and Assessmen of Addiionaliy in VCS Agriculure, Foresry and Oher Land Use (AFOLU) Projec Aciviies mus be used o idenify he poenial alernaive baseline land use scenarios in he projec area and in he modeled waershed area excluded from he projec area. The mehodology provides a sepwise approach o deermine he mos plausible baseline scenario(s) in he projec area and in he excluded area of waershed(s). Demonsraion of addiionaliy: Addiionaliy is demonsraed hrough applicaion of he laes version of he VCS Tool for he Demonsraion and Assessmen of Addiionaliy in VCS Agriculure, Foresry and Oher Land Use (AFOLU) Projec Aciviies. Ex-ane calculaion of baseline GHG emissions: Drainage deph across he waershed(s) of ineres is modeled in he baseline based on he curren and hisoric layou of he relevan drainage sysem (considering any poenial naural damming expeced o occur in he waershed(s) of ineres), curren opographic daa and hisoric climae daa. Baseline CO2 emissions from decomposiion of pea are esimaed by applying he relaionship beween waer levels and CO2 emissions specified in his mehodology or oher equaions from appropriae lieraure as hey may become available in he fuure. CO2 emissions from oxidaion in he baseline are only considered for projec area lands wih suiably hick pea deph (ie, areas where he pea has been compleely depleed are no considered o emi CO2 in he baseline). CH4 and N2O emissions in he baseline are conservaively no accouned for. Calculaion of ex-ane GHG projec emissions: CO2 emissions in he projec scenario are esimaed following he same mehod used in he calculaion of he baseline emissions considering he planned projec inervenion (ie, he esablishmen of dams in drainage waerways). I is conservaively assumed ha emissions may occur over he enire projec area over he enire projec crediing period in he projec scenario. Poenial increases in CH4 emissions are no accouned for because hey are de minimis in comparison o he CO2 emissions reduced by he projec. Page 5

6 Leakage emissions: The condiions under which his mehodology may be applied are such ha i is appropriae or conservaive o no include leakage emissions in he quanificaion of ne emission reducions and/or removals. Furher deails and raionale are provided in Secion 8.3 below. Baseline and projec monioring: The projec aciviy is moniored o verify he implemenaion of he echnical inervenion o rewe he previously drained ropical pealands. Waer levels relaive o he pea surface are modeled a each monioring even based on he curren and hisoric layou of he relevan drainage sysem prior o projec sar, implemenaion of he echnical inervenion and climae daa recorded during he monioring period. Baseline and projec emissions are esimaed following he same mehod used in he calculaion of ex-ane emissions. Acual waer levels in he projec area are measured and compared o modeled waer levels. Mehods are included o ensure conservaive esimaes of waer levels are produced. 3 DEFINITIONS Baseline Period The ime period beween he projec sar dae and he firs monioring even, or he ime period beween monioring evens Excluded Area of Waershed(s) The area wihin he waershed(s) of ineres ha is ouside he projec area Ombrogenous Tropical Pealand Pealand wih a surface isolaed from mineral soil-influenced groundwaer, which only receives waer hrough precipiaion 1 Pea Organic soils wih a leas 65% organic maer and a minimum hickness of 30 cm 2,3 Waershed The enire area ha is drained by one waerway, such ha all flow ha originaes in he area is discharged hrough a single oule Waershed of Ineres The one or more complee waersheds modeled o esimae he impac of projec aciviies on waer levels in he area of hydrological influence 1 Rydin, H and Jeglum, JK The Biology of Pealands. Oxford Universiy Press, UK. 360 p. ISBN13: Rieley, JO. and Page, SE Wise Use of Tropical Pealand: Focus on Souheas Asia. Alerra, Wageningen, The Neherlands. 237 p. ISBN Joosen H, Clarke D (2002) Wise use of mires and pealands Background and principles including a framework for decision-making. Inernaional Mire Conservaion Group / Inernaional Pea Sociey, 304 pp. Page 6

7 Waerway VM0027, Version 1.0 A naural or manmade feaure in a pealand, including rivers and canals, ha conducs waer owards a hydrological oule Acronyms used in his mehodology are lised below: ASCII ASPRS DSM DTM LiDAR PDOP PRA SIMGRO RMSE SRTM SVAT American Sandard Code for Informaion Inerchange American Sociey for Phoogrammery and Remoe Sensing Digial Surface Model Digial Terrain Model Ligh Deecion and Ranging Posiion Diluion of Precision Paricipaory Rural Appraisal Simulaion of Groundwaer model Roo Mean Square Error Shule Radar Topography Mission Soil-Vegeaion-Waer Transfer uni 4 APPLICABILITY CONDITIONS This mehodology applies o projec aciviies which rewe drained ropical pealands hrough he consrucion of permanen and emporary srucures which hold back waer in drainage waerways. Projecs mus mee he condiions below. Noe ha applicabiliy condiions 13 and 14 mus be saisfied a each and every verificaion even. 1. The projec area mus mee he definiion of ombrogenous ropical pealand. 2. The projec area mus exis a an elevaion less han 100m above sea level. 3. The projec area mus exis wihin Malaysia, Indonesia, Brunei or Papua New Guinea (hereafer referred o as Souheas Asia). 4. Mean annual waer level below he pea surface wihin he projec area for he baseline and projec scenarios canno be greaer han 1 meer in deph. Page 7

8 5. The waershed(s) of ineres ha includes he projec area mus comprise one or more complee waersheds. 6. The waershed(s) of ineres canno be hydrologically-conneced o adjacen pealand and non-pealand areas ouside he projec area. 7. The waershed(s) of ineres canno include areas where N-based ferilizers have been, or are planned o be, applied. 8. The projec mus demonsrae a significan difference in he ne GHG benefi beween he baseline and projec scenarios for a leas 100 years. 9. This mehodology is only applicable where he mos plausible baseline scenario is he scenario where he projec area has been drained due o human-induced drainage aciviies and would remain drained in he absence of he projec. 10. A he projec sar dae, i mus be demonsraed ha no agens inend o implemen furher drainage aciviies wihin he projec area. 11. A he projec sar dae, land use aciviies in he projec area canno include deforesaion, planned fores degradaion, land use conversion, crop producion or grazing of animals. 12. The baseline scenario in he waershed(s) of ineres mus resul in equal or lower aboveground ree biomass compared o he projec scenario. 13. Curren and/or poenial fuure land use aciviies in he excluded area of waershed(s) mus no have a significan negaive hydrologic impac on he projec area. Accepable evidence includes land use plans, laws or resource concession righs. This applicabiliy condiion mus be saisfied a validaion and a each verificaion even. Failure o mee his applicabiliy condiion a verificaion will render he projec ineligible for furher crediing. 14. Curren and/or poenial fuure legal land use aciviies aking place wihin he excluded area of waershed(s) mus no be displaced by projec aciviies. This applicabiliy condiion mus be saisfied a validaion and a each verificaion even. Failure o mee his applicabiliy condiion a verificaion will render he projec ineligible for furher crediing. 15. Pealand reweing mus occur hrough permanen and emporary srucures (eg, dams) which hold back waer in drainage waerways, hereby increasing annual average waer levels wihin he projec area. I is no necessary for all drainage waerways wihin he projec area o be dammed by he projec. Page 8

9 16. The projec aciviy canno include he creaion of addiional drainage waerways or oher ypes of infrasrucure ha causes drainage. 17. The projec aciviy canno include any agriculural aciviies. 18. Baseline and projec scenario waer levels mus be modeled using he laes version of he SIMGRO 4 model. The parameers of he model mus be adjused for ombrogenous pealands in Souheas Asia. 5 PROJECT BOUNDARY This secion provides he mehods for deermining he following boundaries ha mus be specified by he projec proponen: The geographic area associaed wih he projec aciviy. The emporal boundaries relevan o he projec aciviy. The sources and associaed ypes of greenhouse gas emissions ha he projec aciviies will impac. 5.1 Geographic Boundary The following geographic boundaries mus be specified: Waershed(s) of Ineres As per he applicabiliy condiions of his mehodology, he modeled waershed(s) of ineres area mus encompass a complee waershed wihin a pea dome. Each modeled waershed covering he projec area mus be self-conained and hus he hydrology wihin he area of he waershed(s) of ineres does no impac he hydrology of oher land areas. Topographic condiions (eg, elevaion, slope) deermine he direcion of waer flow in a region and hus he waershed area. A spaially explici DTM, which characerizes elevaion and slope, mus be used o deermine he spaial exen of all waersheds included in he projec area. Secion provides seps for creaing a DTM of he projec area. Projec Area The pealand reweing projec aciviy may conain more han one discree parcel of land. The projec area is he discree parcel(s) of pealand where he reweing aciviy will impac hydrology. 4 Querner, EP, Povilaiis, A Hydrological effecs of waer managemen measures in he Dovine River basin, Lihuania. Hydrological Sciences Journal. 54: Page 9

10 In addiion, as per he applicabiliy condiions of his mehodology, he projec proponen mus demonsrae ha all land wihin he projec area exiss on ombrogenous ropical pea. This mus be demonsraed using remoe sensing imagery 5 or a DTM and pea hickness model (see Secion below). Excluded Area of Waershed(s) The boundaries of he excluded area of waershed(s) mus be specified. When describing physical areas, he following informaion mus be provided for each discree area: Name of he projec area (eg, comparmen number, local name, waershed name); Unique ID for each discree parcel of land; Map(s) of he area in digial forma; Geographic coordinaes of each polygon verex along wih he documenaion of heir accuracy. Such daa mus be provided in he forma required by he VCS rules; Toal land area; and Deails of land ownership and land user righs. 5.2 Temporal boundary The following emporal boundaries mus be specified: Sar Dae and End Dae of he Hisoric Period for Deermining Climae Variables Baseline emissions are esimaed based on drainage deph as a funcion of long-erm climae variables (among oher parameers). The long-erm average climae variables mus be deermined using daa from weaher saions ha are represenaive of he projec area and mus include a leas 20 years of hisoric daa. Sar Dae and End Dae of he Projec Crediing Period The projec crediing period for WRC projecs mus be beween 20 and 100 years. Baseline and projec scenario GHG emissions are esimaed for he enire projec crediing period. The projec canno claim GHG reducions for longer han he ime i would have aken for all he pea in he 5 Tropical pea swamp foress feaure a unique signaure in mulispecral saellie imagery, when compared o oher, adjacen fores ypes. This is relaed o several physiognomic parameers of he pea swamp fores, such as he hydrologic condiions, a homogenous canopy srucure, small ree crown diameer, among ohers. This makes hem idenifiable in saellie images, in paricular in images which have a band in he micron range of Mid Infrared specrum (eg, Landsa- 5 TM, Landsa-7 ETM+, SPOT-4 and SPOT-5). The specral band responds o differences in moisure (Lillesand, T.M., Kiefer, R.W. Chipman, J.W Remoe sensing and image inerpreaion. 6h Ediion. New York.) and makes hese daases paricularly suiable. The delineaion is carried ou in he GIS by visual inerpreaion of he image in conjuncion wih elevaion analysis based on he SRTM. Page 10

11 enire projec area o be compleely los under he baseline scenario, as deermined by esimaion of he pea depleion ime. Monioring Period Given he monioring procedures of his mehodology, i is recommended, bu no required, ha he minimum duraion of each monioring period be a leas one year, and ha he maximum duraion of each monioring period be five years. Baseline projecions mus be annual and mus be available for each proposed fuure verificaion dae. Dae a Which he Projec Baseline Mus be Revised The esimaion of baseline emissions mus be revised prior o each verificaion even, based on moniored climae variables for he baseline period. Where he baseline scenario is reassessed (in accordance wih VCS rules for baseline reassessmen), he projec proponen mus reassess regulaory surplus and he behavior of agens ha cause changes in hydrology and/or land and waer managemen pracices. 5.3 Carbon Pools Carbon pool Included? Jusificaion/Explanaion Aboveground ree biomass Aboveground non-ree biomass Belowground biomass Yes No No Required for inclusion by VCS rules. I is conservaive o exclude his carbon pool. I is conservaive o exclude his carbon pool. Lier No I is conservaive o exclude his carbon pool. Deadwood No I is conservaive o exclude his carbon pool. Soil Yes Main pool addressed by projec aciviies. Wood Producs No I is conservaive o exclude his carbon pool. Page 11

12 5.4 Sources of Greenhouse Gases Source Gas Included? Jusificaion/Explanaion CO2 Yes Main source and gas o be addressed by projec aciviies. Considered negligible in pealands. N2O Baseline Pea oxidaion N2O No emissions are conservaively no accouned for in he baseline scenario by his mehodology. Considered negligible in drained pealands. CH4 No CH4 emissions from ropical pealands are considered de minimis because hey amoun o less han 5% of he CO2 emissions. 6 CO2 Yes Main source and gas o be addressed by projec aciviies. Considered negligible in ropical Souheas Asia pealands. 7 Projec aciviies increase he Projec Pea oxidaion N2O No waer able in comparison o he baseline and hus N2O emissions will be equal or lower as a resul of projec aciviies. Considered negligible in drained pealands. CH4 No CH4 emissions from ropical pealands are considered de minimis because hey amoun o less han 5% of he CO2 emissions. Sudies of GHG fluxes associaed wih land use change in ropical pealand indicae ha CH4 and N2O fluxes are small and can be considered negligible compared o fluxes of CO2 8. A meaanalysis of changes in CH4 fluxes from he conversion of ropical pea swamp foress indicae ha CH4 emissions from reweing are very low and do no offse he corresponding increase in soil 6 Riley, J.O., Wüs, R.A.J., Jauhiainen, J., Page, S.E., Wösen, H., Hooijer, A., Sieger, F., Limin, S.H., Sahlhu, M Tropical Pealands: Carbon sores, carbon gas emissions and conribuion o climae change processes. In: Srack, M.(Ed.), Pealands and Climae Change. Inernaional Pea Sociey. Sockholm. 7 Esimaed a N2O ha -1 in mea-analysis by Couwenberg, J, Dommain, R, Joosen, H , Greenhouse gas fluxes from ropical pealands in souh-eas Asia. Global Change Biology, 16: doi: /j x 8 Couwenberg, J, Dommain, R, Joosen, H , Greenhouse gas fluxes from ropical pealands in souh-eas Asia. Global Change Biology, 16: doi: /j x; Hirano, T, Jauhiainen, J, Inoue, T, Takahashi, H Conrols on he carbon balance of ropical pealands. Ecosysems 12: ; Murdiyarso, D, Hergoualc h, K, Vercho, L Opporuniies for reducing greenhouse gas emissions in ropical pealands. Proceedings of he Naional Academy of Sciences of he Unied Saes of America 107: 19,655-19,660; Srack, M (ed.) Pealands and Climae Change. Inernaional Pea Sociey. Page 12

13 CO2 emissions from pealand drainage 9. Based on he applicabiliy condiions of he mehodology, he projec aciviies will cause pealand reweing and will no resul in a lower waer able levels han in he baseline and herefore, N2O emissions are excluded. While pealand reweing could poenially cause greaer mehane emissions han in he baseline, he relevance of CH4 emissions in ropical pealands is very low in comparison o he CO2 emissions and are herefore deemed o be de minimis. Peer reviewed lieraure shows ha CH4 emissions are negligibly small in comparison o he CO2 emissions in ropical pealands PROCEDURE FOR DETERMINING THE BASELINE SCENARIO The laes version of he VCS Tool for he Demonsraion and Assessmen of Addiionaliy in VCS Agriculure, Foresry and Oher Land Use (AFOLU) Projec Aciviies mus be used o idenify he poenial alernaive baseline land use scenarios in he projec area. The char below, which reflecs he applicabiliy condiions of his mehodology, mus be used o deermine he mos plausible baseline scenario. 9 Hergoualc h K, Vercho, L Changes in CH4 fluxes from he conversion of ropical pea swamp foress: a mea-analysis. Journal of Inegraive Environmenal Sciences 9(2): Riley, J.O., Wüs, R.A.J., Jauhiainen, J., Page, S.E., Wösen, H., Hooijer, A., Sieger, F., Limin, S.H., Sahlhu, M Tropical Pealands: Carbon sores, carbon gas emissions and conribuion o climae change processes. In: Srack, M.(Ed.), Pealands and Climae Change. Inernaional Pea Sociey. Sockholm. Page 13

14 Has he projec area been drained by human-consruced waerways? No This mehodology is no applicable Yes Is land use conversion, deforesaion, crop producion, planned fores degradaion and/or grazing of animals he exising land use? Yes This mehodology is no applicable No Is here evidence ha demonsraes ha land use conversion, deforesaion, crop producion, planned fores degradaion and/or grazing of animals will no ake place in he baseline scenario? No This mehodology is no applicable No Yes a Is here any evidence ha demonsraes ha no agens inend o implemen furher drainage aciviies wihin he projec area a he projec sar dae? Yes b This mehodology is no applicable No Is here evidence ha demonsraes ha he exising or hisorical land use aciviies will coninue o ake place? Yes c This mehodology is no applicable Is here evidence ha demonsraes ha he hydrology of he waersheds of ineres is drained by exising drainage waerways and will remain similarly drained in he absence of he projec? No This mehodology is no applicable Yes d The mos plausible baseline scenario is ha he projec area has been drained due o human-induced drainage aciviies, and would remain drained in he absence of he projec Page 14

15 a. The projec proponen mus provide evidence ha he lised aciviies will no occur. This mus include iems such as legal permissibiliy, suiabiliy of projec area o land use and/or exising documened baseline managemen plans. b. Accepable evidence includes land use plans, resuls of he PRA, laws or resource concession righs. c. This evidence mus include iems such as legal permissibiliy, common pracice and/or exising managemen and budge plans. d. Evidence mus be presened o demonsrae ha no plans exis for alering waerway drainage in he waersheds of ineres. Long-erm average climae variables (a leas 20 years of daa) ha influence waer able dephs and he iming and quaniy of waer flow mus be used o demonsrae ha waer inpus are expeced o be similar o exising condiions in he absence of he projec. 7 PROCEDURE FOR DEMONSTRATING ADDITIONALITY The laes version of he VCS Tool for he Demonsraion and Assessmen of Addiionaliy in VCS Agriculure, Foresry and Oher Land Use (AFOLU) Projec Aciviies mus be used o demonsrae addiionaliy. 8 QUANTIFICATION OF GHG EMISSION REDUCTIONS AND REMOVALS 8.1 Baseline Emissions Ne GHG emissions in he baseline scenario are deermined as: max C (1) BSL C BSL, 1 Where: ΔC BSL Ne greenhouse gas emissions in he baseline scenario from he coninuaion of pealands in a drained sae ( CO2e) ΔC BSL,, Ne carbon sock change in all pools in he baseline scenario a ime ( CO2e) 1,2,3 max years elapsed since he projec sar dae up o he maximum number of years for sraum i Baseline emissions mus be esimaed for boh he projec crediing period and for 100 years. Page 15

16 8.1.1 Prepare Modeling Daa Baseline CO2 emissions are based on he waer level wih respec o pea surface. These waer levels are modeled based on he curren and hisoric layou of relevan drainage sysems (including any poenial naural damming expeced o occur in he projec area) and he longerm average weaher prior o he projec sar dae. The following seps mus be followed o model waer levels over ime wihin he waershed(s) of ineres: 1) Generae land cover map 2) Generae DTM 3) Generae pea hickness model 4) Collec climae variable daa 5) Delineae waerways 6) Validae SIMGRO model for projec area condiions Generae Land Cover Map A land cover map of he waershed(s) of ineres is required in order o: Perform a deailed accuracy assessmen of he DTM regardless of he opion seleced for generaion of he DTM in Secion Correc radar-derived digial surface models (DSM) for vegeaion if Opion 2 for generaion of he DTM is seleced in Secion Remoe sensing images used mus have a spaial resoluion of 30m or higher. 11,12 Remoe sensing daa mus be geo-referenced ino a common geodeic sysem wih he oher used daases (eg, using he UTM sysem). The arge geomeric accuracy of he image daa is an RMS of 0.5 pixels. The land cover classes mus be validaed by reference daa colleced in he field or high resoluion remoe sensing imagery (resoluion 5 m). Overall classificaion of foresnon-fores mus have an accuracy of 90% or more. 11 Guidance on he selecion of daa sources can be found in Chaper 3A.2.4 of he IPCC 2006 GL AFOLU and in GOFC-GOLD (2011), Reducing greenhouse gas emissions from deforesaion and degradaion in developing counries: a source book of mehods and procedures for monioring, measuring, and reporing. 12 The following saellie sensors are suiable o assess he land cover: Saellie Sensor Geomeric Specral resoluion resoluion MIR/SWIR Landsa-5 TM 30m 7 bands YES Landsa-7 ETM+ 30m 7 bands YES SPOT-4/5 XS 20/10m 4 bands YES Page 16

17 The land cover classes mus be grouped according o average vegeaion heigh. The overall sraificaion mus be based on inernaionally recognized vegeaion classificaion sysems, such as he Inernaional Geosphere-Biosphere Programme land use classificaion sysem, bu he projec proponen may furher refine sraificaion if appropriae for he projec area. The minimum land cover classes are: Fores (lands meeing he inernaionally recognized counry s fores definiion) Shrubs (lands wih woody vegeaion below he minimum heigh crieria in he counry s fores definiion and wih canopy cover greaer han 10%) Grassland (lands wih herbaceous ype of cover; ree and shrub cover mus no exceed 10%) Waer In addiion, in he case ha a radar-derived DSM is used o generae he DTM (Opion 2 in Secion ), he land cover classificaion mus be used o correc he radar daa for vegeaion heigh. In his case he sraificaion mus be creaed from remoe sensing imagery which has been acquired in he same ime range as he radar daa used for creaing he DTM (maximum difference in acquisiion daa +/- 6 monhs). This is necessary in order o assure ha he saellie image shows he same land cover siuaion as elevaion daa Generae DTM A DTM of he pea surface, generaed by 3D modeling wihin a GIS environmen by means of digial elevaion daa, mus exis for he area wihin he waershed(s) of ineres. The DTM is required o deermine he area of he waershed(s) covering he projec area and is a required inpu o creae he pea hickness model as well as a required inpu o SIMGRO for modeling baseline and projec scenario waer levels in he projec area. The DTM may have a larger spaial exen han he waershed(s) of ineres and mus mee he requiremens below. Two DTM creaion opions are presened below. The mehods described under Opion 2, Sep 4 below mus be used o assess he accuracy of he DTM, regardless of which opion is used. If he required daa are available, he DTM mus be derived using airborne LiDAR daa. Oherwise, Opion 2 presened below mus be used o derive he DTM. Page 17

18 Opion 1: Derivaion of DTM wih LiDAR Daa Sep 1: Derive he DTM wih LiDAR Daa If LiDAR daa are used o generae a errain model, he LiDAR poin cloud mus be filered wih a errain adapive filering echnique 13 in order o separae ground poins from vegeaion poins. The echnical specificaions of he LiDAR daa mus mee he following qualiy crieria: Minimum poin densiy is 2 poins per square meer, wih higher poin densiies recommended in order o faciliae more laser reurns from he errain surface. LiDAR daa mus be eiher muliple reurn or full-waveform LiDAR daa wih 2-8 poins per square meer (recommended in foresed areas wih dense vegeaion cover) or firs-las pulse daa. The maximum permissible scan angle mus be 10. The verical accuracy of he LiDAR daa mus be assessed by dgps ground measuremens and mus have an RMSE of < 50 cm. These specificaions faciliae a high accuracy of he LiDAR derived DTM, and limis uncerainy in he errain measuremens. This is a precondiion for a conservaive esimae of emission reducions. I is recommended ha he DTM area be fully covered wih LiDAR daa. However, if full coverage LiDAR daa is no available or canno be acquired, i is allowable o use regularly spaced LiDAR ransecs ha sysemaically cover he DTM area. This is jusified due o he fac ha he opography of ropical pea swamps is usually very even and smooh. In order o faciliae he bes possible represenaion of he errain, ancillary informaion (eg, SRTM digial elevaion model and available saellie images) mus be consuled during planning. The placemen of ransecs mus fulfill he following requiremens: A minimum of 4 ransecs mus be uniformly disribued over he whole area of he DTM. Transecs mus be oriened parallel or in a regularly spaced grid. The ransecs mus accuraely represen errain variaions in he waershed(s) of ineres. The ransecs mus cover he full elevaion range of he waershed(s) of ineres. These LiDAR ransecs mus hen be inerpolaed ino a full coverage DTM by compleing he following seps: 13 Pfeifer, N., Sadler, P. & Briese, C. (2001). Derivaion of digial errain models in SCOP++ environmen. OEEPE Workshop on Airborne Laserscanning and Inerferomeric SAR for Deailed Digial Elevaion Models, Sockholm. Page 18

19 Filering of he LiDAR poin clouds wih a errain adapive filering echnique o separae ground poins from vegeaion poins, such as he Hierarchic Robus Filering (Pfeiffer e al. 2001). Mahemaical modeling of he surface based on he LiDAR poin cloud (eg, wih he Kriging algorihm or a Bézier). The Bézier surface is obained by applying a Caresian produc o he Bézier equaions of a Bézier curve. 14 Sep 2: Assess he accuracy of he LiDAR derived DTM LiDAR derived DTMs mus be validaed wih opographic field measuremens using dgps devices by he mehods described under Opion 2, Sep 4 below. A nework of measuremen poins mus be designed for he whole projec area and errain elevaion mus be measured. The accuracy of he validaion daa mus be a leas hree imes higher han he DTM daase o be assessed. Opion 2: Derivaion of DTM from a DSM In cases where LiDAR daa are no available, a DTM derived from radar daa, including daa from he Shule Radar Topography Mission (SRTM), mus be used. Sep 1: Generaion of surface model Radar daa (eg, SRTM daa 15 or oher superior radar daases as hey become available in he fuure) covering he enire DTM area mus be used o creae a DTM. The minimum horizonal resoluion for he radar daa is 90m while he minimum verical resoluion for radar daa is 1m. Sep 2: Correcion of surface model for vegeaion heigh The DSM derived from radar daa mus be correced for he vegeaion heigh in order o obain a DTM showing he pea dome opography. The fores canopy heigh for differen ypes of pea swamp foress may be derived by comparing vegeaion heigh o errain heigh on foresed and non-vegeaed areas or hrough represenaive field measuremens of ree heigh. To esimae canopy heigh for each land cover class in he land cover map generaed in Secion in he absence of LiDAR, daa field measuremens wihin he DTM area mus have occurred. Canopy heigh mus be measured a locaions for each land cover sraum deermined using represenaive random sampling or sysemaic sampling wih a random iniiaion poin. A each locaion, he heigh of a leas hree represenaive individuals (eg, rees, shrubs) of he dominan canopy layer mus be measured. Sufficien number of locaions mus be measured in 14 Salomon, D Curves and Surfaces for Compuer graphics. 460 p. ISBN-13: The SRTM daa se is a freely available DSM which has an almos global coverage (from 80 N o 80 S), which conains he elevaion of he earh surface (ie, he elevaion including he vegeaion cover). Page 19

20 each land cover sraum o achieve a precision of equal or less han 15% of he mean a he 95% confidence inerval in he esimae of vegeaion heigh for each land cover class. H LC Loc ind1 loc1 Ind H ind, loc, LC Ind Loc (2) Where: H LC H ind,loc,lc Ind Loc LC Mean heigh of vegeaion land cover class LC (m) Heigh of individual ind a sampling locaion loc wihin land cover class LC (m) 1,2,3 Ind individuals measured a sampling locaion loc wihin land cover class LC 1,2,3 Loc locaions of measuremens wihin land cover class LC 1,2,3 LC land cover classes wihin projec area Sep 3: Derive DTM from DSM Radar-derived elevaion profiles placed in a regular spacing over he coverage of he DTM mus hen be analyzed in conjuncion wih he land cover sraificaion in order o subrac he vegeaion heigh of he differen sraa from he corresponden secion of he elevaion profiles. The number of profiles depends on several facors, mos imporanly he area covered by he DTM and homogeneiy of he errain and vegeaion cover in he sudy areas. In order o achieve good inerpolaion resuls he following crieria mus be fulfilled: The profiles mus be oriened o accuraely represen errain variaions in he projec area. The profiles mus cover he full elevaion range of he projec area. The profiles mus cover all vegeaion sraa. The correced elevaion profiles mus hen be modeled wih a polynomial rend funcion in order o compensae for small undulaions in he profile caused by scaer in he elevaion daa. The modeled errain elevaion profiles mus hen be inerpolaed wih he Kriging algorihm ino a full coverage DTM. The adequacy of he number, placemen and spacing of he elevaion profiles is evaluaed by he accuracy assessmen of he DTM. If he DTM mees he accuracy requiremens of his mehodology he number, placemen and spacing of he elevaion profiles are considered adequae. Page 20

21 Sep 4: Accuracy assessmen of he DTM Radar-derived DTMs mus be validaed wih opographic field measuremens (eg, by dgps, Tachymeer or oal saion) or LiDAR derived elevaion measuremens from a LiDAR daase of known accuracy. The mehods described below mus be used o assess he accuracy of radarderived DTMs. The accuracy of LiDAR daases used o validae SRTM-derived DTMs mus also be assessed as described below. The minimum accepable accuracy for he DTM is 1.75m. Due o he fla opography of he pea dome, he daa qualiy of he opographic field measuremens of elevaion mus fulfill he following requiremens: Elevaion daa (LiDAR or field measuremens) used for he validaion of he DTM mus have a relaive accuracy a leas hree imes higher han he DTM daase o be assessed. 16 Horizonal accuracy mus be less han 1m. Verical accuracy of he validaion daa mus be a leas hree imes higher han he DTM daase o be assessed. The validaion poins mus be represenaive of he area covered by he DTM. A minimum number of 20 poins per vegeaion class mus be used. A minimum of 5 saellies mus be available for GPS posiion measuremens. A maximum PDOP of 5 or less mus be achieved. Where he minimum saellie visibiliy or maximum PDOP canno be fulfilled a a given locaion, GPS measuremen mus be aken a a locaion where hese requiremens can be me (he saion ). Then, he X-, Y- and Z- offse from he saion poin mus be measured by raverse or beer conrolled raverse measuremens wih a oal saion or achymeer. The raverse mehod requires he exac deerminaion of wo poins wih GPS and he exac disance and angle beween hese wo reference poins (he saion ). Then, offse poins which are referred o as he raverse mus be measured from he saion. The conrolled raverse mehod is an improvemen over he raverse mehod, and requires anoher saion afer he raverse o assess and correc he measuremen errors in he offse poins. If field measuremens are used o assess he accuracy of he DTM, he accuracy of he DTM mus be calculaed by comparison of he DTM elevaion a he measuremen poins wih he field measured elevaion daa according o he guidelines of he ASPRS Lidar Commiee. 17 The accuracy assessmen mus assess he fundamenal accuracy (accuracy of he DTM on open errain), as well as supplemenal accuracy for he presen ground cover ypes. 16 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 17 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 Page 21

22 Where no field measuremens are available, he accuracy of radar-derived DTMs can VM0027, Version 1.0 alernaively be validaed wih LiDAR derived elevaion measuremens. Since he accuracy of LiDAR derived elevaion daa is dependen of he filering of ground poins, if LiDAR daa is used o validae he radar-derived DTM, he LiDAR daa mus be validaed as described below. When using LiDAR as validaion daa, i mus be assured ha only daa from he acual LiDAR swah is aken, and no from inerpolaed areas beween differen LiDAR swahs. Firs, he errors (difference beween DTM and field measured or LiDAR elevaion) mus be esed for normal disribuion wih a suiable es such as he Kolmogorov-Smirnov (KSA) es, or by calculaing he skewness. 18 If he errors are normally disribued, he Roo Mean Square Error (RMSE) mus be used o deermine he verical accuracy (Accuracy z) of he DTM. RMSE is calculaed wih he equaion: RMSE DTM Q ( Z q1 val, q Z DTM, q ) Q 2 (3) Where: RMSE DTM RMSE in DTM (m) Z val,q Validaion elevaion value q (m) Z DTM,q DTM elevaion value q (m) q 1,2,3 Q sample number Then, verical accuracy (Accuracy z) of he DTM a he 95 percen confidence level mus be calculaed by he equaion: Accuracy z = 1.96 RMSE DTM (4) Where: Accuracy z RMSE DTM Verical accuracy of he DTM (m) Roo Mean Square Error for DTM (m) If he es for normal disribuion fails (ie, he errors feaure an asymmeric disribuion), he use of RMSE is no appropriae for assessing he verical accuracy. In his case, he 95h percenile of 18 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 Page 22

23 he errors mus be calculaed o deermine Accuracy z. 19 Accuracy z hen direcly equals he 95 h percenile. Where field measuremens are used for assessing he accuracy of he DTM, he accuracy of he DTM direcly equals he verical accuracy. Accuracy DTM = Accuracy z (5) Where: Accuracy DTM Accuracy z Accuracy of he DTM (m) Verical accuracy of he DTM (m) Where LiDAR derived elevaion daa are used for assessing he verical accuracy of he radarderived DTM, he uncerainy assessmen mus consider he accuracies of boh daases by error propagaion. The accuracy of he LiDAR daa (Accuracy LiDAR) mus be assessed wih opographic field measuremens of elevaion applying he same mehods and crieria described for assessmen of he verical accuracy of he DTM using opographic field measuremens. Alernaively, if he daase has been validaed by he daa provider and no he projec, i mus be assured ha he accuracy of he daa has been repored in accordance wih he ASPRS guidelines 20 as Tesed (meers, fee) verical accuracy a 95 percen confidence level whenever possible. This requires: Availabiliy of an independen validaion daa source (from a hird pary). Accuracy of he independen daase mus be a leas hree imes higher han he daase assessed. If hese requiremens canno be fulfilled, he accuracy of he LiDAR daase mus be repored as Compiled o mee (meers, fee) verical accuracy a 95 percen confidence level. This may be used where: The validaion daase was measured by he daa provider and no a hird pary. The accuracy of he validaion daase is no hree imes higher han he DTM being validaed. The LiDAR daase used for validaion was validaed, bu ouside he projec area. Accuracy in he radar-derived DTM validaed wih LiDAR daa is calculaed as: Accuracy DTM 2 z 2 LiDAR Accuracy Accuracy (6) 19 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 20 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 Page 23

24 Where: Accuracy DTM Accuracy z Accuracy LiDAR Accuracy of he radar-derived DTM (m) Verical accuracy of he radar-derived DTM as assessed wih LiDAR daa (m) Accuracy of he LiDAR daase (m) Generae Pea Thickness Model The errain model mus be combined wih pea drilling daa o generae a spaially explici model of pea hickness wihin he waershed(s) of ineres. Sep 1: Obain pea hickness daa In order o deermine pea hickness, he deph of pea a each sampling locaion mus be deermined hrough pea drilling using a pea auger such as an Eijkelkampp, unil he mineral soil underneah he pea is reached. Pea drilling locaions in he waershed(s) of ineres mus be deermined using represenaive random sampling or sysemaic sampling. I is accepable o conduc drilling along ransecs ha exend from one boundary of he pea dome o he opposie boundary and inersecs he highes poin of he pea dome. Sampling inervals mus range from 500 o 1500 meers depending on he size of he pea dome and errain accessibiliy. The highes poin mus be deermined using he DTM. In highly inaccessible areas pea hickness can be inerpolaed using a correlaion funcion beween he pea surface and pea hickness daa. 21 Uncerainy in pea drilling daa mus be addressed by assuming he lower bound of he pea hickness model as described below. Sep 2: Esimae pea hickness If drilling measuremens are sysemaically disribued across he waershed(s) of ineres, direc spaial inerpolaion, such as Kriging, mus be applied o esimae pea hickness. In highly inaccessible areas pea hickness may be esimaed using a binominal correlaion funcion beween he pea surface elevaion derived from he DTM and pea hickness daa. The surface elevaion of he pea dome mus be normalized o he elevaion of he boundary of he pea dome wih he equaion: h(norm) = h h(bound) (7) Where: h(norm) h Normalized pea surface elevaion relaive o he pea boundary Terrain elevaion 21 Jaenicke, J, Rieley, JO, Mo, C, Kimman,P, and Sieger,F Deerminaion of he amoun of carbon sored in Indonesian pealands. Geoderma 147: Page 24

25 h(bound) Elevaion a he pea dome boundary For he esablishmen of he correlaion funcion, he surface elevaion is exraced from he DTM a he drilling locaions. Then a binominal rend funcion beween hose variables mus be calculaed wih he equaion: PTh = a h(norm) 2 + b h(norm) + c (8) Where: PTh h(norm) Pea hickness Normalized pea surface elevaion a, b, c Coefficiens of he binominal correlaion funcion, deermined on reference daa The minimum accepable model correlaion beween pea surface elevaion and pea hickness is R² >0.7. Oherwise, pea hickness canno be derived using he correlaion funcion. The pea hickness model mus hen be obained by applying he correlaion funcion o each grid cell of he normalized DTM. The accuracy of he pea hickness model mus be assessed wih validaion pea hickness daa no used for calibraing he model. As he pea hickness model is derived from pea drilling daa and he DTM, firs he calculaed accuracy based on he pea hickness daa mus be combined wih he accuracy of he DTM by error propagaion o deermine he overall verical accuracy in he pea hickness model. The errors (difference beween measured pea hickness and he modeled pea hickness) mus be esed for normal disribuion disribuion wih a suiable es such as he Kolmogorov-Smirnov (KSA) es, or by calculaing he skewness. 22 If he errors are normally disribued, he Roo Mean Square Error (RMSE) mus be used o deermine he accuracy of he pea hickness model. RMSE is calculaed wih he formula: RMSE PTh Q ( PTh q1 val, q PThMOD, q ) Q 2 (9) Where: RMSE PTh RMSE in pea hickness model (m) PTh val,q Validaion pea hickness value q (m) 22 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 Page 25

26 PTh MOD,q Modeled pea hickness value q (m) q 1,2,3 Q sample number Then, accuracy (Accuracy PTh) of he pea hickness model a he 95 percen confidence level mus be calculaed by he equaion: Accuracy PTh = 1.96 RMSE PTh (10) Where: Accuracy PTh RMSE PTh Accuracy of he pea hickness model (m) RMSE for pea hickness model (m) If he es for normal disribuion fails (ie, he errors feaure an asymmeric disribuion), he use of RMSE is no appropriae for assessing he accuracy of he pea hickness model. In his case, he 95h percenile of he errors mus be calculaed o deermine Accuracy Ph. 23 Accuracy PTh hen direcly equals he 95 h percenile. Pea hickness is conservaively esimaed by assuming he lower bound of he esimaed pea hickness is he acual pea hickness a he projec sar dae. PTh Adjused, x, 0 PThx, 0 Accuracy PTh (11) PTh Adjused,x,0 Pea hickness in grid cell x a sar of he projec aciviy adjused for uncerainy in he pea hickness esimae (m) PTh x,0 Pea hickness in grid cell x a sar of he projec aciviy as calculaed from pea hickness model (m) Accuracy PTh Accuracy of he pea hickness model (m) A each verificaion even, pea hickness mus be updaed for he associaed baseline period o updae he esimae of baseline emissions by conservaively assuming a reducion in pea deph due o subsidence. PTh PTh ( S *0.01* ) (12) x, Adjused, x, 0 p Where: PTh x, Pea hickness in grid cell x a sar of baseline period (m) 23 ASPRS Lidar Commiee Verical Accuracy Reporing for Lidar Daa V1 Page 26

27 PTh Adjusedx,0 Pea hickness in grid cell x a he sar of he projec aciviy adjused for uncerainy in he pea hickness esimae (m) Sp Pea subsidence rae (see Secion 8.1.2) 0,1,2,3 number of years elapsed since he sar of he projec (years) During firs baseline period PTh x, = PTh Adjused,x, Collec Climae Variable Daa Long-erm climae variables are deermined using daa from weaher saion(s) represenaive of he waershed(s) of ineres. Precipiaion daa mus be available on he daily ime sep for a climae saion wihin 100 km and wihin ±100 m elevaion of he projec area for 20 years prior o he projec sar dae, hus capuring he range of precipiaion condiions in he area. Addiionally, evaporanspiraion raes of he dominan vegeaion cover(s) mus be available as an inpu o he SIMGRO model. Evaporanspiraion may be assumed o be a consan daily value of 3.5 mm per day, 24 or anoher locaion-specific facor may be used if he projec proponen demonsraes ha i mees he VCS requiremens wih respec o he selecion of appropriae defaul facors, since evaporanspiraion is fairly consan in he humid ropical areas and yearly variaions in evaporanspiraion show low variance. Evaporanspiraion is mainly driven by wind speed, emperaure and air humidiy. These climaic facors are fairly similar for he ropical Souheas Asia region and herefore evaporanspiraion is considered o be fairly uniform across he region. Half day o daily ime seps are required for modeling waer flow in he unsauraed zone and groundwaer; he seleced ime seps for each mus mach bu may vary wihin his range. Daa for he waershed(s) of ineres may be supplied from more han one weaher saion falling wihin 100 km of he waershed(s) of ineres boundary. In his case he relevan saion mus be specified for each of he SVAT-unis in he model. Where more han one weaher saion daa exiss, daa on climae variables may be inerpolaed for he waershed(s) of ineres. If more han one weaher saion mees he locaion requiremens for a given SVAT-uni, for ime periods where daa from he seleced weaher saion is no available, daa from an alernae weaher saion ha mees he locaion requiremens of he SVAT-uni may be subsiued. 24 Takahashi, H., Usup, A., Hayasaka, H., Kamiya, M., Limin, S.H., The imporance of ground waer level and soil moisure of subsurface layer on pea/fores fire in a ropical pea swamp fores. In: Päivänen, J. (Eds.), Wise Use of Pealands. Volume 1. Proceedings of he 12h Inernaional Pea Congress, Tampere, Finland, 6-11 June Inernaional Pea Sociey, Jyväskylä, Finland, p Page 27

28 Using he hisoric daily climae daa, an average precipiaion per day wihin a monh mus be calculaed. This hisoric climae daa will be used o run he SIMGRO model for ex-ane esimaions for he baseline and projec scenarios. For each baseline period, he hisorical climae daa used mus be updaed o updae he esimae of baseline emissions Delineae Waerways Waerways in he waershed(s) of ineres mus be delineaed and informaion on waer characerisics such as widh and deph is measured in he field and recorded as average values for each waerway ype. Delineaion and characerizaion of waerways is compleed by he following seps: Sep 1: Remoe Sensing delineaion of waerways Waerways may be delineaed by combining high resoluion saellie images wih field surveys. 25 High spaial resoluion saellie imagery (10-m or beer such as ALOS or SPOT) may be used o delineae he locaion, lengh, and ouflow of waerways using visual inerpreaion and measuremen ools in a Geographic Informaion Sysem (GIS) or similar sofware. Where waerways canno be delineaed wih high resoluion saellie images, he waerways may be delineaed in he field. Sep 2: Field delineaion of waerways and creaion of waerway classes All idenified waerways delineaed wih high resoluion saellie images mus be confirmed by field checks. Field daa mus also be used o delineae waerways ha canno be delineaed wih high resoluion saellie images. A all idenified waerways, GPS measuremens mus be aken verifying he locaion of he waerway. The oal lengh of waerways may be esimaed based on inerviews wih local communiies, or alernaively GPS measuremens may be aken along idenified waerways delineaing he waerway. All measuremens mus be incorporaed ino a geodaabase of waerway locaions. Waerways mus be sraified ino waerway classes (eg, major river, minor river, major canal, medium canal, hand-dug canal) based on heir physical parameers. I is conservaive o assume a waerway does no exis while modeling baseline emissions, herefore, i is no necessary o ensure all waerways have been idenified. If an idenified waerway canno be field verified, hen i mus be assumed o no exis in he model. 25 Jaenicke, J, Wösen, H, Budiman, A and Sieger, F Planning hydrological resoraion of pealands in Indonesia o miigae carbon dioxide emissions. Miigaion and Adapaion Sraegies for Global Change 15: Page 28