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METHODOLOGY FOR A GLOBAL ANALYSIS OF THE IMPLICATIONS Robert Zomer1*, Antonio Trabucco1, Oliver Van Straaten, Lou Verchot2 and Bart Muys3
1International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lanka. *Corresponding Author: Robert Zomer: r.zomer@cgiar.org
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Overview of the Global Analysis
Within the Kyoto Protocol, countries have significant latitude to define a forest. The most important parameter affecting area designated as forest is the minimum crown density which can be set between 10 and 30 percent. The choice will have implications for the amount of land available in a country for afforestation and reforestation (A/R) activities within the Clean Development Mechanism (CDM). This analysis builds on an earlier analysis of four case study countries (Verchot et al, 2006), to present a global analysis of the effects of decisions about the minimum crown cover on the area available for CDM A/R projects for all Non-Annex I Countries. We used a large collection of publically available global data to perform the geospatial analysis for all Non-Annex 1 countries. We also at the effects of other factors that could exclude land from eligibility for A/R projects. Our objective is to demonstrate how the choice of a high or low threshold value for crown cover will affect the area available for CDM activities and how the limitations imposed by this element of the definition compares to other factors that are likely to limit CDM activities. A range of crown density thresholds from 10% to 30% are delineated, and their effect on land availability for CDM/AR is tested.
In order to test the effect of the minimum crown cover threshold on the availability of land for CDM A/R, and on the definition of forest, the geospatial analysis evaluatied the interaction of crown cover density with other exclusion criteria. The analysis is based on publicly available global datasets, and was performed within ArcGIS, a geographic information system (GIS) environment. In the first step of the spatial analysis, areas delineated as not suitable for reforestation / afforestation projects are excluded. The spatial modeling procedure was implemented using ArcAML programming language. All areas deemed not suitable for CDM-AR projects, as per the following criteria, were excluded:
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Areas with Aridity Index (AI) lower than the specified threshold (AI <0.65)
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Areas above 3500 meters and/or timberline.
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Areas classified as urban, water bodies, or various types of tundra.
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Areas classified as irrigated or under other intensive agricultural production, assuming that these areas are already in high value production, or their conversion may impact on food security.
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Recently deforested areas, that are areas that are identified as forest in the USGS 1993 Landuse Classification, as per guidelines that exclude recently deforested areas from being eligible for CDM-AR.
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Recently reforested areas, that are areas identified as forest in the MODIS12 2001 landuse classification, while defined as other land use type in the USGS 1993 land use classification.
The pre-determined range of minimum crown cover definitions was used to exclude areas with existing "forest" from the remaining land available for CDM A/R. The minimum crown cover density was used to delineate forested area at 5% increments from 10% to 30%, to correspond to the available options for forest definitions under the UNFCC. This resulted in the production of five global scenarios illustrating the implications of the definition of forest, based on crown density combined with the other exclusion criteria. The results of this analysis are delineated by administrative boundaries to tabulate the distribution of CDM-AR suitable areas, and to report the results of this global analysis on a nationa. In addition, the potential for CDM-AR within protected areas is evaluated.
An extensive set of global geospatial datasets has been collected to facilitate this global analysis, within a global raster framework which ranges between 70° North and 60° south. In addition, various intermediate datasets were calculated, in order to allow for estimation of Aridity Index and Treeline on a global basis, as discussed below.
All datasets used for the analyses have been re-projected and processed in two coordinate systems, sinusoidal and geographic. Of these two different coordinate systems, the geographic coordinate system preserves landform shapes with a prospective that is generally easily recognizable to human perception and is therefore useful for map presentation. However geographic projection maintains pixel resolution in decimal degrees, with the equivalent in meters varying at different latitudes. In order to tabulate zonal statistics and carry out areal computation, the dataset in sinusoidal projection was used to represent area extent accurately for all pixels across latitudes (equal-area projection). The cell size for analyses in geographic projection is equal to 0.004497 degrees (15-arc seconds, ~ 500 m at equator and 250 meters at 60 degrees), while the cell size for analyses in sinusoidal projection is 500 meters.
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Crown Cover Density: Tree cover and canopy density estimates were obtained from the MODIS (15 arc-seconds) Vegetation Continuous Fields (MODIS VCF) - Tree Cover Dataset. The tree cover estimates in this dataset are calculated using an automated algorithm for images acquired from the MODIS sensor over one year period (October 2000 – December 2001). This dataset is available online from the University of Maryland’s Global Land Cover Facility (GLCF) at http://glcf.umiacs.umd.edu/data/vcf/.
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SRTM 90m Digital Elevation Dataset: The SRTM-GTOPO 30 dataset from the CLCF was used, with spatial resolution of 15 (or 30) arc seconds. Additionally, void-filled NASA Shuttle Radar Topographic Mission (SRTM) 90m digital elevation data was used for local studies. The void-filled SRTM data can be downloaded from http://srtm.csi.cgiar.org.
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Land-use and Land Cover Datasets: wetlands exclusion criteria were identified and extracted from the USGS 1993 Landuse / LandCover Map. (The USGS 1993 land use classification can be downloaded from http://edcsns17.cr.usgs.gov/glcc/.
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Precipitation Datasets: The precipitation maps and climate data originated from “WorldClim”, a global dataset of preciptation, temperature, and other climate data, interpolated at 30 arc-sec resolution. The full WorldClim global dataset is available for download at http://biogeo.berkeley.edu/worldclim/worldclim.htm.
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Protected Areas: National Parks and other protected areas were identified from the IUCN World Database on Protected Areas. This database is available online through the Global Land Cover Facility (GLCF) as Univ. of Maryland: http://glcf.umiacs.umd.edu/data/wdpa.shtml.
Upper elevation limits for CDM-AR projects
For the purposes of this global analysis, areas above timberline have been estimated as areas with average temperature in the growing season below 6.5 C. The length of the growing season is calculated as the number of months where the average monthly temperature is above 0 C. Spatially distributed monthly average temperature values at 30 arc seconds of spatial resolution are derived from the Worldclim dataset (Hijmans, Cameron et al. 2004). Although treeline can surpass 4000 meters in certain parts of the world, CDM projects are considered unrealistic at height above 3500 meters. Therefore, in addition to the tree line exclusion, all land above 3500 meters is excluded for CDM-AR projects, using the SRTM digital elevation data.
Monthly Potential Evapotranspiration (PET)
Potential (or Reference Crop) evapotranspiration (PET) was estimated on a global scale for the purposes of calculating an Aridity Index (AI). Based on the results of a comparative validation, the Hargreaves (1994) model was chosen to model PET globally for this study. Hargreaves uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and extraterrestrial radiation (RA, radiation on top of atmosphere) to calculate PET, as per the equation below:
PET = 0.0023 • RA • (Tmean + 17.8) • TD0.5 (mm/d)
Monthly values for total precipitation were obtained from the WORLDClim dataset (Hijmans, Cameron et al, 2004). Min, max, and mean temperature were also obtained from this dataset (for years 1960-1990), gridded at a resolution of 30 arc secs, or ~1 km at equator. Subsequently, the average monthly temperature range (the difference between min and max monthly average temperature) was calculated, as required for PET computation.
In a classification of climatic zones proposed by UNEP (1997), the Aridity Index (AI) is used to quantify precipitation deficit over atmospheric water demand:
Aridity Index (AI) = mean annual precipitation / mean annual evapotranspiration
The AI was compared to both the land use classes determined by USGS 1993 (AVHRR sensor) and VEGETATION-SPOT classification from year 2000, and then compared to the MODIS Tree Cover Percentage estimates. Optimal climatic zones for tree plantations were ascertained to have an AI higher than 0.65.
All areas meeting the exclusion criteria within the respective data sets, apart from the crown cover dataset, were selected and merged into one raster layer to produce a map delineating the areas that are likely to be unsuitable for A/R, based upon the biophysical condition, or it’s status as forest or recently deforested area within the landuse classifications.
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