Global Tree Cover and Biomass Carbon on Agricultural Land Database (2000-2010) Data Manual v.1 May 14, 2016

Datasets and Documentation Available Online at: http://www.worldagroforestry.org/global-tree-cover/index.html

Authors: Robert J. Zomer

1,2*

, Antonio Trabucco3,4, Mingcheng Wang1, Jianchu Xu1,2

1. Key Laboratory for Plant Diversity and Biogeography of East Asia (KLPB), Kunming Institute of Botany, Chinese Academy of Science, Kunming 650201, Yunnan, China 2. Centre for Mountain Ecosystem Studies, World Agroforestry Center (ICRAF), East and Central Asia Region, Kunming 650201, China 3. Euro-Mediterranean Center on Climate Change, IAFES Division, Sassari, Italy. 4. Department of Science for Nature and Environmental Resources (DIPNET), University of Sassari, Via De Nicola 9, 07100 Sassari, Italy * Corresponding Author – Email: [email protected]



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Global Tree Cover and Biomass Carbon on Agricultural Land Database

The Global Tree Cover and Biomass Carbon on Agricultural Land Database provides raster data describing percent tree cover and above- and below-ground biomass carbon on agricultural land, globally, with a resolution of 30 arc seconds (nominally 1 km2). The Global Tree Cover and Biomass Carbon on Agricultural Land Database is provided online by the World Agroforestry Center (ICRAF), Nairobi, Kenya, and provides the results and datasets used for the geospatial analysis presented in Zomer et al, 2016. This analysis was part of an on-going research project, focused on Agroforestry for Global Food Security, Environmental Conservation, And Climate Change Mitigation and Adaptation, of the the Center for Mountain Ecosystem Studies / World Agroforestry Center (Kunming Institute of Botany, China), and investigates the geographic extent and socio-ecological characteristics of agroforestry globally, regionally, and nationally. An overview of methods and results are described in the following set of publications: Zomer, R.J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D.A., Trabucco, A., van Noordwijk, M., Wang, M. 2016. Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets. Scientific Reports 6, 29987; doi: 10.1038/srep29987. http://www.nature.com/articles/srep29987 Zomer RJ, Trabucco A, Coe R and Place F. 2009. Trees on Farm: Analysis of Global Extent and Geographical Patterns of Agroforestry. ICRAF Working Paper no. 89. World Agroforestry Centre. Nairobi, Kenya. http://www.worldagroforestry.org/downloads/Publications/PDFs/WP16263.PDF Zomer, R.J., Trabucco, A., Coe, R., Place, F., van Noordwijk, M., Xu, J., 2014. Trees on farms: an update and reanalysis of agroforestry's global extent and socio-ecological characteristics. Working Paper 179. World Agroforestry Center, Bogor, Indonesia. doi:10.5716/ WP14064.PDF http://www.worldagroforestry.org/sites/default/files/WP89_text_only.pdf This research, and the various geospatial analyses presented here, was conducted by Dr. Robert Zomer, Dr. Antonio Trabucco, Wang Mingcheng and Prof. Xu Jianchu at the Center for Mountain Ecosystem Studies, a joint laboratory of the Kunming Institute of Botany, and the East and Central Asia Regional Office of the World Agroforestry Center. Research was partly supported by the National Key Basic Research Program of China (Grant No. 2014CB954100), and National Science Foundation China (Grant No. 31270524). Additional support was also provided from the CGIAR Research Programs on Forests, Trees and Agroforestry (CRP6) and Climate Change, Agriculture, and Food Security (CRP 7). We express our gratitude to the Carbon Dioxide Information Analysis Center (CDIAC), Oakridge National Laboratory and the authors of the “New IPCC Tier 1 Global Biomass Carbon Map”, to the Global Land Cover Facility at the University of Maryland and the authors of the MOD 44B MODIS Vegetation Continuous Fields datasets, and to the Global Land Cover 2000 Database for providing easy access online to the valuable data used in this analysis.



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Use Limitation DISTRIBUTION: Users are prohibited from any commercial, non-free resale, or redistribution without explicit written permission from the ICRAF. Users should acknowledge the Global Tree Cover and Biomass Carbon on Agricultural Land Database as the source used in the creation of any reports, publications, new data sets, derived products, or services resulting from the use of this data set. NO WARRANTY OR LIABILITY: Neither the authors of this dataset, nor ICRAF, can bear any responsibility for the consequences of using it, which are entirely the responsibility of the user. It is inevitable that a dataset of this size will contain some errors and inconsistencies. However, these have been kept to a minimum and when they are identified they are corrected when resources permit. Updates to this dataset are announced through through the ICRAF web site. ACKNOWLEDGMENT AND CITATION: We kindly ask any users to cite this database in any published material produced using this data, and if possible link web pages to the ICRAF Global Tree Cover and Biomass Carbon On Agricultural Land Database website (http://www.worldagroforestry/global-treecover/). The creator of this data set retains full ownership rights over it. The data set may be freely used for non-commercial scientific and educational purposes, provided it is described as the: Global Tree Cover and Biomass Carbon on Agricultural Land Geospatial Database and attributed, using the following citation:

Zomer, R.J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D.A., Trabucco, A., van Noordwijk, M., Wang, M. 2016. Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets. Scientific Reports 6, 29987; doi: 10.1038/srep29987. Geospatial data available online from the ICRAF GeoPortal at: http://www.worldagroforestry.org/global-tree-cover/index.html

DATA USE AND DISTRIBUTION: This database has been generated by not-for-profit institutions with the objective of supplying accessible and useful information to developing country organizations. We actively encourage use of these products for scientific purposes. This is not however the case for commercial purposes. The entire dataset is available for commercial use at a modest cost, but permission must be sought. Commercial sectors interested in using this data should contact Dr. Robert Zomer at: [email protected]





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Global Tree Cover and Biomass Carbon on Agricultural Land Datasets

Datasets are provided online for distribution (in TIFF format), and in Geographic Coordinates (GCS: WGS-84), and should be probably be converted and/or projected to an appropriate format and projection (at up to 1km grid cell resolution) before doing spatial analysis or calculating areal extent. Averaging of annual data 2000 and 2010: Datasets labelled “2000” represent an average value of the years 2000-2002. Datasets labelled “2010” represent an average value of the years 2008-2010. Datasets labelled “Change – 2000-2010” represent the difference between the datasets labelled 2000 and 2010. The dataset year 2000 has been subtracted from the dataset 2010, so that an increase in tree cover (or biomass carbon) from 2000 to 2010 is represented as a positive value, and a decrease as a negative value. The characteristics for the three Global Tree Cover Datasets are listed below: Tree Cover on Agricultural Land – 2000 / Tree Cover on Agricultural Land – 2010 Filename: tc_ag_2000 / Filename: tc_ag_2010 Data value in the Value field represents percent tree cover, as described for MODI VCF Tree Cover Data. Values range from 0 to potentially 100 (but 86 is max.), representing the percent of the grid cell that is covered by tree cover., so that, e.g. 100 % tree cover of the 1km2 grid cell would represent 100 hectares covered by trees, 50 % tree cover would represent 50 hectares covered by trees within those total 100 hectares (1 km2). Change in Tree Cover on Agricultural Land – 2000-2010 Filename: tc_ag_diff Data value in the Value field represents the change in percent tree cover over a decade, from 2000 to 2010. Values range from -77 to +72, with positive value representing an increase in the percent of tree cover from 2000 to 2010, and negative values representing a decrease.



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The characteristics for the three Global Biomass Carbon Datasets are listed below: Biomass Carbon on Agricultural Land – 2000 / Biomass Carbon on Agricultural Land – 2010 Filename: bioc_ag_2000 / Filename: bioc_ag_2010 Data value in the Value field represents the above- and below-ground biomass carbon on agricultural land, as modeled by Zomer et al., 2016. *********************************************************************** Values in the VALUE field are given as tC / km2 (tons of carbon per kilometer square) Or Values in the VALUE field are also equal to: 0.01 tC / ha *********************************************************************** VALUE can be multiplied by 10 to be converted to kg of carbon per hectare (kgC/ha) Field: KG_C_HA VALUE can be divided by 100 to be converted to tons of carbon per hectare (tC/ha). Field: T_C_HA Change in Biomass Carbon on Agricultural Land – 2000-2010 Filename: bioc_ag_diff Data value in the Value field represents the change in the above- and below-ground biomass carbon on agricultural land, as modeled by Zomer et al., 2016, from 2000 to 2010. Positive values represent an increase in the biomass carbon from 2000 to 2010, and negative values representing a decrease. ***********************************************************************

Values in the VALUE field are given as tC / km2 (tons of carbon per kilometer square) Or Values in the VALUE field are also equal to: 0.01 tC / ha *********************************************************************** VALUE can be multiplied by 10 to be converted to kg of carbon per hectare (kgC/ha). Field: DIFF_KG_C_HA VALUE can be divided by 100 to be converted to tons of carbon per hectare (tC/ha). Field: DIFF_T_C_HA



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Materials and Methods – Overview



To quantify estimates of biomass carbon on agricultural land, IPCC Tier 1 default estimates for carbon stored in a variety of land cover types across bioclimatic strata were combined with tree cover estimates based on 250 m resolution MODIS satellite imagery, to provide a global Tier-1 spatial mapping and tabulation, by region and countries, of biomass carbon on agricultural land for the period 2000-2010. The spatial modeling procedure was developed and implemented in ArcGIS 10.3 (ESRI Inc.) using both ArcAML and Python programming languages. Datasets were reprojected to a sinusoidal projection (Sphere_ARC_INFO_Sinusoidal) in order to calculate zonal statistics and carry out areal computations, as it represents area extent accurately across latitudes (i.e., equal-area projection). The cell size for analyses in sinusoidal projection is 1000m (1 km2). These datasets are presented in geographic coordinates (GCS-WGS 84) for data display and mapping, and for online distribution purposes. Assessment of Global Tree Cover on Agricultural Land: The global geospatial analysis to identify tree cover on agricultural land combined a global assessment of tree cover, based upon a MODIS 250 m resolution satellite remote sensing dataset, (Hansen et al., 2003; DiMiceli et al 2011), with the Global Land Cover 2000 (GLC 2000) land-use classification . Tree cover on agricultural land was identified and results mapped and tabulated; globally, by global region, and by countries. A detailed description of this analysis is available online in two working paper reports (Zomer et al, 2009; 2014): Zomer RJ, Trabucco A, Coe R and Place F. 2009. Trees on Farm: Analysis of Global Extent and Geographical Patterns of Agroforestry. ICRAF Working Paper no. 89. Nairobi, Kenya: World Agroforestry Centre http://www.worldagroforestry.org/downloads/Publications/PDFs/WP16263.PDF Zomer, R.J., Trabucco, A., Coe, R., Place, F., van Noordwijk, M., Xu, J., 2014. Trees on farms: an update and reanalysis of agroforestry's global extent and socio-ecological characteristics. Working Paper 179. World Agroforestry Center, Bogor, Indonesia. doi:10.5716/ WP14064.PDF http://www.worldagroforestry.org/sites/default/files/WP89_text_only.pdf







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Geodatasets

The geodatasets used in the analysis are listed below. •

MOD44B MODIS Vegetation Continuous Field (DiMiceli et al 2011) - Collection 5 (2000 through to 2010): •

•

Global Land Cover 2000 (GLC 2000) Database (Bartholomé and Belward, 2005) •

•

Administrative boundaries

CSI-CGIAR Global Aridity Index Database (Trabucco et al., 2009) •



Land-cover categories

GADM database of global administrative areas, version 2.0[J]. 2012. (GADM, 2012) •

•

Percent Tree Cover

Aridity–Wetness Index

Tree-cover data

The MOD44B MODIS/Terra Vegetation Continuous Fields Dataset (VCF) (Hansen 2003) was developed by the University of Maryland and provides global estimates of vegetation cover in terms of woody vegetation, herbaceous vegetation and bare-ground percentages. The updated MOD44B MODIS VCF – Collection 5 dataset (DiMiceli et al 2011) used in the current analysis improves upon the earlier versions and provides data at the resolution of 250 m. A limited amount of validation performed using field data from two sites in Maryland and three sites in Brazil, South America show that the Collection 5VCF product is substantially more accurate than previous versions, with accuracy significantly improved within agricultural areas and forest clearings. (User Guide for VCF Collection 5 – Version 1). This data (and a User Guide for VCF Collection 5 – Version 1) is available online at: http://www.landcover.org/data/vcf/

Land-cover categories

Three agricultural land-use types from the Global Land Cover Class scheme used for the Global Land Cover 2000 database were selected as relevant for the specific objectives of this work: •

Cultivated and Managed Areas (agriculture — intensive),

•

Cropland/Other Natural Vegetation (non-trees: mosaic agriculture/degraded vegetation)

•

Cropland/Tree Cover Mosaic (agriculture/degraded forest).

Although at first the Cropland/Tree Cover Mosaic type seems to identify agroforestry systems, the mix of forest and agriculture does not occur at discrete intervals but is a



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gradient where the two components of landscape-level agroforestry mix within the landscape. The mix of tree cover over agriculture land is depicted along a continuous gradient by the MODIS VCF tree-cover dataset, within the relevant GLC2000 land-cover type. Tree cover shows the percentage of the 1 km2 grid cell occupied by trees, therefore, at a resolution of 1000 m (in an appropriate projection) , the tree-cover percentage can be expressed as hectares (ha) of tree cover per km2. At 100% tree cover, the whole grid cell is occupied, that is, 100 ha/km2. The Global Land Cover 2000 database is available online here: http://www.gvm.jrc.it/glc2000

Administrative boundaries

The GADM database of Global Administrative Areas was used to define both regional and country boundaries. The GADM database is available online here: http://www.gadm.org Aridity–Wetness Index

A global model of aridity (Zomer et al. 2008; Trabucco and Zomer, 2009) was used to stratify ecological conditions based on climatic and agro-ecological characteristics. Aridity is expressed as a function of precipitation, temperature and potential evapotranspiration (PET). Based upon an attempt to classify climatic zones by moisture regime, the Aridity–Wetness Index (AWI) quantifies precipitation deficit over atmospheric water demand as: •

Aridity–Wetness Index (AWI) = MAP / MAE where:
 o o

MAP = mean annual precipitation
 MAE = mean annual evapotranspiration

The AWI dataset is available online here: http://csi.cgiar.org/aridity/

Global Tree Cover on Agricultural Land

To facilitate the global analysis, the VCF Tree Cover – Collection 5 dataset (250 m resolution) grid cells were aggregated to 1 km2 resolution. All the geodatasets were masked to exclude areas which are either non-agricultural land-use types. Tree-canopy cover on agricultural land has been tabulated for all years available in the VCF- C5 dataset, that is, from 2000 to 2010. Variation in the estimates from year to year appears to be high and not consistent with the expected year-to-year change. There is a fair amount of ‘noise’ in the year-to-year estimates, which can be expected from having a

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significant variability associated with the quality of the remote-sensing dataset and seasonal and other confounding factors affecting the classification algorithm used in the VCF-C5 processing. In order to reduce the effect of this variability in estimates of change during the period, we have averaged the first three years of the dataset (2000–2002) and the last three years (2008–2010) and use these averaged results as the beginning and end points for the change analysis. They are further referred to within the text as 2000 and 2010, respectively, to simply presentation of the results. The extent of agricultural land and various associated tree-canopy cover values have been analyzed, compared, mapped and tabulated globally, and by global regions, countries, and aridity-wetness index zones. Within each stratum, or within specific aggregation of strata, zonal statistical values (mean, sum, total area, percentiles, areal distribution, etc.) were summarized to describe those factors of interest for this study. Cumulative agricultural area is presented at decreasing tree-canopy cover to infer at global and subcontinent scales the total area engaged above any specific tree-canopy cover values. In a second stage, the same cumulative distribution of total agricultural land in function of tree-canopy cover has been disaggregated for five different aridity classes (AWI < 0.45 or arid, 0.45 < AWI < 0.6 or semi-arid, 0.6 < AWI < 0.8 or subhumid, 0.8 < AWI < 1.0 or humid, AWI >1.0 or very humid) to show how climate regimes might differentiate specific patterns of interdependence between tree-canopy cover and bioclimatic conditions for different geographical areas. Detailed methodology and analytical results from this analysis are available online in a ICRAF Working Paper (Zomer et al. 2014) at: http://www.worldagroforestry.org/downloads/Publications/PDFS/WP14064.pdf

Spatial datasets from this global analysis of tree cover on agricultural land are available online at: http://www.worldagroforestry.org/global-tree-cover/index.html Global Biomass Carbon Estimates:

For Tier 1 global estimates of biomass carbon, we used the “New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000” (Ruesch and Gibbs, 2008) available from the Carbon Dioxide Information Analysis Center (CDIAC) at the Oakridge National Laboratory. This spatial delineation and global map of biomass carbon stored in above and belowground living vegetation was created using the International Panel on Climate Change (IPCC) Good Practice Guidance (Penman et al 2003; IPCC 2006) for reporting national greenhouse gas inventories. The Global Biomass Carbon Map is stratified into 124 strata (carbon zones), based on FAO ecofloristic zones, and specific continents where that zone is found. In each of those “carbon zones” a carbon value has been calculated for each GLC_2000 landuse class in that zone. These values are available in



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tables, and apply across the whole of each carbon zone. The authors state that this “… spatial database is likely the best available, globallyconsistent map depicting vegetation carbon stocks, circa 2000”. It is based on widely accepted IPCC methods for estimating carbon stocks at the national level. However, the methods employed were not directly linked to ground-based measures of carbon stocks and have not been validated with field data. It is noted that croplands received the same carbon stock value regardless of the type of crop that might be growing. To construct the Global Biomass Carbon Map, Ruesch and Gibbs (2008) used the IPCC GPG Tier-1 method for estimating vegetation carbon stocks using the globally consistent default values provided for aboveground biomass (IPCC 2006). Belowground biomass (root) carbon stocks were added using the IPCC root to shoot ratios for each vegetation type, and then total living vegetation biomass was converted to carbon stocks using the carbon fraction for each vegetation type ( which varies between forests, shrublands and grasslands). All estimates and conversions were specific to each continent, ecoregion and vegetation type (stratified by age of forest). Thus, a total of 124 carbon zones or regions, each with a unique carbon stock value for each of the GLC_2000 Landcover Classes found in that zone, were delineated, based on the IPCC Tier-1 methods and default values. Available online from the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. http://cdiac.ornl.gov/epubs/ndp/global_carbon/carbon_documentation.html Deriving the Global Tier 1 Estimates of Biomass Carbon on Agricultural Land:

The IPCC Good Practice Guidance (Penman et al 2003) and Greenhouse Gas Inventory Guidelines (IPCC 2006) provide recommendations on methods and default values for assessing carbon stocks and emissions at three tiers of detail. Following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) for National Greenhouse Gas Inventories 28 Ruesch & Gibbs (2008) identified a relatively low value (5 t C ha-1) for agricultural land, which has been applied uniformly and globally for Tier 1 estimates within the “Global Biomass Carbon Map for the Year 2000” dataset (Ruesch & Gibbs, 2008). In order to account for the added contribution of tree cover on agricultural land, we use the default Tier 1 biomass carbon value for agricultural land (5tC/ha) as the baseline value, i.e. at 0% treecover the biomass carbon is 5tC/ha (in all carbon zones). We use the biomass carbon value of the GLC_2000 Mixed Forest class (or similar class in case this class is not present) in that same carbon zone as a surrogate biomass carbon value where there is full tree cover on agricultural land (i.e. tree cover percentage = 100). We then assume a linear increase in biomass carbon from 0 to 100 percent tree



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cover where, within a specific grid cell in a specific carbon zone: •

Biomass carbon is equal to the default tier 1 value for agricultural land (5 tC/ha) when there are no trees on that land, o

•

(i.e. tree cover = 0%)

There is an incremental linear increase of tC/ha proportionally as tree cover increases up to the maximum value for Mixed Forest in that specific carbon zone, o

(i.e. biomass carbon values on agricultural land with 100% tree cover are equal to the related Mixed Forest class, for a particular bioclimatic stratum)

Results were tabulated and mapped globally, by region, and by country. Spatial datasets resulting from this global analysis of carbon biomass on agricultural land are available online at: http://www.worldagroforestry.org/global-tree-cover/index.html



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References

Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing 26, 1959–1977 (2005). doi:10.1080/01431160412331291297 DiMiceli, C. M. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000 to 2010, Collection 5, Percent Tree Cover. (University of Maryland, College Park, MD, USA., 2011) http://glcf.umd.edu/data/vcf/. Hansen, M. C., DeFries, R. S. & Townshend, J. Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm. Earth Interactions 7, 10, 1-15, (2003). http://dx.doi.org/10.1175/1087-3562(2003)0072.0.CO;2 Ruesch, A. & Gibbs, H. K. New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000. (Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S., 2008). Available online from the Carbon Dioxide Information Analysis Center [http://cdiac.ornl.gov] doi:10.1234/12345678 Trabucco, A. & Zomer, R. 2009. Global aridity index and global potential evapo-transpiration geospatial database. CGIAR Consortium for Spatial Information. Published online, available from the CGIARCSI GeoPortal at: http://www. csi. cgiar. org/ Zomer R.J., Trabucco A, Coe R and Place F. 2009. Trees on Farm: Analysis of Global Extent and Geographical Patterns of Agroforestry. ICRAF Working Paper no. 89. Nairobi, Kenya: World Agroforestry Centre. http://www.worldagroforestry.org/downloads/Publications/PDFs/WP16263.PDF Zomer, R. J; Bossio, D. A.; Trabucco, A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and Environment. 126:67-80. Zomer, R.J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D.A., Trabucco, A., van Noordwijk, M., Wang, M. 2016. Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets. Scientific Reports 6, 29987; doi: 10.1038/srep29987. Zomer, R.J., Trabucco, A., Coe, R., Place, F., van Noordwijk, M., Xu, J., 2014. Trees on farms: an update and reanalysis of agroforestry's global extent and socio-ecological characteristics. Working Paper 179. World Agroforestry Center, Bogor, Indonesia. doi:10.5716/ WP14064.PDF





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