Uniting expert data from different disciplines
GRAS took strong efforts to carry together scientifically approved, reliable and reputable data from alll relevant sustainability sectors. Below please find all information about how the data was assessed and processed.
1. Assessment and selection of databases
The biodiversity layers in the maps of the Countries show protected areas and biodiverse areas and are compiled from global, inter-regional (European Union), national and sub-national databases. GRAS researched and compiled the best available data sources for each country. The integration of complementary data sources ensures that a complete picture of biodiversity is painted.
The databases have been selected according to the criteria content, data source, data quality, data format and availability of data. Every database must fulfil the following minimum requirements:
- Content: The database shows protected areas, i.e. areas designated by international or national law for nature protection purposes by a governmental authority, or it shows areas that have been identified as biodiverse and/or vulnerable by NGOs, Institutes or Organizations. The database is also eligible if it contains information about potential No Go areas and/or Risk Areas according to the EU Renewable Energy Directive (e.g. primary forests, highly biodiverse grassland).
- Data source: The database is provided by the respective government authority responsible for protected areas. The database is also eligible if it is provided by renowned international NGO, research institutes, UN organization or if it was published in a scientific article.
- Data quality: The database is up-to-date, accurate and it is updated regularly. Additionally the data is displayed in clearly defined polygons.
- Data format: The data is provided in GIS format or can be transferred into GIS format with justifiable effort.
2. Data structuring
3. Classification of No Go Areas and Risk Areas
The biodiversity data integrated in GRAS maps of the different Countries has a broad variety: It comprises protected areas with different designations and objectives, areas identified by NGOs as being biodiverse as well as areas with a certain land cover e.g. primary forests. In order to facilitate interpretation of the biodiversity data, the areas have been classified and compiled into so called „No Go Areas“ and „Risk Areas“. They are displayed as separate layers summarizing all the biodiversity and carbon stock information in the respective country.
The classification is based on the specifications made in article 17 of the EU Renewable Energy Directive (RED). The EU Renewable Energy Directive was originally set up for the biofuels and bioliquids market. Nevertheless, the land-related sustainability requirements are also applicable in the food, feed and chemical market. The sustainability requirements of the RED are the baseline for many certification systems and standards set up by companies. That is why they were used in GRAS as an orientation to classify the areas of various different biodiversity databases into No Go Areas and Risk Areas. The Renewable Energy Directive (RED) defines areas with high priority for nature conservation that are not suitable for sustainable biomass production:
Art. 17, Directive 2009/28/EC: "Biofuels and bioliquids (...) shall not be made from raw material obtained from land with high biodiversity value, namely (...) Primary Forests and other wooded land (...); highly biodiverse grassland (...).
Biofuels and bioliquids (...) shall not be made from raw material obtained from land with high carbon stock, namely (...) wetlands (...); continuously forested areas (...); land spanning more than one hectare with trees higher than five metres and a canopy cover of between 10 % and 30 % (...).
Biofuels and bioliquids (...) shall not be made from raw material obtained from land that was peatland in January 2008, unless evidence is provided that the cultivation and harvesting of that raw material does not involve drainage of previously undrained soil."
No Go Areas, as defined in GRAS, have a high conservation priority and are not suitable for a sustainable biomass production. They are identified using the following indicators:
- Explicitly named in RED (e.g. Ramsar Sites)
- Designation/definition of area is very similar to RED definition of No Go Area: e.g. definition of primary forests from Intact Forest Landscapes and from RED are very similar
- IUCN category Ia, Ib, II or III
- Protection goal of area is to perpetuate the area in its pristine state and allow for an undisturbed development of nature: e.g. National Parks
In contrast, Risk Areas are areas which do not clearly classify as No Go Areas but which are protected, potentially biodiverse and / or carbon stock intensive. Especially, areas identified in land cover maps are rather classified as risk areas because land cover maps can be biased. The following indicators are used to classify Risk Areas:
- IUCN category, IV, V or VI
- Area is grassland, peatland or forested area but the exact characteristics of these areas (e.g. biodiversity, management, canopy cover) are not specified
- Area has a high carbon stock (carbon stock maps are based on land cover maps)
Wherever possible, the classification of protected areas was done with the help of the IUCN categories. IUCN protected area management categories classify protected areas according to their management objectives. The categories are recognized by the United Nations and by many national governments as the global standard for defining and recording protected areas. The IUCN categories are defined as follows:
- IUCN categories Ia, Ib, II, III: Area is designated for nature protection purposes and is strictly protected: only scientific, educational, maintenance and military activities are allowed; agricultural and industrial activities are not allowed
- IUCN categories IV, V, VI: Area is designated for nature protection purposes but certain regulated economic and agricultural activities are allowed by law or can be allowed by the responsible authority
Carbon Stock Methodology
A complete carbon map shows the carbon stored in all of the biomass and soil. For selected Countries, GRAS includes its own carbon layers in displaying the full carbon stock in biomass and soil based on scientifically verified methodology and transparent data sources. For the calculation of a carbon stock associated with a particular land use, the carbon stock stored in biomass and the carbon stock stored in the soil need to be summarized. These two carbon stocks are calculated in separated calculation steps according to the IPCC 2006.
2. Carbon in biomass
The basis for determining the carbon stored in biomass is a land cover map. By associating a typical biomass carbon value with each land cover class in the land cover map, a biomass carbon map can be derived. The typical carbon values were taken from the relevant scientific literature and are transparent for each country. The carbon values include carbon stored in above and below ground living vegetation.
3. Carbon in soil
Carbon stored in the soil concerns the stock of carbon in the soil that is not part of the living biomass, which means all carbon apart from living roots. The GRAS carbon layers are based on the Tier 1 approach of the IPCC 2006 which models soil carbon stocks influenced by climate, soil type, land use, management practices and inputs, which can increase or decrease the carbon content in the soil.
As a first step a suitable soil map needs to be chosen. For GRAS the FAO Harmonized World Soil Database (HWSD) generated by IIASA is used. It shows different soil types in a consistent categorization for the whole world. This first step includes the generation of a map of soil carbon as if the whole area were under natural land cover. This is done in the same fashion as for the biomass carbon map, namely, by associating carbon values to each soil category in the soil map. Carbon values were taken from the IPCC 2006 and show the carbon content in the first 30 centimeters of the soil. As most peatland soils possess carbon stocks in much thicker soil layers, they are not included in this map but shown in separate layers.
As a second step, to determine the actual carbon stock stored in the soil, the carbon stock under natural land cover must be adjusted with the soil use factors that correspond to the current land use. The factors indicate how much the land use type, the management practice and the inputs change the carbon stock stored in the soil compared to a natural land cover.
In order to determine which of these factors apply, the land cover map is used. Thus, for each land cover class, the land use factor, the typical management regime applied for a particular land use in the region and the corresponding typical input are defined. The corresponding values for the factors are taken from the IPCC 2006.
As a last step, to determine the actual carbon stock stored in the soil, the carbon values in the soil under natural vegetation are multiplied with the corresponding soil factors.
4. The total carbon map
The final carbon map is calculated by overlaying and summing the carbon stocks stored in total above and below ground living vegetation map with the actual carbon stock stored in the soil map. The result is a map of the total carbon content of a region.
Land Use Change Methodology
1. Processing of Remote Sensing images
Our methodology is based on detecting land use change (LUC) from MODIS (Moderate-resolution Imaging Spectroradiometer) greenness index time series (resolution 250m x 250m). The index is called Enhanced Vegetation Index (EVI) with values range from 0 to 1, the higher the values the more green the soil cover. From one EVI image we can differentiate between bare soil and green cover. From more than 300 EVI images since the year 2000 in GRAS we can differentiate among the type of green cover, see the history of the land, and indeed detect LUC. Tropical forest would look like a calm sea waves with minor changes around EVI value of 0.6. Deforestation would appear as a clear change in those waves with a drop of EVI to a value below 0.2. We can actually see what happened and when it happened.
GRAS uses that concept to detect land use change after the cut-of-date 1.1.2008. The following MODIS images are used in our analysis (from February 2000 till July 2014):
- Enhanced vegetation index;
- Composite day of the year;
- and Vegetation index quality
2. Preprocessing and Reprojecting
Preprocessing is done on two steps:
- Exporting relevant layers from MODIS Hierarchical Data Format (HDF) container
- Reprojecting the layers’ geographic coordinates system from Sinusoidal, Lambert Azimuthal Equal-Area to WGS84
GRAS preprocess the images using the development framework R and GDAL library on Apple Mac Pro 2,7GHz Intel 12-Core Xeon E5; 68GB RAM; 1 TB GB Flash-Hard drive.
Time Series Smoothing
Smoothing is an important step to exclude outliers, realign EVI data (using composite day of the year layer) and to exclude bad values (using vegetation index quality layer). Time series smoothing is done using modified Whittaker smoothing algorithm. Visual land use change detection is possible from smoothed time series.
3. Automatic Land Use Change Detection
German Aerospace Center (DLR) experts have developed an algorithm for GRAS to detected land use change automatically from smoothed EVI time series. LUC detection algorithm is based on the following three assumptions:
- Agricultural crops reach maximum EVI value (corresponds to maximum leave area index) at certain time of the season;
- The green cover falls down fast from the maximum in the following few weeks;
- Agricultural season for annual crops season is relatively short.
These assumptions are translated into a fuzzy logic approach to detect whether the land was in fact cropped before 2008 or not. If a parcel GRAS algorithm found crop EVI peak after 2008 and no crop EVI peak before 2008, it will be considered that LUC took place.
The algorithm has been tested in several countries. In Chaco, Argentina, for instance, the agreement between visual and automatic LUC detection was 97%. Testing and improving GRAS algorithm will continue to reach a better output and more reliable information for our users.
Social Indices Methodology
1. Used Data
Working and general living, as well as health and education play a major role in the sustainable production of agriculture and forestry materials. The selection of the social indices to be introduced inside the GRAS Tool was made according to the social sustainability issues mentioned in the Renewable Energy Directive. These issues are the consequences of the increased production of biofuel on hunger, working conditions (e.g. loan, slavery, child work), human development and governance as an indicator for political stability and efficiency (e.g. land use rights, absence of violence, corruption). Further, the social consequences of environmental issues related to increasing agricultural production (water issues, use of pesticides, subsidies) were taken into consideration. In addition, the RED is specifically mentioning Conventions of the International Labour Organization, which shall be ratified and implemented by countries that are a significant source of raw material for biofuels consumed within the Community. According to the previously mentioned topics and RED specifications, social indices were selected and displayed inside the GRAS Tool. To mention one example, The Global Hunger Index is an indication for food security, which is a major issue in rural populations and farmers.
All social sources on national level get comprised in an overall GRAS Factor Social that is valid for the whole country. The overall GRAS Factor Social can be displayed as a global ranking of social sustainability risks in countries. For the assessment of sustainability Risk Areas in the GRAS-Index the according overall country GRAS Factor Social is integrated in the calculation. As a convention the overall index is issued on a scale from zero to 1 where a zero marks a perfect sustainability score (a “clean” record) and 1 the worst possible score. In the recent version of the GRAS Factor Social the following indexes were considered:
- Global Hunger Index (GHI)
- World Governance Indicators (WGI)
- Human Development Index (HDI)
- Global Slavery Index (GSI)
- EPI Agricultural Subsidies (EPI AS)
- EPI Pesticide Regulation (EPI PR)
- EPI Water Resources (EPI WR)
- UNICEF Access to Drinking Water (UNICEF WA)
- UNICEF Access to Sanitation (UNICEF WS)
As an additional information the ratification of UN International Labor Organisation (ILO) Core Labor Standards is displayed within the maps tool. However this information is not considered for the overall social factor calculation.
2. Adjustment and weighting of the indices for the overall calculation
The first step in the creation of the GRAS Factor Social is to adjust each index on a 0 to 1 scale. Thus, 0 will be the best result and 1 the worst for all applied indices.
Additionally, a unifying stretching is applied to the adjusted Index values. The following is an example for the Global Hunger Index. The highest value scored by a country in 2014 is 35.6, resulting in a maximum GHI adj of 0.36. However, other indices imply a full minimum to maximum range from zero to one. Thus, GHI might be underrepresented in an overall comparison and implementation in the GRAS Factor Social. With regard to that matter the maximum value of each added adjusted index is stretched. In the case of the GHI example, the value of 35.6 for Country X is adjusted and then divided by its rounded up maximum value:
Afterwards, all indices are summed together for each country. For the calculation of the overall GRAS Factor Social, the used indices are weighted with regard to their impact on the sustainability issues mentioned in the Renewable Energy Directive. These issues are the effects of the increasing production of biomass for biofuels on hunger (represented in GRAS by the Global Hunger Index), working conditions (represented in GRAS by the Global Slavery Index), human development (represented in GRAS by the Human Development Index) and governance (represented in GRAS by the World Governance Indicators) as an indicator for political stability and efficiency (e.g. corruption, land use rights, etc.). In addition, the social consequences of environmental problems (represented in GRAS by the Environmental Performance Index), which are related to the increasing agricultural production (e.g. water problems, use of pesticides, agricultural subventions), were taken into account. For the calculation, all the following indices are weighted equally (0,2): Global Hunger Index (GHI), World Governance Indicators (WGI), Human Development Index (HDI), Global Slavery Index (GSI) and Environmental Performance Index (EPI). The EPI is based on a mix of data from international sources (e.g. UNICEF, which is displayed inside the GRAS Tool as source of the original data) and own indicators. Out of the 19 EPI indicators the five most important (EPI Agricultural Subsidies, EPI Pesticide Regulation, EPI Water Resources, UNICEF Access to Drinking Water and UNICEF Access to Sanitation) were included in the GRAS Factor Social and were weighted equally (0,04).
In the table below, the set weighting factors as well as the adjustments for each index is displayed:
|Global Hunger Index (GHI)||0.2||GHIadj = GHI * 0.01|
|World Governance Indicators (WGI)||0.2||WGIadj=1 - 0.2 * (WGI + 2.5)|
|Human Development Index (HDI)||0.2||HDIadj=1 - HDI|
|Global Slavery Index (GSI)||0.2||GSIadj=GSI * 0.01|
|EPI Agricultural Subsidies (EPI AS)||0.04||EPI ASadj=1 - EPI AS * 0.01|
|EPI Pesticide Regulation (EPI PR)||0.04||EPI PRadj = 1 - EPI PR * 0.01|
|EPI Water Resources (EPI WR)||0.04||EPI WRadj = 1 - EPI WR * 0.01|
|UNICEF Access to Drinking Water (UNICEF WA)||0.04||UNICEF WAadj = 1 - UNICEF WA * 0.01|
|UNICEF Access to Sanitation (UNICEF WS)||0.04||UNICEF WSadj = 1 - UNICEF WS * 0.01|
News and events
Fire season at its peak in South America
The wildfire season of South America is currently at its peak of activity. Stay informed about the latest detected fires in your area of interest and receive a fire alert via e-mail, updated on a daily basis.
October 17th 2018
GRAS at the Sustainable Landscapes Conference, London, 6-7 Nov 2018
Dr. Mohammad Abdel-Razek will present how businesses can support smallholders integration. The Sustainable Landscapes Conference of the Innovation Forum takes place on 6th November 2018 in London. Join GRAS in the session: Farmers - How can business help provide smallholders with the necessary finance and funding that they need?
September 30th 2018
New GRAS Index functionality available
You have now the option to adjust the weighting factors of the GRAS Index of your assessment in the GRAS Tool according to case-and region specific preferences!
September 15th 2018
Honduras now available in the GRAS Tool
Honduras is now available in GRAS. The dataset includes layers on Land Use Change, Biodiversity, Carbon Stock and Social Data. GRAS will add more countries in Central America and other world regions soon.
March 13th 2018
We cordially invite you to join the free webinar on how to “Implement and Monitor Deforestation-Free Supply Chains” with GRAS, taking place on 13 March 2018, 10:00 am (CET) and 5:00 pm (CET). The webinar will give you an insight on new functionalities of GRAS, outline recent case studies and give the attendants the chance to ask their questions. Register for the webinar here.
March 2nd 2018
Addressing the sustainability of sugarcane
In the recent issue of Biofuels International, GRAS is presented as a secure and credible solution provider for companies to prove compliance with national and international sustainability regulations and corporate commitments. The article shows how GRAS can be used to identify deforestation and grassland conversion and determine the exact point in time the land use change took place. The full article can be found here.
February 27th 2018
Sub-national data on Acute Food Insecurity now online for the Democratic Republic of the Congo
Food Security Classification data of FEWS NET (Famine Early Warning Systems Network) for the Democratic Republic of the Congo is now available in GRAS. The dataset includes near and medium term projections, and is updated on a monthly basis.
February 21st 2018
Colombia, South Africa and Peru now available in the GRAS Tool
GRAS added Colombia, South Africa and Peru to the Web Tool. The datasets include layers on Land Use Change, Biodiversity, Carbon Stock and Social Data. More countries will follow soon.
January 31st 2018
GHG values for canola cultivation in Australia and Canada now online
GHG emission values for canola cultivation in Australia and Canada, officially acknowledged by the European Commission, are now available in GRAS. The values have been published in the Commission Implementing Decisions (EU) 2017/2356 and (EU) 2017/2379, respectively.
January 29th 2018
GRAS Fire Alert online now
The new GRAS Fire Alert function is now available for Indonesia and countries in South America. Beside the visualization of latest and historical fires within the tool, the user can now register for an e-mail alert. The user will be informed based on daily updates, in case a fire was identified within an area individually defined by himself.
January 11th 2018
GRAS article in ITC News
GRAS wrote an article in ITC News (2017-2), the alumni magazine of the Faculty of Geo-Information Science and Earth Observation of the University of Twente. The article presents how GRAS provides solutions for a fact-based, objective and credible sustainability reporting and the support of efficient and reliable sustainability certification. The magazine contains results of an interdisciplinary workshop in Enschede, Netherlands.
January 10th 2018
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