Detection of Grasland Conversion in the

European Commission, 29 th April 2016, Brussels Detection of Grasland Conversion in the EU with GRAS Dr. Jan Henke, Dr. Norbert Schmitz, Mohammad Abdel-Razek GRAS Global Risk Assessment GmbH

Content 1 What is GRAS 2 Methodology of Land Use Change Detection and Verification 3 Grassland Conversion in the EU 4 Examples Landsat Enhanced Vegetation Index Time Series 5 GRAS Applications in Sustainable Sourcing 6 Conclusions & Next Steps 2

Challenges Land use change Grassland conversion in EU, South America, North America Companies communicate sustainability/land use change free commitments Pressure by stakeholders on companies to deliver on their promises: Greenpeace Scorecard Cutting Deforestation Out Of Palm Oil Financial institutions ask for evidence on no land use change Certification schemes ask for evidence. Companies have been dropped Governments request it (e.g. Renewable Energy Directive in EU) A credible, fact-based and easy-to use tool to implement, monitor and report on sustainable and ethical supply chains is urgently needed 3

Information on biodiversity, carbon stocks, land use change and social issues is needed 4

GRAS is an independent and comprehensive information provider for biodiversity, land use change, carbon and social information GRAS Services Mapping of: Biodiversity and protection areas Carbon stocks Land Use Change Social indices Calculation of sustainability risk factors and sustainability rankings Certification support Provision of sustainability assessment reports Customized solutions (e.g. supply chain mapping) GRAS data is updated on a quarterly basis GRAS covers 34 countries 5

The technical realization of a user-friendly web-tool took three years and was a major challenge Biodiversity Examined and analysed approx. 70 databases 43 Layers on biodiversity integrated in 7 countries Carbon Stocks Developed new carbon stock maps based on IPCC methodology for 4 countries Land Use Change Algorithms for MODIS and Landsat data Developed Split-screen tool for USA Successful tests in pilot countries Social Issues 9 social-indexes with 24 sub-indices for the calculation of an overall social factor Setup of a 30 Terabyte database Processed by two high-performance computers (MacPro and Workstation ProViz W60; each has 2.7GHz 12-Core Intel Xeon E5; total 24 cores, 64 GB of RAM ) 630 trillion pixels processed Testing of different technical realization options (Open source, Google) Development of a comprehensive LUC algorithm with based on MODIS Verification of Landsat-data 6

GRAS No Go Areas are high priority protected areas (e.g. protected areas with IUCN category I-III) 7

GRAS also offers carbon stock maps and GHG values for cultivation at NUTS2 level 8

GRAS provides social indices related to sustainability and compiles them into a GRAS Social Factor UNICEF Access to Drinking Water and Sanitation Human Development Index GRAS Social Factor 9

GRAS has been developed to support halt of land use change and loss of biodiversity Supported by Core Team Advisors University of Illinois at Chicago 10

LUC detection and verification is a key feature of the GRAS tool Step 1: Producing Landsat and MODIS LUC Heatmaps to identify potential LUC Step 2: Verification through visual inspection of EVI and satellite imagery 12

GRAS adopted sate-of-the-art methodology of land use change detection from MODIS Enhanced Vegetation Index (EVI) VI Quality MODIS HDF Preprocessing EVI Modified Whittaker transformation EVI smoothed Automatic LUC detection LUC map 370 images per scene Composite day of the year 13

DLR (German Aerospace Center) and GRAS collaborated to produce land use change Heatmaps from the EVI time series MODIS Tiles ( 370 images per tile) EVI extraction Re-projection Smoothing MODIS EVI time series Producing EVI time series Producing Heatmaps 14

GRAS Heatmaps delineate possible land use change in areas of interest, followed by a visual verification step Sourcing Area and Land Use Change (LUC) Heatmap Detailed Analysis 50 km April 2015 Grassland Landsat Imagery 2016, Pansharpened and Enhanced April 2016 LUC 15

Land use change Heatmaps and EVI time series together allows for a precise detection of grassland/savanna conversion Cerrado Soybeans 16

EVI shows when the change occurred and what kind of change it is Land cover EVI pattern Explanation Tropical Forest EVI values oscillates slightly around a value of 0.5-0.6 Seasonal variations are minimal Tropical forest to cropland conversion Cerrado The sudden change in EVI pattern in 2008 indicates deforestation EVI after the change shows distinctive seasonal variations, thin and high peaks, hence indicate seasonal crop Cerrado EVI values show more seasonal variations than a forest, lower EVI mean (0.3 0.4), but less than the variations of an annual crop Cerrado conversion to cropland Conversion to annual crop is observed between 2009 2010 Typical pattern of an annual crop is evident 17

GRAS EVI covers 69 countries. Further countries can be added 18

Case study: grassland conversion in Deinstedt (Germany) after the establishment of biogas plant Biogas plant: A 500 kw biogas plant was built in Deinstedt from 2001-2002 The capacity of the biogas plant was extended by 2 x 500 kw from 2005-2007 Due to the biogas plant the red marked areas in the map provided by NABU were converted from grassland to fields with bioenergy maize according to NABU These areas were partly fen and were extensively used before the conversion took place Further information: In Lower Saxony ca. 8% of grassland was converted into arable land from 1991 until 2013 In the Landkreis Rotenburg (Wümme) ca. 17% of grassland was converted into arable from 1991 until 2013 Maize is grown on 63% of the arable land In Deinstedt more than 70% of the arable land is used for the cultivation of maize 20

Areas with potential grassland conversion from 2004 until 2011 located in Deinstedt / Germany were provided by NABU Information provided by NABU Area 1 and 3: Conversion to maize 2004 2008 (mainly in 2006, 2007 and 2008) Area 2: No indication possible Area 4a: Extensive grassland; conversion to maize in 2008, partly in 2010 Area 4b: Extensive grassland; conversion to maize in 2010 Area 4c: Intensive grassland; conversion to maize in 2011 GRAS was asked to verify grassland conversion on the provided areas 21

In case the fields are very small, the detection of grassland conversion by using the MODIS EVI is difficult Challenges: Fields in Europe are often small compared to MODIS EVI pixels (250m x 250m) Scarcity of cloud-free imagery for visual detection Two-images LUC detection approach alone could be misleading Conclusive evidences to prove the conversion of grassland to agricultural areas are at the moment based on visual interpretation, but has not been always successful 22

Evaluated areas do not overlap with NATURA 2000 areas Area 1 Area 3 Area 2 Area 4 Natura 2000 area 23

Corine Landcover map from 2006 shows pasturages and cropping in the analyzed areas Area 3 Area 1 Legend Coniferous forest Pastures Area 2 Non-irrigated arable land Area 4 Land principally occupied by agriculture Discontinuous urban fabric 24

In Google Earth only three images are available between 2000 and 2008. Indicated dates are not correct (image 31/12/2000) Area 3 Area 1 Area 2 Area 4 25

In Google Earth only three images are available between 2000 and 2008. Indicated dates are not correct (image 31/12/2008) Area 3 Area 1 Area 2 Area 4 26

Example Area 1a: Landsat images confirm that there was no bare soil between 2002 and 2008 16/08/2002 09/10/2004 28/10/2008 1 2 3 1 2 3 27

Landsat images show bare soil for the first time in October 2009 (like indicated by the EVI). Since then bare soil is observed in October 15/10/2009 10/10/2010 01/11/2015 4 5 6 LUC in 2009 4 5 6 28

The analyzed area is overlapping with 4 MODIS pixels, which is why the EVI is difficult to interpret 4 pixels 29

The higher resolution of Landsat images will lead to better results in order to detect grassland conversion on small parcels 280 pixels 30

A new methodology has been developed by GRAS to easily detect grassland conversion on very small parcels 0 0.5 1 km GRAS GmbH A new methodology based on high resolution Landsat image (30mx30m) EVI time series to detect grassland conversion has already been tested successfully in Germany A huge amount of Landsat images is needed. Expertise is required to obtain easily interpretable time series for grassland conversion for areas smaller than 1ha Approach being developed in cooperation with BirdLife Europe 31

The methodology to produce EVI time series (Heatmaps and graphs) is a multi-stage process Raw Landsat images 1.2 TB 713 image Co-registration Calibration TOA Conversion to SR Cloud masking Surface Reflectance Band rationing EVI Smoothing & gap filling Smoothed EVI LUC Detection Plotting Heatmap EVI Graph Potential grassland conversion after 2004 Grassland Maize 32

Content 1 What is GRAS 2 Methodology of Land Use Change Detection and Verification 3 Grassland Conversion in the EU 4 Examples of Landsat Enhanced Vegetation Index Time Series 5 GRAS Applications in Sustainable Sourcing 6 Conclusions & Next Steps 33

Content 4 Examples of Landsat Enhanced Vegetation Index Time Series 4.1 Germany - Deinstedt 4.2 Canada - Manitoba 4.2 Guatemala - Escuintla 34

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 EVI of a football ground: It is assumed that no conversion took place 0 100 200 m GRAS GmbH 1 0.75 0.50 0.25 grassland 0.00 35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 GRAS also can detect the land use after the conversion. In this case grassland can be detected after re-seeding in 2012 0 0.5 1 km GRAS GmbH 1 0.75 0.50 0.25 0.00 Grassland Grassland re-seedingd in 2012 Grassland 36

Example 2 shows grassland conversion in 2011, which is in accordance with the provided information by NABU 0 0.5 1 km grassland Grassland conversion 37

Example 3 shows grassland conversion in 2006 0 0.5 1 km grassland Grassland conversion 38

Content 4 Landsat Enhanced Vegetation Index time series: additional examples 4.1 Germany - Deinstedt 4.2 Canada - Manitoba 4.2 Guatemala - Escuintla 39

GRAS identified possible deforestation in 2013 in the quadrate of interest Potential LUC 40

GRAS identified existing batches of deciduous trees (> 30 % coverage) before EU RED cut off date Canada Land Cover Map (2000) Landsat Tree Cover (2000) Landsat Tree Cover (2005) Decidous forest Grassland Tree cover > 30% Tree cover < 30% 41

SPOT and Landsat images confirmed the existence of trees before and after EU RED cut off date SPOT 5 (simulated natural color, pansharpened) Landsat 7 (false-color composite, panshaprened) GRAS processed high resolution imagery to detect whether the LUC occurred before the cut off date SPOT 5 from 2006 (10 m resolution) and Landsat from 2012 (15 m resolution) confirmed the presence of tree batches before and after the cut-off-date In agreement with GRAS preliminary assessment, LUC occurred in 2013 42

Trees from neighboring quadrate showed shadow indicating trees of height more than 10 m 800 m Shadow indicating tall trees rather than shrubs Cleared tree batches (ca. 25 ha) Bing image, 2015 43

Landsat EVI enables the detection of LUC in this case simple and fast Forest to cropland Grassland to cropland 44

Content 4 Landsat Enhanced Vegetation Index time series: additional examples 4.1 Germany - Deinstedt 4.2 Canada - Manitoba 4.2 Guatemala - Escuintla 45

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Landsat EVI allows to distinguish between grassland and sugar cane in Guatemala 2 Sugar Cane 1 Grassland 0 0,5 1 km GRAS GmbH 1 0.75 Grassland Sugar Cane 1 2 0.50 0.25 0.00 46

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 GRAS can detect the conversion from grassland to a sugar cane farm in Guatemala 2013 2015 1 2 GRAS GmbH GRAS GmbH 1 LUC in the end of 2014 0.75 0.50 0.25 0.00 Grassland 47

Palm sourcing area assessment in Kalimantan Sourcing Area and Land Use Change (LUC) Heatmap Detailed Analysis 50 km November 2014 Burn Scars LUC Landsat Imagery 2015, Pansharpened and Enhanced December 2015 49

Fire in Borneo, November 2015 50

Sustainability principles and criteria have been violated in concession areas in Kalimantan 0 100 200 500 km 51

With EVI analysis based on satellite images, deforestation can be detected Natural vegetation Cleared area Cleared area outside concession Established plantation April 2008: natural vegetation September 2009: cleared areas February 2016: established plantations Deforestation Source: Global Risk Assessment Services GmbH 52

New plantations have been established on peatland areas Natural vegetation on peatlands Natural vegetation on peatlands Established plantation on peatlands April 2008: natural vegetation July 2009: natural vegetation February 2016: established plantation Deforestation Source: Global Risk Assessment Services GmbH 53

Conclusion & Next Steps The results of the new methodology tested for the first time and for a small pilot region around Deinstedt show that a detection of grassland conversion is possible In terms of spatial resolution the new developed EVI allows to detect grassland conversion also on small parcels (<1ha) Displaying Heatmaps and providing evidence of land use change (grassland to cropland) would be possible within GRAS if this new approach is implemented on a larger scale (individual countries/ all of EU28) The development of a methodology to detect easily grassland conversion with Heatmaps for whole Europe is possible The development of the new methodology requires comprehensive expert knowledge in remote sensing techniques and time to develop the processing codes as well as process optimizations Due to the huge amount of data, the image processing will take some time and needs an efficient hardware performance Due to the higher resolution of the Landsat satellite images compared to MODIS satellite images the needed storage capacity will increase by many times Heatmaps showing grassland conversion in Europe can be uploaded to the already existing GRAS Web-Tool 55

Thank you! Contact GRAS Global Risk Assessment Services GmbH Hohenzollernring 72 D-50672 Köln Tel.: +49-221-92296183 Fax: +49-221-9415863 info@gras-system.org www.gras-system.org 56