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Results Per-county analysis of forest % cover Agreement between VCF and AVHRR closer than VCF and FE GIS Data Evaluation of the MODIS continuous tree cover.

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Presentation on theme: "Results Per-county analysis of forest % cover Agreement between VCF and AVHRR closer than VCF and FE GIS Data Evaluation of the MODIS continuous tree cover."— Presentation transcript:

1 Results Per-county analysis of forest % cover Agreement between VCF and AVHRR closer than VCF and FE GIS Data Evaluation of the MODIS continuous tree cover map over the UK M. Disney 1* and S. Lafont 2 1 NERC Centre for Terrestrial Carbon Dynamics (CTCD), Dept. Geography, University College London, 26 Bedford Way, London, WC1H 0AP 2 Formerly Mensuration Branch, Forest Research and CTCD; now European Centre for Medium Range Weather Forecasting (ECMWF), Shinfield Park, Reading, Berkshire RG2 9AX, UK *Corresponding author: tel: Purpose of the study Evaluation of MODIS Vegetation Continuous Field (VCF) product VCF is 500m global continuous estimate of % tree cover for global biosphere (carbon) modelling VCF derived from classification of MODIS reflectance data via aggregating high resolution (25m) training data % tree cover predicted from range of multi-temporal phenological metrics Accuracy of VCF key to determining uncertainty in resulting carbon calculations Comparison with other sources including detailed inventory data UK Forest Enterprise Woodland Survey map of UK woodland area (> 2ha) Based on detailed ground surveys and inventory Potential to assess VCF accuracy more closely than perhaps anywhere else Data MODIS VCF global estimate of forest % cover Other data sources for comparison: Forest Enterprise Woodland Survey GIS (down to individual tree level) CEH Land Cover Map, LCM 2000, derived from ~25m resolution EO data augmented with limited ground measurements AVHRR land cover classification (1km) (limited to range 10-80%) FE Woodland Survey Two sources of data: Main survey (woodlands > 2ha) Survey of Small Woodland and Trees: small woods, groups of trees, linear features and individual trees. More details: MODIS VCF product Can be interpreted in two main ways: EITHER assign all non-zero pixels a value of % forest cover (typically causes over-estimate of forest cover). OR assume threshold % cover above which a pixel is considered forest. Over the UK a threhsold value of ~55% gives close agreement to FE % forest cover. Results Nat. Stats. (02)VCFAVHRRLCMFE GIS Total area (km 2 ) Forest (km 2 ) % forest NB : These values are for England + Wales only. AVHRR values assuming classes 80 % = 80 % i.e. MODIS VCF overestimates forest cover in UK by factor of ~2 (93%) Many regions known to be not forest are considered forest in VCF Particular problem over moorland and upland heath All data based on EO overestimates forest cover (including LCM) 1:1 line indicates same % forest cover identified in each pixel by EO and ground data; colour shows the density of points at a given value Better agreement at higher observed forest cover For low observed forest cover MODIS VCF over-estimates significantly Conclusions MODIS VCF overestimates forest cover in UK (x2), compared with FE GIS inventory Errors mainly where semi-natural environments (moorland, heath) misclassified in VCF Spectral confusion between classes in VCF and/or differences between class definitions Other estimates based on EO also overestimate (compared to FE GIS) Carbon fluxes likely to be overestimated in semi-natural environments by models based on VCF e.g. in places such as NW Europe, Canada, Siberia, NZ Solutions: Improved separation of classes in VCF classification decision tree algorithm? Use of structural information where spectral information not sufficient? Area where VCF > 70% AND FE GIS <20% are predominantly mountainous (Cambrian Hills, Pennines, Cheviot Hills) 50% of pixels mistaken for >70% forest cover are moorland, while 15% are improved grassland (set-aside, reclaimed and intensive grazing)


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