Assessing UK greenhouse gas emissions from land use change using EO data
This project “Applying Earth Observation to assess UK land use change: Lot 1 – Coarse Resolution Optical” is one of the three projects BEIS commissioned to assess the feasibility of using Earth Observations data to map Land Cover and Land Use change over the UK to improve emissions inventories.
- The main objectives to be achieved were:
- To assess the feasibility of identifying land use and land use change over time using optical coarse resolution (>250m pixel resolution) EO data, with an overall accuracy of at least 95% and individual class accuracy of at least 90%.
- To develop a costed roadmap for an operational system for tracking Land Use/Land Use Change (LULUC) in the UK from 1990 onwards suitable for incorporation in the emission inventories.
Using a test area covering almost the whole UK (only excluding the far Eastern part), MODIS 500m and AVHRR 5km spatial resolution data were used to derive annual Land Cover / Land Use (LC/LU) products for 2011 and 2012.
Using completely independent reference data to validate the 500m LC/LU product derived using MODIS data an overall classification accuracy of 96.57% and 96.58% for 2011 and 2012 respectively was achieved with all individual accuracies better than 94%. Change detection overall accuracy was 95.66%. Hence the methodology using MODIS 500m data is considered feasible to track LC/LU change over time at this coarse resolution.
Using the same reference data to validate the 5km LC/LU product derived using the AVHRR dataset an overall classification accuracy of 89.47% and 88.89% for 2011 and 2012 respectively was achieved with all individual class accuracies greater than 84% at this coarse resolution. Change detection overall accuracy was 87.92%. Using the specified criteria, the use of the proposed methodology using AVHRR data is not feasible to track LC/LU change over time.
The methodology presented can also produce additional land surface state indicators, such as annual vegetation indices anomalies, e.g. the Normalized Difference Vegetation Index (NDVI) that can indicate the greenness status of the landscape and identify changes in the timing of the vegetation seasonal cycles (phenology).