P. Scholefield

Vegetation Structure From a Fixed Wing UAV System Over Peatland Feature Types

Paul Scholefield, Emma Tebbs, Clare Rowland, Dan Morton, Chris Evans, (CEH) Barry Rawlins, Stephen Grebby, Colm Jordan, Martin Hurst (BGS)

Why are peatlands important?

•  Blanket bog is a globally rare peatland habitat •  10-15% of the world’s blanket bogs are found in

the UK •  Blanket bogs provide valuable ecosystem services

•  biodiversity,  •  carbon  storage  •  hydrological  regula4on  •  poten4al  for  greenhouse  gas  sequestra4on  

•  Despite only covering 3% of the land area of Wales, deep peat soils are estimated to contain approximately 30% of the countries total soil C carbon stock.

Where?

•  Welsh blanket bogs •  Moorhouse NNR, Pennines, County Durham •  Forest of Bowland, Lancashire •  Cheshire Meres and Shropshire Wetlands •  Lowland bogs •  Scottish systems •  A significant number of Countryside Survey

sites

Deliverables •  A ‘definitive’ map of the full extent of peat (> 40 cm) in Wales •  Classification of lowland and upland peat areas into broad

land-use categories •  A detailed assessment of upland blanket bog condition using a

combination of lidar and aerial photography (RGB and CIR) data

•  Development of UAV approaches for assessing habitat extents and condition.

•  An assessment of lowland peat condition based on detailed land-cover data

•  An assessment of utility of UAVs for habitat assessment in remote or “difficult” locations

•  Use aerial photography to provide an improved condition assessment for lowland fens and raised bogs

Why use UAVs for field survey? •  Fields surveyors are

always required for ground truth data.

•  But for difficult conditions and for extensive bog habitats, other approaches may have a reduced impact

•  For mosaiced habitats features can be missed at ground level due to issues with line of site

•  Field work is tiring in wet conditions

UAV systems - software and tools

•  Quest UAV 300 •  Canon 6D, inbuilt GPS •  Canon 450D, NIR •  Lumix LX5 •  Intervalometer

•  Agisoft Photoscan, MicMac, •  Grid.Flightmanager

•  ESRI ArcGIS

•  ENVI/IDL/PCI Geomatica

Image Classification Approach

•  Random forests classifier •  R Script •  Inputs:

•  Spectral data

•  Continuous or categorical

•  DEM

•  Slope •  Outputs:

•  A classified geotiff

•  A class probability layer

Abbeystead Fell

•  Useful training site. •  Grouse moor, and part of the

Grosvenor Estate (Abbeystead Estate).

•  First test of system using the LX5.

Abbeystead Fell

•  Useful training site. •  Grouse moor, and part of the

Grosvenor Estate (Abbeystead Estate).

•  First test of system using the LX5.

Abbeystead Fell

•  Good image resolution following Photoscan processing.

•  Drainage lines visible •  0.07cm per pixel •  Successful DEM generation •  Should be able to extract

slope characteristics •  Possible to yield patch density

metrics

Abbeystead Fell

•  Good image resolution following Photoscan processing.

•  Drainage lines visible •  0.07cm per pixel •  Successful DEM generation •  Should be able to extract

slope characteristics •  Possible to yield patch density

metrics •  Ideal data for the random

forest model.

Moorhouse NNR

•  CEH Long term monitoring site, Environmental Change Network

•  Moor House-Upper Teesdale, is a nature reserve in the Pennine hills of northern England.

•  Large parts of it are upland blanket peatland

Initial Flight

•  Initial classification on imagery collected in May.

•  Camera trigger failed, but some imagery collected.

•  A remote site. •  Needed more batteries. •  Needed more SD cards

Preliminary classification

•  Test area •  7 classes generated •  Showed good

matches with ground survey data

Moorhouse – Training data

•  Vegetation Survey •  1960s

2 Flights Completed June 2014

Results – Orthorectified Geotiff

•  LX5 camera

•  700 images •  Approx 1

sq km

DSM – 0.05m resolution

Slope and Aspect

•  Slope and aspect were prepared for the random forest classification. Classification processing for this 1 sq km takes 4 hours.

Catchment detail – 7 classes

Catchment detail – 11 classes

Hi-Res PGA data – Infoterra – 0.2m Res.

•  Artle Garth Beck

•  0.5 sq km

Test Flight - 0.05m resolution

•  Artle Garth Beck

•  0.5 sq km

PCI Geomatica using NIR and 1m LIDAR

GMEP,  Wales.  BGS  have  es4mated  the  magnitude  (index)  of  local  peat  drainage  associated  with  the  ditches,  taking  account  of  ditch  density,  and  their  orienta4on  rela4ve  to  local  slope.  Used  BGS  1m  LIDAR  data,  NIR  and  RGB  imagery,  and  the  automated  linear  feature  extrac4on.    

Lessons learned

Don’t crash into the tops of 100ft ash trees. As a tree surgeon costs £75.

Learn to crash in safe places

Try to avoid golfers

Conclusions and Next Steps

•  Image capture of Countryside Survey sites is feasible with UAVs but open and remote areas are best.

•  Get qualified to cover a 1 sq km area. •  Don’t underestimate how much time image stitching

consumes •  Good classification results can be obtained relatively

quickly using open source software. •  PCI Geomatica is good for extracting linear features Next Steps : More flights, more habitats, attempt to extract

canopy structure for hedges, trees, and heathlands for improved classification

Don’t always need a UAV…

300  images  taken  at  2  second  intervals  using  a  Canon  6D  with  GPS  Sub-­‐cm  resolu4on