School of ComputingFaculty of Engineering

DART workshop: Airborne remote sensing

David Stott

Overview

● This presentation covers the airborne side of DART:– LiDAR– Spectroscopy (imaging and field)– Aerial photography

● About cropmarks on arable land:– The nature of contrasts in vegetation marks– How we can use this to improve detection

Aims and Objectives

● Look at contrasts over time:– How they change with weather– How they change with land-use

● How to best detect contrasts with different sensors:– What can the sensors detect?– What is the best context to deploy them in?

Archaeological vegetation marks

● Soil differences influence the development & health of the crop

● Visible as local variation in the crop canopy:– Stress and vigour:

● Variations in foliar chemistry● Extreme condtions

– Canopy structure:● Leaf Area Index (LAI) ● Tillering / early growth stage development

Some challenges

● High dimesnionality of data:– Hyperspectral can have 100s of bands– Full wave-form LiDAR– Lots of redundancy

● No unique spectral signature

Some challenges

● High dimesnionality of data:– Hyperspectral can have 100s of bands– Lots of redundancy – Full wave-form LiDAR

● No unique spectral signature– Brute force / classifcation approaches are

problematic– Changes in soils, land use and crop

Methodology: Ground-based

● Multi-temporal ground measurements (monthly):– Spectro-radiometry

● ASD FieldSpec Pro● 350-2500nm @ 3nm (ish) sampling interval

Fiber-optic probe

Tired old laptop (needs an LPT port…)

Reflectance panel

Instrument(20Kg of backpain)

Methodology: Ground-based

● Multi-temporal ground measurements (monthly):– Spectro-radiometry

● ASD FieldSpec Pro● 350-2500nm @ 3nm(ish) sampling interval

● Crop height & density:– Ceptometer (leaf area index)– Surface coverage (near-vertical photos)– Tillering in early growth-stage

Airborne data

● NERC ARSF:

– Eagle and Hawk hyperspectral (VIS-SWIR)

– Full waveform LiDAR

– Survey camera

● Geomatics Group:– CASI hyperspectral

– Discrete LiDAR

– Orthophotography

Methodology: Analyses

● Python software:– Spectral analysis (imagery & spectroradiometry):

● Continuum removal● Vegetation analyses● Red edge position

– LiDAR:● Multi-temporal vegetation mass● Full-waveform

Jun 14th 2011

Jun 29th 2011

Jul 15th 2011

Spectral differences

● Example from Diddington June-July 2011– Spectradiometry shows

good contrast– Continuum removed

spectra from 670nm absorption feature

– Band normalised by area

So...

● Spectroradiometry shows good contrast:– Variations in foliar pigmentation change rapidly– Variations in crop structure remain fairly similar

● Can we use the LiDAR to detect the biomass variations?– Higher spatial resolution (~0.4m vs 1m)

Full waveform

● Looked at correlation between hyperspectral and full waveform LiDAR– Reflectance @ 1062nm (Hawk) & intensity @

1064nm (LiDAR)

Full waveform

● Correlation between archaeological features and full waveform LiDAR

Dataset t p

Vegetation height 42.9721 2.2E-016

Peak sum 12.968 8.56E-014

Maximum intensity 7.9123 1.327E-015

Peak width 0.4164 0.3385

Full waveform

● Sensor only resolves a single return over low, sparse crop

● Very little variation in the width of the return● Intensity is usable● Best results came from using vegetation height

model derived from discrete returns

Conclusions so far

● Different sensors and techniques required on a field by field basis

● This is hard:– Variability of the archaeology– Variablility of its context– Small things in big data– (not even mentioning the weather...)

● Spatial resolution is not the be all & end-all

Further work

● LiDAR:– Scan angle– Spatial analyses

● Statistical comparison of sensors– Comparison of contrasts on and off the features

● Writing it all up

Acknowledgements

● NERC ARSF● Royal Agricultural College● Thornhill Estates● DART community