Use of remote sensing to assess vegetative stress as a proxy for soil contamination.

We report, for the first time, a multimodal investigation of current crude oil reprocessing and storage sites to assess their impact on the environment after 50 years of continuous operation. We have adopted a dual approach to investigate potential soil contamination. The first approach uses conventional analytical techniques i.e. energy dispersive X-ray fluorescence (ED-XRF) for metal analysis, and a complementary metabolomic investigation using hydrophilic liquid interaction chromatography hi-resolution mass spectrometry (HILIC-MS) for organic contaminants. Secondly, the deployment of an unmanned aerial vehicle (UAV) with a multispectral image (MSI) camera, for the remote sensing of vegetation stress, as a proxy for sub-surface soil contamination. The results identified high concentrations of barium (mean 21 017 ± 5950 µg g-1, n = 36) as well as metabolites derived from crude oil (polycyclic aromatic hydrocarbons), cleaning processes (surfactants) and other organic pollutants (e.g. pesticides, plasticizers and pharmaceuticals) in the reprocessing site. This data has then been correlated, with post-flight data analysis derived vegetation indices (NDVI, GNDVI, SAVI and Cl green VI), to assess the potential to identify soil contamination because of vegetation stress. It was found that strong correlations exist (an average R2 of >0.68) between the level of soil contamination and the ground cover vegetation. The potential to deploy aerial remote sensing techniques to provide an initial survey, to inform decision-making, on suspected contaminated land sites can have global implications.

Use of an unmanned aerial vehicle for monitoring and prediction of oilseed rape crop performance.

The flowering stage of oilseed rape (Brassica napus L.) is of vital interest in precision agriculture. It has been shown that data describing the flower production of oilseed rape (OSR), at stage 3, in spring can be used to predict seed yield at harvest. Traditional field-based techniques for assessing OSR flowers are based on a visual assessment which is subjective and time consuming. However, a high throughput phenotyping technique, using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera, was used to investigate the growth stages of OSR (in terms of crop height) and to quantify its flower production. A simplified approach using a normalised difference yellowness index (NDYI) was coupled with an iso-cluster classification method to quantify the number of OSR flower pixels and incorporate the data into an OSR seed yield estimation. The estimated OSR seed yield showed strong correlation with the actual OSR seed yield (R2 = 0.86), as determined using in-situ sensors mounted on the combine harvester. Also, using our approach allowed the variation in crop height to be assessed across all growing stages; the maximum crop height of 1.35 m OSR was observed at the flowering stage. This methodology is proposed for effectively predicting seed yield 3 months prior to harvesting.

Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland.

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.