According to the latest National Statistics on forestry produced by the Forestry Commission (forestry.gov.uk), 73% of the UK public agrees with the statement “trees are good because they remove carbon dioxide from the atmosphere and store it in wood”, and over half of the UK population had visited woodland in the last few years. The forest ecosystems of the UK are therefore vital for our wellbeing. Even though the amount of carbon stored in UK forests is estimated to have increased between 1990 and 2015, the annual rate of additional carbon storage is forecast to peak around 2030 and is expected to fall steadily afterwards.
Climate change, e.g. changes in temperature, variations in seasonal rainfall, and increased numbers of extreme events such as droughts and floods, is expected to have major impacts on UK forest ecosystems, becoming an increasing challenge for forest management and conservation. Evidence suggests that the climate has altered considerably over the past 50 years. These changes have resulted in increased drought and soil saturation (depending on location and time of year) and increased abundance and variety of tree pests and disease. Impacts of climate change on forest ecosystems still need to be better understood and assessed.
In the UK, forest structures and species composition are heavily dependent on and linked to forest management. Managers can alter forest structure and tree species at the landscape or local (stand) scale to improve the forest’s ability to withstand impacts such as drought or pests, and to maximise and maintain ecosystem services – the benefits we gain from forests such as water and carbon storage, timber, biodiversity and tourism. Examples of adaptive management currently being implemented in the UK to increase forest resilience to climate change include actively diversifying the forest system, i.e. planting of a wider range of species and genetic resources, varying felling times and adopting more complex multi-age forest structures . As Suggested by the Forestry Commission, it has become urgent to plant a wider range of tree species of various origins. To study the impact of climate change on forest ecosystems, it is important to understand the influences of climate, management and other environmental factors on forest structure and composition. Monitoring of forest changes can allow better adaptation of management practices.
Nowadays, data acquisition is easier from both ground measurements and remote sensing. Many data are available at different scales. For example, nationwide lidar data from Environment Agency (data.gov.uk/publisher/environment-agency), one metre resolution Lidar data for cities of England and Scotland from NERC Earth Observation Data Centre (http://neodc.nerc.ac.uk/), Bluesky’s National Tree Map (NTM), and open source satellite multispectral images (SPOT, Sentinel-2). Regional high density lidar data can also be obtained periodically, as shown in Figure 1. Accurate forest parameters, e.g. tree height, DBH, crown size, above-ground biomass, and species , can be extracted from high density lidar data. These data enable us to investigate the forest structure and species composition changes, at local, regional and national scales [3, 4].
The project will investigate the use of remote sensing technologies, such as lidar and optical imaging, for forest tree species characterisation and monitoring. Spectral and 3D features of trees will be derived from both imagery and lidar data for species classification. Mutli-temporal data will be collected for change detection and monitoring. A suitable study site will be chosen where multi-source data are available, such as the Aberfoyle Forest Queen Elizabeth II Forest Park, Scotland, where lidar, RGB photos, and hyperspectral images are available.
The project comprises three main tasks:
Task 1 Data collation: This task focuses on collation (and pre-processing) of remote sensing and environmental data sets. Remote sensing datasets, e.g. Environment Agency lidar, satellite imagery, NERC ARF lidar and imagery will be acquired from openly available sources. Our project partner, Forest Research, will provide forest inventory data. Current field data, e.g. species, DBH, Height, LAI, tree positions, will be collected for sample stands to supplement existing inventory data and validate the various data processing steps, i.e. segmentation, structural variable extraction, species classification.
Task 2 Tree parameter extraction: Individual trees will be delineated using a segmentation algorithm developed by Dr Xiao . Based on features directly characterised from lidar, e.g. tree height, crown area, crown volume, stem co-ordinates and canopy density/LAI, other individual tree structural metrics, e.g. DBH and stem volume, can be estimated using linear regression models . Together with features derived from hyperspectral and optical imagery, e.g. spectral reflectance and indices, these will form discriminative metrics for species classification.
Task 3 Multiscale forest species monitoring: To ensure the scalability of the whole system, it is essential to map the species at both stand and landscape levels. Species classification will be undertaken from NERC ARF hyperspectral imagery and lidar metrics at stand-level, which will be used to train classification of high resolution optical satellite data, e.g. commercial SPOT data, at landscape scales. Inventory species data will be incorporated into the classification wherever available. Generalised information from lower resolution lidar on stand structure (e.g. average height, height variation, tree spacing) will be derived by combination with high resolution, limited coverage lidar data. A physiological process-based model will be parameterised using the extracted forest parameters and existing data to simulate forest changes, which will then be validated by remote sensing change detection results.
Each of the tasks will be in close collaboration with the supervisors at Durham University and Forest Research, from field data collection, to forest feature extraction, to species classification, and to multiscale monitoring. The student is expected to have regular meetings with all supervision team members.
The student will spend 6 months on literature review and skill development, including the use of different remote sensing technologies for forest characterisation, methods for classification, and methods for change detection. The remaining time will focus on the Task I: data collation, including field data acquisition and existing digital data collation. A week of fieldwork is expected in late spring.
Multi-source remote sensing data processing. Feature extraction from different data sources at different scales. Data fusion for forest species classification and mapping. Validation against ground measured data. A second fieldwork of one week is planned preferably at the same time of the year.
Multi-temporal data analysis based on features extracted at different spatial scales. This will include individual tree change detection, stand parameters change detection, and landscape forest change detection for forest growth monitoring. A third fieldwork will be conducted. The results are to be compared with physiological simulations. The student will visit Forest Research for at least three months.
The final 6 months will be focused on results dissemination and thesis preparation based on previous publications and associated materials.
The student will be based in the Geospatial Engineering research group within the School of Engineering at Newcastle University, and will benefit from expertise in, e.g. remote sensing, photogrammetry, laser scanning, and woodland structure characterisation, modelling and analysis from co-supervisors in Durham University (Department of Geography) and Forest Research.
The student will develop research skills and broader awareness of on-going research through regular participation in research group meetings, seminar series and participation in external national and international conferences, to support their development as an independent researcher.
The successful applicant is likely to have experience in
geomatics, geography, or ecology, and a strong interest in developing skills in use of new technologies and in field work. Full training will be provided in remote sensing technologies and field validation techniques. Technical skills and knowledge, including image processing, programming and statistics, will be developed based on analysis of the training needs of the individual, through a personalised training plan. The student will also receive broader research skills and career development training provided by the School, Faculty and IAPETUS.
References & further reading
 Brown I., et al. (2016). UK Climate Change Risk Assessment Evidence Report: Chapter 3, Natural Environment and Natural Assets. Adaptation Sub-Committee of the Committee on Climate Changeã€‚
 Ozdemir, I., & Donoghue, D. N. (2013). Modelling tree size diversity from airborne laser scanning using canopy height models with image texture measures. Forest Ecology and Management, 295, 28-37.
 Donoghue, D. N., Watt, P. J., Cox, N. J., & Wilson, J. (2007). Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sensing of Environment, 110(4), 509-522.
 Bonnet S, Gaulton R, Lehaire F, Lejeune P. (2015). Canopy Gap Mapping from Airborne Laser Scanning: An Assessment of the Positional and Geometrical Accuracy. Remote Sensing, 7(9), 11267-11294.
 Xiao, W.; Xu, S.; Elberink, S. O. & Vosselman, G. (2016). Individual Tree Crown Modeling and Change Detection From Airborne Lidar Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 3467-3477
 Yao, W.; Krzystek, P. & Heurich, M. (2012) Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sensing of Environment, 123, 368-380
For eligibility and requirements, please check http://www.iapetus.ac.uk/aboutstudentships/
For further details and informal enquiries, please
contact Dr Wen Xiao (Tel: +44 (0)191 208 6357 or