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Identification of types of tropical forest disturbance using satellite data and artificial intelligence

About This PhD Project

Project Description

Recent advances in computing technology, cloud computing and high-performance computing are paralleled with advanced artificial intelligence (AI) algorithms and significant investment in the European Copernicus Earth Observation programme and its Sentinel satellite missions. AI enables automatic detection of spatial patterns in environmental data such as satellite images based on training data. The paradigm of looking for spatial patterns instead of the historic focus on spectral information in satellite imagery allows the identification of the types of forest disturbances. AI can also be used to accurately estimate from space forest biophysical parameters that are difficult to measure in the ground such as aboveground biomass (Rodriguez-Veiga et al, 2017)

Machine learning / AI (Le Cun et al. 2015) have previously been applied to hyperspectral image classification (Hu et al. 2015), CORINE land cover mapping from Sentinel-1 SAR images (Balzter et al. 2015), forest biomass mapping using a combination or SAR and optical images (Rodriguez-Veiga et al, 2016), providing evidence of slavery from WorldView satellite images (Boyd et al. 2018).
This interdisciplinary studentship (National Centre for Earth Observation and Department of Mathematics) aims to explore the application of AI to operational automated forest monitoring, focusing on tropical forests (cases in Kenyan and Colombian forests). Time-series stacks of multispectral optical and SAR sensors will be input into the AI. The AI will be trained based on measurements collected from in-situ forest inventories and visual interpretation of very high resolution images.

Methods will be drawn from Mathematical Modelling / AI and Earth Observation / Geography. The TensorFlow AI (Abadi et al. 2016) will be implemented on the High-Performance Computing facility SPECTRE-2 at Leicester and linked with an existing 10 m resolution Sentinel-2 image processing chain developed in Python (https://github.com/clcr/pyeo). It will also be adapted to ingest Planet, Sentinel-1 and ALOS-2 PALSAR-2 imagery. Training data for the AI is available from in-situ forest inventories and interpretation of high resolution imagery. Once trained up, the AI will identify forest disturbance types and aboveground biomass loss over time. The outcomes of this analysis will be validated against recently collected forest inventory data interpretation of high resolution imagery. Experiments with different spatial filters and configurations of TensorFlow will be undertaken to optimise the detection of particularly difficult forest disturbances (e.g. gold mining, under canopy crops), and estimation of high forest biomass density levels.

The National Centre for Earth Observation will provide access to its Researcher Forum, staff conferences/workshops and national-level training

The student will be trained in Sentinel data processing on the HPC facility SPECTRE-2 at Leicester. The student will take the new MSc module GY7709 (Satellite Data Analysis in Python), available in 2019-2020, and any other modules deemed suitable, dependent on the background of the student. Complementary individual training in using AI, especially TensorFlow, will be available from the Department of Mathematics. Further training will take place ‘on-the-job’ as part of the research team in NCEO.

Funding details:
This studentship is one of a number of fully funded studentships available to the best UK and EU candidates available as part of the NERC DTP CENTA consortium.
For more details of the CENTA consortium please see the CENTA website: http://www.centa.org.uk
Applicants must meet requirements for both academic qualifications and residential eligibility: http://www.nerc.ac.uk/skills/postgrad/

Entry requirements:
Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject. The University of Leicester English language requirements apply where applicable.
How to apply:
Please refer to the CENTA Studentship application information on our website for details of how to apply
As part of the application process you will need to:
• Complete a CENTA Funding form – to be uploaded to your PhD application
• Complete and submit your PhD application online applying for Geography Research. Indicate project CENTA2-NCEO-BALZ in the

funding section.
• Complete an online project selection form Apply for CENTA2-NCEO-BALZ

Project / Funding Enquiries: Prof Heiko Balzter,

Application enquiries to

Closing date for applications: 21st January 2019 (12pm midday)

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