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Integrated monitoring/integrated data workflows and analysis

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Integrated monitoring/integrated data workflows and analysis

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Image analysis:
-combining images and point cloud data for 3D reconstruction?
-methods for establishing change over time in ROIs? [Is this already a thing?]

Statistical analysis:
-changepoint detection across multiple components of same system? Ties to work CEH have done with LU
-Integrating information from different sources (fixed cameras vs opportunistic obs vs satellite vs drone etc etc)
-Modelling multiple species/functional groups simultaneously

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New technologies & new ways of working elements

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New technologies & new ways of working elements

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- involvement of local communities to collect training data on phenology/ reproduction
- what incentivises local communities to be involved with citizen science?
- local community involved in ongoing monitoring to record phenology across the study area through smart phones
(We talked about this but I don't know how feasible it would be to integrate into a proposal given the other existing elements)

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Different camera set-ups for different needs

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Different camera set-ups for different needs

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Resilience as core idea (connected to tree strategy - leaf & reproductive phenology)

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Resilience as core idea (connected to tree strategy - leaf & reproductive phenology)

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and ideally open source so others can replicate the systems. However, it would make sense if the data would go through UKCEH. We care more about the data and the science than about the economic value

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linking massive flower events to el nino / la nina or other...anomalies changes due to climate

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linking massive flower events to el nino / la nina or other...anomalies changes due to climate

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Seed ID - building image database from collected samples (3D models of fruit & flowers?). Limited use of herbarium samples?

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Seed ID - building image database from collected samples (3D models of fruit & flowers?). Limited use of herbarium samples?

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The limits of detection models for plants

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The limits of detection models for plants

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Oisin raised the point that the shape of plants make it difficult to separate and identify multiple plants in one image. In our example of the seeds and the seedling growth this might not be such a challenge as they should be small and discrete, without overlapping canopies.

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Quantification from images is less frequently applied - possibly area of development

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Quantification from images is less frequently applied - possibly area of development

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'Holy grail' - ID with limited training data

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'Holy grail' - ID with limited training data

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How can species distribution models be improved using image-based data (or vice-verse - need to avoid risk of circularity, using same info in training and testing in a cycle!)

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How can species distribution models be improved using image-based data (or vice-verse - need to avoid risk of circularity, using same info in training and testing in a cycle!)

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Sustainability? Env impact of deep learning? Data/image storage? Avoid lots of reinventing the wheel. How long do I really need to let it run - trade-off and transparency

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Sustainability? Env impact of deep learning? Data/image storage? Avoid lots of reinventing the wheel. How long do I really need to let it run - trade-off and transparency

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Integrating data sources? Object-based and e.g. point clouds, hyperspectral?

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Integrating data sources? Object-based and e.g. point clouds, hyperspectral?

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Training - how can we relieve the burden of training data? How many images are enough for different purposes to give e.g. 80% accuracy?

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Training - how can we relieve the burden of training data? How many images are enough for different purposes to give e.g. 80% accuracy?

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Would need to integrate some type of object tracking together with the object identification to relate the same leaf amongst multiple images if trying to measure growth or herbivory over time.

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scaling: matching up in-situ greening with satellite greening, understanding how different degrees of  leaf phenology synchronicity between individuals translates into satellite derived amplitudes (NDVI, EVI) and other pheno-metrics. Help characterise uncertainty around SOS and EOS derivations from satellite.  Thinking about how pixel heterogeneity impacts satellite signal

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scaling: matching up in-situ greening with satellite greening, understanding how different degrees of  leaf phenology synchronicity between individuals translates into satellite derived amplitudes (NDVI, EVI) and other pheno-metrics. Help characterise uncertainty around SOS and EOS derivations from satellite.  Thinking about how pixel heterogeneity impacts satellite signal

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Understanding impact of e.g. tree disease on canopy and ground flora greening and seasonality?

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Understanding impact of e.g. tree disease on canopy and ground flora greening and seasonality?

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What if we do not have enough data or missing data - how can we use transfer learning methods?

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- (big data) Analysis platforms and tools
- Repository of trustworthy analysis methods and models
- Ethical and transparent use of AI to support image analysis

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Seed fall right through to seedling survival

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Seed fall right through to seedling survival

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If you had cameras pointing towards the ground, you could measure the amount of seed fall during a masting event and track this right through to seedling emergence, survival and growth rather than using separate seed traps and seedling experimental plots (ambitious)

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Reproductive phenology (related to forest structure, environment, other ecological processes e.g. herbivory)

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Reproductive phenology (related to forest structure, environment, other ecological processes e.g. herbivory)

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- Data Labs platforms (future opportunities)
- Use of notebook technologies
- Version controlled data and code (GitHub, Docker, ....)
- Common architecture / /Data common / standardisation

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Understanding the leaf life cycle strategies of trees/plants in a complex, biodiverse community and linking this to community resilience to climate change.  We could expand this and look at reproduction (-> Lindsay's work in Indonesia)

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Understanding the leaf life cycle strategies of trees/plants in a complex, biodiverse community and linking this to community resilience to climate change.  We could expand this and look at reproduction (-> Lindsay's work in Indonesia)

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- Sensor management systems - having a two-way communication to calibrate and re-calibrate sensors (via HTTP or MQTT protocol)
- Data and meta data standards, vocabs and ontologies to enhance data discovery
- FAIR and easy access to analysis platforms

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Fruiting patterns in the canopy to understand fauna movement and survival, herbivory patterns (e.g. caterpillars as food source for birds)

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Fruiting patterns in the canopy to understand fauna movement and survival, herbivory patterns (e.g. caterpillars as food source for birds)

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Following from the work of Saleska group: use camera observation to estimate NEP (instead of expensive flux tower observations) - maybe a long shot but worth a look at?

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Following from the work of Saleska group: use camera observation to estimate NEP (instead of expensive flux tower observations) - maybe a long shot but worth a look at?

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The application of phenocam images to understand the phenological patterns of grasses and trees is a fundamental topic for mixed vegetation, such as savannas.

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Use of seasonal variation information to inform integration with other ecological data

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Use of seasonal variation information to inform integration with other ecological data

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One of the big problems I have when analysing vegetation data is that it occurs at a point in time and space so being able to contextualise that with temporal information would be useful

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Leaf phenology/water relations (over space/between species) and also within canopy position)

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Leaf phenology/water relations (over space/between species) and also within canopy position)

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- Digital infrastructure to support the end to end process - data acquisition, storage, transformation, quality control, analysis ready data, visualisation of the data

- FAIR assets (data, model, methods, etc)
- Automation
- Data integration
- Data discovery

- Systems approach - sensor management system (opportunities and challenges)

- Challenges and opportunities associated with live data streaming

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Leaf area index (how leaf area index varies across time)