Context and objectives
The forecasting of ecosystem response to climate extremes is crucial for understanding and mitigating the impacts of climate change. However, current vegetation models used for scenario simulation and sensitivity analysis are limited by their parametric simplicity and the lack of accurate data to constrain them. This hinders the reliable prediction of ecosystem responses to future climate extremes. To address this challenge, an innovation project called DeepV is proposed, aiming to develop data-driven, spatially explicit ecosystem response models based on remote sensing data and conditional Generative Adversarial Networks (cGANs). These cGANs have shown success in generating realistic data, such as true color and multi-spectral satellite images, using deep learning techniques. The objective of the DeepV project is to expand and explore the use of cGANs for generating fake yet realistic satellite image time series of ecosystem response.
Project outcome
Expected scientific results
The DeepV project expects to yield several significant scientific results. Firstly, the project aims to develop data-driven, spatially explicit ecosystem response models based on remote sensing data and conditional Generative Adversarial Networks (cGANs). These models will provide a better understanding of the spatiotemporal behavior of ecosystems and their dependence on environmental factors and climatic conditions. The incorporation of biophysical constraints and temporal information will further enhance the accuracy and realism of the models. Secondly, the project aims to assess the importance of biophysical constraints on the performance of cGANs in predicting photorealistic satellite images of landscapes Lastly, the validation and assessment of the developed methodologies through comparisons between cGAN-generated and observed Sentinel-2 scenes will provide independent quantitative validation, further strengthening the reliability and applicability of the models. Overall, the expected scientific results of the DeepV project will advance the understanding of ecosystem responses to climate change and provide valuable tools for ecosystem sensitivity assessment and future scenario simulation.
By developing data-driven, spatially explicit ecosystem response models, the project contributes to addressing the impacts of climate change on terrestrial and marine environments. These models inform decision-making for ecosystem management, conservation, and adaptation strategies. By utilizing remote sensing data and cGANs, the project generates realistic satellite image time series for simulating ecosystem responses to future climate extremes. Its outcomes contribute to biodiversity protection, environmental health improvement, and sustainable resource management. The project plays a vital role in understanding and managing climate change impacts on ecosystems, promoting a resilient future.
Expected products and services
The DeepV project will deliver valuable products and services, including data-driven ecosystem response models using remote sensing data and cGANs. These models will enable ecosystem sensitivity assessment and simulation of responses to future climate extremes. An open-source package will be developed, incorporating biophysical constraints and temporal information for wider use. The project's findings will be shared through scientific publications and open source code provided via GitHub.
Potential users
The potential users of the DeepV project's outcomes include researchers and scientists working in the fields of climate change, ecosystem management, and biodiversity conservation. Environmental and land-use planners, policymakers, and decision-makers involved in sustainable development initiatives can also benefit from the project's data-driven ecosystem response models. Furthermore, organizations involved in climate change adaptation and mitigation strategies, such as environmental NGOs and government agencies, may find value in the project's tools and insights for informing their actions and policies.
| Location: | ||
| Website: | http://www.earthmapps.io/projects/DeepV.html | |