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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Forest Drought Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Considering the condition of our planet, anticipating natural disasters has long been a hot topic. This work is becoming more doable thanks to the expansion of earth observation data sources, such as satellite imagery. In this work, our main interest is droughts and their impacts. Recent hot and dry summers in Europe have had a significant impact on forest functioning and structure. In 2018 and 2019, Central Europe experienced two extremely dry and hot summers. These extremes resulted in widespread canopy defoliation and tree mortality. The objective of this study is to create a predictive model for forecasting future satellite imagery that contains information about the greenness of vegetation as measured by the Normalized Difference Vegetation Index (NDVI). We predict NDVI utilising data from the previous months as input to determine where and when drought impacts are triggered. We use a combination of temporal bands from Sentinel 2 and ERA-5 data sources, as well as static data sources such as the NASA SRTM Digital Elevation Model and the Copernicus Landcover Classification Map, as predictors. We will now focus on the forests of Switzerland as a region of interest in order to leverage high-quality model input layers and applications to meet typical stakeholder needs. Widely used vegetation indices and mechanistic land surface models are not effectively informed by the full information contained in Earth observation data and the observed spatial heterogeneity of land surface greenness responses at hillslope-scale resolution. Effective learning from the simultaneous evolution of climate and remotely sensed land surface properties is challenging. Modern deep learning and machine learning techniques, however, have the capacity to generate accurate predictions while also explaining the relationship between climate and its recent history, its position in the landscape, and its influences on vegetation. The task is to predict the future NDVI over forest areas to infer droughts, given past and future weather and surface reflectance. Giving future weather predictions as an input to the model, we are going for a 'guided prediction' approach where the aim is to exploit weather information from forecasting models in order to increase the predictive power of the model. Models are fully data-driven, without feature engineering, and trained on spatio-temporal data cubes, which can be seen as stacked satellite imagery for a specific geo-location and a timestamp of past Sentinel 2 surface reflectance, past (observed) and future (forecasted) climate reanalysis, time-invariant information from a digital elevation model, and a land cover map. In the temporal domain, models are trained on the period between 2018-2019, validated between 05/2021 and 09/2021, and tested between 05/2020 and 09/2020. In this research, we propose a methodology for how to successfully integrate future data from different modalities to go for a "guided-prediction" approach to enhance the predictive power of the models. We also propose a novel, complete guideline for how to effectively create earth observation data cubes. We conducted experiments regarding the model's performance under sparse conditions (clouds). We observed that the proposed model out-performed the baseline. However instead of learning the true signal, model "memorised" of the imputation values used to replace cloudy pixel values. We believe that the reasons for this are the small amount of data to learn from, which effects the generalizability skill of the model, and our chosen cloud removal strategy. |
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