Remote sensing for mapping biodiversity and ecosystem functioning revisited (RS4BEF)

My research is centered on the global-scale investigation of biodiversity and ecosystem functioning (BEF) relationships, employing a combination of in-situ observations, Earth Observation (EO) data, and climate data. The project utilizes satellite remote sensing for broad-scale vegetation monitoring. Key goals include the development of harmonized Earth System Data Cubes (ESDCs) that integrate diverse data sources, the creation of deep learning models for estimating ecosystem functional properties from EO data, and assessing the influence of tree diversity on ecosystem resilience to climate variations. This approach leverages big data and AI, enhancing our understanding of BEF dynamics and ecosystem stability in the face of climate change.

Söchting, M., Mahecha, M. D., Montero,D., & Scheuermann, G. (2024). Lexcube: Interactive Visualization of Large Earth System Data Cubes. In IEEE Computer Graphics and Applications (Vol. 44, Issue 1, pp. 25–37). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/mcg.2023.3321989

Montero, D., Aybar, C., Mahecha, M. D., Martinuzzi, F., Söchting, M., & Wieneke, S. (2023). A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research. In Scientific Data (Vol. 10, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-023-02096-0

Montero, D., Kraemer, G., Anghelea, A., Aybar, C., Brandt, G., Camps-Valls, G., Cremer, F., Flik, I., Gans, F., Habershon, S., Ji, C., Kattenborn, T., Martínez-Ferrer, L., Martinuzzi, F., Reinhardt, M., Söchting, M., Teber, K., & Mahecha, M. (2023). Data Cubes for Earth System Research: Challenges Ahead. California Digital Library (CDL). https://doi.org/10.31223/x58m2v

Martinuzzi, F., Mahecha, M. D., Camps-Valls, G., Montero, D., Williams, T., & Mora, K. (2023). Learning Extreme Vegetation Response to Climate Forcing: A Comparison of Recurrent Neural Network Architectures. Copernicus GmbH. https://doi.org/10.5194/egusphere-2023-2368

Aybar, C., Ysuhuaylas, L., Loja, J., Gonzales, K., Herrera, F., Bautista, L., Yali, R., Flores, A., Diaz, L., Cuenca, N., Espinoza, W., Prudencio, F., Llactayo, V., Montero, D., Sudmanns, M., Tiede, D., Mateo-García, G., & Gómez-Chova, L. (2022). CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01878-2

Montero,D.(2021). eemont: A Python package that extends Google Earth Engine. In Journal of Open Source Software (Vol. 6, Issue 62, p. 3168). The Open Journal. https://doi.org/10.21105/joss.03168