Project Funding: 2018 - 2021

NRA: 2016 NASA: Group on Earth Observations Work Programme   

Funded by NASA, Other US Funding: NASA

Abstract:
The Group on Earth Observations (GEO) Biodiversity Observation Network (BON) was developed to improve the synthesis and acquisition of data on biodiversity for the suite of potential user communities, including scientists, land managers, and policy makers. GEO BON aims to identify Essential Biodiversity Variables (EBVs), of which a category is Ecosystem Function. Within the EBV class of Ecosystem Function, the currently identified variables (Net Primary Productivity, Secondary Productivity, Nutrient Retention, and Disturbance Regime) all describe aspects of ecosystem functioning, however, none of these variables directly quantifies the observed diversity in ecosystem functioning. Just as species composition (including biodiversity) has the capacity to provide information about the resistance and resilience of ecosystems in the face of environmental change, the diversity of ecosystem functioning provides a similar opportunity. With this as our basic premise, and the identification of a potential gap in the set of GEO BON Ecosystem Function EBVs, we propose to develop a classification of Ecosystem Functional Types (EFTs). EFTs are a top-down characterization of the spatial and temporal heterogeneity of ecosystem functioning (regardless of similarities or differences in ecosystem structure), based on areas of the land surface that process energy and matter in similar ways, and potentially show coordinated responses to environmental factors. There is effectively an unlimited number of ways in which ecosystem functioning can be characterized and classified; however, certain EFT classifications are more achievable than others, given limitations of data availability over large spatial extents. The development of an extensive EFT classification, using certain functional variables can be (and has been) accomplished through the use of satellite remote sensing, and this is the approach that we intend to utilize for this effort. While EFTs can be developed globally, we will focus this particular effort on the terrestrial ecosystems of the arctic tundra. We do this because 1) the Arctic is a region that has a relatively high degree of spatial variability in ecosystem functioning, but is also one that has been changing dramatically over the past several decades (and is projected to continue to change), as a result of dynamics in climate and land use, 2) a well-defined classification of the ecosystem structure (vegetation community distribution) of the arctic tundra currently exists in the Circumpolar Arctic Vegetation Map (CAVM) – the region therefore provides an excellent opportunity to identify and evaluate the relationships between ecosystem structural diversity and functional diversity, and 3) the Arctic is currently one of two regional Biodiversity Observation Networks, facilitated by the Circumpolar Biodiversity Monitoring Program (CBMP) – within the Arctic Council, Conservation of Arctic Flora and Fauna (CAFF) Secretariat.

Publications:

Alcaraz-Segura, D., Cabello, J., Arenas-Castro, S., Penas, J., Vaz, A. S. 2022. Remote Sensing in Sierra Nevada: From Abiotic Processes to Biodiversity and Ecosystem Functions and Services in: The Landscape of the Sierra Nevada. Springer International Publishing, 315-327. DOI: 10.1007/978-3-030-94219-9_19

Cazorla, B. P., Cabello, J., Penas, J., Garcillan, P. P., Reyes, A., Alcaraz-Segura, D. 2020. Incorporating Ecosystem Functional Diversity into Geographic Conservation Priorities Using Remotely Sensed Ecosystem Functional Types. Ecosystems. 24(3), 548-564. DOI: 10.1007/s10021-020-00533-4

Guirado, E., Alcaraz-Segura, D., Cabello, J., Puertas-Ruiz, S., Herrera, F., Tabik, S. 2020. Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sensing. 12(3), 343. DOI: 10.3390/rs12030343

Guirado, E., Blanco-Sacristan, J., Rigol-Sanchez, J., Alcaraz-Segura, D., Cabello, J. 2019. A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in Drylands. Remote Sensing. 11(22), 2649. DOI: 10.3390/rs11222649

Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D., Herrera, F. 2019. Whale counting in satellite and aerial images with deep learning. Scientific Reports. 9(1). DOI: 10.1038/s41598-019-50795-9

Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D., Herrera, F. Automatic whale counting in satellite images with deep learning DOI: 10.1101/443671

Liu, L., Smith, J. R., Armstrong, A. H., Alcaraz-Segura, D., Epstein, H. E., Echeverri, A., Langhans, K. E., Schmitt, R. J. P., Chaplin-Kramer, R. 2023. Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity. Remote Sensing. 15(23), 5593. DOI: 10.3390/rs15235593

P. Cazorla, B., P. Garcillan, P., Cabello, J., Alcaraz-Segura, D., Reyes, A., Penas, J. 2021. Patterns of ecosystem functioning as tool for biological regionalization: the case of the Mediterranean-desert-tropical transition of Baja California. Mediterranean Botany. 42, e68529. DOI: 10.5209/mbot.68529

Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., Herrera, F. 2019. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sensing. 11(6), 643. DOI: 10.3390/rs11060643

Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., Herrera, F. 2019. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sensing. 11(6), 643. DOI: 10.3390/rs11060643

Tassone, M. S., Epstein, H. E., Armstrong, A. H., Bhatt, U. S., Frost, G. V., Heim, B., Raynolds, M. K., Walker, D. A. 2024. Drivers of heterogeneity in tundra vegetation productivity on the Yamal Peninsula, Siberia, Russia. Environmental Research: Ecology. DOI: 10.1088/2752-664X/ad220f

Villarreal, S., Guevara, M., Alcaraz-Segura, D., Vargas, R. 2019. Optimizing an Environmental Observatory Network Design Using Publicly Available Data. Journal of Geophysical Research: Biogeosciences. 124(7), 1812-1826. DOI: 10.1029/2018JG004714

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile
• NASA Biological Diversity & Ecological Conservation (BDEC) Project Profile
• NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Project Profile