Clark, Matthew (Matt): Sonoma State University (Project Lead)
Project Funding:
2017 - 2023
NRA: 2016 NASA: Citizen Science for Earth Systems Program
Funded by Other, NASA, Other US Funding: Amanda Whitehurst/Kevin Murphy
Abstract:
Remote sensing plays a critical role in long-term monitoring of biodiversity across spatial and temporal scales. Although traditional remote sensing of diversity has focused on correlative relationships to multispectral bands, landscape heterogeneity from maps, or broad land-cover/ecosystem associations, technological and analytical advances are combining to broaden the potential applications. In particular, lidar provides measurements of three-dimensional vegetation structure that can be related to animal habitat and organismal and functional diversity. Image spectroscopy can detect canopy foliar chemistry, spectral-chemical diversity related to plant functional and floristic diversity, and map plant species and land cover. When placed in space, these sensors can provide the systematic, repeat coverage of measurements over large areas that is needed to track ecosystem change.
Spatial models that characterize aspects of biodiversity, such as species distributions or richness, from remote sensing predictor variables need extensive and representative in situ datasets of taxa of interest for calibration and validation. Citizen science, where public volunteers are enlisted in scientific inquiry, has great potential for collecting large amounts of in situ species presence data while engaging the public in science and environmental issues. In addition, new low-cost technology for automated species identification through sound recordings (bioacoustics) in combination with citizen science may further increase species observations.
This Citizen Science Research investigation has the broad goal of advancing scientific understanding of biodiversity and conservation using a combination of new and existing spaceborne sensors. The main objectives are to: 1) involve citizen scientists in the collection of in situ field information for earth observation of biodiversity; 2) assess the accuracy and precision of bioacoustics for the detection and monitoring of individual species and richness; 3) test the predictive strength of spaceborne imaging spectroscopy, lidar, synthetic aperture radar sensors for spatial modeling of species occupancy and overall species richness; and 4) use occupancy/richness maps to better understand factors related to conserving animal diversity.
We focus our investigation in Sonoma County of northern California, an area with a diverse range of natural vegetation, urban and agricultural gradients. Citizen scientists affiliated with the Audubon society will deploy portable sound recorders and train a

bioacoustics analysis system to automatically identify individual bird species in the recorded soundscapes. To demonstrate the potential of space-borne imaging spectroscopy and lidar to biodiversity monitoring, the science team will simulate Hyperspectral Infrared Imager (HyspIRI) and Global Ecosystem Dynamics Investigation (GEDI) large- footprint lidar data at the county scale. Metrics derived from these simulated data will provide unique, wall-to-wall information on vegetation chemistry and three-dimensional structure that will be linked with in situ citizen science data to model species occupancy and overall richness at the landscape scale. The investigation will have an eight-month prototype phase in a local watershed to demonstrate the bioacoustics technology, capabilities of citizen scientists for automated collection of in situ bird data, and preliminary species distribution and richness maps. In a three-year implementation phase, the investigation will broaden to include bird observations across a range of natural and anthropogenic habitats in the county.
Publications:
Burns, P., Clark, M., Salas, L., Hancock, S., Leland, D., Jantz, P., Dubayah, R., Goetz, S. J. 2020. Incorporating canopy structure from simulated GEDI lidar into bird species distribution models. Environmental Research Letters. 15(9), 095002. DOI: 10.1088/1748-9326/ab80ee
Quinn, C. A., Burns, P., Gill, G., Baligar, S., Snyder, R. L., Salas, L., Goetz, S. J., Clark, M. L. 2022. Soundscape classification with convolutional neural networks reveals temporal and geographic patterns in ecoacoustic data. Ecological Indicators. 138, 108831. DOI: 10.1016/j.ecolind.2022.108831
Quinn, C. A., Burns, P., Hakkenberg, C. R., Salas, L., Pasch, B., Goetz, S. J., Clark, M. L. 2023. Soundscape components inform acoustic index patterns and refine estimates of bird species richness. Frontiers in Remote Sensing. 4. DOI: 10.3389/frsen.2023.1156837
Quinn, C. A., Burns, P., Jantz, P., Salas, L., Goetz, S. J., Clark, M. L. 2024. Soundscape mapping: understanding regional spatial and temporal patterns of soundscapes incorporating remotely-sensed predictors and wildfire disturbance. Environmental Research: Ecology. 3(2), 025002. DOI: 10.1088/2752-664X/ad4bec
More details may be found in the following project profile(s):