Small unoccupied aircraft systems (UAS) are now often used for collecting aerial visible image data and creating 3D digital surface models (DSM) that incorporate terrain and dense vegetation. Lightweight thermal sensors provide another sensor option for generation of sub meter resolution aerial thermal infrared orthophotos that can be used to infer hydrogeological processes. UAS-based sensors allow for the rapid and safe survey of groundwater discharge areas, often present in inaccessible, boggy, and/or dangerous terrain. Visible light and thermal infrared image data were collected March 2018 and March 2019, respectively, at Tidmarsh Farms, a former commercial cranberry bog located in coastal Massachusetts, USA (41°54'17.6"N 70°34'17.4"W), where a comprehensive stream and wetland restoration was performed. Wetland restoration actions at Tidmarsh Farms were made possible by a landowner decision to enroll in the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Wetland Reserve Easement Program. The Massachusetts Department of Fish and Game’s Division of Ecological Restoration (MDER) later became the lead manager for the design, permitting, and implementation of stream and wetland restoration actions on the site. In 2017, after the completion of the largest freshwater wetland restoration in Massachusetts to date, the property was purchased by the Massachusetts Audubon Society who in 2018 opened the Tidmarsh Wildlife Sanctuary to the public.

Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73 95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.

Data Use
License:
Creative Commons Attribution 4.0 License (CC-BY 4.0)
Recommended Citation:
Olsoy PJ, Zaiats A, Delparte DM, Germino MJ, Richardson BA, Roser AV, Forbey JS, Cattau ME, Caughlin TT. 2023. Data from: Demography with drones: Detecting growth and survival of shrubs with unoccupied aerial systems [Dataset]. University of Idaho. https://doi.org/10.7923/xj7r-1d86

Funding
US National Science Foundation and Idaho EPSCoR: OIA-1757324
US National Science Foundation and Idaho EPSCoR: OIA-1826801
US National Science Foundation: BIO-2207158

Ancillary Data Sets
Olsoy P, Zaiats A, Delparte D, Roop S, Roser A, Caughlin TT. 2022. Data from: High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature [Data set]. University of Idaho. https://doi.org/10.7923/B68T-2S83

Dasymetric mapping is a technique used to improve the accuracy of population mapping. In the United States, census data is widely used to analyze the spatial distribution of socio-economic factors. For instance, the American Community Survey (ACS, available at https://www.census.gov/programs-surveys/acs) compiles crucial socio-economic data at the census tract level. While census boundaries cover entire states, the population is not evenly distributed but tends to concentrate in residential areas. Dasymetric mapping, in combination with other datasets like land use and land cover, enhances the precision of mapping results.This notebook applies two python packages including:Tobler, a geostatistic pytho package based on PySAL: https://github.com/pysal/tobler.The EnviroAtlas Intelligent Dasymetric Toolbox by the EPA: https://github.com/USEPA/Dasymetric-Toolbox-OpenSource/tree/masterFor more information about dasymetric mapping, see this publication by Baynes, Neale, and Hultgren (2022).Data used:Open Street Map's residential zonesU.S. 2020 Decennial Census at the census block levelNational Land Cover Dataset (NLCD) from 2019 (indexed in the Virginia Data Cube).Data was called and processed in the Virginia Data Cube: https://datacube.vmasc.org/Funding: This work was made possible by the NASA AIST-21-0031 program, grant number 80NSSC22K1407.Data Description for each layer:Open Street Map (OSM) Residential is a free layer provided by the Open Street Map community that are polygons. AIST_regionCensus are census block polygons from the 2020 deciennial US census clipped to the study region. AIST Census - Clipped to OSM are census block polygons that are clipped to the OSM residential area polygons. Tobler_MAI_totPop are hexagons representing total population through the MAI Tobler function. Tobler_MAI_medFrag are hexagons representing total number of medically fragile population through the MAI Tobler function. Tobler_AI_totPop are hexagons representing total population through the AI Tobler function. Tobler_AI_medFrag are hexagons representing total number of medically fragile population through the AI Tobler function. EPA_totPop are hexagons representing total population through the EPA's IDM open source tool without using an uninhabited mask. EPA_medFrag are hexagons representing total medically fragile population through the EPA's IDM open source tool without using an uninhabited mask. Please note the above data with EPA as a prefix does not represent EPA approved products. The EPA's EnviroAtlas has their own dasymetric output. You may find Jupyter Notebooks that show how to gather this data, powered by the Virginia Datacube, here: https://github.com/ODU-GeoSEA/va-datacube