Benjamin Jones UAF

PO Box 755860 FAIRBANKS AK 99775 United States

9074746794 bmjones3@alaska.edu https://orcid.org/0000-0002-1517-4711 Kenneth Tape kdtape@alaska.edu Jason Clark jaclark2@alaska.edu Ingmar Nitze ingmar.nitze@awi.de Guido Grosse guido.grosse@awi.de Jeff Disbrow disbr007@umn.edu Arctic landscapes are in a state of transition due to changes in climate occurring during both the summer and winter seasons. Scattered observations indicate that beavers (Castor canadensis) have moved from the forest into tundra areas during the last 20 years, likely in response to broader physical and ecosystem changes occurring in Arctic and Boreal regions. The implications of beaver inhabitation in the Arctic and Boreal are unique relative to other ecosystems due to the presence of permafrost and its vulnerability associated with beaver dams and inundation. Our study specifically examines the role of beavers in controlling surface water dynamics and related thermokarst development in low Arctic tundra regions. We mapped the number of beaver dams visible in sub-meter resolution satellite images acquired between 2002 and 2019 for a 100 square kilometer study area (12 years of imagery) near Kotzebue, Alaska and a 430 square kilometer study area (3 years of imagery) encompassing the entire northern Baldwin Peninsula, Alaska. We show that during the last two decades beaver-driven ecosystem engineering is responsible for the majority of surface water area changes and inferred thermokarst development in the study area. This has implications for interpreting surface water area changes and thermokarst dynamics in other Arctic and Boreal regions that may also result from beaver dam building activities. This geospatial dataset provides polygon vector files representing surface water area in a 100 square kilometer study area located near Kotzebue, Alaska. Surface water area maps were created using sub-meter resolution satellite imagery for the years 2002, 2007-2014, and 2017-2019. Image selection focused on cloud-free, ice-free, and calm surface water conditions with images being acquired between late-June and mid-August in a given year. All images were resampled to a spatial resolution of 70 centimeter to match the lowest resolution image in the time series prior to analysis. Within year image dates range from 25 June to 22 August with the average date of image acquisition being 17 July (table 1). Object-based image analysis was conducted in eCognition Essentials 1.3. surface water arctic lakes beavers thermokarst None This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 100 square kilometer area on the northwestern Baldwin Peninsula near Kotzebue, Alaska -162.66348 -162.38882 66.93980 66.81275 2002-08-22 2019-07-08 Benjamin Jones UAF

PO Box 755860 FAIRBANKS AK 99775 United States

9074746794 bmjones3@alaska.edu https://orcid.org/0000-0002-1517-4711 We analyzed changes in surface water extent in the Kotzebue study area using the best available high-resolution panchromatic images available for a particular year. Image selection focused on cloud-free, ice-free, and calm surface water conditions with images being acquired between late-June and mid-August in a given year. All images were resampled to a spatial resolution of 70 centimeter to match the lowest resolution image in the time series prior to analysis. Within year image dates range from 25 June to 22 August with the average date of image acquisition being 17 July (table 1). Object-based image analysis was conducted in eCognition Essentials 1.3. Image pixels were grouped into objects using the multiresolution segmentation algorithm with a scale of 20, a color to shape parameter of 0.1, and a smoothness to compactness factor of 0.4. A supervised classification scheme was developed for each image by selecting water and non-water object samples. At least 500 samples were manually selected for each class in each image. Objects were classified using the object-based k-Nearest Neighbor (KNN) classifier with a k value of 1. The KNN is a simple machine learning algorithm where an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors. Using a k value of 1 emphasizes stark contrasts between disparate object class types. An accuracy assessment was conducted using the eCognition workflow for each image object classification using the input training samples. Based on suitable and available imagery, the Kotzebue study area (100 km2) was mapped each year in 2002, 2007-2014, and 2017-2019 (in total 12 individual years). Classified objects were merged and only those objects classified as water were extracted for further analysis in a GIS (geographic information system) database. All objects smaller than 250 m2 were removed from the surface water inventory, objects that fell outside of the overlapping extent of all images were removed from further analysis, and stable water feature centroids were used to reposition lake polygon files from prior years to match the orthorectification positioning of the 2019 images. Benjamin Jones UAF principalInvestigator Kenneth Tape coPrincipalInvestigator Christopher Arp coPrincipalInvestigator Christopher Larsen coPrincipalInvestigator Amy Breen coPrincipalInvestigator Mikhail Kanevskiy coPrincipalInvestigator NSF 1850578 NSF 1806213 NSF 1929170 Kotzebue_Study_Area_Surface_Water_Polygons.zip Geospatial data on surface water areas in Kotzebue, Alaska. Kotzebue_Study_Area_Surface_Water_Polygons.zip 1984222 75151f2c0a930f9befd43df406ac1e84730a5482 application/zip https://cn.dataone.org/cn/v2/resolve/urn:uuid:5ac377f9-b0df-491f-9e06-05eb0d447ebc OBJECTID Object identification Object identification Shape_Length Length of the lake perimeter in meters meter real Shape_Area Area of the lake in square meters squareMeter real Point WGS_1984_UTM_Zone_3N