20 cm resolution mapping of tundra vegetation communities provides an ecological baseline for important research areas in a changing Arctic environment. (15th October 2019)

• Record Type: Journal Article
• Authors: Greaves, Heather E; Eitel, Jan U H; Vierling, Lee A; Boelman, Natalie T; Griffin, Kevin L; Magney, Troy S; Prager, Case M
• Is Part Of: Environmental research communications. Volume 1:Number 10(2019)
• Publication Date: 2019-10-15
• Subjects: remote sensing -- climate change -- multisensor fusion -- toolik field station -- imnavait creek -- random forest -- vegetation change; Environmental sciences -- Periodicals; 333.705
• DOI: 10.1088/2515-7620/ab4a85
• Languages: English
• ISSNs: 2515-7620

Abstract

Abstract: Arctic tundra vegetation communities are spatially heterogeneous and may vary dramatically from one meter to the next. Consequently, representing Arctic tundra vegetation communities accurately requires very high resolution raster maps (<5 m grid cell size). However, using remotely sensed data to produce maps with sufficient spatial detail at an extent appropriate for understanding landscape-scale ecological patterns is challenging. In this study, we used predictor layers derived from airborne lidar and high-resolution (∼5 cm) 4-band airborne imagery to classify vegetation communities at 20 cm spatial resolution for three landscapes (12.5 km 2 total) near the Toolik Lake research area in the Alaskan Low Arctic. The maps were built using a Random Forest model that was trained and tested on 800 ground reference plots, using classes derived from commonly used legends on existing polygon maps of the area. Withheld test plots (25% of dataset) had a balanced map accuracy of 0.57, kappa of 0.47, and weighted (fuzzy) kappa of 0.65. These maps provide high-resolution plant community information that can serve as important baseline reference data for vegetation monitoring and change detection in this rapidly changing tundra ecosystem, and as validation for coarser scale maps. They also permit fine-scale characterization of landscape phenomena such as community-level nutrient dynamics and wildlife habitat suitability in an important Arctic research site. Our approach demonstrates that very high resolution mapping results can be achieved and validated by integrating high-resolution remote-sensing datasets from multiple sensors in a machine learning model trained on simple field reference data.