Regional Quantitative Cover Mapping of Tundra Plant Functional Types in Arctic Alaska

Abstract

Ecosystem maps are foundational tools that support multi-disciplinary study design and applications including wildlife habitat assessment, monitoring and Earth-system modeling. Here, we present continuous-field cover maps for tundra plant functional types (PFTs) across~125,000 km 2 of Alaska's North Slope at 30-m resolution. To develop maps, we collected a field-based training dataset using a point-intercept sampling method at 225 plots spanning bioclimatic and geomorphic gradients. We stratified vegetation by nine PFTs (e.g., low deciduous shrub, dwarf evergreen shrub, sedge, lichen) and summarized measurements of the PFTs, open water, bare ground and litter using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover). We then developed 73 spectral predictors derived from Landsat satellite observations (surface reflectance composites for~15-day periods from May-August) and five gridded environmental predictors (e.g., summer temperature, climatological snow-free date) to model cover of PFTs using the random forest data-mining algorithm. Model performance tended to be best for canopy-forming PFTs, particularly deciduous shrubs. Our assessment of predictor importance indicated that models for low-statured PFTs were improved through the use of seasonal composites from early and late in the growing season, particularly when similar PFTs were aggregated together (e.g., total deciduous shrub, herbaceous). Continuous-field maps have many advantages over traditional thematic maps, and the methods described here are well-suited to support periodic map updates in tandem with future field and Landsat observations.

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

“…The slope and phenological features with higher quantization scores were the two most pivotal features for forest type identification. The classification accuracy listed in Table 8 was significantly improved by combining phenology information from multi-temporal L8 images and Sentinel-2A imagery, and the overall accuracy was raised at least 22.51% (Scenario 7); this result is in line with the research of [18,53], who reported that the multi-temporal imagery was more effective for plant type cover identification. The feature vegetation index in the bands of red and near-infrared from the Sentinel-2A also contributes to forest type classification, and other features abstracted from Sentinel-2A, DEM and multi-temporal Landsat-8 images (e.g., L8_June b6, L8_March b4, etc.)…”

“…This result is in good agreement with the findings of [19,50], who summarized that time-series images can obtain much higher accuracy of mapping tropical forest types. Macander et al [53] have also used seasonal composite Landsat imagery and the random forest method for plant type cover mapping and found that multi-temporal imagery improved cover classification. Topographic information derived from the DEM can better offer different geomorphologic characteristics.…”

Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska

“…We additionally compared our predictions of shrub AGB against a new regional map of shrub canopy cover (Macander et al 2017) in which shrub canopy cover (%) was mapped across ∼12.5 Mha of the North Slope at a 30 m spatial resolution by linking point-intercept field measurements with Landsat imagery and other geospatial datasets. We performed a pixel-wise comparison between modeled shrub AGB and canopy cover for the area of overlap between the data sets.…”

“…Each point represents the average conditions at a site, while error bars depict plus or minus one standard error in each attribute. (b) The shrub canopy cover was modeled across much of the North Slope using field surveys, Landsat imagery, and various environmental data sets (Macander et al 2017). The relationship between modeled shrub AGB and canopy cover is depicted for 2000 pixels drawn at random from the study area.…”

Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data

“…This is not a new finding (Bratsch et al, 2017;Liu et al, 2017;Macander et al, 2017), but suggests difficulties in capturing soil variation using NDVI as many soil attributes at our site were linked to moss biomass. Indeed, although variation in soil attributes could be statistically significantly explained by variation in NDVI, the soil-NDVI relationships were mostly based on two groups of values, representing the barren and more vegetated sites, and NDVI could not satisfactorily capture variation within the more vegetated areas.…”