Detecting northern peatland vegetation patterns at ultra‐high spatial resolution

• Main Authors: Räsänen, Aleksi, Aurela, Mika, Juutinen, Sari, Kumpula, Timo, Lohila, Annalea, Penttilä, Timo, Virtanen, Tarmo
• Other Authors: orcid:0000-0002-0710-4131, 4100110510, Luonnonvarakeskus
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley
• Subjects: unmanned aerial vehicles; floristic analysis; lidar; northern boreal; very-high spatial resolution; Northern Finland
• Online Access: http://jukuri.luke.fi/handle/10024/545207

Within northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra‐high (centimeter level) to coarse scale. In addition to within‐site spatial heterogeneity, there is evident between‐site heterogeneity, but there is a lack of studies assessing whether different combinations of remotely sensed features and mapping approaches are needed in different types of landscapes. We evaluated the value of different mapping methods and remote sensing datasets and analyzed the kinds of differences present in vegetation patterns and their mappability between three northern boreal peatland landscapes in northern Finland. We utilized field‐inventoried vegetation plots together with spectral, textural, topography and vegetation height remote sensing data from 0.02‐ to 3‐m pixel size. Remote sensing data included true‐color unmanned aerial vehicle images, aerial images with four spectral bands, aerial lidar data and multiple PlanetScope satellite images. We used random forest regressions for tracking plant functional type (PFT) coverage, non‐metric multidimensional scaling ordination axes and fuzzy k‐medoid plant community clusters. PFT regressions had variable performance for different study sites (R2 −0.03 to 0.69). Spatial patterns of some spectrally or structurally distinctive PFTs could be predicted relatively well. The first ordination axis represented wetness gradient and was well predicted using remotely sensed data (R2 0.64 to 0.82), but the other three axes had a less straightforward explanation and lower mapping performance (R2 −0.09 to 0.53). Plant community clusters were predicted most accurately in the sites with clear string‐flark topography but less accurately in the flatter site (R2 0.16–0.82). The most important remote sensing features differed between dependent variables and study sites: different topographic, spectral and textural features; and coarse‐scale and fine‐scale datasets were the most important in different tasks. We suggest that multiple different ...