Current issue: 56(2)
Under compilation: 56(3)
The article gives a proposal for areas that would be suitable for protected areas, situated in state-owned lands in Northern Finland. Eight areas are described in the article, namely Oulankajoki area in Northern Kuusamo, Kutsajoki area in Kuolajärvi, Pyhätunturi in Kemijärvi, Pisavaara in Rovaniemi, Pallastunturi and Ounastunturi area, Malla fells in Kilpisjärvi, Pääskyspahta area in Petsamo and Heinäsaari in Petsamo.
Each of the areas possess special features in Finnish nature, samples of which should be reserved in pristine state. Furthermore, costs of the protection are small. The resident population is, however, in general against protection. The protection should therefore be organized in a way that minimizes the disadvantages caused by limitations to land use, for example grazing, reindeer husbandry, fishing and hunting.
According to Finnish Nature Conservation Act, all wildlife in the conservation areas should be protected. Protection of wolverine and wolf is, however, difficult because of the damages they cause for domestic animals. Protection of bear is regarded to be possible in most of the proposed protected areas.
Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.