Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using data from Landsat 8 satellite imagery, aerial images, and airborne laser scanning. Ground observations from forest stands dominated by either Norway spruce (Picea abies) or Sitka spruce (Picea sitchensis) were coupled with variables derived from each of the three sets of remote sensing data. Random forest, support vector machine, and logistic regression classification models were fit to the data, and the classification accuracy tested by performing a cross-validation. Classification accuracies were compared for different combinations of remote sensing data and classification methods. The overall classification accuracy varied from 0.53 to 0.79, with the highest accuracy obtained using logistic regression with a combination of data derived from Landsat imagery and aerial images. The corresponding kappa value was 0.58. The contribution to the classification accuracy from using airborne data in addition to Landsat imagery was not substantial in this study. The classification accuracy varied between models using data from individual Landsat images.
The study area is within the Fusa and Tysnes municipalities on the western coast of Norway (60°2′N, 5°46′E, 0–500 m above sea level, Figure 1). The forest is naturally dominated by Scots pine and deciduous species, mainly birch (Betula pubescens). From the 1940s and throughout the second part of the twentieth century, regeneration using non-native tree species—such as Sitka spruce—was common in this region on the west coast of Norway. Note that Norway spruce is also considered non-native in parts of this region. The productive forest area is about 260 km2, and the species composition is approximately 13% spruce, 66% pine, and 20% deciduous trees. Three sets of field observations were utilized in the present study, with observations from a total of 240 individual locations. All locations were situated in a spruce-dominated forest, and the proportions of Sitka spruce and Norway spruce were recorded for all locations. A total of 113 locations were dominated by Sitka spruce, and 127 were dominated by Norway spruce. Two of the sets were initially collected as a part of the data acquisition in other research and forest inventory projects.
Field measurements with the main purpose of increasing the number of observations from locations dominated by Sitka spruce was carried out during the summer of 2015. From an initial set of all forest stands in the study area dominated by Sitka spruce, 30 stands were subjectively chosen for measurements. With an initial goal of having the observations evenly spread out in the study area, the selection was ultimately guided by accessibility from, e.g., forest roads. The selection of the 30 stands were carried out prior to visiting the stands in the field, with the exception of a few occasions in which a nearby stand was measured instead due to severe storm felling in the originally chosen stand. Within the selected stands, three locations were subjectively chosen, guided by these criteria: the locations are evenly spread out in the stand and are preferably not close to stand borders. At each of the three locations, the proportions of the basal area of Sitka spruce versus other species were recorded using a relascope.
The data in this sampling event resource has been published as a Darwin Core Archive (DwC-A), which is a standardized format for sharing biodiversity data as a set of one or more data tables. The core data table contains 90 records.
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Researchers should cite this work as follows:
Hauglin, M.; Ørka, H.O. Discriminating between Native Norway Spruce and Invasive Sitka Spruce—A Comparison of Multitemporal Landsat 8 Imagery, Aerial Images and Airborne Laser Scanner Data. Remote Sens. 2016, 8, 363.
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The publisher and rights holder of this work is Norwegian University of Life Sciences (NMBU). This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.
This resource has been registered with GBIF, and assigned the following GBIF UUID: 44ef1f04-b01c-4b42-8baa-679e26ce43bc. Norwegian University of Life Sciences (NMBU) publishes this resource, and is itself registered in GBIF as a data publisher endorsed by GBIF Norway.
Samplingevent; tree species classification; invasive species; Sitka spruce
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|Title||Use of remote sensing for mapping of non-native conifer species|
|Funding||The project was funded by the Norwegian Environment Agency and conducted from November 2014 to November 2015.|
The personnel involved in the project: