Can we discriminating between Native Norway Spruce and Invasive Sitka Spruce?

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.

Reference:

  • [DOI] M. Hauglin and H. O. Ørka, “Discriminating between native norway spruce and invasive sitka spruce—a comparison of multitemporal landsat 8 imagery, aerial images and airborne laser scanner data,” Remote sensing, vol. 8, iss. 5, p. 363, 2016.
    [Bibtex] [Abstract] [Download PDF]

    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.

    @Article{Hauglin2016,
    Title = {Discriminating between Native Norway Spruce and Invasive Sitka Spruce—A Comparison of Multitemporal Landsat 8 Imagery, Aerial Images and Airborne Laser Scanner Data},
    Author = {Hauglin, Marius and Ørka, Hans Ole},
    Journal = {Remote Sensing},
    Year = {2016},
    Number = {5},
    Pages = {363},
    Volume = {8},
    Abstract = {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.},
    Doi = {10.3390/rs8050363},
    ISSN = {2072-4292},
    Owner = {hanso},
    Timestamp = {2016.09.28},
    Url = {http://www.mdpi.com/2072-4292/8/5/363}
    }

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