Details

Autor(en) / Beteiligte

• McInerney, Daniel
• Kempeneers, Pieter
• Marron, Magdalene
• McRoberts, Ronald E.

Titel

Analysis of broadleaf encroachment in coniferous forest plantations using multi-temporal satellite imagery

Ist Teil von

• International journal of applied earth observation and geoinformation, 2019-06, Vol.78, p.130-137

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Landsat data are used to estimate broadleaf encroachment in Irish forests.•A stratified estimator is used to estimate the area and CIs from the error matrix.•Total affected area of encroachment is 20,003 ha (±20%).•Results used by forest managers to revise operational forest management plans. In recent years, several critical issues have been identified concerning the performance of recently established spruce forest plantations in Ireland, in particular coniferous reforestation sites. More specifically, the reforestation of peatland areas in the midlands of Ireland have been subject to encroachment by broadleaf species, such as birch, and willow. These species regenerate naturally and compete with, and outgrow, the coniferous species (commonly Norway spruce) that were planted. In many cases, the growth of these trees is faster than the spruce, resulting in stands being completely encroached by approximately 15 years of age. Given the widespread nature of this problem coupled with the fragmented nature of the Irish forest estate, a project was established to use Earth observation data to predict the spatial distribution and species composition of the affected sites. The spatial distribution and associated area estimates of broadleaf encroachment within State owned spruce forests were assessed using multi-temporal Landsat satellite imagery. The overall accuracy of the encroachment map was 85.23% and a Kappa Index of Agreement of 0.58. The associated area of encroached or broadleaf dominated forests was 20,003 ha with a confidence interval of ±20% calculated using a sample-based estimator.