A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds

Abstract

Assessing forestcoverchange is a key issue forany national forestinventory.This wastested in two studyareas in Switzerland on thebasis of stereo airborne digital sensor (ADS) imagesand advanced digital surface model (DSM) generation techniques based on image point clouds. In the present study, an adaptive multi-scale approach to detectforestcoverchangewithhighspatialandtemporalresolutionwasappliedtotwostudyareasinSwitzerland. The challenge of this approach is to minimize DSM height uncertainties that mayaffect the accuracyof the forest coverchange results. The approach consisted of two steps. In the first step, a ‘change index’ parameter indicated the overallchange status atacoarser scale.The tendencytowards changewas indicated byderivative analysis of the normalized histograms of the difference between the two canopy height models (DCHMs) in different years. In the second step, detection of forest cover change at a refined scale was based on an automatic threshold and a moving window technique. Promising results were obtained and reveal that real forest cover changes can be distinguished from non-changes with a high degree of accuracy in managed mixed forests. Results had a loweraccuracyforforestslocatedonsteepalpineterrain.Amajorbenefitoftheproposedmethodisthatthemagnitude of forestcoverchange of anyspecific region canbe made availablewithin a short timeas often requiredby forest managers or policy-makers, especially after unexpected natural disturbances.