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Kuusenhavuja

New methods for remote sensing supported forest inventory

  • Technology and innovations
  • Environment and natural resources

In this dissertation, Horvitz–Thompson-like estimation of forest characteristics from remote sensing data is studied. In forest remote sensing, the forest is measured with technical devices such as airborne or terrestrial laser scanners. These measurements produce three-dimensional point clouds, from which trees can be extracted with individual tree detection algorithms. These algorithms produce a sample of tree objects and tree attributes from the forest area that has been remotely sensed. Possible attributes are, for example, height, crown diameter, and stem diameter at breast height.

Usually all trees cannot be detected. There can be several reasons for this, but a major one is that some of the trees produce in some way nonvisible areas where other trees can be located and remain undetected. This partial detection produces underestimation of population totals and bias to mean and distribution estimates calculated directly from the attributes of the detected trees.

In this thesis, individual tree detection is thought of as a sampling procedure where trees have different probabilities to be included in the sample based on their attributes. If these probabilities can be approximated, Horvitz–Thompson-like estimators for forest characteristics of interest can be formed. Here we assume that the detection probabilities, or detectabilities, are related to the geometry of the pattern formed by the detected trees. We also assume that the forest is generated by a marked point process. These assumptions give us the tools to approach the calculation of detectabilities, and make the Horvitz–Thompson-like estimation possible. We study the estimation problem in two different remote sensing situations, namely airborne laser scanning and terrestrial laser scanning. We also study the problem of estimating attributes that have not been observed.

The doctoral dissertation of MSc Kasper Kansanen, entitled Horvitz–Thompson-like estimators based on stochastic geometry for forest remote sensing will be examined at the Faculty of Science and Forestry on the 6th of November. The opponent in the public examination will be Research Associate Thomas Opitz, INRAE, France, and the custos will be Professor Lauri Mehtätalo, University of Eastern Finland. The public examination will be held in English.

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Dissertation

Link to the event