Markov point processes for modeling of spatial forest patterns in Amazonia derived from interferometric height

Abstract

The spatial distribution of very large trees in primary Amazon forest is studied with an indicative data set. Very large trees with height larger than 30 m are shown to be highly influential on forest structure, ecology and biomass regime. In particular, they account for a large portion of total above-ground biomass. Their spatial patterns are extracted from airborne SAR data, namely from a digital model of interferometric forest height, by an approach of local maximum filtering. The spatial point patterns describing the distribution of very large trees in the forest within three sample blocks of 100 ha each are modeled by a series of Markov point process models. These models are fitted and assessed by standard spatial statistical methodology. Spatial distribution is regular, and interaction decreases with distance; very large trees are shown to exert repulsive interaction with their neighboring very large trees. The significance of these results for approaches of quantitative forest assessment in primary forests in the Brazilian Amazon is discussed.