Utilizing local variance of simulated high spatial resolution imagery to predict spatial pattern of forest stands

Spatial pattern defined as the distribution of individuals in space, is an important characteristic of forest stands. It provides an insight into the allocation of above-and below-ground resources to a tree, as well as reflec ting,a the stand history, microclimate, and competition between different s pecies over time. The spatial arrangement of trees carl be described as ran dom, aggregated, or regular with a number of statistics existing that chara cterize the spatial pattern of a given tree population. High spatial resolu tion remote sensing is an obvious tool to facilitate the measuring and moni toring of spatial patterns in forest stands. Remote sensing imagery provide s detailed information about forest structure while still allowing large ar eas of forest to be mapped and monitored. The increased availability of hig h resolution imagery coupled with improvements in scene processing and inte rpretation techniques allow additional information, such as texture, to be extracted from this type of imagery. In this paper the spatial pattern of t rees within a forest stand is related to high spatial resolution imagery. T his relationship is developed using a technique that relates scene texture variance to a statistic describing spatial pattern. The technique was teste d on a number of simulated remote sensing scenes by systematically varying the size and spatial distribution of trees using a geometric-optical model. Results indicate that it is theoretically possible to derive the spatial p attern of trees within a high spatial resolution forested scene provided cr own size is estimated a priori. It is also likely that the coral projected foliage cover of the canopy will affect the ability to predict spatial dist ribution based on texture variance. It was concluded that the spatial patte rn of trees within a scene can play a vital role in the amount and degree o f variation existing within imagery. It is important to consider the implic ations of these type of relationships when developing variance-based models of forest structure. (C) Published by Elsevier Science Inc., 2000.