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

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.

[1]  Peter J. Diggle,et al.  Statistical analysis of spatial point patterns , 1983 .

[2]  Remarks on statistical inference and prediction for a hard-core clustering model , 1983 .

[3]  N. Cressie,et al.  Statistics for Spatial Data. , 1992 .

[4]  Douglas A. Maguire,et al.  Modeling the spatial structure of topical forests , 1998 .

[5]  Hans Pretzsch,et al.  Analysis and modeling of spatial stand structures. Methodological considerations based on mixed beech-larch stands in Lower Saxony , 1997 .

[6]  Jeffrey Q. Chambers,et al.  Tree damage, allometric relationships, and above-ground net primary production in central Amazon forest , 2001 .

[7]  Ariel E. Lugo,et al.  Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data , 1989, Forest Science.

[8]  L. Dutra,et al.  Power spectrum analysis of SAR data for spatial forest characterization in Amazonia , 2005 .

[9]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[10]  L. Dutra,et al.  Tropical Forest Measurement by Interferometric Height Modeling and P-Band Radar Backscatter , 2005, Forest Science.

[11]  T. Lovejoy,et al.  Conservation: Rainforest fragmentation kills big trees , 2000, Nature.

[12]  N. Brokaw,et al.  The definition of treefall gap and its effect on measures of forest dynamics. , 1982 .

[13]  K. O. Niemann,et al.  Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution Imagery , 2000 .

[14]  L. Holdridge.  Determination of World Plant Formations From Simple Climatic Data. , 1947, Science.

[15]  R. Saunders,et al.  Poisson limits for a clustering model of strauss , 1977, Journal of Applied Probability.

[16]  J. Chambers,et al.  Ancient trees in Amazonia , 1998, Nature.

[17]  D. Stoyan,et al.  Recent applications of point process methods in forestry statistics , 2000 .

[18]  S. Brown,et al.  Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: Application to tropical Africa , 1995, Environmental monitoring and assessment.

[19]  W. Köppen,et al.  Handbuch der Klimatologie , 1910, Nature.

[20]  E. Laca,et al.  Successional Patterns of Mortality and Growth of Large Trees in a Panamanian Lowland Forest , 1994 .

[21]  W. Köppen.  Das geographische System der Klimate , 1936 .