Characterising forest spatial structure through inhomogeneous second order characteristics

Abstract

Point process theory plays a fundamental role in the analysis and modelling of spatial forest patterns. For instance, the Ripley's K function and its density with respect to the area, i.e. the pair correlation function, have been extensively used to analyse and characterise stationary forest configurations. However, the stationarity condition is not often met in practice when analysing real data. Thus, the development and application of new statistics to measure the degree of inhomogeneity suggests the use of inhomogeneous statistics to describe forest stands. In this paper, we restrict our attention to the inhomogeneous pair correlation function in the context of replicated spatial data. We then analyse the spatial configuration of pure and mixed conifer stands in a case study in Central Catalonia, North-East of Spain. Our results suggest that whilst P. sylvestris tend to be aggregated for short inter-tree distances, P. nigra and P. halepensis keep a minimum interevent distance between trees. Regarding the mixed stands, trees of distinct species tend to be segregated from each other. Tentative explanations for these results are related with site properties, competition effects, shade tolerance and silviculture practices applied in this forest region.

A spatio-temporal Poisson hurdle point process to model wildfires

“…Over the last few years, the occurrence of large wildfire episodis with extreme fire behavior has affected different regions of Europe: Portugal, south-eastern France, Spain and Greece. Wildfires have been studied in many ways, for instance as a spatial point pattern ( [8], [9], [24], [42] and [44]) or through modelling the size of fires ( [1]) or the relative risk of the big fires ( [45]). …”

Spatial statistics in ecological analysis: from indices to functions

“…a heterogeneous Poisson model) or delineating homogenous sub-regions into homogenous regions are required (Wiegand and Moloney 2004). Applications of other than CSR null models can be found in Boyden et al (2005), Camarero et al (2005), Wiegand et al (2007), Jacquemyn et al (2009), Comas et al (2009), Law et al (2009), Picard et al (2009), Eichhorn (2010, Martínez et al (2010), Zhang et al (2010) and Réjou-Méchain et al (2011).…”

Análise espacial de um fragmento florestal baseada no mosaico de dirichlet

“…Entretanto, as configurações espaciais de florestas são, raramente, estacionárias. A fertilidade do solo, a presença de um rio ou simplesmente a heterogeneidade do ambiente podem provocar estruturas florestais não homogêneas (COMAS et al, 2009). …”