Quantifying Landscape-Scale Patterns of Temperate Forests over Time by Means of Neutral Simulation Models

Abstract

Several studies attempt to describe changes in the spatial patterns of forests over time, resorting to the comparison of landscape pattern indices (LPI), but new methods for quantifying landscape differences in a statistical context are necessary. In this paper, we quantified and assessed the statistical significance of the forests pattern changes, which have occurred since the end of WWII in Central Italy (Isernia). To do this; based on the proportion of forest cover (pi) and contagion (H) of three land cover maps (1954-1981-2006); we generated 100 forest maps with predictable results through the midpoint displacement algorithm. Then, for both observed and simulated maps, we computed a set of LPI (number of patches, cohesion, largest forest patch index and area weighted mean shape index) and we derived their empirical distributions; finally, we compared the empirical distributions using the non-parametric Kruskal-Wallis test. Our results show significant changes in the spatial pattern of forests and underline the process of natural forest re-growth, which, in the area, is constrained by -remnants‖ of traditional activities. The adopted approach could be extended to a large ensemble of landscapes and spatial scales and could become a standard procedure when comparing patterns in time.

Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

“…Indeed, the establishment of several new forest nuclei is characteristic of the natural colonization of abandoned lands in Mediterranean ecosystems [34]. In contrast, the observed decrease at larger extents in NP, PD and SI describes the expansion and the coalescence of several secondary forest patches into larger ones [31,34,35,53]. Similar behavior is also evident for the AI index.…”

“…The statistical comparison of two different landscapes is quite difficult to perform because field studies usually address only one or a few landscapes so that no simple test is available for making statistical inferences [9,15,27]. One possible way to compare and test the statistical significance of pattern metric values between two maps is the use of computer-generated simulations (e.g., Neutral Landscape Models) to reproduce a set of maps with spatial characteristics (composition and configuration) that are similar to real-world characteristics [27][28][29][30][31]. Among the neutral landscape models [9,31], the Modified Random Cluster Method (MRC) [32] is able to correctly represent forest aggregation (or fragmentation) caused by human land use pressure [33].…”

“…One possible way to compare and test the statistical significance of pattern metric values between two maps is the use of computer-generated simulations (e.g., Neutral Landscape Models) to reproduce a set of maps with spatial characteristics (composition and configuration) that are similar to real-world characteristics [27][28][29][30][31]. Among the neutral landscape models [9,31], the Modified Random Cluster Method (MRC) [32] is able to correctly represent forest aggregation (or fragmentation) caused by human land use pressure [33]. By varying simulation parameters (the proportion of forest cover pi and the degree of aggregation H), it is possible to obtain different levels of habitat aggregation and patchiness [32].…”

Ecosystem Health Assessment of Mining Cities Based On Landscape Pattern

“…The mining activities of human being and natural factors drive landscape pattern changes, and the analysis of landscape pattern change is one of the most important methods to understand ecological processes. Landscape pattern change is usually quantified by landscape indices in previous studies (Perry et al, 2002;Frate et al, 2013;Sun et al, 2016), and this is a new attempt to analyze the EHA through the landscape indices in the mining area. This study focuses on analyzing the ecosystem health in the mining area response to landscape pattern, using the VOR model.…”

Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada

“…Landsat imagery has been shown to be an effective dataset for the detection of stand level disturbance as a result of timber harvest activity [2,19,24,27]. Repeated land cover mapping and analysis of the same study site over a period of time allows for evaluation of changes in forest cover [18,22,[28][29][30][31]. Many studies make use of multiple image dates in order to detect changes in land cover through time [2,17,19].…”