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

Remote Sensing

Ludovico Frate¹  Santiago Saura³  Michele Minotti²  Paolo Di Martino²  Carmen Giancola⁴ 

[1] Envix-Lab, Department of Biosciences and Territory (DiBT), University of Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy; E-Mail:;Natural Resources and Environmental Planning, Department of Biosciences and Territory (DiBT), University of Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy; E-Mails:;ETSI Montes, Technical University of Madrid, Ciudad Universitaria, s/n 28040, Madrid, Spain; E-Mail:;Global Ecology Lab, Department of Biosciences and Territory (DiBT), University of Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy; E-Mail:

关键词: modified random cluster algorithm;    pattern metrics;    scalogram;    forest regrowth;    stochastic simulations;    central Italy;    statistical significance of change;   

DOI  :  10.3390/rs6109298

来源: mdpi

【 摘 要 】

We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011) with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy) reserve at multiple spatial extents (128, 256 and 512 pixels). We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to detect forest variations over time. Similar procedures can (i) help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii) minimize the bias when comparing pattern metrics at a single extent and (iii) be extended to other landscapes and scales.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland