Next Article in Journal
Spatio-Temporal Dynamics of Land-Use and Land-Cover in the Mu Us Sandy Land, China, Using the Change Vector Analysis Technique
Next Article in Special Issue
Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data
Previous Article in Journal
Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study
Previous Article in Special Issue
Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(10), 9298-9315; doi:10.3390/rs6109298

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

1
Envix-Lab, Department of Biosciences and Territory (DiBT), University of Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy
2
ETSI Montes, Technical University of Madrid, Ciudad Universitaria, s/n 28040, Madrid, Spain
3
Natural Resources and Environmental Planning, Department of Biosciences and Territory (DiBT), University of Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy
4
Global Ecology Lab, Department of Biosciences and Territory (DiBT), University of Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy
*
Author to whom correspondence should be addressed.
Received: 5 May 2014 / Revised: 18 September 2014 / Accepted: 22 September 2014 / Published: 29 September 2014
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
View Full-Text   |   Download PDF [2397 KB, uploaded 29 September 2014]   |  

Abstract

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. View Full-Text
Keywords: modified random cluster algorithm; pattern metrics; scalogram; forest regrowth; stochastic simulations; central Italy; statistical significance of change modified random cluster algorithm; pattern metrics; scalogram; forest regrowth; stochastic simulations; central Italy; statistical significance of change
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Frate, L.; Saura, S.; Minotti, M.; Di Martino, P.; Giancola, C.; Carranza, M.L. Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales. Remote Sens. 2014, 6, 9298-9315.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top