Forest fragmentation in Massachusetts, USA: a town-level assessment using Morphological spatial pattern analysis and affinity propagation. Issue 4 (3rd July 2016)

• Record Type: Journal Article
• Authors: Rogan, J.; Wright, T.M.; Cardille, J.; Pearsall, H.; Ogneva-Himmelberger, Y.; Riemann, R.; Riitters, K.; Partington, K.
• Is Part Of: GIScience & remote sensing. Volume 53:Issue 4(2016)
• Page Start: 506
• Page End: 519
• Publication Date: 2016-07-03
• Subjects: forest fragmentation -- morphological spatial pattern analysis -- affinity propagation -- Massachusetts; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1141448
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address these objectives: 1) characterize the spatial pattern of forest fragmentation in Massachusetts towns using Morphological Spatial Pattern Analysis (MSPA); and (2) identify regional trends using archetypal towns in relation to town history, geography and socioeconomic characteristics. Six fragmentation indicators were calculated using MSPA for each town to represent patterns and processes of fragmentation. We then used these indicators and the proportion of forested land to group towns across Massachusetts with similar patterns of fragmentation. Six representative towns typify different types of forest fragmentation, and illustrate the commonalities and differences between different fragmentation types. The objective selection of representative towns suggests that they might be used as the target of future studies, both in retrospective studies that seek to explain current patterns and in analyses that predict future fragmentation trends.