<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In landscape ecology, forest fragmentation studies with emphasis on effects of scale on fragmentation patch metrics, is an important research area. With increasing availability of satellite data at multiple scales and varied resolutions, it has become important to understand effects of comparing fragmentation metrics acquired from coarse resolution images and those from finer resolution imagery. This is crucial because coarse resolution images such as Landsat imagery, are relatively easier to find because of their cheaper costs, availability and broad coverage, whereas finer resolution imagery is more expensive and therefore, spans only small areas. This paper examines effects of varied spatial resolutions on common fragmentation metrics using Landsat, Sentinel, National Agricultural Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) imagery obtained in November, 2017 of the Whitethorne area near Blacksburg, Virginia. The images are analyzed using FRAGSTATS and ArcGIS software programs. The results show significant differences in fragmentation metrics despite simultaneous acquisition of all images in the same area. Discussion of results obtained in this study centers on the reasons for this disparity, and examines uses of imagery of different resolutions for forest fragmentation analysis.</p></abstract>

In landscape ecology, forest fragmentation studies with emphasis on effects of scale on fragmentation patch metrics, is an important research area. With increasing availability of satellite data at multiple scales and varied resolutions, it has become important to understand effects of comparing fragmentation metrics acquired from coarse resolution images and those from finer resolution imagery. This is crucial because coarse resolution images such as Landsat imagery, are relatively easier to find because of their cheaper costs, availability and broad coverage, whereas finer resolution imagery is more expensive and therefore, spans only small areas. This paper examines effects of varied spatial resolutions on common fragmentation metrics using Landsat, Sentinel, National Agricultural Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) imagery obtained in November, 2017 of the Whitethorne area near Blacksburg, Virginia. The images are analyzed using FRAGSTATS and ArcGIS software programs. The results show significant differences in fragmentation metrics despite simultaneous acquisition of all images in the same area. Discussion of results obtained in this study centers on the reasons for this disparity, and examines uses of imagery of different resolutions for forest fragmentation analysis.

Forest Fragmentation Analysis from Multiple Imaging Formats

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Journal of Landscape Ecology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Forest Fragmentation Analysis from Multiple Imaging Formats"}

In landscape ecology, forest fragmentation studies with emphasis on effects of scale on fragmentation patch metrics, is an important research area. With...

Forest Fragmentation Analysis from Multiple Imaging Formats

Published Online: Mar 16, 2019

Page range: 1 - 15

Received: Nov 08, 2018

Accepted: Dec 10, 2018

DOI: https://doi.org/10.2478/jlecol-2019-0001

Keywords
Forest Fragmentation, Landsat, UAV, forest patches, spatial resolution, Patch metrics

© 2019 Iris E. M. Fynn et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Forest Fragmentation Analysis from Multiple Imaging Formats

Published Online: Mar 16, 2019

Page range: 1 - 15

Received: Nov 08, 2018

Accepted: Dec 10, 2018

DOI: https://doi.org/10.2478/jlecol-2019-0001

KeywordsForest Fragmentation, Landsat, UAV, forest patches, spatial resolution, Patch metrics

© 2019 Iris E. M. Fynn et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Forest Fragmentation, Landsat, UAV, forest patches, spatial resolution, Patch metrics