Remote sensing and GIS are increasingly used as tools for monitoring and managing forests. Remotely sensed and GIS data are now the data sources of choice for capturing, documenting, and understanding forest disturbance and landscape pattern. Sitting astride the fields of ecology, forestry, and remote sensing/GIS, Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches takes you through the general biological or landscape ecological context of forest disturbance to remote sensing and GIS technological approaches and pattern description and analysis, with compelling applied examples of integration and synthesis.
Written by experts, peer-reviewed to adhere to the strictest standards and highest quality criteria, these chapters discuss natural and human-caused forest change and consider factors such as biological setting, monitoring approaches, scale issues, and pattern analysis. The book explores forest disturbance and spatial pattern from an ecological point-of-view within the context of structure, function, pattern, and change. It concludes with a summary of the issues related to detection and mapping of forest disturbances with remotely sensed and GIS data. The authors elucidate how the elements presented, from ecological underpinnings, data considerations, change detection method, and pattern analysis, combine into a problem solving, information generating approach.
You may find this subject covered briefly in a small sub-section in remote sensing forestry texts, or in limited technical detail in the ecology literature. The in-depth, detailed information provided in this book allows you to develop an understanding of the application of BOTH remote sensing and GIS technologies to forest change and the impacts of fire, insect infestation, forest harvesting, and other potential change influences – such as extreme weather events. This book provides guidance on how to master the challenges of capturing and characterizing forest disturbance and spatial patterns.
Contributions by:
Sean Healey (USDA Forest Service Corvallis Oregon USA USDA Forest Service Corvallis Oregon USA USDA Forest Service Corvallis Oregon USA),
John Rogan (Clark University,
Worchester,
Massachusetts,
USA)
Edited by:
Michael A. Wulder,
Steven E. Franklin,
Steven E. Franklin (University of Saskatchewan,
Saskatoon,
Canada)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions:
Height: 234mm,
Width: 156mm,
Weight: 500g
ISBN: 9780367577834
ISBN 10: 0367577836
Pages: 268
Publication Date: 30 June 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
Introduction: Structure, Function, and Change of Forest Landscapes. Identifying and Describing Forest Disturbance and Spatial Pattern: Data Selection Issues and Methodological Implications. Remotely Sensed Data in the Mapping of Forest Harvest Patterns. Remotely Sensed Data in the Mapping of Insect Defoliation. Using Remote Sensing to Map and Monitor Fire Damage in Forest Ecosystems. Integrating GIS and Remotely Sensed Data for Mapping Forest Disturbance and Change. New Directions in Landscape Pattern Analysis and Linkages with Remote Sensing. Characterizing Stand-Replacing Harvest and Fire Disturbance Patches in a Forested Landscape: A Case Study from Cooney Ridge. Conclusion: Understanding Forest Disturbance and Spatial Pattern, Information Needs, and New Approaches. Index.
Michael A. Wulder, Steven E. Franklin
Reviews for Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches
"". . . this book seriously integrates the use of remote sensing and GIS techniques to help forestry researchers, resource management professionals, and conservation biologists meet the challenges of capturing and characterizing forest disturbance and their spatial patterns . . . well-researched and makes a significant contribution to the forestry, remote sensing and landscape ecology bodies of literature."" – Kin M. Ma, Geography and Planning Department, Grand Valley State University, in Photogrammetric Engineering & Remote Sensing, November 2008