Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

Remote Sensing

Riccardo Tortini²  Audrey L. Mayer³  Pieralberto Maianti¹  Yuei-An Liou⁴  Parth Sarathi Roy⁴  Ioannis Gitas⁴ 

[1] Laboratory of Remote Sensing, Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, via Ponzio 31, 20133 Milan, Italy; E-Mail:;Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA; E-Mail:;Department of Social Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA; E-Mail:;Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA; E-Mail

关键词: land use;    small scale forests;    Michigan;    Landsat;    OBCD;   

DOI  :  10.3390/rs70607809

来源: mdpi

【 摘 要 】

Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.