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作者

来源

LANDSCAPE AND URBAN PLANNING,Vol.188,P.143-150

语言

英文

关键字

Small scale forests; Partial harvest; Land use change; Land cover; BAP; C2C; RESOLUTION GLOBAL-MAPS; COVER CHANGE; TIME-SERIES; DISTURBANCE; CONSERVATION; LANDSCAPES; TIMBER; DEGRADATION; MANAGEMENT; ACCURACY

作者单位

[Tortini, R.; Hermosilla, T.; Coops, N. C.] Univ British Columbia, Integrated Remote Sensing Studio, Fac Forest Resources Management, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada. [Mayer, A. L.] Michigan Technol Univ, Dept Social Sci, 1400 Townsend Dr, Houghton, MI 49931 USA. [Mayer, A. L.] Michigan Technol Univ, Sch Forest Resources & Environm Sci, 1400 Townsend Dr, Houghton, MI 49931 USA. [Wulder, M. A.] Nat Resources Canada, Canadian Forest Serv, Pacific Forestry Ctr, 506 West Burnside Rd, Victoria, BC V8Z 1M5, Canada. Tortini, R (reprint author), Univ Calif Los Angeles, Dept Geog, 1255 Bunche Hall, Los Angeles, CA 90095 USA. E-Mail: riccardo.tortini@ubc.ca; almayer@mtu.edu; txomin.hermosilla@ubc.ca; nicholas.coops@ubc.ca; mike.wulder@canada.ca

摘要

The monitoring of forested landscapes dominated by many small private forest owners is difficult or not possible without spatially explicit and up-to-date information on land cover change. Analysis of time series multispectral data from the Landsat series of satellites have the spatial and temporal characteristics required to detect sub-hectare and non-stand replacing harvest events over large areas. We identified harvests that occurred in six western upper Michigan counties from 1985 to 2011 using Landsat best available pixel (BAP) image composites and the Composite2Change (C2C) approach. We detected a total of 7071 harvesting events with size ranging from 0.5 to 171.36 ha and average size of 6.42 ha, and analyzed their temporal trajectory. To gain confidence in our harvest mapping, we compared our findings to the overlapping decade of Global Forest Watch (GFW) data. Agreement between the datasets was high, with 94.24% of the C2C and GFW harvest pixels identified with the same change year and improving to 98.74% within +/- 1 year. This automated harvest detection system, which can capture small and otherwise missed harvests, is valuable to natural resource agencies responsible for monitoring and compliance with regulations over large areas, and researchers requiring estimates of harvest levels and the nature of forest cover status and trends on family forests.