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    Author(s): Rodrigo Pereira Jr.; Johan Zweedea; Gregory P. Asnerb; Keller; Michael
    Date: 2002
    Source: Forest Ecology and Management 168 :77–89
    Publication Series: Miscellaneous Publication
    PDF: Download Publication  (525 B)


    We investigated ground and canopy damage and recovery following conventional logging and reduced-impact logging (RIL) of moist tropical forest in the eastern Amazon of Brazil. Paired conventional and RIL blocks were selectively logged with a harvest intensity of approximately 23 m3 ha1 (geometric volume) in the dry seasons (July–December) of 1996 and 1998. Ground damage (roads þ skid trails þ log decks) in the conventional logging treatments occupied 8.9–11.2% of total operational area. In contrast, ground damage in RIL treatments ranged from 4.6 to 4.8% of the total area. Forest canopy damage was assessed using gap fraction measurements collected with an automated optical canopy analyzer (LAI-2000; Licor Inc.) in March 1999. Canopy opening varied by time since logging. The recently logged (1998) blocks had integrated canopy gap fractions of 21.6 and 10.9% of total area for conventional and RIL blocks, respectively. The blocks logged in 1996 had more closed canopies with 16.5 and 4.9% gap fraction for conventional and RIL blocks, respectively. For comparison, undisturbed forest had a canopy gap fraction of 3.1%. Measurements of ground disturbance and gap fraction using the Licor LAI-2000 generally agree with other field evaluations of RIL and conventional logging. Detailed understanding of canopy structural changes resulting from different logging intensities are critical to the prospect of logging damage estimation using current and future remote sensing products.

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    Pereira Jr., Rodrigo; Zweedea, Johan; Asnerb, Gregory P.; Keller; Michael. 2002. Forest canopy damage and recovery in reduced-impact and conventional selective logging in eastern Para, Brazil. Forest Ecology and Management 168 :77–89


    Amazon, Brazil, Canopy, Gap fraction, Reduced-impact logging, Selective logging

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