Work

Remote sensing proxies of productivity and moisture predict forest stand type and recovery rate following experimental harvest

Year: 2015

Type: article

Source: Forest Ecology and Management

DOI: 10.1016/j.foreco.2015.08.027

Authors: Wiebe Nijland, Nicholas C. Coops, S. Ellen Macdonald, Scott E. Nielsen, Christopher W. Bater, Barry White, Jae Ogilvie, John Stadt

Institutions: University of British Columbia, University of Alberta, Alberta Ministry of Agriculture and Forestry, University of New Brunswick

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Forestry

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Life on land, Climate action

Open Access status: closed

Funders: Alberta Agriculture and Forestry, Natural Sciences and Engineering Research Council of Canada

OpenAlex ID: W2216366647

FWCI: 3.017

Cites: 71

Cited by: 29

Related to: 10