Evaluation of Image-Assisted Forest Monitoring: A Simulation

Forests

Francis A. Roesch²  John W. Coulston⁴  Paul C. Van Deusen⁵  Rafał Podlaski³  Joanne C. White¹ 

[1]id="af1-forests-06-02897">1Southern Research Station, USDA Forest Service, 200 WT Weaver Blvd. Asheville, NC 28804, U

[2]Southern Research Station, USDA Forest Service, 200 WT Weaver Blvd. Asheville, NC 28804, USA

[3]Department of Nature Protection, Institute of Biology, Jan Kochanowski University, ul. Świętokrzyska 15, Kielce 25-406, Poland

[4] E-Mail:

[5]Southern Research Station, USDA Forest Service, 1710 Research Center Drive, Blacksburg, VA 24060, USA

[6] E-Mail:

[7]National Council for Air and Stream Improvement (NCASI), 60 East St., Mount Washington, MA 01258, USA

[8] E-Mail:

关键词: forest monitoring;    sample design;    estimation;    auxiliary information;    remote sensing;   

DOI  :  10.3390/f6092897

来源: mdpi

【 摘 要 】

Fiscal uncertainties can sometimes affect national continuous forest monitoring efforts. One solution of interest is to lengthen the time it takes to collect a “full set” of plot data from five to 10 years in order to reduce costs. Here, we investigate using ancillary information to partially offset this proposed solution’s negative effects. We focus our discussion on the corresponding number of years between measurements of each plot while we investigate how thoroughly the detrimental effects of the reduced sampling effort can be ameliorated with change estimates obtained from temporally-dense remotely-sensed images. We simulate measured plot data under four sampling error structures, and we simulate remotely-sensed change estimates under three reliability assumptions, integrated with assumptions about the additional unobserved growth resulting from the lengthened observation window. We investigate a number of estimation systems with respect to their ability to provide compatible annual estimates of the components of change during years spanned by at least half of the full set of plot observations. We show that auxiliary data with shorter observation intervals can contribute to a significant improvement in estimation.

【 授权许可】

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