Semantic Scholar Open Access 2016 715 sitasi

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Joanne C. White N. Coops M. Wulder M. Vastaranta T. Hilker +1 lainnya

Abstrak

Abstract Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.

Topik & Kata Kunci

Penulis (6)

J

Joanne C. White

N

N. Coops

M

M. Wulder

M

M. Vastaranta

T

T. Hilker

P

P. Tompalski

Format Sitasi

White, J.C., Coops, N., Wulder, M., Vastaranta, M., Hilker, T., Tompalski, P. (2016). Remote Sensing Technologies for Enhancing Forest Inventories: A Review. https://doi.org/10.1080/07038992.2016.1207484

Akses Cepat

Informasi Jurnal
Tahun Terbit
2016
Bahasa
en
Total Sitasi
715×
Sumber Database
Semantic Scholar
DOI
10.1080/07038992.2016.1207484
Akses
Open Access ✓