Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Bogdan M. Strimbu"'
Autor:
Rong Fang, Bogdan M. Strimbu
Publikováno v:
Information Processing in Agriculture, Vol 10, Iss 3, Pp 334-346 (2023)
As a complement to traditional estimates of stem dimensions from numerical models, terrestrial light detection and ranging (Lidar) provides direct stem diameter and volume values using cylindrical models constructed from point clouds. This study used
Externí odkaz:
https://doaj.org/article/12a3b700552c490ea549f59536a39e0a
Autor:
Sudeera WICKRAMARATHNA, John GOETZ III, Jon SOUDER, Benjamin PROTZMAN, Brian SHEPARD, Sorin HERBAN, Francisco MAURO, Hailemariam TEMESGEN, Bogdan M. STRIMBU
Publikováno v:
Notulae Botanicae Horti Agrobotanici Cluj-Napoca, Vol 51, Iss 3 (2023)
Arguably the most popular remote-sensing products are classified images. However, there are no definitive procedures to assess classification accuracy that simultaneously consider resources available and field efforts. The explosive usage of unmanned
Externí odkaz:
https://doaj.org/article/029eef3baf7745a4b54269fece6a9a93
Autor:
Todd West, Bogdan M. Strimbu
Publikováno v:
Data, Vol 9, Iss 1, p 16 (2024)
The Elliott State Research Forest comprises 33,700 ha of temperate, Douglas-fir rainforest along North America’s Pacific Coast (Oregon, United States). In 2015, naturally regenerated stands at least 92 years old covered 49% of the research area and
Externí odkaz:
https://doaj.org/article/7e73bf865b31473b923024c7d1154456
Publikováno v:
Forests, Vol 14, Iss 2, p 297 (2023)
Sycamore is a valuable tree not only economically but also ecological and culturally. Even though it has a vigorous regeneration system from its stump, its coppice management has triggered limited formal investigations. Therefore, the present study f
Externí odkaz:
https://doaj.org/article/d6c0f1c88d114a609b44af275435dd2b
Publikováno v:
Remote Sensing, Vol 14, Iss 8, p 1938 (2022)
Traditional inventories require large investments of resources and a trained workforce to measure tree sizes and characteristics that affect wood quality and value, such as the presence of defects and damages. Handheld light detection and ranging (Li
Externí odkaz:
https://doaj.org/article/e75aff78b73f40a6a5cf018ebae3485e
Publikováno v:
Forests, Vol 13, Iss 2, p 305 (2022)
Background: Tethered cut-to-length and cable yarding systems with tethered falling equipment are increasingly used to harvest trees from slopes exceeding 30–60% more safely and at reduced financial cost than less mechanized harvest systems. Existin
Externí odkaz:
https://doaj.org/article/0c76c2ee75484359a8ffeb7b5eced97f
Publikováno v:
Forests, Vol 12, Iss 3, p 280 (2021)
Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five o
Externí odkaz:
https://doaj.org/article/f85733dd82994fb985fc455422abfca9
Publikováno v:
Data, Vol 6, Iss 2, p 17 (2021)
The Willamette Valley, bounded to the west by the Coast Range and to the east by the Cascade Mountains, is the largest river valley completely confined to Oregon. The fertile valley soils combined with a temperate, marine climate create ideal agronom
Externí odkaz:
https://doaj.org/article/b55fcf9fedd245a5a87db0088c8b98d8
Publikováno v:
Forests, Vol 11, Iss 12, p 1311 (2020)
Estimation using a suboptimal method can lead to imprecise models, with cascading effects in complex models, such as climate change or pollution. The goal of this study is to compare the solutions supplied by different algorithms used to model ozone
Externí odkaz:
https://doaj.org/article/8f4e82dd2d764ab89a9ccc3f60042d32
Publikováno v:
Remote Sensing, Vol 12, Iss 20, p 3325 (2020)
Forest species classifications are becoming increasingly automated as advances are made in machine learning. Complex algorithms can reach high accuracies, but are not always suitable for small-scale classifications, which may benefit from simpler con
Externí odkaz:
https://doaj.org/article/cedc2595323c4efc8e43f49e5165019f