Zobrazeno 1 - 10
of 1 276
pro vyhledávání: '"Brian A. Johnson"'
Autor:
Brian A. Johnson, Chisa Umemiya, Damasa B. Magcale-Macandog, Ronald C. Estoque, Masato Hayashi, Takeo Tadono
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 122, Iss , Pp 103452- (2023)
National monitoring of forests is essential for tracking progress towards various global environmental goals, including those of the Kunming-Montreal Global Biodiversity Framework and the Paris Agreement. Inconsistent national definitions of “fores
Externí odkaz:
https://doaj.org/article/c43a27b9d20f4b5a9c8c4af69527734c
Autor:
Kamran Ali, Brian A. Johnson
Publikováno v:
Sensors, Vol 22, Iss 22, p 8750 (2022)
Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To
Externí odkaz:
https://doaj.org/article/5f3b4f54690e46a7babe2d85ed95755a
Autor:
Shahab Jozdani, Dongmei Chen, Wenjun Chen, Sylvain G. Leblanc, Christian Prévost, Julie Lovitt, Liming He, Brian A. Johnson
Publikováno v:
Remote Sensing, Vol 13, Iss 14, p 2658 (2021)
Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features.
Externí odkaz:
https://doaj.org/article/ecf5100189f945cdadf87f57afce481c
Autor:
Brian A. Johnson
Publikováno v:
Remote Sensing, Vol 7, Iss 10, Pp 13436-13439 (2015)
Much remote sensing (RS) research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC) classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-n
Externí odkaz:
https://doaj.org/article/0a51d9905e014068910afaff76502ac4
Autor:
Brian A. Johnson, Milben Bragais, Isao Endo, Damasa B. Magcale-Macandog, Paula Beatrice M. Macandog
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 4, Iss 4, Pp 2292-2305 (2015)
Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA) methods are becoming widely-used in remote sensing because single-scale/single-level (SS-GEOBIA) methods are often unable to obtain an accurate segmentation and classification
Externí odkaz:
https://doaj.org/article/0facfcf4c59a43c8befb0fe42c8f3152
Publikováno v:
Land, Vol 9, Iss 2, p 39 (2020)
In this study, we measured and characterized the relative dielectric constant of mineral soils over the 0.3−3.0 frequency range, and compared our measurements with values of three dielectric constant simulation models (the Wang, Dobson, and Mironov
Externí odkaz:
https://doaj.org/article/b4588d30ee884edf957e7b0f98040d04
Publikováno v:
Remote Sensing, Vol 10, Iss 11, p 1711 (2018)
Generally, the characterization of land surface roughness is obtained from the analysis of height variations observed along transects (e.g., root mean square (RMS) height, correlation length, and autocorrelation function). These surface roughness mea
Externí odkaz:
https://doaj.org/article/4ec3e4297794441a93512c63c93b9ba3
Publikováno v:
Remote Sensing, Vol 10, Iss 7, p 1134 (2018)
This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost
Externí odkaz:
https://doaj.org/article/c982915e623c4663ace452327c577528
Autor:
Brian A. Johnson, Shahab E. Jozdani
Publikováno v:
Remote Sensing, Vol 10, Iss 1, p 73 (2018)
The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is a
Externí odkaz:
https://doaj.org/article/9a7562745ed1402dbcc02f29234a95c3
Publikováno v:
Current Research in Toxicology, Vol 7, Iss , Pp 100191- (2024)
Chemical risk assessment still primarily relies on extrapolation of data from high-confidence in vivo studies. Emerging 21st Century Toxicology tools and approaches have potential to figure more prominently in chemical risk assessment, but many chall
Externí odkaz:
https://doaj.org/article/8a967a6bb6bc4db3bd374a1bd2046525