Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Joseph F Knight"'
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3511 (2023)
Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study
Externí odkaz:
https://doaj.org/article/074d6fdc6caa4787ace9c1f2593ebb3c
Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis
Publikováno v:
Remote Sensing, Vol 15, Iss 4, p 989 (2023)
Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue; RGB) s
Externí odkaz:
https://doaj.org/article/612b3a688dca4c53834a017838b3d044
Autor:
Audrey C. Lothspeich, Joseph F. Knight
Publikováno v:
Remote Sensing, Vol 14, Iss 11, p 2662 (2022)
The means to accurately monitor wetland change over time are crucial to wetland management. This paper explores the applicability of LandTrendr, a temporal segmentation algorithm designed to identify significant interannual trends, to monitor wetland
Externí odkaz:
https://doaj.org/article/b0ad146b400a4f4ab9ebc5bac29af0c6
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3303 (2021)
Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly cha
Externí odkaz:
https://doaj.org/article/e1cb6f1ef92e45a38d622de41e99baf6
Autor:
Zachary P D Marston, Theresa M Cira, Joseph F Knight, David Mulla, Tavvs M Alves, Erin W Hodgson, Arthur V Ribeiro, Ian V MacRae, Robert L Koch
Publikováno v:
Journal of Economic Entomology. 115:1557-1563
Spectral remote sensing has the potential to improve scouting and management of soybean aphid (Aphis glycines Matsumura), which can cause yield losses of over 40% in the North Central Region of the United States. We used linear support vector machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00681a8d9753f05b6881816362e46fe0
https://doi.org/10.1093/jee/toac077
https://doi.org/10.1093/jee/toac077
Autor:
Tyler J. Nigon, Ce Yang, Gabriel Dias Paiao, David J. Mulla, Joseph F. Knight, Fabián G. Fernández
Publikováno v:
Remote Sensing, Vol 12, Iss 8, p 1234 (2020)
The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk
Externí odkaz:
https://doaj.org/article/f7767eb6f1a9456f9e8ae339b5fa7a3e
Autor:
Trevor K. Host, Matthew B. Russell, Marcella A. Windmuller-Campione, Robert A. Slesak, Joseph F. Knight
Publikováno v:
Remote Sensing, Vol 12, Iss 8, p 1341 (2020)
Ash trees (Fraxinus spp.) are a prominent species in Minnesota forests, with an estimated 1.1 billion trees in the state, totaling approximately 8% of all trees. Ash trees are threatened by the invasive emerald ash borer (Agrilus planipennis Fairmair
Externí odkaz:
https://doaj.org/article/1efd4c0c6a0f4b8dbb0b49f95f89d1f3
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 12:1-21
Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods fol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d591b924bd85eef18a9c705edd1c3f3
https://doi.org/10.1145/3466688
https://doi.org/10.1145/3466688
Publikováno v:
Geospatial Information Handbook for Water Resources and Watershed Management ISBN: 9781003175025
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e05928f4846bd8a7f5d6035b8246e09
https://doi.org/10.1201/9781003175025-5
https://doi.org/10.1201/9781003175025-5
Publikováno v:
Remote Sensing, Vol 5, Iss 7, Pp 3212-3238 (2013)
Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniq
Externí odkaz:
https://doaj.org/article/c46e0423377e48828e545f741bdc50a1