Items where Subject is "G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography"

Up a level
Export as [feed] Atom [feed] RSS 1.0 [feed] RSS 2.0
Group by: Creators | Item Type
Jump to: A | B | D | G | S | W
Number of items at this level: 13.

A

Aebischer, Philippe; Sutter, Michael; Reidy, Beat (1 November 2020). Measuring sward height and dry matter yield of pastures using multispectral imagery from UAV and a random forest algorithm In: DIGICROP 2020. Online. 1.-10. November 2020.

Aebischer, Philippe; Sutter, Michael; Reidy, Beat (4 March 2020). Weidedrohne – Messung der Grashöhe auf Basis eines automatisierten UAV Systems In: 40. Wissenschaftlich-Technische Jahrestagung der DGPF. Stuttgart. 4.-6. März 2020.

B

Baltensweiler, Andri; Walthert, Lorenz; Zimmermann, Stephan; Nussbaum, Madlene (2022). Hochauflösende Bodenkarten für den ­Schweizer Wald Schweizerische Zeitschrift für Forstwesen, 173(6), pp. 288-291. Schweizerischer Forstverein 10.3188/szf.2022.0288

D

Duruz, Solange; Flury, Christine; Matasci, Giona; Joerin, Florent; Widmer, Ivo; Joost, Stéphane; Davoli, Roberta (2017). A WebGIS platform for the monitoring of Farm Animal Genetic Resources (GENMON) PLoS One, 12(4), e0176362. Public Library of Science (PLoS) 10.1371/journal.pone.0176362

G

Garosi, Younes; Ayoubi, Shamsollah; Nussbaum, Madlene; Sheklabadi, Mohsen (2022). Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran Geoderma Regional, 29, e00513. Elsevier 10.1016/j.geodrs.2022.e00513

Garosi, Younes; Ayoubi, Shamsollah; Nussbaum, Madlene; Sheklabadi, Mohsen; Nael, Mohsen; Kimiaee, Iman (2022). Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment International Journal of Remote Sensing, 43(18), pp. 6856-6880. Taylor & Francis 10.1080/01431161.2022.2147037

S

Schaller, Christoph; Ginzler, Christian; van Loon, Emiel; Moos, Christine; Dorren, Luuk (27 May 2022). Improving Local Maxima-based Individual Tree Detection using statistically modelled Forest Structure Information In: EGU General Assembly 2022. Vienna, Austria & Online. 23–27 May 2022. 10.5194/egusphere-egu22-11723

Schaller, Christoph; Ginzler, Christian; van Loon, Emiel; Moos, Christine; Seijmonsbergen, Arie C.; Dorren, Luuk (2023). Improving country-wide individual tree detection using local maxima methods based on statistically modeled forest structure information International Journal of Applied Earth Observation and Geoinformation, 123, p. 103480. Elsevier 10.1016/j.jag.2023.103480

Scheller, Katharina (28 October 2022). Critical Cartographies to Sustain and Care for Biodiversity in Central Europe In: Counterparts: Exploring Design Beyond the Human: Swiss Design Network Research Summit. Zurich. 27–28 October 2022.

Scheller, Katharina (3 June 2022). Mapping for Green Cities: Analyse und Exploration am Beispiel der städtischen Baumkartierung In: Design x Sustainability: Materiality, Systems, Shared Prosperity: 18. Jahrestagung der Deutsche Gesellschaft für Designtheorie und -forschung DGTF (pp. 110-113). Deutsche Gesellschaft für Designtheorie und -forschung 10.25368/2022.297

Scheller, Katharina (January 2022). Mapping for Green Cities: Analyse und Exploration am Beispiel der städtischen Baumkartierung Hochschule der Künste Bern

Steich, Kelly; Kamel, Mina; Beardsley, Paul; Obrist, Martin K.; Siegwart, Roland; Lachat, Thibault (2016). Tree cavity inspection using aerial robots In: IROS 2016 : 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4856-4862). Piscataway, NJ: IEEE 10.1109/IROS.2016.7759713

W

Weber, Dominique; Ginzler, Christian; Flückiger, Stefan; Rosset, Christian (2018). Potenzial von Sentinel-2-Satellitendaten für Anwendungen im Waldbereich Schweizerische Zeitschrift für Forstwesen, 169(1), pp. 26-34. Schweizerischer Forstverein 10.3188/szf.2018.0026

This list was generated on Fri Mar 29 03:36:35 2024 CET.
Provide Feedback