Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany
Author:
Funder
Bundesministerium für Verkehr und Digitale Infrastruktur
Publisher
Springer Science and Business Media LLC
Subject
Pollution,Ecology,Global and Planetary Change
Link
https://link.springer.com/content/pdf/10.1007/s00267-023-01826-9.pdf
Reference63 articles.
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2. Adam M, Urbazaev M, Dubois C, Schmullius C (2020) Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters. Remote Sens 12(23):3948. https://doi.org/10.3390/rs12233948
3. Anderson K, Hancock S, Disney M, Gaston KJ (2016) Is waveform worth it? A comparison of Li DAR approaches for vegetation and landscape characterization. Remote Sens Ecol Conserv 2:5–15. https://doi.org/10.1002/rse2.8
4. Antropov, O; Rauste, Y; Tegel, K; Baral, Y; Junttila, V; Kauranne, T et al. (2018): Tropical forest tree height and above ground biomass mapping in Nepal using Tandem-X and ALOS PALSAR data. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
5. Astola H, Häme T, Sirro L, Molinier M, Kilpi J (2019) Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens Environ 223:257–273
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