Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany-Reference-Cited by-同舟云学术

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Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany

Published:2023-05-26 Issue:3 Volume:72 Page:657-670
ISSN:0364-152X
Container-title:Environmental Management
language:en
Short-container-title:Environmental Management

Author:

Lehmler Sebastian,Förster Michael,Frick Annett

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.

1. Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience Remote Sens 57(1):1–20. https://doi.org/10.1080/15481603.2019.1650447

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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