Urban tree species mapping using hyperspectral and lidar data fusion

By Michael Alonzo, Bodo Bookhagen, Dar A. Roberts. 2014

486 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/269b5dee4172b75c6fa03cce291699862bf08304

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Lorenzo Bruzzone
2. Francesca Bovolo
3. Gustau Camps-Valls
4. Luis Gómez-Chova
5. Jon Atli Benediktsson
6. Pedram Ghamisi
7. Devis Tuia
8. Michael Alonzo

In Environmental Engineering

1. Michael Alonzo
2. Kim Calders
3. Mathias I. Disney
4. Gustau Camps-Valls
5. Gabriele Moser
6. Lorenzo Bruzzone
7. Peter M. Atkinson
8. Pasi Raumonen

In Global and Planetary Change

1. Pedram Ghamisi
2. Jon Atli Benediktsson
3. Francesca Bovolo
4. Lorenzo Bruzzone
5. Douglas C. Morton
6. Bruce Douglas Cook
7. Hans-Erik Andersen
8. Gustau Camps-Valls

Citations in the corpus

• Will remote sensing shape the next generation of species distribution models? (2015, methodology): “LiDAR coupled with multispectral or hyperspectral data has also been used for identifying tree species (Jones et al. 2010; Heinzel and Koch 2011; Dalponte et al. 2012; Alonzo et al. 2014; Ghosh et al. 2014).”
• Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “[125] also worked at the single-tree/crownobject level and added HSI to the airborne lidar data set to map urban tree species.”
• Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion (2018, background): “There is ample evidence of successful tree crown segmentation in forests with relatively simple structure such as sparse oak woodlands [38], urban forests [39], and even-aged Norway spruce stands [23].”
• Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “because of the improved discrimination capability obtained for several land-cover classes with both data sources [20].”
• Tree species classification using structural features derived from terrestrial laser scanning (2020, methodology): “, 2010; Alonzo et al., 2014). Fusion of airborne and groundbased data is, however, still a technical challenge and has not been fully investigated for TLS. Although our classification method is fully automatic, collecting TLS data to build the QSMs is time consuming and has only been done for limite”