The following 7 publications cited the product CMS: LiDAR-derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013.
| Year | Citation |
|---|---|
| 2024 | Burns, P., C.R. Hakkenberg, and S.J. Goetz. 2024. Multi-resolution gridded maps of vegetation structure from GEDI. Scientific Data. 11(1). https://doi.org/10.1038/s41597-024-03668-4 |
| 2022 | Zhang, Y. and J. Liu. 2022. Estimating forest aboveground biomass using temporal features extracted from multiple satellite data products and ensemble machine learning algorithm. Geocarto International. 38(1). https://doi.org/10.1080/10106049.2022.2153930 |
| 2021 | Cooper, S., A. Okujeni, D. Pflugmacher, S. van der Linden, and P. Hostert. 2021. Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping. International Journal of Applied Earth Observation and Geoinformation. 98:102307. https://doi.org/10.1016/j.jag.2021.102307 |
| 2020 | Girolamo-Neto, C.D., L.Y. Sato, I.D. Sanches, I.C.O. Silva, J.C.S. Rocha, and C.A. Almeida. 2020. OBJECT BASED IMAGE ANALYSIS AND TEXTURE FEATURES FOR PASTURE CLASSIFICATION IN BRAZILIAN SAVANNAH. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. V-3-2020:453-460. https://doi.org/10.5194/isprs-annals-V-3-2020-453-2020 |
| 2020 | Lowner, M.O., N.C. Bandelow, M. Gerke, F. Hillen, L. Klein, A. Schmidt, and T. Siefer. 2020. TOWARDS INNOVATIVE PARTICIPATION-ORIENTED PLANNING OF INFRASTRUCTURE MEASURES. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLIII-B4-2020:55-61. https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-55-2020 |
| 2020 | Zhang, Y. and S. Liang. 2020. Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map. Remote Sensing. 12(16):2559. https://doi.org/10.3390/RS12162559 |
| 2018 | Xiao, F., C. Li, Z. Wu, and Y. Wu. 2018. NMSTREAM: A SCALABLE EVENT-DRIVEN ETL FRAMEWORK FOR PROCESSING HETEROGENEOUS STREAMING DATA. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. IV-4:243-246. https://doi.org/10.5194/isprs-annals-IV-4-243-2018 |