SUNConferences, New Frontiers in Forecasting Forests 2018

Font Size: 
MODELLING STAND VARIABLES OF PINE FOREST USING SENTINEL-2A DATA AND THE RANDOM FOREST APPROACH
Stéfano Arellano-Pérez, Miguel Ángel González-Rodriguez, Fernando Castedo-Dorado, Carlos Antonio López-Sánchez, César Pérez-Cruzado, Juan Gabriel Álvarez-González, Ana Daría Ruiz-González

##manager.scheduler.building##: Wallenburg Research Centre (STIAS)
##manager.scheduler.room##: Main auditorium
Date: 2018-09-27 12:05 PM – 12:10 PM
Last modified: 2018-09-12

Keywords


volume, aboveground biomass, vegetation indices

References


Diéguez-Aranda, U., Rojo Alboreca, A., Castedo-Dorado, F., Álvarez González, J.G., Barrio-Anta, M., Crecente-Campo, F., et al., 2009. Herramientas selvícolas para la gestión forestal sostenible en Galicia. Consellería do Medio Rural, Xunta de Galicia. Santiago de Compostela, España.

Gregoire, T.G., Næsset, E., McRoberts, R.E., Ståhl, G., Andersen, H.-E., Gobakken, T., Ene, L., Nelson, R., 2016. Statistical rigor in Lidar-assisted estimation of aboveground forest biomass. Remote Sensing of Environment, 173, pp.98–108.

Hall, R.J., Skakun, R.S., Arsenault, E.J., Case, B.S., 2006. Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225, pp.378-390.

Kauranne, T., Joshi, A., Gautam, B., Manandhar, U., Nepal, S., Peuhkurinen, J., Hämäläinen, J.,  Junttila, V., Gunia, K., Latva-Käyrä, P., Kolesnikov, A., Tegel, K., Leppänen, V., 2017. LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon. Remote Sensing, 9, pp.154.

Liaw, A., Wiener, M., 2002. Classification and Regression by randomForest. R News 2(3), pp.18-22.

Puliti, S., Saarela, S., Gobakken, T., Ståhl, G., Næsset, E., 2018. Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference. Remote Sensing of Environment, 204, pp.485-497.

R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (Available on:  https://www.R-project.org/.)

Therneau, T., Atkinson, B., Ripley, B., 2017. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-11. https://CRAN.R-project.org/package=rpart

Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., Ryu, S.-R., 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93, pp.402–411


An account with this site is required in order to view papers. Click here to create an account.