Prediction of butt rot volume in Norway spruce forest stands using harvester, remotely sensed and environmental data

by   Janne Räty, et al.

Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, the maps of BR damages are typically lacking in forest information systems. We predicted timber volume damaged by BR at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). We utilized random forest (RF) models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). We found that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation achieved an RMSE of 11.4 m^3ha^-1 (pseudo R^2: 0.66) whereas the mapping case resulted in a pseudo R^2 of 0.60. When the spatially distinct k-means clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo R^2 associated with the mapping case were 15.6 m^3ha^-1 and 0.37, respectively. This indicates that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages.


page 4

page 7

page 14

page 15

page 16


Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

The age of forest stands is critical information for many aspects of for...

Estimating forest biodiversity in airborne laser scanning assisted inventories using spatial measures

With recent developments in remote sensing technologies, plot-level fore...

A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data

Accurate mapping of forests is critical for forest management and carbon...

Gaussian process regression for forest attribute estimation from airborne laser scanning data

While the analysis of airborne laser scanning (ALS) data often provides ...

Spatial airborne laser scanning features for predicting forest biodiversity indices

With recent developments in remote sensing technologies, plot-level fore...

Magnify Your Population: Statistical Downscaling to Augment the Spatial Resolution of Socioeconomic Census Data

Fine resolution estimates of demographic and socioeconomic attributes ar...

Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data

Policy measures and management decisions aiming at enhancing the role of...

Please sign up or login with your details

Forgot password? Click here to reset