Nearest neighbor imputation of logwood volumes using bi-temporal ALS, multispectral ALS and aerial images

ABSTRACT

We examine the nearest neighbor (NN) imputation of species-specific logwood volumes using airborne laser scanning (ALS) data and aerial images. We compare different remote sensing (RS) data combinations as predictor variables in an area-based prediction of logwood volumes using separate training and validation data. We include multispectral leaf-on ALS data, bi-temporal leaf-off ALS data and aerial images in the analyses. Two response configurations are used in the NN imputations: (1) simultaneous imputation in which species-specific logwood volumes are response variables, and (2) separate imputation by tree species in which the attributes of one tree species at a time are response variables. Although an unrealistic alternative in practical implementation, the combination of leaf-on and leaf-off ALS metrics as predictors proved to be the most successful RS data combination, according to the RMSE values associated with the predicted species-specific and dominant logwood volumes. The results showed that older leaf-off ALS data perform well in combination with leaf-on ALS data. In general, predictive performance was better with simultaneous imputation than with separate imputation by tree species. Our finding promotes an awareness of how best to utilize various RS data in future forest inventories.

KEYWORDS:

• Area-based approach
• bi-temporal ALS
• diameter distribution
• logwood volume
• multispectral ALS
• nearest neighbor imputation

Acknowledgements

We acknowledge the support provided by the Strategic Research Council of the Academy of Finland for the FORBIO project (decision number 314224), led by Prof. Heli Peltola at the School of Forest Sciences, UEF. We would like to express our gratitude to Prof. Heli Peltola and Prof. Jyrki Kangas for the acquisition of the financial support for the fieldwork needed to conduct this study. We also would like to thank the Finnish Society of Forest Science for the scholarship awarded to the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the authors.

Availability of data and material

The raw leaf-off ALS datasets supporting the conclusions of this article are available in the repository of National Land Survey Finland, https://tiedostopalvelu.maanmittauslaitos.fi/tp/kartta. The multispectral ALS data, and the field data will not be shared due to the ownership of the data.

Data deposition

No data deposition.

ORCID

Janne Räty http://orcid.org/0000-0002-6578-8965

Additional information

Funding

This study is a contribution to the project Comparative test to predict species-specific diameter distributions in forest information systems financed by the Finnish Forest Centre. This study was also supported by the project Sustainable, climate-neutral, and resource-efficient forest-based bioeconomy (FORBIO, decision number 314224), funded by the Strategic Research Council at the Academy of Finland. The Finnish Society of Forest Sciences supported this work with a scholarship granted to the corresponding author.