Considerations towards a Novel Approach for Integrating Angle-Count Sampling Data in Remote Sensing Based Forest Inventories

Melanie Kirchhoefer,Gerald Kändler,Johannes Schumacher,Petra Adler

doi:10.3390/f8070239

Abstract

Integration of remote sensing (RS) data in forest inventories for enhancing plot-based forest variable prediction is a widely researched topic. Geometric consistency between forest inventory plots and areas for extraction of RS-based predictive metrics is considered a crucial factor for accurate modelling of forest variables. Achieving geometric consistency is particularly difficult with regard to angle-count sampling (ACS) plots, which have neither distinct shape nor distinct extent. This initial study considers a new approach for integrating ACS and RS data, where the concept of ACS is transferred to RS-based metrics extraction. By using the relationship between tree height and diameter at breast height (DBH), pixels of a RS-based canopy height model are extracted if their value suggests a DBH that would lead to inclusion in an angle-count sample at the given distance to the plot centre. Different variations of this approach are tested by modelling timber volume in national forest inventory plots in Germany. The results are compared to those achieved using fixed-radius plots. A root mean square error of approximately 42% is achieved by both the new and fixed-radius approaches. Therefore, the new approach is not yet considered sufficient for overcoming all difficulties concerning the integration of ACS plot and RS data. However, possibilities for improvement are discussed and will be the subject of further research.

Highlights

The utilisation of remote sensing (RS) data—especially aerial images—for gaining information about forests has a long tradition [1], but for a long time, information extraction was laborious and expensive
This maximum distance was measured during the terrestrial inventory measurements at each plot and was used as the fixed radius for the respective plot centre
The initial, uncorrelated variables used for modelling for each data set were mean, npix, cv, volout, and meanDTM

Summary

Introduction

The utilisation of remote sensing (RS) data—especially aerial images—for gaining information about forests has a long tradition [1], but for a long time, information extraction was laborious and expensive. Dense image matching allows automatic generation of high-resolution digital surface models (DSM) from aerial stereo images, which enable the extraction of 3D data for forest stands Another means of extracting this kind of information is the utilisation of airborne laser scanning (ALS), which directly measures 3D points for DSM generation. Height information extracted from RS data sources was found to be well correlated with heights measured in the field e.g., [2] This relationship was further used to model and predict many other forest variables, among them timber volume and biomass e.g., [3]. In the second variation the median of all maximum distances (of maxDistind) was calculated and used as fixed radius for all plot centres (maxDistmed). Six metres is a threshold that resulted from the expert interviews of forest management planning at ForstBW—the Baden‐Württemberg state office for forestry In the second variation the median of all maximum distances (of maxDistind )

Methods

Results

Discussion

Conclusion