Evaluation of a Smartphone App for Forest Sample Plot Measurements

Mikko Vastaranta,Ville Luoma,Ninni Saarinen,Markus Holopainen,Juha Hyyppä,Eduardo Latorre

doi:10.3390/f6041179

Mikko Vastaranta, Ville Luoma + Show 4 more

Open Access

https://doi.org/10.3390/f6041179

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Abstract

We evaluated a smartphone app (TRESTIMATM) for forest sample plot measurements. The app interprets imagery collected from the sample plots using the camera in the smartphone and then estimates forest inventory attributes, including species-specific basal areas (G) as well as the diameter (DgM) and height (HgM) of basal area median trees. The estimates from the smartphone app were compared to forest inventory attributes derived from tree-wise measurements using calipers and a Vertex height measurement device. The data consist of 2169 measured trees from 25 sample plots (32 m × 32 m), dominated by Scots pine and Norway spruce from southern Finland. The root-mean-square errors (RMSEs) in the basal area varied from 19.7% to 29.3% and the biases from 11.4% to 18.4% depending on the number of images per sample plot and image shooting location. DgM measurement bias varied from −1.4% to 3.1% and RMSE from 5.2% to 11.6% depending on the tree species. Respectively, HgM bias varied from 5.0% to 8.3% and RMSE 10.0% to 13.6%. In general, four images captured toward the center of the plot provided more accurate results than four images captured away from the plot center. Increasing the number of captured images per plot to the analyses yielded only marginal improvement to the results.

Highlights

Forest resource information is collected using sampling, measurements and models [1]
The root-mean-square errors (RMSEs) in the total basal area varied from 19.7% to 29.3% and the biases from 11.4% to 18.4%, depending on the number of the images per sample plot and image shooting locations
The basal area of Norway spruce was estimated with the smallest bias with TPC4 imaging configuration (−0.5%) and the smallest RMSE with TPC8 configuration (21.6%)

Summary

Introduction

Forest resource information is collected using sampling, measurements and models [1]. One of the commonest practices in remote sensing-aided forest mapping is to select field plot locations using sampling, measure forest inventory attributes, such as basal-area (G), mean diameter, and height from the field plots and link field-measured forest inventory attributes and metrics derived from remote sensing data to develop predicative models further used in deriving forest attribute maps [2,4]. With current phase-shift scanners, it takes 2–4 minutes to measure the surrounding area with a radius of 70–120 m, as the applied pulse density at a 10-m distance is still 6.3 mm. The drawbacks of laser scanning systems are related to the price of the required equipment (20,000 € or more for TLS) and the limited number of commercial programs for data processing of the point cloud data

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