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Open AccessArticle

Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys

School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
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Forests 2019, 10(8), 643; https://doi.org/10.3390/f10080643
Received: 23 June 2019 / Revised: 20 July 2019 / Accepted: 27 July 2019 / Published: 29 July 2019
(This article belongs to the Special Issue Geographic Information Systems and Their Applications in Forests)
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Abstract

In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys. View Full-Text
Keywords: forest GIS surveys; terrestrial photogrammetry; LBS; MobileNets; measurement tool forest GIS surveys; terrestrial photogrammetry; LBS; MobileNets; measurement tool
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Fan, G.; Chen, F.; Li, Y.; Liu, B.; Fan, X. Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys. Forests 2019, 10, 643.

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