New Method for Forest Resource Data Collection Based on Smartphone Fusion with Multiple Sensors

Tree parameter measurement is an important part of forest resource monitoring. Smartphones play an important role in forest resource surveys. Although sensors inside smartphones, such as gyroscopes and angle sensors, can meet the needs of the public for entertainment or games, the measurement accuracy in professional forest resource monitoring is slightly insufficient. In this paper, a method of collecting tree measurement factors based on personal smart space fusion with a variety of high-precision sensors is proposed. First of all, a high-precision attitude sensor measurement module and a laser ranging module are organically integrated and packaged in a black box. The smartphone is then connected to the sensor box using a magnet sheet, and the working personnel can determine key parameters in the forest stand by holding it. Finally, in order to verify the accuracy of the method, the measured values in this paper are compared with the reference values. The root mean square error (RMSE) of the tree position in the X and Y directions was 0.114 m and 0.147 m, the relative deviations (rBias) were 0.95% and 0.39%, and the average RMSE was 0.186 m. The RMSEs measured by tree height and diameter at breast height (DBH) were 0.98 m and 2.24 cm, the relative root mean square error (rRMSE) was 5.87% and 13.46%, and the relative deviations (rBias) were −1.40% and −1.06%, respectively. Therefore, the method of forest stand parameter measurement based on personal smart space fusion multitype sensors proposed in this paper can be effectively applied to forest resource data collection.

[1]  M. D. Nelson,et al.  Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information , 2008 .

[2]  Matthieu Molinier,et al.  Relasphone - Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping , 2016, Remote. Sens..

[3]  Antonio Villasante,et al.  Measurement errors in the use of smartphones as low- cost forestry hypsometers , 2014 .

[4]  Zixuan Qiu,et al.  Application of UAV Photogrammetric System for Monitoring Ancient Tree Communities in Beijing , 2018, Forests.

[5]  L. Wallace,et al.  Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds , 2016 .

[6]  Tarmo Virtanen,et al.  Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement , 2017 .

[7]  Lauri Mehtätalo,et al.  Stand density estimators based on individual tree detection and stochastic geometry , 2016 .

[8]  Jonathon J. Donager,et al.  UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA , 2017 .

[9]  Guan Fangli,et al.  Tree DBH measurement method based on smartphone and machine vision technology , 2018 .

[10]  Terje Gobakken,et al.  Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories , 2019, Remote Sensing of Environment.

[11]  P. Zhang,et al.  Carbon sequestration potential of forest vegetation in China from 2003 to 2050: Predicting forest vegetation growth based on climate and the environment , 2020 .

[12]  P. Surový,et al.  Forest Stand Inventory Based on Combined Aerial and Terrestrial Close-Range Photogrammetry , 2016 .

[13]  David Pothier,et al.  Comparison of relative accuracy and time requirement between the caliper, the diameter tape and an electronic tree measuring fork , 1995 .

[14]  Yicheng Lin,et al.  Application of a Continuous Terrestrial Photogrammetric Measurement System for Plot Monitoring in the Beijing Songshan National Nature Reserve , 2018, Remote. Sens..

[15]  Annika Kangas,et al.  A Mobile Phone Application for the Collection of Opinion Data for Forest Planning Purposes , 2015, Environmental Management.

[16]  Nikolay S. Strigul,et al.  Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring , 2018, Remote. Sens..

[17]  Jonathan P. Dash,et al.  Comparison of high-density LiDAR and satellite photogrammetry for forest inventory , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[18]  Juha Hyyppä,et al.  Evaluation of a Smartphone App for Forest Sample Plot Measurements , 2015 .

[19]  Bin Chen,et al.  Passive measurement method of tree diameter at breast height using a smartphone , 2019, Comput. Electron. Agric..

[20]  Christopher P. Quine,et al.  An investigation of the potential of digital photogrammetry to provide measurements of forest characteristics and abiotic damage , 2000 .

[21]  Julián Tomaštík,et al.  Horizontal accuracy and applicability of smartphone GNSS positioning in forests , 2016 .

[22]  Rüdiger Grote,et al.  Estimation of crown radii and crown projection area from stem size and tree position , 2003 .

[23]  Jan-Peter Mund,et al.  UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring , 2019, Remote. Sens..

[24]  Maria Immacolata Marzulli,et al.  Estimating tree stem diameters and volume from smartphone photogrammetric point clouds , 2019, Forestry: An International Journal of Forest Research.

[25]  Marek Pierzchała,et al.  Applications of Remote and Proximal Sensing for Improved Precision in Forest Operations , 2017 .

[26]  Yan Tingting,et al.  Passive Measurement Method of Tree Height and Crown Diameter Using a Smartphone , 2020, IEEE Access.

[27]  David L. Evans,et al.  Use of Global Positioning System (GPS) for Forest Plot Location , 1992 .

[28]  Juha Hyyppä,et al.  Autonomous Collection of Forest Field Reference - The Outlook and a First Step with UAV Laser Scanning , 2017, Remote. Sens..

[29]  Yi Lin,et al.  A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements , 2010 .

[30]  Gherardo Chirici,et al.  National Forest Inventory contributions to forest biodiversity monitoring , 2012 .

[31]  T. W. Payn,et al.  Plantation forests, climate change and biodiversity , 2013, Biodiversity and Conservation.

[32]  T. Sunderland,et al.  EDITORIAL: Forests, Biodiversity and Food Security , 2011 .

[33]  Guangpeng Fan,et al.  Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys , 2019, Forests.

[34]  Rafal Kazmierczak,et al.  Reliable Technology of Centimeter GPS/GLONASS Surveying in Forest Environments , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[35]  P. Němec Comparison of modern forest inventory method with the common method for management of tropical rainforest in the Peruvian Amazon. , 2015 .

[36]  Julián Tomastík,et al.  Tango in forests - An initial experience of the use of the new Google technology in connection with forest inventory tasks , 2017, Comput. Electron. Agric..