{"title":"伐木机作业自动化生产分析的初步验证:机载计算机数据与激光雷达清单的整合","authors":"Steffen Lahrsen, Omar Mologni, Zexi Liu, Dominik Röser","doi":"10.1007/s10342-024-01732-7","DOIUrl":null,"url":null,"abstract":"<p>This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency.</p>","PeriodicalId":11996,"journal":{"name":"European Journal of Forest Research","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Preliminary validation of automated production analysis of feller buncher operations: integration of onboard computer data with LiDAR inventory\",\"authors\":\"Steffen Lahrsen, Omar Mologni, Zexi Liu, Dominik Röser\",\"doi\":\"10.1007/s10342-024-01732-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency.</p>\",\"PeriodicalId\":11996,\"journal\":{\"name\":\"European Journal of Forest Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Forest Research\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s10342-024-01732-7\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Forest Research","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s10342-024-01732-7","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0
摘要
本研究利用机载计算机数据,对森林采伐作业中的全自动生产分析协议进行了开发和初步验证。通过整合 FPDat II 数据记录器的点火状态、运动状态和机器位置数据以及激光雷达森林资源数据,本研究旨在准确预测关键生产指标,如生产时间、覆盖面积、收获量以及单台机器的总体生产率。通过与传统的现场数据采集方法进行直接比较,仔细研究了这种全自动数据采集和分析方法的功效,重点是伐木机作业。研究结果表明,生产时间记录(0.9%)和机器覆盖面积(-1.9%)的差异极小,收获量(-4.4%)和生产率(-5.3%)的差异稍大。在个别班次中,特别是当多台机器同时运行或 FPDat II 数据记录器对机器的跟踪覆盖范围不完整时,对覆盖面积估计的差异更为明显。这项研究是了解机载计算机数据在森林作业远程生产分析中的能力和局限性的关键一步。通过全面分析,它有助于林业的数字化转型,强调了自动化工具在提高采伐效率方面所面临的挑战和机遇。
Preliminary validation of automated production analysis of feller buncher operations: integration of onboard computer data with LiDAR inventory
This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency.
期刊介绍:
The European Journal of Forest Research focuses on publishing innovative results of empirical or model-oriented studies which contribute to the development of broad principles underlying forest ecosystems, their functions and services.
Papers which exclusively report methods, models, techniques or case studies are beyond the scope of the journal, while papers on studies at the molecular or cellular level will be considered where they address the relevance of their results to the understanding of ecosystem structure and function. Papers relating to forest operations and forest engineering will be considered if they are tailored within a forest ecosystem context.