用于估算滚动阻力和森林交通性的高分辨率收割机数据

IF 2.6 2区 农林科学 Q1 FORESTRY
Aura Salmivaara, Eero Holmström, Sampo Kulju, Jari Ala-Ilomäki, Petra Virjonen, Paavo Nevalainen, Jukka Heikkonen, Samuli Launiainen
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引用次数: 0

摘要

有关地形条件的信息是规划环境和经济上可持续的森林采伐作业,避免对土壤造成负面影响的先决条件。目前的土壤数据比较粗糙,而且用传统方法收集这些数据成本高昂。可以利用森林采伐机来估算滚动阻力系数(\(\mu _{RR}\)),它是森林交通性的代表。利用使用的发动机功率、行驶速度和机器倾角的时空数据,可以计算出采伐区的\(\mu _{RR}\)。本研究描述了 2021 年无霜期在芬兰南部寒带森林景观中收集的大量高分辨率数据,覆盖了大约 50 公里的收割机路线。我们报告了在消除以前对计算陡坡上的\(\mu _{RR}\) 的一些限制方面所取得的进展,使计算在\(-10^{\circ }\) 到\(+10^{\circ }\)的坡度范围内进行,速度范围为0.6-1.2 ms\(^{-1}\) 。我们描述了 4 月至 8 月期间 11 个试验点之间和内部的 \(\mu _{RR}\)变化特征。站点平均值从 0.14 到 0.19 不等,并且在站点之间存在显著差异。通过模拟土壤的水文状态以及关于森林和地形的开放空间数据,我们确定了最能解释站点内 \(\mu _{RR}\) 极端值的特征。一些与湿度相关的指数,如具有不同阈值的水深指数,可以解释\(\mu _{RR}\)极值,而与生物量相关的林分属性通过与地点和土壤特性的联系间接地解释了这些极值。从实际操作数据中获取\(\mu _{RR}\)扩展了基于收割机的大规模数据收集能力,并为建立数据驱动的交通性预测模型铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

High-resolution harvester data for estimating rolling resistance and forest trafficability

High-resolution harvester data for estimating rolling resistance and forest trafficability

Information on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient (\(\mu _{RR}\)), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, \(\mu _{RR}\) can be computed for harvest areas. This study describes an extensive, high-resolution data on \(\mu _{RR}\) collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating \(\mu _{RR}\) on steeper slopes, enabling the calculation within a \(-10^{\circ }\) to \(+10^{\circ }\) slope range with a speed range of 0.6–1.2 ms\(^{-1}\). We characterise the variation in \(\mu _{RR}\) both between and within 11 test sites harvested during the April-August period. The site mean \(\mu _{RR}\) varies from \(\sim\) 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of \(\mu _{RR}\) within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the \(\mu _{RR}\) extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining \(\mu _{RR}\) from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

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来源期刊
CiteScore
5.10
自引率
3.60%
发文量
77
审稿时长
6-16 weeks
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.
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