Aura Salmivaara, Eero Holmström, Sampo Kulju, Jari Ala-Ilomäki, Petra Virjonen, Paavo Nevalainen, Jukka Heikkonen, Samuli Launiainen
{"title":"用于估算滚动阻力和森林交通性的高分辨率收割机数据","authors":"Aura Salmivaara, Eero Holmström, Sampo Kulju, Jari Ala-Ilomäki, Petra Virjonen, Paavo Nevalainen, Jukka Heikkonen, Samuli Launiainen","doi":"10.1007/s10342-024-01717-6","DOIUrl":null,"url":null,"abstract":"<p>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 (<span>\\(\\mu _{RR}\\)</span>), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, <span>\\(\\mu _{RR}\\)</span> can be computed for harvest areas. This study describes an extensive, high-resolution data on <span>\\(\\mu _{RR}\\)</span> 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 <span>\\(\\mu _{RR}\\)</span> on steeper slopes, enabling the calculation within a <span>\\(-10^{\\circ }\\)</span> to <span>\\(+10^{\\circ }\\)</span> slope range with a speed range of 0.6–1.2 ms<span>\\(^{-1}\\)</span>. We characterise the variation in <span>\\(\\mu _{RR}\\)</span> both between and within 11 test sites harvested during the April-August period. The site mean <span>\\(\\mu _{RR}\\)</span> varies from <span>\\(\\sim\\)</span> 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 <span>\\(\\mu _{RR}\\)</span> within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the <span>\\(\\mu _{RR}\\)</span> extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining <span>\\(\\mu _{RR}\\)</span> 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.</p>","PeriodicalId":11996,"journal":{"name":"European Journal of Forest Research","volume":"22 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-resolution harvester data for estimating rolling resistance and forest trafficability\",\"authors\":\"Aura Salmivaara, Eero Holmström, Sampo Kulju, Jari Ala-Ilomäki, Petra Virjonen, Paavo Nevalainen, Jukka Heikkonen, Samuli Launiainen\",\"doi\":\"10.1007/s10342-024-01717-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (<span>\\\\(\\\\mu _{RR}\\\\)</span>), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, <span>\\\\(\\\\mu _{RR}\\\\)</span> can be computed for harvest areas. This study describes an extensive, high-resolution data on <span>\\\\(\\\\mu _{RR}\\\\)</span> 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 <span>\\\\(\\\\mu _{RR}\\\\)</span> on steeper slopes, enabling the calculation within a <span>\\\\(-10^{\\\\circ }\\\\)</span> to <span>\\\\(+10^{\\\\circ }\\\\)</span> slope range with a speed range of 0.6–1.2 ms<span>\\\\(^{-1}\\\\)</span>. We characterise the variation in <span>\\\\(\\\\mu _{RR}\\\\)</span> both between and within 11 test sites harvested during the April-August period. The site mean <span>\\\\(\\\\mu _{RR}\\\\)</span> varies from <span>\\\\(\\\\sim\\\\)</span> 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 <span>\\\\(\\\\mu _{RR}\\\\)</span> within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the <span>\\\\(\\\\mu _{RR}\\\\)</span> extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining <span>\\\\(\\\\mu _{RR}\\\\)</span> 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.</p>\",\"PeriodicalId\":11996,\"journal\":{\"name\":\"European Journal of Forest Research\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-09\",\"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/s10342-024-01717-6\",\"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/s10342-024-01717-6","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FORESTRY","Score":null,"Total":0}
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.
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.