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ArticleJournal articleGlobal trend in forest sector’s contribution to job creation and income
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The forest sector plays a vital role in achieving several targets of the Sustainable Development Goals (SDGs). The sector is considered to be a significant source of growth and employment as well as essential for sustenance. Well quantified information of forest-based employment and sectoral contribution to national economies are thus crucial in supporting relevant stakeholder’s decision processes towards sustainable development. This study combines the most recent available statistics from the Global Forest Resources Assessments (FRA), United Nations Industrial Development Organization (UNIDO), World Bank and FAOSTAT associated with employment, income as well as demographical indicators to quantify the total number of global formal and informal employment in the forest sector. In this study, the forest sector encompasses forestry and logging, manufacture of wood and products of wood, manufacture of paper and paper products and manufacture of furniture. The results show detailed trends about formal (visible) employment figures on the different subsectors such as forestry or wood industry among others on a global scale. Other key indicators such as labour productivity and sub-sectoral contribution to GDP are presented disaggregated by major geographical regions. It becomes evident that persons, to whom forests are the primary source of livelihood, are not captured well by the published statistics. Our analysis reveals that two-third of entire forestry and logging-based employment is informal and highly concentrated in developing nations. It is important to mention that informal employment in this context is an ambiguous term. It does not correctly describe, for example, forest work for subsistence. Hence, with a comprehensive literature review, this study sheds further light on the aspects of informal employment in the global forest sector. Based on the results, conclusions are drawn on how to enhance employment statistics related to the forest sector. Keywords: forest-related employment, informal employment, labour productivities ID: 3485590 -
DocumentOther documentLIDAR image–based fuel construction in a computational fluid dynamics simulation domain
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.LiDAR image-based vegetation fuel construction in a computational fluid dynamic (CFD) simulation domain was investigated. Using LiDAR images to convey fuel information to CFD would improve the accuracy of wildfire spread prediction. The obtained vegetation information using LiDAR appears as point signals in LiDAR images, and the point signals were dispatched to nodes using the K-D tree algorithm. Then, each node is transferred to the meshing algorithm along with the number of signals and location information. In a CFD domain, 3-dimension vegetation fuel information is reconstructed, and fuel mass is estimated by using the number of signals within each mesh. It appears that utilizing LiDAR images to obtain fuel information improves the accuracy in fuel shapes and mass distribution compared to the conventional way that assigns pre-determined shape and mass distribution for each vegetation. It is expected that the outcomes of this research would improve the liability of CFD-based wildfire prediction. Keywords: Sustainable forest management, Research, Climate change ID: 3617419 -
ArticleJournal articleCoupling machine learning and forest simulations to promote the applicability of long-term forest projections under climate change perspectives
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Projecting forest dynamics is the foundation for sound decision support in adaptive forest management. However, due to their complexity, many forest modeling techniques addressing global changes in terrestrial ecosystems are limited to scientific applications. Integrating conventional research and artificial intelligence technologies has the potential to bridge research and practical use. In this study, we propose a Machine Learning (ML) framework that facilitates the implementation of long-term forest projections under climate change scenarios. Our approach combines ML and forest simulations based on process-based models to project forest dynamics. The goal is to leverage the complementary strength of process-based and state-of-the-art ML models to improve predictions at a reduced computational cost. We use environmental data and periodic field measurements at a national scale to train ML models to predict forest growth. By integrating process-based simulations we investigate how the additional variables can improve the prediction accuracy. The proposed hybrid ML framework identifies forest dynamics processes and drivers across spatial and temporal scales, contributing at many levels to the climate change adaptation: from increasing awareness of the climate-induced hazards to enhancing education and assisting in sustainable natural resource management and planning. Keywords: adaptive forest management, climate change, forest growth modelling, machine learning ID: 3623078
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