Azelia Dwi Rahmawati
Department of Forest Management Science, Faculty of Forest and Environment, IPB University
Summary: The concept of sustainable forest management is known as a concept that considers various forest functions with values and interests that are in line with social, economic, and ecological needs. The implementation of this concept is considered less optimal because often the main focus of forest management is only on forest sustainability, without considering the welfare of workers as the managers who preserve it. This study aims to identify the level of risk vulnerability of Occupational Health and Safety (OSH) disorders in the form of a source of natural hazards through monitoring the condition of the forest area using the Geographic Information System (GIS) approach. Multi-Criteria Decision Making Analytical Hierarchy Process (AHP) is carried out as a weighting of parameters in the peat forest area.
The three main factors of natural hazards (environmental biophysical, climate, and threat) are used to classify various spatial analysis techniques. This classification includes 12 parameters that can potentially result in occupational accidents, including: slope, elevation, soil type, vegetation density, accessibility, rainfall, temperature, wind speed, sunlight intensity, humidity, wildlife potential, and abnormal tree conditions. These parameters are processed through various spatial analysis and are categorized into five classification classes. Three experts from the fields of OSH, harvesting, and terrain knowledge use the AHP approach to assess each parameter's weight. The weighting results reveal that the criteria for threats and parameters related to the abnormal tree condition at the study location have the highest weight.
The middle class, with an area of 53467.51 ha, or 59.095%, dominated the risk map of the occupational accident area, while the high class, with an area of 30.78 ha, or 0.034%, was the highest work accident risk class. The Moran index spatial correlation autocorrelation reveals a significant and positive correlation between the modeling results and a history of occupational accidents, whereas the Local Indicator Spatial Analysis (LISA) significance pattern displays a low spatial cluster relationship (Low-Low), indicating data heterogeneity due to deviations in specific areas. Estate managers can consider the results and recommendations to improve risk control programs when formulating risk management policies with the aim of implementing sustainable forest management that prioritizes worker welfare.
Keywords: analytical hierarchy process, natural hazard, occupational safety and health, remote sensing
Azelia Dwi Rahmawati
Department of Forest Management Science, Faculty of Forest and Environment, IPB University
azeliadr@gmail.com drazelia@apps.ipb.ac.id