Speaker
Description
Annual logging planning with optimization solutions relies on measurable criteria for felling area conditions and suitable weather. Increased data precision improves the reliability of the optimization assessing logging feasibility and simulates the effects of varying weather scenarios. Which leads to effective process management by centralizing planning of harvesting . The study aimed to improve the precision of regression-based forecasting models predicting logging conditions through the integration of satellite data with previously used meteorological center inputs, leveraging the satellite data’s higher spatial resolution, better spatial coverage and spatial variability detection of the satellite data. Secondly, geospatial information system (LVM GEO) data was integrated with meteorological measurements, implementing finer special data(10X10km) improving the precision of area-specific forecasts. The study was realized on data of 12000 final fellings and 5000 commercial-thinning of JSC “Latvijas valsts meži” (Latvia’s State Forests) from year 2017 to 2024. For each felling area were assigned points that characterize the proportion of bad and extreme logging conditions from total cutting volume in the last 7 days in each company region. The 7-day average of adverse logging conditions correlated best with weather constraints. Felling areas were additionally characterized by felling volume combined with the proportion of the area with groundwater levels up (according to the LVM groundwater model), and the distinct impacts of each forest type. Furthermore, for each felling area, satellite-derived temperature and precipitation data over 7-, 15-, and 30-day periods during logging obtained from ERA5 and Copernicus repositories, were analyzed in parallel with measurements from 26 Latvian State Geological and Meteorological Center weather stations. Further a regression analysis was performed to develop a predictive model for assessing logging conditions and assigning “points”(lower points – easier logging, higher points – harder logging) of each felling area. Geospatial information system data that has proven to be significant (p<0.05) is the proportion of the felling area where the groundwater level reaches up to 0.1 m in combination with the felling volume in addition to forest types (p<0.05). By regression analysis the weather condition impact on logging possibilities was set. Equation allows to calculate the maximum logging points under defined weather constraints. Satellite data that has proven to be significant (p<0.05) are daily maximum air temperature at 2 m height, daily minimum air temperature at 2 m height, daily mean air temperature at 2 m height, soil moisture at depth, soil moisture index, precipitation sum, snow depth, soil temperature at depth. The additional factors allowed to increase the coefficient of determination by 1% on average for the felling data equation, and by 8.5% on average for the weather conditioning equation. As a result, using seven years of weather data, three meteorological scenarios—optimal, moderate, and suboptimal for logging—were defined per region. Monthly 15- and 30-day average temperatures and 30-day precipitation were used to calculate logging condition indices via two predictive models contained in information system and optimization system providing objective criteria to identify the most suitable month for harvesting each area.
| Keywords | forecasting; logging; satellite; efficiency |
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