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Sirvi Autor "Ivanovs, J." järgi

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  • Pisipilt
    Kirje
    Development of Latvian land use and land use change matrix using geospatial data of National forest inventory
    (2019) Krumsteds, L.L.; Ivanovs, J.; Jansons, J.; Lazdins, A.
    Land use and land use change calculation matrix is one of the most important parts of the national greenhouse gas (GHG) inventory in land use, land use change and forestry (LULUCF) sector providing information of an overall summary and changes in land use at a national level over a specified period of time. Information on land use and land use changes are further used to calculate other parameters important for determination of GHG emissions and carbon stock changes in living and dead biomass, soil and litter, as well as basic information on the impact of applied climate change mitigation measures. Calculations of land use change can be carried out in a partly automated process using GIS tools, which makes calculations easier to perform, reduces time consumption for this task and occasional mistakes due to manual operations. The aim of this study is to improve the methodology for development of land use and land use change matrix in the national GHG inventory system using geospatial data of National forest inventory (NFI) and auxiliary data sources. The developed system uses geospatial NFI data and auxiliary information provided by the land parcel information system (LPIS) and stand-wise forest inventory, and it improves accuracy and consistency of the land use and land use change matrix, providing the ability to apply the same land use accounting method for the whole reporting period since 1990 without a significant increase of uncertainty. The developed method determines land use changes in a 5-year period by comparing three successive NFI cycles. To determine the actual land use category in a particular year, we adjusted weights for different land use categories. Interpolation is used to determine year-by-year transitions.
  • Pisipilt
    Kirje
    Identification of wet areas in forest using remote sensing data
    (2018) Ivanovs, J.; Lupikis, A.
    Aim of this study is to evaluate different remote sensing indices to detect spatial distribution of wet soils using GIS based algorithms. Ar ea of this study represents different soil types on various quaternary deposits as well as different forest types. We analyzed 25 sites with the area of 1 km 2 each in central and western part of Latvia. Data about soil characteristics like thickness of pea t layer and presence of reductimorphic colors in soil was collected during field surveys in 228 random points within study sites. ANOVA test for comparing means of different soil wetness classes and binary logistic regression analysis for evaluating the ac curacy of different remote sensing indices to model spatial distribution of wet areas are used for analysis. Main conclusion of this study is that for different quaternary deposits and soil texture classes different algorithms for soil wetness prediction s hould be used. Data layers for predicting soil wetness in this study are various modifications and resolutions of digital elevation model like depressions, slope and SAGA wetness index as well as Sentinel - 2 multispectral satellite imagery. Accuracy of soil wetness classification of soils on moraine, fluvial and eolian sediments exceeds 94%, whereas on the clayey sediments it is close to 80%.
  • Pisipilt
    Kirje
    Mapping drainage ditches in agricultural landscapes using LiDAR data
    (Estonian University of Life Sciences, 2022) Melniks, R.; Ivanovs, J.; Lazdins, A.; Makovskis, K.
    The aim of this study is to develop a method for identification of the drainage ditch network, which can be used for surface runoff modeling and to increase accuracy of estimation of greenhouse gas (GHG) emissions in croplands and grasslands, using remote sensing data. The study area consists of 11 objects throughout Latvia with a total area of 145 km2 . Digital elevation models (DEMs) in two resolutions, which were created using three different interpolation methods, were used for the analysis. Several multi-level data filtering methods were applied to identify ditch network, including flow patterns, which can be used in surface runoff process. The method we developed correctly identified 85–89% of ditches, depending on the DEM used, in comparison to the reference data. Mapped ditches are located within 3 m range of the reference data in 89–93% of cases. The elaborated model is robust and uses openly available source data and can be used for large scale ditch mapping with sufficient accuracy necessary for hydrological modelling and GHG accounting in the national inventories.