Regional greenhouse gas net emission intensities by land cover category in Finland

The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing. <strong>Data description</strong> <em>Data format.</em> The data are distributed as GeoTiff raster files, which can be read using most GIS-software. <em>Units and definitions</em>. The land cover net emission intensities are shared as raster data with a 250m-by-250m resolution in the ETRS-TM35FIN projected coordinate system. Negative values correspond to sinks (only sinks of C/CO₂ considered). The emission intensities are reported as total emission intensities in carbon dioxide equivalents (gCO₂-eq m^-2) based on the 100-year global warming potential as reported in the IPCC 5^th assessment report (Myhre et al. 2013, p. 73). All cells which do not include emissions from the corresponding land use are classified as <code class="language-sql">NULL</code>s or <em>no data</em>, which should be taken into account if combining raster layers. Where the source data report emission coefficients in the amount of the main element (e.g. C for CO₂ or N for N₂O) they have been converted to the amounts of the corresponding gas using the standard atomic weights of the relevant atoms (C: 12.011, O: 15.999, N: 14.007, H: 1.008) before conversion to carbon dioxide equivalents. See the related publication for the values of the emission coefficients used and further methodological details (Holmberg et al. 2023). <em>Data processing</em>. Data processing for the production of the 250m-by-250m emission intensity raster maps was conducted using GRASS GIS 8.2 (GRASS Development Team, 2022). Land cover emissions derived from vector data (rivers, lakes, agricultural land) were rasterized at a resolution of 1m² with the emission intensity as the raster cell value. For rivers, linear features representing rivers having a width of 2 to 5 meters were converted first to areal features by creating a buffer of 1.75 meters to represent an average width of 3.5 meters (see <em>Rivers</em> below). The buffer was created <em>without caps</em> so that the total length of the linear segments was not changed. The buffered river features were merged with the areal features removing the potentially overlapping parts. For all source raster data, the data were available at a 16m-by-16m meter resolution. Emission intensities were aggregated to 250m-by-250m by first summing over the original raster cells intersecting with each aggregate cell while accounting for the proportion of each cell overlapping with the aggregated cell and then multiplying by the area of the original cell. The resulting raster values were divided by the total area of the aggregate cell to acquire average emission intensities. Hence, rasters including a lower proportion of the corresponding land use have lower emission intensities. <strong>Thematic layers</strong> <em>Cropland</em>. CO₂ emissions from cropland were estimated for mineral soils and organic soils separately using emission coefficients from the national greenhouse gas inventory report for 2023. Averaged emission coefficients for the years 2010–2020 for southern and northern Finland were used for mineral soils (Statistics Finland 2023b, Table 3_App_6j). For organic soils separate emission coefficients were used for annual and perennial crops (IPCC 2014, Table 2.1). Cropland and crop data were acquired from the Finnish Food Authority’s Land parcel register for year 2020. Soils were classified into mineral and organic soils by intersecting the field parcels with the soil body layer of the Finnish soil database (Lilja et al. 2006, Lilja et al. 2017). Data files: Net missions from cropland on mineral soils: <code>cropland_mineral_250m_250m_mean.tif</code> Net missions from cropland on organic soils: <code>cropland_organic_250m_250m_mean.tif</code> <em>Forests</em>. The net emissions from forests are estimated as the balance of carbon sequestration due to gross primary production of trees and understory vegetation and carbon loss due to harvested biomass, and emission from decomposition of harvest residues, litter, and soil organic matter. Forest productivity is modelled using the process-based forest growth model PREBAS (Minunno et al. 2016, 2019, Junttila et al. 2023, Mäkelä et al. 2023). The initial state for the forest model for the three main forestry species Scots pine, Norway spruce, and Silver birch is derived from the multi-source national forest inventory (MS-NFI; version 2015) and harvesting intensities are modelled on the basis of the Finnish national statistics (National Resources Institute Finland 2023). The PREBAS forest net emissions represent annual averages for the period 2017–2025. CO₂ emissions from decomposition on mineral soils are estimated with the soil carbon model YASSO07 (Liski et al. 2005, Tuomi et al. 2009). On drained peatlands, in addition to CO₂ emissions due to peat and litter decomposition, the soil emissions include the CH₄ and N₂O emissions. The net emissions due to CO₂, CH₄ and N₂O from drained peatland (Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023) are calculated using emission coefficients for nutrient rich sites (herb-rich and blueberry type), and nutrient poor sites (lingonberry, dwarf-shrub, and lichen type). Data files: Net missions from forest on mineral soils: <code>forest_mineral_250m_250m_mean.tif</code> Net missions from forest on organic soils: <code>forest_organic_250m_250m_mean.tif</code> <em>Lakes</em>. Emissions of CO₂ and CH₄ were estimated for lakes using size dependent emission coefficients. The lakes were classified into five size classes with emission coefficients for CO₂ evasion (Kortelainen et al. 2006), CH₄ diffusion (Juutinen et al. 2009) and ebullition (Bastviken et al. 2004) as well as the CH₄ emissions due to the macrophytes <em>Phragmites australis</em> and <em>Equisetum fluviatile</em> (Juutinen et al. 2003, Bergström et al. 2007, 2011). The lake date was from the Shoreline10 data by the Finnish Environment Institute. Data files: Net emissions from lakes: <code>lakes_250m_250m_mean.tif</code> <em>Rivers</em>. CO₂ emissions from rivers were estimated using emission coefficients based on the width of the stream. The width dependent emission coefficients were derived from stream order specific emission coefficients of Swedish rivers (Humborg et al. 2010) by classifying the rivers into width groups and with the emission coefficients chosen based on the stream order specific coefficient of corresponding average width. The river emissions were calculated from the Shoreline10 data by the Finnish Environment Institute which represents rivers wider than 5 m as areal features, and rivers &lt; 5 m wide as linear features. For rivers &lt; 5m wide, an average width of 3.5 m was assumed. Data files: Net emssions from rivers: <code>rivers_250m_250m_mean.tif</code> <em>Undrained mires</em>. Total net emissions were estimated for undrained mires in Finland using average emission coefficients for CH₄ (Minkkinen and Ojanen 2013), CO₂ (Sallantaus 1994 , Turunen et al. 2002), and N₂O (Minkkinen et al. 2020). The emission coefficients represent the long term accumulation of carbon as well as the emission of CH₄ and from N₂O peatland. Peatland sites were extracted from the multi-source national forest inventory (MS-NFI; version 2019; see also Mäkisara et al. 2022) and undrained mires were delineated using data provided by the Natural Resources Institute Finland. The undrained mires were classified into four classes using the MS-NFI data: 1) productive forested mires, 2) sedge fens, 3) other open and sparsely treed fens and 4) ombrotrophic bogs, which mainly differ in their emission coefficients for methane (Minkkinen and Ojanen 2013). Data files: Net missions from undrained mires: <code>undrained_mires_250m_250m_mean.tif</code>本文所发布数据相关的方法已在关联出版物（Holmberg et al. 2023, Junttila et al. 2023）中详细阐述。本文件旨在说明数据集及数据预处理步骤。本次数据发布的目标是提供芬兰土地覆盖在温室气体排放中作用的区域评估。相关出版物中的结果基于大型行政区划单元——芬兰本土的NUTS 3区域（芬兰统计局，2023a）。尽管本数据集受所用方法与源数据精度限制，但仍可用于更局域的评估，例如市级尺度。本数据代表土地覆盖的时间快照。除土壤图、河流与湖泊外，所有土地覆盖数据的时间范围为2015-2020年，基于登记数据或遥感影像获取。 <strong>数据描述</strong> <em>数据格式。</em> 数据以GeoTiff栅格文件形式分发，可通过绝大多数地理信息系统（GIS，Geographic Information System）软件读取。 <em>单位与定义。</em> 土地覆盖净排放强度以栅格数据形式共享，投影坐标系为ETRS-TM35FIN，分辨率为250m×250m。负值代表汇（仅考虑碳/二氧化碳（CO₂）汇）。排放强度以二氧化碳当量（gCO₂-eq m⁻²）的总排放强度形式报告，基于政府间气候变化专门委员会（IPCC，Intergovernmental Panel on Climate Change）第五次评估报告所采用的百年全球变暖潜势（Myhre et al. 2013, p. 73）。所有未包含对应土地利用排放的栅格单元将被分类为<code class="language-sql">NULL</code>值或<em>无数据</em>，在合并栅格图层时需考虑这一点。若源数据以主元素（例如CO₂对应的碳（C）、N₂O对应的氮（N））的形式提供排放系数，则需先通过相关原子的标准原子量（C: 12.011, O: 15.999, N: 14.007, H: 1.008）将其转换为对应气体的排放量，再进一步转换为二氧化碳当量。所用排放系数的具体数值与更多方法学细节可参阅关联出版物（Holmberg et al. 2023）。 <em>数据处理流程。</em> 250m×250m分辨率的排放强度栅格图通过GRASS GIS 8.2（GRASS开发团队，2022）生成。源自矢量数据（河流、湖泊、农用地）的土地覆盖排放数据首先被栅格化为1m²分辨率的栅格，以排放强度作为栅格单元值。对于河流，宽度为2至5米的线状要素首先通过创建1.75米的缓冲区转换为面状要素，以代表平均3.5米的宽度（详见下文<em>河流</em>部分）。缓冲区创建时<em>不添加端点帽</em>，以确保线状段的总长度保持不变。经缓冲处理的河流面状要素将与原有面状要素合并，并移除可能存在的重叠部分。所有源栅格数据的原始分辨率均为16m×16m。将排放强度聚合至250m×250m分辨率时，首先对与每个聚合单元相交的原始栅格单元求和，同时考虑每个原始单元与聚合单元的重叠比例，再乘以原始单元的面积。最终得到的栅格值将除以聚合单元的总面积，以得到平均排放强度。因此，包含对应土地利用比例较低的栅格，其排放强度也相对更低。 <strong>专题图层</strong> <em>耕地。</em> 耕地的二氧化碳（CO₂）排放分别针对矿质土壤与有机土壤进行估算，所用排放系数源自2023年国家温室气体清单报告。针对矿质土壤，采用芬兰南部与北部2010-2020年的平均排放系数（芬兰统计局，2023b, 表3_App_6j）。针对有机土壤，一年生作物与多年生作物采用不同的排放系数（IPCC 2014, 表2.1）。耕地与作物数据源自芬兰食品管理局2020年的土地地块登记册。通过将农田地块与芬兰土壤数据库的土体图层相交，将土壤划分为矿质土壤与有机土壤（Lilja et al. 2006, Lilja et al. 2017）。数据文件：矿质土壤耕地净排放：<code>cropland_mineral_250m_250m_mean.tif</code>；有机土壤耕地净排放：<code>cropland_organic_250m_250m_mean.tif</code> <em>森林。</em> 森林的净排放量估算为树木与林下植被的总初级生产固碳量，与生物量收获、收获残留物、凋落物及土壤有机质分解导致的碳损失之间的平衡。森林生产力采用基于过程的森林生长模型PREBAS进行模拟（Minunno et al. 2016, 2019; Junttila et al. 2023; Mäkelä et al. 2023）。针对三大主要造林树种——欧洲赤松、挪威云杉与银桦，森林模型的初始状态源自多源国家森林清查（MS-NFI, Multi-source National Forest Inventory; 2015版），收获强度基于芬兰国家统计数据建模（芬兰自然资源研究所，2023）。PREBAS模型的森林净排放量代表2017-2025年的年均值。矿质土壤的二氧化碳（CO₂）分解排放采用土壤碳模型YASSO07进行估算（Liski et al. 2005, Tuomi et al. 2009）。在排水泥炭地中，除泥炭与凋落物分解产生的CO₂排放外，土壤排放还包括甲烷（CH₄）与一氧化二氮（N₂O）排放。排水泥炭地的CO₂、CH₄与N₂O净排放量（Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023）采用富营养位点（草本与蓝莓型）与贫营养位点（越橘、矮灌木与地衣型）的排放系数进行计算。数据文件：矿质土壤森林净排放：<code>forest_mineral_250m_250m_mean.tif</code>；有机土壤森林净排放：<code>forest_organic_250m_250m_mean.tif</code> <em>湖泊。</em> 湖泊的CO₂与CH₄排放采用与尺寸相关的排放系数进行估算。将湖泊划分为五个尺寸等级，所用排放系数涵盖CO₂逸散（Kortelainen et al. 2006）、CH₄扩散（Juutinen et al. 2009）与冒泡排放（Bastviken et al. 2004），以及大型水生植物<em>芦苇（Phragmites australis）</em>和<em>水问荆（Equisetum fluviatile）</em>导致的CH₄排放（Juutinen et al. 2003, Bergström et al. 2007, 2011）。湖泊数据源自芬兰环境研究所的Shoreline10数据集。数据文件：湖泊净排放：<code>lakes_250m_250m_mean.tif</code> <em>河流。</em> 河流的CO₂排放采用与河流宽度相关的排放系数进行估算。基于瑞典河流的按河级划分的排放系数（Humborg et al. 2010），将河流划分为不同宽度组别，并根据对应平均宽度的河级专属排放系数确定宽度相关的排放系数。河流排放数据源自芬兰环境研究所的Shoreline10数据集，该数据集将宽度大于5米的河流表示为面状要素，宽度小于5米的河流表示为线状要素。对于宽度小于5米的河流，假设其平均宽度为3.5米。数据文件：河流净排放：<code>rivers_250m_250m_mean.tif</code> <em>未排水沼泽。</em> 采用针对甲烷（CH₄）（Minkkinen and Ojanen 2013）、CO₂（Sallantaus 1994, Turunen et al. 2002）与N₂O（Minkkinen et al. 2020）的平均排放系数，估算芬兰境内未排水沼泽的总净排放量。该排放系数代表长期碳积累以及泥炭地CH₄与N₂O的排放。从多源国家森林清查（MS-NFI; 2019版; 另见Mäkisara et al. 2022）中提取泥炭地位点，并利用芬兰自然资源研究所提供的数据划定未排水沼泽范围。利用MS-NFI数据将未排水沼泽划分为四类：1）生产力较高的森林沼泽，2）莎草沼泽，3）其他开阔与稀疏林木沼泽，4）寡营养泥炭沼泽，四类沼泽的主要差异在于甲烷排放系数（Minkkinen and Ojanen 2013）。数据文件：未排水沼泽净排放：<code>undrained_mires_250m_250m_mean.tif</code>