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44 results for “Forest inventory data”
Cooperative Alaska Forest Inventory (CAFI): I - Tree Inventory Data 1994-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the tree data of the CAFI. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): II - Seedling Inventory Data 1994-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the seedling data of the CAFI. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): III - Vegetation Data 1994-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the vegetation data of the CAFI. The protocol has been changed in 2021. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
Cooperative Alaska Forest Inventory (CAFI): IV - Sapling Inventory Data 2022-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the sapling data of the CAFI. Sapling data is only available after 2022 due to a protocol change. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.
AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots
<p>This dataset consists of three zip archives containing valuable visual and measurement data related to tree assessments conducted using the Apple Vision Pro (AVP) technology. The first zip archive, <strong>images.zip</strong>, includes images taken in the forest, presented in .PNG and .JPG formats. These images capture various aspects of the study area and the measurement process.</p> <p>The second archive, <strong>videos_app_HR.zip</strong>, features videos recorded with the AVP using the "Handsruler" app, which focuses on measuring diameter at breast height (dbh) at 22 designated sample plots. Each video file is labeled with a numeric identifier that corresponds to the specific sample plot number, allowing for easy reference and organization.</p> <p>The third archive, <strong>videos_app_TM.zip</strong>, contains videos from the "Tape Measure" app, documenting dbh measurements taken at 17 sample plots. Similar to the previous videos, the file names indicate the respective sample plot numbers.</p> <p>In addition to the visual data, the dataset includes a comma-separated values (CSV) file named <strong>information_all_trees.csv</strong>, which consolidates all reference data regarding individual trees and sample plots. Each row in this file represents a single tree and includes several columns, each providing specific details about the measurements and observations.</p> <p>The column headers in <strong>information_all_trees.csv</strong> are as follows:</p> <ul> <li><strong>PLOT_ID</strong>: The numeric identifier for each sample plot.</li> <li><strong>tree_species_short</strong>: Abbreviation of the tree species.</li> <li><strong>caliper_dbh</strong>: The manually measured dbh of the tree in centimeters.</li> <li><strong>AVP_App1_dbh</strong>: The dbh measurement obtained from the AVP app "Handsruler" in centimeters.</li> <li><strong>AVP_App2_dbh</strong>: The dbh measurement obtained from the AVP app "Tape Measure" in centimeters.</li> <li><strong>res_App1</strong>: The difference between the dbh measured by the "Handsruler" app (AVP_App1_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>res_App2</strong>: The difference between the dbh measured by the "Tape Measure" app (AVP_App2_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>tree_species</strong>: The Latin name of the tree species, with genus and species connected by an "_".</li> <li><strong>tree_class</strong>: Classification of the tree into a species-specific category.</li> <li><strong>date</strong>: The date of the recordings.</li> <li><strong>time_App_1_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Handsruler" app, in minutes.</li> <li><strong>time_App_2_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Tape Measure" app, in minutes.</li> <li><strong>time_manual_caliper_min</strong>: The duration of all dbh measurements at the entire sample plot conducted manually, in minutes.</li> <li><strong>measuring_person</strong>: The individual field worker for conducting all dbh measurements (manual and both AVP apps) at the sample plot.</li> <li><strong>mean_slope_degrees</strong>: The average slope of the terrain across the sample plot, expressed in degrees.</li> </ul> <p>This comprehensive dataset provides essential insights into the effectiveness of the AVP technology for measuring tree dimensions and contributes to ongoing research in forest management and ecological studies. The included videos and images serve as a visual reference for the measurement processes, while the CSV file encapsulates the quantitative data necessary for analysis. Each row in the CSV file represents a single tree, facilitating detailed examinations of individual measurements and comparisons across different sample plots.</p>
LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots
<p>In forest inventory, trees are usually measured by handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory is nowadays redesigned, since modern laser scanner technology became available. Laser scanner generate massive data in the form of 3D point clouds. Novel methodology is currently developed to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK), and (ii) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contains digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.</p>
iLAUT – iPad laser scanner data from Austrian forest Inventory plots
<p>The estimation of stand- and individual tree information is one of the major goals of forest inventory. Conventionally, field data in forest inventory are collected at tree level on sample plots by means of manual measurements (e.g., caliper, tape). In recent years, modern laser-supported sensors and automatic routines for feature extraction were increasingly used instead of the traditional forest inventory methods. In 2020, Apple (Apple Inc. Cupertino, California, USA) implemented a LiDAR (Light Detection and Ranging) sensor into the new 4th Generation of Apple iPad Pro. Consequently, LiDAR-generated 3D point clouds can nowadays be recorded with consumer-level devices for the first time. Novel methodology is able to provide estimates of the terrain height, tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to show recent iPad 3D point clouds of forest inventory sample plots and to test new software routines for the automatic measurement of trees. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 21 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) the iPad pro (Apple Inc. Cupertino, California, USA), and (ii) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK). The data also contains application videos of the iPad, digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.</p>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022
This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data
This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Data from : Tree inventory data from permanent plots in French forest reserves
<p>We present a dataset resulting from the first round of a national monitoring program of forest reserves. It contains 9538 permanent plots, distributed across 111 study sites in mainland France (including Corsica). Notably focusing on dead wood measurement, this protocol has primarily been applied in strict forest reserves and special nature reserves (sensu Bollmann et Braunisch 2013), with 68% (6494) of the plots being currently located in strict forest reserves (unmanaged) and 24,7% (2363 plots) in forests unmanaged for at least 50 years. Sites cover a large variety of ecological conditions, from lowland to subalpine forests, but with an underrepresentation of Mediterranean forests (Table 1). The protocol assesses all the stages of a tree's life cycle, from seedling to decomposed lying dead wood. On each plot, a combination of three sampling techniques was used: (i) fixed area inventory for regeneration, standing dead trees, living trees and coarse woody debris (CWD) with diameter over 30 cm, (ii) transect lines for CWD with diameter < 30 cm, and (iii) fixed angle plot method for living trees with a diameter at breast height (DBH) > 30 cm (using a relascopic angle of 3%). Measurements include: exact tree location (azimuth, distance), species, diameter(s), tree-related microhabitats, decay stage and bark cover, seedling cover. With the ongoing climate change, the program network can also provide important information to monitor changes in forest ecosystems. It can also be used as forest management monitoring or conservation status assessment.</p>
CABO forest inventory survey data: Canopy-level spectra, species abundances, and environmental conditions
<p>This dataset contains the forest inventory survey data as aqcuired by airborne imaging spectroscopy (i.e., continuum removed spectral reflectance) and field inventory surveys (i.e., relative species abundance) for 65 field plots along a temperate-to-boreal forest gradient in southern Québec, Canada. Additionally, it contains plot-level average environmental variables (e.g., elevation, slope, etc.) and species-mean foliar traits. This data supports the manuscript titled "Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis" <em>in press </em>at <em>Ecological Monographs </em>(ID: ECM23-0177) and accompanying code (Crofts 2024) is archieved on Zenodo (<a href="https://doi.org/10.5281/zenodo.10735410)">doi: 10.5281/zenodo.10735410)</a>. Please refer to the methods section of the associated paper for methodological details, with additional details available at protocols.io (<a href="https://doi.org/10.17504/protocols.io.q26g7rn23vwz/v2">doi: 10.17504/protocols.io.q26g7rn23vwz/v2).</a> This data was aqcuired as a part of the Canadian Airborne Biodiversity Observatory (CABO; caboscience.org) and has the licence Creative Commons by Attribution (cc-by). Note: this dataset is also published of EcoSiS (<a href="https://ecosis.org/package/cabo-canopy-level-spectra-from-forest-sites)">https://ecosis.org/package/cabo-canopy-level-spectra-from-forest-sites)</a></p>
Figure 1 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
Figure 1. Map of Micronesia showing jurisdictions included in the FIA program and locations of Micronesia Challenge protected areas as of 2018.
Figure 2 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
Figure 2. Estimated percentage of all trees by diameter class (in inches) by jurisdiction across Micronesia. FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.
High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data
<p>We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m<sup>2</sup> for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.</p> <p>For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40<a> European countries, </a>forming a continuous coverage of the western part of the European continent.</p> <p>A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to <a>73 % </a>the South-Eastern area.</p> <p>The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.</p> <p>The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.</p> <p>Corresponding authors: <a href="mailto:Jukka.Miettinen@vtt.fi">Jukka.Miettinen@vtt.fi</a>, <a href="mailto:Johannes.Breidenbach@nibio.no">Johannes.Breidenbach@nibio.no</a></p>
National forest inventory data for a size-structured forest population model
<p>In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, i.e. the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short timeseries from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade-tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.</p>
Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
<p><strong> Introduction</strong></p> <p>A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO<sub>2</sub> emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO<sub>2</sub> emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).</p> <p><strong>Time series data </strong></p> <p>The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (<em>Pinus sylvestris</em> L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).</p> <p>The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018).</p> <p>Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.</p> <p>The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO<sub>2</sub> release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.</p> <p>The pre-prepared input data are given in files, see Table 1 for descriptions.</p> <p> </p> <p> </p> <p>Table 1. Description of input data files.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description of data</strong></p> </td> </tr> <tr> <td> <p>basal.areas.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual average basal area (m<sup>2</sup> ha<sup>-1</sup>) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> <p>Values of tree species or group correspond to:</p> <p>1 Scots pine</p> <p>2 Norway spruce</p> <p>3 Broadleaved species</p> </td> </tr> <tr> <td> <p>biomass.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual biomass (biomass, t ha<sup>-1</sup> of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>dead_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha<sup>-1</sup> of dry mass; lognat_litter).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>ghgi_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha<sup>-1</sup>) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of ground correspond to litter deposition environment:</p> <p>above Above-ground litter</p> <p>below Below-ground litter</p> </td> </tr> <tr> <td> <p>lognat_decomp.csv</p> </td> <td> <p>Time series of years 1990-2022 for C, t ha<sup>-1</sup> of dry mass, decomposed from logging residues and natural mortality by region.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>logyasso_weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> </td> </tr> <tr> <td> <p>total_area.csv</p> </td> <td> <p>Areas (ha) of drained peatland forests remaining forest land by region and peat_type.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>The R-scripts</strong></p> <p>The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO<sub>2</sub> emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.</p> <p>Guidance for running the R-scripts is given in the README.txt.</p> <p><strong>References</strong></p> <p>Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.</p> <p>LUKE Statistics</p> <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> <p>Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).</p> <p>Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014.</p> <p>R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.</p> <p>Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.</p>
National forest inventory data for a size-structured forest population model
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CABO forest inventory survey data: Canopy-level spectra, species abundances, and environmental conditions
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Stand inventory data (overstory and understory) in northern New Mexico forests affected by western spruce budworm (Choristoneura freemani Razowski) defoliation, 2012-2013
Stand Selection Stands were selected based on data provided by the United States Forest Service insect and disease aerial survey maps. The following criteria was used for stand selection: 1. At least 50% of pre-2000 species composition comprised of the same host tree; 2. No forest treatments within previous 20 years; and 3. Similar slope, aspect, vegetation association and elevation. Stands ranged from west-central New Mexico to north-central New Mexico. Sampling was completed in the summers of 2012 and 2013 in the Mount Taylor stands and the summer of 2013 for the remainder of the stands. Plots A randomized, systematic grid of ten clusters of two 0.02 ha plots were established using GIS software and exported to a handheld GPS. One plot of each cluster was located on the intersection of the grid (‘grid plots’) and the second located 50m at a random azimuth from the established grid plot (‘cluster plots’). This methodology was shown to improve sampling efficiency for stand characteristics pertaining to western spruce budworm within a set allowable error (Lynch 2003). Five 0.001 ha nested regeneration plots were also established (described below). Plot Characteristics Vegetation association was assessed using the Plant Associations of Arizona and New Mexico habitat typing guide. Canopy cover was recorded using a GRS densiometer in 1m increments on two 15.96 m transects bisecting plot center running north to south and east to west. Measurements will begin at 1m and extend to 15m totaling 15 measurements on the north to south transect. The east to west transect will exclude the measurement at 8m to avoid repeated measurements. Canopy cover was calculated by the number of canopy “hits” divided by the total number of measurements taken Overstory Measurements Species and diameter at breast height (DBH) was measured for all trees greater than 12.7 cm in diameter occurring in plot. DBH was considered to be 1.37 meters above ground. The height and canopy base height of each t
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