Logging industries, annual 21 principal statistics (revenues, expenses, salaries, employment, stocks, etc.), by North American Industry Classification System (NAICS), total and 6-digit level.

A summary of various datasets on logging concessions, exports, forest cover are presented here.

Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the central Cascade Mountain area, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest patches identified in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras of logging trends that align with prevailing regulations and economic conditions. We used multiple logistic regression to determine the biophysical and anthropogenic factors that influence fine-scale selection of harvest stands in each time period. Results show that private forestland became significantly reduced and more fragmented from 1985 to 2014. Variables linked to parameters of site conditions, location, climate, and vegetation greenness consistently distinguished harvest selection for each distinct era. This study demonstrates the utility of annual LULC data for investigating the underlying factors that influence land cover change.

Forests cover over ************ hectares of the Earth's landmass, around ** percent of the total land area. As of 2023, worldwide forest area measured some **** billion hectares, slightly down from approximately **** billion hectares in 1990. From 1990 to 2022, no country saw a greater percentage change in forest area than Côte d'Ivoire, which lost more than half of its forests.

From 1990 and up until 2010, South America was the region in the world with the highest rate of forest loss, with an estimated 5.2 million hectares of net forest lost per year in the first decade of this century. Since then, the destruction of South American forests has slowed down to an average of 2.6 million hectares per year, the second largest forest loss rate in the world after Africa. The figures suggest that, despite reforestation efforts, forest areas in South America continue to be endangered by massive deforestation and wildfires.

Market Size statistics on the Logging industry in the US

Taiwan Taichung City Forest main product logging data, including: logging quantity, forest, area, clear-cut, selective cutting, standing timber volume, timber, firewood. Bamboo, area, number of plants. Production quantity, timber, firewood, branches and leaves, bamboo and other data. The statistics are surveyed once every quarter, reported by the district office to the county government for consolidation, and the data can be provided after the end of each quarter and two months later.

Graph and download economic data for Employment for Agriculture, Forestry, Fishing and Hunting: Logging (NAICS 113310) in the United States (IPUAN113310W010000000) from 1987 to 2024 about hunting, forestry, fishing, logging, agriculture, NAICS, IP, employment, and USA.

This table contains data described by the following dimensions (Not all combinations are available): Geography (16 items: Canada; Atlantic Region; Newfoundland and Labrador; Prince Edward Island; ...) Principal statistics (16 items: Total revenue; Revenue from logging activities; Total expenses; Total salaries and wages, direct and indirect labour; ...) North American Industry Classification System (NAICS) (3 items: Logging; Logging (except contract); Contract Logging).

Russia had the largest forest area in the world in 2023, amounting to around 815 million hectares, more than twice that of Canada, whose forest area amounted to 346 million hectares. The forestry industry in Canada With the third largest forest area in the world, Canada’s forestry industry is a significant contributor to the country’s gross domestic product. In 2023, the nominal GDP of Canada’s forest industry reached more than 27 billion Canadian dollars, with the wood product manufacturing sector alone contributing around 13.3 billion Canadian dollars in nominal GDP. A comparison of Canadian provinces shows that British Colombia has the largest forestry and logging industry in the country, followed by Quebec and Ontario. The Amazon rainforest in Brazil Brazil has the second largest forest area in the world after Russia, with total forest areas in the South American country amounting to approximately 494 million hectares in 2022. This is largely because around 62 percent of the Amazon rainforest is located in Brazil. The Amazon rainforest is the world’s largest rainforest, what some call “the lungs of the planet”. However, in recent years, deforestation has been a salient issue in the Amazon, with illegal logging and wildfires raging across the rainforest have contributed to very high deforestation rates. Indeed, around 8,000 square kilometers were destroyed in the Brazilian Amazon in 2023. Deforestation and its impact on climate change has spurred opposition to the logging industry, which was the sector responsible for the most killings of environmental activists in 2021.

In 2023, preliminary figures showed that the gross domestic product (GDP) from forestry and logging in Indonesia amounted to approximately ****** trillion Indonesian rupiah. The GDP from forestry and logging in Indonesia has gradually increased since 2014.

This data set contains the results of a survey of logging damage in a 18 ha plot (300 m N-S, 600 m E-W) east (upwind) of the eddy flux tower at km 83, Tapajos National Forest, Para, Brazil. Data collected include type of damage, snap height, and log dimensions, as well as calculated biomass of stems and canopy either damaged or removed in logging. There are two comma-delimited data files with this data set.

130 views (3 recent) Dataset extent Map data © OpenStreetMap contributors. Metsäkeskus publishes monitoring information on felling intentions in accordance with the forest use declaration. The forest owner is obliged to make a forest use notification to Metsäkeskus in advance. The obligation under the Forest Act applies to all commercial logging with the minor exceptions mentioned in the Act. The forest use notification contains, among other things, information on the location, area and felling method of the planned felling. Map view of forest use notifications submitted to Metsäkeskus The weekly monitoring compiles an estimate of the amount of harvest accumulation (m³ / ha) and the accumulation of carrier money (€) from the beginning of the year. The map service displays incoming forest use notifications as location information. Statistics on logging methods are also produced annually.

This spreadsheet consist of data and graphs from deep well 58-32 stress testing from 6900 - 7500 ft depth. Measured stress data were used to correct logging predictions of in situ stress. Stress plots shows pore pressure (measured during the injection testing), the total vertical in situ stress (determined from the density logging) and the total maximum and minimum horizontal stresses. The horizontal stresses were determined from the DSI (Dipole Sonic Imager) and corrected to match the direct measurements.

Forest area for pacific island countries

Data on Forest Inventory and Analysis (FIA) includes information on Palau's forests 2013-2014. The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. PNW data are combined with data from all states in the U.S. and stored in the national FIADB. Data for any state can be accessed on the national website (see links to national tools below). Please be aware that some documents may be very large. The PNW-FIA Program shifted from a periodic to an annual inventory system in 2001. Periodic inventories sampled primarily timberland plots outside of national forests and most reserved areas, in a single state within a 2- or 3-year window. Typically, re-assessments occurred every ten years in the West. For the annual inventory in the Pacific Northwest all forested plots are now sampled, with one-tenth of the plots in any given state being visited annually. A full annual inventory cycle is complete in ten years. To download and use the FIA Database, follow this link https://www.fs.fed.us/pnw/rma/fia-topics/inventory-data

Overview This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland (UMD), Google, USGS, and NASA, measures areas of tree cover loss across all global land (except - Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval, and additional images used for 2023 and 2024. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time, and is biome-dependent. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced. This data set has been updated five times since its creation, and now includes loss up to 2024 (Version 1.12). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2024, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Since the entire historical timeseries was not reprocessed with the updated methodology, time-series assessments should be performed with caution. Read more about the Version 1.12 update here and access on GEE here. When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold. Resolution: 30mGeographic Coverage: Global land area (excluding Antarctica and other Arctic islands).Update Frequency: AnnualContent Date: 2001-2024

OverviewThis emissions layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2e emissions/ha, between 2001 and 2024). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil organic carbon) and greenhouse gases (CO2, CH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance.Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data; separate rasters for emissions for each year are not available from GFW. All input layers were resampled to a common resolution of 0.00025 × 0.00025 degrees each to match Hansen et al. (2013).We have made several updates to the model since its original release. For documentation through the current version, please refer to this blog. For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to this article.Three variations of emissions rasters are available for download:megagrams CO2e emissions/ha in pixels with >30% tree cover density (TCD) in 2000 or tree cover gain: Used for visualizing (mapping) emissions according to the default GFW TCD threshold because it represents the density of emissions per hectare. You would use this if you want to only include emissions in pixels that are more conservatively defined as forest.megagrams CO2e emissions/pixel in pixels with >30% TCD in 2000 or tree cover gain: Used for calculating the emissions in an area of interest (AOI) according to the default GFW TCD threshold because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. You would use this if you want to only include emissions in pixels that are more conservatively defined as forest.megagrams CO2e emissions/pixel in pixels with any amount of tree cover in 2000 or tree cover gain: Used for calculating the emissions in an area of interest (AOI) without any TCD threshold because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. This would represent the total emissions from tree cover loss in the AOI without applying a TCD threshold. You would use this if you want to include emissions in pixels that have low (<30%) TCD in 2000.The values in the megagrams CO2e/pixel layers were calculated by adjusting the emissions per hectare by the size of each pixel, which varies by latitude. Tree cover density in 2000 is according to Hansen et al. (2013) and tree cover gain between 2000 to 2020 is according to Potapov et al. (2022)Related Open Data Portal layers: Forest Carbon Removals, Net Forest Carbon FluxGoogle Earth Engine: asset (megagrams CO2e emissions/ha in pixels with >30% TCD) and visualization scriptResolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: AnnualDate of Content: 2001-2024CautionsData are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.Values are applicable to forest areas only (canopy cover >30 percent and >5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis.Although emissions in each pixel are associated with a specific year of disturbance, emissions over an area of interest reflect the total over the model period of 2001-2024. Thus, values must be divided by 24 to calculate average annual emissions.Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation.Emissions reflect a gross estimate, i.e., carbon removals from any regrowth that occurs after disturbance are not included. Instead, gross carbon removals are accounted for in the companion forest carbon removals layer.Emissions data contain temporal inconsistencies. Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer here for additional information.Forest carbon emissions do not reflect carbon transfers from ecosystem carbon pools to the harvested wood products (HWP) pool.This dataset has been updated since its original publication. See Overview for more information.

Data were collected in the logging concession at the Fazenda Rohsamar in the municipality of Juruena in northwestern Mato Grosso. Estimates of damage associated with logging operations were made after logging operations were complete in 2003 and 2004. Damage associated with gaps created by felling single trees was estimated in 54 individual gaps. Characteristics of the single harvested tree were recorded and included species, DBH, commercial height, total height, and canopy proportions. Damage to all surrounding trees was recorded. Stratified transects in two logging blocks were used to estimate damage associated with road building and skid trails. Twenty-six transects were established in Block 5 and 21 transects in Block 18 to assess the frequency of damage by log skidders and tree felling. The boundaries between different types of damage were noted along the transect and the length in meters of that damage type along the transect was recorded. From this information, the area of the logging block affected by road building and skid trails was determined.The Gap Survey and the Logging Damage Transects Survey data are provided in comma-separated ASCII files. A third file provides the coordinates of the starting points for the Survey Transects.

The Study’s subject: Forestry is besides the Agriculture one of the most important form of land use by area. Wood harvest indicates the intensity of use of forestal products. Data of wood harvest are the basis for the analysis of forestry profitability. The lumber industry is the forestry’s most important income source, therefore the harvested lumber is sorted in different intended use groups and assessed by a statistical classification. Another important task of forestry is the maintenance of the forest. In this way a fundamental contribution to the care and conservation of the agricultural landscape is made by forestry. These requirements for the forestry should be described by selected statistical indexes and parameters, supplying information about - the forestry entrepreneurs and forestry area,- the development of wood harvest and therefore the intensity of forest use,- the usage of wood as lumber for industry ,- damages of the forest by forest fires,- finally, the total balance-sheet of forestry and foreign trade balance of wood. The description of forest enterprises, forestry areas, and logging is made according to the type of tenure in forestry. There are the following three types of tenure: (a) State Forest (Forest owned by the single federal German States, Forest Trust and the National Forest owned by the German federal government) (b) corporate forest (c) private forest (a) State Forest: This forests are designated as state-owned National Forest. The forest owned by the Federal Republic of Germany, although the state forest, usually referred to as the Federal Forestry. The forests owned by the state are supervised by the Federal Forest Service. The National Forest covers 3.7 percent of the forested area and is located mainly in military used areas and along federal waterways and highways. Federal forests therefore are usually subject of a special purpose.The forests owned by the German federal states predominantly stems from former properties of landlords or sovereigns that in the context of the Enlightenment was transformed from private possession of the former ruling families into state ownership. Another case is ecclesiastical possessions which became state ownership by expropriation under the secularization beginning of the 19th century. (b) Corporate Forest: This forest areas are according to §3 paragraph 3 of the National Forest Act owned by public corporations as for example communities or towns (often called as ‘Kommunalwald’ (= corporate forests), Stadtwald (= urban woodland or city forest), ‘Gemeindewald’ (= communal forests) ). (c) Private forest: Private forests are possessions of natural, legal persons, or business partnerships. In Germany, the area of private woods is around 47% of the total forest area, and therefore this form of forest ownership has the highest proportion of all forms of ownership in Germany. The study’s aim: The aim was the compilation of long time series on the basis of the publications of official statistics. An attempts has been made to cover a period from the start of official statistics from 1871 until to the present in 2010 with statistical parameters of German forestry. For the period of the German Empire (1871 – 1938/39) especially for forest enterprises and for forest areas time series date could be collected from the issues of the statistical yearbook of the German Empire. In the case of the former German Democratic Republic (between 1945-1989) which is since 1990 the area of the new Länder (Brandenburg, Mecklenburg-Western Pomerania, Saxony, Saxony-Anhalt, Thuringia), no information for forestry companies and their forest areas could be found in the publications of official statistics former GDR. In this case the reporting period starts in 1990, the period of time after the German reunification. In the case of logging statistics and usage of wood, however, statistical information for the former GDR was available and could be included into this compilation. The values for total wood balance, and foreign trade balance for timber again refers to the territory of the former Federal Republic of Germany and within the borders of 3rd October 1990. The following topics are covered by the data:A) farms and forest land in total and by ownership (state forest, corporate and community forestry, private forestry);B) logging (logging =) by types of trees and forms of ownership;C) damage caused by forest fires;D) total wood balance and foreign trade balance for wood.