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152 results for “Forest inventory”
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.
Tree Inventories for Validating Terrestrial Lidar Measurements at Harvard Forest 2007-2014
Our objective is to improve the measurements of canopy structure and biomass of a forest stand and detect their annual changes via a ground-based laser scanning technology, also known as terrestrial lidar (TLS). A TLS instrument utilizes lasers to scan an environment, measure 3D locations of objects encountered by lasers and detect intensities of laser lights scattered by those objects back to the TLS instrument. TLS have shown abilities and is being further explored to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, aboveground biomass and other useful forest structural parameters rapidly and accurately. Three TLS instruments used in this project include: (1) the Echidna (R) Validation Instrument (EVI), built by CSIRO Australia; (2) Dual-Wavelength Echidna® Lidar (DWEL), built by Boston University, University of Massachusetts, Lowell, University of Massachusetts, Boston and CSIRO Australia; (3) Compact Biomass Lidar (CBL), built by University of Massachusetts, Boston. To validate the forest structural parameters retrieved using these TLS instruments, we set up a one-ha (100 m by 100 m) forest site and collected tree inventory data including: tree location, tree species, DBH, tree height and crown dimension since 2007 with a two-year gap of 2008 and 2009. Lidar data are available from the ORNL DAAC (http://dx.doi.org/10.3334/ORNLDAAC/1045).
Vegetation Inventory of Harvard Forest 1986-1993
The three main tracts of the Harvard Forest (3000 acres) in Petersham MA have been sampled every 10-30 years since 1907. Though methods have varied, each survey has involved mapping forest stands followed by intensive sampling. This inventory was completed in 1986-1993.
Biomass Inventories at Harvard Forest EMS Tower since 1993
In 1993, we installed 40 circular, 10 m radius biometric plots in the footprint of the EMS tower on Prospect Hill. We randomly placed the plots within 100 m increments along ten 500 m transects that extend from the tower in the northwest and southwest directions. In 2001, we removed three plots (G3, H3, H4) from the datasets and ceased measurements there due to their inundation by a beaver pond. In 1999, we installed 6 additional circular, 10 m radius biometric plots on the Simes Lot, adjacent to Prospect Hill to study the effects of a selective harvest that occurred there in the winter of 2000-01. In the summer of 2001, we expanded the harvested plots in size to 15 m radius and ceased measurements at one plot (X4) because it was unaffected by the harvest. The harvest also affected three of the original tower plots (A4, A5, B5), which were expanded in size as a part of the harvest plot group. Consequently, there are 34 tower plots and 8 harvest plots. We have taken the following ecological measurements at each site: tree growth, woody debris, litter, leaf area increment (LAI), leaf chemistry, and soil respiration and moisture.
Inventory of Ants at the Black Rock Forest in Cornwall NY 2006-2015
Ants are key indicators of ecological change, but few studies have investigated how ant assemblages may respond to dramatic changes in vegetation structure in temperate forests. Pests and pathogens are causing widespread loss of dominant canopy tree species; ant species composition and abundance may be very sensitive to such losses. Prior to the experimental removal of red oak trees to simulate effects of sudden oak death and examine the long-term impact of oak loss at the Black Rock Forest (Cornwall, New York), we carried out a rapid assessment of the ant assemblage in the 10-hectare experimental area. We also determined the efficacy in a northern temperate forest of five different collecting methods - pitfall traps, litter samples, tuna-fish and cookie baits, and hand collection - routinely used to sample ants in tropical systems. A total of 33 species in 14 genera were collected and identified; the myrmecines Aphaenogaster rudis and Myrmica punctiventris, and the formicine Formica neogagates were the most common and abundant species encountered. Ninety-four percent (31 of 33) of the species were collected by litter sampling and structured hand sampling together, and we conclude that in combination, these two methods are sufficient to assess species richness and composition of ant assemblages in northern temperate forests. Using new, unbiased estimators, we project that 38-58 ant species are likely to occur at Black Rock Forest. Loss of oak from these forests may favor Camponotus species that nest in decomposing wood and open-habitat specialists in the genus Lasius.
Long-term dynamics of tropical rain forests in permanent inventory plots, La Selva, Costa Rica (1969-1995)
Three permanent plots comprising a total of 12.4 ha were established in 1969 in tropical rain forest at La Selva Biological Station, near Puerto Viejo de Sarapiquí, in the Caribbean lowlands of Costa Rica. The plots were established in old-growth forest on three contrasting landforms: Plot 1 (4.4 ha) on old alluvial terrace; Plot 2 (4.0 ha) in swamp forest and rolling hills; and Plot 3 (4.0 ha) on steeply dissected terrain with residual soils. The data archived here include plot inventories carried out at five census dates over a period of 27 years. The inventory starting dates were 1969; 1982; 1985; 1989; and 1995. All stems 10 cm dbh or greater were tagged with a permanent numbered tag; measured in diameter at breast height and above buttresses to the nearest mm; mapped on the ground to the nearest m; and identified to species. At each census, live trees were re-measured, dead trees were recorded along with information on the manner of death, other details on the condition of the tree were noted, and new recruits were tagged, mapped, measured, and identified. The archived data include these five components: (1) The master data file, including comprehensive data on all tagged individuals in the three plots for the five censuses from 1969-1995. Each line in the data set represents an individual tagged tree or liana. The data array comprises 8689 lines (the number of tagged individuals) x 48 columns of data. The lines in the data set are ordered first by Plot number (1, 2, 3); next by subplot within each plot; and then by tag number within each subplot. (2) A list of column identifiers, describing in detail the information represented in each of the 48 columns within the master data file. The list gives a description of the data in each column, the units of measurement, and a guide to the interpretation of zeroes in the data. (3) A key to codes used in the field to describe the condition of individual trees. (4) A taxonomic reference list, including all species found
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.
Cooperative Alaska Forest Inventory (CAFI): V - Photo Collection 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 collection of photos taken at the sites.
Forest Inventory for Tree Demography and Carbohydrate Reserves at Harvard Forest 2009-2011
The objective of this study is to establish a network of forest plots spanning the eastern U.S. focused on understanding how regional scale climate patterns affect patterns of tree demography (e.g. growth, mortality, dieback). Within each research site we also aim to understand the landscape-scale variability in demographic rates and how these rates are affected by edaphic variables by aligning plots along primary environmental gradients (elevation, soils, hydrology, fire return interval). The demographic data obtained will also be compared to regional and landscape-scale patterns in carbon reserves in adults and sapling by sampling root and stem concentrations of nonstructural carbohydrates (TNC) and relate these to demographic patterns, life-history traits, and plant C:N ratios. Besides addressing important ecological questions directly, this study is designed to improve the representations of vegetation dynamics and carbohydrate reserves in regional and landscape-scale forest ecosystem models--two of the least data-constrained processes in such models--by parameterizing and validating the modules for these processes in the Ecosystem Demography model (ED v2.1). This data set contains two years of census data for eight mapped plots distributed across Harvard Forest along the aforementioned primary environmental gradients.
Vegetation Inventory of Harvard Forest 1937
The three main tracts of the Harvard Forest (3000 acres) in Petersham, MA have been sampled every 10-30 years since 1907. Though methods have varied, each survey has involved mapping forest stands followed by intensive sampling. The 1937 forest inventory was conducted one year before 75% of the standing timber at Harvard Forest was blown down by the 1938 hurricane. This valuable data set shows maximum vegetation development since agricultural abandonment. Data collected in this inventory include tree volume by species and presence/absence of advance regeneration, shrubs, herbs and bryophytes.
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>
New England Enhanced Forest Inventory
Light detection and ranging (LiDAR) has become a common tool for generating remotely sensed forest inventories. However, regional modeling of forest attributes using LiDAR has remained challenging due to varying parameters between LiDAR datasets, such as pulse density. Here we develop a regional model using a three dimensional convolutional neural network (CNN). We then apply our model to publicly available data over New England, generating maps of fourteen forest attributes at a 10 m resolution over 85 % of the region. Attributes include aboveground biomass (kg), total biomass (kg), tree count (#), percent conifer (%), basal area (m^2), mean height (m), quadratic mean diameter (cm), percent spruce/fir (%), percent white pine (%), inner bark volume (m^3), merchantable volume (m^3), and spruce/fir volume (m^3. All values correspond to the amount per pixel cell (I.E. kg of biomass found within that pixel). Map/model performance was assessed using the USFS’s FIA inventory, which constituted an independent dataset free from spatial autocorrelation. More data can be found in the following pre-print: Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J. A., Cook, B. D., & Weiskittel, A. R. (2019). Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Forest Inventories. bioRxiv, 580514.
Hubbard Brook Experimental Forest: Watershed 1 Tree Inventory, 1996 - ongoing
In order to evaluate the role of Ca supply in regulating the structure and function of base-poor forest and aquatic ecosystems, the Ca content of soil was increased through the application of wollastonite (CaSiO3) in October 1999. The watershed is forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 90 % of its area, and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 10%. Forest inventory surveys were initiated in 1996 and repeated at 5 year intervals. This data set includes 2016 inventory measurements. The data consists of a total inventory of all trees ≥10 cm diameter-at-breast-height (dbh) on the whole of the watershed (11.8 ha), as measured in each of the 200 25 m x 25 m plots. Trees ≥ 2 to ≤10 cm dbh were subsampled using a 3 meter wide strip along one edge of each 25 m x 25 m plot. With the addition of tree tags in 2006 on all trees ≥10 cm dbh, tracking of individual trees is now possible nd trees that grow into the ≥10 cm dbh size class are tagged each survey. The data consist of the diameters (dbh) of all the trees ≥10 cm dbh, live and dead, in the whole of the watershed (about 9000 individual stems) and an additional 3000-4000 saplings. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Watershed 5 Tree Inventory, 1982 - ongoing
A whole-tree harvest was conducted during the dormant season of 1983-1984 in order to assess ecosystem response to whole-tree logging operations. Pre-harvest forest inventory surveys were conducted in 1982 on the whole of the watershed. Post-harvest surveys were conducted in 1990, 1994 and every 5 years thereafter. This data set includes data for 1982 – 2019 surveys. The hydrology has been monitored since 1962 and stream water chemistry monitored since 1963. In 1982, before the clearcut, the watershed was forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 85 % of its area and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 15%. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Watershed 6 Tree Inventory, 1965 - ongoing
The watershed is forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 90% of its area and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 10%. Forest inventory surveys were initiated in 1965, repeated in 1977, and repeated at 5 year intervals after that. This data set includes all inventories from 1965 to 2022 (11 surveys). The inventory consists of a total inventory of all trees ≥10 cm diameter-at-breast-height (dbh) (over 11,000 individual stems overtime) on the whole of the watershed (13.23 ha, 549−792 m in elevation), as measured in each of the 208 grid cells (= plots; 25 m x 25 m, 625 m2). Trees ≥2 to <10 cm dbh were subsampled using a 3 meter wide strip along one edge of each 25 m x 25 m plot. While the specifics of the inventory design varied between watersheds and over time, the core measurements were consistent. Differences between exact inventory methods over time are detailed in the Methods. The surveys include 6000 – 7000 live trees and another 2000-3000 dead standing trees. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES) and funded largely through the Long-term Ecological Research (LTER) program through NSF since 1988. The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
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>
LAUTx - Individual Tree Point Clouds From Austrian Forest Inventory Plots
<p>This dataset contains manually segmented tree point clouds from Personal Laser Scanning (PLS) data, and additionally automatic segmented trees from the same point clouds. The raw point cloud data has been published in LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots (<a href="https://doi.org/10.5281/zenodo.3698956">https://doi.org/10.5281/zenodo.3698956</a>) and six of those plots were processed for this data. Purpose of this data is to serve as benchmarking for automatic tree segmentation algorithms.</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>
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