These polygons depict areas where invasive plants were removed and native trees and/or shrubs were planted.

Analysis of ‘Forest Restoration Tracking’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/058e17c5-124d-437f-b12c-7db73421181b on 13 February 2022.

--- Dataset description provided by original source is as follows ---

These polygons depict areas where invasive plants were removed and native trees and/or shrubs were planted.

--- Original source retains full ownership of the source dataset ---

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

Results obtained from monitoring activities using acoustic monitoring on forest restoration at HLENR.

This data publication contains twenty-four GeoTIFF files for four significant geographic areas (SGAs) in Alabama, Florida, and Georgia. The extent of the SGAs are defined within the America’s Longleaf Range-wide Conservation Plan for Longleaf (2009). A raster grid file is provided for the extent of each SGA within each state and shows the amount of pine basal area per acre (BAA), the amount of all species BAA, the amount of pine trees per acre (TPA), the amount of all species TPA, dominant forest type classification, visually identified classification, the probability of an area being composed primarily of longleaf pine BAA, and the probability of an area being composed primarily of regeneration. These raster surfaces were created using machine learning relationships between FIA plot information (2010-2015) and NAIP imagery (2013) and are intended to be used to help quantify existing conditions of forested ecosystems and help prioritize longleaf restoration efforts across the four SGAs.Intended use for these datasets include: helping quantify existing conditions of forested ecosystems and helping to prioritize Longleaf restoration efforts across four significant geographic areas described in America’s Longleaf Range-wide Conservation Plan for Longleaf (2009).Original metadata date is 03/06/2017. Minor metadata updates made on 9/14/2018, 07/02/2019, and 09/16/2024.

Effectiveness monitoring of a randomized selection of 10% of Fisheries Habitat Restoration (Fisheries Restoration Grants Program and Forest Land Anadromous Restoration) habitat restoration projects in the North Coast Hydrologic Region. Survey assesses the effectiveness of habitat restoration projects and treatment types based on qualitative assessment ratings of habitat response from a sample of individual treatment features for a range of project types. Survey assesses the effectiveness of given treatment types through both qualitative ratings and quantitative metrics evaluated both pre- and post-restoration project implementation. 6.276 MB

This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000236. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.

Raw data of reference values collected in the literature and from a monitoring database in the Brazilian Atlantic Forest.

Plot number and location with tree health metrics. Visit https://dataone.org/datasets/p68.ds60_20241030_0300 for complete metadata about this dataset.

Analysis of ‘Collaborative Forest Landscape Restoration Program: Line (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3cdad543-88c0-40f8-af2d-21738c21ee97 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

--- Original source retains full ownership of the source dataset ---

Fractional tree cover facilitates the depiction of forest density and its variations. However, it remains challenging to estimate fractional tree cover from satellite data, leading to substantial uncertainties in forest cover changes analysis. We generated a global annual fractional tree cover dataset (GLOBMAP FTC) from 2000 to 2021 with a resolution of 250 m using the MODIS land surface reflectance products. MODIS annual observations were realigned at the pixel level to a common phenology with the midpoint date of growing season centered in the calendar year. Twelve features that can differentiate between trees and herbaceous vegetation were extracted from the realigned MODIS observations. A massive training data, consisting of 465.88 million sample points, was collected across the globe from four high-resolution forest cover products, including ESA WorldCover, GlobeLand30, PALSAR FNF, and ESRI Land Cover. A feedforward neural network model was trained to predict tree cover. Global and regional analysis demonstrated that the dataset could characterize the forest cover changes, including both abrupt forest loss and gradual forest gain. This dataset can be applied for assessment of forest cover dynamics and monitoring the progress of forest restoration projects and policies.

The dataset is provided by 296 to 323 1200 km × 1200 km tiles for each year, aligning with the MODIS standard tiling system. The data are stored in the Sinusoidal projection with Geotiff format. The files are designated with the naming structure "GLOBMAPFTC.AYYYY001.hHHvVV.V01.tif", where “YYYY” represents the year of the data, while “HH” and “VV” indicate the horizontal and vertical tile indices respectively, aligning with the MODIS standard tile. The valid range of tree cover value is 0-100, with a scale factor of 1.0 and the unit of percentage (%).

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012.

Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.

Analysis of ‘Collaborative Forest Landscape Restoration Program: Polygon (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f506759a-e292-4de7-a4ee-531e124ef998 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

--- Original source retains full ownership of the source dataset ---

This dataset contains comprehensive statistics on stand structure, rejuvenation, and vegetation for each forest reserve included in the monitoring program on strict forest reserves in Flanders (Belgium). The data collection and processing methodology used are described in Vandekerkhove et al., 2021.

The dataset encompasses information on 15 distinct strict forest reserves and covers one to three consecutive forest inventories following a 10-year cycle.

The "site_info.xlsx" file provides details on the 15 sites, including central coordinates, surface area, forest type, and the year when they were set aside. For detailed descriptions of the information provided, please refer to the "_metadata_site_info.xlsx" file.

The “statistics_per_reserve.zip” file contains 12 separate csv-files with the following data:

stat_dendro(_by)(_diam)(_species).csv : statistics on basic stand structure attributes (volume, basal area, number of trees per hectare, …) for living and dead standing trees (DBH-threshold 5cm): overall values and values per diameter class and/or species

stat_carbon.csv : statistics on biomass and carbon stock related to living trees

stat_logs(_by)(_decay)(_species).csv : statistics on volume per hectare of lying deadwood: values per decay stage and/or species

stat_reg(_by)(_height)(_species).csv : statistics on rejuvenation (numbers per ha of young trees - seedlings up to trees with DBH< 5cm): overall values and values per heightclass and/or species

stat_veg.csv : statistics on vegetation characteristics, including number of species, moss, herb, shrub, tree and waterlayer cover, cumulated canopy cover and soil disturbance by game

stat_herbs.csv : species-specific mean cover, percentage of plots where each species occurs

For descriptions of the common fields used in the above csv-files, please consult the "_metadata_statistics.xlsx" file.

For detailed and comprehensive information regarding the meaning and characteristics of the calculated variables, please refer to the "_metadata_variables.xlsx" file.

Plot-level results regarding dendrometry and regeneration are published separately here, while information on vegetation can be found here.

For any inquiries or further information, please contact Kris.vandekerkhove@inbo.be or Anja.leyman@inbo.be. The provided csv-files will be updated as required to address any issues or include data from additional surveys. . Please check for updated versions periodically.

We ask the users of the dataset to notify us of its use.

As global tree-growing efforts have escalated in the past decade, copious failures and unintended consequences have prompted many reforestation best practices guidelines. The extent to which organizations have integrated these ecological and socioeconomic recommendations, however, remains uncertain. We reviewed websites of 99 intermediary organizations that promote and fund tree-growing projects to determine how well they report following best practices. Nearly half the organizations stated tree or area planting targets, but only 25% had measurable, time-bound objectives. Most organizations discussed the benefits local communities would receive from trees, but only 38% reported measures of these outcomes. Non-profit organizations with greater prior experience converged more closely on best practices, and their level of scientific expertise was positively associated with clearer project selection standards. Although many tree-growing organizations acknowledge the importance of clear goals, local community involvement, and monitoring, our results raise questions regarding whether long-term benefits are being achieved and emphasize the need for stronger public accountability standards.

This lidar dataset was collected as part of an NCALM Seed grant for Cassie Lumbrazo at the University of Washington. This study used airborne lidar to evaluate the hydrologic effects of forest restoration in Washington State’s Eastern Cascade Mountains. The study area consists of sections located northwest of Ellensburg, WA, and covers approximately 62.7 km2. Publications associated with this dataset can be found at NCALM's Data Tracking Center

Monitoring data collected in 550 areas undergoing restoration in the Brazilian Atlantic Forest.

This dataset consists of repeat vegetation cover maps of multiple Willamette River restoration sites where restoration activities were implemented to increase the area of floodplain forests. Beginning in the early 21st century, large-scale restoration programs have been implemented along the Willamette River, Oregon, to address historical losses of floodplain habitats for native fish (Keith and others, 2022). For much of the Willamette River floodplain, direct enhancement of floodplain habitats through restoration activities is needed because the underlying hydrologic, geomorphic, and vegetation processes that historically created and sustained complex floodplain habitats have been fundamentally altered by dam construction, bank protection, large wood removal, land conversion, and other influences (for example, Hulse and others, 2002; Wallick and others, 2013). Floodplain forest vegetation cover was derived from R Random Forest classification of 2009, 2011, 2018, and 2020 aerial i ...

Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests. Current manual forest carbon stock inventory methods of measuring single trees by hand are time, labour, and cost-intensive and have been shown to be subjective. They can lead to substantial overestimation of the carbon stock and ultimately distrust in forest financing. The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.

In this paper, we present ReforesTree, a benchmark dataset of forest carbon stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we show that a deep learning-based end-to-end model using individual tree detection from low cost RGB-only drone imagery is accurately estimating forest carbon stock within official carbon offsetting certification standards. Additionally, our baseline CNN model outperforms state-of-the-art satellite-based forest biomass and carbon stock estimates for this type of small-scale, tropical agro-forestry sites. We present this dataset to encourage machine learning research in this area to increase accountability and transparency of monitoring, verification and reporting (MVR) in carbon offsetting projects, as well as scaling global reforestation financing through accurate remote sensing.

The data consists of date and time of use, activity and number of individuals visiting specific sites. This data was collected by using camera traps to monitor visitors of two mitigation areas that are being restored to riparian forests.

The data consists of date and time of use, activity and number of individuals visiting specific sites. This data was collected by using camera traps to monitor visitors of two mitigation areas that are being restored to riparian forests.