A Forest Mask classifies forest/non forest land coverages (a binary forest land classification). The forest mask product is the basis for other products such as forest type classification or vegetation stress monitoring. These products can be made in High Resolution, Very High Resolution or with LIDAR.

Forest Mask	Product Specifications
Product Description	A binary forest-non forest classification. The Forest Mask product is the basis for other products such as forest type classification or vegetation stress monitoring. The product adapts to the definition of forest valid in each country
Main Applications	• Forest Inventory • Forest and Natural Resources Management • Land Use Land Cover Planning and Dynamics monitoring • Environmental Impact Assessment • Deforestation and Degradation analyses • Biomass estimation and carbon offsets projects • Canopy cover fraction • Biodiversity conservation • Forest fire-fighting plans
Provider	GMV
Format	Classification image in Tiff format (.tif) and metadata (.txt)
Data Source	Sentinel-2 imagery
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0,1 ha. (equivalent to 10 pixels/10m pixel)
Temporal Coverage	Several images are required to include seasonal foliage conditions in the same reference year
Spatial Coverage	Global

This product provides a supervised image classification of the main forest types.

The Stand Height product represents the dominant height of trees within the stands.

Forest Age product is only necessary when the forest management plan does not specify the stands dominant ages. Forest age is calculated by management stand using a multi-temporal analysis of historical satellite data.

Burnt Scars detection starts with the production of a baseline (forest mask). Change detection algorithms are applied to detect the changes in the forest due to wildfires between two given dates.
Detection of changes in the forest due to wildfires between two given dates.

Detection of areas where forest has been cut. It requires a forest mask for a given initial date. Change detection algorithms are used to detect forest changes due to logging practices.

Digital Terrain Model (MDT or DEM) describes the elevation of the Area of Interest. Derived from it, maps of Slope and Aspect represent different terrain characteristics that are of great interest for users.

Site Index is an indicator of the forest biological productivity; this indicator is a baseline for many forest management activities such as forest inventory. The site index can be obtained only if two consecutive LIDAR flights are available together with information on site index curve.

Stand Density is a measure of how many trees are growing per unit area. Stand density is typically predicted with a moderate degree of precision from LIDAR cloud metrics.

Basic wood density is one of the most important properties of wood. At stand level is measured by extracting wood cores from the trees and laboratory analysis. This property is a wood quality characteristic very important for pulp industry: when it increases, raw material needs are lower and yields are bigger. For that product, a mathematical model predicting basic wood density is built using Satellite, LiDAR, climatic and Physiographic data. Depending on the needs of the user, this wood density can be calculated, in field, to validate and to improve the accuracy of the model.

Wood Density Ranking	Product Specifications
Product Description	This product predicts and maps basic wood density for Eucalyptus globulus forest plantations with ages ranging from 8 to 20 years old. Basic wood density (kg/m3) is one of the most important wood technological properties, particularly relevant for pulp production. It was measured extracting wood cores form standing trees and, after that, laboratory analysis was performed. Basic wood density values obtained at tree level were validated with industrial wood density analysis. At stand level, the mean basic wood density value was correlated with different remote data related with infrared bands of Sentinel-2, climatic and physiographic data. A mathematical prediction model was developed integrating multi-temporal Sentinel-2 bands from 2017 to 2019 during growing season, altitude above sea level, slope and annual mean precipitation. This product was developed with 87 field plots located along the Eucalyptus globulus distribution area in Galicia (Spain) and north of Portugal. The algorithm was obtained using PLS (Partial Least Squares Regression) and a cross validation leave one out was performed (R2adj=0,79 a RMSE=17 kg/m3). Other preliminary prediction model were explored and mapped for Quercus robur in Croatia and Picea abies in Czech Republic, showing interesting relationship with drought and pest susceptibility.
Main Applications	Basic Wood Density prediction/optimized CO2 stock calculation/ potential indirect drought and pest susceptibility
Provider	MADERA Plus
Format	Classification image in vector format (.shp) and metadata (.txt)
Input Data	Multi-temporal series of Sentinel 2 data, climatic data and physiographic data. Depending on the AOI other data could be also included in the model: LiDAR
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0.09 ha (3×3 pixel)
Temporal Coverage	Single sampling time
Spatial Coverage	The spatial coverage is limited to the forest species and bioclimatic condition. At this moment, algorithms were developed for Eucalyptus globulus from Galicia and North of Portugal. Preliminary algorithms were also developed for Quercus robur and Picea abies.

Stiffness or Modulus of elasticity (MOE) is a measure of the resistence of wood to deformation under applied load and is a good indicator of wood quality for structural uses. At tree level, Stiffness is determined by sound stress waves, to estimate velocity, and by green wood density data using the folowing ecuation MOE=V2xdg . After that a mathematical model predicting MOE is built using Satellite, LiDAR and Physiographic data.

Wood Stiffness	Product Specifications
Product Description	This product predicts and maps wood stiffness in Pinus sylvestris forest stands with at least 60 years old. Wood stiffness or Modulus of Elasticity (MOE) measures wood measures wood resistance to deformation which is essential for quality structural uses. At tree level, stiffness is obtained basing on longitudinal wave velocity and outerwood density on standing trees. This product was developed with 28 field plots located along Roncal Valley in Navarre (Spain). The algorithm was obtained using PLS (Partial Least Squares Regression) and a cross validation leave one out was performed (R2adj=0,75 a RMSE= 885 MPa).
Main Applications	Wood quality characterization for structural uses
Provider	MADERA Plus
Format	Classification image in vector format (.shp) and metadata (.txt)
Input Data	Multi-temporal series of Sentinel 2 data, LiDAR statistics and Physiographic data. Depending on the AOI, other data could be also included
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0.09 ha (3×3 pixel)
Temporal Coverage	Single sampling time
Spatial Coverage	The spatial coverage is limited to the forest species. At this moment preliminary algorithms were developed for Pinus sylvestris and will be validated in Pinus pinaster.

This product is based on Wood Stiffness product. Once the values of wood stiffness are obtained, the strength class is assigned using the normative of structural wood classification. This product was generated in 2019 for Pinus sylvestris in FORESNA 2.

Wood Strength	Product Specifications
Product Description	This product is based on wood stiffness values. Thereafter, the Strength Class is assigned using the normative of structural wood classification. Product available for Pinus sylvestris.
Main Applications	Wood quality characterization for structural uses
Provider	MADERA Plus
Format	Classification image in vector format (.shp) and metadata (.txt)
Input Data	Wood Stiffness
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0.09 ha (3×3 pixel)
Temporal Coverage	Single sampling time
Spatial Coverage	The spatial coverage is limited to the forest species. At this moment preliminary algorithms were developed for Pinus sylvestris and will be validated in Pinus pinaster

Volume describes m3/ha of wood, the same way biomass describes t/ha of available biomass and CO2 represents the carbon absorbes by a forest in that moment (t CO2/ha).

Volume, Above Ground Biomass & CO2 Stock	Product Specifications
Product Description	Volume is the available quantity of wood for industry or other purposes (m3/ha). AGB describes t/ha of available biomass and CO2 stocks represents the carbon absorbed by the forest (t CO2/ha). Three different outputs will be provided
Main Applications	• Forest Inventory • Environmental Impact Assessment • Biomass Estimation and Carbon Offset projects • Sustainable Forest Management and Exploitation
Provider	FÖRA
Format	Classification image in shape format and metadata
Input Data	LiDAR data: either free or fee-paying data. If LiDAR data are not available on the AOI of interest, private flights to collect LiDAR data would need to be costed
Spatial Reference	ETRS89
Spatial Resolution	20/25 m (depending on data from users it can be personalized)
Minimum Mapping Unit (MMU)	0.04/0.0625 ha (depending on data from users it can be personalized)
Temporal Coverage	Single date (year depending on LiDAR data availablility and Area Of Interest) except for Area Of Interest FORESNA and UFE (two dates were available)
Spatial Coverage	Limited to availability of LiDAR information

Biotic Damage is produced when an activation is raised because the concurrence of a pest or disease outbreak. The objective is detect the forest loss due the catastrophic event by analysing an image just after the event and an image just before the event. The output of this product is the forest area affected by the biotic damages and an actualized forest mask after the event.

Biotic Damage	Product Specifications
Product Description	This product detects the concurrence of a pest outbreak and diseases, estimates the forest loss and the area affected, eventually updating the Forest Mask
Main Applications	• Near-Real Time Pest and Diseases Damage Assessment • Environmental Impact Assessment
Provider	GMV
Format	Classification image in raster format (.tif) and metadata (.txt)
Data Source	Sentinel-2 imagery
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0,1 ha. (equivalent to 10 pixels/10m pixel)
Temporal Coverage	Several images are required to perform a monitoring of subsequent pest outbreak and diseases as of reference date
Spatial Coverage	Global

Drought Damage is produced when an activation is raised because a drought event which was detected by meteorological stations. The objective is detect the forest loss due the catastrophic event by analysing an image just after the event and an image just before the event. The output of this product is the forest area affected by the drought event and an actualized forest mask after the event.

Drought Damage	Product Specifications
Product Description	This product estimates affected drought area on the forest. Multi-temporal vegetation indices and climate data are combined with Machine Learning algorithms to correlate forest health decay with drought periods. The output provides a map showing the areas damage caused during the drought episode.
Main Applications	• Drought Monitoring • Climate Change Monitoring • Environmental Impact Assessment • Water Resources Management
Provider	GMV
Format	Classification image in raster format (.tif) and metadata (.txt)
Input Data	Sentinel-2 imagery and Drought Datasets provided by European Drought Observatory (EDO)
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0,1 ha. (equivalent to 10 pixels/10m pixel)
Temporal Coverage	Since 2016
Spatial Coverage	Global

A geo-data base of basins and stream network based on the international cartographic standard MGCP. The Basin Stream delineation seeks a data base capable of estimating the flood prone areas along the stream banks. The stream network is most important to monitor the health condition of riparian forests.

Basin Stream Network	Product Specifications
Product Description	A geo-database of the hydrographic network, adapted from the international cartographic standard Multi-National Geospatial Co-Production (MGCP). Working scale, 1:5,000. Features: rivers, streams and water bodies. Basin delineated with DEM, watershed hierarchy included. Features are attributed with descriptive data for consultation. INSPIRE standards apply.
Main Applications	• Flood risk and erosion (soils and riverbeds) • Water resources and forest restoration • Environmental Assessment
Provider	GMV
Format	Vector format (.shp) and metadata (.txt).
Input Data	Very High Resolution (VHR) imagery and Copernicus EU-DEM v1.0, hybrid SRTM and ASTER GDEM (25m)
Scale	Representation: 1:5,000; Feature extraction, 1:2,000
Temporal Coverage	–

Biodiversity map represents the variety of dominant tree species applying diversity indices. Habitat fragmentation map represents the isolation between forest/species patches.

Biodiversity-Habitat Fragmentation	Product Specifications
Product Description	The Biodiversity product represents intrinsic diversity of forest community calculated through indices concerning dominance, abundance, and uniformity. The Habitat fragmentation product is obtained by classifying spatial patterns of forest patches and measuring forest area density to quantify forest fragmentation. These two products are derived with different methodologies and provide independent outputs.
Main Applications	• Landscape Ecology • Ecosystem Restoration • Environmental Impact Assessment
Provider	GMV
Format	Classification image in raster format (.tif) and metadata (.txt)
Input Data	Sentinel-2 imagery
Spatial Reference	WGS 1984
Spatial Resolution	10 m
Minimum Mapping Unit (MMU)	0,1 ha. (equivalent to 10 pixels/10m pixel)
Temporal Coverage	Multitemporal
Spatial Coverage	Global

Physical asset accounts for forest and other wooded lands (land accounts) describe the area of land and related changes over an accounting period. This product is developed for land covers derived from forest and other wooded land data obtained from satellite and LIDAR images. The reporting unit is Ha.

Land Physical Account	Product Specifications
Product Description	Physical asset accounts for forest and other wooded lands (land accounts) describe the area of land and related changes over an accounting period. This product is developed for land covers derived from forest and other wooded land data obtained from satellite and LiDAR images. The reporting unit is Ha.
Main Applications	System of Environmental-Economic Accounting (UN-SEEA)
Provider	EFI
Format	Product in raster format and Excel file (.xlsx)
Input Data	Raw Satellite data: none; MSF satellite-based Products: Forest Mask and Main Forest Types (multitemporal: at two points in time); non-EO data (strict natural reserves extent, tree species information)
Spatial Reference	WGS 1984
Spatial Resolution	100m
Minimum Mapping Unit (MMU)	1 ha (100m x 100m pixel)
Temporal Coverage	Multitemporal – this product compare two different time period to assess the changes in flows
Spatial Coverage	Regional, Provincial

The wood physical asset account records the volume of timber resources and their changes of a given area over an accounting period. This product is developed for data obtained from LIDAR images. The reporting unit is m3.

Physical Wood Account	Product Specifications
Product Description	The timber physical asset account records the volume of timber resources and their changes of a given area over an accounting period. This product is developed for data obtained from LiDAR images. The reporting unit is m3.
Main Applications	System of Environmental-Economic Accounting (UN-SEEA)
Provider	EFI
Format	Product in raster format and Excel file (.xlsx)
Input Data	Raw Satellite data: none; MSF satellite-based Products: As “Land Physical Accounts” product (Forest Mask and Main Forest Types)+ Burnt Scars (multitemporal: various depending of timeseries to analyse) and Clear Cuts (multitemporal: various depending of timeseries to analyse); MSF LIDAR-based Products: Volume (multitemporal: at two points in time), DEM-Elevation, Slope, Aspect and Site Index; Non-EO data As “Land Physical Accounts” product (strict natural reserves extent, tree species information)
Spatial Reference	WGS 1984
Spatial Resolution	100m
Minimum Mapping Unit (MMU)	1 ha (100m x 100m pixel)
Temporal Coverage	Multitemporal – this product compare two different time period to assess the changes in flows
Spatial Coverage	Regional, Provincial

The wood monetary asset account records the value of timber resources and their changes of a given area over an accounting period. This product is developed for data obtained from LIDAR images. The reporting unit is euro.

Monetary Wood Account	Product Specifications
Product Description	The timber physical asset account records the value of timber resources and their changes of a given area over an accounting period. This product is developed for data obtained from LiDAR images. The reporting unit is euro.
Main Applications	System of Environmental-Economic Accounting (UN-SEEA)
Provider	EFI
Format	Product in raster format and Excel file (.xlsx)
Input Data	Raw Satellite data: none; MSF satellite-based Products: As “Physical Woods Accounts” product (Forest Mask, Main Forest Types, Burnt Scars and Clear Cuts) + Forest Age (multitemporal: at two points in time); LIDAR-based Products: As “Physical Woods Accounts” product (Volume, DEM, and Site Index) and Forest Age (multitemporal: at two points in time) + Forest Height (multitemporal/ at two points in time); Non-EO data As “Land Physical Accounts” product (strict natural reserves extent, tree species information)+ information on wood prices for the corresponding tree species.
Spatial Reference	WGS 1984
Spatial Resolution	100 m
Minimum Mapping Unit (MMU)	1 ha (100m x 100m pixel)
Temporal Coverage	Multitemporal – this product compare two different time period to assess the changes in flows
Spatial Coverage	Regional, Provincial