This dataset was generated by the TU Wien Department of Geodesy and Geoinformation.European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788.The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel.Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe.Dataset RecordThe forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below.Code AvailabilityFor the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThe computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

The crown (canopy) closure map was derived from three main sources. The entirety of the Montana portion was obtained from the USGS National Land Cover Database (NLCD) 2001 tree canopy layer. The mountainous portions of Alberta and BC were acquired from the Foothills Model Forest Grizzly Bear Project (FMFGBP). The areas of BC and Alberta not covered by the FMFGBP were modeled by extending the FMFGBP model with Landsat images from 2001 as well as a terrain model. The Crown Closure model was then clipped to forested areas. The Crown Managers Partnership 2001 land cover map was used to define forested areas. This dataset was developed by the Crown Managers Partnership, as part of a transboundary collaborative management initiative for the Crown of the Continent Ecosystem, based on commonly identified management priorities that are relevant at the landscape scale. The CMP is collaborative group of land managers, scientists, and stakeholder in the CCE. For more information on the CMP and its collaborators, programs, and projects please visit: http://crownmanagers.org/ Data represents canopy cover circa 2001-2006; Dataset was first published in November 2012

Characterization of land cover change in the past is fundamental for understanding the evolution and present state of the earth system, the amount of carbon and nutrient stocks in terrestrial ecosystems, and the role played by land-atmosphere interactions in influencing climate. The estimation of land cover changes using palynology is a mature field, as thousands of sites in Europe have been investigated over the last century. Nonetheless, a quantitative land cover reconstruction at continental scale has been largely missing. Here we present a series of maps detailing the evolution of European forest cover during last 12000 years. Our reconstructions are based on the Modern Analogue Technique (MAT): a calibration dataset is built by coupling modern pollen samples with the corresponding satellite-based forest cover data. Fossil reconstructions are then performed by assigning to every fossil sample the average forest cover of its closest modern analogues. The occurrence of fossil pollen assemblages with no counterparts in modern vegetation represents a known limit of analogue-based methods. To lessen the influence of no-analogue situations, pollen taxa were converted into Plant Functional Types prior to running the MAT algorithm. We then interpolate site-specific reconstructions for each timeslice using a four-dimensional gridding procedure to create continuous gridded maps at continental scale. The performance of the MAT is compared against methodologically independent forest cover reconstructions produced using the REVEALS method; MAT and REVEALS estimates are most of the time in good agreement at a trend level, yet MAT regularly underestimates the occurrence of densely forested situations, requiring the application of a bias correction procedure The calibrated MAT-based maps draw a coherent picture of the establishment of forests in Europe in the early Holocene with the greatest forest cover fractions reconstructed between ~8500 and 6000 cal. yr. BP. This forest maximum is followed by a general decline in all parts of the continent, likely as a result of anthropogenic deforestation. The continuous spatial and temporal nature of our reconstruction, its continental coverage and gridded format make it suitable for climate, hydrological, and biogeochemical modelling, among other uses.

This data set is a grid map of forest fragmentation based on 1-km resolution land cover maps for the globe cover (Loveland and Belward 1997; Loveland et al. 1991, 2000; Olsen and Watts 1982). Measurements in analysis windows from 81 km 2 (9 x 9 pixels, small scale) to 59,049 km 2 (243 x 243 pixels, large scale) were used to characterize the fragmentation around each forested pixel. Six categories of fragmentation (interior, perforated, edge, transitional, patch, and undetermined) are identified from the amount of forest and its occurrence as adjacent forest pixels. Interior forest exists only at relatively small scales; at larger scales, forests are dominated by edge and patch conditions.

At the smallest scale, there were significant differences in fragmentation among continents; within continents, there were significant differences among individual forest types. Tropical rain forest fragmentation was most severe in North America and least severe in Europe and Asia. Forest types with a high percentage of perforated conditions were mainly in North America (five types) and Europe and Asia (four types), in both temperate and subtropical regions. Transitional and patch conditions were most common in 11 forest types, of which only a few would be considered as naturally patchy (e.g., dry woodland). The five forest types with the highest percentage of interior conditions were in North America; in decreasing order, they were cool rain forest, coniferous, conifer boreal, cool mixed, and cool broadleaf.

References:

Loveland, T. R. and A. S. Belward. 1997. The IGBP-DIS Global 1 km Land Cover Data Set, DISCover First Results. International Journal of Remote Sensing 18: 3289-3295.

Loveland, T. R., J. W. Merchant, D. O. Ohlen, and J. F. Brown. 1991. Development of a Land-Cover Characteristics Database for the Conterminous U.S. Photogrammetric Engineering and Remote Sensing 57: 1453-1463.

Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, L. Yang, and J. W. Merchant. 2000. Development of a Global Land Cover Characteristics Database and IGBP DISCover from 1-km AVHRR Data. International Journal of Remote Sensing, 21(6/7): 1303-1330.

Olsen, J. S. and J. A. Watts. 1982. Major World Ecosystems Complex Map. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current cropland extent maps are either inaccurate, have coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland area map for the African continent is therefore recognised as a gap in the current crop monitoring services. Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35 SouthTemporal Coverage: 2019Spatial Resolution: 10 x 10 meterUpdate Frequency: TBDNumber of Bands: 3 BandsParent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMADSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)

Digital Earth Africa’s cropland extent maps for Eastern, Western, and Northern Africa show the estimated location of croplands in these countries for the period of January to December 2019:

Eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and BurundiWestern: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-BissauNorthern: Morocco, Algeria, Tunisia, Libya and EgyptSahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and DjiboutiSouthern: South Africa, Namibia, Botswana, Lesotho and Eswatini

Cropland is defined as:

"a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date."

This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.

The provisional cropland extent maps have a resolution of 10 metres and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built separately using extensive training data from Eastern, Western, and Northern Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.

Independent validation datasets suggest the following accuracies:

The Eastern Africa cropland extent map has an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has an overall accuracy of 94.0 %, and an f-score of 0.91The Sahel Africa cropland extent map has an overall accuracy of 87.9 %, and an f-score of 0.78The Southern Africa cropland extent map has an overall accuracy of 86.4 %, and an f-score of 0.75

The algorithms for all regions tend to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur, they tend to be focussed around wetlands and seasonal grasslands which spectrally resemble some kinds of cropping.

Available BandsBand IDDescriptionValue rangeData typeNoData/Fill valuemaskcrop extent (pixel)0 - 1uint80probcrop probability (pixel)0 - 100uint80filteredcrop extent (object-based)0 - 1uint80

mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.

prob: This band displays the prediction probabilities for the ‘crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.

filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm (see this paper for details on the algorithm used). During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the ‘salt and pepper’ effect typical of classifying pixels is diminished.

More details on this dataset can be found here.

CAMRIS incorporates the Australian estuarine database, which includes the National Estuaries Study (Bucher and Saenger 1989, http://dx.doi.org/10.1111/j.1467-8470.1991.tb00726.x). Attributes include location, name, climatic variables, run-off coefficients, land use, flood frequency, water quality, habitat types including seagrass/mangrove/saltmarsh, fisheries/conservation/amenity values, administration, literature and threats.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy: Assumed to be correct. +/- one degree. Non Quantitative accuracy: The estuaries coverage contains 1566 points and the following attributes:

ESTUARY_NO : Inventory number, contains a letter prefix to denote State in which estuary lies. Estuaries are numbered clockwise around the continent.

NAME : Name of major input stream used to identify an estuary unless the estuary itself is named.

GEO_ZONE : Set of 12 coastal geographical zones (ACIUCN 1986).

CLIM_ZONE : Set of 3 named climatic zones.

CATCH_AREA : Catchment Area (sq km).

AVE_ANN_RF : Mean annual rainfall (mm), recorded at station nearest estuary.

RUNOFF_COEF : Runoff figure, best approximation to a catchment average rainfall, usually the average value for the respective drainage basin.

MAX_TIDAL_RANGE : Maximum tidal range (m).

WATER_AREA : Water area (sq km).

SAND-MUD_AREA : Sand and Mud Area (sq km).

MANGROVE_AREA : Area of Mangroves (sq km).

SEAGRASS_AREA : Area of Seagrass (sq km).

SALTMARSH_AREA : Area of Saltmarsh (sq km).

ESTUARINE_AREA : Est area of estuary (sq km).

GALLOWAY_SECTION : Galloway section number - each 3x10km strip is numbered, clockwise around the coast.

LONGITUDE : Longitude of estuary site (dd).

LATITUDE : Latitude of estuary site (dd).

LANDUSE_CODE : % catchment clearance.

FLOOD_REGIME : Frequency of flooding.

WATER-QUAL : Subjective assessment of water quality only.

MANGROVE_COVER : Degree Mangrove cover.

SEAGRASS_COVER : Degree Seagrass cover.

SALTMARSH_COVER : Degree Saltmarsh cover.

FISH_VALUE : Importance of an estuary as a commercial or amateur fishing ground.

FISH_THREAT : Threats to fisheries.

CONS_VALUE : Qualitative conservation values.

CONS_THREAT : Threats to conservation.

AMENITY_VALUE : Amenities value.

ECO_STATUS : Effects of human activity.

RESEARCH : Depth of information used to assess estuary.

ADMIN : Statutory classifications that restricts use.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Projected the estuaries point coverage to geographics with the WGS84 spheroid. The coverage has been attributed with information taken from the Bucher and Saenger (1989) National estuaries inventory.

CSIRO: Data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary of data processing follows:

1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and Complete book of Australian Weather).

2. From the information held in r-BASE a BASE Table was generated incorporating specific fields.

3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point.

4. BASE Table imported into SPANS and a BASE Map generated.

5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level).

6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn).

7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid.

8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats.

9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times.

The Harvard Forest plot is dominated by eastern hemlock and northern hardwood species, and will make an excellent comparison with several other hardwood plots in North America and China at similar latitudes. This plot is part of a global array of large-scale plots established by ForestGEO, which recently expanded sampling efforts into temperate forests to explore ecosystem processes beyond population dynamics and biodiversity. The Harvard Forest was designed to include a continuous, expansive, and varied natural forest landscape that will yield opportunities for the study of forest dynamics and demography while capturing a large amount of existing science infrastructure (e.g., eddy flux towers, gauged sections of a small watershed, existing smaller permanent plots) that will enable the integrated study of ecosystem processes (e.g., biogeochemistry, hydrology, carbon dynamics) and forest dynamics .

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. This plot site was situated in Malaysia Sabah Kabili-Sepilok Forest Reserve. The plot site had the following geographical features; Moisture type: Moist, Elevation: Lowland, Edaphic Type: White Sand, Forrestry: Old-growth.

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. This plot site was situated in Brazil, Pará, Caxiuanã National Forest. The plot site had the following geographical features; Moisture type: Moist, Elevation: Lowland, Edaphic Type: Terra Firma, Substrate:Mixed, Geology: Pre-Quaternary,Forrestry: Old-growth.

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times.

Armstrong Redwoods State Natural Reserve is a state park of California in the United States established to preserve 805 acres (326 ha) of coast redwoods (Sequoia sempervirens). The reserve is located in Sonoma County, just north of Guerneville. The reserve is in a temperate rainforest. The climate is mild and wet. The park receives an average of 55 inches (1.4 m) of rainfall per year, almost all between September and June. Abundant fog during the summer months helps to maintain the moist conditions required by the coast redwoods.

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. This plot site was situated in Gabon Estuaire l'Arboretum Raponda Walker. The plot site had the following geographical features; Moisture type: Moist, Elevation: Lowland, Edaphic Type: Terra Firma, Composition: Monodominent, Forrestry: Secondry Older.

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. This plot site was situated in Peru Madre De Dios Tambopata National Reserve. The plot site had the following geographical features; Moisture type: Moist, Elevation: Lowland, Edaphic Type: Terra Firme, Composition Mixed Forrest , Substrate Geology: Pre-Holocence, Forrestry: Old-growth.

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

McArthur FFDI for historical data at 39 high quality stations in Australia. Stations cover the entire continent, although spacing is sparse in some locations.

Values of 95th,90th,75th,50th,25th and 10th percentile of daily FFDI estimated over standard meteorological seasons (i.e. DJF, MAM, JJA, SON). Top and Bottom value for each season are also provided. Reliable data presented here extend from JJA 1972 through MAM 2017. The data used in the paper focus on the 90th percentile.

Seasonal statistics for components of FFDI (i.e. rainfall, KBDI, max temperature, relative humidity, drought factor, wind speed) also given.

Latitude/longitude of the stations, number of individual observations in each season, and time indicators also provided. Sorry, station names not included...couldn't get the netCDF to contain a string variable!!

The netCDF-3 file describes the format and variables included in more detail.

The calculation of FFDI follows the methodology described in Lucas (2010). Station selection is as in Clarke et al (2013). Underlying meteorological data come from Bureau of Meteorology surface weather stations

This version of the data set supports the submission/publication of Harris and Lucas (2019) in PLOS ONE

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. This plot site was situated in French Guiana, Cayenne, Nourague Nautre Reserve. The plot site had the following geographical features; Moisture type: Moist, Elevation: Lowland, Edaphic Type: Terra Firma, Substrate:Mixed, Geology: Pre-Quaternary,Forrestry: Old-growth.

The project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.

1. Large, severe disturbances drive many forest ecosystems over the long term, but pose management uncertainties when human experience with them is limited. Recent continent-scale outbreaks of bark beetles across the temperate Northern Hemisphere have raised major concerns as to whether coniferous forests will regenerate back towards pre-outbreak condition and meet possible reforestation objectives. To date, however, analyses of post-outbreak regeneration across broad spatial and temporal scales have been rare, and entirely lacking for many regions. 2. Following a series of large, severe ( 99% overstorey mortality) outbreaks of spruce bark beetles Ips typographus in Central Europe, we capitalized on an extensive forest inventory data set (n = 615 plots across 7000 ha) to evaluate regeneration dynamics in Norway spruce Picea abies forests across the Bohemian Forest Ecosystem (spanning Germany and the Czech Republic). We asked whether neighbourhood effects (conspecific advance regeneration of spruce) would support prompt regeneration back to spruce forest, or whether the rapid, severe canopy mortality would overwhelm this influence and promote pioneer and broadleaf species. We tracked 15 years of post-outbreak regeneration dynamics (occupancy, density, height, composition) of all tree species and evaluated initial variations in successional pathway and structure. 3. Median tree regeneration density increased from 400 trees ha−1 at the time of outbreak to 2000 trees ha−1 within a decade, and occupancy increased from 58% to 76%. The increases were driven by spruce, which primarily recruited from advance regeneration, gradually occupying greater height classes. Only one broadleaf/pioneer species increased in relative proportion, for a brief (<3-year) period before declining again. Nevertheless, both pure spruce and spruce–broadleaf stands were common and, coupled with wide variations in density and height, contributed to diverse early-successional structure. 4. Synthesis and applications. Contrary to common expectations, spruce beetle outbreaks in Central Europe effectively promoted their host in the long term. Outbreak-affected forests are naturally self-replacing even after severe canopy mortality, when positive neighbourhood effects of conspecific advance regeneration lead to rapid replacement of the dominant species. Thus, natural regeneration may be considered among the most effective ways to meet possible reforestation objectives in forests destroyed by beetles.