Forest area (% of land area) in Greenland was reported at 0.00054 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Greenland - Forest area (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on February of 2026.

Forest area (sq. km) in Greenland was reported at 2.2 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Greenland - Forest area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2026.

Greenland Forests With Long Term Management Plans: % of Total Forest Area data was reported at 0.000 % in 2025. This stayed constant from the previous number of 0.000 % for 2020. Greenland Forests With Long Term Management Plans: % of Total Forest Area data is updated yearly, averaging 0.000 % from Dec 1990 (Median) to 2025, with 6 observations. The data reached an all-time high of 0.000 % in 2025 and a record low of 0.000 % in 2025. Greenland Forests With Long Term Management Plans: % of Total Forest Area data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Greenland – Table GL.OECD.GGI: Environmental: Forest Resources: Non OECD Member: Annual.

Greenland Naturally Regenerating Forests: % of Total Forest Area data was reported at 0.000 % in 2025. This stayed constant from the previous number of 0.000 % for 2020. Greenland Naturally Regenerating Forests: % of Total Forest Area data is updated yearly, averaging 0.000 % from Dec 1990 (Median) to 2025, with 6 observations. The data reached an all-time high of 0.000 % in 2025 and a record low of 0.000 % in 2025. Greenland Naturally Regenerating Forests: % of Total Forest Area data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Greenland – Table GL.OECD.GGI: Environmental: Forest Resources: Non OECD Member: Annual.

GL: Forest Area: % of Land Area data was reported at 0.001 % in 2015. This stayed constant from the previous number of 0.001 % for 2014. GL: Forest Area: % of Land Area data is updated yearly, averaging 0.001 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 0.001 % in 1996 and a record low of 0.001 % in 2015. GL: Forest Area: % of Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank: Land Use, Protected Areas and National Wealth. Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

GL: Forest Area data was reported at 2.200 sq km in 2015. This stayed constant from the previous number of 2.200 sq km for 2014. GL: Forest Area data is updated yearly, averaging 2.200 sq km from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 2.200 sq km in 2015 and a record low of 2.200 sq km in 2015. GL: Forest Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank: Land Use, Protected Areas and National Wealth. Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

Automatic categorization and segmentation of land cover is of great importance for sustainable development, autonomous agriculture, and urban planning. DeepGlobe Land Cover Classification Challenge introduces the challenge of automatic classification of land cover types. This problem is defined as a multi-class segmentation task to detect areas of urban, agriculture, rangeland, forest, water, barren, and unknown.

Acknowledgements

This dataset was obtained from Land Cover Classification Track in DeepGlobe Challenge . For more details on the dataset refer the related publication - DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

Any work based on the dataset should cite:

@InProceedings{DeepGlobe18,
 author = {Demir, Ilke and Koperski, Krzysztof and Lindenbaum, David and Pang, Guan and Huang, Jing and Basu, Saikat and Hughes, Forest and Tuia, Devis and Raskar, Ramesh},
 title = {DeepGlobe 2018: A Challenge to Parse the Earth Through Satellite Images},
 booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
 month = {June},
 year = {2018}
}

Terms & Conditions

The DeepGlobe Land Cover Classification Challenge and hence, the dataset are governed by DeepGlobe Rules, The DigitalGlobe's Internal Use License Agreement, and Annotation License Agreement.

Data

• The training data for Land Cover Challenge contains 803 satellite imagery in RGB, size 2448x2448.
• The imagery has 50cm pixel resolution, collected by DigitalGlobe's satellite.
• The dataset contains 171 validation and 172 test images (but no masks).

Label

• Each satellite image is paired with a mask image for land cover annotation. The mask is a RGB image with 7 classes of labels, using color-coding (R, G, B) as follows.

  • Urban land: 0,255,255 - Man-made, built up areas with human artifacts (can ignore roads for now which is hard to label)
  • Agriculture land: 255,255,0 - Farms, any planned (i.e. regular) plantation, cropland, orchards, vineyards, nurseries, and ornamental horticultural areas; confined feeding operations.
  • Rangeland: 255,0,255 - Any non-forest, non-farm, green land, grass
  • Forest land: 0,255,0 - Any land with x% tree crown density plus clearcuts.
  • Water: 0,0,255 - Rivers, oceans, lakes, wetland, ponds.
  • Barren land: 255,255,255 - Mountain, land, rock, dessert, beach, no vegetation
  • Unknown: 0,0,0 - Clouds and others

• File names for satellite images and the corresponding mask image are id _sat.jpg and id _mask.png. id is a randomized integer.

• Please note:

  • The values of the mask image may not be pure 0 and 255. When converting to labels, please binarize them at threshold 128.

Greenland GL: Forest Rents: % of GDP data was reported at 0.000 % in 2015. This stayed constant from the previous number of 0.000 % for 2014. Greenland GL: Forest Rents: % of GDP data is updated yearly, averaging 0.000 % from Dec 1970 (Median) to 2015, with 46 observations. Greenland GL: Forest Rents: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank: Land Use, Protected Areas and National Wealth. Forest rents are roundwood harvest times the product of regional prices and a regional rental rate.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

Study Aim

We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for Betula nana L. (dwarf birch, Betulaceae) and Salix glauca L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.

Data Description

Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.

Nuup Kangerlua (Godthåbsfjord)

Betula nana and Salix glauca presence-absence, abundance, community species richness

Kangerluarsunnguaq (Kobbefjord)

Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height

Methods

Field survey in Nuup Kangerlua

We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).

Field survey in Kangerluarsunnguaq

We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of Betula nana in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).

Biotic variables

We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.

Climate variables

We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).

Topography and terrain wetness indicator variables

We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.

Computer code

Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)

Third-party data

Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

グリーンランドの国土面積に占める森林面積の割合の統計データです。最新の2023年の数値「0%」を含む1990～2023年までの推移表や他国との比較情報を無料で公開しています。csv形式でのダウンロードも可能でEXCELでも開けますので、研究や分析レポートにお役立て下さい。

This data set contains the Laser Vegetation Imaging Sensor (LVIS) imagery over Greenland.

Raw data used in the publication by Kjær et al. (2025), titled "Grazing by sea urchins is a potential co-driver of Greenland kelp forest distribution.". This dataset supports a study that assesses the distribution of kelp forests in relation to sea urchin density along the western coast of Greenland.

自然再生森林：占森林总面积的百分比在12-01-2025达0.000%，相较于12-01-2020的0.000%保持不变。自然再生森林：占森林总面积的百分比数据按年更新，12-01-1990至12-01-2025期间平均值为0.000%，共6份观测结果。该数据的历史最高值出现于12-01-2025，达0.000%，而历史最低值则出现于12-01-2025，为0.000%。CEIC提供的自然再生森林：占森林总面积的百分比数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的格陵兰岛 – Table GL.OECD.GGI: Environmental: Forest Resources: Non OECD Member: Annual。

δ13Cenamel and δ18Oenamel values of pretreated and untreated samples.

有长期管理计划的森林：占森林总面积的百分比在12-01-2025达0.000%，相较于12-01-2020的0.000%保持不变。有长期管理计划的森林：占森林总面积的百分比数据按年更新，12-01-1990至12-01-2025期间平均值为0.000%，共6份观测结果。该数据的历史最高值出现于12-01-2025，达0.000%，而历史最低值则出现于12-01-2025，为0.000%。CEIC提供的有长期管理计划的森林：占森林总面积的百分比数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的格陵兰岛 – Table GL.OECD.GGI: Environmental: Forest Resources: Non OECD Member: Annual。