Potential Natural Vegetation biomes global predictions of classes (based on predictions using the BIOMES 6000 dataset's 'current biomes' category.)
Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate that would exist at a given location non-impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This dataset contains results of predictions of
- (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions),
- (2) distribution of forest tree species in Europe based on detailed occurrence records (1,546,435 ground observations), and
- (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points).
To report an issue or artifact in data, please use this link.
To access and visualize maps outside of Earth Engine, use this page.
If you discover a bug, artifact or inconsistency in the LandGIS maps or if you have a question please use the following channels:
潜在的自然植被生物群落的全球预测类别（基于使用BIOMES 6000数据集的 "当前生物群落 "类别的预测。
潜在自然植被（PNV）是指在某一特定地点不受人类活动影响而存在的与气候平衡的植被覆盖。PNV对于提高公众对土地退化的认识和估计土地潜力非常有用。该数据集包含以下预测结果
(1) 基于BIOME 6000数据集（8057个基于花粉的现代遗址重建）的全球生物群落分布。
(2) 基于详细的发生记录（1,546,435次地面观测）的欧洲森林树种的分布，以及
(3) 全球每月吸收光合有效辐射的分数（FAPAR）值（30,301个随机抽样的点）。
要报告数据中的问题或假象，请使用此链接。
要访问和可视化地球引擎以外的地图，请使用这个页面。
如果您发现LandGIS地图中的错误、伪装或不一致，或者您有问题，请使用以下渠道。
关于代码的技术问题和疑问
一般问题和评论
Dataset Availability
2001-01-01T00:00:00 - 2002-01-01T00:00:00
Dataset Provider
Collection Snippet
Copied
ee.Image("OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01")
Resolution
1000 meters
Bands Table
| Name | Description |
|---|---|
| biome_type | Potential distribution of biomes |
Class Table: biome_type
| Value | Color | Color Value | Description |
|---|---|---|---|
| 1 | #1c5510 | tropical evergreen broadleaf forest | |
| 2 | #659208 | tropical semi-evergreen broadleaf forest | |
| 3 | #ae7d20 | tropical deciduous broadleaf forest and woodland | |
| 4 | #000065 | warm-temperate evergreen broadleaf and mixed forest | |
| 7 | #bbcb35 | cool-temperate rainforest | |
| 8 | #009a18 | cool evergreen needleleaf forest | |
| 9 | #caffca | cool mixed forest | |
| 13 | #55eb49 | temperate deciduous broadleaf forest | |
| 14 | #65b2ff | cold deciduous forest | |
| 15 | #0020ca | cold evergreen needleleaf forest | |
| 16 | #8ea228 | temperate sclerophyll woodland and shrubland | |
| 17 | #ff9adf | temperate evergreen needleleaf open woodland | |
| 18 | #baff35 | tropical savanna | |
| 20 | #ffba9a | xerophytic woods/scrub | |
| 22 | #ffba35 | steppe | |
| 27 | #f7ffca | desert | |
| 28 | #e7e718 | graminoid and forb tundra | |
| 30 | #798649 | erect dwarf shrub tundra | |
| 31 | #65ff9a | low and high shrub tundra | |
| 32 | #d29e96 | prostrate dwarf shrub tundra |
数据使用：
This is a human-readable summary of (and not a substitute for) the license.
You are free to - Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially.
This license is acceptable for Free Cultural Works. The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms - Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
这是对许可证的可读摘要（而不是替代）。
你可以*地--分享--以任何媒介或格式复制和再传播这些材料，适应--为任何目的重新混合、改造和建立这些材料，甚至是商业性的。
此许可证可用于*文化作品。只要你遵守许可条款，许可人就不能撤销这些*。
在以下条款下--署名--你必须给予适当的荣誉，提供许可证的链接，并说明是否进行了修改。你可以以任何合理的方式这样做，但不能以任何方式暗示许可人认可你或你的使用。
类似共享 - 如果你重新混合、改造或建立在材料的基础上，你必须在与原始材料相同的许可下分发你的贡献。
没有额外的限制--你不得应用法律条款或技术措施，在法律上限制他人做许可证允许的任何事情。
数据引用：
Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC. (2018) Global Mapping of Potential Natural Vegetation: An Assessment of Machine Learning Algorithms for Estimating Land Potential. PeerJ Preprints. 10.7287/peerj.preprints.26811v1
https://doi.org/10.7910/DVN/QQHCIK
代码：
var dataset = ee.Image("OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01");
var visualization = {
bands: ['biome_type'],
min: 1.0,
max: 32.0,
palette: [
"1c5510","659208","ae7d20","000065","bbcb35","009a18",
"caffca","55eb49","65b2ff","0020ca","8ea228","ff9adf",
"baff35","ffba9a","ffba35","f7ffca","e7e718","798649",
"65ff9a","d29e96",
]
};
Map.centerObject(dataset);
Map.addLayer(dataset, visualization, "Potential distribution of biomes");