Google Earth Engine——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。

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:

• Technical issues and questions about the code
• General questions and comments

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

EnvirometriX Ltd

Collection Snippet

Copied

ee.Image("OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01")

Resolution

1000 meters

Bands Table

Class Table: biome_type

数据使用：

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No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

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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");