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WRI/Google DeepMind 全球森林消失驱动因素 2001-2022 v1.0
此数据集以 1 公里的分辨率绘制了 2001 年至 2022 年全球树冠覆盖率减少的主要原因。这些数据由世界资源研究所 (WRI) 和 Google DeepMind 制作,使用基于收集的一组样本训练的全球神经网络模型 (ResNet) 开发而成。 农业 森林砍伐 森林 森林生物量 google landandcarbon -
WRI/Google DeepMind 全球森林消失驱动因素 2001-2023 v1.1
此数据集以 1 公里的分辨率绘制了 2001 年至 2023 年全球树冠覆盖率损失的主要驱动因素。这些数据由世界资源研究所 (WRI) 和 Google DeepMind 制作,使用基于收集的一组样本训练的全球神经网络模型 (ResNet) 开发而成。 农业 森林砍伐 森林 森林生物量 google landandcarbon -
WRI/Google DeepMind 全球森林消失驱动因素 2001-2024 v1.2
此数据集以 1 公里的分辨率绘制了 2001 年至 2024 年全球树冠覆盖率减少的主要原因。这些数据由世界资源研究所 (WRI) 和 Google DeepMind 制作,使用基于收集的一组样本训练的全球神经网络模型 (ResNet) 开发而成。 农业 森林砍伐 森林 森林生物量 google landandcarbon
Land & Carbon Lab
[null,null,[],[],[],null,["# Land & Carbon Lab\n\nLand and Carbon Lab, founded by World Resources Institute and the Bezos Earth Fund in 2021, develops breakthroughs in geospatial monitoring to help governments, businesses and communities power solutions for sustainable landscapes. Global Forest Watch, established in 2014 by a consortium of partners led by the World Resources Institute, is a forest monitoring initiative that provides open access to data about the current status of forests and recent forest change. \n[](https://landcarbonlab.org/) \n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2022 v1.0](/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022) |\n | This dataset maps the dominant driver of tree cover loss from 2001-2022 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) [google](/earth-engine/datasets/tags/google) [landandcarbon](/earth-engine/datasets/tags/landandcarbon) |\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2023 v1.1](/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023) |\n | This dataset maps the dominant driver of tree cover loss from 2001-2023 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) [google](/earth-engine/datasets/tags/google) [landandcarbon](/earth-engine/datasets/tags/landandcarbon) |\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2024 v1.2](/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024) |\n | This dataset maps the dominant driver of tree cover loss from 2001-2024 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) [google](/earth-engine/datasets/tags/google) [landandcarbon](/earth-engine/datasets/tags/landandcarbon) |"]]