-
コートジボワール BNETD 2020 土地被覆地図
コートジボワール BNETD 2020 土地被覆地図は、コートジボワール政府が、国立機関である国立調査局技術開発地理情報デジタルセンター(BNETD-CIGN)を通じて、欧州連合の技術的および財政的支援を受けて作成しました。手法 分類 森林伐採 森林 土地被覆 土地利用-土地被覆 -
IPCC 地上バイオマスのティア 1 推定値のグローバル 2020 森林分類、V1
このデータセットは、2020 年のステータス/状態別に区分された世界中の森林のクラスを約 30 m の解像度で提供します。このデータは、2006 年 IPCC ガイドラインの国別温室効果ガス インベントリの 2019 年改訂版で、自然林の地上部の乾燥木質バイオマス密度(AGBD)のティア 1 推定値の生成をサポートしています。 地上 バイオマス 炭素 分類 森林 森林バイオマス -
世界 3 クラスの PALSAR-2/PALSAR 森林/非森林地図
2017 ~ 2020 年の 4 つのクラスを含むこのデータセットの新しいバージョンは、JAXA/ALOS/PALSAR/YEARLY/FNF4 にあります。グローバルな 25 m 解像度の PALSAR-2/PALSAR SAR モザイク内の SAR 画像(後方散乱係数)を分類して、強い後方散乱ピクセル ... alos alos2 classification eroc forest forest-biomass -
世界 4 クラスの PALSAR-2/PALSAR 森林/非森林地図
グローバルな森林/非森林マップ(FNF)は、25 m 解像度のグローバル PALSAR-2/PALSAR SAR モザイク内の SAR 画像(後方散乱係数)を分類して生成されます。これにより、強い後方散乱ピクセルと低い後方散乱ピクセルがそれぞれ「森林」と「非森林」として割り当てられます。ここでの「森林」とは、次のような自然の森林を指します。 alos alos2 classification eroc forest forest-biomass
Datasets tagged classification in Earth Engine
[null,null,[],[[["\u003cp\u003eThe webpage provides access to various global and regional forest classification datasets.\u003c/p\u003e\n"],["\u003cp\u003eDatasets include land cover maps, forest/non-forest classifications, and biomass estimations.\u003c/p\u003e\n"],["\u003cp\u003eData sources include satellite imagery from PALSAR-2/PALSAR and organizations like BNETD and NASA.\u003c/p\u003e\n"],["\u003cp\u003eThese datasets support research on deforestation, forest monitoring, and carbon stock assessments.\u003c/p\u003e\n"],["\u003cp\u003eUsers can leverage these resources to analyze forest cover change and contribute to environmental studies.\u003c/p\u003e\n"]]],[],null,["# Datasets tagged classification in Earth Engine\n\n-\n\n |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Cote d'Ivoire BNETD 2020 Land Cover Map](/earth-engine/datasets/catalog/BNETD_land_cover_v1) |\n | The Cote d'Ivoire BNETD 2020 Land Cover Map was produced by the Ivorian Government through a national institution, the Center for Geographic Information and Digital from the National Study Office Techniques and Development (BNETD-CIGN), with technical and financial support from the European Union. The methodology ... |\n | [classification](/earth-engine/datasets/tags/classification) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [landcover](/earth-engine/datasets/tags/landcover) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Global 2020 Forest Classification for IPCC Aboveground Biomass Tier 1 Estimates, V1](/earth-engine/datasets/catalog/NASA_ORNL_global_forest_classification_2020_V1) |\n | This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse ... |\n | [aboveground](/earth-engine/datasets/tags/aboveground) [biomass](/earth-engine/datasets/tags/biomass) [carbon](/earth-engine/datasets/tags/carbon) [classification](/earth-engine/datasets/tags/classification) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) |\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Global 3-class PALSAR-2/PALSAR Forest/Non-Forest Map](/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF) |\n | A newer version of this dataset with 4 classes for 2017-2020 can be found in JAXA/ALOS/PALSAR/YEARLY/FNF4 The global forest/non-forest map (FNF) is generated by classifying the SAR image (backscattering coefficient) in the global 25m resolution PALSAR-2/PALSAR SAR mosaic so that strong and low backscatter pixels ... |\n | [alos](/earth-engine/datasets/tags/alos) [alos2](/earth-engine/datasets/tags/alos2) [classification](/earth-engine/datasets/tags/classification) [eroc](/earth-engine/datasets/tags/eroc) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) |\n\n-\n\n |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Global 4-class PALSAR-2/PALSAR Forest/Non-Forest Map](/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF4) |\n | The global forest/non-forest map (FNF) is generated by classifying the SAR image (backscattering coefficient) in the global 25m resolution PALSAR-2/PALSAR SAR mosaic so that strong and low backscatter pixels are assigned as \"forest\" and \"non-forest\", respectively. Here, \"forest\" is defined as the natural forest with ... |\n | [alos](/earth-engine/datasets/tags/alos) [alos2](/earth-engine/datasets/tags/alos2) [classification](/earth-engine/datasets/tags/classification) [eroc](/earth-engine/datasets/tags/eroc) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) |"]]