Google Earth Engine ——基于MODIS数据集JRC/GWIS/GlobFire/v2/FinalPerimetersMCD64A1的火灾边界数据集
Fire boundaries based on the MODIS dataset MCD64A1. The data were computed based on an algorithm that relies on encoding in a graph structure a space-time relationship among patches of burned areas.&a...
Google Earth Engine ——美国LANDIFRE火灾LANDFIRE FRG (Fire Regime Groups) v1.2.0数据集
LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. De....
Google Earth Engine ——美国LANDIFRE火灾数据集LANDFIRE MFRI (Mean Fire Return Interval) v1.2.0数据集内包含多种数据要素
LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. De....
Google Earth Engine ——美国LANDIFRE火灾数据集LANDFIRE/Fire/PLS/v1_2_0数据集内包含多种数据要素
LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. De....
Google Earth Engine ——2017-2018年伊朗土地覆盖/土地利用数据集KNTU/LiDARLab/IranLandCover/V1
The Iran-wide land cover map was generated by processing Sentinel imagery within the Google Earth Engine Cloud platform. For this purpose, over 2,500 Sentinel-1 and over 11,000 Sentinel-2 images were....
Google Earth Engine ——美国LANDIFRE植被数据集包括。生物物理设置（BPS)环境场地潜力（ESP)现有植被冠层(EVC)现有植被高度（EVH）现有植被类型（EVT)数据集
Google Earth Engine ——美国LANDIFRE植被数据集包括。生物物理设置（BPS)环境场地潜力（ESP)现有植被冠层(EVC)现有植被高度（EVH）现有植被类型（EVT)数据集
Google Earth Engine ——ALOS World 3D - 30m (AW3D30) 是一个全球数字表面模型 (DSM) 数据集
ALOS World 3D - 30m (AW3D30) is a global digital surface model (DSM) dataset with a horizontal resolution of approximately 30 meters (1 arcsec mesh). The dataset is based on the DSM dataset (5-meter ....
Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取铁，预测平均值和标准偏差数据集
iSDAsoil Extractable IronExtractable iron at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation.Pixel values must be back-transformed with exp(x/10)-1.In areas of dense jungle....
Google Earth Engine ——2001-2017年使用坡度、化学和物理土壤特性得出的土壤肥力能力分类，预测平均值和标准偏差数据集
iSDAsoil Fertility Capability ClassificationSoil fertility capability classification derived using slope, chemical, and physical soil properties. For more information about this layer, please visit t....
Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量，预测平均值和标准偏差数据集
Clay content at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation.In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artefacts such....
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