High-resolution global map of closed-canopy coconut palm

The file ‘GlobalCoconutLayer_2020_v1-2.zip’ contains 878 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut palm layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: [0] Other land covers that are not coconut palm. [1] Coconut palm. The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip’ contains the 20-meter coconut palm classification aggregated to 1 km. The value of each pixel represents the coconut palm area (in squared meters) within the 1-km pixel. The file ‘Validation_points_GlobalCoconutLayer_2020_v1-2.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files: [0] Other land covers that are not coconut palm. [1] Coconut palm. The attribute ‘Class’ contains the following values: [0] Land cover could not be determined because sub-meter resolution data was not available. [1] Other land covers that are not coconut palm. [2] Sparse coconut palm. Low density of coconut palms; between 1 and 4 coconut palms within the 20-meter pixel. [3] Dense open-canopy coconut palm; more than 4 coconut palms within the 20-meter pixel but coconut trees do not reach the full canopy closure. [4] Closed -canopy coconut palm; more than 4 coconut palms within the 20-meter pixel and coconut palms fully cover the ground. [5] Palm species that are not coconut palm. Changelog v1-2: - Pixels classified as class ‘coconut’ were reclassified to class ‘other’ in West Bengal.文件`GlobalCoconutLayer_2020_v1-2.zip`包含878幅100×100公里的GeoTIFF格式栅格瓦片。该栅格文件是利用卷积神经网络（Convolutional Neural Network）对哨兵-1（Sentinel-1）与哨兵-2（Sentinel-2）年度合成数据进行分类后得到的2020年椰子种植图层。影像空间分辨率为20米，包含两类地物： [0] "非椰子棕榈的其他土地覆被" [1] "椰子棕榈" 文件`GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip`包含聚合至1公里分辨率的20米椰子分类结果。每个像素的数值代表该1公里像素范围内的椰子棕榈种植面积（单位：平方米）。 文件`Validation_points_GlobalCoconutLayer_2020_v1-2.shp`包含10200个用于验证该产品的采样点。每个点均包含属性`Class`（通过亚米级影像目视解译得到的真实类别）与`predClass`（卷积神经网络预测的类别）。`predClass`的取值与前述栅格文件一致： [0] "非椰子棕榈的其他土地覆被" [1] "椰子棕榈" 属性`Class`的取值如下： [0] 因无亚米级影像数据，无法确定土地覆被类型 [1] "非椰子棕榈的其他土地覆被" [2] 稀疏椰子棕榈：椰子棕榈种植密度较低，20米像素内椰子棕榈株数介于1至4株之间 [3] 开放冠层密植椰子棕榈：20米像素内椰子棕榈株数超过4株，但未达到完全冠层闭合状态 [4] 闭合冠层椰子棕榈：20米像素内椰子棕榈株数超过4株，且椰子棕榈完全覆盖地面 [5] 非椰子棕榈的其他棕榈物种 更新日志 v1-2： - 西孟加拉邦地区被分类为"椰子"的像素被重新归类为"其他"类别。