Liu Yang, Liu Ronggao, Chen Jilong, Wei Xuexin, Qi Lin, Zhao Lei
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2024 Aug 1;11(1):832. doi: 10.1038/s41597-024-03671-9.
Fractional tree cover facilitates the depiction of forest density and its changes. However, it remains challenging to estimate tree cover from satellite data, leading to substantial uncertainties in forest cover changes analysis. This paper generated a global annual fractional tree cover dataset from 2000 to 2021 with 250 m resolution (GLOBMAP FTC). MODIS annual observations were realigned at the pixel level to a common phenology and used to extract twelve features that can differentiate between trees and herbaceous vegetation, which greatly reduced feature dimensionality. A massive training data, consisting of 465.88 million sample points from four high-resolution global forest cover products, was collected to train a feedforward neural network model to predict tree cover. Compared with the validation datasets derived from the USGS circa 2010 global land cover reference dataset, the R value, MAE, and RMSE were 0.73, 10.55%, and 17.98%, respectively. This dataset can be applied for assessment of forest cover changes, including both abrupt forest loss and gradual forest gain.
分数树木覆盖度有助于描述森林密度及其变化。然而，从卫星数据估算树木覆盖度仍然具有挑战性，这导致森林覆盖变化分析中存在很大的不确定性。本文生成了一个2000年至2021年的全球年度分数树木覆盖度数据集，分辨率为250米（GLOBMAP FTC）。MODIS年度观测数据在像素级别重新调整为共同物候，并用于提取十二个能够区分树木和草本植被的特征，这大大降低了特征维度。收集了来自四个高分辨率全球森林覆盖产品的4.6588亿个样本点组成的大量训练数据，以训练前馈神经网络模型来预测树木覆盖度。与源自美国地质调查局2010年左右全球土地覆盖参考数据集的验证数据集相比，R值、平均绝对误差（MAE）和均方根误差（RMSE）分别为0.73、10.55%和17.98%。该数据集可用于评估森林覆盖变化，包括突然的森林损失和逐渐的森林增加。
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