Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

中文翻译：

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印度通迪保护区林地森林生物量估算的快速评估和验证方法

光学遥感数据可在全球范围内免费获得。然而，用于快速，准确和精确地上森林生物量（AGB）评估的卫星图像处理和分析仍然充满挑战和困难。本文旨在开发一种从光学遥感数据进行精确，准确和快速评估森林AGB的新方法。通常，地面森林AGB是使用经验模型从地面数据中获取的，这些数据来自生物物理参数，例如树木密度，高度和从不同海拔地层采集的胸高（DBH）的直径。植被覆盖的地面部分（FVC）在每个地面样本位置进行了计算。然后，计算了来自光学遥感影像的植被覆盖率（FVC）。在方法实施的第一阶段，开发了地面FVC与地面森林AGB之间的关系模型。在第二阶段，建立图像FVC与地面FVC之间的关系模型。最后，将这两个模型融合以得出图像FVC和森林AGB之间的关系模型。利用Sentinel-2影像作为测试数据证明了所开发方法的有效性，并使用了印度东部贾坎德邦州Dhanbad区的通迪保护区作为测试地点。来自开发模型的结果经过地面验证，并且还与先前开发的基于树冠投影面积（CPA）的森林AGB估算方法的结果进行了比较。与基于CPA的方法相比，已开发方法的结果显示出卓越的精度功能。通过这种方法获得的测试地点的平均森林AGB估计值显示每公顷463吨，与该测试点之前的估计值相符。