Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wild fires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are essential for disaster management and policy making. In recent years, foresters have relied on hand-crafted features or bi-temporal change detection methods to detect change in the remote sensing imagery to estimate the forest area. Due to manual processing steps, these methods are fragile and prone to errors and can generate inaccurate (i.e., under or over) segmentation results. In contrast to traditional methods, we present AI-ForestWatch, an end to end framework for forest estimation and change analysis. The proposed approach uses deep convolution neural network-based semantic segmentation to process multi-spectral space-borne images to quantitatively monitor the forest cover change patterns by automatically extracting features from the dataset. Our analysis is completely data driven and has been performed using extended (with vegetation indices) Landsat-8 multi-spectral imagery from 2014 to 2020. As a case study, we estimated the forest area in 15 districts of Pakistan and generated forest change maps from 2014 to 2020, where major afforestation activity is carried out during this period. Our critical analysis shows an improvement of forest cover in 14 out of 15 districts. The AI-ForestWatch framework along with the associated dataset will be made public upon publication so that it can be adapted by other countries or regions.

中文翻译：

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AI-ForestWatch：基于语义分割的端到端框架，用于使用多光谱遥感图像进行森林估计和变化检测

森林变化检测对于可持续森林管理至关重要。由于森林砍伐（例如由于开发活动引起的野火或伐木）或植树造林导致的森林面积变化改变了森林总面积。此外，它还会影响可用于商业目的的存量、碳排放导致的气候变化以及森林栖息地生物多样性评估，这些对于灾害管理和政策制定至关重要。近年来，林业人员依靠手工制作的特征或双时态变化检测方法来检测遥感影像的变化来估计森林面积。由于手动处理步骤，这些方法很脆弱且容易出错，并且会产生不准确（即不足或过度）的分割结果。与传统方法相比，我们提出了 AI-ForestWatch，森林估算和变化分析的端到端框架。所提出的方法使用基于深度卷积神经网络的语义分割来处理多光谱星载图像，通过从数据集中自动提取特征来定量监测森林覆盖变化模式。我们的分析完全由数据驱动，并使用 2014 年至 2020 年的扩展（带植被指数）Landsat-8 多光谱图像进行。作为案例研究，我们估计了巴基斯坦 15 个地区的森林面积，并生成了来自2014 年至 2020 年，在此期间进行重大造林活动。我们的批判性分析表明，15 个地区中有 14 个地区的森林覆盖率有所改善。