发表机构
DAI Labs, K.K.（DAI实验室株式会社）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种两阶段统计-编码器级联方法，利用Sentinel-1时间序列实现近实时森林异常检测，克服云层遮挡和季节性后向散射干扰，输出可审计的置信度与风险等级。
AI 中文摘要
热带森林监测对于全球气候稳定性和生物多样性保护至关重要。为了满足对快速、可靠的森林损失检测的迫切需求——这对于及时干预非法采伐、供应链透明度、土地利用治理和碳市场标准至关重要——我们引入了一种两阶段统计-编码器级联方法，利用Sentinel-1时间序列进行近实时异常检测。我们的系统旨在克服遥感中的两个基本挑战：限制光学监测的云层覆盖问题，以及导致SAR系统将自然水分变化误认为森林损失的季节性后向散射变化。该架构整合了两种不同的分析引擎以确保高保真检测：（1）对共配准的Sentinel-1 VH后向散射进行自适应、稳健统计的z-score检验，使用同季节历史基线；（2）基于在稳定森林斑块上训练的卷积自编码器的潜在空间结构相似性（SSIM）的学习型确认门控。仅当两个阶段都同意时，候选扰动才被确认为警报，并根据其重复发生历史分配置信度分数和低/中/高风险等级。该系统生成每个警报的可审计置信度分数和以公顷为单位的面积估计，可直接与监测、报告和核查（MRV）工作流程、可持续森林管理、实地操作检查和环境风险评估兼容。除了其主要应用外，该模型的灵活性还允许用于从选择性采伐到大规模农业侵占测绘、洪水测绘等关键环境应用。
英文摘要
Tropical forest monitoring is essential for global climate stability and biodiversity preservation. To address the urgent need for rapid, reliable detection of forest loss which is essential for timely intervention against illegal logging, supply chain transparency, land-use governance and carbon market standards, we introduce a two-stage statistics-encoder cascade for near-real-time anomaly detection using Sentinel-1 time series. Our system is designed to overcome two fundamental challenges in remote sensing: the cloud-cover limitations that restrict optical monitoring and seasonal backscatter variation that causes SAR systems to mistake natural moisture changes for forest loss. The architecture integrates two distinct analytical engines to ensure high-fidelity detection: (1) an adaptive, robust-statistics z-score test on co-registered Sentinel-1 VH backscatter, same-season historical baseline and (2) a learned confirmation gate based on the latent-space structural similarity (SSIM) of a convolutional autoencoder trained on stable-forest patches. A candidate disturbance is confirmed as an alert only when both stages agree, and is assigned a confidence score and a Low/Medium/High risk tier from its repeat-occurrence history. The system produces per-alert auditable confidence scores and area-in-hectares estimates directly compatible with Monitoring, Reporting and Verification (MRV) workflows, sustainable forestry management, operational field checks and environmental risk assessments. Beyond its primary application, the model's flexibility allows for critical environmental applications ranging from selective logging to large-scale agricultural encroachment mapping, flood mapping and so on.
