用连续土地扰动算法监测水体变化，实现精准动态追踪。
Water Mapping and Change Detection Using Time Series Derived from the Continuous Monitoring of Land Disturbance Algorithm
- 基于COLD算法提取时序数据，识别水体分布与变化趋势。
- 可准确估计稳定期水体频率，识别扰动后面积增减趋势。
- 适合环境监测、生态评估人员用于实时水体变化分析。
面对日益严峻的环境挑战，精确监测和预测水体变化对可持续管理与保护至关重要。连续土地扰动（COLD）算法为实时分析土地变化（如森林砍伐、城市扩张、农业活动及自然灾害）提供了有效工具，有助于及时干预和科学决策。本文评估了该算法在估算水体范围及追踪像素级水体变化趋势方面的有效性。结果表明，基于COLD的数据可可靠估计稳定期内的水体频率，并准确划定水体边界；同时，能够评估扰动后的水体面积变化趋势，判断其频率是否上升、下降或保持不变。
原文摘要 · Abstract (English)
Given the growing environmental challenges, accurate monitoring and prediction of changes in water bodies are essential for sustainable management and conservation. The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a valuable tool for real-time analysis of land changes, such as deforestation, urban expansion, agricultural activities, and natural disasters. This capability enables timely interventions and more informed decision-making. This paper assesses the effectiveness of the algorithm to estimate water bodies and track pixel-level water trends over time. Our findings indicate that COLD-derived data can reliably estimate estimate water frequency during stable periods and delineate water bodies. Furthermore, it enables the evaluation of trends in water areas after disturbances, allowing for the determination of whether water frequency increases, decreases, or remains constant.
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