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Threshold- and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.rse.2020.112026
Adrian Ochtyra , Adriana Marcinkowska-Ochtyra , Edwin Raczko

Abstract Numerous algorithms are used in remote sensing to detect changes in vegetation. Majority of them require several tunable parameters or can only detect abrupt forest disturbances. The aim of this study was to develop a new threshold- and trend-based vegetation change monitoring algorithm (TVCMA) that can detect abrupt and gradual changes in vegetation within forested and non-forested areas. To test the algorithm, the Polish and Slovak Tatra Mountains were used as the study area. Strong winds and bark beetle outbreaks (BBOs) are the primary causes of vegetation disturbances in this region. An annual time series of vegetation indices from 1984 to 2016 was used as the input. The long time span necessitated the use of scenes from the Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Fifty-one images were atmospherically and topographically corrected. The collected in situ data included the chlorophyll content, leaf area index, absorbed photosynthetically active radiation, and spectral signatures of non-forest vegetation, dwarf pine, and forest stands in 190 sample plots. To select the vegetation indices (VIs) most suitable for disturbance detection, ten satellite-based VIs were correlated with the acquired field data. The normalized difference moisture index (NDMI) was found to be more sensitive to vegetation disturbances and more resistant to data noise than any other tested index. The TVCMA uses two separate approaches, namely, thresholding, which indicates where and when the disturbances occurred, and a regression analysis, which presents the general trend in the time series for each pixel. The number of detected disturbances, the Spearman's correlation coefficient between the modeled trend line and satellite observations, and p-values were calculated. Different threshold values were tested to identify the value that yielded the most accurate results. By using 200 randomly selected validation points, we achieved an 83.3% producer's accuracy for disturbances (PAD), 46.3% user's accuracy for disturbances (UAD), and 97.8% overall accuracy (OA). These results confirm the potential of TVCMA for monitoring abrupt and gradual changes in vegetation. Moreover, the simplicity and data-driven character of the proposed algorithm make it suitable for multi-temporal analyses of other types of satellite data.

中文翻译:

基于年际多时相归一化差异水分指数系列的阈值和趋势植被变化监测算法:以塔特拉山脉为例

摘要 遥感中使用了多种算法来检测植被的变化。它们中的大多数需要几个可调参数或只能检测突然的森林干扰。本研究的目的是开发一种新的基于阈值和趋势的植被变化监测算法 (TVCMA),可以检测森林和非森林区域内植被的突然和逐渐变化。为了测试算法,波兰和斯洛伐克塔特拉山脉被用作研究区域。强风和树皮甲虫爆发 (BBO) 是该地区植被干扰的主要原因。使用 1984 年至 2016 年的年度植被指数时间序列作为输入。长时间跨度需要使用来自 Landsat Thematic Mapper (TM)、Enhanced Thematic Mapper Plus (ETM+) 和 Operational Land Imager (OLI) 的场景。五十一张图像经过大气和地形校正。收集的原位数据包括 190 个样地中非森林植被、矮松和林分的叶绿素含量、叶面积指数、吸收的光合有效辐射和光谱特征。为了选择最适合干扰检测的植被指数 (VI),将十个基于卫星的 VI 与采集的现场数据相关联。发现归一化差异水分指数 (NDMI) 对植被干扰更敏感,并且比任何其他测试指数更能抵抗数据噪声。TVCMA 使用两种不同的方法,即阈值法,它指示扰动发生的地点和时间,以及回归分析,它呈现每个像素的时间序列的总体趋势。计算了检测到的干扰的数量、建​​模趋势线和卫星观测之间的 Spearman 相关系数以及 p 值。测试了不同的阈值以识别产生最准确结果的值。通过使用 200 个随机选择的验证点,我们实现了 83.3% 的生产者干扰准确度 (PAD)、46.3% 用户干扰准确度 (UAD) 和 97.8% 的总体准确度 (OA)。这些结果证实了 TVCMA 在监测植被突然和逐渐变化方面的潜力。此外,所提出算法的简单性和数据驱动特性使其适用于其他类型卫星数据的多时相分析。并计算了 p 值。测试了不同的阈值以识别产生最准确结果的值。通过使用 200 个随机选择的验证点,我们实现了 83.3% 的生产者干扰准确度 (PAD)、46.3% 用户干扰准确度 (UAD) 和 97.8% 的总体准确度 (OA)。这些结果证实了 TVCMA 在监测植被突然和逐渐变化方面的潜力。此外,所提出算法的简单性和数据驱动特性使其适用于其他类型卫星数据的多时相分析。并计算了 p 值。测试了不同的阈值以识别产生最准确结果的值。通过使用 200 个随机选择的验证点,我们实现了 83.3% 的生产者干扰准确度 (PAD)、46.3% 用户干扰准确度 (UAD) 和 97.8% 的总体准确度 (OA)。这些结果证实了 TVCMA 在监测植被突然和逐渐变化方面的潜力。此外,所提出算法的简单性和数据驱动特性使其适用于其他类型卫星数据的多时相分析。s 干扰精度 (UAD) 和 97.8% 的总体精度 (OA)。这些结果证实了 TVCMA 在监测植被突然和逐渐变化方面的潜力。此外,所提出算法的简单性和数据驱动特性使其适用于其他类型卫星数据的多时相分析。s 干扰精度 (UAD) 和 97.8% 的总体精度 (OA)。这些结果证实了 TVCMA 在监测植被突然和逐渐变化方面的潜力。此外,所提出算法的简单性和数据驱动特性使其适用于其他类型卫星数据的多时相分析。
更新日期:2020-11-01
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