Substantial land surface greening has been observed globally over the last few decades. This greening has been primarily attributed to climate change and CO₂ concentrations, but recent research studies have emphasized the large role of human land use in this process, especially agricultural land abandonment (ALA). The aim of this study was to investigate whether long-term gradual greening of agricultural land can aid in mapping abandoned land across Poland. Trend estimation and temporal segmentation were applied to the 1986–2019 annual Landsat-derived Normalized Difference Vegetation Index (NDVI) time series to detect periods of long-term greening and to assess its relationship with actual information on ALA. The results show that long-term greening is widespread in Poland (covering up to 60% of its territory), regardless of former and current land uses, most of which has stemmed from modifications within stable land use classes (as they cover 91% of Poland). The highest greening rates and intensities were observed due to conversions from agricultural to non-agricultural land use, which strongly suggests land abandonment and the proceeding succession. However, some proportion of managed agricultural land has also experienced high-intensity greening, and a large proportion of abandoned agricultural land is not greening intensively. Thus, setting an exact greening intensity threshold allowing to clearly distinguish the status of land was not possible. This study also presents the consistency of the Landsat annual time series and its good performance for long-term greening detection, indicating that temporal segmentation may better capture ALA patterns than trend estimation

在过去的几十年中，全球范围内均已观察到大量的地面绿化。这种绿化主要归因于气候变化和CO ₂集中，但最近的研究强调了人类土地使用在这一过程中的重要作用，特别是农业土地遗弃（ALA）。这项研究的目的是调查长期逐步绿化农业土地是否可以帮助在波兰各地绘制荒地的地图。将趋势估计和时间分段应用于1986-2019年度Landsat衍生的归一化差异植被指数（NDVI）时间序列，以检测长期绿化的时期并评估其与ALA实际信息的关系。结果表明，波兰的长期绿化十分普遍（覆盖了其领土的60％），无论以前和当前的土地利用如何，其大部分源于稳定土地利用类别的修改（因为它们涵盖了91％的土地利用）。波兰）。观察到最高的绿化率和强度是由于农业用地向非农业用地的转化，这强烈表明土地被遗弃和进行中。但是，一定比例的经管理的农业用地也经历了高强度的绿化，并且很大一部分废弃的农业用地并未进行密集的绿化。因此，不可能设置确切的绿化强度阈值以清楚地区分土地状况。这项研究还展示了Landsat年度时间序列的一致性及其在长期绿化检测中的良好表现，表明时间分段可能比趋势估计更好地捕获ALA模式 这有力地表明了土地的遗弃和后续的继承。然而，部分被管理的农业用地也经历了高强度的绿化，并且很大一部分废弃的农业用地没有被密集地绿化。因此，不可能设置确切的绿化强度阈值以清楚地区分土地状况。这项研究还展示了Landsat年度时间序列的一致性及其在长期绿化检测中的良好表现，表明时间分段比趋势估计可能更好地捕获ALA模式 这有力地表明了土地的遗弃和后续的继承。但是，一定比例的经管理的农业用地也经历了高强度的绿化，并且很大一部分废弃的农业用地并未进行密集的绿化。因此，不可能设置确切的绿化强度阈值以清楚地区分土地状况。这项研究还展示了Landsat年度时间序列的一致性及其在长期绿化检测中的良好表现，表明时间分段比趋势估计可能更好地捕获ALA模式 设置确切的绿化强度阈值以明确区分土地状况是不可能的。这项研究还展示了Landsat年度时间序列的一致性及其在长期绿化检测中的良好表现，表明时间分段比趋势估计可能更好地捕获ALA模式 设置确切的绿化强度阈值以明确区分土地状况是不可能的。这项研究还展示了Landsat年度时间序列的一致性及其在长期绿化检测中的良好表现，表明时间分段比趋势估计可能更好地捕获ALA模式