Abstract

Agricultural land abandonment is a dynamic process characterized by both significant spectral variability and spectral similarity to areas of agricultural land. The identification of abandoned agricultural land (AAL) based on remote sensing data must be preceded by field surveys that are focused on the acquisition of the physiognomic characteristics (the composition of species, height of vegetation, textures, and clustering into patterns) of these areas. This study aims to document the physiognomic and spectral differences between AAL and other land cover/land use classes in Slovakia. The Normalized Difference Vegetation Index (NDVI), derived from Sentinel-2 time series for vegetation from April to September 2018, was applied. NDVI values were calculated for each Sentinel-2 scene, and NDVI profiles for selected samples were used to create phenological profiles for each AAL and land cover/land use class. The dispersion of the NDVI values for these classes, their median, and the root mean square error between NDVI data show that overgrowth by herbaceous plants is characterized by more significant dynamics (0.40–0.75), resulting in better spectral discriminability than classes overgrown by shrubs and trees (0.70–0.80). Field survey data are a fundamental prerequisite for the correct explanation of the discriminability of these AAL classes.

摘要

农业土地放弃是一个动态过程，其特征在于与农业用地区域具有显着的光谱可变性和光谱相似性。基于遥感数据识别废弃农业用地 (AAL) 之前必须进行实地调查，重点是获取这些地区的地貌特征（物种组成、植被高度、纹理和聚类模式） . 本研究旨在记录斯洛伐克 AAL 与其他土地覆盖/土地利用类别之间的地貌和光谱差异。应用了归一化差异植被指数 (NDVI)，该指数源自 2018 年 4 月至 9 月植被的 Sentinel-2 时间序列。计算每个 Sentinel-2 场景的 NDVI 值，选定样本的 NDVI 剖面用于为每个 AAL 和土地覆盖/土地利用类别创建物候剖面。这些类别的 NDVI 值的分散、它们的中位数和 NDVI 数据之间的均方根误差表明，草本植物的过度生长具有更显着的动态特征 (0.40–0.75)，从而导致比灌木过度生长的类别具有更好的光谱辨别力和树木（0.70–0.80）。实地调查数据是正确解释这些 AAL 类别的可辨别性的基本前提。导致比灌木和树木过度生长的类（0.70-0.80）具有更好的光谱辨别力。实地调查数据是正确解释这些 AAL 类别的可辨别性的基本前提。导致比灌木和树木过度生长的类（0.70-0.80）具有更好的光谱辨别力。实地调查数据是正确解释这些 AAL 类别的可辨别性的基本前提。