Cabello Javier, Escudero-Clares Montserrat, Martos-Rosillo Sergio, Casas J Jesús, Cintas Juanma, Zakaluk Thomas, Salinas-Bonillo María J
Andalusian Center for Global Change (ENGLOBA), Ctra. de Sacramento s/n, La Cañada de San Urbano, 04120, Almería Spain.
Dept. of Biology and Geology, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, 04120, Almería Spain.
Data Brief. 2025 Jun 9;61:111760. doi: 10.1016/j.dib.2025.111760. eCollection 2025 Aug.
This dataset provides a spatially explicit classification of potentially groundwater-dependent vegetation (pGDV) in the Sierra Nevada Protected Area (Southern Spain), generated using Sentinel-2 imagery (2019-2023) and ecohydrological attributes derived from NDVI time series. NDVI metrics were calculated from cloud- and snow-filtered Sentinel-2 Level 2A images processed in Google Earth Engine. Monthly NDVI values were used to extract three ecohydrological indicators: dry-season NDVI, dry-wet seasonal NDVI difference, and interannual NDVI variability. Based on quartile classifications of these indicators, 64 ecohydrological vegetation classes were defined. These were further clustered into three levels of potential groundwater dependence using hierarchical clustering techniques, differentiating between alpine and lower-elevation aquifer zones. The dataset includes raster layers (GeoTIFF) of the ecohydrological classes and pGDV types at 10 m spatial resolution, a CSV file with descriptive statistics for each class, and complete metadata. All spatial layers are projected in ETRS89 / UTM Zone 30N (EPSG: 25830) and are ready for visualization and analysis in standard GIS platforms. Partial validation of the classification was performed using spring location data and the distribution of hygrophilous plant species from official conservation databases. This available dataset enables reproducible analysis of vegetation-groundwater relationships in dryland mountain ecosystems. It supports comparative research across regions, facilitates the study of groundwater buffering effects on vegetation function, and offers a transferable framework for ecohydrological classification based on remote sensing. The data can be reused to inform biodiversity conservation, groundwater management, and climate change adaptation strategies in the Mediterranean and other water-limited mountain regions.
该数据集提供了内华达山脉保护区(西班牙南部)潜在依赖地下水植被(pGDV)的空间明确分类,使用哨兵 - 2影像(2019 - 2023年)以及从归一化植被指数(NDVI)时间序列得出的生态水文属性生成。NDVI指标是根据在谷歌地球引擎中处理的经过云和气溶胶滤波的哨兵 - 2二级A类图像计算得出的。每月的NDVI值用于提取三个生态水文指标:旱季NDVI、干湿季NDVI差异以及年际NDVI变异性。基于这些指标的四分位数分类,定义了64个生态水文植被类别。利用层次聚类技术,将这些类别进一步聚类为三个潜在地下水依赖级别,区分了高山和低海拔含水层区域。该数据集包括10米空间分辨率的生态水文类别和pGDV类型的栅格图层(GeoTIFF)、一个包含每个类别的描述性统计数据的CSV文件以及完整的元数据。所有空间图层均投影到ETRS89 / UTM 30N带(EPSG: 25830),并可在标准GIS平台中进行可视化和分析。使用泉水位置数据和来自官方保护数据库的喜湿植物物种分布对分类进行了部分验证。这个可用数据集能够对旱地山区生态系统中的植被 - 地下水关系进行可重复分析。它支持跨区域的比较研究,便于研究地下水对植被功能的缓冲作用,并提供了一个基于遥感的生态水文分类可转移框架。这些数据可重新用于为地中海及其他水资源有限山区的生物多样性保护、地下水管理和气候变化适应策略提供信息。