# Vegetation dynamics inside Mediterranean vineyards: A dataset for tracking changes using unmanned aerial vehicles

## TL;DR

This paper introduces a dataset from Mediterranean vineyards that tracks vegetation changes using drones over four years to study the impact of service crops on grapevine performance.

## Contribution

The novelty lies in combining field sampling with long-term UAV data to study service crops' effects on vineyards.

## Key findings

- UAV data captured detailed vineyard characteristics like elevation and vegetation indices over four years.
- Processed data includes spatial vectors and point clouds at 5 cm resolution for vineyard analysis.
- The dataset supports algorithm calibration for distinguishing vines from inter-row vegetation.

## Abstract

Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021–06–10 and 2022–07–29 for the first vineyard, and between 2023–06–08 and 2025–03–12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.