Abstract

Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop’s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.

Highlights

  • Nowadays, there is a wide range of agricultural activities where geospatial technologies can support management and decision-making by collecting geographic data, 2D/3D modelling, and mapping

  • Estimation of volume changes will be presented to answer the question: “What is the impact of the co-registration method in Differential Digital Terrain Models (DTMs) quality for the calculation of mo3.1

  • This study showed the potential of unmanned aerial vehicle (UAV) data in the estimation of soil volume change for a soil tillage operation

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Summary

Introduction

There is a wide range of agricultural activities where geospatial technologies can support management and decision-making by collecting geographic data, 2D/3D modelling, and mapping. Some of these technologies are global navigation satellite systems (GNSS), remote sensing and photogrammetry, airborne laser scanning (ALS), sensors (insitu or mobile), and spatial analysis tools for raster/vector within a geographic information systems (GIS) environment. Remote sensing and photogrammetry can ensure the fast identification of crop areas that need more care or water, detect possible deficiencies in the irrigation of different sites, and optimise resources. This will contribute to a better distribution of nutrients, fertilisers, and water without waste, increasing agricultural production or preventing its loss

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