Digital Elevation Model: comparative study of data from different sources

Abstract

Precision agriculture technologies aim to optimize agricultural productivity and sustainability. An essential aspect of achieving this optimization is acquiring information about the terrain's elevation, enabling an understanding of the topographic nuances that shape agricultural dynamics. The altimetry data of the agricultural landscape holds particular significance as it influences essential factors such as the operations of agricultural machinery, susceptibility to soil erosion, and other critical aspects impacting crop yields and environmental health. Furthermore, remote sensing is a valuable addition, offering a rapid and cost-effective means to Digital Elevation Models (DEMs).
This study aims to conduct a comparison of the altimetry data for an agricultural field obtained through a topographic survey using a total station, cross-referencing it with data from two platforms: Google Earth and Earthdata by NASA. Using both datasets, we seek to assess potential disparities and gain valuable insights into the accuracy and reliability of the altimetry information provided by each source.
To achieve this goal, we constructed four digital elevation models in the ArcGIS TM software: DEM_ET, DEM_GEb (low point density), DEM_GEa (high point density), and DEM_ED from NASA and then subjected them to processing. The altimetry values were analyzed using a correlation matrix in R. This study was carried out in a 31-hectare experimental field located in Quinta do Quinto, in the area of Vale de Figueira.
The statistical analysis indicated a positive correlation between the data from the topographic survey and the three established DEMs. We found a correlation coefficient (r) of 0.96 for DEM_GEb, 0.96 for DEM_GEa, and 0.91 for DEM_ED. Furthermore, a correlation of 0.99 between DEM_GEa and DEM_GEb.
Overall, our study showcased that the elevation model derived from Google Earth, despite being generated with low point density, has the potential to serve as a reliable data source for determining the altimetry of a field. These finding contributes to future agricultural projects, as it reduces costs and need for labor-intensive topographic surveys while facilitating more efficient land management decisions.