Engaging with the Canopy—Multi-Dimensional Vegetation Mark Visualisation Using Archived Aerial Images

A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multiple RS datasets to overcome limitations and produce comparatively detailed outcomes. However, there are still knowledge gaps in examining the effectiveness of these RS approaches in enhancing the detection of archaeological remains/areas. Thus, this review paper is likely to deliver valuable comprehension for archaeological studies to fill knowledge gaps and further advance exploration of archaeological areas/features using RS along with DL approaches.

Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images

The awareness of spatial and temporal variations in site-specific crop parameters, such as aboveground biomass (total dry weight: (TDW), plant length (PL) and leaf area index (LAI), help in formulating appropriate management decisions. However, conventional monitoring methods rely on time-consuming manual field operations. In this study, the feasibility of using an unmanned aerial vehicle (UAV)-based remote sensing approach for monitoring growth in rice was evaluated using a digital surface model (DSM). Approximately 160 images of paddy fields were captured during each UAV survey campaign over two vegetation seasons. The canopy surface model (CSM) was developed based on the differences observed between each DSM and the first DSM after transplanting. Mean canopy height (CH) was used as a variable for the estimation models of LAI and TDW. The mean CSM of the mesh covering several hills was sufficient to explain the PL (R2 = 0.947). TDW and LAI prediction accuracy of the model were high (relative RMSE of 20.8% and 28.7%, and RMSE of 0.76 m2 m−2 and 141.4 g m−2, respectively) in the rice varieties studied (R2 = 0.937 (Basmati370), 0.837 (Nipponbare and IR64) for TDW, and 0.894 (Basmati370), 0.866 (Nipponbare and IR64) for LAI). The results of this study support the assertion of the benefits of DSM-derived CH for predicting biomass development. In addition, LAI and TDW could be estimated temporally and spatially using the UAV-based CSM, which is not easily affected by weather conditions.

Structure from motion photogrammetry in ecology: Does the choice of software matter?

Image-based modeling, and more precisely, Structure from Motion (SfM) and Multi-View Stereo (MVS), is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimeter-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software-Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset (n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets (n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for "High," "Medium," and "Low" quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using "High" and "Medium" quality settings are of small overall magnitude.

The Potential of Drones and Sensors to Enhance Detection of Archaeological Cropmarks: A Comparative Study Between Multi-Spectral and Thermal Imagery

This paper presents experimentation carried out at the Roman Republican city of La Caridad (Teruel, Spain), where different tools have been applied to obtain multispectral and thermal aerial images to enhance detection of archaeological cropmarks. Two different drone systems were used: a Mikrokopter designed by Tecnitop SA (Zaragoza, Spain) and an eBee produced by SenseFly Company (Cheseaux-sur-Lausanne, Switzerland). Thus, in this study, we have combined in-house manufacturing with commercial products. Six drone sensors were tested and compared in terms of their ability to identify buried remains in archaeological settlements by means of visual recognition. The sensors have different spectral ranges and spatial resolutions. This paper compares the images captured with different spectral range sensors to test the potential of this technology for archaeological benefits. The method used for the comparison of the tools has been based on direct visual inspection, as in traditional aerial archaeology. Through interpretation of the resulting data, our aim has been to determine which drones and sensors obtained the best results in the visualization of archaeological cropmarks. The experiment in La Caridad therefore demonstrates the benefit of using drones with different sensors to monitor archaeological cropmarks for a more cost-effective assessment, best spatial resolution and digital recording of buried archaeological remains.

Surfaces from the Visual Past: Recovering HighResolution Terrain Data from Historic Aerial Imagery for Multitemporal Landscape Analysis

Historic aerial images are invaluable sources of aid to archaeological research. Often collected with large-format photogrammetric quality cameras, these images are potential archives of multidimensional data that can be used to recover information about historic landscapes that have been lost to modern development. However, a lack of camera information for many historic images coupled with physical degradation of their media has often made it difficult to compute geometrically rigorous 3D content from such imagery. While advances in photogrammetry and computer vision over the last two decades have made possible the extraction of accurate and detailed 3D topographical data from high-quality digital images emanating from uncalibrated or unknown cameras, the target source material for these algorithms is normally digital content and thus not negatively affected by the passage of time. In this paper, we present refinements to a computer vision-based workflow for the extraction of 3D data from historic aerial imagery, using readily available software, specific image preprocessing techniques and in-field measurement observations to mitigate some shortcomings of archival imagery and improve extraction of historical digital elevation models (hDEMs) for use in landscape archaeological research. We apply the developed method to a series of historic image sets and modern topographic data covering a period of over 70 years in western Sicily (Italy) and evaluate the outcome. The resulting series of hDEMs form a temporal data stack which is compared with modern high-resolution terrain data using a geomorphic change detection approach, providing a quantification of landscape change through time in extent and depth, and the impact of this change on archaeological resources.