scholarly journals Thermal and Multispectral Remote Sensing for the Detection and Analysis of Archaeologically Induced Crop Stress at a UK Site

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 61
Author(s):  
Katherine James ◽  
Caroline J. Nichol ◽  
Tom Wade ◽  
Dave Cowley ◽  
Simon Gibson Poole ◽  
...  
Keyword(s):  
Data Collection ◽  
Vegetation Index ◽  
Agricultural Practices ◽  
Archaeological Site ◽  
Electromagnetic Spectrum ◽  
Thermal Imager ◽  
Thermal Sensors ◽  
Large Area ◽  
Normalised Difference Vegetation Index

In intensively cultivated landscapes, many archaeological remains are buried under the ploughed soil, and detection depends on crop proxies that express subsurface features. Traditionally these proxies have been documented in visible light as contrasting areas of crop development commonly known as cropmarks. However, it is recognised that reliance on the visible electromagnetic spectrum has inherent limitations on what can be documented, and multispectral and thermal sensors offer the potential to greatly improve our ability to detect buried archaeological features in agricultural fields. The need for this is pressing, as ongoing agricultural practices place many subsurface archaeological features increasingly under threat of destruction. The effective deployment of multispectral and thermal sensors, however, requires a better understanding of when they may be most effective in documenting archaeologically induced responses. This paper presents the first known use of the FLIR Vue Pro-R thermal imager and Red Edge-M for exploring crop response to archaeological features from two UAV surveys flown in May and June 2019 over a known archaeological site. These surveys provided multispectral imagery, which was used to create vegetation index (VI) maps, and thermal maps to assess their effectiveness in detecting crop responses in the temperate Scottish climate. These were visually and statistically analysed using a Mann Whitney test to compare temperature and reflectance values. While the study was compromised by unusually damp conditions which reduced the potential for cropmarking, the VIs (e.g., Normalised Difference Vegetation Index, NDVI) did show potential to detect general crop stress across the study site when they were statistically analysed. This demonstrates the need for further research using multitemporal data collection across case study sites to better understand the interactions of crop responses and sensors, and so define appropriate conditions for large-area data collection. Such a case study-led multitemporal survey approach is an ideal application for UAV-based documentation, especially when “perfect” conditions cannot be guaranteed.

Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls

2015 ◽  
Vol 3 (2) ◽  
pp. 58-67 ◽  
Author(s):  
Jan Rudolf Karl Lehmann ◽  
Keturah Zoe Smithson ◽  
Torsten Prinz
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Vegetation Index ◽  
Archaeological Site ◽  
Unmanned Aerial Systems ◽  
Surrounding Water ◽  
Normalised Difference Vegetation Index ◽  
Remote Sensing Techniques ◽  
Aerial Systems

Remote sensing techniques have become an increasingly important tool for surveying archaeological sites. However, budgeting issues in archaeological research often limit the application of satellite or airborne imagery. Unmanned aerial systems (UAS) provide a flexible, quick, and more economical alternative to commonly used remote sensing techniques. In this study, the buried features of the archaeological site of the Kleinburlo monastery, near Münster, Germany, were identified using high-resolution color–infrared (CIR) images collected from a UAS platform. Based on these CIR images, a modified normalised difference vegetation index (NDVIblue) was calculated, showing reflectance spectra of vegetation anomalies caused by water stress. In the presented study, the vegetation growing on top of the buried walls was better nourished than the surrounding plants because very wet conditions over the days previous to data collection caused higher levels of water stress in the surrounding water-drenched land. This difference in water stress was a good indicator for detecting archaeological remains.

scholarly journals Optimal Spatial Resolution for the Detection and Discrimination of Archaeological Proxies in Areas with Spectral Heterogeneity

2020 ◽  
Vol 12 (1) ◽  
pp. 136 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Archaeological Site ◽  
Area Of Interest ◽  
First Case

Subsurface targets can be detected from space-borne sensors via archaeological proxies, known in the literature as cropmarks. A topic that has been limited in its investigation in the past is the identification of the optimal spatial resolution of satellite sensors, which can better support image extraction of archaeological proxies, especially in areas with spectral heterogeneity. In this study, we investigated the optimal spatial resolution (OSR) for two different cases studies. OSR refers to the pixel size in which the local variance, of a given area of interest (e.g., archaeological proxy), is minimized, without losing key details necessary for adequate interpretation of the cropmarks. The first case study comprises of a simulated spectral dataset that aims to model a shallow buried archaeological target cultivated on top with barley crops, while the second case study considers an existing site in Cyprus, namely the archaeological site of “Nea Paphos”. The overall methodology adopted in the study is composed of five steps: firstly, we defined the area of interest (Step 1), then we selected the local mean-variance value as the optimization criterion of the OSR (Step 2), while in the next step (Step 3), we spatially aggregated (upscale) the initial spectral datasets for both case studies. In our investigation, the spectral range was limited to the visible and near-infrared part of the spectrum. Based on these findings, we determined the OSR (Step 4), and finally, we verified the results (Step 5). The OSR was estimated for each spectral band, namely the blue, green, red, and near-infrared bands, while the study was expanded to also include vegetation indices, such as the Simple Ratio (SR), the Atmospheric Resistance Vegetation Index (ARVI), and the Normalized Difference Vegetation Index (NDVI). The outcomes indicated that the OSR could minimize the local spectral variance, thus minimizing the spectral noise, and, consequently, better support image processing for the extraction of archaeological proxies in areas with high spectral heterogeneity.

Lidar plots — a new large-area data collection option: context, concepts, and case study

2012 ◽  
Vol 38 (5) ◽  
pp. 600-618 ◽  
Author(s):  
Michael A. Wulder ◽  
Joanne C. White ◽  
Christopher W. Bater ◽  
Nicholas C. Coops ◽  
Chris Hopkinson ◽  
...  
Keyword(s):  
Data Collection ◽  
Large Area ◽  
Area Data

Sensors key to advances in precision agriculture

Sensor Review ◽  
2017 ◽  
Vol 37 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Robert Bogue
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Agricultural Practices ◽  
Content Type ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Physical And Chemical Variables ◽  
Physical And Chemical ◽  
The Impact ◽  
Agricultural Machines

Purpose This study aims to illustrate the growing role that sensors play in agriculture, with an emphasis on precision agricultural practices. Design/methodology/approach Following a short introduction, this study first provides an overview of agricultural measurements and applications. It then discusses the importance of airborne and land-based optical sensing techniques and the role of the normalised difference vegetation index. Sensors used on conventional and robotic agricultural machines are considered next, and fixed sensors and sensor networks are then discussed. Finally, brief concluding comments are drawn. Findings This shows that much modern agriculture is a high-technology business which relies on a multitude of sensor-based measurements. Sensors are based on a diversity of optical and other technologies and measure a wide range of physical and chemical variables. They are deployed in the air, on agricultural machines and in the field and generate data that can be used to enhance productivity and reduce both costs and the impact on the environment. Originality/value This provides a detailed insight into the important role played by sensors in modern agricultural practices.

scholarly journals Vegetation monitoring using multispectral sensors — best practices and lessons learned from high latitudes

2019 ◽  
Vol 7 (1) ◽  
pp. 54-75 ◽  
Author(s):  
Jakob J. Assmann ◽  
Jeffrey T. Kerby ◽  
Andrew M. Cunliffe ◽  
Isla H. Myers-Smith
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Control Point ◽  
Lessons Learned ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Vegetation Monitoring ◽  
Technological Advances ◽  
Normalised Difference Vegetation Index ◽  
Multispectral Sensors

Rapid technological advances have dramatically increased affordability and accessibility of unmanned aerial vehicles (UAVs) and associated sensors. Compact multispectral drone sensors capture high-resolution imagery in visible and near-infrared parts of the electromagnetic spectrum, allowing for the calculation of vegetation indices, such as the normalised difference vegetation index (NDVI) for productivity estimates and vegetation classification. Despite the technological advances, challenges remain in capturing high-quality data, highlighting the need for standardized workflows. Here, we discuss challenges, technical aspects, and practical considerations of vegetation monitoring using multispectral drone sensors and propose a workflow based on remote sensing principles and our field experience in high-latitude environments, using the Parrot Sequoia (Pairs, France) sensor as an example. We focus on the key error sources associated with solar angle, weather conditions, geolocation, and radiometric calibration and estimate their relative contributions that can lead to uncertainty of more than ±10% in peak season NDVI estimates of our tundra field site. Our findings show that these errors can be accounted for by improved flight planning, metadata collection, ground control point deployment, use of reflectance targets, and quality control. With standardized best practice, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.

scholarly journals Urban Growth Derived from Landsat Time Series Using Harmonic Analysis: A Case Study in South England with High Levels of Cloud Cover

2021 ◽  
Vol 13 (16) ◽  
pp. 3339
Author(s):  
Matthew Nigel Lawton ◽  
Belén Martí-Cardona ◽  
Alex Hagen-Zanker
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Urban Growth ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Ground Truth ◽  
Data Availability ◽  
Change Analysis ◽  
Normalised Difference Vegetation Index

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period.

On-line Tropical Epidemiology -A Case-Study from The Gambia

1987 ◽  
Vol 26 (02) ◽  
pp. 73-76 ◽  
Author(s):  
Kathryn Rowan ◽  
P. Byass ◽  
R. W. Snow
Keyword(s):  
Data Collection ◽  
Field Trial ◽  
Error Rates ◽  
Database Management System ◽  
Bed Nets ◽  
Completion Rates ◽  
On Line ◽  
Access To Data ◽  
Rapid Processing

SummaryThis paper reports on a computerised approach to the management of an epidemiological field trial, which aimed at determining the effects of insecticide-impregnated bed nets on the incidence of malaria in children. The development of a data system satisfying the requirements of the project and its implementation using a database management system are discussed. The advantages of this method of management in terms of rapid processing of and access to data from the study are described, together with the completion rates and error rates observed in data collection.

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