Georeferencing Process Research Articles

GeoPick ‐ A web application for georeferencing natural history collections following best practices

Georeferencing is a key process in the digitization of natural history collections as it assigns spatial coordinates to preserved specimen collecting locations, facilitating their use in ecological, evolutionary and conservation research. Georeference data in public repositories such as GBIF is often missing or incomplete, jeopardising their use in research and limiting the return on investment made by public institutions. Despite the existence of guidelines for best practices for georeferencing and widely accepted standards for biodiversity data, there is a lack of a simple yet effective software tool that offers the implementation of both concepts. We present GeoPick with the aim to offer the collections community a standards‐compliant tool that eases the georeferencing process, making it more cost‐effective, and which, by applying best practices, contributes to the betterment of the occurrence data in public repositories. GeoPick also offers the possibility of collaboration between users and institutions through the sharing of georeferences. The tool is hosted by GBIF at https://geopick.gbif.org, and is open source. Its code is available at a public GitHub repository (https://github.com/rtdeb/GeoPick).Keywords: Darwin Core, georeferencing, natural history collections, point‐radius method, web application, Well Known Text Format, WKT

Accuracy Evaluation for Plan-Reliefs and Historical Maps Created during WWI in Northern Italy

The availability of digital copies of historical artifacts modeling the territory through the so-called “plan-reliefs” is important for many reasons: the preservation of the artifact if the physical object is damaged or destroyed, the possibility of creating virtual showrooms and providing researchers a tool to study the object combining information from different sources. For these reasons, a set of plan-reliefs created during World War I on the Italian front and kept by the Italian Historical War Museum of Rovereto (Italy) was surveyed to create digital models of the surfaces, which were georeferenced in the ETRS89 datum. A set of historical military maps of the same period was georeferenced to overlay the sets to the surface in the digital representation and to try to infer clues about the cartographic sources used in the historical artifact creation. The best transformation for georeferencing the maps is different depending on the map scale, map origin, conservation status and number of Ground Control Points. The georeferencing process precision and accuracy were evaluated. The digital models created in this study were compared to the official Digital Terrain Model (DTM) provided by the Regions or the autonomous provinces. The results demonstrate the feasibility of the approach, and the combination of the models with the georeferenced maps is used by historians to describe the process used in the creation of plan-reliefs.

Evaluating a Method for Georeferencing Agricultural Fields

The US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has begun a modernization effort to supplement survey data with non-survey data to improve estimation of agricultural quantities. As part of this effort, NASS has begun georeferencing farms on its list frame by linking geospatial data on agricultural fields with farm records on the list frame. Although many farms can be linked to geospatial data acquired by the Farm Service Agency (FSA), this linkage is not possible for farmers who do not participate in FSA programs, which may include members of some underrepresented groups in US agriculture. Thus, NASS has developed a georeferencing process for non-FSA farms, combining automatic and manual field identification, county assessor parcel data, record linkage, and classification surveys. This process serves the dual purpose of linking farms already on the list frame to geospatial data sources and identifying new farms to add to NASS’s list frame. This report evaluates the output of the non-FSA georeferencing process for 11 states, with a focus on farms added to the list frame via georeferencing. Substantial percentages (>25% for each category) of the new farms added via georeferencing were urban or suburban farms, were small, had livestock, or were in counties with Amish settlements. The georeferencing process shows promise adding farms from these groups that have historically been less well covered in NASS surveys.

Spatio-Temporal Analysis of Ground Movement Using Unmanned Aerial Vehicle Photogrammetry in Gampong Lamkleng, Aceh Besar

Ground movement is one of the most frequent disasters causing major damages in Indonesia. Unmanned Aerial Vehicle (UAV) has been widely used as a rapid observation method to obtain detailed characterization of ground movement. Often, active landslide area is difficult to access. This hinders close monitoring and observations of the ground movement. This study aims to demonstrate the use of UAV as tools for monitoring and observations on active ground movement area and to validate the results. For this purpose, the study was conducted at Gampong Lamkleng, Aceh Besar-Indonesia using spatio-temporal analysis by UAV photogrammetry. The UAV was chosen because it is easy to use, practical, and safe for landslide area that are relatively small and difficult to reach. Aerial photographs were processed using the Agisoft Metashape software in modeling and analyzed using Quantum GIS (QGIS) and ArcGis. The observation results show that the largest ground movement occurred in January 20 to 23, 2021 which was related to precipitation rates measured at a nearby rainfall station. The movement volume was 1,411.063 m3 and a rate of ground movement reaching 0.69 m/day due to heavy rain. The estimated value of losses is IDR 1,055,854,000. The UAV images analysis were compared to actual ground elevation measured using Real-Time Kinematic (RTK). The validation show that the accuracy based on comparison between photogrammetric and RTK measurement was at agreeable rate (99%). Otherwise, the accuracy performed on 19 check point using Root Mean Square Error analysis found that the accuracy was still very low. The low RMSE value is due to the georeferencing process using the Ground Control Point not being carried out.

Giving Historical Photographs a New Perspective: Introducing Camera Orientation Parameters as New Metadata in a Large-Scale 4D Application

The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.

Changes in Glacial Area Extent in Auyuittuq National Park, Pangnirtung

With global temperatures rising four times faster in the Canadian Arctic compared to global averages, glaciers in the Canadian Arctic Archipelago (CAA) are starting to shrink at an alarming rate (Paul et al., 2020). To quantify rates of change, frequent updates of glacial outlines to provide an accurate database for monitoring are needed (Schaffer et al., 2017). Rundle, Nerutusoq, and Fort Beard Glaciers are all located on the Penny Ice Cap within Auyuittuk National Park, Baffin Island, Canada. Using a combination of aerial photography and ArcGIS Pro, I examined the historical and recent changes in glacial area extents. High resolution historical aerial photographs from the National Air Photo Library dating to September 1959 were manually georeferenced onto ArcGIS Pro. Photogrammetry techniques were then used to combine the aerial photographs into one combined orthographic image. The historic glaciers were then outlined using a manual technique based on the pixel size (8 μm) of the images using ArcGIS Pro. This allows me to compare glacial outlines from 2010 using the Randolph glacier inventory and 2022 using manual outline techniques described, respectively. Because using a manual technique can create accuracy limitation, specifically during the georeferencing process, I compared results for both time periods using an automatic method. Results from my work will provide estimates of changes in glacier area over time, the relative precision of different methods, and weather rates of shrinkage have increased over time. ReferencesPaul, F., Rastner, P., Azzoni, R. S., Diolaiuti, G., Fugazza, D., Le Bris, R., Nemec, J., Rabatel, A., Ramusovic, M., Schwaizer, G., & Smiraglia, C. (2020). Glacier shrinkage in the Alps continues unabated as revealed by a New Glacier Inventory from sentinel-2. Earth System Science Data, 12(3), 1805–1821. https://doi.org/10.5194/essd-12-1805-2020 Schaffer, N., Copland, L., & Zdanowicz , C. (2017). Ice velocity changes on Penny Ice Cap, Baffin Island, since the 1950s. Journal of Glaciology, 63(240), 716–730. https://doi.org/10.1017/jog.2017.40

Ispitivanje uticaja broja orijentacionih tačaka na geometrijsku tačnost UAV fotogrametrijskih proizvoda formiranih pomoću pristupa strukture iz pokreta

The positional and vertical accuracy of UAV aerial photogrammetry products generated using the Structure from Motion (SfM) approach depends on various factors, such as flight plan parameters, camera quality, camera calibration, the SfM algorithm used, and the georeferencing process. The influence of the quantity of Ground Control Points (GCPs) on the geometric quality of generated models and the stability of camera calibration parameters assessed through self-calibration in the block-aerotriangulation process was investigated in this study. Three software systems were used to process the collected UAV photogrammetry images: Pix4D Mapper, Agisoft Metashape, and Trimble Inpho UASMaster. Standard statistical quality assessments were employed to assess the accuracy of the block-aerotriangulation. The research findings indicate that augmenting the quantity of GCPs enhances model reliability and decreases the RMSE values of vertical deviation on the control points. The RMSE values of vertical deviation on the check points for all three used software systems converged to approximately twice the value of the average spatial resolution. Additionally, the RMSE values of positional deviation on check points converged to the value of the average spatial resolution.

PRODUCING DIGITAL MAPS FOR THE DISTRIBUTION OF MINERALS IN IRAQ USING GEOGRAPHIC INFORMATION SYSTEMS (GIS)

Considering the huge amount of information, it becomes so difficult to deal with and understand only in condition they were organized, classified and saved as data base that can be deal with automatically and to make use of it without interfering with its meaning and significance. Here comes the need to invent new methods and systems to save such information and deal with. An example of such systems is Management Information System and Geographic Information System. The ability of geographic information systems to prepare the geographic database and represent it as digital maps is verified by processing mineral data. This research aims to produce digital maps of mineral materials in Iraq by converting a paper map (geological map) at a scale (1:1000000) into a digital map using the survey process and modifying the coordinate system for this map using the georeferencing process. Then the layers are extracted from this map using the digitization process, and these layers are represented, and the proportion of these materials on these layers is represented as a circular sector. The purpose of a geodatabase is to create features in GIS datasets and to determine the relationship between features that are shown on maps as layers, where each layer represents a specific type of feature that was used for spatial analysis.

Georeferencing Historic Collection Data

Collection data from historic collections often contain vague or non-specific location information of where the specimen was found. Now, during the mass-digitization era of natural history collections, this presents a challenge as we intend to georeference these locations without specific details of where they were found. In a case study at the Museum für Naturkunde Berlin (MfN), a system was developed to georeference these vague locations. There are three types of geospatial vector data that should be considered in georeferencing: points, lines, and polygons. In most collections, objects were taken from a place that can be represented by a point coordinate (x, y or latitude and longitude). However, if these coordinates were not captured at the time, or information has been lost, making a polygon (i.e. a bounding area) is more appropriate for georeferencing a collection site (Hill 2009). Many databases and standards, however, expect point coordinate information with a field to account for uncertainty. TDWG’s Darwin Core Standard includes terms for decimalLatitude, decimalLongitude, geodeticDatum, and coordinateUncertaintyInMeters, among additional georeferencing fields (Wieczorek et al. 2012). Therefore, the following process results in relevant information to fulfill this standard. MfN’s Neuroptera collection required the georeferencing of their historic collection of specimens found in Germany. Their data contained verbatim place names for the objects collected between 1758 and 1906. Some of these places were vague and non-specific, referring to entire states or cities, while others were more detailed descriptions. This georeferencing process ultimately resulted in a searchable table of place names in Germany, at different administrative levels. For each, there was a latitude, longitude and uncertainty radius assigned. Open geospatial data sources of polygon boundaries of Germany’s regions at different administrative levels were used as a basis for the project. These were transformed with QGIS. First, the polygon layer was used to create a circular polygon, encompassing the entire area. Next, a measurement of each circle’s circumference was taken and stored in the data’s attribute table. Then the centroids of each circle were calculated. These became the latitudes and longitudes for each area. This visualisation is shown in Fig. 1. Finally, the data was tidied, and the radius of the circle was calculated — this became the uncertainty measurement for each area, as it was measured from the centroid of the polygon to the maximum possible distance edge of the polygon. In total there were six output tables (see Fig. 2) — the four administrative levels, and two additional levels for Berlin, which is organised differently (Thorn 2022). These tables allowed a user to search for the verbatim place name they had in the data, and assign coordinates and an uncertainty radius to it. This method for georeferencing historic locations could be replicated in different countries, eventually creating a comprehensive database that would aid in georeferencing historic (and recent) collections. If this project were to be used more widely, additional outputs could be created using historic boundaries. The outputs from this process can be repeatedly reused, therefore saving collection staff from manually finding coordinates for everything in their collections. While currently the output tables still have to be searched to find relevant data, this may be automated in the future, creating an efficient georeferencing process.

AN INTERDISCIPLINARY APPROACH FOR UNVEILING AND ENHANCING THE FIRST WORLD WAR HERITAGE IN THE LANDSCAPE

Abstract. More than one hundred years ago, the European landscape was profoundly transformed by multiple factors related to the Great War, which altered its territorial and environmental ecosystems, giving the landscape the value of a cultural asset with a civilizational value. After more than a century, the "signs of history" remain in the contemporary world at different levels of recognition, very often in a state of abandonment and at risk of loss. This contribution presents the elaboration of a non-invasive and "from remote" operative method, based on the analytical interpretation of historical cartography and historical aerial photographs to reconstruct the evolutionary dynamics of the landscape and recognize the areas where the impact of the conflict has been stronger. The georeferencing process of the sources in the QuantumGIS environment made it possible to elaborate interesting overlaps between cartographies and photographs belonging to different temporal frames. In this way, the dynamics of landscape transformation and land cover/use have been analyzed in relation to the destructive impact of the war to identify the areas in which it is more likely to find material remains of the vestiges, whose recognition becomes a prerequisite for the future practices of protection, transformation, and enhancement.

Promoting the heritage of the city of San Cristobal de La Laguna through a temporal link with a 16th century map

There is an increasing interest in the conservation of historical cities since they provide a link to the roots of their communities and bring cultural and economic benefits to their inhabitants. In this paper, we present an approach to promote the knowledge of the UNESCO World Heritage city of San Cristóbal de La Laguna in the Canary Islands, Spain. The city was founded in 1496 and has a unique value due to its urban design. In this work, we present a web application that allows a user to locate places and addresses of the current city in its first known map authored by Leonardo Torriani in the 16th century. To build this application we have georeferenced the ancient map in the current cartography. The georeferencing process needs the identification of homologous ground control points in the coordinate systems of both the old map and the current cartography, and the definition of a transform between them. Best results were obtained with the non-parametric natural transform interpolation leading to a global mean error of 4.9 m that reduces to 3.2 m in the historical city centre. To provide a fast response to the user of the web application, a technique to precompute offline the natural transform is presented. The web application has a simple front-end where the user fills the current city address in a form. This activates a query to obtain the geographical coordinates of the address that are transformed to map coordinates using the pre-computed transformation. These map coordinates are used by a map viewer in the front end that locates the user address in the ancient map. To test the performance of the web application, the load of the system has been analysed obtaining a latency of 1.4 s in 50 concurrent users. Results show that the web application provides accurate results in the historical centre while providing satisfactory response times.Highlights:- This study investigates the issue of georeferencing the first historic map (16th century) of San Cristóbal de La Laguna and the possibility to geolocate current city addresses.- By using the natural interpolation method, the georeferencing errors were diminished below 4 m in most of the historic city.- A user-friendly web application has been designed which precisely locates current directions in the old map providing a valuable tool for the promotion of the heritage of the city.

Accuracy Assessment of a UAV Direct Georeferencing Method and Impact of the Configuration of Ground Control Points

Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal RMSE of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the RMSE gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level.

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications.

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

Image Georeferencing using Artificial Neural Network Compared with Classical Methods

Georeferencing process is one of the most important prerequisites for various geomatics applications; for example, photogrammetry, laser scan analysis, remotely sensing, spatial and descriptive data collection, and others. Georeferencing mostly involves the transformation of coordinates obtained from images that are inhomogeneous due to accuracy differences. The georeferencing depends on image resolution and accuracy level of measurements of reference points ground coordinates. Accordingly, this study discusses the subject of coordinate’s transformation from the image to the global coordinates system (WGS84) to find a suitable method that provides more accurate results. In this study, the Artificial Neural Network (ANN) method was applied, in addition to several numerical methods, namely the Affine divided difference, Newton’s divided difference, and polynomial transformation. The four methods were modelled and coded using Matlab programming language based on an image captured from Google Earth. The image was used to determine reference points within the study area (University of Baghdad campus). The findings of this study showed that the ANN enhanced the results by about 50% in terms of accuracy and 90% in terms of homogeneity, compared with the other methods.

Toponym-assisted map georeferencing: Evaluating the use of toponyms for the digitization of map collections.

A great deal of information is contained within archival maps—ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map toponym (place name) labels. It is the first study to demonstrate these methods across a wide range of both simulated and real-world maps. We find that toponym-based georeferencing is sufficiently accurate to be used for data extraction purposes in nearly half of all cases. We make our implementation available to the wider research community through fully open-source replication code, as well as an online georeferencing tool, and highlight areas of improvement for future research. It is hoped that the practical implications of this research will allow for larger and more efficient processing and digitizing of map information for researchers, institutions, and the general public.

Assessment of potential locations for evacuation in Depok Municipality using images from UAV

This study aims to perform assessment of public facilities in Perumnas Depok Utara, Depok Municipality, Indonesia for evacuation locations using images from UAV. A UAV device, a DJI Phantom 4 was flown at 100 metres height to capture seven locations of public facilities located within Perumnas Depok Utara residential area. Raw images from DJI Phantom 4 underwent image processing and georeferencing process to generate smooth aerial images. Public facilities locations were identified from acquired images and later digitized to generate layers of assessment parameters. Each locations were scored based on rating of all criteria’s parameters. Seven criteria serve as foundation layers for evacuation assessment namely size, ownership, availability, capacity, infrastructure, transportation and safety. Study result shows that Jawa Soccer Field is the first rank of potential evacuation location while Balai Rakyat Meeting Place is the least potential evacuation location. Total area for potential evacuation locations around 43,000 m2 and can accommodate 28,961 people or equal to 60.5% of Beji Village’s population.

Fun and Easy Georeferencing with a New Online Tool from South Africa

A new online tool for georeferencing specimen records has been developed through the Natural Science Collections Facility (NSCF) in South Africa to address the need for rapid, high quality georeferencing of specimen collections in the region (Fig. 1). A dataset of specimen records with Darwin Core fields dwc:scientificName, dwc:country, optional dwc:stateProvince, dwc:locality or dwc:verbatimLocality, optional dwc:recordedBy, and a record identifier such as dwc:occurrenceID (see dwc.tdwg.org/terms for definitions) is first uploaded into the tool and a team of georeferencers then work to georeference the dataset. Fuzzy string matching is used to group similar locality strings and to search for potential matching georeferences from a georeference database. The tool aims to improve efficiency by storing georeferenced localities so that they can be reused when the same locality is encountered again in other datasets. Thus, a locality only needs to be georeferenced once, and that georeference is reused for any other permutations of that locality string. A georeference includes the most important metadata from the Darwin Core standard: a measure of uncertainty, dwc:georeferenceDate, dwc:georeferencedBy, dwc:georeferenceProtocol, dwc:georeferenceSources, and the all too often neglected dwc:geodeticDatum. dwc:georeferencedByID is included for recording the ORCID iD of the georeferencer to facilitate attribution further down the data publication pipeline. In theory, the process of georeferencing should become more efficient with time as the georeference database grows. The georeferencing process is gamified by showing each georeferencer their own numbers of georeferenced records as they work, and they can see activity of fellow georeferencers as the dataset statistics update in real time. Dataset owners can also see overall progress with the dataset and numbers of records georeferenced by each team member, which may be useful for management purposes. Once a dataset is completed, it is downloaded with the new georeferences so these can be incorporated back into the original source database. Within the landscape of currently available georeferencing tools the system presented here is specifically placed to facilitate the management of the georeferencing process for a dataset by a team of georeferencers. The georeferencing workflow still requires a full suite of tools for finding coordinates for localities, such as a GIS, gazetteers and online resources, as well as a specific georeferencing protocol. It essentially replaces the use of spreadsheets for doing georeferencing, or doing georeferences directly in a collection database, which can be inefficient. Related to this, it includes a quality assurance process whereby georeferences are checked for correctness and adherence to the protocol being used, and for identifying geographic and environmental outliers for each species within the dataset. In this way the tool supports current workflows and best practices for georeferencing (e.g. Chapman and Wieczorek (2020), Zermoglio et al. (2020)). The technology stack includes Firebase as the primary database, ElasticSearch for fuzzy string matching, and the user interface is built with the modern Javascript framework Svelte. The tool has been in use by the NSCF since April 2021 after being populated with approximately 300 000 existing georeferences for southern Africa from various sources, including the South African National Biodiversity Institute (SANBI) Gazetteer and several collections databases. While initial emphasis in developing the tool has focussed on southern Africa, the tool can be extended to other regions easily. Please contact data@nscf.org.za for further information.

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

Improved Georeferencing: Three essential guiding documents

To understand biological and geological events and the history of collected samples, it is essential to determine and communicate location information accurately. The accuracy of a georeference depends upon the circumstances of the event. Historical collections depend on having clear verbatim locality descriptions, the correct interpretation of data written on labels, and on the availability of gazetteers and maps of suitable scale and time. Observation and tracking data localities depend on GPS (Global Positiioning System) accuracy, and on presence or absence of nearby obstructions such as buildings, forest cover, cliffs, etc. Marine data depend on the accurate determination of the surface location and the techniques to position a dive event from that location and to determine its depth and extent. Many people are using smartphones or maps such as Google Earth and Google Maps to determine their georeferences – but are they suitable and accurate enough to determine locations and elevations? New editions of the Georeferencing Best Practices (Chapman and Wieczorek 2020), the Georeferencing Quick Reference Guide (Zermoglio et al. 2020), and the Georeferencing Calculator Manual (Bloom et al. 2020), were published earlier this year and address all the issues listed above and many more. These documents were based on earlier versions but have been updated and improved considerably – adding information for marine biomes, caves, lithographic stratifications, transects, and the use of elevation, as well as including many more illustrations and examples. The expansion of an extensive georeferencing glossary adds to consistency in the use of terms The trio of documents now provides consistent guidance about how to georeference diverse locality types and detailed instructions on how to calculate uncertainty using many different coordinate reference systems and datums (horizontal and vertical) along with the importance of recording this information. Finally, they provide guidance on how to set up a georeferencing project and how to relate the results to the Darwin Core Standard (Darwin Core Task Group 2009). For the last decade, Darwin Core (Wieczorek et al. 2012) has been one of the preferred standards for sharing biodiversity data, including associated location information. Darwin Core currently has 44 terms in its Location class, allowing sharing from administrative divisions, to elevations and depths, coordinates in different formats, and georeference metadata, among others. Although Darwin Core provides definitions for each of its terms, their correct use is sometimes poorly understood, resulting in information being captured incorrectly, or not captured, documented or shared at all. We will re-introduce these documents, discuss their content, importance, and differences from previously published versions. The newly revised documents provide guidance on capturing and documenting georeferences, clarifying the georeferencing process and showing how to capture information using Darwin Core appropriately. They will improve the location data associated with biological events and our understanding of these events.

Archival Aerial Images Georeferencing: A Geostatistically-Based Approach for Improving Orthophoto Accuracy with Minimal Number of Ground Control Points

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.