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Volunteers are a key part of the archaeological labour force and, with the growth of digital datasets, these citizen scientists represent a vast pool of interpretive potential; yet, concerns remain about the quality and reliability of crowd-sourced data. This article evaluates the classification of prehistoric barrows on lidar images of the central Netherlands by thousands of volunteers on the Heritage Quest project. In analysing inter-user agreement and assessing results against fieldwork at 380 locations, the authors show that the probability of an accurate barrow identification is related to volunteer consensus in image classifications. Even messy data can lead to the discovery of many previously undetected prehistoric burial mounds.

Pediatric Speech Sound Disorders (SSDs) are conventionally diagnosed using auditory-perceptual assessments, heavily relying on International Phonetic Alphabet (IPA) transcriptions. This approach, while prevalent, is increasingly criticized due to inherent perceptual biases, limited sensitivity to subtle speech motor variations, and insufficient reflection of underlying speech mechanisms. This paper critically re-examines a widely used diagnostic classification system for pediatric SSDs, namely Dodd's Model of Differential Diagnosis (MDD), emphasizing the limitations of perceptual methods and advocating for instrumental techniques to address significant ground truthing issues. Critical analysis in this paper integrates evidence from perceptual research, instrumental phonetics, and speech motor development studies, highlighting discrepancies between traditional classification methods and modern instrumental data. Findings indicate profound limitations in current auditory-perceptual classification methods, particularly regarding their inability to detect subtle motoric impairments such as jaw sliding, covert motor contrasts, and undifferentiated tongue gestures. Evidence from instrumental studies supports a speech-motor rather than purely cognitive-linguistic basis for many pediatric SSDs, revealing significant inadequacies in current clinical practices. To avoid the narrow interpretation of \"motor speech\" as referring only to childhood apraxia of speech (CAS) or dysarthria, we explicitly broaden its scope to include a wider range of motoric influences on SSDs. Given these critical ground truthing concerns, the paper proposes adopting instrumental-based methodologies that offer greater precision in identifying underlying motor-based impairments, thereby promoting a more accurate and nuanced understanding of pediatric SSDs. Furthermore, the discussion advocates for adopting a dimensional rather than categorical classification framework, emphasizing gradual developmental trajectories and foundational speech motor skills. Aligning with modern precision medicine principles, the proposed approach aims to refine diagnostic accuracy, improve intervention effectiveness, and ultimately enhance clinical outcomes for children with SSDs.

Ecological niche models (ENMs) are often used to predict species distribution patterns from datasets that describe abiotic and biotic factors at coarse spatial scales. Ground‐truthing ENMs provide important information about how these factors relate to species‐specific requirements at a scale that is biologically relevant for the species. Chimpanzees are territorial and have a predominantly frugivorous diet. The spatial and temporal variation in fruit availability for different chimpanzee populations is thus crucial, but rarely depicted in ENMs. The genetic and geographic distinction within Nigeria–Cameroon chimpanzee (Pan troglodytes ellioti) populations represents a unique opportunity to understand fine scale species‐relevant ecological variation in relation to ENMs. In Cameroon, P. t. ellioti is composed of two genetically distinct populations that occupy different niches: rainforests in western Cameroon and forest–woodland–savanna mosaic (ecotone) in central Cameroon. We investigated habitat variation at three representative sites using chimpanzee‐relevant environmental variables, including fruit availability, to assess how these variables distinguish these niches from one another. Contrary to the assumption of most ENM studies that intact forest is essential for the survival of chimpanzees, we hypothesized that the ecotone and human‐modified habitats in Cameroon have sufficient resources to sustain large chimpanzee populations. Rainfall, and the diversity, density, and size of trees were higher at the rainforest. The ecotone had a higher density of terrestrial herbs and lianas. Fruit availability was higher at Ganga (ecotone) than at Bekob and Njuma. Seasonal variation in fruit availability was highest at Ganga, and periods of fruit scarcity were longer than at the rainforest sites. Introduced and secondary forest species linked with anthropogenic modification were common at Bekob, which reduced seasonality in fruit availability. Our findings highlight the value of incorporating fine scale species‐relevant ecological data to create more realistic models, which have implications for local conservation planning efforts. We examined specific abiotic factors and biotic conditions predicted by ecological niche models (ENMs) to differentiate two distinct genetically distinct populations of the Nigeria–Cameroon chimpanzee (Pan troglodytes ellioti) at a fine geographic scale using chimpanzee‐relevant variables, such as fruit availability. The results revealed that while ENMs are useful for understanding habitat suitability at a broad scale, more attention needs to be given to incorporating species‐specific requirements at a spatial and temporal scale that is biologically relevant for the species.

Street food stands (SFS) are an understudied element of the food environment. Previous SFS studies have not used a rigorous approach to document the availability, density, and distribution of SFS across neighborhood income levels and points of access in Mexico City. A random sample (n = 761) of street segments representing 20 low-, middle-, and high-income neighborhoods were assessed using geographic information system (GIS) and ground-truthing methods. All three income levels contained SFS. However, SFS availability and density were higher in middle-income neighborhoods. The distribution of SFS showed that SFS were most often found near homes, transportation centers, and worksites. SFS availability near schools may have been limited by local school policies. Additional studies are needed to further document relationships between SFS availability, density, and distribution, and current structures and processes.

Accurate surveying of vegetated areas presents significant challenges due to obstructions that obscure visibility and compromise the precision of measurements. This paper introduces a methodology employing the DJI Zenmuse L2 Light Detection and Ranging (LiDAR) sensor, which is mounted on a Matrice 350 RTK drone. The DJI Zenmuse L2 sensor excels at capturing detailed terrain data under heavy foliage, capable of collecting 1.2 million points per second and offering five returns, thus enhancing the sensor's ability to detect multiple surface responses from a single laser pulse. In a case study conducted near a creek heavily obscured by tree coverage, traditional aerial imaging techniques are found insufficient for capturing critical topographic features, such as the creek banks. Employing LiDAR, the study aims to map these obscured features effectively. The collected data is processed using DJI Terra software, which supports the accurate projection and analysis of the LiDAR data. To validate the accuracy of the data collected from the LiDAR sensor, traditional survey methods are deployed to ground truth the data and provide an accuracy assessment. Ground control points (GCPs) are established using a GNSS receiver to provide geodetic coordinates, which then assist in setting up a total station. This total station measures vertical and horizontal angles, as well as the slope distance from the instrument to positions underneath the tree coverage on the ground. These measurements serve as checkpoints to validate the accuracy of the LiDAR data, thus ensuring the reliability of the survey. This paper discusses the potential of integrating LiDAR data with traditional surveying data, which is expected to enhance the ability of surveyors to map environmental features efficiently and accurately in complex and vegetated terrains. Through detailed procedural descriptions and expected outcomes, the study aims to provide valuable insights into the strategic application of geospatial technologies to overcome common surveying challenges.

The IUCN Sampled Red List Index (SRLI) is a policy response by biodiversity scientists to the need to estimate trends in extinction risk of the world's diminishing biological diversity. Assessments of plant species for the SRLI project rely predominantly on herbarium specimen data from natural history collections, in the overwhelming absence of accurate population data or detailed distribution maps for the vast majority of plant species. This creates difficulties in re-assessing these species so as to measure genuine changes in conservation status, which must be observed under the same Red List criteria in order to be distinguished from an increase in the knowledge available for that species, and thus re-calculate the SRLI. However, the same specimen data identify precise localities where threatened species have previously been collected and can be used to model species ranges and to target fieldwork in order to test specimen-based range estimates and collect population data for SRLI plant species. Here, we outline a strategy for prioritizing fieldwork efforts in order to apply a wider range of IUCN Red List criteria to assessments of plant species, or any taxa with detailed locality or natural history specimen data, to produce a more robust estimation of the SRLI.

The aim of this study was to validate a Dutch commercial dataset containing information on the types and locations of food retailers against field audit data. Field validation of a commercial dataset (“Locatus”) was conducted in February 2019. Data on the location and classification of food retailers were collected through field audits in 152 streets from four urban and four rural neighborhoods in the Netherlands. The classification of food retailers included eight types of grocery stores (e.g., supermarkets, bakeries) and four types of food outlets (e.g., cafés, take away restaurants). The commercial dataset in the studied area listed 322 food retailers, whereas the field audit counted 315 food retailers. Overall, the commercially available data showed “good” to “excellent” agreement statistics (>0.71) with field audit data for all three levels of analysis (i.e., location, classification and both combined) and across urban as well as rural areas. The commercial dataset under study provided an accurate description of the measured food environment. Therefore, policymakers and researchers should feel confident in using this commercial dataset as a source of secondary data.

This paper presents the results of a three-year survey (2021–2023), conducted in an area of approximately 356 km2 in Iraqi Kurdistan with the aim of identifying previously undetected archaeological sites. Thanks to the development of a multi-temporal approach based on open multispectral satellite data, greater effectiveness was achieved for the recognition of archaeological sites when compared to the use of single archival or freely accessible satellite images, which are typically employed in archaeological research. In particular, the Google Earth Engine services allowed for the efficient utilization of cloud computing resources to handle hundreds of remote sensing images. Using different datasets, namely Landsat 5, Landsat 7 and Sentinel-2, several products were obtained by processing entire stacks of images acquired at different epochs, thus minimizing the adverse effects on site visibility caused by vegetation, crops and cloud coverage and permitting an effective visual inspection and site recognition. Furthermore, spectral signature analysis of every potential site complemented the method. The developed approach was tested on areas that belong to the Land of Nineveh Archaeological Project (LoNAP) and the Upper Greater Zab Archaeological Reconnaissance (UGZAR) project, which had been intensively surveyed in the recent past. This represented an additional challenge to the method, as the most visible and extensive sites (tells) had already been detected. Three years of direct ground-truthing in the field enabled assessment of the outcomes of the remote sensing-based analysis, discovering more than 60 previously undetected sites and confirming the utility of the method for archaeological research in the area of Northern Mesopotamia.

Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments.

Capturing forest disturbances over time is increasingly important to determine the ecosystem's capacity to recover as well as aiding a timely response of foresters. With changes due to climate change increasing the frequencies, a better understanding of forest disturbances and their role in historical development is needed to, on the one hand, develop forest management approaches promoting ecosystem resilience and, on the other hand, provide quick and spatially explicit information to foresters. A large, publicly available satellite imagery spanning more than two decades for large areas of the Earth's surface at varying spatial and temporal resolutions represents a vast, free data source for this. The challenge is 2-fold: (1) obtaining reliable information on forest condition and development from satellite data requires not only quantification of forest loss but rather a differentiated assessment of the extent and severity of forest degradation; (2) standardized and efficient processing routines both are needed to bridge the gap between remote-sensing signals and conventional forest condition parameters to enable forest managers for the operational use of the data. Here, we investigated abiotic and biotic disturbances based on a set of ground validated occurrences in various forest areas across Germany to build disturbance response chronologies and examine event-specific patterns. The proposed workflow is based on the R-package “npphen” for non-parametric vegetation phenology reconstruction and anomaly detection using MODIS EVI time series data. Results show the potential to detect distinct disturbance responses in forest ecosystems and reveal event-specific characteristics. Difficulties still exist for the determination of, e.g., scattered wind throw, due to its subpixel resolution, especially in highly fragmented landscapes and small forest patches. However, the demonstrated method shows potential for operational use as a semi-automatic system to augment terrestrial monitoring in the forestry sector. Combining the more robust EVI and the assessment of the phenological series at a pixel-by-pixel level allows for a changing species cover without false classification as forest loss.