Mapping Efforts Research Articles

Multi resolution appraisal of Cork Harbour estuary: An object based image analysis approach

Lithic sedimentary benthic habitats are often organised by the overlying hydrodynamic regime, generating seabed sedimentary bedforms (SSBs) whose morphology and sediment composition are controlled by the current flow properties including speed, direction, and duration. Furthermore, SSBs can manifest at a variety of spatial scales reflecting different aspects of the hydrodynamic regime that operate at distinct intensities and temporal scales. Acoustic seabed mapping of SSBs permits the quantification of the complex seafloor hydrodynamics without in situ measurements. As such, seabed mapping efforts must determine the minimum spatial resolution required to effectively capture the complete seabed hydrodynamic picture. Here, we provide a semi-automated, object-based image analysis (OBIA) of SSBs occurring over 0.5, 1.0, 1.5, 2.0, and 2.5 m spatial resolutions of multibeam echosounder (MBES) data acquired in Cork Harbour, Ireland, to ascertain the optimum resolution at which to derive current velocity attributes. Estimations of seabed sediment grain size were completed through the angular range analysis (ARA) of co-located 400 kHz multibeam backscatter data. Results show that current flow estimations derived at 0.5, 1.0 and 1.5 m resolutions of MBES bathymetry agree with previous in situ current speed measurements. Moreover, sediment grain size estimations produced during the ARA of the 400 kHz backscatter data displayed a clear pattern of sediment resuspension at each section of the harbour, and this was effectively depicted at 0.5, 1.0, and 1.5 m resolution of MBES bathymetry data. However, current flow estimates derived from 2.0 and 2.5 m resolution failed to portray the short-term current variation induced by the ebb phase of the tide. Therefore, it is recommended that MBES data with a pixel size of >50 % of the wavelength of the SSB observed is insufficient for inclusion in an OBIA workflow.

Three-dimensional imaging of martian glaciated terrain using Mars Reconnaissance Orbiter Shallow Radar (SHARAD) observations

We present a three-dimensional radargram of a 13° x 11° region of Deuteronilus Mensae, produced from 457 Mars Reconnaissance Orbiter Shallow Radar (SHARAD) observations. We assess the viability of the 3D imaging algorithms developed to work with SHARAD observations in mid-latitude regions of interest. The quality of subsurface imaging in topographically complex regions is highly improved due to the proper positioning and mitigation of off-nadir reflections in the resulting image. The initial analysis of debris-covered glaciers using 3D radargrams has yielded results that are consistent with previous 2D-based mapping efforts. However, the use of 3D radargrams has enabled the analysis of 3× more area resulting in more accurate estimates of total ice volume, indicating over 9% more ice than previously estimated. Such discrepancies have important implications for Mars climate history studies and in situ resource utilization. The success of this imaging study paves the way for future 3D radargrams in the martian mid-latitudes and stands to increase our confidence in the distribution of many subsurface features of interest.

A case study on joint species distribution modelling with bird atlas data: Revealing limits to species' niches

Growing interest in biodiversity mapping has spurred the development of species distribution atlases, often mainly based on citizen-science projects. Atlas data have been frequently exploited to model species' ecological niches and distributions on a species-by-species basis. However, spatial autocorrelation and phylogenetic relatedness among species complicate the statistical description of species' niches. Also, the effects of species' traits and co-occurrences on species-habitat relationship are commonly disregarded. In this work, we build a hierarchical multi-species model based on a major citizen-science project (the third Spanish breeding bird atlas) that simultaneously accounts for spatial, phylogenetic and trait-based dependencies. We predict the distributions of species niches, species richness and community traits along regional ecological gradients. Climate, habitat associations and species' traits all contribute (in this order) to structuring species' distributions. Species richness increases towards intermediate climatic conditions and with aquatic habitat cover and decreases with increasing forest and woody agricultural land cover. Species were distributed along regional climate gradients in accordance with their global thermal niches. Forest habitats favoured assemblages dominated by generalist, small-sized and cold-dwelling species with limited migratory behaviour. Increasing sampling effort augmented the model performance. Model performance was weaker for rare species and those with decreasing population sizes, likely due to their low niche saturation. Overall, we show that ecological relationships generalize from local to large scales and may be eludicated from atlases based on citizen-science mapping efforts.

Mapping Utopia as an Enclosed Renaissance Garden

ABSTRACT This article presents previous attempts to map Utopia, a fictional island in the book Utopia by Thomas More, and proposes another possibility, based on the assumption that Utopia is an enclosed garden on an artificial island that is not flat, but has piedmont slopes of 2.5:1. Our mapping experiment is founded on the Renaissance analogy between the domestication of wild nature, and the civilization of the human beings in More’s enclosed garden; and on the anecdotal similarities between the Inca Empire and Utopia. This approach is not only consistent with the island’s dimensional specifications, which have hampered previous mapping attempts due to their mathematical incompatibility, but it also addresses the educative capacity of utopian life not considered by previous mapping efforts. Utopia, as a result, is to be understood as a series of performative spatial arrangements where the physical and geographical specifications would inform the vernacular life of its inhabitants in the direction of self-enhancement.

Mapping offshore anthropogenic infrastructure associated with Cannery Row, Monterey Peninsula, California USA

“Cannery Row” is well known from the novel of the same name written by John Steinbeck in 1945. It historically extends back to the early 1940’s when sardines were being heavily fished in Monterey Bay and an extensive network of canneries were established in Monterey. To support these canneries and the fishing vessels delivering fish to the shore-based processing facilities, an extensive network of seabed infrastructure was developed. Once the fisheries collapsed, the canneries shut down and the “Row” became derelict with the seabed infrastructure left to deteriorate in place. Today the row has been revitalized by an invigorating tourist industry brought to the peninsula with the establishment of the Monterey Bay Aquarium (MBA) nearly 40 years ago. New seabed infrastructure was constructed to support the aquatic exhibits of the MBA but unlike the previous canneries’ infrastructure, this infrastructure was constructed with the conservation of the unique and spectacular nearshore marine environment in mind.Using modern geophysical instrumentations consisting of Multi-Beam Echosounder (MBES) and 3.5 kHz Compressed High Intensity Radar Pulse (CHIRP) sub-bottom profiling systems we mapped the seafloor geology, imaged abandoned and present-day seabed infrastructure, and characterized the benthic habitats. From this mapping effort we were able to assess the artifacts of the seafloor and illustrate how modern seafloor processes such as sediment transport is altering the infrastructure. Much of the abandoned submarine infrastructure provides hard substrate within a dynamic unconsolidated sand bottom that is habitat for encrusting and sessile organisms, contributing to the diversity and complexity of the marine ecosystem of the area. In addition, a prominent granodiorite exposure upon which the dynamic sand onlaps is prime holdfast foundation for kelp that in turn provides habitat for fish. Although not pristine, the impacts of previous industrial disturbances and anthropogenic impacts are now well mitigated with the area set aside as a natural reserve and educational center piece within the Monterey Bay National Marine Sanctuary.

Detection of invasive plants using NAIP imagery and airborne LiDAR in coastal Alabama and Mississippi, USA

Invasive plants have imposed severe threats to native ecosystems worldwide. Triadica sebifera (Tallow tree) and Ligustrum sinense (Chinese privet) are among the most prolific invasive species in the southern United States (US) that needs urgent assessment to protect coastal ecosystems. The lack of spatially explicit assessments of these invasives, coupled with the increasing availability of high-resolution remotely sensed data, represents an opportunity to produce a distribution map for subsequent monitoring. The overall goal of this study was to develop spatially comprehensive maps of Tallow tree and Chinese privet in ecologically sensitive coastal regions, where both invasives have become well established. The study was conducted in three coastal sites within Alabama and Mississippi: (1) Mobile Tensaw River Delta, (2) Bon Secour National Wildlife Refuge, and (3) Mississippi Sandhill Crane National Wildlife Refuge. We implemented three image classification methods, representing unsupervised, supervised, and machine learning techniques, respectively: (1) ISODATA, (2) Maximum Likelihood (ML), and (3) Random Forest (RF). For each classification, a 1 m National Agriculture Imagery Program (NAIP) orthoimage was first examined, then integrated with vegetation structure and topography parameter derived from airborne light detection and ranging (LiDAR). The maximum Overall Accuracy (OA) of 87.5% was obtained using RF model with NAIP stacked image integrated with LiDAR derived variables. Overall, findings highlight the potential for accurately characterizing both Tallow tree and Chinese privet using readily available remote sensing data. Mapped products from this study represent a spatially comprehensive baseline inventory of crucial invasive species and will serve to inform the development of a framework for broader-scale mapping and monitoring efforts.

Integrating habitat features into spatio-temporal biomass dynamics models for a better understanding of stock productivity: a case study of sea scallop in the Bay of Fundy

Abstract Recent efforts in ocean mapping of seafloor habitat have made data increasingly available. For bottom-dwelling and/or sessile species, there is often a strong relationship between population productivity and habitat, and stock assessment models are likely to be improved by the inclusion of habitat. Here, we extend a recently developed spatio-temporal biomass dynamics model to allow habitat to inform probabilities of non-zero tows and catchability. Simulation experiments demonstrate the ability of this new approach to reliably capture population trends over time and space, with the applicability of the method further demonstrated using data from the Canadian Maritimes Inshore Sea Scallop Fishery in the Bay of Fundy. This habitat-informed spatio-temporal biomass dynamics model better captures underlying processes, reduces uncertainty, thereby improving our understanding of stock status from which fisheries management decisions can be based.

Mapping Abandoned Uranium Mine Features Using Worldview-3 Imagery in Portions of Karnes, Atascosa and Live Oak Counties, Texas

Worldview-3 (WV3) 16-band multispectral data were used to map exposed bedrock and mine waste piles associated with legacy open-pit mining of sandstone-hosted roll-front uranium deposits along the South Texas Coastal Plain. We used the “spectral hourglass” approach to extract spectral endmembers representative of these features from the image. This approach first requires calibrating the imagery to reflectance, then masking for vegetation, followed by spatial and spectral data reduction using a principal component analysis-based procedure that reduces noise and identifies homogeneous targets which are “pure” enough to be considered spectral endmembers. In this case, we used a single WV3 image which covered an ~11.5 km by ~19.5 km area of Karnes, Atascosa and Live Oak Counties, underlain by mined rocks from the Jackson Group and Catahoula Formation. Up to 58 spectral endmembers were identified using a further multi-dimensional class segregation method and were used as inputs for spectral angle mapper (SAM) classification. SAM classification resulted in the identification of at least 117 mine- and mine waste-related features, most of which were previously unknown. Class similarity was further evaluated, and the dominant minerals in each class were identified by comparison to spectral libraries and measured samples of actual Jackson Group uranium host rocks. Redundant classes were eliminated, and SAM was run a second time using a reduced set of 23 endmembers, which were found to map these same features as effectively as using the full 58 set of endmembers, but with significantly reduced noise and spectral outliers. Our classification results were validated by evaluating detailed scale mapping of three known mine sites (Esse-Spoonamore, Wright-McCrady and Garbysch-Thane) with published ground truth information about the vegetation cover, extent of erosion and exposure of waste pile materials and/or geologic information about host lithology and mineralization. Despite successful demonstration of the utility of WV3 data for inventorying mine features, additional landscape features such as bare agricultural fields and oil and gas drill pads were also identified. The elimination of such features will require combining the spectral classification maps presented in this study with high-quality topographic data. Also, the spectral endmembers identified during the course of this study could be useful for larger-scale mapping efforts using additional well-calibrated WV3 images beyond the coverage of our initial study area.

Drones, communities and nature: pitfalls and possibilities for conservation and territorial rights

Since the early 2010s, small drones have become key tools for environmental research around the globe. While critical voices have highlighted the threat of ‘green securitisation’ and surveillance in contexts where drones are deployed for nature conservation, Indigenous peoples and local communities (IPLCs) worldwide have also begun using drones – most often in alliance with non-governmental organisations or researchers – exploring this technology’s potential to advance their own territorial, political and socio-ecological goals. Against this backdrop, this paper examines six different experiences in five countries where communities are using small drones in areas of high ecological and cultural diversity with international significance for nature conservation. We highlight the ways that communities deploy drones – both in terms of their motivations and actual use strategies. We also reflect upon the opportunities and barriers that IPLCs and their collaborators encounter in designing and implementing meaningful drone strategies, explicitly considering social, economic and political challenges. Finally, we consider the socio-ecological outcomes that community drone use enables across these sites along with the ways that drones engender more biocultural and territorial approaches to conservation through IPLC-led monitoring and mapping efforts. In conclusion, we suggest that effective, meaningful and appropriate deployment of drones, especially with IPLCs as protagonists in their use, can support nature conservation together with the recognition and protection of biocultural and territorial rights. Given the mounting demands for conservation to counter intertwined global socio-environmental crises, community drones may play a role in amplifying the voices and territorial visions of IPLCs.

A Novel Approach to Geographically Mapping Effective Family-Based Services: Feasibility and Quality Comparison with 2-1-1.

Community social services are often fragmented and difficult to navigate. This presents a barrier to programs, such as health navigation, that focus on connecting individuals to available services. Existing service mapping efforts, such as those generated by 2-1-1, are helpful but limited in the specificity they provide, particularly with regard to whether services are based on evidence-informed principles. This may lead to a distrust of service quality or poor referral match. We developed a novel service mapping protocol to identify local, evidence-informed, family-based services, and compared results to 2-1-1's resource list. Our mapping protocol identified more evidence-informed services than 2-1-1 and collected greater detail related to accessibility. Recommendations for integrating this approach into routine community mapping efforts (e.g., 2-1-1) or as a stand-alone approach are discussed.

HERITAGE OF THE HINDU KUSH: A PRELIMINARY ASSESSMENT OF CULTURAL HERITAGE IN NURISTAN, AFGHANISTAN

As the last part of Afghanistan to be converted to Islam (in 1896), Nuristan retains a distinct material and intangible cultural heritage. However, for nearly forty years, Nuristan has been virtually inaccessible to researchers of cultural heritage due to protracted insecurity in the province. With the Taliban in control of the country once more, conflict has largely ceased, enabling access to many hitherto inaccessible parts of the country, including Nuristan. There is now a window of opportunity to conduct much-needed work on the state of cultural assets across the province. The authors conducted two separate field visits between May and July 2022 to undertake a preliminary assessment of Nuristan’s intangible and intangible cultural heritage and to conduct a feasibility study for future mapping and conservation efforts. This paper presents initial findings from the fieldwork and highlights the urgent need for investing in cultural protection work in Nuristan.

Identification and Genomic Localization of Autosomal sdY Locus in a Population of Atlantic Salmon (Salmo salar).

The determination of sex in salmonid fishes is controlled by genetic mechanisms, with males being the heterogametic sex. The master sex-determining gene, the sexually dimorphic gene on the Y chromosome (sdY), is a conserved gene across various salmonid species. Nevertheless, variations in the genomic location of sdY have been observed both within and between species. Furthermore, different studies have reported discordances in the association between the sdY and the phenotypic gender. While some males seem to lack this locus, there have been reports of females carrying sdY. Although the exact reasons behind this discordance remain under investigation, some recent studies have proposed the existence of an autosomal, non-functional copy of sdY as a potential cause. In this study, we confirmed the presence of this autosomal sdY in the SalmoBreed strain of Atlantic salmon using a genotyping platform through a novel approach that allows for high-throughput screening of a large number of individuals. We further characterized the segregation profile of this locus across families and found the ratio of genetically assigned female-to-male progeny to be in accordance with the expected profile of a single autosomal sdY locus. Additionally, our mapping efforts localized this locus to chromosome 3 and suggested a putative copy on chromosome 6.

Satellite-Derived Bathymetry Mapping on Horseshoe Island, Antarctic Peninsula, with Open-Source Satellite Images: Evaluation of Atmospheric Correction Methods and Empirical Models

Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island’s shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R2 around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance–depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10–20 m depth intervals and in the entire range of (0–20 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation.

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records.

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.

The Newfoundland and Labrador mosaic founder population descends from an Irish and British diaspora from 300 years ago

The founder population of Newfoundland and Labrador (NL) is a unique genetic resource, in part due to its geographic and cultural isolation, where historical records describe a migration of European settlers, primarily from Ireland and England, to NL in the 18th and 19th centuries. Whilst its historical isolation, and increased prevalence of certain monogenic disorders are well appreciated, details of the fine-scale genetic structure and ancestry of the population are lacking. Understanding the genetic origins and background of functional, disease causing, genetic variants would aid genetic mapping efforts in the Province. Here, we leverage dense genome-wide SNP data on 1,807 NL individuals to reveal fine-scale genetic structure in NL that is clustered around coastal communities and correlated with Christian denomination. We show that the majority of NL European ancestry can be traced back to the south-east and south-west of Ireland and England, respectively. We date a substantial population size bottleneck approximately 10-15 generations ago in NL, associated with increased haplotype sharing and autozygosity. Our results reveal insights into the population history of NL and demonstrate evidence of a population conducive to further genetic studies and biomarker discovery.

Land Use Misclassification Results in Water Use, Economic Value, and GHG Emission Discrepancies in California’s High-Intensity Agriculture Region

California’s San Joaquin Valley is both drought-prone and water-scarce but relies on high-intensity agriculture as its primary economy. Climate change adaptation strategies for high-intensity agriculture require reliable and highly resolved land use classification data to accurately account for changes in crop water demand, greenhouse gas (GHG) emissions, and farmgate revenue. Understanding direct and indirect economic impacts from potential changes to high-intensity agriculture to reduce groundwater overdrafts, such as reductions in the cultivated area or switching to less water-intensive crops, is unachievable if land use data are too coarse and inconsistent or misclassified. This study quantified the revenue, crop water requirement, and GHG emission discrepancies resulting from land use misclassification in the United States’ most complex agricultural region, California’s San Joaquin Valley. By comparing three commonly used land use classification datasets—CropScape, Land IQ, and Kern County Agriculture—this study found that CropScape led to considerable revenue and crop water requirement discrepancies compared to other sources. Crop misclassification across all datasets resulted in an underestimation of GHG emissions. The total revenue discrepancies of misclassified crops by area for the 2016 dataset comparisons result in underestimations by CropScape of around USD 3 billion and overestimation by LIQ and Kern Ag of USD 72 million. Reducing crop misclassification discrepancies is essential for crafting climate resilience strategies, especially for California, which generates USD 50 billion in annual agricultural revenue, faces increasing water scarcity, and aims to reach carbon neutrality by 2045. Additional investments are needed to produce spatial land use data that are highly resolved and locally validated, especially in high-intensity agriculture regions dominated by specialty crops with unique characteristics not well suited to national mapping efforts.

Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa

Current mangrove mapping efforts, such as the Global Mangrove Watch (GMW), have focused on providing one-off or annual maps of mangrove forests, while such maps may be most useful for reporting regional, national and sub-national extent of mangrove forests, they may be of more limited use for the day-to-day management of mangroves and for supporting the Global Mangrove Alliance (GMA) goal of halting global mangrove loss. To this end, a prototype change mangrove loss alert system has been developed to identify mangrove losses on a monthly basis. Implemented on the Microsoft Planetary Computer, the Global Mangrove Watch v3.0 mangrove baseline extent map for 2018 was refined and used to define the mangrove extent mask under which potential losses would be identified. The study period was from 2018 to 2022 due to the availability of Sentinel-2 imagery used for the study. The mangrove loss alert system is based on optimised normalised difference vegetation index (NDVI) thresholds used to identify mangrove losses and a temporal scoring system to filter false positives. The mangrove loss alert system was found to have an estimated overall accuracy of 92.1%, with the alert commission and omission estimated to be 10.4% and 20.6%, respectively. Africa was selected for the mangrove loss alert system prototype, where significant losses were identified in the study period, with 90% of the mangrove loss alerts identified in Nigeria, Guinea-Bissau, Madagascar, Mozambique and Guinea. The primary drivers of these losses ranged from economic activities that dominated West Africa and Northern East Africa (mainly agricultural conversion and infrastructure development) to climatic in Southern East Africa (primarily storm frequency and intensity). The production of the monthly mangrove loss alerts for Africa will be continued as part of the wider Global Mangrove Watch project, and the spatial coverage is expected to be expanded to other regions over the coming months and years. The mangrove loss alerts will be published on the Global Mangrove Watch online portal and updated monthly.

Brain Mapping in Neurosurgery

Background and Aim: Brain mapping is the study of the anatomy and function of the Central Nervous System (CNS). Brain mapping has many techniques and these techniques are permanently changing and updating. From the beginning, brain mapping was invasive and for brain mapping, electrical stimulation of the exposed brain was needed. However, nowadays brain mapping does not require electrical stimulation and often does not require any complex involvement of patients. To perform brain mapping, functional and structural neuroimaging has an essential role. The techniques for brain mapping include noninvasive techniques (structural and functional magnetic resonance imaging [fMRI], diffusion MRI [dMRI], magnetoencephalography [MEG], electroencephalography [EEG], positron emission tomography [PET], near-infrared spectroscopy [NIRS] and other non-invasive scanning techniques) and invasive techniques (direct cortical stimulation [DCS] and intracarotid amytal test [IAT] or wada test). Methods and Materials/Patients: This is a narrative study on brain mapping in neurosurgery. To provide up-to-date information on brain mapping in neurosurgery, we precisely reviewed brain mapping and neurosurgery articles. Using the keywords “brain mapping”, “neurosurgery”, “brain mapping techniques”, and “benefits of brain mapping”, all of the related articles were obtained from Google Scholar, PubMed, and Medline and were precisely studied. Results: To perform an effective and safe neurosurgical intervention, precise information about the structural and functional anatomy of the brain is obligatory. Based on the information on brain mapping, the selection of suitable patients for the operation, the plan of appropriate operative approach, and good surgical results can be acquired. To provide this information, we can use brain mapping techniques that were formerly applied in neuroscientific brain mapping efforts with noninvasive techniques, such as fMRI, MEG, dMRI, PET, etc and invasive techniques, such as DCS, IAT, etc. Conclusion: Functional brain mapping is a constantly evolving fact in neurosurgery. All stages in obtaining a functional image are complex and need knowledge of the basic physiologic and imaging features.

Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

Rapid early-season maize mapping without crop labels

Maize (Zea mays), the second most-produced crop worldwide, serves as the cornerstone for global food security and human livelihood. Early-season maize mapping benefits maize production forecasting and other pre-harvest decision-making applications. However, most existing early-season mapping efforts rely heavily on either the current-year or historical crop labels to train classifiers, limiting the potential applications to new regions lacking crop labels. To explore the possibility of maize mapping only using satellite data in the early season, we proposed a Multi-temporal Gaussian Mixture Model (MGMM) to map maize planting areas without any crop labels. A chlorophyll content relevant proxy, named the Red-edge position (REP), was selected as model input, based on the truth that summer maize tends to show a higher chlorophyll content than other summer crops (e.g., soybean, cotton, peanut, sunflowers, etc.) in the peak season. The novel early-season mapping framework using the REP-based MGMM (MGMM-REP) was applied in four diverse areas (Iowa and Georgia in the US, Heilongjiang province (HLJ) in China, and Grand-Est in France). The MGMM-REP could generate maize maps more than two months before harvest with reasonable accuracy (F1 ≥ 77%) using all the available Sentinel-2 (S2) images and the Google Earth Engine platform (GEE). Our early-season maps agreed well with the existing crop maps and official statistics. The correlation coefficient (R) of the maize acreage between our early-season maps and statistics was higher than 0.94. The high inter-class difference of REP between maize and other summer crops could increase the F1 score by 2–47% compared to the other commonly used Vegetation indices (VIs). Since MGMM-REP does not rely on crop labels, it had the potential to be transferred to label-scarce maize-cropped regions and contribute to the international commodity trade and food security forecast.