Km2 Area Research Articles

Internal structure of the volcanic island of Surtsey and surroundings: Constraints from a dense aeromagnetic survey

Surtsey, a young basaltic island off the south coast of Iceland, was built by volcanic activity in 1963–1967 from a pre-eruption oceanic seafloor depth of 130 m. An aeromagnetic survey was carried out in October 2021 over a 60 km2 area covering Surtsey and its surroundings. It aimed to explore the internal structure and the possible existence of basaltic intrusions associated with the five vents active at different times over the 3.5 years of eruptive activity. The survey line spacing was 200 m and the flying altitude was generally 90 m a.s.l. The strongest anomalies (amplitude ∼700 nT) are caused by the 30–100 m thick subaerially erupted lava field on the southern part of Surtsey, formed in two episodes of effusive activity:1964–1965 and 1966–1967. 2D spectral analysis and Euler deconvolution indicate that the causative bodies of anomalies outside the island of Surtsey are located within the uppermost 300 m of the seafloor and their horizontal dimensions are similar to or smaller than their depth. 3D forward modeling of the island and its surroundings, constrained by observations during the formation of the island and drill cores extracted in 1979 and 2017, is consistent with an absence, at all vents, of pillow lava and therefore effusive activity in their opening phases. However, the data support the existence of a 10–20 m thick pillow lava field on the seafloor, 2.5–3 km2 in area, extending about ∼1 km to the south of Surtsey. The field is considered to have been fed by magma reaching the seafloor via channelized intrusive flow through the foreset breccia constituting the submarine part of an emerging lava delta during the early stage of effusive eruption in May–July 1964. The general scarcity of significant magnetic bodies within the edifices is consistent with magma fragmentation dominating the submarine eruptions from the onset of activity. A small magnetic anomaly is observed over the submarine edifice of Surtla, built during short-lived activity over ∼10 days in 1963–1964. This anomaly is consistent with observed subaqueous weak or moderate explosive activity that may have allowed a dyke to be preserved within the submarine tephra mound. More violent Surtseyan activity was observed at other vents, however, and may have destroyed any initial dykes that, if preserved, might have been resolved magnetically. Indications of magnetized volcanic rocks of unknown age predating the Surtsey eruption are found beneath the flank of the ephemeral island of Jólnir, the southernmost of the Surtsey vents.

Landscape evolution during Holocene transgression of a mid‐latitude low‐relief coastal plain: The southern North Sea

AbstractLow‐relief coastal landscapes are at major risk of rising sea levels, as vertical changes in relative sea level have far‐reaching lateral effects. Integration of a dense 2D grid of seismic reflection data with sedimentological and geotechnical data obtained in two offshore wind farm zones allows detailed documentation of postglacial landforms and environmental change over a 1,021 km2 area in the western sector of the southern North Sea. Following the retreat of Last Glacial Maximum ice sheets from the southern North Sea, the resulting postglacial terrestrial landscape provided a surface for peatland formation as climate started to warm and the water table rose in response to relative sea‐level rise. Southward‐draining fluvial networks formed contemporaneously with the peatlands, and remnants of this terrestrial wetland landscape are buried beneath Holocene marine sediments. Distinctive isolated incisional features and discrete widening of fluvial channels that cut through the peats are interpreted as either tidal ponds or relict tidal channels. These features record the evolution of this landscape through the Early Holocene as marine transgression inundated a low‐relief coastal plain. The erosion of the peatlands observed in the cores, the patchy preservation of the organic wetland landscape, and the lack of preserved barrier systems recorded by the seismic reflection data suggest that the rate of relative sea‐level rise outpaced sediment supply during the Late Postglacial and Early Holocene in this area of the southern North Sea. In a regional context, the southward draining river channels contrast to northward fluvial drainage to the North Sea, pointing to a subtle drainage divide in the palaeolandscape and the presence of a low‐relief land bridge separating the North Sea and the English‐Channel/La Manche during the Early Holocene. The documented scenario of rising sea levels combined with decreasing sediment supply in a low‐relief setting is a situation faced by many global deltas and coastlines, which makes the southern North Sea a crucial archive of coastal landscape change.

Transformation of Silifke—A Historic Town in Anatolia in the Ottoman Period

This article focuses on the Ottoman Period urban fabric of Silifke, a crucial historical town in Turkey. The aim of this paper is to develop a research framework for the transfer of cultural heritage and cultural continuity in multi-layered historical cities. In this context, investigating the level of sociocultural transition and physical permeability between layers is the main problem of the article. The scope of the study consists of an approximately 1.5 km2 (150 hectares) area within the boundaries of the 3rd Degree archaeological site determined in 1995 by the Adana Cultural and Natural Heritage Conservation Regional Board. In the case area, there are building remains and 58 registered buildings, 40 of which were built during the Ottoman Period. The findings and evaluations were examined in three parts: the periods before the Ottoman Period, the Ottoman Period, and the Republican Period. Analyses were developed independently or overlapped from archive documents such as the Presidential Ottoman Archive (BOA), provincial yearbooks, engravings of travelers, photographs, aerial photographs of different years, current maps, zoning plans, and conservation plans. Then, we made descriptions and inferences about urban change/transformation, which is affected by the political, economic, social, cultural, and environmental factors of the town and will shape the future change/transformation and management of the town. The ultimate goal is to set up a basis for the Silifke town center that will guide future interventions and design and planning policies for cultural continuity.

Landcover-based detection of rapid impacts of extreme storm on coastal landscape

On September 24, 2022, Post-Tropical Hurricane Fiona made landfall in Atlantic Canada and caused unprecedented damages to the coastal communities and ecosystems therein. The aftermath triggered local government and communities in Prince Edward Island (PEI), Canada to rethink current policies and practices for coastal protection in the context of climate change. This historic hazard represents the escalating frequency and intensity of extreme weather events that globally threaten coastal regions, accelerating coastal erosion and endangering communities. This study employs landcover-based detection to assess rapid storm impact of Fiona on coastline of PEI using Sentinel-2 satellite images, to gauge the efficacy of landcover-based detection and quantify storm-induced coastal environmental changes. Our results indicate that, following Fiona, over 51 km2 coastal land loss due to the erosion at beach foreshore and inundation at tidal flat, and over 11 km2 sand dune loss mainly on the PEI north shore. This constitutes a 3.5 % loss of coastal land resources within the 1798 km2 PEI coastal zone. Fiona also caused over 194 km2 area in coastal buffer zone showed temporal fluid-mud from the eroded sediments of sand dunes, cliffs, and tidal flats, suggesting the significant sediment loss from vertical structures in addition to the direct retreat. The landcover-based method can be regarded as a valuable tool for the storm impacts on coastal environments. Based on the coastal change pattern, more sustainable coastal protection and adaptation measures should be developed, focusing on reducing hydrodynamic intensity and improving erosion capacity, with consideration of the increasing likelihood of more intense and frequent storm events in a warming climate.

Mapping of groundwater potential zones of Khordha District using GIS and AHP approaches

The most dependable source of fresh water is groundwater. Groundwater supplies are severely threatened by a number of factors, including urbanization, industrialization, and population growth. The amount, quality and variables affecting groundwater supplies are significantly impacted by climate variability. The fall in groundwater levels is often exacerbated by poor quality surface water resources and unreliable monsoons. Therefore, in order to supplement the groundwater supply, it is important to locate and define the groundwater potential zone (GPZ). The analysis is conducted for the Khordha district, where groundwater rather is a primary source for agricultural uses. In order to determine the possible groundwater zones, many factors, including geomorphology, geology, elevation, slope, precipitation, soil type, soil texture, drainage density (DD), lineament density (LD), Land use/Land cover (LULC), and lineament density (LD), are constructed as separate layers in the geographical information system (GIS) backdrop. The multi-criteria decision-making technique and the Analytic Hierarchy Process (AHP), which enable pairwise evaluation of criteria impacting the potential zone, were utilized to establish the weights for the different layers and after that, the weighted overlay analysis (WOA) tool in ArcGIS10.8 was used to produce the final groundwater potential map. The output map of specified region was delineated into five new classes-very good, good, moderate, poor, and very poor of which 12% (325.1745 km2) falls under ‘very low’, 22% (603.9765 km2) under ‘low’, 26% (700.7715 km2) under ‘moderate’, 26% (694.2591 km2) under ‘high’, 14% (376.7553 km2) under ‘very high’. Approximately 1395 km2 area concerning 52% of study region, falls under ‘high’ and ‘very high’ categories of GPZ. Validation of the generated GPWZ map was done with data acquired from Central groundwater board. The accuracy assessment was done by kappa coefficient error matrix, and based on overall accuracy, the obtained map was 81.538% accurate to field value. As dependable results were produced with the proposed methodology, future management plans incorporating natural and artificial recharge practices can be created in these locations with effectiveness.

Seismic Imaging of Shallow Depths using Ambient Noise Tomography in the Hyderabad Region, Eastern Dharwar Craton, India

ABSTRACT Hyderabad and its surrounding region are situated within Eastern Dharwar Craton, primarily composed of Archean granite. We use ambient noise tomography to obtain the 3-D shallow subsurface shear seismic velocity structure of 150 x 200 km2 area around Hyderabad. We used continuous waveforms that were recorded by a digital seismic network with 10 three-component broadband seismographs from November 2020 to December 2021. Using the vertical component of waveforms, the Empirical Green’s functions (EGFs) have been estimated, and the group velocity dispersion curves in the time period range of 1.0–10 s have been extracted from these. We inverted these dispersion curves to estimate 3-D variation of shear wave velocity by using direct surface wave tomographic method. The results suggest that the subsurface velocity structure is heterogeneous. The shear wave velocity varies from 3.4 to 3.7 km/s in the shallow subsurface. The limited seismicity in this area is mostly confined to the boundary between high and low-velocity changes.

Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches.

Insufficient freshwater recharge and climate change resulted in seawater intrusion in most of the coastal aquifers in Pakistan. Coastal aquifers represent diverse landcover types with varying spectral properties, making it challenging to extract information about their state hence, such investigation requires a combination of geospatial tools. This study aims to monitor erosion along the major coastal aquifers of Pakistan and propose an approach that combines data fusion into the machine and deep learning image segmentation architectures for the erosion and accretion assessment in seascapes. The analysis demonstrated the image segmentation U-Net with EfficientNet backbone achieved the highest F1 score of 0.93, while ResNet101 achieved the lowest F1 score of 0.77. Resultant erosion maps indicated that Sandspit experiencing erosion at 3.14 km2 area. Indus delta is showing erosion, approximately 143 km2 of land over the past 30years. Sonmiani has undergone substantial erosion with 52.2 km2 land. Miani Hor has experienced erosion up to 298 km2, Bhuri creek has eroded over 4.11 km2, east Phitii creek over 3.30 km2, and Waddi creek over 3.082 km2 land. Tummi creek demonstrates erosion, at 7.12 km2 of land, and East Khalri creek near Keti Bandar has undergone a measured loss of 5.2 km2 landlinked with quantified reduction in the vertical sediment flow from 50 (billion cubic meters) to 10 BCM. Our analysis suggests that intense erosions are primarily a result of reduced sediment flow and climate change. Addressing this issue needs to be prioritized coastal management and climate change mitigation framework in Pakistan to safeguard communities. Leveraging emerging solutions, such as loss and damage financing and the integration of nature-based solutions (NbS), should be prioritized for the revival of the coastal aquifers.

Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA.

Mapping flooded areas utilizing Google Earth Engine and open SAR data: a comprehensive approach for disaster response

One of the most destructive and frequent natural disasters in the world, flash floods cause millions of people to be displaced annually in addition to seriously harming livelihoods and infrastructure. It affects many ecological components and applications related to water management, natural resources, agriculture, human health, and economics. Himachal Pradesh saw an unprecedented amount of rainfall in June and July 2023, which resulted in exceptionally strong monsoon conditions from July 7 to July 10. Operating out of the Meteorological Centre in Shimla, the India Meteorological Department reported widespread, unusually heavy rainfall throughout the state during this time. Cloud cover often obstructs optical satellite data during the monsoon season, which has led to the investigation of alternate techniques for mapping floods. the whole satellite image processing process was carried out using Google Earth Engine (GEE). The Kangra District's 5739 km2 area was chosen as the study's area of interest. In these circumstances, traditional methods of mapping and monitoring flood-prone areas frequently fall short because of poor visibility and overcast skies during bad weather. Technology such as Synthetic Aperture Radar (SAR) becomes a ray of hope during these desperate times. When SAR data is combined with Google Earth Engine, it becomes even more user-friendly. Using SAR data and the powerful cloud processing platform Google Earth Engine (GEE), this study suggests a flood mapping technique. When it comes to mapping flood areas, the strength of SAR data and GEE surpasses boundaries and challenges. It is evidence of both technological advancement and human inventiveness. Such tools are more critical than ever as the world deals with the increasing effects of climate change and the rising frequency of extreme weather events.

Bottom simulating reflections across the northern Gulf of Mexico slope

Bottom simulating reflections (BSRs) are widely used indicators for natural gas hydrate in reflection seismic data. BSRs, however, are typically studied on a local scale at a single site and less frequently on a large, regional scale. Herein, we map and describe BSRs over a 68,000 km2 area across the northern Gulf of Mexico slope, using publicly released industry-acquired 3D seismic data. We map 74 BSR zones that cover a total area of 2060 km2. To understand the characteristics of these BSR zones, we classify them based on their BSR characteristics in the seismic data (continuous, discontinuous, and clustered) and the geological settings in which they appear (structural, stratigraphic, and venting). The most common type of BSRs are discontinuous BSRs; such BSRs were mapped over ∼60% of the total BSR area. Discontinuous BSRs occur most commonly in structural or stratigraphic settings. Discontinuous BSRs indicate that gas and hydrate are likely concentrated in discrete layers crossing the base of the hydrate stability zone (HSZ). Continuous BSRs occur in 27% of the total BSR area and they primarily occur in venting and stratigraphic settings. At vent sites, hydrate formation likely reduces sediment permeability and focuses fluid flow laterally underneath the base of the HSZ, likely resulting in a continuous BSR along the area of lateral gas flow. Clustered BSRs, chaotic sequences of high amplitude reflections, occur in only 13% of the total BSR area and are only found within structural settings. In addition, the majority of clustered BSR zones are close to paleochannels, suggesting that clustered BSRs may be more common where coarser-grained sediments exist.

Key breeding habitats of threatened golden eagles across Eastern Canada identified using a multi-level, multi-scale habitat selection approach

ContextIn a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions.ObjectivesThe objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles (Aquila chrysaetos). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection).MethodsUsing tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection.ResultsEagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles.ConclusionHere we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.

Spatial analysis of dynamic groundwater potential maps of Nagpur district of Maharashtra using the AHP and RS-GIS tools

ABSTRACT Identification of groundwater potential (GWP) is essential for groundwater management. Recently some areas of Nagpur district have faced water scarcity with severe groundwater level fluctuation (GWLF). The study incorporates the dynamic behaviour of rainfall, land use/cover, fractional impervious surface (FIS), and GWLF from 2017 to 2022 along with topographical wetness index (TWI), topographical ruggedness index (TRI), lineament density, drainage density, slope, soil, and geomorphology. The study employs the geographical information system (GIS)-based multi-criteria decision-making approach, analytical hierarchy process, and remote sensing for spatiotemporal GWP mapping. The weighted overlay tool of ArcGIS 10.5 was used to derive final GWP maps. Critically, the northwestern part of the study area experienced major shifts in GWP, 448 km2 area has increased under the poor GWP category representing a decline in recharge probability. The safe GWP category diminished by nearly 531 km2 which exacerbates the problem whereas, high GWP showed very less changes. The most sensitive parameters are identified using an area-sensitivity approach, which reveals that TRI, slope, soil, geomorphology, rainfall, and TWI cause 35, 29, 20, 19, 11, and 11% area changes, respectively, while maximizing their weights. The validation of GWP maps shows good agreement with pre- and post-monsoon well data. The methodology and results may serve for GWP appraisal of similar regions.

Post-fire stabilization of thaw-affected permafrost terrain in northern Alaska

In 2007, the Anaktuvuk River fire burned more than 1000 km2 of arctic tundra in northern Alaska, ~ 50% of which occurred in an area with ice-rich syngenetic permafrost (Yedoma). By 2014, widespread degradation of ice wedges was apparent in the Yedoma region. In a 50 km2 area, thaw subsidence was detected across 15% of the land area in repeat airborne LiDAR data acquired in 2009 and 2014. Updating observations with a 2021 airborne LiDAR dataset show that additional thaw subsidence was detected in < 1% of the study area, indicating stabilization of the thaw-affected permafrost terrain. Ground temperature measurements between 2010 and 2015 indicated that the number of near-surface soil thawing-degree-days at the burn site were 3 × greater than at an unburned control site, but by 2022 the number was reduced to 1.3 × greater. Mean annual ground temperature of the near-surface permafrost increased by 0.33 °C/yr in the burn site up to 7-years post-fire, but then cooled by 0.15 °C/yr in the subsequent eight years, while temperatures at the control site remained relatively stable. Permafrost cores collected from ice-wedge troughs (n = 41) and polygon centers (n = 8) revealed the presence of a thaw unconformity, that in most cases was overlain by a recovered permafrost layer that averaged 14.2 cm and 18.3 cm, respectively. Taken together, our observations highlight that the initial degradation of ice-rich permafrost following the Anaktuvuk River tundra fire has been followed by a period of thaw cessation, permafrost aggradation, and terrain stabilization.

Ghost roads and the destruction of Asia-Pacific tropical forests.

Roads are expanding at the fastest pace in human history. This is the case especially in biodiversity-rich tropical nations, where roads can result in forest loss and fragmentation, wildfires, illicit land invasions and negative societal effects1-5. Many roads are being constructed illegally or informally and do not appear on any existing road map6-10; the toll of such 'ghost roads' on ecosystems is poorly understood. Here we use around 7,000 h of effort by trained volunteers to map ghost roads across the tropical Asia-Pacific region, sampling 1.42 million plots, each 1 km2 in area. Our intensive sampling revealed a total of 1.37 million km of roads in our plots-from 3.0 to 6.6 times more roads than were found in leading datasets of roads globally. Across our study area, road building almost always preceded local forest loss, and road density was by far the strongest correlate11 of deforestation out of 38 potential biophysical and socioeconomic covariates. The relationship between road density and forest loss was nonlinear, with deforestation peaking soon after roads penetrate a landscape and then declining as roads multiply and remaining accessible forests largely disappear. Notably, after controlling for lower road density inside protected areas, we found that protected areas had only modest additional effects on preventing forest loss, implying that their most vital conservation function is limiting roads and road-related environmental disruption. Collectively, our findings suggest that burgeoning, poorly studied ghost roads are among the gravest of all direct threats to tropical forests.

Effect of Seismicity and Tectonic‐Glacial Interactions on Submarine Megaslides

AbstractEnhanced sedimentation at glacial margins can produce submarine megaslides (&gt;10,000 km3). We report a single megaslide in the Surveyor Fan, Gulf of Alaska. Minimum extant size is ∼16,124 km2 in area and ∼9,080 km3 in volume. Slope failure occurred ∼1.2 Ma at the onset of the mid‐Pleistocene transition (MPT). With accretion along the Aleutian‐Alaska Trench, the original volume is conservatively ∼16,280 km3, with only a 140 km run‐out due to its blocky, high shear strength nature. We suggest the megaslide was triggered by a major sediment influx at the onset of the MPT, when glacial‐interglacial cycles shifted from 41 to 100 Kyr. The absence of repeat megaslides may reflect a changing balance between seismic strengthening and sediment flux, where later sedimentation driven by cross‐shelf ice streams results in thin, fluidized, non‐cohesive slides. Continued accretion of the Surveyor Fan and megaslide also reduces critical wedge taper, further inhibiting major failure.

240 Impact of a Large Geographical Area on Outcomes in Acute Traumatic Intracranial Extra-axial Haemorrhage Requiring Urgent Surgical Evacuatio

INTRODUCTION: The Princess Alexandra Hospital (PAH) Department of Neurosurgery services a population of 1.85 million people over a 420,000 km2 area in Queensland, Australia. Increased distance from a neurosurgical centre has previously been correlated with poorer outcomes in traumatic brain injury. METHODS: This single-centre retrospective study identified patients aged 16-85 who underwent evacuation of an acute traumatic subdural (ASDH) or extradural haemorrhage (EDH) between 2016 and 2022. Initial Glasgow Coma Score (GCS), pupillary abnormalities, time to intervention, distance from PAH and the Extended Glasgow Outcome Scale (GOS-E) at 6 months were recorded. RESULTS: 106 and 50 patients underwent evacuation of ASDH and EDH respectively. In the ASDH group, age was inversely correlated (p = 0.03, r2 = 0.04) and the motor component of the initial GCS was directly correlated (p = 0.002, r2 = 0.09) with GOS-E at 6 months. Distance from PAH directly correlated with an increased time to intervention (p&lt;0.0001, r2 = 0.23) but did not correlate with GOS-E at 6 months (p = 0.09). There was no difference between survivors and non-survivors regarding distance from PAH (43.43 ± 75.05 km vs. 25.56 ± 41.32 km, p = 0.15) or time to operation (276.14 ± 192.77 mins vs. 217.40 ± 147.27 mins, p = 0.08). This was also found for survivors with poor (GOS-E &lt;6) and fair (GOS-E = 6) recovery at 6 months. In the EDH group there was no correlation between distance, time to operative intervention, age or initial motor score with outcomes at 6 months. CONCLUSIONS: Whilst increased distance from treatment centre correlated with increased time to surgical intervention for ASDH, it did not impact on survival or functional outcomes at 6 months. For ASDH requiring urgent evacuation, increasing age and severity of primary brain injury are predictors of poorer long-term outcomes.

Comparative assessment of eDNA metabarcoding and longline deployments for elasmobranch surveying across a large tropical marine park network

Abstract Medium‐ to large‐scale marine protected areas (MPAs) are playing an increasingly important role in global marine biodiversity conservation. A key question is “how do we collect relevant data on biodiversity and population trends to inform their design and measure success?” This question is particularly relevant for marine taxa that are difficult to survey, such as sharks and rays, and where populations may occur over vast areas. Environmental DNA (eDNA) metabarcoding has proven to be an effective sampling method and may provide a solution to these challenges; however, it remains unclear how its sampling effectiveness compares with traditionally used methods for elasmobranch surveying. Here, we directly compared the efficacy of eDNA metabarcoding and demersal longline deployments to survey elasmobranchs across 31 sites over a 55,000 km2 area of the Kimberley and Roebuck Australian Marine Parks in Australia's North‐west Marine Parks Network. In total, we documented 49 unique elasmobranch taxa, 36 of which were detected by eDNA and 32 (from a total of 815 captured sharks) by longline. A combined approach yielded over 34% more elasmobranch taxa than either method alone. Site community compositions varied between the two survey methods; notably eDNA was able to detect species from outside of the immediate sampling area, although this was still consistent with a detection radius of a few kilometres, highlighting a particular use in rugose reef habitats where it is difficult to deploy longlines. In investigating the quantitative use of eDNA data, we report that eDNA metabarcoding read abundance (count) data was poorly correlated with longline aggregate catch (count and biomass) data across raw, relative and rank abundance measures. However, we found that in multivariate analyses, both binary (presence–absence) and abundance (after square‐root transformation) datasets produced highly similar ordination patterns, largely segregating method (eDNA vs. longline), followed by latitude and depth. Lastly, we identified required levels of eDNA replication and longline deployments to maximize captured elasmobranch diversity. This study sets an integrated, georeferenced baseline and long‐term monitoring approach for the management and conservation of elasmobranch diversity within this unique marine park network.

Spatializing soil elemental concentration as measured by X-ray fluorescence analysis using remote sensing data

Spatial soil information has significantly contributed to public policy and planning. The use of X-ray fluorescence spectrometry (XRF) in soil science, primarily in laboratory settings or for isolated point analyses, has been well documented. This study broadens the application of XRF data by integrating it with remote sensing data for a comprehensive understanding of soil patterns and processes. Focusing on a 2574 km2 area in Brazil, we utilized a lab-based XRF instrument to measure the total concentrations of key chemical elements in the soil, such as Al, Si, Ti, and Fe. These geochemical data were then spatialized using the digital soil mapping (DSM) framework. We employed a synthetic bare soil image and elevation data as covariates in the analysis. The spatial accuracy assessment yielded R2 values ranging from 0.65 to 0.81 for Fe, 0.62 to 0.78 for Ti, 0.52 to 0.63 for Si, and 0.48 to 0.63 for Al. The digital soil maps of geochemical elements aligned well with existing pedological, and geological maps as evaluated using multiple correspondence analysis. The integration of spatialized XRF data with DSM and remote sensing techniques shows significant promise in assessing soil variations across landscapes based on their chemical composition.

Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop–rice cropping systems using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang-Gan-Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites, with an overall accuracy of 91.92%. There were 7348 km2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one-tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Extraction of Forest Road Information from CubeSat Imagery Using Convolutional Neural Networks

Forest roads provide access to remote wooded areas, serving as key transportation routes and contributing to human impact on the local environment. However, large animals, such as bears (Ursus sp.), moose (Alces alces), and caribou (Rangifer tarandus caribou), are affected by their presence. Many publicly available road layers are outdated or inaccurate, making the assessment of landscape objectives difficult. To address these gaps in road location data, we employ CubeSat Imagery from the Planet constellation to predict the occurrence of road probabilities using a SegNet Convolutional Neural Network. Our research examines the potential of a pre-trained neural network (VGG-16 trained on ImageNet) transferred to the remote sensing domain. The classification is refined through post-processing, which considers spatial misalignment and road width variability. On a withheld test subset, we achieve an overall accuracy of 99.1%, a precision of 76.1%, and a recall of 91.2% (F1-Score: 83.0%) after considering these effects. We investigate the performance with respect to canopy coverage using a spectral greenness index, topography (slope and aspect), and land cover metrics. Results found that predictions are best in flat areas, with low to medium canopy coverage, and in the forest (coniferous and deciduous) land cover classes. The results are vectorized into a drivable road network, allowing for vector-based routing and coverage analyses. Our approach digitized 14,359 km of roads in a 23,500 km2 area in British Columbia, Canada. Compared to a governmental dataset, our method missed 10,869 km but detected an additional 5774 km of roads connected to the network. Finally, we use the detected road locations to investigate road age by accessing an archive of Landsat data, allowing spatiotemporal modelling of road access to remote areas. This provides important information on the development of the road network over time and the calculation of impacts, such as cumulative effects on wildlife.