Km2 Area Research Articles

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.

Emissivity of the Earth as a Cancer Predictor

Aim: The anticancer properties of locally applied infrared radiation are widely discussed in the literature. Earth's global emissivity, or the infrared radiation emitted by our planet, is inhomogeneous and varies based on the geophysical characteristics of the region where one resides. This study aimed to test the hypothesis: does local emissivity influence the prevalence of malignant diseases in a region. Materials and Methods: We performed planimetric comparison of two World maps: 1. NASA Spacecraft Map of Earth's Global Emissivity; 2. GLOBOCAN 2020 Global Cancer Data. For comparison we used “Image Compare” tool from Oxford Robotics Research Group. For the area measurements we used FreeMapsTools calculator. Results: The total assessed area was 127,750,000 square kilometers. The distribution according to emissivity was as follows: 107,050,000 km² of area with emissivity above 0.94 and 20,700,000 km² of area with emissivity below 0.94. The distribution according to cancer incidence was as follows: 28,150,000 km² of area with cancer incidence lower than 180 per 100,000 and 99,600,000 km² of area with cancer incidence more than 180 per 100,000. Cancer incidence was found to be statistically significantly associated with higher emissivity of the defined area (p-value < 0.0001). Conclusion: We found a significant association between areas of higher emissivity on our planet and higher cancer incidence. Our findings underscore the need to include environmental factors, in our case infrared radiation exposure, as potential predictor in the deployment of cancer predictive models, such as those involving machine learning and artificial intelligence.

Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery 0.6 m from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km2 areas across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered ∼ 19.3% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.

Application and Evaluation of Stage–Storage–Discharge Methodology in Hydrological Study of the Southern Phosphate Mining Model Domain in Southwest Florida

This hydrological study investigated a combined rating methodology tested on a 14,090 km2 area in Southwest Florida. The approach applied the Hydrological Simulation Program-Fortran (HSPF) over a 23-year period and was validated by 28 stream gauging stations. The regional hydrological complexity includes natural and agricultural areas, as well as extensive phosphate mining and urbanizing areas. This application is a novel and efficient methodology for generating stage–storage–discharge relationships using a geographic information system (GIS), empirical equations, and spreadsheets for 148,000 isolated and connected alluvial wetlands within the model domain. The validation metrics used to evaluate the applied methodology for populating the stage–storage–discharge relationship demonstrated the model effectiveness in simulating a range of hydrological events across various regions. For discharge prediction, the Nash–Sutcliffe efficiency values surpassed 0.7 at most stations, with an average of 0.67, and the average R squared was 0.74. This methodology, when applied, achieved a root-mean-square error of 4 m3/s for discharge prediction and 0.47 m for stage prediction. However, limitations emerged in simulating baseflow (low flows), highlighting the need for integrated modeling approaches to accurately capture groundwater–surface water interactions. The research provides an improved means for modeling regional water resources and lays the groundwork for enhanced hydrological modeling in watersheds with complex alluvial and isolated wetland systems.

Sample Size Optimization for Digital Soil Mapping: An Empirical Example

In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation.

Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Urban inundation disasters caused by extreme rainfall events are becoming increasingly severe. However, the numerical inundation model based on physical process has relatively slow computational speed and struggle to meet the demands of current forecasting and early warning systems. Addressing how to integrate machine learning technology into rapid urban inundation prediction, especially when dealing with limited training samples, and selecting the most appropriate machine learning algorithms for different rain patterns, is a crucial and pressing issue. To tackle this problem, this work combines a coupled hydrological-hydrodynamic model known for its high-computational accuracy with efficient machine learning algorithms. Taking different rain patterns into account, a rapid urban inundation prediction method is proposed. This method utilizes data-driven results from the coupled hydrological-hydrodynamic model and constructs multiple machine learning algorithms based on the conclusions drawn from the analysis regarding the most suitable machine learning algorithms for different rain patterns. The results show that for single-peak, double-peak, and uniform rain patterns, the Ridge algorithm, KNN algorithm, and RF algorithm should be respectively employed. The method achieves a mean absolute percentage error of 5.32 %, 7.73 %, and 2.49 % for single-peak, double-peak, and uniform rainfall scenarios and can predict inundation within a 3.68 km2 area in 14.07 s. This method offers high prediction accuracy and fast computational speed, not only meeting the requirements of daily forecasting and early warning but also providing a new direction for the application of machine learning technology in urban inundation prevention.

Multi‐geophysical methods for characterizing fractures in an open pit mine, western Bushveld Complex, South Africa

AbstractIn the Bushveld Complex, South Africa, open pit mines are faced with a challenge of rock slope stability due to geological structures (fractures, faults and dykes) that compartmentalize the rock mass. Geophysical surveys (seismics, magnetics and electrical methods) were conducted in a 0.2 km2 area at Tharisa mine, with the goal to delineate fractures that may be potential conduits for water migration into the pit. Special processing techniques were applied to the dataset to obtain good quality seismic, magnetic and resistivity models. The P‐wave velocity models show distinct low velocities in the centre of the seismic profile, indicating the presence of weak zones associated with faulting or fracturing. Seismic reflection method was used to image the deeper discontinuities and mineralization contacts. Near surface reflections are observed throughout the profiles and are correlated with the contact between the chromitite and host rock. Ground magnetic surveys were conducted to delineate dykes and fractures. De‐trending and de‐culturing techniques were applied on the magnetic data for correcting regional and temporal variations. The low magnetic regions indicate the presence of fracture systems in the subsurface, whereas the high magnetic region is correlated with the dolerite dyke that crosscuts the pit. The electrical resistivity tomography exhibits linear low resistivity contrast zones that differentiate between the fractured and undisturbed hard rock at an estimated depth of 4–10 m. Resistivity shows discontinuities that suggests the presence of fracturing and dyke‐host rock contacts. Correlation among magnetics, P‐wave velocity models, resistivity section and seismic data is evident when overlaying the different datasets, implying that the low magnetic regions are highly weathered and prone to fracturing. The integration of geophysical data is encouraging, because it was able to image the depth to the bedrock, fractures within the host rock and dyke in a complex mining environment.

Environmental variables and bottom trawling effort affect the complexity and distribution of soft-bottom octocorals and burrowing megafauna communities in the Gulf of Cádiz

Bottom trawling significantly impacts benthic communities, reducing habitat complexity and biodiversity. The distribution and abundance of key species in the northeastern Gulf of Cádiz, Spanish waters of Atlantic Ocean, such as soft-bottom octocorals and burrowing megafauna, were examined using video observations and Spatial Distribution Models developed with Random Forest. The study was conducted in the Site of Community Importance “Volcanes de fango del golfo de Cádiz" focusing on depths between 300 and 950 metres depth. The effects of environmental variables and bottom trawling on octocoral distribution and abundance were assessed, and management strategies for habitat protection were proposed. Bottom trawling was found to decrease habitat suitability for large octocoral species while the abundance of Norway lobster was positively correlated with certain values of bottom trawling effort. The study suggests that bottom trawling causes significant reduction in octocoral distributions, substrate homogenization, and habitat differentiation. A 200 km2 area is recommended for a specific bottom trawling regulation to protect soft-bottom octocorals and support the recovery of commercial burrowing megafauna.

Comparative Spatial Modeling of Bretziella fagacearum Distribution in Northern Wisconsin, United States.

Bretziella fagacearum, the ascomycete fungus oak wilt, is considered a virulent threat to North American oaks, but the influence of the physical environment on this pathosystem remains unclear, particularly at the forest scale. This study explored the influence of terrain and soil factors on B. fagacearum infections, applying discrete and continuous spatial models to investigate the question, besides proximity to other infections, which environmental factors influenced B. fagacearum incidence? Locations of infections were recorded from 586 confirmed B. fagacearum sites, identified from 2004 through 2021 in a 76 km2 area of deep, sandy glacial outwash in Chequamegon-Nicolet National Forest, northern Wisconsin. Public datasets derived from remote sensing were incorporated as covariates, describing terrain elevation (USGS 10-m DEM), soil physical and chemical properties (POLARIS), and forest composition (WiscLand2). Spatial models included generalized additive models (GAMs) and Neyman-Scott cluster process models. The results indicated that spatial dependence and the distribution of oak forests were the most important drivers of B. fagacearum distribution in this area, with more minor influence from elevation, hill shade, and drainage patterns. Comparison between modeling approaches indicated that-at this scale and in this area-the most accurate models were those that included host distribution, spatial dependence, and quantitative terrain and soil descriptions. However, a close approximation could be attained using nonlinear models (GAMs) that incorporated only host distribution and spatial dependence.

High-resolution digital outcrop model of the faults, fractures, and stratigraphy of the Agardhfjellet Formation cap rock shales at Konusdalen West, central Spitsbergen

Abstract. Structure-from-motion (SfM) photogrammetry has become an important tool for the digitalisation of outcrops as digital outcrop models (DOMs). DOMs facilitate the mapping of stratigraphy and discontinuous structures like folds, faults, and fractures from the centimetre to kilometre scale. With pristine, treeless exposures, the outcropping strata in Svalbard, Arctic Norway, hold exceptional potential for analogue studies and are ideally suited for the acquisition of high-resolution DOMs. Here, we present the acquisition, processing, and integration of the Konusdalen West digital model data set, comprising both DOM and derived digital terrain model (DTM) data. Drone-based image acquisition took place over 2 weeks in July and August 2020. The Konusdalen West DOM and DTM cover a 0.12 km2 area and span a 170 m elevation difference. The DOM covers the upper two-thirds of the mudstone-dominated Late Jurassic–Early Cretaceous Agardhfjellet Formation. The Agardhfjellet Formation and its time equivalents are regional cap rocks for CO2 sequestration and petroleum accumulations on the Norwegian Continental Shelf. A total of 15 differential GNSS control points were used to georeference and quality assure the digital data assets, 5 of which function as reference checkpoints. SfM processing of 5512 acquired images resulted in high-confidence, centimetre-scale resolution point clouds, textured mesh (DOM), tiled model, orthomosaics, and a DTM. The confidence-filtered dense cloud features an average inter-point distance of 1.57 cm and has an average point density of 3824.9 points per metre. The five checkpoints feature root mean square errors of 2.0 cm in X, 1.3 cm in Y, 5.2 cm in Z, and 5.7 cm in XYZ. Increased confidences and densities are present along the western flank of the Konusdalen West outcrop, where a fault fracture network in mudstone-dominated strata is best exposed and photographed most extensively. Top and side view orthomosaics feature maximum resolutions of 8 mm per pixel, enabling the mapping of faults, formation members, marker beds, fractures, and other sub-centimetre features. Additional structural measurements and observations were taken in June 2021 to place the data in the geological context. Data described in this paper can be accessed at Norstore under https://doi.org/10.11582/2022.00027 (Betlem, 2022b).

Delineation of potential alternative agriculture region using RS and AHP-based GIS techniques in the drought prone upper Dwarakeswer river basin, West Bengal, India

Delineation of potential alternative agriculture region is an essential tactics of the drought prone to mitigate the harmful effect of drought for maintaining food security and sustainable economy. These alternatives can be integrated into existing cropping systems for providing additional income to farmers. The present study deals with site suitability analysis of alternative agriculture in the drought-prone upper Dwarakeswer river basin considering 15 parameters using Analytical Hierarchy Process (AHP) based Geographic Information System (GIS) techniques. The alternative agriculture zones are then delineated on the basis of 15 separate thematic layers such as Slope, landuse and landcover, soil depth, ground water, distance from roads, soil texture, distance from settlements, ruggedness index, drainage density, soil drainage, proximity to ponds, distance from vegetation, distance from streams, nitrogen and organic carbon. Finally, seven alternative agricultural zones are identified; these are plantation agriculture, integrated farming, fruit culture, folk rice culture, medicinal plant farming, mixed farming and horticulture covering an area of 96 km2, 234 km2, 295 km2, 341 km2, 298 km2, 199 km2 and 74 km2 respectively. This is also to note that the ‘Not Suitable Zone’ has been identified accounting 397 km2 area and covering 20.5% of total study area. A total of 350 alternative agricultural sites are identified for validation purpose and the computed kappa coefficient (k) of 95.34 denote a overall accuracy of 96%.

Habitat suitability modelling of Koklass pheasant (Pucrasia macrolopha) in moist temperate forest.

The decreasing status of on IUCN of Koklass pheasant (Pucrasia macrolopha) belongs to the family Phasianidae and the order Galliform needs the attention of researchers. The species with habitats as low as 6,000 feet and as high as 11,000 feet certainly cover a broad variety of habitats, such a wide altitude range embraces a diverse range of habitats. Insufficient research has been conducted on the suitability of moist temperate forests as a potential habitat for the Koklass pheasant. Therefore, this study was carried out to explore habitat suitability in 15 different sites which were located in the 4 districts of Hazara Division using GIS data science and environmental variables. A random sampling technique was used for laying out the transect. Overall, 45 line transects (Length 2-4 km, Width 10-30 m) were laid out in study sites. The size of sample plots for trees was 10x10m, for shrubs (4 x4m), and herbs and grasses 1x1m. The other habitat parameters like elevation, slope, cover, and frequency of plant at each point were also considered. We found the uneven distribution of Koklass pheasant in the Hazara Division. There were 59 occurrence points identified and highlighted the distribution of Koklass pheasant in the study area. Although all environmental variables were preferred by Koklass pheasant in its habitat statistical analysis proved that slope, level of disturbance, tree and shrub frequency of habitat contributed mostly to the presence of Koklass in each study site except the contribution of soil and herbs. The potential suitable habitat of Koklass pheasant was estimated to be 439.6 km2 areas starting from Abbottabad to Mansehra in the Hazara division. Awareness and enforcing legal protection are recommended for the conservation of Koklass Pheasant in Moist temperate forest.

Predicting the current habitat refugia of Himalayan Musk deer (Moschus chrysogaster) across Nepal.

Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field-based surveys during 2018-2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation-related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long-term forces of climate change.

Performance Analysis of 4x4 MIMO and 8x8 MIMO Antena Implementation of Private 5G Networks in Industrial Area

Private 5G represents a promising wireless network technology that offers a wide range of opportunities for mobile operators. It enables them to meet the increasing data demands of their customers and provide enhanced capacities and user experiences. However, before deploying Private 5G, it's crucial to prepare the foundation properly, especially when choosing the right antena and Radio Frequency (RF) band. This research project focuses on evaluating the implementation of 4x4 MIMO and 8x8 MIMO antenas in the Greater Jakarta industrial area, covering a 35 km2 area, operating at a frequency of 2300 MHz with a 40 MHz bandwidth. The study involved data rate measurements and determined the number of required sites based on different modulation schemes. The findings revealed that using an 8x8 MIMO antena resulted in a data rate twice as high as that achieved with a 4x4 MIMO antena and required only a quarter of the sites. Additionally, simulation results demonstrated that the 8x8 MIMO antena provided better SS-RSRP and SS-SINR values compared to the 4x4 MIMO antena, with values of -91.51 dBm and 8.52 dB

KIRSAL VE KENTSEL ALANDA MAHALLE: KÖYLERDE, KENT VE KASABALARDA, BÜYÜKŞEHİRLERDE MAHALLE KAVRAMI

Bu çalışmada Türkiye'de mevcut mahalle kavramı, yapısı, gelişimi, kırsal ve kentsel özellikleri ve bunların farklı yönleri temelinde açıklanmaya çalışılacaktır. Öncelikle kırsal alan ve kentsel alan ifadeleri ele alınacak, kırsal alanda mahalle kavramının ne anlama geldiği, kentsel alanda kurulan mahallelerin varlığı ve büyükşehirlerde kırsal mahalle kavramının anlamı ve nedenleri araştırılacak ve karşılaştırılacaktır. Çalışmada yapılacak açıklamalar literatür ve hukuki metinlere dayandırılacaktır. Kırsal ve kentsel alanlardaki mahalle türleri karşılaştırılacak, sorunları sıralanacak ve öneriler getirilmeye çalışılacaktır.
 Türkiye'de üç tür mahalle uygulaması bulunmaktadır. Kırsal alanlarda bazı köylerin bünyesinde bulunan mahalleler, şehir ve kasabalarda belediyeler tarafından kurulan mahalleler, büyükşehirlerde kırsal mahalle olarak adlandırılan, daha önce köy veya belde belediyesi iken mahalleye dönüştürülen kırsal karakterli yerleşmeler. Bu çalışmada, bahsedilen üç farklı mahalle tipi açıklanmaya çalışılmıştır.
 Türkiye'de TÜİK, ülkenin kırsal nüfusunu hesaplarken yerleşim yerleri ölçeğinde köy, belde ve köy altı yerleşim yerlerini (oba, kom, mezra, divan gibi) esas almaktadır. Bazen nüfus büyüklüğü de bir ölçü olarak kullanılmaktadır. Örneğin Türkiye'de Köy Kanunu kent ölçütünü 20 binin üzerindeki nüfusa dayandırmaktadır. Ancak, nüfus büyüklüğü kentsel ve kırsal karakteri ölçmek için her zaman tek başına yeterli değildir.
 Avrupa Birliği'nde uygulanmakta olan DEGURBA (kentleşme derecesi) istatistik sistemi 2022 yılından itibaren Türkiye'de de uygulanmaya başlanmıştır. Köy ve mahalle bazında coğrafyanın her 1 km2'lik alanının nüfus büyüklüğü ve farklı sosyo-ekonomik işlevi dikkate alınarak bu 1 km2'lik plakalar yoğun kentsel alan, orta yoğun kentsel alan ve kırsal alan olarak kodlanmaktadır. Bu yeni kır-kent alan sınıflandırması köy ve mahalle düzeyinde yapıldığı için o yerleşmenin idari statüsünün daha etkin bir şekilde belirlenmesine olanak sağlamaktadır.

Analysis of landslide susceptibility prediction accuracy with an event-based inventory: The 6 February 2023 Turkiye earthquakes

Landslide susceptibility assessment is a complex challenge explored by various scientists, but not fully resolved. In this study, we produced the landslide susceptibility map of a large region covering 38,500 km2 area in South-East Turkiye severely affected by the 6 February 2023 Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6) using an inventory produced in previous years. We employed random forest regression with a total of nine geomorphological and environmental features and evaluated the results using the co-seismic inventory with 2611 landslides compiled here. Although high accuracy was obtained from pixel-based assessments of test data split from the learning set, the independent validation set of co-seismic landslides showed that attention needs to be paid unseen features such as rare lithological units. Given the significant damage caused by latent hazards with the Kahramanmaras earthquakes, producing reliable inventories and precise landslide susceptibility maps is crucial for risk reduction and minimizing damages.

Geospatial liquefaction probability models based on sand boils occurred during the 2017 M5.5 Pohang, South Korea, earthquake

The 2017 Pohang earthquake, triggered by an enhanced geothermal system, had a moment magnitude (M) of 5.5 and caused liquefaction in Pohang City, South Korea. Notably, approximately 600 sand boils were observed only in the soft Quaternary alluvial sediments. This contrasts with most historic liquefaction cases that occurred in earthquakes with M ≥ 6.0. In addition, ground and building settlements were reported in Pohang City. To assess the liquefaction hazard for the Pohang earthquake within the city, we define a 14.4 km2 area where the majority of liquefaction was observed. Using the liquefaction data, we develop eight geospatial liquefaction probability models using logistic regression (LR) and random forest (RF) algorithms (five LR- and three RF-based models). These models consider geospatial data (peak ground acceleration, peak ground velocity, slope-derived average shear wave velocity of the upper 30 m, compound topographic index, distance to the nearest water, and roughness) and geotechnical data (averaged standard penetration test N values up to 20 m depth, depth to rock). We evaluate the performances of our eight models and existing global and regional geospatial models using four indicators: accuracy, balanced accuracy, F1 score, and area under the receiver operating characteristic curve. The three RF-based models outperform the other models, followed by the best LR-based model and the global geospatial model from Zhu et al. (2015). Finally, we apply the best RF-based model to the entire Pohang City and evaluate its applicability to other regions and earthquakes. The estimated liquefaction probabilities agree well with the observed distributions of the sand boils and settlements. These geospatial liquefaction models provide a valuable tool for continuously mapping liquefaction hazards in areas with similar geological and geotechnical conditions to Pohang.