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  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 401 - 277 of 277 Journals sorted by number of followers
Jambura Geo Education Journal     Open Access   (Followers: 49)
AGU Advances     Open Access   (Followers: 15)
Arctic     Open Access   (Followers: 9)
Visión Antataura     Open Access   (Followers: 9)
Environmental Research : Climate     Open Access   (Followers: 8)
Environmental and Sustainability Indicators     Open Access   (Followers: 7)
Journal of Public Space     Open Access   (Followers: 7)
Oxford Open Climate Change     Open Access   (Followers: 7)
Evolutionary Human Sciences     Open Access   (Followers: 6)
Geographia     Open Access   (Followers: 6)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal   (Followers: 5)
Remote Sensing in Earth Systems Sciences     Hybrid Journal   (Followers: 5)
Geography and Sustainability     Open Access   (Followers: 5)
Population and Economics     Open Access   (Followers: 5)
Football(s) : Histoire, Culture, Économie, Société     Open Access   (Followers: 5)
The Geographic Base     Open Access   (Followers: 5)
People and Nature     Open Access   (Followers: 4)
Ecosystems and People     Open Access   (Followers: 4)
Earth Systems and Environment     Hybrid Journal   (Followers: 4)
GeoHumanities     Hybrid Journal   (Followers: 4)
Earth System Governance     Open Access   (Followers: 4)
International Journal of Cartography     Hybrid Journal   (Followers: 3)
Advances in Cartography and GIScience of the ICA     Open Access   (Followers: 3)
Progress in Disaster Science     Open Access   (Followers: 3)
Brill Research Perspectives in Map History     Full-text available via subscription   (Followers: 3)
Wellbeing, Space & Society     Open Access   (Followers: 3)
Environmental Science : Atmospheres     Open Access   (Followers: 3)
Nomadic Civilization : Historical Research / Кочевая цивилизация: исторические исследования     Open Access   (Followers: 3)
Plants, People, Planet     Open Access   (Followers: 2)
African Geographical Review     Hybrid Journal   (Followers: 2)
AAG Review of Books     Hybrid Journal   (Followers: 2)
Area Development and Policy     Hybrid Journal   (Followers: 2)
Asian Journal of Geographical Research     Open Access   (Followers: 2)
Biogeographia : The Journal of Integrative Biogeography     Open Access   (Followers: 2)
Computational Urban Science     Open Access   (Followers: 2)
Journal of the Bulgarian Geographical Society     Open Access   (Followers: 2)
Załącznik Kulturoznawczy / Cultural Studies Appendix     Open Access   (Followers: 2)
KN : Journal of Cartography and Geographic Information     Hybrid Journal   (Followers: 1)
Resilience : International Policies, Practices and Discourses     Hybrid Journal   (Followers: 1)
Papers in Applied Geography     Hybrid Journal   (Followers: 1)
Journal of Geography, Environment and Earth Science International     Open Access   (Followers: 1)
Agronomía & Ambiente     Open Access   (Followers: 1)
Offa's Dyke Journal     Open Access   (Followers: 1)
Regional Studies Journal     Open Access   (Followers: 1)
UNM Geographic Journal     Open Access   (Followers: 1)
Studies in African Languages and Cultures     Open Access   (Followers: 1)
Revue de géographie historique     Open Access   (Followers: 1)
Boletín de Estudios Geográficos     Open Access  
Proyección : Estudios Geográficos y de Ordenamiento Territorial     Open Access  
Parks Stewardship Forum     Open Access  
Scandinavistica Vilnensis     Open Access  
East/West : Journal of Ukrainian Studies     Open Access  
Tidsskrift for Kortlægning og Arealforvaltning     Open Access  
Les Cahiers d’Afrique de l’Est     Open Access  
Mappemonde : Revue trimestrielle sur l'image géographique et les formes du territoire     Open Access  
IBEROAMERICANA. América Latina - España - Portugal     Open Access  
Scripta Nova : Revista Electrónica de Geografía y Ciencias Sociales     Open Access  
Coolabah     Open Access  
Biblio3W : Revista Bibliográfica de Geografía y Ciencias Sociales     Open Access  
Ar@cne     Open Access  
Journal of Cape Verdean Studies     Open Access  
Punto Sur : Revista de Geografía     Open Access  
RIEM : Revista Internacional de Estudios Migratorios     Open Access  
Revista Brasileira de Meio Ambiente     Open Access  
Sasdaya : Gadjah Mada Journal of Humanities     Open Access  
Revista Eletrônica : Tempo - Técnica - Território / Eletronic Magazine : Time - Technique - Territory     Open Access  
Periódico Eletrônico Geobaobás     Open Access  
PatryTer     Open Access  
Espaço Aberto     Open Access  
AbeÁfrica : Revista da Associação Brasileira de Estudos Africanos     Open Access  
Mosoliya Studies     Open Access  
New Approaches in Sport Sciences     Open Access  
International Journal of Geoheritage and Parks     Open Access  
Watershed Ecology and the Environment     Open Access  
Sémata : Ciencias Sociais e Humanidades     Full-text available via subscription  
Geoingá : Revista do Programa de Pós-Graduação em Geografia     Open Access  
Revista Uruguaya de Antropología y Etnografía     Open Access  
Rocznik Toruński     Open Access  
Southern African Journal of Environmental Education     Open Access  
Proceedings of the ICA     Open Access  
Mediterranean Geoscience Reviews     Hybrid Journal  
Journal of Geospatial Applications in Natural Resources     Open Access  
Revista Geoaraguaia     Open Access  
TRIM. Tordesillas : Revista de investigación multidisciplinar     Open Access  
Geopauta : Revista de Geografia da Universidade Estadual do Sudoeste da Bahia     Open Access  

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Remote Sensing in Earth Systems Sciences
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2520-8195 - ISSN (Online) 2520-8209
Published by Springer-Verlag Homepage  [2468 journals]
  • Assessment of Ecosystem Service Value Variation Over Land Use and Land
           Cover Dynamics in the Beles River Basin, Ethiopia

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      Abstract: Abstract The Beles River Basin is branded as a potential national economic growth corridor. As a result, tremendous developments took place in the basin. However, the effects of changes in land use and land cover (LULC) on ecosystem service values (ESV) have not been properly assessed. This study aimed to assess the ESV status in the Beles River Basin to provide relevant information to stakeholders and policymakers from 1986 to 2019. Satellite imagery and participatory assessments of community perceptions were used to evaluate changes in LULC over the years. All images were classified using the maximum likelihood algorithm (MLA), a supervised classification technique. The LULC types were classified with an overall accuracy ranging from 89.3 to 92.1%, with a kappa coefficient of 0.87 to 0.90. The results indicated a reduction in forests (71.0%), woodlands (11.2%), and grazing lands (1.8%), while there was an increase in croplands (3568.0%), water bodies (178.1%), and built-up areas (9.4%) over the past 33 years. The total ESV in the basin were estimated to be US$1.1 billion in 1986, US$909.4 million in 2002, and US$836.5 million in 2019. The ESV losses accounted for 22.9% (US$249.3 million) over the past three decades. The observed changes in LULC also affected individual ecosystem services. The decline in ESV highlights the effects of environmental degradation in the basin. Thus, sustainable land management is indispensable to ensure the sustainability of ecosystem services in the basin.
      PubDate: 2024-06-08
       
  • Statistical Downscaling of Remote Sensing Precipitation Estimates Using
           MODIS Cloud Properties Data over Northeastern Greece

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      Abstract: Abstract The aim of this study is to spatially downscale the daily precipitation data from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG), utilizing cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Cloud optical thickness (COT), cloud effective radius (CER), and cloud water path (CWP) are used to statistically downscale IMERG precipitation estimates from 0.1 to 0.01° spatial resolution, using the Multivariate Linear Regression (MLR) and residual correction methods. The downscaled precipitation estimates were subsequently validated using in situ rain gauge measurements. The residual corrected IMERG downscaled precipitation estimates were found to be more accurate than the downscaled predicted precipitation without the implementation of the residual correction algorithm (up to 37%), with a respective decrease of the Root Mean Square Error (RMSE) (up to 75%), Normalized Root Mean Square Error (NRMSE) (up to 79%), and the Percent Bias (PB) (up to 98%). In addition, the final downscaled product after the MLR method implementation with residual correction was better correlated with the rain gauge observations than the initial IMERG product (up to 20%). Thus, the implementation of the MLR method in conjunction with the residual correction algorithm is an efficient tool for downscaling remote sensing products with a coarse spatial resolution.
      PubDate: 2024-06-08
       
  • Image Processing of Landsat-8 OLI Satellite Data for Mapping of
           Alkaline-Carbonatite Complex, Southern India

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      Abstract: Abstract The alkaline-carbonatite igneous intrusion occupies a very less aerial extent on the earth’s surface. However, it is an important source rock for Rare Earth Elements (REE), Large Ion Lithophile Elements (LILE), and radioactive elements. The origin of the alkaline-carbonatite suite is always related to tectonic settings such as continental drift and suture zones. In this context, the Samalpatti and Sevathur alkaline-carbonatite complex is an interesting area among various geoscientists. The moderate resolution Landsat-8 OLI satellite data is digitally processed using ENVI 5.3 image processing software to interpret various lithologies in the terrain. Different rock types, including syenite, pyroxenite, dunite, carbonatite, and epidote hornblende gneiss, cover the Samalpatti-Sevathur complex. The carbonatites are emplaced as elongated bodies, dykes, and scattered nodules in ultramafic formation. The mapping of ultramafic formations is essential since it contains carbonatite emplacement in the complex. The seven-band OLI data covered under the visible, near-infrared, and shortwave infrared spectrum were used to generate color composite images, band ratios, principal component analysis (PCA) images, and support vector machine (SVM) classified output. The digitally processed satellite images are helpful in the interpretation of different rock types, particularly the ultramafic formation. Out of various outputs, the FCC, the composite image B652, color composite images from ratio output, PCA composite images in RGB filters, and SVM-classified output are suitable for interpreting different lithologies in the terrain. The results obtained through the interpretation of digitally processed satellite outputs were validated with the help of published geology maps, field sample locations, and photomicrographs.
      PubDate: 2024-05-11
       
  • Influence of Chemical and Granular Organic Fertilizer with Hormone Mixed
           Formula (HO) on Yield, Quality of Yield, Cost, and Profit of Chili
           (Capsicum annuum Linn.) Production

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      Abstract: Abstract This study was aimed at the development of fertilizer management innovation for improvement of yield, quality of yield, and cost–benefit for chili production. In this research, 4 formulas of new innovation fertilizers called HO fertilizers (chemical and granular organic fertilizer with hormone mixed fertilizer (HO) and one chemical fertilizer (15–15-15) applied at the rate of 50 and 100 kg/rai (0.16 ha) were introduced. The experiment site was located at Moo 9 village, Kaeng Sopha subdistrict, Wang Thong district, Phitsanulok Province, Thailand, during June–September 2018. Data were analyzed with analysis of variance (ANOVA), comparing the difference of mean by DMRT at 95% confidence level. The results of fertilizers analyzed showed that HO group fertilizers contained major nutrients (N-P-K) 10.0–12.7%, while the chemical fertilizer was 15–15-15% and the HO group had a high level of secondary and micronutrients, organic matter with pH 6.5–6.8, which is suitable for plant uptake. The analysis of active ingredients (capsaicin, dihydrocapsaicin, capsaicinoids, and capsaicinoids mg/plant) showed that all HO fertilizers had higher active ingredients than chemical fertilizer and found that T9 (HO-3, 100 kg) was the highest content significantly different from other treatments. The cost and profit analyzed showed that all of HO fertilizers had higher income than chemical fertilizer even applied with 50 or 100 kg/rai. From the results above, T9 (HO-3, 100 kg) was the most influenced fertilizer to yields, yield components, yield quality, and active ingredients of the chili. The T9 (HO-3, 100 kg) method showed the highest cost, but it got the highest yields also. Therefore, T9 (HO-3, 100 kg) was the best method for chili production, its providing contribution margin of 49, 334 Baht per rai (0.06 ha) when the chemical was 17,958 Baht per rai, which means T9 (HO-3, 100 kg) got income above chemical fertilizer (T3, 100 kg) about 2.7 times which amount of 31,376 Baht per rai different.
      PubDate: 2024-05-03
       
  • Wireless Spatial Analysis-Based Predictive Analysis and Environmental Data
           Optimisation Using Machine Learning Model

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      Abstract: Abstract A significant quantity of sensor data has been used recently to construct a variety of Internet of Things (IoT)-based methods as well as applications. They have been extensively employed in urban sustainable development and in mobile data reception for WSN (wireless sensor networks), for instance. Correct interpretation as well as reuse of sensor data from many domains is essential for maximising the use of data from numerous sources for decision-making. The purpose of this project is to provide new methods for predictive analysis based on spatial data modelling and machine learning-based environment data optimisation. Predictive Bayesian spatial Markov neural network is used for the environment spatial data predictive analysis. Then, lion grey moath binary optimisation is used to optimise the data. Environmental data is subjected to an experimental examination in terms of F-1 score, recall, accuracy and precision. The results of the study showed that optimizing models for precise water quality prediction may be achieved by combining artificial intelligence models with optimisation routines.
      PubDate: 2024-03-09
      DOI: 10.1007/s41976-024-00103-5
       
  • Characterization of Surface Spectral Emissivity Retrieved from EE9-FORUM
           Simulated Measurements

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      Abstract: Abstract FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) has been approved to be the ninth Earth Explorer mission of the European Space Agency and is scheduled for launch in 2027. The core FORUM instrument is a Fourier transform spectrometer, which will, for the first time, measure the upwelling spectral radiance in the far-infrared (FIR) and mid-infrared (MIR) portions of the Earth’s spectrum. These radiances will be processed up to level 2, to determine mainly the vertical profile of water vapor, surface spectral emissivity, and cloud parameters. In this paper, we assess the performance of the FORUM surface spectral emissivity product based on all-sky sensitivity study. In the FIR, we find that the retrieval error is mainly driven by the precipitable water vapor (PWV) in clear-sky conditions. In dry atmospheres, FIR emissivity can be retrieved with an error less than 0.01. In cloudy conditions, small errors can be achieved for optically thin clouds, especially for small values of the PWV. In the MIR, we observe that a large thermal contrast between the surface and the lowest atmospheric layers increases the sensitivity of the measurements to the surface emissivity in clear-sky conditions and an emissivity retrieval error less than 0.01 can usually be achieved. In cloudy conditions, small errors can be achieved for optically thin clouds, especially for large values of the surface temperature. Applying a coarser retrieval grid further reduces retrieval error, at the expense of an increased emissivity smoothing error.
      PubDate: 2024-02-23
      DOI: 10.1007/s41976-024-00102-6
       
  • Flood Susceptibility Map of Periyar River Basin Using Geo-spatial
           Technology and Machine Learning Approach

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      Abstract: Abstract Floods are among nature’s most destructive disasters because they create extremely extensive damage to structures, the environment, and people. Therefore, it is important to determine the causes of floods as well as areas that are vulnerable to flooding, which can be done by performing a flood susceptibility model. This research identified flood-prone locations in the Periyar River Basin using historical flood records from 2000 to 2020 and some of the conditioning features. The ten variables considered in the present study include elevation, slope, aspect, flow direction, drainage density, rainfall, Normalized Difference Water Index (NDWI), Stream Power Index (SPI), Sediment Transport Index (STI), and Topographic position Index (TPI). In order to create a flood susceptibility map and examine the correlation between flood incidence, the logistic regression (LR), support vector machine, naive Bayes, random forest, AdaBoost, gradient boost, and extreme gradient boost models were developed and validated. The model accuracies were measured using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). In addition to this, some other indices like precision, recall, sensitivity, specificity, F1 score, and overall accuracy matrices are used for model evaluation. The results demonstrated that every model can identify flood-prone locations with reasonable accuracy. However, compared to other models, the random forest model showed a better performance and prediction rate (AUC = 94). Furthermore, all models indicated that low-lying places near water bodies and in the western region of the study area had the largest probability of flooding. According to the study, machine learning techniques are a useful tool for mapping and predicting flood-prone areas and for creating flood mitigation strategies and plans.
      PubDate: 2024-01-18
      DOI: 10.1007/s41976-024-00101-7
       
  • Deciphering Forest Cover Losses and Recovery (1990–2022) Using Satellite
           Data in Behali Reserve Forest of Northeastern Himalaya

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      Abstract: Abstract Protected and reserved forest areas are evidently experiencing the problem of extensive forest deforestation due to illegal encroachment by the surrounding communities. Assessing forest cover change and its dynamics plays a significant contribution to the management of the forest biodiversity and ecosystem. The present study aims to assess the forest cover change (i.e., loss and recovery) in Behali Reserve Forest (BRF) using multi-temporal satellite data from 1990 to 2022. The remote sensing satellite data of 1990, 2000, 2010 (from Landsat-5) and 2022 (from Landsat-8) were employed to analyse forest cover change using the supervised classification method. The overall accuracy was reported about 93.5% with a kappa coefficient of 0.91. The result showed considerable forest cover losses of 25.6% from 1990 to 2000, but there was a marginal gain of 3.44% in BRF from 2000 to 2010 and 0.19% from 2010 to 2022. The net change of forest to non-forest was nearly 21.34 km2 over the span of 1990–2022 which accounted for a net loss of forest cover of 22.88%. The forest dynamic map from 1990 to 2022 showed an area of 48.6 km2 (33.4%) as non-forest remained non-forest, 68.11 km2 (46.8%) as forest remained forest, 25.2 km2 (17.3%) as deforestation and 3.7 km2 (2.5%) as afforestation. This study found hotspots of deforestation and degradation in the upper part of BRF due to the encroachment of the Arunachali people. Therefore, the study suggests the declaration of this reserve forest as a wildlife sanctuary to control the rate of rising forest deforestation and encroachment in the region.
      PubDate: 2023-12-29
      DOI: 10.1007/s41976-023-00100-0
       
  • Maritime Intercontinental Trade in Submerged Poompuhar Port City—A
           Case Study

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      Abstract: Abstract The objective of this paper is to analyze the complex relationships between maritime connectivity, trade, and domestic production from the ancient vanished harbor Poompuhar, which is well described in the Tamil literature: Silappadikaram, Manimekalai, Pattinapalai, and Akananuru. Many ancient ports such as Nagapattinam, Korkai, Alangulam, Periyapattinam, and Pooumpuhar along Tamil Nadu’s coastline played a significant role in the oceanic trade and commerce before the beginning of the Christian Era. These ancient ports/harbors vanished or submerged along the seashore due to coastal corrosion, sea level changes, tectonic plate movement, or other natural calamities. The Sangam literature vividly describes Poompuhar port’s location, habitation, and town planning. This ancient port town, dating back to the fourth century BCE, is now being rebuilt digitally and preserved so that the whole India can learn about the early existence of Poompuhar’s trade and culture, as it still remains unknown. Through the millennia, trade operations have encouraged exchanges between far-off lands connected by networks of maritime trade routes. Maritime networks functioned as a major means of connecting with other nations across various seas and land routes connecting different sections of the subcontinent. This research can lead to the development of archiving and preserving all cultural heritages, which can promote tourism in India. We can also discover the hidden remains and truth of India’s ancient cultures and heritages. We can be brought into the light so that everyone can know how the ancient Indian people lived and maintained their maritime oceanic trade and culture. This study focuses mainly on the archeological artifacts discovered at Poompuhar (Kaveripoompattinam), which allow us to describe the ancient trade routes. The artifacts demonstrate numerous routes for making contact with coastal and inland centers.
      PubDate: 2023-12-01
      DOI: 10.1007/s41976-023-00084-x
       
  • High-Resolution Soil Moisture—a European Airborne Campaign Using NASA
           Goddard’s Scanning L-Band Active Passive (SLAP)

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      Abstract: Abstract A summer 2021 European airborne field campaign—the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign—presented an opportunity to explore passive soil moisture sensing with footprints as small as 100 × 200 m, contributing a key measurement to LIAISE and providing a valuable opportunity to gain detailed insight into the water/energy/carbon exchanges at such plot-scale resolution over a 17 × 5 km area. NASA Goddard’s Scanning L-band Active Passive (SLAP) sensor—an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite—made nine soil moisture flights near Lleida, Spain, during 15–29 July. Soil moisture imagery and histograms demonstrate sensitivity to spatial and temporal patterns spanning irrigated and non-irrigated areas and their response to both irrigation and precipitation events followed by drydowns. Comparisons with point-scale ground truth at two sites—one within the irrigated zone and one in the non-irrigated zone—are good. Soil moisture differences are within the error bars of the ground truth values and within one standard deviation of the SLAP moisture values except for the afternoon flight of July 24. The overly dry retrieved values of that flight were likely the result of the extremely dry surface conditions and the simplified uniform ancillary data values used for this analysis. Future analyses using higher-fidelity ancillary data will explore these differences.
      PubDate: 2023-12-01
      DOI: 10.1007/s41976-023-00099-4
       
  • Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake
           Using Remote Sensing Data

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      Abstract: Abstract The summer season in India is marked by a severe shortage of water, which poses significant challenges for daily usage and agricultural practices. With unpredictable weather patterns and irregular rainfall, it is crucial to monitor and maintain water bodies such as domestic ponds and lakes in urban areas to ensure they provide clean and safe water for regular use, free from industrial pollutants. In this research paper, we propose an innovative ensemble deep learning approach (e-DLA) that leverages deep learning models to predict the turbidity of Dooskal Lake, located in Telangana, India, using remote sensing data. The proposed approach utilizes various deep learning models, including bagging, boosting, and stacking, to analyze the complex relationships between remote sensing data and turbidity levels in the lake. The study aims to provide accurate and efficient predictions of turbidity levels, which can aid in the management and conservation of water resources in the region. Hyperparameter tuning is employed, and dynamic climatic features are extracted and integrated with the ensemble learning global protective intelligent algorithm to reveal the complex relationship between in situ and measured values of turbidity during the measuring timeline. The proposed approach provides accurate predictions of turbidity levels, enabling the implementation of effective control measures to maintain water quality standards. Experimental results demonstrate that the proposed approach significantly reduces prediction errors compared to existing deep learning models. Overall, this research highlights the potential of machine learning techniques in monitoring and maintaining water resources, particularly in urban areas, to support sustainable water management and usage, and addresses an urgent and pressing issue in India and around the world.
      PubDate: 2023-11-18
      DOI: 10.1007/s41976-023-00098-5
       
  • Comparative Analysis of CMIP5-Based Monsoon Season Rainfall Against
           Satellite-Based Estimations over India

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      Abstract: Abstract The present study is designed to assess the rainfall pattern from Climate Model Inter-comparison Project Phase 5 (CMIP5) based on the satellite-derived rainfall products, Tropical Rainfall Measuring Mission (TRMM) over the Indian Region utilising daily as well as monthly rainfall data during monsoon season, ranging from 1st June to 30th September (JJAS). In this context, five best defined global climate models (GCMs) that participated in CMIP5 archive along with its multi-model mean (MMM) have been analysed to investigate the rainfall pattern during JJAS in terms of spatial map and time series under the forcing scenarios i.e., Representative Concentration Pathway 4.5 (RCP 4.5) and Representative Concentration Pathway 8.5 (RCP 8.5) from 2006 to 2018 over Indian region. On the other hand, spatial maps and time series have also been generated using TRMM rainfall data at daily (TRMM 3B42v7) and monthly (TRMM 3B43v7) scales during the reference time period. Thereafter, comparative study of the JJAS rainfall pattern between CMIP5 models and TRMM products has been carried out, whether the GCMs are able to simulate rainfall data reasonably well compared to satellite-derived estimates or not under various forcing scenarios over this region' Based on the assessment, it is noted that CMIP5 models have the ability to simulate daily mean monsoon season rainfall; however, it underestimates the rainfall intensity at daily scale over the north-east and south-west parts of India. Moreover, statistical analysis indicated more biases in the western coast and the north-eastern parts of India where it receives the highest amount of rainfall during JJAS. The outcomes presented here may be useful for assessing the reliability of CMIP5 models to project the rainfall pattern in near future under the various warming scenarios over the Indian Region.
      PubDate: 2023-11-06
      DOI: 10.1007/s41976-023-00096-7
       
  • Insights About the Spatial and Temporal Characteristics of the
           Relationships Between Land Surface Temperature and Vegetation Abundance
           and Topographic Elements in Arid to Semiarid Environments

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      Abstract: Abstract This study utilized multiple linear regression (MLR) and geographically weighted regression (GWR) to explore the connections between land surface temperature (LST) and four critical factors (vegetation abundance, elevation, slope, aspect of slope) in Jordan, an arid to semiarid country, during daytime and nighttime across all seasons in one year, yielding important insights. (1) Rates of change in LST in response to variations in vegetation abundance and elevation were consistently negative in both daytime and nighttime throughout all seasons. However, daytime showed a stronger influence of vegetation abundance, while nighttime had a more pronounced effect from elevation. (2) Rates of change in LST in response to changes in slope and aspect of slope were consistently negative during daytime and positive during nighttime across all seasons. (3) The most influential factor on LST varied by season, with vegetation abundance and slope being significant during daytime, while slope and elevation played significant roles during nighttime. (4) LST lapse rates consistently displayed negative values, with nighttime lapse rates being higher across all seasons. Overall, this research highlights GWR’s advantages over MLR in capturing local nuances in LST-influencing factor relationships. However, it also emphasizes the need for additional variables to fully explain year-round variations.
      PubDate: 2023-10-21
      DOI: 10.1007/s41976-023-00095-8
       
  • GIS-assisted Flood-risk Potential Mapping of Ilorin and its Environs,
           Kwara State, Nigeria

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      Abstract: Abstract The incessant reoccurrence of flooding disasters across Nigeria has mandated an urgent outlook on flood-risk management techniques. Ilorin and its environs have suffered immensely from annual flood reoccurrence. This study aims to assess flood risk within Ilorin and its environs and proffer adequate flood mitigation strategies that governments and policymakers can adopt to placate future flooding events within the state. Satellite imagery data were acquired and analyzed for flood-risk assessment of the area. Ten highly influential flood causative factors were synergized using Multi-Criteria Decision-Making techniques in this research; they are Land Surface Temperature, Elevation, Soil Moisture Index, and Distance to Stream, Drainage Density, Stream Power Index, Normalized Difference Vegetation Index, Land Use Land Cover, Slope, and Topographic Wetness Index. Findings showed that approximately 47.2% of the study area had low flood risk, while moderate and high flood-risk zones occupied 33.5% and 19.29%, respectively. Most parts of Ilorin and its environs are safe from flood disasters; only about one-quarter of the total area under investigation lies in the high flood-risk zones; these areas mostly fall within the shores of major streams, rivers, and dams within the state. A plot of previous flood cases in the state placed the affected areas in the high and moderate zones of flood risk, confirming the efficacy of geospatial techniques in flood-risk assessment. It is hoped that this study's findings and recommendations can be implemented to prevent future devastating flooding occurrences within the state.
      PubDate: 2023-10-11
      DOI: 10.1007/s41976-023-00093-w
       
  • Integrating Sentinel-2 Derivatives to Map Land Use/Land Cover in an
           Avocado Agro-Ecological System in Kenya

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      Abstract: Abstract Reliable, readily available, and appropriate land use/land cover (LULC) information is fundamental for coherent land and natural resources management, especially in data-scarce environments that are complex and heterogeneous. This study took a holistic approach for evaluating the classification accuracy of LULC classes in an avocado production system in Kenya using different classification scenarios and the random forest (RF) machine learning (ML) algorithm in Google Earth Engine (GEE). We integrated sentinel-2 (S2) spectral bands, vegetation indices (VIs), and phenological variables in two classification routines, pixel- and polygon-based procedures, and assessed their performance and importance in mapping LULC classes. To assess the LULC classification accuracy, a confusion matrix and a pattern-based assessment were used. This study demonstrated that the polygon-based classification procedure was the best (overall accuracy > 75% for confusion matrix and > 0.7 for pattern-based accuracy assessment methods) in mapping out complex landscapes when compared to the pixel-based classification procedures. Combining S2 reflectance with vegetation indices, red-edge (RE) vegetation indices, and phenological metrics can considerably improve LULC classification accuracy.
      PubDate: 2023-09-15
      DOI: 10.1007/s41976-023-00090-z
       
  • Impact of Urbanization on Temperature Trend Using Geospatial Techniques in
           Ondo State, Nigeria

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      Abstract: Abstract The conversion of natural/vegetated surfaces into residential, commercial, and industrial areas has contributed to increased land surface temperature (LST). The knowledge of surface temperature is essential to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environmental interactions. This paper aims to establish the impact of urbanization on land surface temperature. Hence, remote sensing and GIS techniques were applied to Landsat data to examine the land use for three (3) periods within thirty-two (32) years (1984–2016). Also, the LST was obtained by converting the thermal band to a surface temperature map, while the relationship between land use and temperature emission was established using zonal statistical analyses. The result showed a rapid urbanization footprint at the expense of vegetation and forest land use categories. Furthermore, the land surface temperature increased rapidly over the period of study with a noticeable spatio-temporal variation that is modified by land-use features. The built-up area has the highest LST, which justifies the steady temperature increase over time due to urban expansion. Also, rock outcrop contributes to UHI because of their thermal conductivity. While water bodies, forests, and vegetation regulate the surface temperature of their immediate surrounding. The level of development of each local government area affects its average temperature, thus confirming the presence of UHI. Finally, the result highlighted rock outcrop, built-up, and fallow land as a significant driver of high urban heat intensity value.
      PubDate: 2023-09-05
      DOI: 10.1007/s41976-023-00091-y
       
  • MPM-Net: a Data-Driven Approach for Forecasting Indian Heatwave and Cold
           Wave Events Using Dehazing and Ensemble Learning Technique

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      Abstract: Abstract This paper proposes a data-driven approach for forecasting Indian heatwave and cold wave events by dehazing high-resolution multispectral remote sensing images. The proposed method utilizes the Multi-Path Multi-Scale Dehazing Network (MPM-Net). This well-trained network provides a precise calculation of atmospheric light and transmission maps, resulting in high-resolution quality restoration of hazy images. The denoised images extract dynamic climatic features integrated with an ensemble learning global protective intelligent algorithm. This approach identifies the complex relationship between daily and past events on the measuring timeline, leading to a solid prediction of the heatwave and cold wave events. Furthermore, it facilitates solid control measures based on the prediction results. Evaluation outcomes reveal that the proposed technique notably reduces prediction errors compared to existing deep learning models. This research significantly contributes to the weather forecasting niche and emphasizes the vital role of dehazing in improving the accuracy of predictions.
      PubDate: 2023-08-01
      DOI: 10.1007/s41976-023-00089-6
       
  • Spatio-temporal Investigation of the Urban Thermal Comfort in Khulna City
           and Surrounding Areas

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      Abstract: Abstract Rapid urbanization is posing a serious threat to high-density urban areas worldwide, particularly in developing countries, with urban thermal environment degradation becoming an impending concern. Such dreadful climatic events force these cities to examine and monitor the spatial variation of the microscale thermal environment in and around the city. Our study aimed to analyze the spatiotemporal variation of urban thermal comfort in the Khulna Development Authority (KDA) area, utilizing the Urban Thermal Field Variance Index (UTFVI). The results indicate a worsening trend in urban thermal comfort, with the land surface temperature (LST) rising by approximately 4.75℃ between 2009 and 2020. During the same time, the minimum and maximum LST increased by 4.85℃ and 5.85℃, respectively, and the Urban Heat Island (UHI) increased by approximately 10%. We also found a strong positive correlation between multiple spectral indices and LST. Our analysis of the built-up area and vegetation indices showed an opposing trend, establishing a gradual decline in vegetation coverage and an increase in building footprint as the apparent causes of the temperature increase. This study's findings regarding the degradation of the thermal environment will guide policymakers to take action and implement measures to mitigate the effects.
      PubDate: 2023-06-23
      DOI: 10.1007/s41976-023-00088-7
       
  • An Automatic Detection of Citrus Fruits and Leaves Diseases Using Enhanced
           Convolutional Neural Network

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      Abstract: Abstract Accurate and timely detection of diseases present in citrus crops is a crucial task for effective crop management and the prevention of yield loss. Traditional methods of disease detection, such as visual inspection, can be time-consuming and prone to human error. In this paper, we propose a novel approach for automatic and accurate disease detection using convolutional neural networks (CNNs). By investigating the numerous images of infected citrus fruits and leaves, the proposed CNN model has attained prominent recognition and classification accuracy results. The proposed Enhanced-CNN (E-CNN) model is trained using diverse collection of three benchmark datasets such as A Citrus Fruits and Leaves Dataset, Citrus Pest and Disease dataset and Citrus Leaf dataset. Due to careful investigation on layer details and image pre-processing techniques, the proposed E-CNN model secures remarkable performance in citrus fruit and leaf disease detection and type classification. The proposed model achieves significant improvement in disease detection and classification performance by securing the average f1 score 92.06, precision score 95.14, recall score 96.67, recognition accuracy 98% and classification accuracy 99%. These results are comparatively higher than earlier approaches and show more than 6% improvements in disease detection and classification performance. We believe that this unique approach has the potential to significantly improve disease management practices in the citrus industry, helping to improve crop yield and reduce the spread of diseases.
      PubDate: 2023-06-19
      DOI: 10.1007/s41976-023-00086-9
       
  • MIMO- Multi Channel Synthetic Aperture Radar System with Higher Visibility
           for Autonomous Vehicles to Identify Mobile and Immobile Objects

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      Abstract: Abstract MIMO-based SAR system provides the autonomous vehicle with a more complete and accurate representation of its environment, allowing it to detect mobile (such as other vehicles on the highway) and immobile (such as road signs and obstacles) objects with greater accuracy. The improved visibility provided by the MIMO-based SAR system enhances the safety and efficiency of the autonomous vehicle, enabling it to make more informed decisions and navigate its surroundings with greater confidence. An autonomous vehicle equipped with a MIMO-based SAR system drives on a highway. The system uses multiple transmit and receive antennas to gather information about the environment and detect objects in the surroundings. The radar signals transmitted by the multiple antennas are reflected by the objects in the environment and collected by the received antennas. The data collected by the antennas are processed using the estimation of signal parameters via rotational invariance techniques (ESPRIT) to produce high-resolution images of the environment, which are then analyzed by the vehicle’s control system. The system can detect objects in the environment with greater accuracy and range than traditional radar systems, allowing the autonomous vehicle to make more informed decisions and navigate its surroundings more confidently.
      PubDate: 2023-06-15
      DOI: 10.1007/s41976-023-00087-8
       
 
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  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 401 - 277 of 277 Journals sorted by number of followers
Jambura Geo Education Journal     Open Access   (Followers: 49)
AGU Advances     Open Access   (Followers: 15)
Arctic     Open Access   (Followers: 9)
Visión Antataura     Open Access   (Followers: 9)
Environmental Research : Climate     Open Access   (Followers: 8)
Environmental and Sustainability Indicators     Open Access   (Followers: 7)
Journal of Public Space     Open Access   (Followers: 7)
Oxford Open Climate Change     Open Access   (Followers: 7)
Evolutionary Human Sciences     Open Access   (Followers: 6)
Geographia     Open Access   (Followers: 6)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal   (Followers: 5)
Remote Sensing in Earth Systems Sciences     Hybrid Journal   (Followers: 5)
Geography and Sustainability     Open Access   (Followers: 5)
Population and Economics     Open Access   (Followers: 5)
Football(s) : Histoire, Culture, Économie, Société     Open Access   (Followers: 5)
The Geographic Base     Open Access   (Followers: 5)
People and Nature     Open Access   (Followers: 4)
Ecosystems and People     Open Access   (Followers: 4)
Earth Systems and Environment     Hybrid Journal   (Followers: 4)
GeoHumanities     Hybrid Journal   (Followers: 4)
Earth System Governance     Open Access   (Followers: 4)
International Journal of Cartography     Hybrid Journal   (Followers: 3)
Advances in Cartography and GIScience of the ICA     Open Access   (Followers: 3)
Progress in Disaster Science     Open Access   (Followers: 3)
Brill Research Perspectives in Map History     Full-text available via subscription   (Followers: 3)
Wellbeing, Space & Society     Open Access   (Followers: 3)
Environmental Science : Atmospheres     Open Access   (Followers: 3)
Nomadic Civilization : Historical Research / Кочевая цивилизация: исторические исследования     Open Access   (Followers: 3)
Plants, People, Planet     Open Access   (Followers: 2)
African Geographical Review     Hybrid Journal   (Followers: 2)
AAG Review of Books     Hybrid Journal   (Followers: 2)
Area Development and Policy     Hybrid Journal   (Followers: 2)
Asian Journal of Geographical Research     Open Access   (Followers: 2)
Biogeographia : The Journal of Integrative Biogeography     Open Access   (Followers: 2)
Computational Urban Science     Open Access   (Followers: 2)
Journal of the Bulgarian Geographical Society     Open Access   (Followers: 2)
Załącznik Kulturoznawczy / Cultural Studies Appendix     Open Access   (Followers: 2)
KN : Journal of Cartography and Geographic Information     Hybrid Journal   (Followers: 1)
Resilience : International Policies, Practices and Discourses     Hybrid Journal   (Followers: 1)
Papers in Applied Geography     Hybrid Journal   (Followers: 1)
Journal of Geography, Environment and Earth Science International     Open Access   (Followers: 1)
Agronomía & Ambiente     Open Access   (Followers: 1)
Offa's Dyke Journal     Open Access   (Followers: 1)
Regional Studies Journal     Open Access   (Followers: 1)
UNM Geographic Journal     Open Access   (Followers: 1)
Studies in African Languages and Cultures     Open Access   (Followers: 1)
Revue de géographie historique     Open Access   (Followers: 1)
Boletín de Estudios Geográficos     Open Access  
Proyección : Estudios Geográficos y de Ordenamiento Territorial     Open Access  
Parks Stewardship Forum     Open Access  
Scandinavistica Vilnensis     Open Access  
East/West : Journal of Ukrainian Studies     Open Access  
Tidsskrift for Kortlægning og Arealforvaltning     Open Access  
Les Cahiers d’Afrique de l’Est     Open Access  
Mappemonde : Revue trimestrielle sur l'image géographique et les formes du territoire     Open Access  
IBEROAMERICANA. América Latina - España - Portugal     Open Access  
Scripta Nova : Revista Electrónica de Geografía y Ciencias Sociales     Open Access  
Coolabah     Open Access  
Biblio3W : Revista Bibliográfica de Geografía y Ciencias Sociales     Open Access  
Ar@cne     Open Access  
Journal of Cape Verdean Studies     Open Access  
Punto Sur : Revista de Geografía     Open Access  
RIEM : Revista Internacional de Estudios Migratorios     Open Access  
Revista Brasileira de Meio Ambiente     Open Access  
Sasdaya : Gadjah Mada Journal of Humanities     Open Access  
Revista Eletrônica : Tempo - Técnica - Território / Eletronic Magazine : Time - Technique - Territory     Open Access  
Periódico Eletrônico Geobaobás     Open Access  
PatryTer     Open Access  
Espaço Aberto     Open Access  
AbeÁfrica : Revista da Associação Brasileira de Estudos Africanos     Open Access  
Mosoliya Studies     Open Access  
New Approaches in Sport Sciences     Open Access  
International Journal of Geoheritage and Parks     Open Access  
Watershed Ecology and the Environment     Open Access  
Sémata : Ciencias Sociais e Humanidades     Full-text available via subscription  
Geoingá : Revista do Programa de Pós-Graduação em Geografia     Open Access  
Revista Uruguaya de Antropología y Etnografía     Open Access  
Rocznik Toruński     Open Access  
Southern African Journal of Environmental Education     Open Access  
Proceedings of the ICA     Open Access  
Mediterranean Geoscience Reviews     Hybrid Journal  
Journal of Geospatial Applications in Natural Resources     Open Access  
Revista Geoaraguaia     Open Access  
TRIM. Tordesillas : Revista de investigación multidisciplinar     Open Access  
Geopauta : Revista de Geografia da Universidade Estadual do Sudoeste da Bahia     Open Access  

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JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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