1,766 results on '"Google Earth"'
Search Results
2. LandslideNet: A landslide semantic segmentation network based on single-temporal optical remote sensing images.
- Author
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Zhu, Xinyu, Zhang, Zhihua, He, Yi, Wang, Wei, Yang, Shuwen, and Hou, Yuhao
- Subjects
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OPTICAL remote sensing , *OPTICAL resolution , *DEEP learning , *REMOTE sensing , *LANDSLIDES , *HAZARD mitigation - Abstract
Swiftly and accurately acquiring the spatial distribution, location, and magnitude of landslides while documenting them in a landslide cataloging database can furnish crucial information for precise disaster mitigation measures and secondary hazard prevention. The extraction of landslides using existing semantic segmentation algorithms may give rise to issues such as false detection and missed detection due to the diverse shape and texture features of landslides in remote sensing images, the abundance of spectral features, and the complexity of the environment. In this article, we proposed LandslideNet, a novel model specifically designed for accurate segmentation of landslides in single-temporal high spatial resolution optical remote sensing images. By constructing a landslide image dataset and employing the LandslideNet model, we successfully identify and segment landslides with high precision. Quantitative experimental results demonstrate that our LandslideNet achieves superior performance compared to widely used semantic segmentation models including U-Net, PSPNet, Deeplabv3+, HRNetv2, Segformer and GELAN-c with F1-score , mIoU , FWIoU , mPA and OA reaching 72.53 %, 78.41 %, 99.86 %, 83.33 % and 99.93 % respectively. Moreover, our model exhibits lower complexity while demonstrating improved capability in detecting landslides with complex shapes and different sizes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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3. From earth to sky: Large-scale archaeological settlement patterns in southernmost South America based on ground surveys, UAV LiDAR, and open access satellite imagery.
- Author
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Eidshaug, Jo Sindre P., Bjerck, Hein B., Breivik, Heidi M., Risbøl, Ole, Tivoli, Angélica M., and Zangrando, Atilio Francisco J.
- Abstract
AbstractLarge-scale archaeological surveys in coastal areas can be challenging. Factors such as remoteness, logistics, accessibility, and weather conditions can render fieldwork time-consuming and expensive in rugged archipelagic settings. Remote sensing encompasses several methods that can be used for increasing the survey area and promoting new archaeological insights. Moreover, it can be an affordable option. Google Earth (among others) collects aerial and satellite imagery from third parties and makes them freely available from anywhere in the world. In southern Tierra del Fuego, clusters of ring-shaped shell middens are conspicuous archaeological features. They are abundant in the central part of the Beagle Channel, but it has not been feasible to study their distribution across the Fuegian archipelago because we lack data from many remote areas where fieldwork is not easily conducted. Remote sensing provides a viable option for large-scale surveying due to the general visibility of these midden clusters. In this study, we establish confidence in open access satellite imagery identifications of ring-shaped middens in Tierra del Fuego based on comparisons with UAV LiDAR and conventional ground surveys, arguing that the results are appropriate for studies of large-scale trends. We provide the results from a full desk-based survey of satellite imagery provided by Google Earth, ESRI, and Microsoft Bing, covering 3000 km of coastline up to 1 km inland in southern Tierra del Fuego. Moreover, we determine the geographical distribution of ring-shaped middens and explore large-scale trends in coastal settlement patterns and landscape use—showing that ring-shaped middens are not evenly distributed across the Fuegian archipelago but strongly related to the sheltered areas of the Beagle and Murray Channels. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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4. A digital, inquiry-based lesson on the rain shadow effect for middle school.
- Author
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Ryan, Thomas R. and Cole, Merryn L.
- Subjects
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INQUIRY-based learning , *AIR masses , *MIDDLE schools , *CLIMATE change , *ACADEMIC achievement - Abstract
AbstractThis article introduces a middle-school level, NGSS-aligned, inquiry-based approach to teaching weather and climate concepts in grades six through eight using free online resources including Google Earth. Inquiry-based learning has been shown to improve student achievement, but Earth’s climate systems are complex and macroscopic, making inquiry-based teaching difficult. This tends to result in a teacher-centric approach to teaching climate in middle school, where lectures and authoritative videos predominate. Unfortunately, in this educational setting, many people develop climate misconceptions and tend to view climate change as a minor issue or a matter of personal belief. This lesson offers an alternative approach in which technology provides a scaffold to inquiry-based learning. After an unusual weather phenomenon such as a snowman in the desert is introduced, students use Google Earth to explore the contrasting forest and desert found on opposite sides of some mountain ranges. With Nullschool Earth, students explore daily historical wind data to model prevailing winds and develop an understanding of how patterns in the motion of air masses create the rain shadow effect. This lesson helps students begin to model the complex Earth systems that create climate and to understand the distinction between weather and climate. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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5. The Effect of Applying A Problem-Based Learning Model Using Google Earth on Understanding Hydrometeorological Disaster Mitigation at SMAN 1 Tanjung Palas Tengah.
- Author
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Bashori, Imam and Khotimah, Nurul
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HAZARD mitigation , *EXPERIMENTAL design , *SAMPLING (Process) , *JUDGMENT sampling , *SCIENTIFIC observation - Abstract
This study aims to reveal whether there is a positive influence in the application of the problem-based learning model using google earth media on the understanding of hydrometeorological disaster mitigation in class XI of SMA N1 Tanjung Palas Tengah. The research design used in this study is a nonequivalent control group design with a quasi-experimental research method (Quasi Experiment). The sampling technique used in this research is purposive sample. The research instruments used are test instruments in the form of multiple-choice questions, as well as non-test instruments in the form of observation and documentation. The results of the study showed that there was a positive influence in the application of the problem-based learning model using google earth media on the understanding of hydrometeorological disaster mitigation in class XI at SMAN 1 Tanjung Palas Tengah in the form of a significant increase in the average class score. This is based on the results of N-Gain obtained a value of 66.96% and the t-test obtained a value of Sig. (2-tailed) of 0.001. This is also supported by the results of observational studies that show a positive response from the research subjects. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Mobile Smartphones as Tools for ICT Integration in Geography Teaching.
- Author
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Gubevu, Bongumusa Welcome Selby and Mncube, Vusumzi Sthembiso
- Subjects
CAREER development ,DIGITAL learning ,CELL phones ,VIRTUAL reality ,JUDGMENT sampling - Abstract
This article seeks to reflect on the opportunities that mobile smartphones (MSPs) present as ICT integration tools in teaching geography. The more extensive study, underpinned by the Professional Development Framework for Digital Learning (PDFDL) in ICT integration, employed a qualitative research approach. Lensed by the Professional Development Framework for Digital Learning (PDFDL), the article used the qualitative approach to garner insights from the participants regarding using MSPs as tools to integrate ICT in geography teaching. Data collection tools included interviews, observations, and document reviews. Researchers sampled (n = 4) schools, interviewed and observed (n = 13) teachers, and interviewed (n = 10) learners and (n = 8) parents in the province of KwaZulu-Natal. Furthermore, they used a purposive sampling technique to access the participants, basing the research on the premise that MSPs promote virtual reality for an array of learners. As the findings revealed, although some participants viewed the use of MSPs as a distractor in the learning space, teachers felt compelled to heed the call to modify their teaching pedagogies, such that they integrated mobile phones fruitfully in their teaching. The findings further revealed that such a paradigm shift would benefit homeschooling and facilitate a dual teaching mode at learning institutions. Curriculum planners are responsible for helping teachers accept that uncertainty is the only certainty about the future, considering the volatility, uncertainty, complexity, and augmentation (VUCA) challenges brought on by the COVID-19 pandemic. Extended lockdown periods accelerated the use of MSPs in teaching, requiring every stakeholder in the educational space to become a life-long learner by using a range of technologies and platforms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. Assessing Geospatial Accuracy in Mapping Applications: A Focus on Google Earth.
- Author
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Alqahtani, Thaar
- Subjects
VECTOR data ,RESEARCH personnel ,TEST methods ,QUANTITATIVE research ,UNIVERSITY research - Abstract
Google Earth, among other online mapping platforms, offers an interactive mapping platform that has become indispensable for academic and research applications. It serves as a primary reference and a foundational tool for map creation, providing open-source, cost-free imagery that meets the user needs of the mapping community. As a contemporary repository of high-resolution images of Earth's landmass, Google Earth has vast potential for scientific exploration and remains an underexploited resource. Its rapid expansion and consistent reliability make it a favored source for mapping and routing tasks. However, this research underscores the crucial aspect of Google Earth's positional accuracy, which is at the heart of this study. A comparative analysis between the positional accuracy of Google Earth and traditional ground surveying maps was conducted. The Wilcoxon rank test and quantitative methods were used to evaluate coordinate discrepancies, revealing significant discrepancies between the two datasets. This study aims to provide a rigorous assessment of Google Earth's utility and accuracy in scientific and academic contexts, emphasizing its role and reliability as a critical resource for researchers and practitioners in the field of mapping. The results revealed displacement changes in both the northing and the easting coordinates. For the northing coordinates, the displacement increases when moving eastward and decreases when moving westward. For the easting coordinates, the displacement increases when moving northward and decreases when moving southward. This pattern highlights spatial discrepancies and the varying impact of location on the dataset's accuracy, emphasizing the need for targeted corrections to enhance data accuracy. These key findings provide valuable insights that could significantly contribute to optimizing mapping practices and efficiently exploiting this vast, yet underexplored, digital resource. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR).
- Author
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Prabandari, Amanah Anggun and Mandini Manessa, Masita Dwi
- Abstract
Copyright of Jurnal Tanah dan Sumberdaya Lahan is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
9. A Spatio-temporal Change Analysis of Umri Landslide: A Case Study from Haryana, India
- Author
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Prakasam, C., Sarkar, Pranati, Shaw, Rajib, Series Editor, Chatterjee, Uday, editor, Lalmalsawmzauva, K.C., editor, Biswas, Brototi, editor, and Pal, Subodh Chandra, editor
- Published
- 2024
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10. Hydraulic Modelling for Flood Inundation Mapping to Assess the Impact of Check Dam in Araniyar River
- Author
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Muthiah, Krishnaveni, Ganapathy, Ragavi, Subramanian, Vijai, Kostianoy, Andrey G., Series Editor, Carpenter, Angela, Editorial Board Member, Younos, Tamim, Editorial Board Member, Scozzari, Andrea, Editorial Board Member, Vignudelli, Stefano, Editorial Board Member, Kouraev, Alexei, Editorial Board Member, Gourbesville, Philippe, editor, and Caignaert, Guy, editor
- Published
- 2024
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11. Radio Propagation Modeling and Simulation Using Ray Tracing
- Author
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Yun, Zhengqing, Iskander, Magdy F., Lakhtakia, Akhlesh, editor, Furse, Cynthia M., editor, and Mackay, Tom G., editor
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- 2024
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12. Monitoring Tree Cover Change Using the CuSum Algorithm: A Case Study in the Southern Western Ghats of Kerala
- Author
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Anjitha, A. S., Nitish Sri Surya, N., Reddy, C. Sudhakar, and Asok, Smitha V.
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- 2024
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13. Is ward-level calculation of urban green space availability important?--A case study on Vellore city, India, using the histogram-based spectral discrimination approach.
- Author
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Gaikadi, Sangeetha and Kumar, S. Vasantha
- Subjects
PUBLIC spaces ,PER capita ,SUPPORT vector machines ,URBAN planners - Abstract
How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and objectbased methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20-40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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14. The Spatial Distribution Characteristics and Possible Influencing Factors of Landslide Disasters in the Zhaotong Area, Yunnan Province of China.
- Author
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Wang, Wantong, Ma, Siyuan, Yan, Wujian, and Yuan, Renmao
- Subjects
LANDSLIDES ,RIVER channels ,DISASTERS ,REMOTE-sensing images ,RAINFALL ,DISASTER relief ,DATABASES - Abstract
The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10° to 40° and near river channels. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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15. A Box in the Desert: Using Open Access Satellite Imagery to Map the 151st Infantry Brigade's Field Defences on the Gazala Line, 1942.
- Author
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Gregory, Derwin
- Subjects
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REMOTE-sensing images , *INFANTRY , *LIBYAN Conflict, 2011- , *WORLD War II , *DESERTS - Abstract
At the end of May 1942, the Axis Afrika Korps launched an assault on the Allied Eighth Army's defences of the Gazala Line in Libya: the Gazala Line was located to the west of Tobruk, and stretched south into the Libyan desert. By the time the Axis attacked the Gazala Line, the Allies' defences consisted of a series of boxes which were defended by the different brigades of the Eighth Army. In this article, the results of a survey of the field defences of the 151st Infantry Brigade using open access satellite imagery is discussed. This research will demonstrate that the 151st Infantry Brigade's box was primarily designed to defend against a frontal assault. In addition, the survey demonstrates the value of open access satellite imagery for understanding Second World War desert battles. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
16. Vertical Accuracy of Google Earth Data.
- Author
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El-Ashmawy, Khalid L. A.
- Subjects
GEOGRAPHIC information systems ,GLOBAL Positioning System ,STANDARD deviations ,LAND use mapping ,CADASTRAL maps - Abstract
Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. When one thing leads to another: <italic>Pole Station Antarctica: December 15th 8am 1956</italic>.
- Author
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MacDonald, Kristie
- Abstract
This short paper illuminates a research-creation project titled
Pole Station Antarctica: December 15th 8am 1956 (2012 – Ongoing). This artwork is an evolving collection of envelopes sharing the same postmark – mailed from the South Pole at the same time, on the same day. As the collection grows its shifting contents illustrate with increasing clarity global flows to and from the South Pole during the mid-twentieth century. When observed en mass, the cumulative minutia of place names, postage stamp designs, and decorative embellishments expose the complicated cultural and historical circumstances of this particular mailing – pointing towards America’s geopolitical motivations at the height of the cold war, the spread of Second World War military logistics and transportation technologies in the post war era, and the effects of navigation technologies on remote areas of Antarctica. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
18. FINDING COLONIES OF BLACK-HEADED GULLS CHROICOCEPHALUS RIDIBUNDUS USING GOOGLE EARTH.
- Author
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VALLE, ROBERTO G., CORREGIDOR-CASTRO, ALEJANDRO, and SCARTON, FRANCESCO
- Subjects
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GULLS , *COLONIAL birds , *ARTIFICIAL satellites , *REMOTE-sensing images , *FISH farming , *IMAGE analysis - Abstract
We explore the possibility of identifying Black-headed Gull Chroicocephalus ridibundus colonies in the saltmarshes of the Lagoon of Venice, Italy, using Google Earth satellite images. One reproductive season was considered (June 2017), based on the images available on Google Earth. This species builds nests clustered around tidal pools and tidal creeks, providing a dark background to reveal the white gulls. Images of the southern part of the lagoon (excluding fish farms) were analyzed by dividing it into sectors (n = 403) using the Google Earth grid at an elevation of 100 m above ground level. The results of the satellite count were compared with field data collected in the same season. Image analysis revealed five colonies, with excellent sensitivity (100%) but only good specificity (88%), due to the presence of numerous clear areolae falling within the spectral range of nests; these consisted of plastic litter and dry, stranded vegetation. Overall, our results indicate that Black-headed Gull colonies can be found in marsh-island habitat using Google Earth. While this approach presents sub-optimal specificity due to both the abundance of whitish debris and low image resolution, future developments in software capabilities hold the potential to overcome these limitations and enhance the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
19. Feasibility Study of Rainwater Harvesting from Large Rooftops (Case Study: Ahvaz City, Iran)
- Author
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Amirreza Shahriari, Mojtaba Pili dezfouli, Mahziar Basitnejad, Tooba Taheri Talavari, and Mohammad Amin Maddah
- Subjects
ahvaz ,google earth ,runoff ,water saving ,Water supply for domestic and industrial purposes ,TD201-500 - Abstract
Humanity is currently facing one of its greatest challenges a shortage of renewable and accessible water resources. One of the best and most cost-effective solutions for sustainable water resource utilization is rainwater harvesting system (RWS). Rooftops of buildings can act as water micro-catchment surfaces and store rainwater before it turns into runoff losses. In order to investigate the potential of rainwater conservation and recycling, in this study, the feasibility of utilizing rainwater harvesting from rooftops of public and large buildings of Ahvaz city was evaluated. To achieve the goal, we utilized annual precipitation data, the Statistical Yearbook of the Ahvaz Metropolitan Area, and satellite imagery from Google Earth. Large buildings were categorized into 6 groups, and the total rooftop area suitable for implementing a rainwater harvesting system was estimated. It was calculated that approximately 222,708 m3 of rainwater could be harvested annually from the rooftops of these buildings. The volume of harvestable water was distributed among different building categories, with industrial sites having the largest share, followed by governmental offices and educational centers. The results form the Sotnikova equations showed that by help of RWS could meet 11.42% of Ahvaz's industrial water demand, leading to a reduction in dependency on external water sources and economic savings. Recycled rainwater could supply up to 34.56% of the water demand for urban parks in Ahvaz.
- Published
- 2024
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20. An Approach to Large-Scale Cement Plant Detection Using Multisource Remote Sensing Imagery.
- Author
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Li, Tianzhu, Ma, Caihong, Lv, Yongze, Liao, Ruilin, Yang, Jin, and Liu, Jianbo
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CEMENT plants , *GREENHOUSE gases , *CARBON emissions , *CEMENT industries , *REMOTE sensing , *ENVIRONMENTAL protection - Abstract
The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world's carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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21. The Yinshan Mountains Record over 10,000 Landslides.
- Author
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Sun, Jingjing, Xu, Chong, Feng, Liye, Li, Lei, Zhang, Xuewei, and Yang, Wentao
- Subjects
LANDSLIDES ,REMOTE sensing ,HUMAN-computer interaction - Abstract
China boasts a vast expanse of mountainous terrain, characterized by intricate geological conditions and structural features, resulting in frequent geological disasters. Among these, landslides, as prototypical geological hazards, pose significant threats to both lives and property. Consequently, conducting a comprehensive landslide inventory in mountainous regions is imperative for current research. This study concentrates on the Yinshan Mountains, an ancient fault-block mountain range spanning east–west in the central Inner Mongolia Autonomous Region, extending from Langshan Mountains in the west to Damaqun Mountains in the east, with the narrow sense Xiao–Yin Mountains District in between. Employing multi-temporal high-resolution remote sensing images from Google Earth, this study conducted visual interpretation, identifying 10,968 landslides in the Yinshan area, encompassing a total area of 308.94 km
2 . The largest landslide occupies 2.95 km2 , while the smallest covers 84.47 m2 . Specifically, the Langshan area comprises 331 landslides with a total area of 11.96 km2 , the narrow sense Xiao–Yin Mountains include 3393 landslides covering 64.13 km2 , and the Manhan Mountains, Damaqun Mountains, and adjacent areas account for 7244 landslides over a total area of 232.85 km2 . This research not only contributes to global landslide cataloging initiatives but also serves as a robust foundation for future geohazard prevention and management efforts. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
22. COASTLINE CHANGES OF DALIAN CITY BASED ON GOOGLE EARTH HISTORICAL IMAGES.
- Author
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JIANG Shan, ZHOU Li, MA Hong-wei, ZHU Wei, NI Jin, MA Shi-min, ZHANG Jing, and HUAN Heng-fei
- Subjects
COASTAL changes ,RECLAMATION of land ,COASTS ,GEOGRAPHIC information systems ,ENVIRONMENTAL sciences ,ZONING - Abstract
To study the environmental changes and evolution rule of coastal zones in Dalian City in the last 40 years, the paper uses GIS technology to extract and analyze the coastline information during 1984-2018 based on the historical images from Google Earth. The results show that the coastline has grown in three stages: low-high-low, separated by the years of 2004 and 2014. The growth rate of sea reclamation is highly correlated to the GDP growth curve. Spatially, the coastline changes are mainly distributed in the bay and estuary areas, with the main types of land reclamation and sea mariculture. The land reclamation area is mainly concentrated in the central and southern coast of municipal district and Changxing Island Development Zone, while the sea reclamation area is scattered in Wafangdian, Pulandian and Zhuanghe areas in the north and east. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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23. 结合多源专题数据和目视解译的大区域密集湿地样本数据生产.
- Author
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彭, 凯锋, 蒋, 卫国, 侯, 鹏, 凌, 子燕, 牛, 振国, 毛, 德华, and 黄, 卓
- Abstract
Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
24. РАЗРАБОТКА АЛГОРИТМА ДОЛГОВРЕМЕННОГО УЧЕТА ИСПОЛЬЗОВАНИЯ СЕЛЬХОЗЗЕМЕЛЬ С ПРИМЕНЕНИЕМ ГИС-ТЕХНОЛОГИИ И ПРОМЕЖУТОЧНЫХ ДАННЫХ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ
- Author
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Шогелова, Н. Т., Сартин, С. А., Смаилов, Н. М., Толеубекова, Ж. З., and Первиков, А. В.
- Abstract
The development of algorithms for agricultural land management based on geographic information systems is a promising direction in the field of precision agriculture. The application of GIS enables the collection, processing, and analysis of geospatial data on land parcels, which facilitates more accurate and efficient planning of agricultural activities. This method contributes to improving the efficiency and sustainability of the agrarian sector, including by optimizing the use of resources and reducing the negative impact on the environment, and contributes to increasing yields and improving the quality of agro-products. The present article is devoted to the development of methodology of planned arrangement of agricultural land using modern geoinformation technologies. The article presents key stages and algorithms necessary for the organization of crop rotation and effective management of agricultural land. Using the Google Earth platform and remote sensing data, the authors analyze the land plot, define goals and objectives, develop a crop rotation scheme, and select crops. The article presents a specific algorithm for delineating the boundaries of agricultural fields, cataloging them, and evaluating the stage of crop rotation. The results of the research allow optimizing the use of land resources and increasing crop yields. The methodology proposed in the article has important practical significance for agricultural enterprises and can be used to improve farmland management. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Utilization of Satellite Imagery for Mapping the Distribution of Seagrass on Buhung Pitue Island.
- Author
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Dwi Rosalina, Arafat, Yasser, Jamil, Khairul, Wahda, A. Nurtasya, Rombe, Katarina Hesty, Khasanah, Ruly Isfatul, and Dini Sofarini
- Subjects
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REMOTE-sensing images , *SEAGRASSES , *SEAGRASS restoration , *COVID-19 pandemic , *OCEAN temperature , *REMOTE sensing - Abstract
Buhung Pitue Island has seagrass beds those which are spread almost evenly along its coast. Research using remote sensing technology in an effort to support seagrass conservation in Indonesia needs to be carried out. Spatial data is relatively easy to obtain because there are many types of images with various spatial resolutions. The image can be obtained on google earth. Analysis of the distribution of seagrass areas was obtained by digitizing on screen in ArcGIS software, namely in seagrass areas where the boundaries are known. Digitizing is conducted by enlarging the seagrass area in the downloaded image, performing radiometric and geometric corrections, and digitizing to create a shapefile (shp) storing the location, shape, and attributes of geographic features. The seagrass distribution area of Buhung Pitue Island was of 36.5 Ha in 2014 and was of 39.6 in 2021. The rate of change in area from 2014 to 2021 was of 0.085% (an increase of 3.1 ha). The distribution area of seagrass has increased due to natural factors and restrictions on human activities during the COVID-19 pandemic. In addition, another factor supporting the increase in seagrass distribution is the abundance of Enhalus acoroides seagrass species growing and spreading over long distances. The sea surface temperature was high, which was 30.37 °C, while the current speed was categorized as slow because it was around 0.01 m/s. Although the results are obtained from high-resolution imagery, an accuracy test still needs to be conducted. [ABSTRACT FROM AUTHOR]
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- 2024
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26. Elaboración de un mapa temático de los cultivos agroecológicos mediante el uso de Google Earth.
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Vélez Duque, Pedro, Centanaro Quiroz, Paulo, Javier Martillo, Juan, and Alvarado Barzallo, Arturo
- Abstract
Copyright of Salud, Ciencia y Tecnología is the property of Fundacion Salud, Ciencia y Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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27. High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach
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Lulu Qi, Yue Zhou, Kristof Van Oost, Jiamin Ma, Bas van Wesemael, and Pu Shi
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Soil erosion ,Sentinel-2 ,Bare soil composite ,Soil spectroscopy ,Google Earth ,Unsupervised classification ,Science - Abstract
Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.
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- 2024
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28. Global lokalisering
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Gustaf Marcus
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Michel Houellebecq ,Marie Darrieussecq ,Google Earth ,Google Maps ,Google Street View ,GeoGuessr ,Language and Literature - Abstract
Global localization. Google Earth and the experience of place attachment in GeoGuessr, Houellebecq and Darrieussecq The purpose of this article is to analyze literary texts and other cultural practices that relate to or use content from Google Earth and, more generally, to outline what I call a contemporary glocal experience of place. The article discusses the browser game GeoGuessr and the interactive music video “The Wilderness Downtown” by Arcade Fire, along with the novel La Mer à l’envers by Marie Darrieussecq, and different works, including photographs and poems, by Michel Houellebecq. In all these instances, the transmissions, slippages, and tensions between a situated and embodied experience of place and global technological systems play a prominent role. The article shows how GeoGuessr and “The Wilderness Downtown” explore this tension from opposite points of departure: while the game fosters a sense of place within locales that are essentially unknown to the player, the music video integrates images from places that are related to the viewer’s personal memories into an otherwise impersonal animated video. In turn, Darrieussecq’s novel combines and narrativizes these opposing perspectives. Here, Calais represents a globalized space that can only be understood through glocalizing technologies, such as geoposititioning and interactive mapping. But Clève, the hometown of the protagonist, becomes unmoored through the same interaction with these technologies. Finally, the article highlights the “Houellebecq perspective” in novels, poems, and photographs. In these recurring scenes, the world is seen from above (through satellite imagery, on maps, or through airplanes windows). They revolve around the tension between the aerial view and the “horizon structure”, which permits the subject to place itself in the world and in relation to others.
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- 2024
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29. Is ward-level calculation of urban green space availability important?—A case study on Vellore city, India, using the histogram-based spectral discrimination approach
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Sangeetha Gaikadi and S. Vasantha Kumar
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urban green space ,Google Earth ,histogram analysis ,spectral discrimination ,support vector machine ,3D LIDAR ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and object-based methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20–40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees.
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- 2024
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30. Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation
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Jiayu Cheng, Yihang Zhu, Yiying Zhao, Tong Li, Miaojin Chen, Qinan Sun, Qing Gu, and Xiaobin Zhang
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Remote sensing ,Peach orchard mapping ,Unmanned aerial vehicle ,Google Earth ,Sentinel-2 ,Semantic segmentation ,Physical geography ,GB3-5030 ,Environmental sciences ,GE1-350 - Abstract
Peach cultivation holds a significant economic importance, and obtaining the spatial distribution of peach orchards is helpful for yield prediction and precision agriculture. In this study, we introduce a new U-Net semantic segmentation model, utilizing ResNet50 as a backbone network, augmented with an Efficient Multi-Scale Attention (EMA) mechanism module and a LayerScale adaptive scaling parameter. To address style differences between images from Unmanned Aerial Vehicle (UAV), Google Earth, and Sentinel-2 satellite, we incorporate Cycle-Consistent Generative Adversarial Networks (CycleGAN). This synthesis ensures that UAV images conform to a comparable style found in Google Earth and Sentinel-2 images, while feature details of high spatial resolution UAV images are transferred to Google Earth and Sentinel-2 images through transfer learning. The results demonstrate that using ResNet50 as a backbone network for the U-Net model yields higher accuracy compared to using VGG16 for the U-Net model. Specifically, the Mean Intersection over Union (MIoU) values for UAV and Sentinel-2 images are higher by 0.49 % and 0.95 %, respectively. The MIoU values for UAV, Google Earth, and Sentinel-2 images increased by 0.87 %, 1.71 %, and 1.74 %, respectively, with the introduction of EMA. Additionally, with the introduction of LayerScale adaptive scaling parameters, the MIoU values increased by 0.31 %, 0.33 %, and 1.44 %, respectively, further enhancing the segmentation accuracy of the model. After applying CycleGAN and transfer learning, the MIoU increased by 1.02 %, 0.15 %, and 1.57 % for UAV, Google Earth, and Sentinel-2 images, respectively, resulting in MIoU values of 97.39 %, 92.08 %, and 84.54 %. The comparative analysis with DeepLabV3+, PSPNet, and HRNet models demonstrates the superior mapping performance of the proposed method. Moreover, the method exhibits good generalization and mapping speed across six test sites in the research area. Overall, this approach ensures high precision and efficiency in peach orchard mapping, accommodating various spatial resolutions, and holds potential for addressing diverse requirements in peach orchard mapping applications.
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- 2024
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31. INOVASI PEMETAAN NAGARI DENGAN TEKNOLOGI GOOGLE EARTH DI PASIR TALANG SELATAN KECAMATAN SUNGAI PAGU KABUPATEN SOLOK SELATAN
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Danny Hidayat, Fauziah Putri Khuzaimah, and Rafly Putra Pratama
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survey ,peta ,nagari ,administrasi ,google earth ,Technology - Abstract
Nagari Pasir Talang Selatan belum memiliki peta nagari yang sempurna sebagai penunjang administrasi kelengkapan kantor dan data nagari. Peta Nagari merupakan hal yang mutlak dimana harus dimiliki oleh masing-masing Nagari. Oleh karena itu, pemerintah Nagari Pasir Talang Selatan harus memiliki sebuah peta Nagari untuk membantu dalam pemerintahan Nagari. Peta Nagari sangat membantu dalam mendukung perencanaan pembangunan suatu wilayah dan sebagai dasar dalam pembaharuan rencana detail dalam tata ruang kota untuk itu maka diperlukan program kerja pembuatan peta Nagari ini. Tujuan kegiatan ini membahas pemanfaatan Google Earth sebagai alat yang efektif dalam pembuatan peta administrasi untuk Nagari Pasir Talang Selatan. Hal ini dapat memberikan kontribusi yang signifikan dalam penyediaan data geospasial yang akurat dan informasi administrasi Nagari. Kegiatan ini dilaksanakan di Nagari Pasir Talang Selatan Kecamatan Sungai Pagu Kabupaten Solok Selatan, Provinsi Sumatera Barat. Metode pelaksanaan dalam pembuatan peta nagari yaitu dimulai dari survei lokasi, menentukan titik koordinat, dan menarik garis peta yang dibuat sesuai dengan yang tergambar pada peta di google erath. Penggunaan teknologi Google Earth, dapat menghasilkan peta administrasi yang akurat dan terkini. Hasil kegiatan ini terlaksananya pembuatan peta Nagari sampai selesai dengan sempurna, yang dapat membantu dalam mempermudah pihak-pihak yang memerlukan informasi mengenai Nagari tersebut dan memudahkan pihak pemerintahan Nagari untuk memantau penggunaan lahan di Nagari tersebut. Program ini merupakan langkah positif dalam pengembangan Nagari Pasir Talang Selatan bahkan membawa manfaat besar bagi masyarakat dan pemerintah daerah. Untuk meningkatkan efektivitas program ini, perlu dilakukan pengembangan dan pemutakhiran peta nagari berdasarkan perkembangan di wilayah tersebut.
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- 2023
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32. Mobile Smartphones as Tools for ICT Integration in Geography Teaching
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Bongumusa Welcome Selby Gubevu and Vusumzi Sthembiso Mncube
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animation ,augmentation ,Google Earth ,mobile smartphones ,tablets ,VUCA ,Education - Abstract
This article seeks to reflect on the opportunities that mobile smartphones (MSPs) present as ICT integration tools in teaching geography. The more extensive study, underpinned by the Professional Development Framework for Digital Learning (PDFDL) in ICT integration, employed a qualitative research approach. Lensed by the Professional Development Framework for Digital Learning (PDFDL), the article used the qualitative approach to garner insights from the participants regarding using MSPs as tools to integrate ICT in geography teaching. Data collection tools included interviews, observations, and document reviews. Researchers sampled (n = 4) schools, interviewed and observed (n = 13) teachers, and interviewed (n = 10) learners and (n = 8) parents in the province of KwaZulu-Natal. Furthermore, they used a purposive sampling technique to access the participants, basing the research on the premise that MSPs promote virtual reality for an array of learners. As the findings revealed, although some participants viewed the use of MSPs as a distractor in the learning space, teachers felt compelled to heed the call to modify their teaching pedagogies, such that they integrated mobile phones fruitfully in their teaching. The findings further revealed that such a paradigm shift would benefit homeschooling and facilitate a dual teaching mode at learning institutions. Curriculum planners are responsible for helping teachers accept that uncertainty is the only certainty about the future, considering the volatility, uncertainty, complexity, and augmentation (VUCA) challenges brought on by the COVID-19 pandemic. Extended lockdown periods accelerated the use of MSPs in teaching, requiring every stakeholder in the educational space to become a life-long learner by using a range of technologies and platforms.
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- 2024
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33. Introduction
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Goudie, Andrew and Goudie, Andrew
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- 2023
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34. Real World Path Generation for Non-holonomic Systems with Obstacle Avoidance Using RRT* and Google Earth
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Sapthagirivasan, S., Seshath, M., Srivarshan, S., Kumar, T. V. K. Sushil, Karakoc, T. Hikmet, Series Editor, Colpan, C Ozgur, Series Editor, Dalkiran, Alper, Series Editor, Letnik, Tomislav, editor, Marksel, Maršenka, editor, Ekmekci, Ismail, editor, and Ercan, Ali Haydar, editor
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- 2023
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35. Testing the Application of Integrated Digital Photography and Image Processing (IDIP) to Calculate the Characteristics of Urban Greening Over Time: A Pilot Project for Oxford, UK
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Thornbush, Mary J. and Oncel, Suphi S., editor
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- 2023
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36. Evaluation of the Efficiency of SfM-Photogrammetry in Obtaining DEM from Google Earth Images
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Ahmed A Elashiry and Omar Al Khalil
- Subjects
digital elevation model (dem) ,srtm ,structure from motion (sfm) ,google earth ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
In this study, we seek to evaluate the efficiency of SfM Photogrammetry in obtaining Digital Elevation Model (DEM) from the free Google Earth browser, by simulating the aerial photography of the terrain displayed by this browser, calculating the resulting block of images, and obtaining DEM, and evaluate its accuracy to know the type of works in which this model can be used. Control data for image orientation are provided using Google Earth and free online services.The results showed that the huge number of dense point cloud generated by applying SfM to Google Earth images has contributed to obtaining a DEM with a vertical accuracy better than the vertical accuracy of the free digital elevation model SRTM1. The achieved vertical accuracy of the DEM produced by SfM was 3.58 m, whereas the vertical accuracy of the free SRTM1 was 4.65 m compared to a DEM derived 1/25000 scale topographic map. This type of DEM can be used in works that do not require high vertical accuracy such as hydrological works (watersheds) dealing with SRTM models.
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- 2023
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37. ROUTE ANALYSIS LEARNING MODULE BASED ON GOOGLE EARTH ROAD CLASSIFICATION FOR GEOMATICS COURSE
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Sunar Rochmadi, Bening Jannati Rupi, Nuryadin Eko Raharjo, Nur Hidayat, Wisnu Rachmad Prihadi, and Rudi Nur Syamsudin
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Route Analysis ,Google Earth ,Learning Module ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Due to limited learning resources and low student grades in the Geomatics course, it is very important to carry out this research to improve the quality of special learning activities in the Geomatics course. This research aims to develop a route analysis learning module based on Google Earth Road Classification. This research uses the Thiagarajan Research and Development (R&D) model, known as 4D design. The data collection technique uses a questionnaire to reveal the validity of the product based on material experts, media experts, and user responses. The result of this research is a product development using a 4D R&D model with stages (1) defining, which refers to needs based on competency in Geomatics subjects, especially in operating the Google Earth application; (2) design, which includes four learning activities on A4 paper size and 12pt Arial font; (3) development, which was completed with validation results from material experts totaling 122 scores with an average of 4.07 (suitable); from media experts, 201 scores with an average of 4.28 (very appropriate); as well as the development stage. user feasibility results of 3666 scores with an average of 4.52 (very feasible); (4) the dissemination stage, which is carried out by distributing learning modules to lecturers in geomatics courses and uploading the products developed to Google Drive to be used as learning media. The implications of this research will provide changes to learning activities, and the existence of learning modules is expected to improve student learning outcomes.
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- 2024
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38. An essential update on the inventory of landslides triggered by the Jiuzhaigou Mw6.5 earthquake in China on 8 August 2017, with their spatial distribution analyses
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Jingjing Sun, Xiaoyi Shao, Liye Feng, Chong Xu, Yuandong Huang, and Wentao Yang
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Jiuzhaigou earthquake ,Landslide catalogue ,Spatial distribution ,Coseismic landslide ,Google Earth ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
On August 8, 2017, a magnitude Mw6.5 (Ms7.0) earthquake occurred in Jiuzhaigou County, Aba Prefecture, in the northern part of Sichuan Province, China, with a focal depth of 20 km and an epicenter located at (33.2°N, 103.8°E). Due to the significant magnitude of the earthquake, a large number of coseismic landslides were triggered. Despite previous research conducted by experts on the landslides caused by the Jiuzhaigou earthquake, the actual number of landslides has been severely underestimated in the previously published papers. Through field surveys and visual interpretation of high-resolution remote sensing images before and after the mainshock, we have established a detailed inventory of earthquake-induced landslides. The results indicate that the event caused a minimum of 9428 landslides covering a total area of 18.82 km2. These landslides are mainly distributed in the IX intensity area of the earthquake. The landslides mainly consist of medium-sized landslides and debris flows. They predominantly occur in areas with an altitude ranging from 2600 m to 3600 m, with slopes greater than 30° and facing east and southeast. The Lower Carboniferous and Middle Carboniferous formations are more prone to triggering landslides, and landslides are more concentrated within 1 km of roads and in forested areas. Additionally, as the distance from roads and the epicenter increases, the values of LAP and LND decrease, indicating a positive correlation between the two. There are more landslides within 2 km from the fault and within a range of 6 km–9 km from the epicenter. In conclusion, this study provides a comprehensive landslide inventory with broader coverage and increased accuracy. It also conducts a comprehensive analysis of the spatial distribution patterns of landslides. This contributes to a deeper understanding of the causes of coseismic landslides and further research on the impact of landslides in affected areas.
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- 2024
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39. Combining remote sensing, habitat suitability models and cellular automata to model the spread of the invasive shrub Ulex europaeus.
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Gränzig, Tobias, Clasen, Anne, Fassnacht, Fabian Ewald, Cord, Anna, and Förster, Michael
- Abstract
Modeling the past or future spread patterns of invasive plant species is challenging and in an ideal case requires multi-temporal and spatially explicit data on the occurrences of the target species as well as information on the habitat suitability of the areas at risk of being invaded. Most studies either focus on modeling the habitat suitability of a given area for an invasive species or try to model the spreading behavior of an invasive species based on temporally or spatially limited occurrence data and some environmental variables. Here we suggest a workflow that combines habitat suitability maps, occurrence data from multiple time steps collected from remote sensing data, and cellular automata models to first reconstruct the spreading patterns of the invasive shrub Ulex europaeus on the island Chiloé in Chile and then make predictions for the future spread of the species. First, U. europaeus occurrences are derived for four time steps between 1988 and 2020 using remote sensing data and a supervised classification. The resulting occurrence data is combined with occurrence data of the native range of U. europaeus from the GBIF database and selected environmental variables to derive habitat suitability maps using Maxent. Then, cellular automata models are calibrated using the occurrence estimates of the four time steps, the suitability map, and some additional geo-layer containing information about soils and human infrastructure. Finally, a set of calibrated cellular automata models are used to predict the potential spread of U. europaeus for the years 2070 and 2100 using climate scenarios. All individual steps of the workflow where reference data was available led to sufficient results (supervised classifications Overall Accuracy > 0.97; Maxent AUC > 0.85; cellular automata Balanced Accuracy > 0.91) and the spatial patterns of the derived maps matched the experiences collected during the field surveys. Our model predictions suggest a continuous expansion of the maximal potential range of U. europaeus, particularly in the Eastern and Northern part of Chiloé Island. We deem the suggested workflow to be a good solution to combine the static habitat suitability information—representing the environmental constraints—with a temporally and spatially dynamic model representing the actual spreading behavior of the invasive species. The obtained understanding of spreading patterns and the information on areas identified to have a high invasion probability in the future can support land managers to plan prevention and mitigation measures. [ABSTRACT FROM AUTHOR]
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- 2023
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40. A benchmark dataset for deep learning-based airplane detection: HRPlanes.
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Bakirman, Tolga and Sertel, Elif
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REMOTE-sensing images ,DEEP learning ,AIRPLANE defects ,AUTOMATION ,BENCHMARKING (Management) - Abstract
A irplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection. [ABSTRACT FROM AUTHOR]
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- 2023
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41. Open Foris Collect Earth: a remote sensing sampling survey of Azerbaijan to support climate change reporting in the land use, land use change, and forestry.
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Bassullu, Caglar and Sanchez-Paus Díaz, Alfonso
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LAND use ,REMOTE sensing ,CLIMATE change mitigation ,CLIMATE change ,LAND cover ,FORESTS & forestry - Abstract
Land use, land use change, and forestry (LULUCF) are critical in climate change mitigation. Producing or collecting activity data for LULUCF is essential in developing national greenhouse gas inventories, national communications, biennial update reports, and nationally determined contributions to meet international commitments under climate change. Collect Earth is a free, publicly accessible software for monitoring dynamics between all land use classes: forestlands, croplands, grasslands, wetlands, settlements, and other lands. Collect Earth supports countries in monitoring the trends in land use and land cover over time by applying a sample-based approach and generating reliable, high-quality, consistent, accurate, transparent, robust, comparable, and complete activity data through augmented visual interpretation for climate change reporting. This article reports forest extent estimates in Azerbaijan, analyzing 7782 0.5-ha sampling units through an augmented visual interpretation of very high spatial and temporal resolution images on the Google Earth platform. The results revealed that in 2016, tree cover existed in 31.9% of total land, equal to 2,751,167 ha and 1,301,188 ha or 15.1% of the total land, with a 5.4% sampling error covered by forests. The estimate is 15 to 25% higher than the previous estimates, equal to 169,418 to 260,888 ha of forest that was never reported in previous studies. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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42. The Open Landslide Project (OLP), a New Inventory of Shallow Landslides for Susceptibility Models: The Autumn 2019 Extreme Rainfall Event in the Langhe-Monferrato Region (Northwestern Italy).
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Licata, Michele, Buleo Tebar, Victor, Seitone, Francesco, and Fubelli, Giandomenico
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LANDSLIDES ,LANDSLIDE hazard analysis ,RAINFALL ,AUTUMN ,HUMAN settlements ,TEMPERATE climate - Abstract
Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset's suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis. [ABSTRACT FROM AUTHOR]
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- 2023
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- View/download PDF
43. Spatiotemporal distribution of mangrove along the Egyptian Red Sea coast and analysis of hydrological impact on growth patterns
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Sewilam, H., Hassan, B. T., and Khalil, B. S.
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- 2024
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44. Evaluation of Precipitation Data using CHIRPS and PERSIANN Models (Case Study: Bandar Abbas)
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H. siasar and A. Salari
- Subjects
precipitation pattern ,satellite imagery ,google earth ,persiann and chirps satellites ,Agriculture ,Agriculture (General) ,S1-972 - Abstract
Access to large precipitation data with appropriate accuracy can play an effective role in irrigation planning and water resources management. Satellite images generate high, wide, cheap, and up-to-date data is a good way to estimate precipitation. In this research, the Google Earth engine system and precipitation products from satellite images of PERSIANN and CHIRPS models in daily, monthly, and annual time intervals were used to evaluate and validate the amount of precipitation in Bandar Abbas station during the statistical period of 1983-2020. The results showed that the precipitation estimation by PERSIANN and CHIRPS satellites on a monthly and annual scale is more accurate than the daily scale. The highest correlation coefficient and the least RMSE belonged to the PERSIANN algorithm on monthly and annual scales. The value of the correlation coefficient in the PERSIANN algorithm on daily, monthly, and annual scales is equal to 0.32, 0.83, and 0.94, respectively. The correlation coefficient in the CHIRPS algorithm in daily, monthly, and annual scales is equal to 0.24, 0.71, and 0.90, respectively. The coefficient of determination (R2) of PERSIANN and Chrips algorithms on a monthly scale were 0.89 and 0.70, respectively, and for an annual scale were 0.88 and 0.80, respectively. The general conclusion of this study indicated that the accuracy of the two algorithms in determining the spatial pattern of rainfall on a monthly and annual scale is appropriate, and the PERSIANN algorithm had a higher accuracy on a monthly time scale.
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- 2023
45. The Spatial Distribution Characteristics and Possible Influencing Factors of Landslide Disasters in the Zhaotong Area, Yunnan Province of China
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Wantong Wang, Siyuan Ma, Wujian Yan, and Renmao Yuan
- Subjects
Zhaotong area ,landslide disasters ,influencing factors ,distribution pattern ,Google Earth ,statistical analysis ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10° to 40° and near river channels.
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- 2024
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46. Accuracy Assessment of Google Earth and Open-source Digital Elevation Models in China Using GPS on Field Control Points.
- Author
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Xuwan Zhang, Lei You, Mingjun Deng, Yuan Kou, and Yin Yang
- Subjects
DIGITAL elevation models ,SURFACE of the earth ,STANDARD deviations - Abstract
Google Earth (GE) provides accurate and reliable global high-resolution images and can obtain the coordinates of any point on Earth's surface. A digital elevation model (DEM) is a dataset that quantitatively reflects the elevation of Earth's surface. Although GE and DEMs can be used to obtain the coordinates of any position on Earth, their open-access data can be affected by various factors, thereby inducing undesirable precision variability. Therefore, it is essential to estimate the accuracy of GE and open-source DEMs. In this study, 325 high-precision GPS survey points in 16 regions in China were used to evaluate the horizontal and vertical accuracies of GE and the elevation accuracy of two open-source DEMs. GE had a high horizontal accuracy with a root mean square error (RMSE) of 2.495 m and an error range of 1.090--4.844 m. The elevation accuracy of GE (RMSE = 2.610 m) was lower than those of TanDEM-X (RMSE = 2.055 m) and AW3D30 (RMSE = 2.373 m) DEMs. At the same time, the impacts of slope, aspect, and feature type on the accuracy of these data are studied and analyzed. The results show that the accuracy of control data are closely related to the characteristics of the study area. Overall, these findings indicate that for future studies in China, GE can be used to acquire horizontal data, whereas TanDEM-X and AW3D30 are more suitable for elevation data that have higher precision and provide a reference for relevant research on geographic information. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. Evaluation of the Efficiency of SfM-Photogrammetry in Obtaining DEM from Google Earth Images.
- Author
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Elashiry, Ahmed A. and Al Khalil, Omar
- Subjects
- *
DIGITAL elevation models , *AERIAL photography , *TOPOGRAPHIC maps , *DIGITAL photogrammetry - Abstract
In this study, we seek to evaluate the efficiency of SfM (Structure from Movement) Photogrammetry in obtaining Digital Elevation Model (DEM) from the free Google Earth browser, by simulating the aerial photography of the terrain displayed by this browser, calculating the resulting block of images, and obtaining DEM, and evaluate its accuracy to know the type of works in which this model can be used. Control data for image orientation are provided using Google Earth and free online services. The results showed that the huge number of dense point clouds generated by applying SfM to Google Earth images has contributed to obtaining a DEM with a vertical accuracy better than the vertical accuracy of the free digital elevation model SRTM1. The achieved vertical accuracy of the DEM produced by SfM was 3.58 m, whereas the vertical accuracy of the free SRTM1 was 4.65 m compared to a DEM derived 1/25000 scale topographic map. This type of DEM can be used in works that do not require high vertical accuracy, such as hydrological works (watersheds) dealing with SRTM models. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. De l'usage de Google Earth pour une contre-cartographie critique de l'extension carcérale.
- Author
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de Dardel, Julie and Blanc, Jean-Sébastien
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- 2023
- Full Text
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49. Using Open Foris Collect Earth in Kyrgyzstan to support greenhouse gas inventory in the land use, land use change, and forestry sector.
- Author
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Bassullu, Caglar and Martín-Ortega, Pablo
- Subjects
LAND use ,GREENHOUSE gases ,CLIMATE change mitigation ,FORESTS & forestry ,REMOTE-sensing images - Abstract
The Kyrgyz Republic (Kyrgyzstan) is one of the countries most vulnerable to the adverse effects of climate change in Central Asia. The land use, land use change, and forestry (LULUCF) sector is critical in climate change mitigation in Kyrgyzstan and is integral to national greenhouse gas (GHG) inventories. However, consistent, complete, and updated activity data is required for the LULUCF sector to develop a transparent GHG inventory. Collect Earth (CE), developed by the Food and Agriculture Organization of the United Nations (FAO), is a free, user-friendly, and open-source tool for collecting activity data for the LULUCF sector. CE assists countries in developing GHG inventories by providing consistent and complete land representation. This article reports an estimate of land use and land-use change dynamics in Kyrgyzstan, based on analyzing 13,414 1-hectare (ha) sampling units through an augmented visual interpretation approach using satellite imagery at the very high spatial and temporal resolution available through the Google Earth platform. The results show that in 2019, forests covered 1.36 million ha or 6.83% of the total land with a 6.23% uncertainty. This estimate was 5 to 16% higher than previous estimates, detecting an additional 63,024 to 188,164 ha of forestland that had not been reported previously. The new estimates suggest an average increase of 10.4% in the current forestlands of Kyrgyzstan. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. Quercus branti) تلفيق آماروسى وكوكدارث براى بوآوردزيستتوده وذخيوه كوبن درختان بلوط ايوانى مطالعه موردى: منطقه دادآبادخومآب ٠د) (Lindl
- Author
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محسن يوسفوندمفرد, جواد سوسنى, حامد نقوى, كامبيز ابرارى واجارى, and نقى شعبانيان
- Subjects
ALLOMETRIC equations ,OAK ,BIOMASS - Abstract
Zagros forests cover a large area of the Iran and play an important role in soil protection storing underground water and other socio-economic services. Forests play an important role in the natural storage of carbon on a global scale and their protection is one of the important and sustainable strategies to deal with global warming. Therefore, the sustainable management of these areas requires information to understand and predict the changes of these forests. This research aimed to estimate the biomass and amount of carbon storage of Iranian oak trees, which are one of the most important elements of the Zagros forests, by using the combinathion of field inventory and Google Earth images. According to the results of the previous study in this area, the average diameter of the crown is a suitable variable for estimating the biomass of the Iranian oak species. For this purpose, the average diameter of the crown of the Iranian oak trees in the Dadabad region of Lorestan in an area of 488 hectares, using Google Earth images by ImageJ software was measured. The results showed that the amount of biomass on the ground for the Iranian oak species was 9.68 tons per hectare. Also, the amount of carbon storage estimated for the oak forests 6.427 tons per hectare. The results of this study and its comparison with other studies showed that non-destructive methods such as remote sensing and the use of satellite images and their integration with local allometric equations, allow the estimation of the amount of biomass and carbon storage in large areas with proper accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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