Search

In this study, we used high-resolution optical satellite images on the Google Earth platform to map large-scale landslides in Xianyang City, Shaanxi Province, China. After mapping, a comprehensive and detailed large-scale landslide inventory that contains 2924 large-scale landslides was obtained. We analyzed the spatial distribution of landslides with seven influencing factors, including elevation, slope angle, aspect, curvature, lithology, distance to a river, and distance to the fault. Landslide Number, Landslide Area, Landslide Number Density (LND), and Landslide Area Percentage (LAP) were selected as indexes for the spatial distribution analysis. The results show that the number and area of landslides in the elevation range of 1000–1200 ​m is the highest. The highest number of landslides was observed in the slope angle of 25°–30°. North-facing slopes are prone to sliding. The area and number of landslides are the largest when the slope curvature ranges from −1.28 to 0. The LND and LAP reach their maxima when the slope curvature is less than −2.56. Areas covered by the Tertiary stratum with weakened fine-grained sandstone and siltstone show the highest LND and LAP values. Regarding distance to a river, the LAP peaks in the range of 300–600 ​m, whereas the LND peaks in an area larger than 2100 ​m. The values of LND and LNP rise as the distance from the faults increases, except for the locations 30 ​km away from active faults. This phenomenon is because active faults in this area pass through the plain areas, while landslides mostly occur in mountainous areas. The cataloging of landslide development in Xianyang City provides a significant scientific foundation for future research on landslides. In addition, the spatial distribution results are useful for landslide hazard prevention decisions and provide valuable references in this area. [ABSTRACT FROM AUTHOR]

In this study, we obtained a regional landslide database of Jiyuan City based on the high-resolution remote sensing images of Google Earth platform. As a result, 605 landslides were identified, with a total area of 24.53 ​km2. Based on the GIS platform, the landslide number density (LND) and landslide area percentage (LAP) were used to statistically analyze the spatial distribution of landslides in the study area. Elevation, slope, aspect, curvature, drainage and stratum were selected as the influencing factors. The results show that the elevation interval where landslides are most likely to occur is 399–650 m. The slope most prone to landsliding is 44.6°–70.6°. The landslides with high frequency appear on the slope facing northwest. Slopes with curvature between −1–0 and 0–1 have relatively high landslide frequency, but no indication that whether on concave or convex. The water system in the entire study area has less impact on landslides. Landslides are most likely to occur in Neogene and Paleogene (N ​+ ​E) strata, while the number of landslides in the Permian (P) stratum is the highest, which means a high incidence. The results can provide a scientific basis for landslide hazard prevention and mitigation in the study area, and also for other regions.

The design of a copter has been carried out, that can be used to set up air interference, reducing the probability of impact of the enemy air defense systems, reconnaissance operations, and the transfer of military cargo. The developed hexacopter is capable of carrying a useful load of 2.5-3.0 kg, at a speed of up to 45 km/h, based on a Pixhawk family flight controller using the Arducopter firmware. Stability of the flight of a quadcopter on a frame of 850 mm during a gusty wind of 7-8 m/s in navigation modes for firmware Arducopter ver.4.0.7 has been experimentally tested. The use of software dynamic notch filters to reduce the impact of vibrations from running motors on the readings of the accelerometer and gyroscope is considered. [ABSTRACT FROM AUTHOR]

Copyright of TURKAV Institute of Public Adminsitration Journal of Social Sciences / TÜRKAV Kamu Yönetimi Enstitüsü Sosyal Bilimler Dergisi is the property of TURKAV Institute of Public Adminsitration Journal of Social Sciences 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.)

This paper proposes a novel approach for automatic 3D surface reconstruction from uncalibrated and multi-view Google Earth images by using a multi-view stereo method and 3D projective to metric transformation. Without the Rational Polynomial Coefficients, it is impossible to obtain the metric reconstruction of the 3D surface from multi-view satellite images. We solve the uncalibrated multi-view satellite image problem by employing a multi-view stereo vision technique followed by a projective to metric transformation. The virtual pose parameters of the satellite images are obtained by using COLMAP, and the virtual 3D projective reconstruction is done by using EnSoft3D. For projective to metric transformation, we propose to employ 3D homography transformation. Eight 3D correspondence pairs on the viewing frustums between the virtual reference camera and the ideal nadir camera are used to derive a 3D homography matrix. Using the 3D homography matrix, we finally obtain the metric reconstruction of 3D surface up to an unknown height scale in a reference coordinate system of the Google Earth desktop software. Experiments are done in several world locations on Google Earth including building and vegetation areas. Reconstruction error analysis with the Data Fusion Contest 19 dataset is also presented. The average of MAE and RMSE of five tile regions in the dataset are 1.596 m and 2.083 m, respectively. [ABSTRACT FROM AUTHOR]

Shoreline analysis helps to understand the coastal dynamism for decision-making in coastal management. As there are still doubts in transect-based analysis, this study attempts to understand the influence of transect intervals in shoreline analysis. Shorelines were delineated on high-resolution satellite images in Google Earth Pro for twelve beaches in Sri Lanka under different spatial and temporal scales. Shoreline change statistics were calculated using Digital Shoreline Analysis System in the ArcGIS 10.5.1 software under 50 transect interval scenarios, and influence of the transect interval for shoreline change statistics were interpreted using standard statistical methods. Transect interval error was calculated with respect to the 1 m scenario as this has the best beach representation. Results revealed that there is no any significant difference (p > 0.05) of shoreline change statistics between 1 and 50 m scenarios in each beach. Furthermore, it was found that the error was extremely low up to 10 m scenario and then after it was subject to fluctuate in an unpredictable manner (R2 < 0.5). Overall, the study concludes that the influence of the transect interval is negligible and 10-m transect interval is ideal in shoreline analysis for the highest efficacy in small sandy beaches. [ABSTRACT FROM AUTHOR]

Geoheritage constitutes a natural resource that promotes sustainable rural tourism by creating employment and activities that allow population settlement in unpopulated areas with great natural heritage. The objective of this work is to value the singularity and variety of the geosites, which show a wide geodiversity, with lithological, geomorphological, tectonic and natural process diversity (fluvial, coastal, sedimentary, etc.). In the "Arosa estuary" (Galicia, Spain), seven Geosites have been identified, described and valued, determining their scientific, educational and tourist-recreational potential and obtaining values between 565 and 660 points. The state of conservation and risk of degradation is analyzed in order to proceed with their geoconservation, promoting sustainable geo-tourism. The values of degradation susceptibility range between 0.03 and 3 points, and anthropic degradation between 0.33 and 1.73 points. A 3D virtual itinerary is made using Google Earth, implementing descriptive sheets, interpreted diagrams and photographs, and analysis of the geological processes. An interactive virtual flight is presented for academic and tourist purposes to promote geotourism. The virtual tour also has geomatic didactic elements: geoapp and georeferenced thematic cartographies. These resources are helpful for the unknown geoheritage of the population that lives or visits the "Arosa estuary", favoring sustainable development and fostering attitudes and skills of respect for nature. [ABSTRACT FROM AUTHOR]

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 (R 2 ) 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. [ABSTRACT FROM AUTHOR]

Introduction: Discernable air pollution occurs in most developing countries due to rapid urbanization which can be parameterized by air, humidity, population density, temperature, contaminants, exorbitant fossil fuel consumption, and inadequate transportation. Nitrogen dioxide (NO2), one of the most widely recognized air pollutants, has a detrimental impact on human health explicitly or implicitly and considerably influences on atmospheric composition. Materials and methods: In this study, NO2 intensity was analyzed from 2018 with aiming to monitor spatiotemporal changes in Dhaka and its surrounding areas with the Tropospheric Monitoring Instrument (TROPOMI) sensor data. Copernicus Sentinel-5 Precursor satellite data was used in the Google Earth Engine platform to get the result. Results: The results revealed a strong relationship (R2=0.9478) between the NO2 concentration and high population density and the temporal variation is higher during the pre-monsoon than throughout the post-monsoon. The reason behind is the lack of sunlight and the difficulty to break down the NO2, which causes the removal of NO2 from the atmosphere to proceed more slowly. In contrast, Land Use and Land Cover (LULC) are also impacted by the high concentration which is remains in the built-up area. Conclusion: This research mainly considered that how NO2 concentration measured from satellite images with temporal variation within a year and what factors strongly influence raising NO2 levels. This model can be used for policy-making to take proper initiatives to reduce NO2 concentrations. The result showed significant uses of TROPOMI with relating population density and LULC in Dhaka and its surrounding areas of Bangladesh.

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.

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 km2. 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.

This essay uses Google Earth images to examine urban morphologies in Mexico City. Vertical views of the world embraced by cartographers and planners have long legitimated claims to authority, truth, and temporal power. Since its introduction in 2008, Google satellite view has only reinforced such presumptions, particularly given the company's entangled relations with the U.S. Geospatial Intelligence Agency. Nevertheless, aerial photographs provide an undeniably useful source for architects and urbanists to study city form and metropolitan expansion. The vertical view is particularly valuable for its capacity to illuminate spatial relations that are otherwise difficult to trace on the ground, but which nonetheless shape everyday human experience. The goal of this essay is to discern a range of city forms in the rapidly expanding metropolis, and to contemplate the ways in which urban morphology frames everyday life in one of the world's largest conurbations. It is part of a longer-term study of Mexico City's urbanism based on fieldwork, mapping, and spatial analysis.

The primary objective of toll roads is to stimulate economic growth. As the pivotal road network on Java Island, the Trans-Java Toll Road has a significant impact on regional development and spatial planning. However, there has been a lack of research focusing on the specific influence of this toll road on spatial planning. Therefore, this study aims to comprehensively examine the spatial and temporal implications of the Trans-Java Toll Road. To achieve this goal, the Normalized Difference Built-Up Index (NDBI) method is employed using satellite imagery. This method is widely recognized for its effectiveness in detecting changes in built-up areas through remote sensing techniques. The calculated NDBI values reveal substantial expansion of built-up areas in the cities and regencies traversed by the Trans-Java Toll Road. Notably, two specific areas with the highest rates of change are investigated: Gresik Regency and Bekasi Regency, where the built-up areas have experienced remarkable growth of over 186.9% and 61.8% respectively over a five-year period. To validate these findings, Google Earth imagery is utilized for two different years, namely 2016 and 2021. The observed changes in these regions are predominantly attributed to the economic growth facilitated by the establishment of industrial areas. Consequently, the expansion of industrial zones and settlements has resulted in a notable reduction in wetland areas and a simultaneous increase in built-up areas.

The location of debris flow occurring in a gully determines the observable differences in its formation, evolution and effects. In this piece of research, we propose a new method for debris flow gully classification based on the locations of the debris flows occurring in the gullies. It is termed the three-section method (TSM). It includes eight different types of gullies with different digital identifications (IDs) and susceptibility degrees (SDs). By taking the Jiangjia Gully (JJG), in Yunnan, China, as a case study site, the main gully and the sub-gullies at different levels were identified using a hydrological analysis method. Then, the gullies were divided into different types using the new classification TSM. The results show that there are seven different types of debris flow gullies in the JJG. The number of different types varied greatly in gullies at different levels. In particular, the topological diagram of debris flow gullies was drawn after simplifying the shape of the gullies, and it was a good way to understand the characteristics of debris flow gullies. Finally, the relationships were explored between the hypsometric integrals (HIs), surface exposures (SEs) and susceptibility degrees (SDs), and a new calculation model construction method for determining the degree of debris flow susceptibility was proposed. This model, using the above method, can not only be used to calculate the SDs of debris flows in the gullies, but can also be instrumental in pointing out the approximate locations of the debris flow commonly and easily occurring in the gullies. We hope that our research can provide a new concept for the assessment of debris flow susceptibility. [ABSTRACT FROM AUTHOR]

Abandoned boats represent a pervasive and growing problem in the coastal zone that has received little regulatory or scientific attention. In this study, we show how Google Earth can be employed to remotely identify abandoned boats and provide information on their size, age and condition. Based on specified criteria for abandonment, a survey of southern England (1700 km in length) revealed the presence of 266 boats that were mainly located in the intertidal zones of estuaries and inlets and that were often contained within clusters. Boat length ranged from 2.6 to 60 m (median = 10.5 m), and since 2004 abandonment has been increasing at a rate of about eight per year. The majority of boats appeared to be of timber construction and exhibited evidence of damage and water-sediment ingression. The environmental impacts are likely to be most significant where boats are clustered on protected mudflats. [ABSTRACT FROM AUTHOR]

Extreme rainfall events can make disproportionally larger contributions than common rainfalls to soil erosion rates. Yet assessing the importance of different soil erosion processes during such events remains a critical challenge, especially due to a lack of field observations. Based on detailed field surveys and remote‐sensing analyses, we therefore provide erosion budgets for three representative catchments in the Chinese Loess Plateau, following an exceptional rainstorm event (July 26, 2017; estimated return period: 200 years). Our results show that the associated catchment‐scale erosion intensities were 2815 ± 529 t km‐2 for rill erosion, 787 ± 486 t km‐2 for gully erosion, 4433 ± 657 t km‐2 for landsliding and 3927 ± 1009 t km‐2 for the collapse of implemented soil and water conservation measures (SWCMs, specifically terraces and check‐dams). Hence, landslides (37 ± 4%) and SWCMs collapses (33 ± 4%) were the most important sediment sources, followed by rill (24 ± 3%) and gully erosion (7 ± 4%). The lower contribution of gully erosion likely results from earlier vegetation restoration efforts. Overall, our results indicate that successful erosion control during extreme events strongly depends on integrative and robust catchment management strategies. Specifically, the spatial intersecting of forestland and grassland can play a large role, with reforestation on problematic slopes being an important strategy limiting rill and gully erosion. Sufficient consideration should be given to the planning and maintenance of terraces and check‐dams. The latter should be constructed with good flood‐release facilities, meanwhile many small‐scale dams likely work better than a few large ones. Likewise, (unpaved) roads should receive attention as they can also be a significant runoff and sediment source. [ABSTRACT FROM AUTHOR]

The great majority of the world population resides nowadays in urban areas. Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the São Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, São Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution "goodness-of-fit" metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25–0.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65–0.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions. [ABSTRACT FROM AUTHOR]

Obtaining terrain elevation data is essential for numerous engineering applications. However, regardless of the technique used to obtain the elevation data, there will be errors intrinsic to the instruments and to the measurement technique used, as well as depending on the model of Earth representation, which are plane, spherical or ellipsoidal. In this perspective, this work proposes a comparative approach between the elevation data obtained from the geodesic coordinates, provided by Global Navigation Satellite System (GNSS), and those obtained from Google Earth (GE) and Shuttle Radar Topography Mission (SRTM). The data collection was carried out in the urban section between km 125 to km 145,500 of Dom Pedro-I Highway (SP-065), State of São Paulo, Brazil. The quality of the results is evaluated based on the Mean Quadratic Error (RMSE). The main results showed that the elevation data obtained through the SRTM show greater agreement or adjustment to the GNSS data when compared to the higher quality and less variation than those obtained through the GE, since they have smaller RMSE and are closer to the average elevations calculated for the highway segment analyzed. However, it is worth noting that the use of GE can be a good alternative for low-cost preliminary studies. [ABSTRACT FROM AUTHOR]

The present work was developed in Paraguai river corridor, located between Cabaçal river mouth and the city of Cáceres, Mato Grosso, and aimed at identifying and mapping, in detail, river features present in this geographic cut. For such, the study interpreted and identified river features through satellite image from August 2018, made available software Google Earth Pro, through which vectorization of river features was possible, using "polygon" tool in detail, provided by the good image resolution. Later, a folder containing the vectorized features was transferred to SIG ArcGis® to generate georeferenced maps representative of the study. Two field activities occurred in July 2019 in order to assist in the accuracy of the features mapping. Four maps were generated and classified in compartments I, II, III and IV. In the scope of the segment studied, 27 submerged bars, 140 side bars, 04 central bars, 29 marginal cords, 10 marginal dikes, 12 islands, 160 lagoons, 15 bays, and 28 tailing ponds were identified. The methodology adopted led to satisfactory results that can be base for discussion on the river corridor dynamics. It's worth mentioning that the genesis and maintenance of the features identified are due to Paraguai river dynamics and climate changes, consistent with the inadequate soil use in the river basin to which the study area belongs, and can influence the emergence of new features and/or the disappearance of other features. [ABSTRACT FROM AUTHOR]

GeoArt deals with spectacular creations on the earth (Geo) by Mother Nature in the form of artistic (Art) developments of landforms and landscapes, which are appreciated when viewed from the air or sky-space. Initially Geoart was considered for any artistic natural or geological feature, and irrespective of scale. The stunning beauty of satellite views was brought out by NASA in a book 'Earth as Art' in 2012. Many considered these as 'Geoarts' relating it with geosciences and art. Large numbers of spectacular GeoArts are identified by the author through surfing of Google Earth images of India and neighbouring countries. Most of these are found related to human and animal-bird lives. Some, however, have attained manifestations of divine forms relating to God/Goddess or their association; hence, are regarded as Divine GeoArts. Brief details of 20 such spectacular Divine GeoArts are presented in the paper. The approachable sites of some of these amazing GeoArts may find its place in future geotourism as these, like other landforms and landscapes, have potentiality to communicate scientific perception for their development through natural agencies or divine forces of Mother Nature. [ABSTRACT FROM AUTHOR]

The establishment of a regional historical landslide inventory plays an indispensable role in landslide assessment and prevention. In this study, based on the Google Earth platform, an inventory of ancient landslides in Hualong County, Qinghai Province was established. The inventory includes 3517 ancient landslides with individual areas ranging from 2354.6 m2 to 12.44 km2. The dominant characteristics include an elevation of 2600–2800 m, slope of 10–20°, aspects SW, W, and NW, mudstone and sandstone of Paleoproterozoic, Neoproterozoic and Quaternary loess, 8–10 km from faults, 0–1 km from rivers, cultivated and grassland types, NDVI of 0.25–0.3, and an average precipitation in the range of 480–500 mm. In addition, the geometric analysis of landslides shows that the average height and length of ancient landslides in the study area are 151.92 m and 429.52 m, respectively. The power law relationship between the two is L = 0.41 × H1.37. The ancient landslide inventory of this study exhibits an integrated pattern of the development characteristics and spatial distribution of landslides in the Tibetan Plateau and the upper Yellow River basin, as well as providing a significant reference for subsequent landslide susceptibility mapping in the area. [ABSTRACT FROM AUTHOR]

Pada kegiatan PKM ini tim pelaksana melakukan kerjasama atau bermitra dengan SMAN 8 Kecamatan Tomoni Kabupaten Luwu Timur yang beralamat di Jalan Pendidikan No. 3. Permasalahan yang terjadi pada mitra menunjukkan adanya berbagai keterbatasan terkait dengan sumber belajar, media pembelajaran serta kemampuan siswa Kelas XII IPS SMAN 8 pada mata pelajaran geografi belum mampu memanfaatkan sumber-sumber dan media pembelajaran geografi yang lebih modern dengan memanfaatkan Teknologi Informasi (TI) yang saat ini cukup tersedia saat ini. Manfaat dalam program PKM ini adalah sebagai upaya meningkatkan kualitas pembelajaran geografi, menigkatkan kompetensi pengetahuan dan kemampuan skill siswa Kelas XII IPS SMAN 8 Luwu Timur yang mempelajari matapelajaran geografi dalam hal penerapan teknologi SIG seperti pembuatan peta dan penggunaan aplikasi Google Earth dan Story Map. Kegiatan PKM yang dilakukan berupa bentuk seminar, workshop atau pelatihan untuk siswa kelas XII IPS SMAN 8 Luwu Timur yang mempelajari matapelajaran geografi. Melalui PKM ini peserta kelas XII IPS yang mengikuti kegiatan PKM ini diberikan pengetahuan atau penguatan tentang Sistem Informasi Geografis dan penggunaan aplikasi google earth dan story map yang dapat diterapkan dalam pembelajaran geografi. Dari kegiatan PKM ini peserta sudah mampu menerapkan aplikasi Google Earth salah satunya menentukan lokasi atau mengambil data koordinat, menghitung luas lahan, melakukan digitasi jalan maupun sungai dan peserta sudah mampu memvisualisasikan data spasial dengan memanfaatkan aplikasi story map.

Riparian forests in Iran are valuable ecosystems which have many ecological values. Because of destruction of these forests in recent years, providing spatio-temporal information from area and distribution of these ecosystems has been receiving much attention. This study was performed for mapping distribution, area and density of riparian forests in southern Iran using Sentinel-2A, Google Earth, and field data. First Sentinel-2A satellite image of the study area was provided. The field work was performed to take the training areas and to assess the forest density of riparian forests in Khuzestan province. In the first part of this study, after selecting training areas as pixel-based samples on the Sentinel-2A satellite image, supervised classification of image was performed using support vector machine (SVM) algorithm to classify the distribution of riparian forests. After classification of Sentinel-2A satellite image, the boundary of riparian forests map was checked and corrected on Google Earth images. In the second part of this study, field data, Normalized Difference Vegetation Index (NDVI), and regression model were used to assess the density of riparian forests. Finally, the accuracy of the final riparian forest map (showing both distribution and density of riparian forests) was assessed using Google Earth images. Results showed that the final riparian forest map (showing both distribution and density of riparian forests) with overall accuracy 89% and kappa index 0.81 had a good accuracy for classifying the distribution and density of riparian forests in Khuzestan province. These results demonstrate the accuracy of SVM algorithm for classifying the distribution of riparian forests and also capability of NDVI for classifying the density of riparian forests in this study. Results also showed that regression model (R2 = 0.97) is reliable for estimating riparian forest density. The results demonstrated that there are 68447.18 ha of riparian forest around the main rivers in Khuzestan province, mainly distributed in the northwest and southeast of the province. From this area, 54694.15 ha have been covered by dense forests and 13753.03 ha by sparse forests. Results of this research have created the useful data of area, distribution and density of riparian forests in 10-m spatial resolution which is necessary for conservation and management of these forests in southern Iran. It is suggested that mapping area, distribution and density of these forests would be performed using SVM algorithm and NDVI in the certain temporal periods for protective management of these ecosystems in time series. [ABSTRACT FROM AUTHOR]

First, with our use of IUCN range maps to estimate the number of species per watershed, as in other broad-scale biodiversity assessments, we assumed that a given species is present across its entire range. Prioritizing conservation in sub-Saharan African lakes based on freshwater biodiversity and algal bloom metrics Keywords: algal blooms; freshwater biodiversity; Google Earth Engine; lakes; remote sensing; species richness; sub-Saharan Africa; África sub-sahariana; biodiversidad de aguas dulces; floración de algas; Google Earth; lagos; riqueza de especies; teledetección; ; ; ; ; ; ; EN algal blooms freshwater biodiversity Google Earth Engine lakes remote sensing species richness sub-Saharan Africa África sub-sahariana biodiversidad de aguas dulces floración de algas Google Earth lagos riqueza de especies teledetección 1 13 13 10/04/22 20221001 NES 221001 INTRODUCTION As the global human population and associated food demand continue to increase, so does the need to use land for agriculture (Tscharntke et al., 2012). Watershed colors are based on level 3 and level 4 HydroBASINS (see Lehner & Grill, 2013) gl Measuring freshwater biodiversity For both obligate freshwater species and freshwater-dependent vertebrate species in each country, we used the HydroBASINS data set (format 2 [i.e., inserted lakes]) and International Union for the Conservation of Nature (IUCN) datasets. [Extracted from the article]

Shelterbelts (or windbreaks) can effectively improve the microclimate and soil conditions of adjacent farmland and thus increase crop yield. However, the individual contribution of these two factors to yield changes is still unclear since the short-term effect from the microclimate and the accumulated effect from the soil jointly affect crop yield. The latter (soil effect) is supposed to remain after shelterbelt-cutting, thus inducing a post-cutting legacy effect on yield, which can be used to decompose the shelterbelt-induced yield increase. Here, we develop an innovative framework to investigate the legacy effect of post-cutting shelterbelt on corn yield by combining Google Earth and Sentinel-2 data in Northeastern China. Using this framework, for the first time, we decompose the shelterbelt-induced yield increase effect into microclimate and soil effects by comparing the yield profiles before and after shelterbelt-cutting. We find that on average, the intensity of the legacy effect, namely the crop yield increment of post-cutting shelterbelts, is 0.98 ± 0.03%. The legacy effect varies depending on the shelterbelt–farmland relative location and shelterbelt density. The leeward side of the shelterbelt-adjacent farmland has a more remarkable legacy effect compared to the windward side. Shelterbelts with medium–high density have the largest legacy effect (1.94 ± 0.05%). Overall, the legacy effect accounts for 47% of the yield increment of the shelterbelt before cutting, implying that the soil effect is almost equally important for increasing crop yield compared to the microclimate effect. Our findings deepen the understanding of the mechanism of shelterbelt-induced yield increase effects and can help to guide shelterbelt management. [ABSTRACT FROM AUTHOR]

Cadastral marks constitute a dense source of information for topographical surveys required to update cadastral maps. Historically, in Italy, cadastral marks have been the cartographic network for the implementation of mapping updates. Different sources of cadastral marks can be used by cadastral surveyors. In recent years, the cadastre is moving toward a digital world, and with the advancement of surveying technology, GNSS CORS technology has emerged in the positioning of cadastral marks. An analysis of congruence among cadastral marks using GNSS CORS and official maps is missing. Thus, this work aims to analyze the positional accuracy of some cadastral marks, located in Palermo, Italy, with regard to the official maps produced by the cadastral bureau, the local cartography, and Google Earth maps. A survey of 60 cadastral marks was carried out by conventional GNSS NRTK procedures, with the lateral offset method due to their materialization (mostly building edges), which is not always directly detectable. The cadastral marks' positioning was obtained from different maps: cadastral maps and related monographic files, numerical technical maps, and Google Earth maps, to check their coordinate congruence. A statistical approach was performed to check whether the distribution frequencies of the coordinate's differences belonged to the bivariate normal distribution for the planimetric coordinates and the univariate normal distribution for the altimetric component. The results show that the hypothesis of a normal distribution is confirmed in most of the pairs, and specifically, most of the analyses indicate that the highest congruencies seem to characterize the coordinates determined by using the GNSS and with those that can be deduced by the numerical technical maps. The results obtained experimentally show centimetric accuracies obtained by the GNSS NRTK survey, in both the planimetric and altimetric components, while the accuracies obtained from the georeferencing of the cadastral maps show differences in the order of 0.4–0.8 m. Meanwhile, the differences resulting from comparing the technical cartography produced by the local authority and Google Earth maps show greater criticalities, with a metric order of magnitude. [ABSTRACT FROM AUTHOR]

Landslides are a typical geological hazard that endangers people's lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent to compile a complete inventory of landslides in a specific region. The investigation object of this study is Baoji City, Shaanxi Province, China. Using the multi-temporal high-resolution remote sensing images from Google Earth, we preliminarily completed the cataloging of large-scale (area > 5000 m2) landslides in the study area through visual interpretation. The inventory was subsequently compared with the existing literature and hazard records for improvement and supplement. We identified 3422 landslides with a total area of 360.7 km2 and an average area of 105,400 m2 for each individual landslide. The largest landslide had an area of 1.71 km2, while the smallest one was 6042 m2. In previous studies, we analyzed these data without describing the data sources in detail. We now provide a shared dataset of each landslide in shp format, containing geographic location, boundary information, etc. The dataset is significantly useful for understanding the distribution characteristics of large-scale landslides in this region. Moreover, it can serve as basic data for the study of paleolandslide resurrection. Dataset: https://doi.org/10.5281/zenodo.6668092. Dataset License: Creative Commons Attribution 4.0 International. [ABSTRACT FROM AUTHOR]

The demand on the part of the society of cartographic documents has grown all over the world, including Brazil. The advance of geotechnologies associated with the publication of data free of charge allows this acquisition to be more accessible. Now it is possible to use high resolution images, for example, the images of the Google Earth platform. The goal of this study is to evaluate the positional accuracy of the images from the Google Earth platform for selected applications. The study areas were selected through the random sample process, making the selection of a state by region. The selected states were in Alagoas (northeast), Río de Janeiro (southeast), Santa Catarina (south), Goiás (central-west) y Tocantins (north). Control points measured in the field with GNSS were used to evaluate the positional accuracy. The results obtained in this study indicate that the image provided by the Google Earth platform has a positional accuracy compatible with a scale of 1:25 000 class B of the EPC (Standard of Cartographic Precision) of Brazil, which indicates its solo use for mapping in on the scale of 1:25 000. [ABSTRACT FROM AUTHOR]

Due to the popularity of Google Earth, users often use the platform's geographic data for scientific and spatial planning purposes. However, Google claims that these data are only approximations and therefore their positional accuracy is not officially documented. In view of the improvement in obtaining these data, there is a need to assess the quality of the cartographic product. Thus, this work aims to evaluate the planimetric quality of point data obtained from Google Earth, based on Descriptive Statistics, Trend Analysis and the Brazilian Standard for Cartographic Precision (PEC-PCD). For this, two georeferenced orthophotos with a spatial resolution of 5 cm were used as a reference, one in an urban area and another in a rural area in the state of Pernambuco, Brazil. In the results, a positional error of 1.26 m was found in the urban area and 4.65 m in the rural area. According to the PEC-PCD analysis, the planimetric data generated by Google Earth meet the technical specifications at scales of less than 1:5000, in densely urbanized areas, and 1:10,000, in non-urbanized areas. Thus, in places with greater availability of high resolution satellite images, it was observed that the data present a more significant precision. In this sense, Google Earth represents an attractive source of positional data that can be used for spatial analysis and preliminary surveys. [ABSTRACT FROM AUTHOR]

Effective mangrove mapping needs reliable indices that can characterize mangroves from other land cover types on remote sensing data. Currently, a variety of vegetation- and mangrove indices are available, while a comparative assessment of their efficiency seems essential. The aim of this study was to evaluate six vegetation indices and seven mangrove indices developed for Sentinel-2 imagery to obtain a robust approach in mangrove mapping within Google Earth Engine (GEE) cloud computing platform. The rasterized indices were classified by support vector machine. The final maps were evaluated by area under curve (AUC) of receiver operating characteristic (ROC) in addition to common accuracy assessment criteria. Results showed that mangrove indices were more reliable than vegetation indices. Amongst the vegetation indices, Modified Chlorophyll Absorption in Reflectance Index (MCARI) (AUCmangrove from 0.91 to 0.92) achieved the highest AUC values, while MFI (Mangrove Forest Index) returned the highest values amongst the mangrove indices (AUCmangrove from 0. 93 to 0.95). All in all, results revealed that MFI on Sentinel-2 imagery in GEE was efficient in mangrove mapping within the study sites. [ABSTRACT FROM AUTHOR]