scholarly journals Fleroff goes digital: georeferenced records from "Flora des Gouvernements Wladimir" (Fleroff, 1902)

2021 ◽  
Vol 9 ◽  
Author(s):  
Alexey P. Seregin ◽  
Yurii Basov
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Expert Knowledge ◽  
Rare Plants ◽  
Modern Trend ◽  
Moscow Oblast ◽  
Global Biodiversity Information Facility ◽  
Novgorod Oblast ◽  
Vladimir Oblast ◽  
Data Coverage

Global Biodiversity Information Facility (GBIF) has uneven data coverage across taxonomic, spatial and temporal dimensions. Temporal imbalances in the data coverage are particularly dramatic. Thus, 188.3M GBIF records were made in 2020, more than the whole lot of the currently available pre-1986 electronic data. This underscores the importance of reliable and precise biodiversity spatial data collected in early times. Biological collections certainly play a key role in our knowledge of biodiversity in the past. However, digitisation of historical literature is underway, being a modern trend in biodiversity data mining. The grid dataset for the flora of Vladimir Oblast, Russia, includes many historical records borrowed from the "Flora des Gouvernements Wladimir" by Alexander F. Fleroff (also known as Flerov or Flerow). Intensive study of Fleroff's collections and field surveys exactly in the same localities where he worked, showed that the quality of his data is superb. Species lists collected across hundreds of localities form a unique source of reliable information on the floristic diversity of Vladimir Oblast and adjacent areas for the period from 1894 to 1901. Since the grid dataset holds generalised data, we made precise georeferencing of Fleroff's literature records and published them in the form of a GBIF-mediated dataset. A dataset, based on "Flora des Gouvernements Wladimir. I. Pflanzengeographische Beschreibung des Gouvernements Wladimir" by Fleroff (1902), includes 8,889 records of 654 taxa (mainly species) from 366 localities. The majority of records originate from Vladimir Oblast (4,611 records of 534 taxa from 195 localities) and Yaroslavl Oblast (2,013 records of 409 taxa from 66 localities), but also from Nizhny Novgorod Oblast (942 records), Ivanovo Oblast (667 records) and Moscow Oblast (656 records). The leading second-level administrative units by the number of records are Pereslavsky District (2,013 records), Aleksandrovsky District (1,318 records) and Sergievo-Posadsky District (599 records). Georeferencing was carried out, based on the expert knowledge of the area, analysis of modern satellite images and old topographic maps. For 2,460 records, the georeferencing accuracy is 1,000 m or less (28%), whereas for 6,070 records it is 2,000 m or less (68%). The mean accuracy of records of the entire dataset is 2,447 m. That accuracy is unattainable for most herbarium collections of the late 19th century. Some localities of rare plants discovered by Fleroff and included into the dataset were completely lost in the 20th century due to either peat mining or development of urban areas.

scholarly journals Measuring Walkability with GIS—Methods Overview and New Approach Proposal

2021 ◽  
Vol 13 (4) ◽  
pp. 1883
Author(s):  
Agnieszka Telega ◽  
Ivan Telega ◽  
Agnieszka Bieda
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Modern World ◽  
Single Family ◽  
New Approach ◽  
Parks And Recreation ◽  
Gis Tools ◽  
Point Data ◽  
Pedestrian Infrastructure ◽  
Gis Methods

Cities occupy only about 3% of the Earth’s surface area, but half of the global population lives in them. The high population density in urban areas requires special actions to make these areas develop sustainably. One of the greatest challenges of the modern world is to organize urban spaces in a way to make them attractive, safe and friendly to people living in cities. This can be managed with the help of a number of indicators, one of which is walkability. Of course, the most complete analyses are based on spatial data, and the easiest way to implement them is using GIS tools. Therefore, the goal of the paper is to present a new approach for measuring walkability, which is based on density maps of specific urban functions and networks of generally accessible pavements and paths. The method is implemented using open-source data. Density values are interpolated from point data (urban objects featuring specific functions) and polygons (pedestrian infrastructure) using Kernel Density and Line Density tools in GIS. The obtained values allow the calculation of a synthetic indicator taking into account the access by means of pedestrian infrastructure to public transport stops, parks and recreation areas, various attractions, shops and services. The proposed method was applied to calculate the walkability for Kraków (the second largest city in Poland). The greatest value of walkability was obtained for the Main Square (central part of the Old Town). The least accessible to pedestrians are, on the other hand, areas located on the outskirts of the city, which are intended for extensive industrial areas, single-family housing or large green areas.

Fomes fomentarius. [Descriptions of Fungi and Bacteria].

2021 ◽  
Author(s):  
D. W. Minter
Keyword(s):  
New York ◽  
Rhode Island ◽  
Tamil Nadu ◽  
Conservation Status ◽  
Uttar Pradesh ◽  
Voronezh Oblast ◽  
Moscow Oblast ◽  
Novosibirsk Oblast ◽  
Novgorod Oblast ◽  
Fomes Fomentarius

Abstract A description is provided for Fomes fomentarius. Sporophores of this fungus are found on both living and dead trees, where the fungus causes a decay of heartwood. Some information on its associated organisms and substrata, dispersal and transmission, habitats and conservation status is given, along with details of its geographical distribution (Africa (São Tomé and Principe, Somalia, Tunisia), Asia (Azerbaijan, China (Hong Kong), Cyprus, Georgia, India (Himachal Pradesh, Jammu & Kashmir, Karnataka, Meghalaya, Sikkim, Tamil Nadu, Uttarakhand, Uttar Pradesh, West Bengal), Iran, Japan, Kazakhstan (Akmola, Aktobe, Almaty, East Kazakhstan, Kostanay, North Kazakhstan, Pavlodar, South Kazakhstan, West Kazakhstan), Kyrgyzstan, Mongolia, Nepal, North Korea, Pakistan, Russia (Altai Krai, Altai Republic, Buryatia, Chelyabinsk Oblast, Irkutsk Oblast, Khabarovsk Krai, Novosibirsk Oblast, Primorsky Krai, Sakha Republic, Sakhalin Oblast, Tyumen Oblast, Zabaykalsky Krai), South Korea, Tajikistan, Turkey, Uzbekistan), Central America (Panama), Atlantic Ocean (Portugal (Madeira)), Europe (Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Kosovo, Latvia, Luxembourg, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia (Komi Republic, Krasnodar Krai, Moscow Oblast, Nizhny Novgorod Oblast, Orenburg Oblast, Republic of Karelia, Saratov Oblast, Voronezh Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, UK), North America (Canada (Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Ontario, Quebec, Saskatchewan), USA (Alabama, Alaska, California, Connecticut, District of Columbia, Florida, Idaho, Iowa, Kentucky, Maine, Massachusetts, Michigan, Minnesota, Montana, Nebraska, New Hampshire, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, South Dakota, Tennessee, Vermont, Virginia, Washington, West Virginia, Wisconsin)), South America (Brazil (Minas Gerais, Santa Catarina), Chile)).

scholarly journals Relationships between landslides and land use changes in the Sanbagawa and Mikabu Belts, Shikoku, Japan

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Roxanne Lai ◽  
Takashi Oguchi
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Abandonment ◽  
Crystalline Schist ◽  
Agricultural Abandonment ◽  
Landslide Kinematics

<p><strong>Abstract.</strong> Changing land use is an increasingly important issue as human habits, behaviors, and needs change. There has been an increase in land and agricultural abandonment in some places of the world. In Japan, movement of the population from rural to urban areas have resulted in much land and agricultural abandonment. In 2016, a land ministry survey showed that 4.1 million hectares of land in Japan had unclear ownership, with farmland making up 16.9% of the total. As vegetation cover changes after land abandonment, this temporal and spatial effect may have important effects on geomorphic processes such as landslide susceptibility and landslide kinematics.</p><p>Here we track long-term land use changes over vegetated landslide areas of the Sanbagawa and Mikabu Belts of Shikoku Island, Japan. The Sanbagawa and Mikabu Belts are metamorphic belts that run across Southwest Japan, and are home to numerous large crystalline schist landslides, including the widely-studied slow but continuously moving Zentoku landslide. Villages and communities have been built on these landslide areas due to historical and cultural factors, as well as the fertility of the soil. Consequently, given the changing land uses including land abandonment in these landslide areas over time, we use long-term high-resolution land cover vegetation datasets to examine first the long-term land use changes, and then use statistical methods to explore their relationships with landslide susceptibility and kinematics. Mapping of spatial data and their analysis using GIS constitute a core part of the research. The results suggest interconnections between land use changes and land movement.</p>

scholarly journals Estimation of Building Heights and DEM Accuracy Assessment Using ICESat-2 Data Products

2021 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Goddu Pavan Sai Goud ◽  
Ashutosh Bhardwaj
Keyword(s):  
Spatial Data ◽  
Rural Areas ◽  
Urban Areas ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Population Expansion ◽  
Cost Effective ◽  
Absolute Error ◽  
Google Earth ◽  
Surface Geometry

The use of remote sensing for urban monitoring is a very reliable and cost-effective method for studying urban expansion in horizontal and vertical dimensions. The advantage of multi-temporal spatial data and high data accuracy is useful in mapping urban vertical aspects like the compactness of urban areas, population expansion, and urban surface geometry. This study makes use of the ‘Ice, cloud, and land elevation satellite-2′ (ICESat-2) ATL 03 photon data for building height estimation using a sample of 30 buildings in three experimental sites. A comparison of computed heights with the heights of the respective buildings from google image and google earth pro was done to assess the accuracy and the result of 2.04 m RMSE was obtained. Another popularly used method by planners and policymakers to map the vertical dimension of urban terrain is the Digital Elevation Model (DEM). An assessment of the openly available DEM products—TanDEM-X and Cartosat-1 has been done over Urban and Rural areas. TanDEM-X is a German earth observation satellite that uses InSAR (Synthetic Aperture Radar Interferometry) technique to acquire DEM while Cartosat-1 is an optical stereo acquisition satellite launched by the Indian Space Research Organization (ISRO) that uses photogrammetric techniques for DEM acquisition. Both the DEMs have been compared with ICESat-2 (ATL-08) Elevation data as the reference and the accuracy has been evaluated using Mean error (ME), Mean absolute error (MAE) and Root mean square error (RMSE). In the case of Greater Hyderabad Municipal Corporation (GHMC), RMSE values 5.29 m and 7.48 m were noted for TanDEM-X 90 and CartoDEM V3 R1 respectively. While the second site of Bellampalli Mandal rural area observed 5.15 and 5.48 RMSE values for the same respectively. Therefore, it was concluded that TanDEM-X has better accuracy as compared to the CartoDEM V3 R1.

scholarly journals SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 127-134 ◽  
Author(s):  
M. A. Dogon-Yaro ◽  
P. Kumar ◽  
A. Abdul Rahman ◽  
G. Buyuksalih
Keyword(s):  
Digital Image ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Structural Complexity ◽  
Urban Ecosystems ◽  
Field Work ◽  
Urban Trees ◽  
Decision Makers ◽  
Image Datasets

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

scholarly journals Geographic Information Systems (GIS) in Urban Planning

2015 ◽  
Vol 1 (1) ◽  
pp. 85
Author(s):  
Sonila Xhafa ◽  
Albana Kosovrasti
Keyword(s):  
Sustainable Development ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Urban Planning ◽  
Urban Space ◽  
Spatial Data ◽  
Urban Areas ◽  
Geographic Information ◽  
Social Infrastructure ◽  
Urban Centers

Geographic information systems can be defined as a intelligent tool, to which it relates techniques for the implementation of processes such as the introduction, recording, storage, handling, processing and generation of spatial data. Use of GIS in urban planning helps and guides planners for an orderly development of settlements and infrastructure facilities within and outside urban areas. Continued growth of the population in urban centers generates the need for expansion of urban space, for its planning in terms of physical and social infrastructures in the service of the community, based on the principles of sustainable development. In addition urbanization is accompanied with numerous structural transformations and functional cities, which should be evaluated in spatial context, to be managed and planned according to the principles of sustainable development. Urban planning connects directly with land use and design of the urban environment, including physical and social infrastructure in service of the urban community, constituting a challenge to global levels. Use of GIS in this field is a different approach regarding the space, its development and design, analysis and modeling of various processes occurring in it, as well as interconnections between these processes or developments in space.

scholarly journals GNSS SATELLITE VISIBILITY ANALYSIS BASED ON 3D SPATIAL INFORMATION IN URBAN AREAS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 123-128
Author(s):  
Y.-H. Lu ◽  
J.-Y. Han
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Spatial Information ◽  
Signal Transmission ◽  
Point Clouds ◽  
Satellite System ◽  
Topographic Effects ◽  
Analysis Technique ◽  
Satellite Visibility ◽  
Global Navigation Satellite

Abstract. Global Navigation Satellite System (GNSS) is a matured modern technique for spatial data acquisition. Its performance has a great correlation with GNSS receiver position. However, high-density building in urban areas causes signal obstructions and thus hinders GNSS’s serviceability. Consequently, GNSS positioning is weakened in urban areas, so deriving proper improvement resolutions is a necessity. Because topographic effects are considered the main factor that directly block signal transmission between satellites and receivers, this study integrated aerial borne LiDAR point clouds and a 2D building boundary map to provide reliable 3D spatial information to analyze topographic effects. Using such vector data not only reflected high-quality GNSS satellite visibility calculations, but also significantly reduced data amount and processing time. A signal obstruction analysis technique and optimized computational algorithm were also introduced. In conclusion, this paper proposes using superimposed column method to analyze GNSS receivers’ surrounding environments and thus improve GNSS satellite visibility predictions in an efficient and reliable manner.

scholarly journals IDENTIFICATION OF DEVELOPMENT DETERMINANTS OF GREEN INFORMATION SYSTEMS FOR URBAN AREAS – POLISH CASE STUDY

2020 ◽  
Vol 19 (1) ◽  
pp. 45-60
Author(s):  
Magdalena Nowak ◽  
Agnieszka Dawidowicz ◽  
Ryszard Źróbek ◽  
Mai Do Thi Tuyet
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Green Infrastructure ◽  
Urban Areas ◽  
Impact Factors ◽  
Green Is ◽  
Data Infrastructure ◽  
Urban Greenery ◽  
The Creation ◽  
Green Information Systems

The green information systems (green IS) address the demand for information about green spaces in both urban and non-urbanized areas. This systems are part of green infrastructure (GI) and National Spatial Data Infrastructure (NSDI). GI are very important for the urban environment, and it improves the quality of life. There are various types of urban greenery. The green IS can support the management, maintenance, monitoring, protection and revitalization of urban greenery and all GI. This systems contribute to the sustainable development of urban areas, the development of smart and green cities and spatially enabled societies where community members are involved in local projects. In Poland, few cities have so far taken the effort to create a green IS due to the costs of starting and maintaining the system. Municipalities give up the creation of this system because it is not a good first necessity. However, green infrastructure is developing in Poland and there is a strong demand for green IS for easier GI management. Therefore, the aim of the research was to identify various determinants (factors) that may affect the development of green IS in Poland. Analysisof determinants is necessary and important from the point of view of knowledge of mechanisms affecting the development of green IS and may be useful to develop a strategy for further activities promoting the creation of green IS in all cities in Poland. The research results provided the basis for distinguishing groups of impact factors due to their specificity and showed which instruments are applied to them taking into account global and local initiatives.

scholarly journals GIS-based delineation of local climate zones: The case of medium-sized Central European cities

2016 ◽  
Vol 24 (3) ◽  
pp. 2-12 ◽  
Author(s):  
Jan Geletič ◽  
Michal Lehnert
Keyword(s):  
Decision Making ◽  
Urban Areas ◽  
Expert Knowledge ◽  
Mapping Method ◽  
Rural Settlements ◽  
Climate Zones ◽  
Local Climate ◽  
Central European ◽  
European Cities ◽  
Local Climate Zones

Abstract Stewart and Oke (2012) recently proposed the concept of Local Climate Zones (LCZ) to describe the siting of urban meteorological stations and to improve the presentation of results amongst researchers. There is now a concerted effort, however, within the field of urban climate studies to map the LCZs across entire cities, providing a means to compare the internal structure of urban areas in a standardised way and to enable the comparison of cities. We designed a new GIS-based LCZ mapping method for Central European cities and compiled LCZ maps for three selected medium-sized Central European cities: Brno, Hradec Králové, and Olomouc (Czech Republic). The method is based on measurable physical properties and a clearly defined decision-making algorithm. Our analysis shows that the decision-making algorithm for defining the percentage coverage for individual LCZs showed good agreement (in 79–89% of cases) with areas defined on the basis of expert knowledge. When the distribution of LCZs on the basis of our method and the method of Bechtel and Daneke (2012) was compared, the results were broadly similar; however, considerable differences occurred for LCZs 3, 5, 10, D, and E. It seems that Central European cities show a typical spatial pattern of LCZ distribution but that rural settlements in the region also regularly form areas of built-type LCZ classes. The delineation and description of the spatial distribution of LCZs is an important step towards the study of urban climates in a regional setting.

Tremella mesenterica. [Descriptions of Fungi and Bacteria].

2022 ◽  
Author(s):  
D. W. Minter
Keyword(s):  
South Carolina ◽  
Rhode Island ◽  
Prince Edward Island ◽  
Conservation Status ◽  
Voronezh Oblast ◽  
Moscow Oblast ◽  
Omsk Oblast ◽  
Murmansk Oblast ◽  
Mato Grosso ◽  
Novgorod Oblast

Abstract A description is provided for Tremella mesenterica, a parasite on mycelium of (perhaps exclusively) Peniophora spp. Some information on its associated organisms and substrata, dispersal and transmission, habitats and conservation status is given, along with details of its geographical distribution (Africa (Benin, Democratic Republic of the Congo, Morocco, South Africa, Tunisia), Asia (Armenia, Azerbaijan, China (Hong Kong, Sichuan, Yunnan), Georgia, India (Chhattisgarh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Meghalaya, Sikkim), Iran, Israel, Japan, Kazakhstan (Almaty, East Kazakhstan), Lebanon, Malaysia, Philippines, Russia (Altai Krai, Amur Oblast, Irkutsk Oblast, Jewish Autonomous Oblast, Kamchatka Krai, Khabarovsk Krai, Khanty-Mansi Autonomous Okrug, Omsk Oblast, Primorsky Krai, Sakha Republic, Sakhalin Oblast, Sverdlovsk Oblast, Tyumen Oblast, Yamalo-Nenets Autonomous Okrug), South Korea, Sri Lanka, Taiwan, Tajikistan, Turkey, Turkmenistan, Uzbekistan), Australasia (Australia (Australian Capital Territory, New South Wales, Northern Territory, Queensland, South Australia, Tasmania, Victoria, Western Australia), New Zealand), Caribbean (Jamaica, Puerto Rico), Central America (Costa Rica, Honduras, Panama), Europe (Austria, Belarus, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Liechtenstein, Lithuania, Luxembourg, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia (Arkhangelsk Oblast, Belgorod Oblast, Bryansk Oblast, Chuvash Republic, Ivanovo Oblast, Kaliningrad Oblast, Kaluga Oblast, Kirov Oblast, Komi Republic, Kostroma Oblast, Krasnodar Krai, Kursk Oblast, Leningrad Oblast, Mari El Republic, Moscow Oblast, Murmansk Oblast, Nizhny Novgorod Oblast, Novgorod Oblast, Perm Krai, Pskov Oblast, Republic of Adygea, Republic of Bashkortostan, Republic of Dagestan, Republic of Mordovia, Republic of Tatarstan, Tula Oblast, Tver Oblast, Udmurt Republic, Vladimir Oblast, Voronezh Oblast, Yaroslavl Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, UK), Indian Ocean (Réunion), North America (Canada (Alberta, British Columbia, New Brunswick, Newfoundland and Labrador, Northwest Territories, Nova Scotia, Ontario, Prince Edward Island, Quebec, Saskatchewan, Yukon), Mexico, USA (Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming)), Pacific Ocean (USA (Hawaii)), South America (Argentina, Brazil (Bahia, Mato Grosso do Sul, Minas Gerais, Paraná, Rio Grande do Sul, Rio de Janeiro, Santa Catarina, São Paulo), Chile, Colombia, Ecuador, French Guiana, Guyana, Peru, Venezuela)).

Sign in / Sign up

Export Citation Format

Share Document