scholarly journals Regularities in species niches reveal the World’s climatic regions

2019 ◽  
Author(s):  
Joaquín Calatayud ◽  
Magnus Neuman ◽  
Alexis Rojas ◽  
Anton Eriksson ◽  
Martin Rosvall
Keyword(s):  
Conservation Planning ◽  
Geographical Information ◽  
Climate Classification ◽  
Climatic Regions ◽  
Evolutionary Studies

Although classifications of the Earth’s climates date back to the ancient Greeks, the climatic regions shaping the distribution of animals remain poorly resolved. Here we present a classification of global climates based on regularities in realised niches of 3657 amphibians, 7204 reptiles, 10684 birds and 4574 mammals. We found 16 main climatic regions that are mostly consistent across groups and previous plant expert-based classifications, confirming the existence of major climatic restrictions for life. The results also suggest that differences among groups likely relate to their particular adaptations and dispersal capabilities. We further show how the integration of species niche classifications with geographical information provides valuable information on potential mechanisms shaping the climatic regions. Our climate classification has applications in several disciplines, including conservation planning and ecological and evolutionary studies.

scholarly journals Bioclimatic Approach for Climate Classification of Nigeria

2020 ◽  
Vol 12 (10) ◽  
pp. 4192
Author(s):  
Tolulope Dorcas Mobolade ◽  
Parastoo Pourvahidi
Keyword(s):  
Climate Change ◽  
Vernacular Architecture ◽  
Mechanical Equipment ◽  
Climatic Data ◽  
Climate Classification ◽  
Climatic Regions ◽  
The People ◽  
Harsh Climate ◽  
Cool Climate

One of the fundamental determinants of buildings is the protection of the people who live and work within them from a harsh climate, but a lot of buildings in Nigeria are no longer providing the required comfort needed. The gas emissions through the use of mechanical equipment and lack of energy efficiency in buildings are the major causes of climate change. The way architecture responds to climate change is important. Thus, this research attempted, by using the new bioclimatic chart, to prepare the new climate classification of Nigeria. The research was aimed at establishing a bioclimatically based approach for architecture in Nigeria. By retrieving the climatic data from thirty-six Nigerian meteorological stations about characteristics of each region, bioclimatic analysis was achieved. According to the bioclimatic analysis of this research, Nigeria can be divided into five different climatic regions, such as hot-dry, hot-humid, temperate-dry, temperate-humid, and temperate-dry with a cool climate. We aimed to prove that the climate classification gives the proper answer, dependent upon the vernacular architecture analyzed on Nigeria.

scholarly journals Climate classification of Asian university forests under current and future climate

2020 ◽  
Vol 25 (3) ◽  
pp. 136-146
Author(s):  
Yen-Jen Lai ◽  
Nobuaki Tanaka ◽  
Sangjun Im ◽  
Koichiro Kuraji ◽  
Chatchai Tantasirin ◽  
...  
Keyword(s):  
Future Climate ◽  
Climate Classification

scholarly journals A Geographical Factor of Interest Recommended Strategies in Location Based Social Networks

2018 ◽  
Vol 7 (3.27) ◽  
pp. 32
Author(s):  
Bulusu Rama ◽  
K Sai Prasad ◽  
Ayesha Sultana ◽  
K Shekar
Keyword(s):  
User Modeling ◽  
Life Experiences ◽  
Real Life ◽  
Geographical Information ◽  
Huge Number ◽  
Fast Development ◽  
New Guidelines ◽  
Location Based Social Networks ◽  
Legal Guidelines

The fast development of area based administrations (LBSNs) has extensively advanced individuals' city lives and pulled in a huge number of recent years. Area based informal organizations (LBSNs) allow clients to registration at a real region and offer step by step rules on purposes of-intrigue (POIs) with their pals each time and anyplace. Such check-in behavior can make daily real-life experiences spread rapidly via the Internet. Moreover, such check-in records in LBSNs can be totally exploited to understand the basic legal guidelines of humans’ every day motion and mobility. This paper centers on evaluating the scientific classification of client displaying for POI proposals through the information investigation of LBSNs. First, we quickly introduce the shape and records traits of LBSNs, then we current a formalization of user modeling for POI suggestions in LBSNs. Contingent upon which sort of LBSNs records used to be completely used in buyer displaying forms for POI proposals, we separate client demonstrating calculations into four classifications: pure check-in data-based consumer modeling, geographical information-based consumer modeling, spatial-temporal information-based consumer modeling, and geo-social information-based consumer modeling. At finally, condensing the current works, we bring up the future difficulties and new guidelines in five possible aspects  

Cartography and GIS

2019 ◽  
Author(s):  
Вячеслав Раклов ◽  
Vyacheslav Raklov
Keyword(s):  
Real Estate ◽  
Land Management ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Gis Mapping ◽  
History Of ◽  
Gis Environment ◽  
Composition Structure ◽  
Classification Of Maps

The textbook considers the basic concepts of cartography, the history of its development, as well as the classification of maps and the main elements of the map, the issues of mathematical cartography, the main stages of creating maps, the factors, types and methods of cartographic generalization. Separate sections of the manual are devoted to cartographic signs and methods of image on maps of thematic content, the development of cartographic scales and methods of use of maps in land management and cadastre. Separately, the issues of the functioning of geographical information systems (GIS): their composition, structure, technology for creating thematic maps in the GIS environment. The manual concludes with a section on GIS mapping for real estate cadastre, environmental protection and land monitoring, as well as recommendations on the choice of GIS and requirements for cartographic documentation of real estate cadastre. Recommended for students studying in the field of "land Management", "Land cadastre", "Urban cadastre".

Objective climate classification of Slovenia

2017 ◽  
Vol 37 ◽  
pp. 848-860 ◽  
Author(s):  
Katja Kozjek ◽  
Mojca Dolinar ◽  
Gregor Skok
Keyword(s):  
Climate Classification

scholarly journals Pemetaan Kawasan Lahan Pertanian Pangan Berkelanjutan di Kuta Utara dan Mengwi Berbasis Penginderaan Jauh dan Sistem Informasi Geografis

2021 ◽  
Vol 11 (2) ◽  
pp. 176
Author(s):  
KARTIKA DEWI OKTAFIANTI ◽  
INDAYATI LANYA ◽  
NI MADE TRIGUNASIH
Keyword(s):  
Remote Sensing ◽  
Agricultural Land ◽  
Field Survey ◽  
Spatial Information ◽  
Image Interpretation ◽  
Rice Fields ◽  
Geographical Information ◽  
Sustainable Food ◽  
Physical Conditions

Mapping of Sustainable Food Agricultural Land at North Kuta and Mengwi Districts Based on Remote Sensing and Geographical Information System. Sustainable Food Agricultural Land (LP2B) is a field of agricultural land designated to be protected and developed consistently in order to produce staple food for national food independence, resilience and sovereignty. The Badung Regency Government has determined the area and location of LP2B but it has not been accompanied by a spatial information map. This study aims to map subak rice fields in 2019 as well as mapping of LP2B based on the physical conditions of the area and the environment in North Kuta and Mengwi Districts based on remote sensing and GIS. The method used consists of image interpretation, field survey and numerical classification. The results showed that the distribution of subak rice fields in North Kuta and Mengwi Districts was 4967.22 ha. The distribution of rice fields in North Kuta District is 850.15 ha and in Mengwi District is 4117.07 ha. In the classification of LP2B areas, the recommended area is model 1 (234.88 ha), model 2 (939.76 ha) and model 3 (2048.63 ha). The recommendation areas are in model 1 (1489.91 ha), model 2 (1101.52 ha) and model 3 (2047.53 ha). The conditional recommendation area is in model 1 (2969.50 ha), model 2 (2048.49 ha) and model 3 (873.39 ha). Not recommended area in model 1 (270.81 ha), model 2 (875.33 ha) and model 3 (0 ha).

scholarly journals Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data

2016 ◽  
Vol XLI-B3 ◽  
pp. 173-180 ◽  
Author(s):  
A. Bellakaout ◽  
M. Cherkaoui ◽  
M. Ettarid ◽  
A. Touzani
Keyword(s):  
Point Cloud ◽  
Geographical Information ◽  
Gis Mapping ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Second Phase ◽  
Variation Analysis ◽  
Height Variation ◽  
Topological Relationship

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. <br><br> This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. <br><br> This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.

scholarly journals Thornthwaite climate classification of TurkeyTürkiye Thornthwaite iklim sınıflandırması

2016 ◽  
Vol 13 (3) ◽  
pp. 3973 ◽  
Author(s):  
Erkan Yılmaz ◽  
İhsan Çiçek
Keyword(s):  
Large Scale ◽  
Potential Evaporation ◽  
Grid Data ◽  
Climate Classification ◽  
Environmental Media ◽  
Monthly Average ◽  
Topographical Effects ◽  
Average Precipitation ◽  
Effectiveness Index

It is possible to see different climatic types together in Turkey having very different habitats, ecosystems and environmental media by the mathematical and special position where it is located in. The creation of large-scale weather maps and databases to reflect this diversity is necessary for the clarification of the elements that are affected by this diversity. This diversity could not be reflected sufficiently due to the reason that the weather maps which have been created so far today are not large-scale. In this study, it is aimed to create a map and a database concerning Turkey, which is detailed, in which the topographical effects are reflected, which shows the ecosystem differences and which is digitally accessible. For this purpose, the Thornthwaite climate classification in which the sub-climatic types can be digitally separated was preferred. First of all, the monthly average precipitation and temperature data were arranged at 805.000 points of Turkey and then this data was analyzed according to the Thornthwaite climate classification method. The results obtained were submitted as grid data at the resolution of 1 km in 4 different categories and an integrated climate class map by the combination of them. According to the results of the analysis, it was determined that there are 8 different precipitation effectiveness index classes, 8 different temperature effect index classes, 6 different drought and moistness index classes and 8 different potential evaporation index classes in Turkey. It was seen that the number of the integrated index classes created by the combination of them is 233. Accordingly, it is possible to say that there are 233 different environmental conditions in Turkey. ÖzetBulunduğu matematiksel ve özel konum itibariyle çok farklı habitat, ekosistem ve çevresel ortam barındıran Türkiye’de farklı iklim tiplerini bir arada görmek mümkündür. Bu çeşitliliği yansıtacak büyük ölçekli iklim haritalarının ve veri tabanlarının oluşturulması, bu çeşitlilikten etkilenen ögelerin açıklanması için gereklidir. Günümüze kadar yapılan iklim haritalarının büyük ölçekli olmamaları nedeniyle bu çeşitlilik yeteri kadar yansıtılamamıştır. Bu çalışmada, Türkiye’ye ait ayrıntılı, topografik etkilerin yansıtıldığı, ekosistem farklılıklarını gösteren ve sayısal olarak ulaşılabilecek bir harita, veri tabanını üretmek amaçlanmıştır. Bu amaçla, alt iklim tiplerinin sayısal olarak ayrılabildiği Thornthwaite iklim sınıflandırması tercih edilmiştir. Önce, Türkiye’de 805.000 noktasında aylık ortalama yağış ve sıcaklık verileri düzenlenmiş, ardından bu veriler Thornthwaite iklim sınıflandırma yöntemine göre analiz edilmiştir. Elde edilen sonuçlar 4 farklı kategoride 1 km çözünürlükte grid verisi olarak sunulmuş ve bunların birleştirilmesi ile tümleşik iklim sınıfı haritası oluşturulmuştur. Analiz sonuçlarına göre Türkiye’de 8 farklı yağış etkinlik indisi, 8 farklı sıcaklık tesiri indisi, 6 farklı kuraklık ve nemlilik indisi ve 8 farklı potansiyel buharlaşma indis sınıfının olduğu belirlenmiştir. Bunların birleştirilmesi ile oluşturulan tümleşik indis sınıflarının ise 233 olduğu görülmüştür. Buna bağlı olarak Türkiye’de 233 farklı çevresel koşul olduğunu söylemek mümkündür.

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