geospatial modelling
Recently Published Documents


TOTAL DOCUMENTS

89
(FIVE YEARS 40)

H-INDEX

12
(FIVE YEARS 4)

Author(s):  
Kezia Faith OBBUS ◽  
James Nicko FLORDELİS ◽  
Earl Godfred VELOS ◽  
Numerıano Amer GUTİERREZ
Keyword(s):  

2021 ◽  
Vol 906 (1) ◽  
pp. 012072
Author(s):  
Robert Sasik ◽  
Jakub Srek ◽  
Alessandro Valetta

Abstract The modelling means the world object cognition based on the analogy. This analogy presents an idea and material imitation of some properties of the existing world. It is processed by various anthropogenic objects, in which the chosen properties are presented, defined and characterised as shapes and relations of original objects. The simplified objects are created. These objects are specially created only for world study. These types of objects are called models. To edit the digital terrain model correctly, it is necessary to understand the geospatial modelling.


2021 ◽  
pp. e01032
Author(s):  
Joel Efiong ◽  
Devalsam Imoke Eni ◽  
Josiah Nwabueze Obiefuna ◽  
Sylvia James Etu

2021 ◽  
Author(s):  
Johan van den Hoogen ◽  
Niamh Robmann ◽  
Devin Routh ◽  
Thomas Lauber ◽  
Nina van Tiel ◽  
...  

Geospatial modelling can give fundamental insights in the biogeography of life, providing key information about the living world in current and future climate scenarios. Emerging statistical and machine learning approaches can help us to generate new levels of predictive accuracy in exploring the spatial patterns in ecological and biophysical processes. Although these statistical models cannot necessarily represent the essential mechanistic insights that are needed to understand global biogeochemical processes under ever-changing environmental conditions, they can provide unparalleled predictive insights that can be useful for exploring the variation in biophysical processes across space. As such, these emerging tools can be a valuable approach to complement existing mechanistic approaches as we aim to understand the biogeography of Earth's ecosystems. Here, we present a comprehensive methodology that efficiently handles large datasets to produce global predictions. This mapping pipeline can be used to generate quantitative, spatially explicit predictions, with a particular emphasis on spatially-explicit insights into the evaluation of model uncertainties and inaccuracies.


Sign in / Sign up

Export Citation Format

Share Document