scholarly journals comments on “Estimation of land-use change using a Bayesian data assimilation approach”

2017 ◽  
Author(s):  
Anonymous
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Bayesian Data Assimilation

scholarly journals Estimation of land-use change using a Bayesian data assimilation approach

2017 ◽  
Author(s):  
Peter Levy ◽  
Marcel Van Oijen ◽  
Gwen Buys ◽  
Sam Tomlinson
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Likelihood Function ◽  
Skewed Distribution ◽  
Data Sources ◽  
Modelling Uncertainty ◽  
Bayesian Data Assimilation ◽  
Posterior Estimate

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land-use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/ (doi pending).

Identifying a land use change cellular automaton by Bayesian data assimilation

2014 ◽  
Vol 53 ◽  
pp. 121-136 ◽  
Author(s):  
Judith A. Verstegen ◽  
Derek Karssenberg ◽  
Floor van der Hilst ◽  
André P.C. Faaij
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Cellular Automaton ◽  
Bayesian Data Assimilation

A Bayesian data assimilation approach to estimating land-use change

2021 ◽  
Author(s):  
Peter E. Levy
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Joint Probability ◽  
Data Cube ◽  
Joint Probability Distribution ◽  
Multiple Sources ◽  
Time Data ◽  
Bayesian Data Assimilation

<p>The aim of this work was to make improved estimates of land-use change in the UK, using multiple sources of data. We applied a method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with national-scale census data, whilst retaining the detailed information available from several other sources. We produced a time series of maps describing our best estimate of land-use change given the available data, as well as the full posterior distribution of this space-time data cube. This quantifies the joint probability distribution of the parameters, and properly propagates the uncertainty from input data to final output. The output data has been summarised in the form of land-use vectors. The results show that we can provide improved estimates of past land-use change using this method. The main advantage of the approach is that it provides a coherent, generalised framework for combining multiple disparate sources of data, and adding further sources of data in future is straightforward.</p>

scholarly journals Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach

2018 ◽  
Vol 15 (5) ◽  
pp. 1497-1513 ◽  
Author(s):  
Peter Levy ◽  
Marcel van Oijen ◽  
Gwen Buys ◽  
Sam Tomlinson
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Likelihood Function ◽  
Skewed Distribution ◽  
Data Sources ◽  
Modelling Uncertainty ◽  
Bayesian Data Assimilation ◽  
Posterior Estimate

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/.

scholarly journals Detecting systemic change in a land use system by Bayesian data assimilation

2016 ◽  
Vol 75 ◽  
pp. 424-438 ◽  
Author(s):  
Judith A. Verstegen ◽  
Derek Karssenberg ◽  
Floor van der Hilst ◽  
André P.C. Faaij
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Systemic Change ◽  
Land Use System ◽  
Bayesian Data Assimilation

Assessment of land-use change in coastal areas through geographical information systems

2020 ◽  
Vol 13 (10) ◽  
Author(s):  
Verónica Lango-Reynoso ◽  
Karla Teresa González-Figueroa ◽  
Fabiola Lango-Reynoso ◽  
María del Refugio Castañeda-Chávez ◽  
Jesús Montoya-Mendoza
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Geographical Information Systems ◽  
Design Methodology ◽  
Scientific Information ◽  
Coastal Ecosystems ◽  
Coastal Areas ◽  
Geographical Information ◽  
Coastal Zones ◽  
Computer Based

Objective: This article describes and analyzes the main concepts of coastal ecosystems, these as a result of research concerning land-use change assessments in coastal areas. Design/Methodology/Approach: Scientific articles were searched using keywords in English and Spanish. Articles regarding land-use change assessment in coastal areas were selected, discarding those that although being on coastal zones and geographic and soil identification did not use Geographic Information System (GIS). Results: A GIS is a computer-based tool for evaluating the land-use change in coastal areas by quantifying variations. It is analyzed through GIS and its contributions; highlighting its importance and constant monitoring. Limitations of the study/Implications: This research analyzes national and international scientific information, published from 2007 to 2019, regarding the land-use change in coastal areas quantified with the digital GIS tool. Findings/Conclusions: GIS are useful tools in the identification and quantitative evaluation of changes in land-use in coastal ecosystems; which require constant evaluation due to their high dynamism.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

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