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 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).

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/.

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

scholarly journals Estimating historical land use patterns in Amazonia for hydrological studies

2017 ◽  
Vol 8 (4) ◽  
pp. 189-197
Author(s):  
Christiane Cavalcante Leite ◽  
Marcos Heil Costa ◽  
Ranieri Carlos Ferreira de Amorim
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Land Use Change ◽  
Census Data ◽  
Land Use Patterns ◽  
Use Patterns ◽  
The Past ◽  
The World ◽  
Historical Land Use ◽  
Past Land Use

The evaluation of the impacts of land-use change on the water resources has been, many times, limited by the knowledge of past land use conditions. Most publications on this field present only a vague description of the past land use, which is usually insufficient for more comprehensive studies. This study presents the first reconstruction of the historical land use patterns in Amazonia, that includes both croplands and pasturelands, for the period 1940-1995. During this period, Amazonia experienced the fastest rates of land use change in the world, growing 4-fold from 193,269 km2 in 1940 to 724,899 km2 in 1995. This reconstruction is based on a merging of satellite imagery and census data, and provides a 5'x5' yearly dataset of land use in three different categories (cropland, natural pastureland and planted pastureland) for Amazonia. This dataset will be an important step towards understanding the impacts of changes in land use on the water resources in Amazonia.

scholarly journals Assessment of the Finnish wolf population combines DNA captures, citizen observations and mortality data using a Bayesian state-space model

2021 ◽  
Author(s):  
Samu Mäntyniemi ◽  
Inari Helle ◽  
Ilpo Kojola
Keyword(s):  
Current Knowledge ◽  
Joint Probability ◽  
Assessment Model ◽  
Assessment Process ◽  
Mortality Data ◽  
Joint Probability Distribution ◽  
Sources Of Information ◽  
Wolf Population ◽  
Multiple Sources ◽  
Data Set

Assessment of the Finnish wolf population relies on multiple sources of information. This paper describes how Bayesian inference is used to pool the information contained in different kind of data sets (point observations, non-invasive genetics, known mortalities) for the estimation of the number of territories occupied by family packs and pairs. The output of the assessment model is a joint probability distribution, which describes current knowledge about the number of wolves within each territory. The joint distribution can be used to derive probability distributions for the total number of wolves in all territories and for the pack status within each territory. Most of the data set comprises of both voluntary-provided point observations and DNA samples provided by volunteers and research personnel. The new method reduces the role of expert judgement in the assessment process, providing increased transparency and repeatability.

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
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