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/.

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>

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.

Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages

2019 ◽  
Keyword(s):  
Land Use ◽  
Human Activity ◽  
Human Activities ◽  
Census Data ◽  
Lessons Learned ◽  
Data Sources ◽  
Aquatic Biota ◽  
Human Environment ◽  
Freshwater Habitats ◽  
Landscape Scales

<i>Abstract.</i>—Over the past decade, numerous studies have identified correlative relationships between aquatic biota and human activities at landscape scales. In addition to demonstrating the pervasive effects of these activities on aquatic biota, these findings have encouraged researchers to suggest that predictive relationships between human activities and aquatic biota could be used to enhance diagnostic power of biological assessments, predict future changes in species distributions, and inform land-use planning. However, to achieve these important goals, descriptions of human activities will need to become more detailed than the simple land use/land cover classifications frequently used. Our purpose is to highlight four sources of human activity data (existing geographic information system layers, census data, remotely sensed images, and visual landscape surveys) that can be used to increase the level of detail with which the human environment is described. Strengths and weaknesses of each data source are discussed and methods for adapting those data to aquatic studies are described by drawing on experiences from studies in the agricultural landscapes of southern Manitoba and southwestern Ontario, Canada. Based on the observations and lessons learned from our previous experiences, we make recommendations for how researchers can identify and apply the data sources that best meet their needs. We also discuss challenges and possible solutions for applying the described data sources as well as for improving data availability in the future. Moreover, we encourage aquatic researchers to allot more time to detailed description of human activities because we believe this to be an effective approach to improving our ability to predict the effects of human activity and thus better assist decision makers in protecting aquatic ecosystems.

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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