Testing an Integrated Land Use and Transportation Modeling Framework for a Small Metropolitan Area

2009 ◽  
Vol 2133 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Brian Voigt ◽  
Austin Troy ◽  
Brian Miles ◽  
Alexandra Reiss
Keyword(s):  
Land Use ◽  
Metropolitan Area ◽  
Modeling Framework ◽  
Transportation Modeling

scholarly journals Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

2021 ◽  
Vol 13 (4) ◽  
pp. 631
Author(s):  
Kyle D. Woodward ◽  
Narcisa G. Pricope ◽  
Forrest R. Stevens ◽  
Andrea E. Gaughan ◽  
Nicholas E. Kolarik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Resource Use ◽  
Training Data ◽  
Systems Science ◽  
Participatory Mapping ◽  
Land Use Patterns ◽  
Modeling Framework ◽  
Use Patterns ◽  
Human Habitation

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

Urban environmental design of land use in Tokyo metropolitan area

1990 ◽  
Vol 15 (1-2) ◽  
pp. 133-137 ◽  
Author(s):  
Hironori Watanabe ◽  
Hirotoshi Yoda ◽  
Toshio Ojima
Keyword(s):  
Land Use ◽  
Metropolitan Area ◽  
Environmental Design ◽  
Tokyo Metropolitan ◽  
Tokyo Metropolitan Area

The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE

2021 ◽  
Author(s):  
Peter H. Verburg ◽  
Žiga Malek ◽  
Sean P. Goodwin ◽  
Cecilia Zagaria
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
Land Use Changes ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Regional Modelling ◽  
Model Application ◽  
Climatic Regions ◽  
Single Region

The Conversion of Land Use and its Effects modeling framework (CLUE) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modeling of competition between land use types. Being one of the most widely used spatial land use models, CLUE has been applied all over the world on different scales. In this document, we demonstrate how the model can be used to develop a multi-regional application. This means, that instead of developing numerous individual models, the user only prepares one CLUE model application, which then allocates land use change across different regions. This facilitates integration with the Integrated Economic-Environmental Modeling (IEEM) Platform for subnational assessments and increases the efficiency of the IEEM and Ecosystem Services Modeling (IEEMESM) workflow. Multi-regional modelling is particularly useful in larger and diverse countries, where we can expect different spatial distributions in land use changes in different regions: regions of different levels of achieved socio-economic development, regions with different topographies (flat vs. mountainous), or different climatic regions (dry vs humid) within a same country. Accounting for such regional differences also facilitates developing ecosystem services models that consider region specific biophysical characteristics. This manual, and the data that is provided with it, demonstrates multi-regional land use change modeling using the country of Colombia as an example. The user will learn how to prepare the data for the model application, and how the multi-regional run differs from a single-region simulation.

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