Advancing Land Change Modeling: Opportunities and Research by National Research Council, Division on Earth and Life

By National Research Council, Division on Earth and Life Studies, Board on Earth Sciences and Resources, Geographical Sciences Committee, Committee on Needs and Research Requirements for Land Change Modeling

Everyone is always altering the land floor via development, agriculture, strength construction, and different actions. adjustments either in how land is utilized by humans (land use) and within the plants, rock, structures, and different actual fabric that conceal the Earth's floor (land conceal) might be defined and destiny land swap should be projected utilizing land-change versions (LCMs). LCMs are a key ability for figuring out how people are reshaping the Earth's floor long ago and current, for forecasting destiny panorama stipulations, and for constructing guidelines to control our use of assets and the surroundings at scales starting from someone parcel of land in a urban to large expanses of forests world wide.

Advancing Land switch Modeling: possibilities and study Requirements describes quite a few LCM ways, indicates counsel for his or her applicable program, and makes thoughts to enhance the mixing of commentary options into the versions. This record presents a precis and assessment of numerous modeling techniques, and their theoretical and empirical underpinnings, relative to advanced land-change dynamics and procedures, and identifies a number of possibilities for extra advancing the technology, information, and cyberinfrastructure fascinated about the LCM firm. as a result of a number of versions on hand, the file specializes in describing the types of techniques used besides chosen examples, instead of offering a assessment of particular types. also, simply because all modeling techniques have relative strengths and weaknesses, the document compares those relative to varied reasons. Advancing Land swap Modeling's innovations for evaluation of destiny facts and learn wishes will let version outputs to raised support the technological know-how, coverage, and decisionsupport groups.

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Extra resources for Advancing Land Change Modeling: Opportunities and Research Requirements

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Then the maximum-likelihood algorithm estimates the equation’s coefficients so the regression curve fits as closely as possible to the data, given the form of the monotonic sigmoidal relationship. The coefficients indicate whether the assumed monotonic relationships are increasing or decreasing, and at what rate. The logistic regression might also include interactions among the explanatory variables. Diagnostic measurements help to interpret the fitted coefficients of the regression equation. In comparison with logistic regression, machine learning algorithms do not require strong assumptions concerning a particular form of a mathematical equation to express a relationship between the land cover map(s) and the map(s) of explanatory variable(s).

A challenge for other data sources, such as field surveys and censuses, is how to sustain consistent collection efforts over the long term to create time-series data that could be more useful for LCMs. An Abundance of New Data Sources The growth of new data sources from satellites, aircraft, ground sensors, and “citizen science” presents new opportunities to measure and characterize land systems. The massive increase in data-gathering methods and data sets has not been matched by parallel increases in approaches for turning raw geographic data into more meaningful information about land systems or inputs to LCMs.

Furthermore, interactions in cellular models are almost exclusively represented by spatial neighborhoods, mostly ignoring interactions through social and other networks. , 1999) is an issue that makes it difficult to transfer model parameterizations across case studies and scales. , up to 20 years). However, in cellular models run over large extents and used to inform climate change assessments, the simulations are extended to periods of up to one century and over such longer time scales, feedbacks in socioecological systems become increasingly important.

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