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The Relative Timing of Human Migrati...
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Shih, Hsiao-chien.
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The Relative Timing of Human Migration and Land-Cover and Land-Use Change: An Evaluation of Northern Taiwan from 1990 to 2015.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
The Relative Timing of Human Migration and Land-Cover and Land-Use Change: An Evaluation of Northern Taiwan from 1990 to 2015./
作者:
Shih, Hsiao-chien.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
120 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Contained By:
Dissertations Abstracts International82-03B.
標題:
Geographic information science. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27994768
ISBN:
9798672103587
The Relative Timing of Human Migration and Land-Cover and Land-Use Change: An Evaluation of Northern Taiwan from 1990 to 2015.
Shih, Hsiao-chien.
The Relative Timing of Human Migration and Land-Cover and Land-Use Change: An Evaluation of Northern Taiwan from 1990 to 2015.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 120 p.
Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Thesis (Ph.D.)--University of California, Santa Barbara, 2020.
This item must not be sold to any third party vendors.
Urban land expansion can be driven by and drive population growth, but determining cause-effect relationships of urban land expansion and population growth is challenging due to the temporal resolution of decadal censuses for most countries. The relative timing of urban land expansion and population change was explored based on a case study for north Taiwan from 1990 to 2015. Data on urban land expansion were derived from a dense time series of Landsat satellite imagery, and population change from annual population registers at the district level.A novel algorithm for identifying the starting time of urban expansion was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) proportion maps derived from Landsat surface reflectance imagery. The time of urbanization estimated by logistic regression was determined from the Impervious cover time series. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of urban expansion estimated within ± 2.4 years.The random forest (RF) classification routine was applied to Landsat image inputs for detailed urban land-use classification. Input features were extracted from a 2015 Landsat image dataset, and the top 10 most contributing features for RF classifiers were the six spectral wavebands, gray level co-occurrence matrix (GLCM) homogeneity of V-I-S, and Vegetation temporal variation. The Landsat image was tested with different input features, and the land-use maps derived from the top 10 features yielded the most accurate map. Areal extents of annual urban land uses (Residential, Employment, and Transportation corridor categories) were derived by applying a conditional statement based on the urbanization date of the urban expansion map and overlaying on the 2015 land-use map. The relative timing and general relationship between urban land-use and population dynamics was explored at the district level. Linear regression was run to understand the general relationship between population and land use. The relative timing between population growth and land-use change was estimated with lagged correlation. Residential land use and change was most related to population and change. Population growth occurred 2.5 years later than Residential land expansion based on the median time lag.
ISBN: 9798672103587Subjects--Topical Terms:
3432445
Geographic information science.
Subjects--Index Terms:
Land use change
The Relative Timing of Human Migration and Land-Cover and Land-Use Change: An Evaluation of Northern Taiwan from 1990 to 2015.
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Urban land expansion can be driven by and drive population growth, but determining cause-effect relationships of urban land expansion and population growth is challenging due to the temporal resolution of decadal censuses for most countries. The relative timing of urban land expansion and population change was explored based on a case study for north Taiwan from 1990 to 2015. Data on urban land expansion were derived from a dense time series of Landsat satellite imagery, and population change from annual population registers at the district level.A novel algorithm for identifying the starting time of urban expansion was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) proportion maps derived from Landsat surface reflectance imagery. The time of urbanization estimated by logistic regression was determined from the Impervious cover time series. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of urban expansion estimated within ± 2.4 years.The random forest (RF) classification routine was applied to Landsat image inputs for detailed urban land-use classification. Input features were extracted from a 2015 Landsat image dataset, and the top 10 most contributing features for RF classifiers were the six spectral wavebands, gray level co-occurrence matrix (GLCM) homogeneity of V-I-S, and Vegetation temporal variation. The Landsat image was tested with different input features, and the land-use maps derived from the top 10 features yielded the most accurate map. Areal extents of annual urban land uses (Residential, Employment, and Transportation corridor categories) were derived by applying a conditional statement based on the urbanization date of the urban expansion map and overlaying on the 2015 land-use map. The relative timing and general relationship between urban land-use and population dynamics was explored at the district level. Linear regression was run to understand the general relationship between population and land use. The relative timing between population growth and land-use change was estimated with lagged correlation. Residential land use and change was most related to population and change. Population growth occurred 2.5 years later than Residential land expansion based on the median time lag.
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https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27994768
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