Leveraging remote sensing time series to characterize annual land-cover dynamics in greater Houston over two decades

Authors: Christopher Hakkenberg*, Department of Statistics, Rice University, Matthew Dannenberg, School of Natural Resources and the Environment, University of Arizona, Conghe Song, Department of Geography, University of North Carolina at Chapel Hill, Katherine Ensor, Department of Statistics, Rice University
Topics: Remote Sensing, Urban Geography, Hazards, Risks, and Disasters
Keywords: land cover land use change (LCLUC), remote sensing, time series, Landsat, Houston, urbanization, flooding, NLCD
Session Type: Paper
Day: 4/12/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Napoleon D1, Sheraton 3rd Floor
Presentation File: No File Uploaded


The Houston-Galveston metropolitan area has experienced extraordinary growth over the past two decades. While census data provides a unique insight into the nature of that growth, its sparse temporal frequency limits its application for assessing fine-grained change. This study takes a different approach to quantifying how the greater Houston area is changing, agnostic to ground sampled data on development and population growth. Instead, this research leverages over twenty years of satellite observation to synoptically characterize the spatio-temporal dynamics of land cover change on an annual basis from 1997-2017. Driven by a demand for spatio-temporal accuracy and consistency, we employ 262 Landsat images across three space-borne observation platforms to classify land cover change using a three-part algorithmic procedure: automated training data selection of NLCD cover using Automatic Adaptive Signature Generalization (AASG), random forests image classification, and spatio-temporal filtering, allowing for an annual, spectrally-based classification of the NLCD’s Developed - Open class, otherwise classified using non-spectral, decadal, ancillary data. The classification time series is validated using accuracy comparison with NLCD products, as well as independent high resolution satellite data from Ikonos, Quickbird, and Worldview sensors. Results reveal specific urbanization morphologies of the Houston area, and through fine spatio-temporal resolution change date maps, the timing and rate of land cover conversion. In addition to its methodological innovations in the field of remote sensing, results confirm that extraordinary scale of urbanization in Houston, especially in floodplains and ecologically sensitive areas, of great interest in assessing the extent of devastation during Hurricane Harvey.

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