Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006

Abstract

A 34 year time series of Landsat imagery for a portion of Snohomish and King Counties, Washington (the Snohomish Water Resource Inventory Area (WRIA)) was analyzed to estimate the amount ofland that was converted into impervious surface as a result of urban and residential development. Spectral unmixing was used to determine the fractional composition of vegetation, open, and shadow for each pixel. Unsupervised and supervised classification techniques were then used to derive preliminary land cover maps for each time period. Digital orthophotos were used to create agricultural, forest management, high elevation, and riparian masks. In conjunction with established Urban Growth Areas (UGAs), these masks were utilized for the application of spatial rules that identified impervious surface as a surrogate for urban and residential development. Temporal rules, that minimized classification error, were developed based on each pixel's classified trajectory over the time series of imagery. Overall cross-date classification accuracy for impervious v. non-impervious surface was .95%. The results of the analysis indicate that the area of impervious surface in the Snohomish WRlA increased by 255% over 34 years, from 3285 ha in 1972 to 11,652 ha in 2006. This approach demonstrates the unique value of the 35 year Landsat archive for monitoring impervious surface trends in rapidly urbanizing areas.

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

“…All other TM and ETM+ images in the time series were radiometrically normalized to the COST image by means of MADCAL (Multivariate Alteration Detection and Calibration algorithm [51] as recommended by Schroeder et al [52]. To normalize MSS data with the TM and ETM+ time series we used Tasseled Cap Transformation (TCT) components with MADCAL rather than the individual spectral bands [53]. The coefficients used to create the TCT components were derived statistically from images and empirical observations and are specific to each imaging sensor.…”

“…(For all TM/ETM+ images TCT was applied using the coefficients for reflectance data [55] and we obtained brightness (TCB), greenness (TCG), and wetness (TCW). Brightness and greenness define the vegetation plane [58] and provide a practical bridge between the earlier MSS imagery and more recent TM and ETM+ imagery [53].…”

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

“…Because of the advantage of high temporal frequency, many quite subtle disturbance events of forest, such as defoliation, diseases, insect pests and regeneration, can be captured based on the change of vegetation spectral attribution (Goodwin et al, 2008;Hermosilla, Wulder, White, Coops, & Hobart, 2015;Zhu, Woodcock, & Olofsson, 2012). In addition to its wide applications in forest ecosystems, such method has also been applied to quantify changes of impervious surfaces in urban environments (Powell, Cohen, Yang, Pierce, & Alberti, 2008;Schneider, 2012), coral reef health (Palandro et al, 2008) and fire events (Röder, Hill, Duguy, Alloza, & Vallejo, 2008). Pre-classification change detection techniques, whether using dense time series image data or not, generally only generates "change" vs. "no-change" maps, but do not specify the type of change (Berberoglu & Akin, 2009;Lu et al, 2004;Singh, 1989).…”

Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level

“…With the concept of the V-I-S (vegetation-impervious surface-soil) model proposed by Ridd [12], the spectral mixture analysis (SMA) technique has been widely used for mapping the impervious surface fractions [13][14][15][16][17][18]. Meanwhile, other methods were devised for characterizing impervious surfaces, such as the index analysis [19][20][21], the regression model [22][23][24][25], and the knowledge-based expert system [14,26,27]. The advent of high spatial resolution remotely sensed images since the 1990s, e.g., IKONOS (launched 1999) and Quick Bird (2001), also enables the incorporation of structure and texture features for quantifying impervious surfaces [28,29].…”