Geology topics

George Z. Xian

Publications and source records attributed to George Z. Xian.

57 records · Page 4Linked to original sources

USGS2006

Analysis of urban land use change in the Las Vegas metropolitan area using multi-temporal satellite imagery

Urban development has expanded rapidly in Las Vegas, Nevada, over the last fifty years. To assess urban land use change in the area, a sub-pixel change detection approach has been used to map urban extent and its temporal changes by determining sub-pixel level impervious surface areas from Landsat satellite remote sensing data in conjunction with digital orthophotography. Sub-pixel percentages of imperviousness are mapped from the 1980s to the 2000s for the Las Vegas metropolitan area using a regression tree model. The spatial-temporal distribution of vegetation in the urban area has also been quantified using the same approach. Analysis suggests that spatial and temporal changes in impervious surface are useful indicators of spatial extent, intensity, and potentially, the types of urban land cover and land use change. Results indicate that areas of 40–60% imperviousness have experienced the largest increase in Las Vegas, suggesting that areas of medium to high development density areas represent the major urban land uses in the region. Urban area fractional vegetation cover has the highest coverage in medium-density urban areas of Las Vegas.

Nevada↗

USGS2003

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Photogrammetric Engineering and Remote Sensing↗

USGS2002

Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data

Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically, we present preliminary results of mapping forest types and average basal area by different forest types at 30-meter resolution. Input data into the decision tree model included Landsat-7 ETM+ spring, summer and fall greenness, brightness and wetness of the tasseled cap transformation, topographic data layers such as slope and elevation, and forest variables measured on inventory plots in the Mid-Atlantic region. Using decision-tree models, eight forest types were successfully identified in training cases and mapped for the entire mapping area. Forest basal area per unit area (conifer and deciduous) was estimated as well using regression tree models. Cross-validation conducted for both forest types and basal area showed that discrete forest type estimation error was 35% and continuous basal area relative errors were between 58 and 72%. Accuracy was higher in homogeneous forested lands and lower in areas with fragmented forest cover. The study demonstrated that decision tree and regression tree methods are efficient for large-area vegetation mapping if sufficient large-amount of reference data are available.

Conference Paper↗