The authors build on a recent development in urban geographic theory, providing evidence of an oscillatory behavior in spatiotemporal patterns of urban growth. With the aid of remotely sensed data, the spatial extent of urban areas in the Houston (USA) metropolitan region from 1974 to 2002 was analyzed by spatial metrics. Regularities in the spatial urban growth pattern were identified with temporal periods as short as thirty years by means of spatial metric values, including mean nearest-neighbor distance, mean patch area, total number of urban patches, and mean patch fractal dimension. Through changes in these values, a distinct oscillation between phases of diffusion and coalescence in urban growth was revealed. The results suggest that the hypothesized process of diffusion and coalescence may occur over shorter time periods than previously thought, and that the patterns are readily observable in real-world systems.
Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.
In this study, a computer program, the Land Surface Temperature Calculator (LST Calculator) was developed in Visual Basic . NET (2008) which employs the Radiative Transfer Equation (RTE) method. The LST Calculator is a valuable tool to study thermal environments using the thermal band of Landsat TM/ETM+ data and make the land surface temperature retrieval much simpler and faster. LST can be retrieved with this tool by inputting required parameters in the program. Thus, this program will be helpful to those who are interested in studying thermal environment. A Landsat Enhanced Thematic Mapper Plus (ETM+) image of Dallas -Fort Worth Metroplex was used as a case study for demonstration purposes.
Land Surface Temperature (LST) is an essential climate parameter, related to surface energy balance. The new instrument which was called Thermal Infrared Sensor (TIRS) carried on board of the new generation of Landsat 8 captures the temperature of the Earth's surface in two bands, band 10 and band 11 with spatial resolution of 100m. The main objective of this study was to develop a tool making the LST retrieval process quite simple and automated. In this study, Radiative Transfer Equation (RTE) method has been employed in ArcGIS Model Builder to retrieve LST from Landsat 8 satellite imagery. The user just inputs required bands (Band4, Band5, and Band10) and a couple of parameters then the tool outputs the final LST imagery automatically. The tool first makes the conversions to top of atmosphere (TOA) radiance and reflectance. Then NDVI is calculated based on NIR and RED bands reflectances. Land surface emissivity is calculated based on NDVI Thresholds Method (NDVI-THM) which was developed by Sobrino et al. (2008). Finally, the tool calculates land surface temperatures in degrees Celsius.
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