The Florida Geographer. Multi-temporal Composite Trend Classification using DMSP-OLS Images

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1 The Florida Geographer Multi-temporal Composite Trend Classification using DMSP-OLS Images Dolores Jane Forbes, Charles Roberts Department of Geosciences Florida Atlantic University Introduction Given the plethora of satellite sensors with lengthy service records, and the subsequent increase in multi-temporal data, there is a growing need for multi-temporal analysis methods for satellite data (Gillanders, et al., 2008). By utilizing time series satellite data, we can address not only change in landscape spatial pattern, but also examine those changes through time (Gillanders, et al., 2008). This study proposes a new method for supervised classification of trends utilizing a multitemporal composite of images from the Defense Meteorological Satellite Program (DMSP) Optical Line Scan (OLS) instruments. Examination of remotely sensed multi-temporal composites offers us the best chance to describe patterns and structure as they change not only through space but also time. The use of multi-temporal data for long-term monitoring of landscape spatial pattern can provide the means to identify a greater range of processes of landscape modification (Gillanders, et al., 2008). Furthermore, a more complete multi-temporal image sequence consisting of consecutive time steps allows for a more inclusive and informative trajectory, or trending, of change (Gillanders, et al., 2008). The DMSP-OLS constellation of satellites constructs images consisting of visible light present on the Earth s surface at night. Orbital revisit period for the DMSP-OLS system is daily, making it invaluable for recording even small changes at night (Elvidge, et al., 1999). What also sets the DMSP-OLS system apart from other sources of night side data is its high-gain sensor, giving it the capability of recording very low levels of light visible on the Earth s surface (Elvidge, et al., 1999). The DMSP-OLS system incorporates a photo-multiplier tube to enhance all visible and near-infrared sources it detects. The DMSP-OLS satellite data has been utilized for many different studies involving physical, social and economic indicators. It has been shown at varying small scales that the DMSP-OLS satellite dataset correlates especially well with economic statistics. Economic activity and the DMSP-OLS night-time imagery lit area was correlated by Elvidge, et al., (1997) at the national level, including the United States, and at the sub-national level with radiance values in Europe and the United States (Doll, et al., 2006). In addition, the DMSP-OLS night-time imagery was shown to correlate highly with GDP at an even larger scale, the MSA level, in Florida (Forbes, 2011). The DMSP-OLS data archive lends itself well for temporal change analysis of economic trends. Remotely sensed satellite time series data is an invaluable record of changes occurring on the Earth s surface. An examination of any remotely sensed time series data reveals that some of the data is static and some is dynamic, sometimes changing rapidly (Jenson, 2005). It is important that these changes be inventoried accurately so that the physical and human processes at work 63

2 Forbes & Roberts can be more fully understood (Jensen, 2005). The method proposed in this study seeks to define a fast and accurate supervised classification of changes occurring over time using a multitemporal composite of the DMSP-OLS images. Time series data consists of sequences that follow non-random order. The analysis of time series data is based on the assumption that successive values in the data represent consecutive measurements taken at regular time increments. The main goal of time series analysis requires that the underlying patterns and trends occurring over time be identified and described. Doing so means we can then interpret and integrate time series data with other data to better understand the nature of the phenomenon. We might then possibly extrapolate both the data itself and the explained patterns so as to predict future events. Research Objective There are many examples of change detection methods within the literature that examine spatial patterns based on only two dates of imagery (Gillanders, et al., 2008; Jensen, 2005). These typically involve comparisons of pairs of landscape pattern indices derived from thematic maps representing a beginning, or reference point in time, and an end point in time (Gillanders, et al., 2008). Many change detection techniques employ the use of indices, and the analysis of change is an examination of change occurring in these indices (Jensen, 2005). These types of studies typically quantify land use/land cover (LU/LC) change and seek to evaluate the spatial arrangement and complexity of land cover types (Jensen, 2005). The creation of these indices requires informed but subjective image analysis in creating the LU/LC descriptions that comprise the basis for the change detection (Jensen, 2005). In addition, many of these change detection techniques require expertise within the study area so as to adequately choose the thresholds that determine change and no change. Therefore, many change detection techniques are not suitable for large study areas or for regions that are unfamiliar to the analyst (Jensen, 2005). Quantification and visualization techniques for time series analysis of remotely sensed data are needed to decrease or remove subjectivity from results while simultaneously describing location and temporal trends. We seek, therefore, a method that is suitable for small or large study areas that also does not require large scale expertise. The method proposed in this study attempts to address both these needs using a multitemporal time series analysis over a large study area. This proposed method also accounts for the use of more than two images, which gives us the ability to identify a greater range of processes of landscape change, including rates and dynamics (Gillanders, et al, 2008). A multi-temporal approach to landscape pattern analysis presents considerable challenges in data processing, analysis, and subjective interpretation. However, it also provides an opportunity to characterize and quantify the complexity of spatial and temporal patterns and processes within the landscape (Gillanders et al., 2008). For intuitive reasons, a time series of remotely sensed raster grids presents additional problems in analysis over conventional statistical time series analysis. The proposed method in this study makes use of all data available, but minimizes processing needs by utilizing a tool readily available within ENVI 4.7. There are two basic classes of patterns with time series data: trend and seasonality. Trends represent a general systematic linear or non-linear component that changes over time and does 64

3 The Florida Geographer not repeat. Seasonality, on the other hand, repeats itself in systematic intervals over time. In conventional analysis of time series data, we seek techniques to separate trend and seasonality patterns from the error in time series data by understanding the pattern of identified trends. In conventional statistical analysis this involves moving averages, linear regression over time or exponential smoothing (weighted moving averages). Many change detection methods currently employed for remotely sensed data reflect some form of these conventional statistical techniques (Jensen, 2005). However, we also know from conventional statistical analysis that as long as the time series data is monotonous, it can be adequately approximated with a linear function. This study seeks a method to identify trending in composited satellite-based remote sensing data representing multiple coverages over time by identifying and classifying linear functions of trend spatially. The research question to be answered here is to devise a classification method that decreases subjectivity and the need for regional expertise while quantitatively describing trends in digital multi-temporal remotely sensed data. Region of Interest The region of interest for this study is a bounding rectangle that encompasses the entire administrative boundary of the state of Florida, including the Florida Keys, portions of the Atlantic Ocean, the Straits of Florida and the Gulf of Mexico. The original DMSP-OLS images from the National Geophysical Data Center comprise global coverage in 30 arc second grids spanning 180 to 180 degrees longitude and 75 to 65 degrees latitude. The original global coverage was subset to the study extent, which is bounded in the upper left corner at N W and in the lower right corner at N W (see Figure 1). Time Period The time period used in this study encompasses the years from 2005 to 2009 inclusive. This time period was selected not only for availability of the data but also because it corresponds to severe economic fluctuations occurring over the same time period. All the data from this time period is readily available from a single sensor in the DMSP-OLS constellation, eliminating the need for cross-sensor calibration. Data The DMSP-OLS night light image data used in this study was downloaded from the National Geophysical Data Center s (NGDC) DMSP-OLS web site. The NGDC is charged with archival responsibilities for the data generated by the DMSP-OLS constellation. Multiple types of products are available. This study utilizes their stable night lights product for the years 2005 to 2009 inclusive. The NGDC pre-processes the data prior to providing its DMSP-OLS products. For this study, NGDC pre-processing includes removal of lunar illumination so as to increase contrast of light sources on the ground, exclusion of sunlit data based on the solar elevation angle, removal of clouds, and removal of temporally inconsistent light sources such as biomass burning events and bad scan lines (Elvidge, et al., 1999). In addition, data used in each yearly averaged mosaicked image comes from the center half of the 3000 km wide OLS swaths. Lights in the center half of the swath have been shown to have better geolocation and more consistent radiometry (Elvidge, et al., 1999). Acceptable data for each year s period was averaged to create a stable night lights data set. The resulting data set is therefore an average of each cloud-free 65

4 Forbes & Roberts image available throughout the stated time period, minus temporally unstable lights as well as images from full moon periods (Elvidge, et al., 1999). It is important to note that the OLS has no on-board calibration and the gain settings are not recorded in the data stream. While the cloud-free composites were each produced using the same algorithms and stringent data selection criteria, the digital number (DN) values are not strictly comparable from one year to the next. To address this issue in the selected data files, specific pixels in the center of large water bodies and along coastal area throughout the scene were examined across all years to determine if inter-calibration between years was necessary. The pixels were selected from areas that are assumed to always be unlit, such as the center of Lake Okeechobee. All selected points returned the same brightness values across all years, so inter-calibration between years was deemed unnecessary. Atmospheric correction is a well-known requirement for the use of remotely sensed data, especially for satellite-based data over airborne data. To address atmospheric correction issues in the data, it was assumed that because the yearly data files are an average of acceptable data values across all years, atmospheric interference is at the same or nearly the same level for all years, and is ignored. Because the images used in this study comprise yearly averages over each given time period, it is assumed that seasonality within the time series has already been smoothed. This leaves only the trend and assumed error of some unknown magnitude within the dataset. Next, co-registration of the images was examined both visually and by using the ENVI Change Detection map tool with the automatic co-registration option. The method proposed in this study employs examination of raster-to-raster changes through time. Therefore, accuracy in image registration and co-registration is even more critical for this method (Yuan and Elvidge, 1999). Registration issues were identified between the year 2009 image file and all other years, in the north/south direction. Co-registration of the 2009 file to all other files was accomplished using the Shift Tool in ArcGIS to shift the 2009 image one-half raster north. First, the north-south length of each raster was calculated from the average distance of a decimal degree between latitude 23 and 32 (the study area extent) on the WGD84 spheroid using the calculation: 111, (cos 2φ ) (cos 4φ ). This created an average raster length for the study area latitudes. The shift magnitude per raster was then determined by choosing a representative image for all other years and visually examining co-incidence between the two images. To assist in visual analysis of the results, a detailed Florida coast and county boundary shape file was obtained from the Florida Department of Environmental Protection. In addition, a roadway shape file of base map routes for the entire state of Florida was obtained from the Florida Department of Transportation. Automated detection of land-cover change in satellite imagery is complicated by many adverse temporal factors: 1) differences in band passes and spatial resolutions; 2) spatial misregistrations; 3) variations in the radiometric response of the sensors; 4) differences in the distribution of cloud and cloud shadow; 5) variations in solar irradiance and solar angles; and 6) variations in atmospheric scattering and absorption (Yuan and Elvidge, 1999). For this study, 66

5 The Florida Geographer differences in band passes and spatial resolutions were minimized by using data from the same sensor, in this case the F16 sensor in the DMSP-OLS constellation. All other adverse issues were addressed or normalized by pre-processing of the data. Methodology Well known remote sensing change detection techniques fall into three main classes: 1) Visual Change Detection; 2) Post-classification Comparison; and 3) Image Algebra (Jensen, 2005). Visual change detection methods include write memory insertion, which involves the assignment of different bands of a multi-temporal composite to different computer monitor color guns for visual analysis of change. This method works best with three or fewer layers. Applying this method to datasets with more than three layers makes this method awkward at best and involves ignoring any data over the three layer limit during some part of the analysis. In addition, the analysis following this method is subjective, and is dependent on the skill and regional expertise of the analyst. Post-classification comparisons are utilized frequently in change detection between Time 1 and Time 2, but these methods require each layer to be classified prior to change detection and analysis. DMSP-OLS data consists of gray-scale imagery of lights detected at night, and is not suitable for traditional land cover/land use classification techniques. Accuracy of postclassification comparison methods depends on the accuracy of the individual classification (Yuan & Elvidge, 1999) which again is dependent on the skill and expertise of the analyst as well as the suitability of the data on which the classification is performed. Post-classification methods also require some knowledge of the study area to assist in selection of the thresholds that define the classifications. Image algebra techniques for change detection include band subtraction, band ratioing, or index subtraction (Jensen, 2005). Again, these techniques are suitable for change detection of two input layers, but as the number of layers increases, the complexity of calculations and the resulting analysis also increases. In addition, accuracy of image algebra methods depends on the discrimination of change and no-change values by the use of arbitrary thresholds selected by the analyst (Yuan & Elvidge, 1999). For similar reasons, spectral change vector analysis, another image algebra technique for change detection in remotely sensed data, is unsuitable for multitemporal composite data. Arguably, the best method for multi-temporal digital remotely sensed data over large areas is multi-temporal compositing. However, traditional methods in change detection using these methods come with many hazards during analysis (Jensen, 2005). For example, principal components analysis (PCA) is very good at discriminating those factors accounting for the most variance in the data over time, but it is difficult to create definitions of the components that are found. PCA works very well for small areas in which regional expertise can assist in the interpretation and clarification of the resulting components (Jensen, 2005). There are limitations involved in all methods outlined above, and most change detection methods require subjective classification methods or expertise on the part of the analyst. To address these limitations, this study proposes a method of trend classification that reduces or eliminates 67

6 Forbes & Roberts subjectivity yet retains and incorporates all available data in all layers while also providing output that may not require expertise on the part of the analyst. To achieve the objectives for reducing or eliminating subjectivity while incorporating all data layers available, this method treats the data set in this study as a data cube, in a manner similar to how hyperspectral data is analyzed, using spectral profiles. However, in this case, the more appropriate terminology is not spectral profiles but rather time change profiles, as each raster in the resulting machine classification map is classified based on the profile of changes over time that occurred in that exact location. The output from this method is therefore unique, objectively-derived cluster classes that illustrate trends over the time span of the data. The first step used in this method was to develop a spectral library, hereafter referred to as a time change profile library (see Table 1). The time change profile library was then used as input to the Spectral Angle Mapper (SAM) tool in ENVI 4.7 for supervised classification of trends, and to generate a trend map for the study area. Twenty-one time change profiles were devised to capture trend. These time change profiles were then mapped to four trend classes: 1) increasing trend; 2) decreasing trend; 3) change with no clear trend; and 4) no change. These four trend classes were then combined with one last class, unclassified, for a total of five classes in the resulting supervised classification trend map. While the number of years in this study is discrete, the possible normalized brightness values for any given image are theoretically unlimited, especially for images that originate with floating point attribute values. This means there are theoretically an unlimited number of time profile plots. In this study, discrete scaled values were chosen for the normalized brightness values (0, 0.5, 1.0) for development of the trends used in this study. This study s trend plots use three discrete values for the y-axis (brightness values), with the five years being discrete values for the x-axis (time). This simplification of three values for the brightness values means that there are 35 or 243 actual potential plot trends for this data set. This study utilizes a subset of twenty-one plot trends out of the possible 243 plots trends combined with a very loose tolerance to capture trend. The tolerance setting for the SAM tool was adjusted to minimize the number of unclassified rasters within the scene (Figure 3). It should be noted that the majority of rasters in the chosen scene represent large water bodies, including the Gulf of Mexico, the Florida Straits, Lake Okeechobee, and the Atlantic Ocean. The tolerance setting for the SAM tool was adjusted to minimize the number of unclassified rasters within the scene (Figure 3). 68

7 The Florida Geographer Table 1: Time Change Profile Library Time Change Profile Library Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Plot 6 Plot 7 Plot 8 Plot 9 Plot 10 Plot 11 Plot 12 Plot 13 Plot 14 Plot 15 Plot 16 Plot 17 Plot 18 Plot 19 Plot 20 Plot 21 Source: Author 69

8 Forbes & Roberts Table 1: Time Change Profile Library The DMSP-OLS is a spectrally coarse, 6-bit (26= 64) system, resulting in Digital Number values ranging from 0 to 63. The minimum value for all rasters in all time periods is 0 and the maximum Tolerance Setting Number of Unclassified Rasters 1,415,729 1,351,255 value is 63. The co-registered images for years ,148, to 2009 were normalized and then stacked ,105,231 in year order into a multi-temporal composite ,105,231 The co-registered images for the five images were normalized to floating point values ranging from 0.0 to 1.0 using the following equation: These normalized images were then stacked into a composite image. The SAM process was then executed with the chosen tolerance of 1.0 (radians) for the spectral angle (see Table 2). As noted, this tolerance value was chosen to minimize the number of unclassified rasters. The default ENVI 4.7 Maximum angle (in radians) for the SAM tool is Figure 1: Spectral Angle Mapper Output Trend Map Source: Author 70

9 The Florida Geographer Results Output from the ENVI SAM tool is a three band (RGB) image depicting four trend classes plus a class for no change (Figure 1). Specific colors (red, green, blue, white, and black) were chosen to differentiate between the five classes in the output image. The output image was flattened to an 8-bit image with color table for ease of symbolization in ArcMap 9.3. In Figure 1, gray corresponds to No Change, and matches Plots 3, 4 and 5 in the Time Change Profile library. Black pixels are unclassified, and therefore did not match any of the 21 time trend library plots. White pixels represent change over time without identifiable trend, and correspond to Plots 6, 7, 14, 15, 16 and 17 in Figure 1. Green pixels correspond to increasing trends (Plots 2, 9, 10, 12, 19 and 20 in Figure 1) while red pixels depict decreasing trend (Plots 1, 8, 11, 13, 18 and 21 in Figure 1). Initial analysis of the results was performed visually across the entire scene. It appears this method correctly distinguishes settlement areas from forest. On land, the unclassified (unlit) areas correctly appear in rural areas around the Big Bend coastline of Florida as well as the National and State forests west of the state capitol in Tallahassee, Florida. Water bodies, including the Gulf of Mexico, bays, oceans, Lake Okeechobee and the Everglades are correctly represented as unclassified rasters. The overall scene shows decreasing trends (red) dominating in Florida, Georgia and the north coast of Cuba, with increasing trends (green) dominant in Mississippi, Alabama and Louisiana. This may be indicative of the economic collapse experienced in Florida during the study time period, contrasted with the rebuilding of the Gulf states after the devastation of the hurricane seasons during the same time period. Some of the oil platforms located in the Gulf of Mexico off the coast of Mississippi and Louisiana are visible in the trend map. The oil platforms appear to have diminishing trends in lights on the platforms closest to land, with increasing trends on platforms located further from shore. This contrasts with the overall increasing lights on the coasts to the north. The coast of Cuba also shows decreasing trends along the coastline, with some increase shown inland. In Figure 2, a composite image has been created by overlaying the SAM Output Trend Map over the 2009 night lights image. Combined with a Florida shape file of administrative boundaries (blue lines), cities (gold), and roadways (purple lines) the resulting image also shows all five classes created by the SAM tool. Green is increasing trend and appears, as expected, on the outer parts of saturated rasters (light gray). Red areas are considered having decreased in light over the time period. The grey areas in the image correspond to areas with no change, and appear very bright where there is light on the DMSP-OLS image behind. Black areas are areas of no change. Lake Okeechobee appears as the nearly square black area in the center of the image towards the left, and has municipal boundaries (blue lines) meeting in its center. Note that the lights recorded by the DMSP-OLS sensor extend well over the water itself. It is assumed this is light reflected off the water from the shoreline developed areas. 71

10 Forbes & Roberts Figure 2: Lake Okeechobee, Florida Source: Author As noted previously, the spectral resolution of the DMSP-OLS sensor is coarse, with data values ranging from 0 to 63, the highest limit of its 6-bit storage capabilities. Within highly populated urban areas, rasters in the original DMSP-OLS images quickly become saturated at the 6-bit ceiling imposed by this limitation, so it is not surprising that urban/developed areas correspond to no change values. The coarse resolution of the DMSP-OLS sensor means that highly urbanized areas are usually saturated pixels, and therefore do not exhibit change over time. Focusing strictly on the Florida Peninsula as a whole, the majority of decreasing trends appear around the urban areas on the coastlines, compared to the center of the state which appears to be experiencing isolated regions of increase. Also apparent in the image is the bloom that extends out beyond the coastlines of Florida. This bloom is particularly extensive in the waters off southeast Florida and the Clearwater-St. Petersburg peninsula west of Tampa Bay, the most densely populated region on the Florida peninsula. Interestingly, trend changes appear in the water off the coasts of Florida and around the Florida Keys. It may be possible that these change trends are a reflection of a reduction (or increase) in lights on the land, even though the rasters on the land, already saturated, show no change. An examination of the bloom surrounding the Florida Keys suggests that this may be true. 72

11 The Florida Geographer Figure 3: Florida Keys Source: Author Figure 4: Jacksonville, Florida Source: Author 73

12 Forbes & Roberts Table 3: Sampled Increasing Trend (Green) Test Rasters TREND COORDINATES TIME CHANGE PROFILE Green (increasing) N E Plot N E N E N E N E N E 74

13 The Florida Geographer Table 4: Sampled No Change (Blue) Test Rasters TREND COORDINATES TIME CHANGE PROFILE Blue (no change) N E Plot N E N E N E N E N E 75

14 Forbes & Roberts Table 5: Sampled Decreasing Trend (Red) Test Rasters TREND COORDINATES TIME CHANGE PROFILE Red (decreasing) N E Plot N E N E N E N E N E 76

15 The Florida Geographer Table 6: Sampled No Clear Trend (White) Test Rasters TREND COORDINATES TIME CHANGE PROFILE White (no clear trend) N E Plot N E N E N E N E N E 77

16 Forbes & Roberts In Figure 3, a second composite image has been created as in Figure 2. Figure 3 shows the Florida Keys and the southwester portion of the Florida Peninsula. This thematic map again shows green as increasing trend, red as decreasing trend, black for unlit and grey for no change. The 2009 DMSP-OLS night light image is the background for this image. Blue lines were used for administrative boundaries and purple lines for roadways. Because the sensed night light exceeds the actual land boundaries, change in light only appear in the waters adjacent to land masses and islands. The SAM Output assigns a decreasing trend to the light that appears at Flamingo, Florida, which appears as a red area surrounded by black in the center of the image. For each of the output classes, multiple rasters were randomly selected and their Time Change Plots were examined to determine if they matched the intended trend (increasing, decreasing, no clear trend, and no change). The following four tables show the results for selected rasters for each of the four output classes. A cursory examination of all lighted locations within Florida show that they correspond well with roadways, urban areas and residential development. Unexpected trends adjacent to these urban areas within the resulting map might be explained by proximity to adjacent urban areas and/or roadways. This suggests some type of change in land use or land cover at the unexpected trend locations. Conclusion While these initial results are encouraging, a qualitative visual analysis cannot be fully completed until an accuracy assessment for this method has been devised. Once an accuracy assessment of the method has been completed, more in-depth visual, statistical and spatial analyses can be performed. In addition to an accuracy assessment for the method itself, a quantitative study to determine the best tolerance level used in the Spectral Angle Mapper tool in ENVI 4.7 needs to be performed. Ground truthing to determine if the original images accurately reflect changes that have occurred at those locations would certainly increase the acceptance of this method as a useful tool for change detection in multi-temporal composites. While it is not possible to return in time to test the lights captured in previous years, it should be possible to ground truth the multi-temporal composite images by using known LU/LC changes. The multi-temporal night light images tell us that there is light burning at night at specific locations, and the trend map tells how that light has changed. This should coincide with changes in LU/LC. Additional questions that need to be answered within the accuracy assessment include how well the image matches the twenty-one plots in the time trend library, and whether these twenty-one plots well represent all potential trends. The initial results suggest that the tolerance was well set, but an examination of this issue needs to be addressed. If this method is found to have acceptable accuracy, many additional suggestions for further research are apparent. It is well recorded in the literature that the DMSP-OLS images exaggerate urban extent due to blooming (Small, et al., 2005). One area of investigation would be an examination of blooming along all coastlines to determine if the blooming is entirely due to spatial resolution constraints with the DMSP-OLS sensor, or is in some part due to the urban 78

17 The Florida Geographer areas adjacent to the blooming. The relative flatness of Florida s terrain encourages light propagation from the dome of light that occurs over cities. Does the trend map represent increasing and decreasing light pollution around cities? Also of interest based on the results of this study would be an examination of the changes in lights shown in water bodies off the coastlines of Florida, especially off the most populated portions of the Florida coast. Is this bloom entirely due to atmospheric interference? An examination of the lights off the coast of Miami-Dade county suggests otherwise, as the bloom follows the shape of the land. It may be possible to quantify trends in the saturated pixels by examining the changes in adjacent reflected lights in the water. It may also be possible that the amount of light reflected in these locations is related to water depth. It would be interesting to determine if the differences between blooming off the coasts is due to changes in the amount of light on the land, change in depth of nearshore waters, or changes in adjacent population density. There are many other methods for multi-temporal change detection procedure utilizing dense data stacks (Gillanders, et al., 2008)., including spectral information divergence (SID), spectral gradient angle, and Euclidean distance, to name just a few. The SAM tool only examines shape, and not magnitude. The best tool to achieve acceptable accuracy results can vary based upon the application and data. An examination of these additional methods to determine the best method for this application is warranted. If acceptable accuracy can be attained, the night light data may yield sufficient information for prediction of future trends. Acknowledgements The author wishes to express sincere appreciation to Dr. Caiyun Zhang for her support, encouragement and assistance in the completion of this study. The author also wishes to thank the anonymous reviewers for their time and their comments on the draft manuscript. 79

18 Forbes & Roberts References Doll, C. N. H CIESIN thematic guide to night-time light remote sensing and its applications. Palisades NY: Center for International Earth Science Information Network of Columbia University: Columbia University. Doll, C. N. H., Muller, J., & Morley, J. G Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57(1), Elvidge, C. D., Baugh, K. E., Dietz, J. B., Bland, T., Sutton, P. C., & Kroehl, H. W Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sensing of Environment, 68(1), Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), Forbes, D. J Statistical Correlation Between Economic Activity and DMSP-OLS Night Light Images in Florida. Papers of Applied Geography Conference Redlands, California. In Press. Gillanders, S., et al Multitemporal Remote Sensing of Landscape Dynamics and Pattern Change: Describing Natural and Anthropogenic Trends. Progress in Physical Geography 32.5 (2008): Kramer, H. J Observation of the Earth and its Environment - Survey of Missions and Sensors ( 2nd ed.). Berlin & New York: Springer-Verlag. National Geophysical Data Center. DMSP Operational Linescan System. Last accessed June 1, Small, C., Pozzi, F., & Elvidge, C. D. (2005). Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sensing of Environment, 96(3),