941 The Land-cover Change Mapper (LCM) and its Application
to Timber Harvest Monitoring in Western Canada
Guillermo Castilla, Richard H. Guthrie, and Geoffrey J. Hay
Full Article (7.5Mb PDF)
We introduce an automated change detection and delineation tool for remote sensing images: the Land-cover Change Mapper (LCM). LCM rapidly generates a polygon vector layer (shapefile) of regions deemed to have undergone significant change in land-cover. In its simplest usage, LCM requires two single band or multi-band co-registered images of the same scene acquired at different dates, and as the only user defined parameter, the minimum size for change regions. The main advantages of this tool are that (a) it is fully unsupervised, (b) it is exceptionally fast, (c) it is robust to geometric misregistration errors and variations in illumination, and (d) it produces visually pleasing outlines that resemble those obtained through manual digitization. We describe how the tool works, illustrate its application to monitoring forest clearcuts on a 1,000 km2 area in Western Canada using SPOT imagery, compare it to a commercial tool, and report on its thematic and spatial accuracy. A freeware LCM version is available on the Internet.
951 Enhancing Binary Change Detection Performance
Using A Moving Threshold Window (MTW) Approach
Jungho Im, Jinyoung Rhee, and John R. Jensen
This study introduced a new concept, the Moving Threshold Window (MTW), for binary change detection. An automated MTW-based calibration model was developed and evaluated using a case study. The MTW-based model is free from the assumption of symmetry for difference and ratio types of change-enhanced images, unlike traditional binary change detection methods. The MTW-based calibration model outperformed the traditional binary change detection methods based on the Symmetric Threshold Window (STW) for both single and multiple change-enhanced images of the study area. In most of the calibrations, the optimum thresholds resulting in the highest Kappa coefficient were asymmetric. Three major factors may explain the asymmetric characteristics of the optimum thresholds, including: different atmospheric conditions found in the two dates of imagery, different look angles associated with the two dates of imagery, and the nature of the change information. Multiple change-enhanced images generally produced higher accuracies than single change enhanced
images using both the MTW- and STW-based models.
963 Range of Categorical Associations for Comparison of
Maps with Mixed Pixels
Robert Gilmore Pontius Jr. and John Connors
This paper presents a method to compare maps that contain pixels that have partial membership to multiple categories, i.e., mixed or soft classified pixels. The method quantifies ranges for associations among categories based upon possible variations in sub-pixel spatial allocation. The paper derives the mathematical equations for constructing the range of associations based on three types of cross-tabulation matrices, the greatest matrix, the random matrix, and the least matrix. We demonstrate how the analysis can be combined with multiple resolution map comparison to specify the resolution at which clusters exist on a single map or between two maps. The method produces a range that reflects the amount of uncertainty in the categorical associations. We illustrate the procedure with both a simple example and data from the Plum Island Ecosystems study site in Massachusetts, USA.
971 Projection of Land Use Change Patterns Using Kernel
Bo Wu, Bo Huang, and Tung Fung
Change analysis is probably a natural step following the detection of changes using remote sensing data. One significant topic in change analysis is to model the changes in relation to their driving factors and to project future land-use patterns. While logistic regression (LR) has been widely used in change modeling, this paper presents an improved method, kernel logistic regression (KLR), to model the nonlinear relationship between land-use change and various causal factors such as population, distance to road and facilities, surrounding land-use, and others. Traditional KLR models contain one coefficient for each training sample, rendering it inappropriate for applications of land-use change analysis with more than a few thousand samples. A feature vectors selection method for the KLR model has therefore been proposed to impose sparsity and control complexity. To test the effectiveness of KLR, a case study was implemented to model rural-urban land-use conversion in the city of Calgary, Canada during the periods 1985 to 1990 and 1990 to 1999. The KLR model was compared with a commonly used LR model in terms of the Percentage of Correct Prediction (PCP), Area under Curve (AUC), and McNamara’s test, and the results consistently demonstrated the better performance of KLR.
981 A Disturbance Inventory Framework for Flexible and
Reliable Landscape Monitoring
J. Linke, G.J. McDermid, D.N. Laskin, A.J. McLane, A. Pape, J. Cranston, M. Hall-Beyer, and S.E. Franklin
Remote sensing plays a key role in landscape monitoring, but our handling of these data in a multi-temporal time series is not yet fully developed. Of particular concern is the presence of spatial and thematic errors in independently created maps that distort measures of landscape pattern and constrain the reliability of change analysis. In addition, there is a need to incorporate continuous attributes of cover gradients for flexible map representations that support a variety of applications. In this paper, we present a framework for generating temporally and categorically dynamic land-cover maps that provide such a reliable and adaptable foundation. The centerpiece is a spatio-temporal disturbance-inventory database, created through semi-automated change detection and conditioned with boundary-matching procedures, which can be used to backdate and update both continuous and categorical reference maps. We demonstrate our approach using multi-annual Landsat imagery from a forested region in west-central Alberta, Canada, between the years 1998 and 2005.
997 Quantifying Impacts of Land Ownership on Regional
Forest NDVI Dynamics: A Case Study at Bankhead
National Forest in Alabama, USA
Xiongwen Chen and Rory Fraser
Identifying the impacts of human activities on local and regional ecological processes is important for protecting essential ecological functions. In this study, the spatial and temporal dynamics of Normalized Difference Vegetation Index (NDVI) at three adjacent areas with different proportions of private land (6 percent, 20 percent and 55 percent) in the Bankhead National Forest in Alabama, USA from 1998 to 2004 were examined. The phase coupling, synchrony, and information entropy of NDVI at multiple temporal scales for each of these areas were examined. A higher proportion of private land (e.g., 55 percent) resulted in decrease of annual mean, coefficient of variance, seasonal maximum, and absolute value of rate of increase/ rate of decrease for NDVI values as well as increase in seasonal minimum NDVI and decrease in spatial coupling and synchrony of NDVI dynamics. Thus, a higher proportion of private land could affect regional NDVI dynamics in complex and ecologically significant ways.
1005 Individual Object Change Detection for Monitoring the
Impact of a Forest Pathogen on a Hardwood Forest
Tim De Chant and Maggi Kelly
Sudden oak death (SOD) has caused widespread mortality in a number of tree and shrub species throughout coastal California. As a result, canopy changes are directly visible from remotely sensed imagery. To quantify changes in horizontal canopy structure to the oak woodlands in China Camp State Park, California, USA, a heavily hit area, we developed a novel change detection technique that tracks changes to individual objects. Using 4-band, 1 m spatial resolution aerial photography, we classified four annual images (2000 to 2003) with object-based image analysis (OBIA) and employed a GIS for our change detection technique. We identified 352 gaps that contained SOD mortality in 2000 and persisted through 2003. Their median areas and perimeters did not change significantly in that time. However, those gaps that increased in size tended to be smaller than those that decreased, indicating increased mortality in newly infected areas. Our new change detection method allowed us to monitor these gaps one-by-one, revealing ecologically meaningful results that would otherwise be obscured in a landscape-scale analysis.
1015 Spatial Change Optimization: Integrating GA with
Visualization for 3D Scenario Generation
Magesh Chandramouli, Bo Huang, and Lulu Xue
Urban spatial analysis is becoming an increasingly complex problem due to the overwhelming demands imposed by the population and several other factors. Consequently, tools are needed to solve complex urban spatial problems that are multiobjective in nature. This study presents a multiobjective optimization approach to generating alternative land use scenarios and offers a visual evaluation tool for assessing the Pareto solutions. Typically, with genetic algorithms (GA), decision makers are finally left with alternative solutions in the form of the Pareto set, from which one or a few more will be chosen. Hence, a visualization tool is employed in this study, whereby the decision makers can better evaluate the alternative solutions from the Pareto set. Modeling futuristic land uses is devised as an optimization problem wherein spatial configurations are created through the use of evolutionary algorithms. With the goal of sustainable urban land use planning, the evolutionary algorithm is designed for multiple objectives, such as maximization of per capita green space, maximization of urban housing density, maximization of public service space, and conflict resolution among neighboring land uses. The results evince the validity of the GA framework and also corroborate the utility of the virtual scenarios.