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The paper provides an account of how three key relief organizations worked together after the devastating Haiti earthquake to produce the first damage assessment based mainly on the use of remotely-sensed imagery. This assessment was jointly conducted by the World Bank (WB), the United Nations Institute for Training and Research (UNITAR) Operational Satellite Applications Programme (UNOSAT), and the European Commission’s Joint Research Centre (JRC). This paper discusses the data sources used for the assessment, the methodologies employed to evaluate building damage, and a set of independent studies to validate the final damage results. Finally, a vision of the role of remote sensing technologies in future disasters is presented that serves as a road map for methodological improvements.

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.

The objective of this paper is to examine the scaling-up effect on the relationship between landscape patterns and land surface temperatures based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four TERRA ASTER images were acquired to derive the land-use and land-cover (LULC) patterns and land surface temperatures (LST) in different seasons. Each LULC and LST image was resampled to eight spatial scales: 15, 30, 60, 90, 120, 250, 500, and 1,000 m. The scaling-up effect on the spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. Optimal spatial resolutions were determined on the basis of the minimum distance in the landscape metric spaces. The results show that the patch percentages of LULC and LST patches were not strongly affected by the scaling-up process in different seasons. The patch densities and landscape shape indices and LST patches kept decreasing across the scales without distinct seasonal differences. Thirty meters was found to be the optimal resolution in the study of the relationship between urban LULC and LST classes. Ninety meters was found to be the optimal spatial resolution for assessing the landscape-level relationship between LULC and LST patterns. This paper may provide useful information for urban planners and environmental practitioners to manage urban landscapes and urban thermal environments as a result of urbanization.

This paper discusses the calibration and assessment of a cellular automata model for urban growth modeling. A number of transition rules are introduced in the cellular automata model to consider the most influential urbanization factors, such as land-cover maps obtained from satellite images and population density from the census. The transition rules are calibrated both spatially and temporally to ensure the modeling accuracy. Spatially, each township (about 6 miles 3 6 miles) in the study area is used as a calibration unit such that the spatial variability of the urban growth process can be taken into account. The temporal calibration is performed by using a sequence of remote sensing images from which the land-cover information at different years is extracted. As for the assessment, fitness (for urban level match) and two types of modeling errors (for urban pattern match) are introduced as the evaluation criteria. The study shows that the use of images reduces the need for a large number of input data. Evaluation on the rule variogram reveals that the transition rule values are correlated spatially and vary with the urbanization level. The paper reports the study outcome over the city of Indianapolis, Indiana for the past three decades using Landsat images and the population data.

The Hellenic Cadastre Program (HCP) of Greece aims at developing a modern cadastral system for the first time in the Hellenic history. This paper is focused on the issues related to cadastral forestlands digital mapping, an indispensable part of HCP. Mapping the forestlands is a challenge of multiple disciplines. It includes photogrammetry, photointerpretation, Geographic Information Systems (GIS), and a clear understanding of the current institutional and legislative setting. The process requires both historical and current information pertaining to land-cover in order to identify forestland changes over time. Historical and current digital orthoimagery is generated through photogrammetric operations. Forestlands are delineated in a spatiotemporal environment; state property rights in forestlands are allocated and land ownership is established within the framework of HCP. This paper demonstrates that the integration of airborne remote sensing and collateral data with a GIS is an effective approach for cadastral forest mapping. The produced GIS databases and large-scale Forest Maps may serve as a data foundation towards a land register of forests.

This paper proposes a technique to extract urban built-up land features from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery taking two cities in southeastern China as examples. The study selected three indices, Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI) to represent three major urban land-use classes, built-up land, open water body, and vegetation, respectively. Consequently, the seven bands of an original Landsat image were reduced into three thematic-oriented bands derived from above indices. The three new bands were then combined to compose a new image. This considerably reduced data correlation and redundancy between original multispectral bands, and thus significantly avoided the spectral confusion of the above three land-use classes. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image as the spectral clusters of the classes are well separated. Through a supervised classification, a principal components analysis, or a logic calculation on the new image, the urban built-up lands were finally extracted with overall accuracy ranging from 91.5 to 98.5 percent. Therefore, the technique is effective and reliable. In addition, the advantages of SAVI over NDVI and MNDWI over NDWI in the urban study are also discussed in this paper.

The SRTM dataset is available at the UsGs seamless website with one arc-second pixel spacing for the U.S. Recently, the value for horizontal resolution has been questioned. One paper (Smith and Sandwell, 2003) suggests that 60 meters may be more accurate, implying that the resolution is twice the provided spacing. For users of this data, the horizontal resolution is very important for their analyses. Hence, this paper addresses this important question by using two different approaches: coherence spectra and step-response. The coherence spectra approach uses statistical techniques to compare the SRTM dataset against a more accurate one, while the step response approach uses the observed step response in many areas of the dataset to estimate the width of the averaging function used to produce the SRTM data. Results from this study show that the resolution is between 1 and 1.6 pixels, depending on the local variability of the elevation data; with higher resolution near sharp edges and corners, and lower resolution in smoother areas.

Spatially explicit models are now widely used for conducting ecological research and for managing natural resources, due in part to the difficulty of undertaking empirical work at landscape scale. Unfortunately, error assessment and analysis of the predictive ability of such models is not well-developed, and has relied on the Kappa statistic and information-based measures. As has been pointed out, however, such approaches are limited by virtue of their global nature and weak hypotheses. As it turns out, the literature on map accuracy does provide a way of assessing model performance, and the goal of this paper is to adapt this literature to the need for evaluating the predictive ability of spatially explicit models. To this end, the paper first considers inference using the Kappa statistic. This is followed by a commentary on information theory, and a critique of both the Kappa statistic and information-based approaches given their global structure and underlying null hypotheses. A probabilistic treatment of alternative measures recently suggested follows, as does a direct adaptation of map inference to the modeling case. Examples of the proposed measures are given, using an application of logistic regression applied to land-cover changes that have recently occurred in the Muskegon River watershed of the State of Michigan.

For over a century, our national map consisted of the thousands of paper topographic quadrangle maps created and maintained by the U.S. Geological Survey (USGS) at scales ranging from 1:250,000 to 1:24,000. As long as the Nation’s spatial data were embodied in paper maps, the linkage between private sector data providers and the USGS was tenuous: the USGS contracted for mapping services and map publishers might use quadrangle maps as the basis for a commercial map, but the relationship rarely went further. As the computer age impinged on mapping of the Nation, the linkage between public and private mapping grew tighter. Private companies became important resources in public map database compilation, and public digital maps seeded new private data products. The National Map vision promulgated in 2001 by the USGS opens new avenues for public-private collaboration on this vital resource. Commercial data providers can work with government at all levels to their mutual benefit to achieve The National Map vision.

The social-economic statistics collected from local governments are the main access for the central government to achieve national economic circumstance, especially for China. However, the statistics of almost 10% of national counties are missing or inconsistent due to the statistical caliber change in the wave of urbanization during economic development. Some researchers proposed to apply a night luminosity product to solve such issue. However, it lacks the ability to distinguish between the wealthy populations with a dense distribution and the less developed places. In this paper, the publicly available daytime Landsat images are used to estimate economic statistics. An end-to-end multi-task deep learning framework is constructed to estimate the county-level economy of Mainland China and the overall accuracy of this model achieves higher than 85%. The experiments show that our model provides a potential strategy to make up for the missing statistics and examines the reliability of the statistics collected for the central government.

In this paper, we demonstrate an approach to align aerial images to airborne lidar data by using common object features (tree tops) from both data sets under the condition that conventional correlation-based approaches are challenging due to the fact that the spatial pattern of pixel gray-scale values in aerial images hardly exist in lidar data. We extracted tree tops by using an image processing technique called extended-maxima transformation from both aerial images and lidar data. Our approach was tested at the Angelo Coast Range Reserve on the South Fork Eel River forests in Mendocino County, California. Although the aerial images were acquired simultaneously with the lidar data, the images had only approximate exposure point locations and average flight elevation information, which mimicked the condition of limited information availability about the aerial images. Our results showed that this approach enabled us to align aerial images to airborne lidar data at the single-tree level with reasonable accuracy. With a local transformation model (piecewise linear model), the RMSE and the median absolute deviation (MAD) of the registration were 9.2 pixels (2.3 meters) and 6.8 pixels (1.41 meters), respectively. We expect our approach to be applicable to fine scale change detection for forest ecosystems and may serve to extract detailed forest biophysical parameters.

Web mapping and virtual globes are increasingly used to communicate 3D geospatial results to fellow researchers and the public. The lack of analytical functionality nonetheless restricts their utility in research, including archaeology. Integrating both 3D and analytical functionalities should result in a 4D archaeological GIS that can be used throughout the archaeological workflow. This paper investigates the feasibility of extending a virtual globe to such a user-friendly system. This involves a technical assessment by comparing the characteristics of virtual globes with user, data, functional, and organizational requirements. The prototypical implementation consequently uses Cesium® as basis. This prototype served for the practical feasibility evaluation. The usability test with two Flemish archaeological organizations has shown broad support for a low-threshold 4D ArcheoGIS from its potential end-users. Although public activities, analyses, and fieldwork preparations were mentioned as application domains, extending the system to fit archaeological workflows or a cyberinfrastructure is necessary.

Earth observation imagery collected by the series of Landsat satellites constitutes the longest and most consistent collection of moderate spatial resolution remotely sensed data. Feedback from the Landsat user community has played an important role in assessing its utility and making improvements to future Landsat missions. The United States Geological Survey (USGS) and the National Aeronautical and Space Administration (NASA) continue to input from user communities. This paper describes the outcome of soliciting input from experts associated with AmericaView (AV), a non-profit organization aimed at promoting remote sensing applications in the US. The AV community identified the importance of rigorously calibrated Landsat data for LULC applications at the state level and made several recommendations for future Landsat missions. Recommendations suggested by the community of AV experts provide extremely valuable insights to USGS and NASA to further their discussions about the design and operation of future Landsat missions.

In this paper the distribution of 3-second elevation error in the data from the Shuttle Radar Topography Mission (SRTM) over the whole of China and its associations with topographic and land cover factors were systematically evaluated. The landscape features extracted from different datasets at more than 500,000 sites were used to determine the variation pattern in the errors by the method of single factor analysis. The results showed that the topographic attributes derived from SRTM data could adequately represent the terrain of China. However, there were extended and observable areas with abnormalities in a small proportion of the data. Slope was the dominant factor affecting elevation error compared with other landscape features (aspect, vegetation, etc.). The mean errors in glaciers, deserts and wetlands were −1.05 m, −2.03 m and −2.43 m, and 1.05 m in built-up areas. In general the elevation errors in the SRTM data formed a complex pattern of variation across China.

While the U.S. government does not charge for downloading Landsat images, the images have value to users. This paper demonstrates a method that can value Landsat and other imagery to users. A survey of downloaders of Landsat images found: (a) established US users have a mean value of $912 USD per scene; (b) new US users and users returning when imagery became free have a mean value of $367 USD per scene. Total US user benefits for the 2.38 million scenes downloaded is $1.8 billion USD. While these benefits indicate a high willingness-to-pay among many Landsat downloaders, it would be economically inefficient for the US government to charge for Landsat imagery. Charging a price of $100 USD a scene would result in an efficiency loss of $37.5 million a year. This economic information should be useful to policy-makers who must decide about the future of this and similar remote sensing programs.

Information on the county level population distribution is essential for supporting a wide variety of local planning pro¬cesses. Previous researchers found that impervious surfaces had a strong linear relationship with population. But few studies have reported estimating population in a county area of China with impervious surfaces. In this paper, a population distribution model for a county area in China was established using demographic information at the county level (i.e., total population, non-agricultural population, and agricultural population) and impervious surfaces derived from Land- sat TM images. The model was conducted to distribute the population of Fujin County, Heilongjiang Province, China. Population data of 123 villages and 11 towns of Fujin County were used to evaluate the model. The results indicated that the model performs better than area-based approaches, particularly in the estimation of town populations (17.86 percent mean relative error). The results of village population estima¬tion were acceptable, with a mean relative error of 25.3 percent. As a result, impervious surfaces cannot provide height information and identify non-residential areas in developed areas of zhens (urban cores of towns). The model suggests the difficulty of non-agricultural population estimation.

As an important indicator of cropping intensity, Cropping Index (CI) is defined as the number of peaks in the Vegetation Index (VI) time-series curve in a year. The existing CI mapping algorithms (e.g., the cross-fitting and the second order difference algorithm) are vulnerable to noise contained in the VI time series and need a priori knowledge and some extra constraints which could not be directly derived from the VI time-series data. In this paper, a shape-matching method is developed which can map CI directly from the preprocessed VI time-series data without the de-noising processes. This shape-matching method utilizes a decision-making process to find out the true peaks in the VI time-series curve based on a rank order mathematical morphology algorithm. The processing procedure involves five steps: (a) determination of the temporal moving window size, (b) detection of local maximum/minimum points, (c) exclusion of false maximum/minimum points, (d) determination of the threshold for the minimum growth amplitude, and (e) mapping of CI. This shape-matching method only needs two input parameters, the temporal moving window size and the threshold for the minimum growth amplitude, which can be both directly derived from the VI time-series data with some selected test pixels. Moreover, the response of the shape-matching method is relatively insensitive to the exact values of its design parameters, making it more flexible and effective in adapting to other regions. This new method is applied to map the CIs in Jiangsu Province, China, in 2010, based on the Enhanced Vegetation Index (EVI) time-series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) product. The overall CI mapping accuracy for the shape-matching method is 80 percent, which is much higher than the CI mapping accuracy of 60 percent for the second order difference algorithm. This shape-matching method can be further applied to other regions with a grid-search for its optimal parameters using some test pixels.

In this paper, a novel method for detecting earthquake damage to urban road networks using very high resolution (VHR) satellite images is proposed. The study focuses on road blockage caused by rubble from collapsed buildings. The road regions were first extracted from a pre-event road map. The damage to the road network was then detected from the post-event VHR image of the road regions using object-based classification, where both spectral and texture information was used. The damage to adjacent buildings was also extracted and used to refine the initial result of the road damage detection. The proposed method was evaluated using bitemporal VHR images and the road map of Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. The experimental results showed that the proposed method outperformed the direct use of various data combinations in road damage detection and achieved the best performance, with an overall accuracy (OA) of 81.50 percent and a Kappa coefficient of 61.23 percent. In particular, when the refinement step with building damage information in the area adjacent to the road was applied, the OA and Kappa were significantly improved.

The paper reports on the activity of the Center for Satellite based Crisis Information (ZKI) of the German Aerospace Center (DLR) in the aftermath of the devastating earthquake in Port-au-Prince, Haiti on 12 January 2010. DLR/ZKI closely coordinated with the European Global Monitoring for Environment and Security (GMES) program and the International Charter Space and Major Disasters. All DLR/ZKI damage maps are based on a specific analysis approach, including pre-processing procedures and visual interpretation on a grid-basis. As the satellite-based mapping response globally was so extensive for this event, problems resulting from the large number and inconsistency of satellite maps generated internationally are addressed. In order to avoid this kind of "mapping challenge" in future the setting-up of an international working group to elaborate global guidelines and cooperation procedures for better coherence of international satellite rapid mapping efforts for extreme events such as the Haiti earthquake is suggested.

This paper introduces a multi-level morphological active contour (MMAC) algorithm to identify and delineate tree crowns in mountainous forest based on rasterized airborne lidar data. MMAC is a generalized tree crown mapping algorithm which can accommodate multiple heads in a crown as well as overlapping crowns. The MMAC algorithm comprises three steps: bottom up erosion (BUE) which identifies stand candidates, top down dilation (TDD) which estimates the crown periphery, and an active contour model (ACM) which delineates crown contours. Three sample plots were selected in Alishan National Scenic Area, Taiwan, (predominantly alder, sugi, and red cypress) for evaluation of the algorithm. When compared with ground survey data, the algorithm achieved an average detection accuracy of 24 percent omission error and 13 percent commission error in identifying individual trees in mountainous forest stands. Detection accuracy is potentially related to stand density.

High spatial resolution images have been increasingly used for urban land-use/land-cover classification, but the high spectral variation within the same land-cover, the spectral confusion among different land-covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land-cover classification with QuickBird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land-cover classification performance.

This paper analyzes the geo-positioning capability of high-resolution satellite images in a very special metropolitan environment where within a relatively small region there are a large number of densely populated skyscrapers and high buildings. A pair of QuickBird stereo images covering downtown Shanghai were used for analysis. Multi-source data including GPS survey data, aerial images, and lidar data collected in the same area were used for Ground Control Points (GCPS) and Independent Check Points (ICPS) measurements and accuracy analysis. The vendor-provided Rational Function Model followed by a translation and scale correction model in image space was used to introduce the ground control and improve geo-positioning accuracy. The experimental results revealed that there is a clear dependency of geo-positioning accuracy on elevation where the GCPS are placed. Using the vendor-provided RFM and several GCPS across all the elevation ranges improves the overall geo-positioning accuracy.

Airborne laser scanning technology is primarily perceived as a means for gathering detailed three-dimensional information about the surface and objects on it. The dense 3D data contain information about surface features and geohazards, some of which are of subtle form. Geohazards are usually embedded within the terrain, and scarcely form distinct shape-transition to their surroundings; therefore their detection is challenging. We address in this paper detection of subtle terrain features and demonstrate it on collapse sinkholes. Collapse sinkholes are surface depressions whose formation has severe effect on the environment and on regional development. We present an autonomous model for their extraction and characterization. Sinkholes within the studied regions appear in various size, forms, from their embryonic to a well developed formation. The level of sinkhole detection is high, and as demonstrated, the model performs well under varying landforms and surface texture, with little influence on the correctness of the extracted sinkholes. As the results show, features of approximately 20 cm deep can be identified and separated from their surroundings in the data.

This paper presents a multi-scale approach for delineating individual tree crowns (ITC) from high spatial resolution imagery. By analyzing the evolution of image gradients over the scale-space constructed with orthogonal wavelets, tree crown boundaries are effectively strengthened while the textures resulted from tree branches and twigs are largely suppressed. Two scale consistency checks, a scale and a geometric consistency check, were devised to account for tree crown's radiometric and geometric characteristic. After an edge-enhanced image was acquired, a previously developed marker-controlled watershed segmentation method was adopted to delineate ITC. An experiment was carried out in a study site in California. Field measurements of crown size of 58 trees were compared with those derived from aerial imagery. An R square value of 0.68 was achieved. It was found that crown size was underestimated from the photo interpretation compared to that from the ground survey. The result can be attributed to the fact that pixels lying on the tree crown boundaries are poorly represented in the image.

Remote sensing technology still faces challenges when it comes to monitoring tasks that must be able to stand up to validation from technical, scientific, and practical points of view, in other words, when entering into established, fully operational workflows. In this paper, we present an approach for delineating and monitoring aggregated spatial units relevant to regional planning tasks, which has been fully validated within a 3,654 km 2 area in the Stuttgart Region of southwestern Germany. This has been achieved by developing algorithms for semi-automated (geo-) object-based class modeling of biotope complexes, which are aggregated, functionally homogenous (but not necessarily spectrally homogeneous) units. High levels of complexity in the target classes and the need for integration of auxiliary geodata as a priori knowledge meant that different methods of information extraction were required to be combined in an operational workflow, and that new validation strategies were needed for quality assessment. A total of 31,698 biotope complexes were delineated for the entire Stuttgart Region, with an average size of 11.5 ha for each complex. Approximately 86 percent of the biotope complex boundaries were shown to have been correctly delineated.

In this paper, cadastre agricultural cartography was enriched using crop raster maps obtained from remote sensing images. The work demonstrates the implications of applying two new terms: fidelity and purity. Per-pixel classifications and polygon enrichments were compared taking into account: (a) the consequences of using a more or less conservative strategy at the classification stage, using fidelity, and (b) the consequences of using modal thresholds at the enrichment stage when deciding which category each polygon is to be assigned to, using purity. More than 300,000 pixels and 2,800 polygons were used to measure the thematic accuracy of ten agricultural categories by means of confusion matrices. These were computed at pixel, polygon, and area level. Thematic accuracy was calculated in the classical way and without taking into account unclassified pixels as errors, as well as by paying special attention to the consequences for the classified area. The results show that polygon enrichment is a useful methodology, achieving thematic accuracies of 95.6 percent, when optimum parameters are used, while classifying 87.4 percent of the area.

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.

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.

This paper proposes a comprehensive framework for the accuracy assessment of taxonomically diverse LULC maps. A widely accepted difficulty in assessing such maps is associated with the vagueness in the interpretation of complex landscapes. For every class of the map, this method quantified the thematic and positional fuzziness of accuracy, induced by this difficulty. The labeling protocol consisted of a fuzzy comparison between the map and a reference maplet, for which degrees of positional and thematic tolerance can be user-defined. The construction of reference maplets permitted a flexible analysis (comparable with the assessment of other maps) of the positional fuzziness of the reference dataset and of the vagueness of the assessment process, while the alternate evaluation protocol, based on traditional point like data collection, did not allow such analysis.

This paper explores the relationship between remote sensing measurements of land surface temperature and biophysical/socioeconomic data by utilizing the association rule mining technique. The surfaces associated with urban uses typically radiate more heat as compared to its rural counterparts. There is a need to quantitatively analyze this contrast in temperature and the biophysical and social characteristics which influence it. Furthermore, in order to consider the urban heat island (UHI) effect, a parameterization is required to account for the urban surface characteristics impacts on the magnitude of land surface temperature (LST). The association rule mining model has demonstrated to bring in additional quantitative information concerning the relationships among urban parameters. The ASTER data from 2000 was used for the selection of appropriate variables to be used in the model. This information was then used for generating association rules between land-use land-cover (LULC) and LST information from 2000, 2001, and 2004. The results thus obtained quantitatively described the relationships between various urban parameters. It was found that there was little change in the percentage area of the LULC types from 2000 to 2004. This made the comparison of the results possible. In the case of the 2000 data, it was found that forest and impervious surfaces had strong association with temperature and scaled normalized difference vegetation index (SNDVI). Specific zones such as hospitals and universities had negative association with water. The comparison of data from 2000, 2001, and 2004 suggests that impervious surface and the zoning category of airport had a strong association. Nevertheless, the information extracted needs to be analyzed in greater detail in order to arrive at robust decision rules. Overall, the model so developed has demonstrated to be effective in predicting associations between urban LST and pertinent factors. This model could be useful for urban planners and environmental managers in quantifying rules that characterize a particular urban landscape.

The aim of this paper was to examine the potential of Ikonos satellite images for estimating boreal forest stand characteristics using frequency distributions of radiometric values. The spectral features selected for use in the estimation were medians, standard deviations, and the parameters of the two-parametric Weibull distribution derived from the standwise spectral histograms of the Ikonos image. Ancillary map information, such as land-use and peatland classes, was also included. The method of estimation was non-parametric k-most similar neighbors (K-MSN) method. The most accurate results were achieved using spectral features that were derived from the multispectral images. The lowest RMSEs for the mean total stem volume, basal area, and mean height were 52.2 m3/ha (31.3 percent), 5.6 m2/ha (25.3 percent), and 3.1 m (20.6 percent), respectively. When only the panchromatic image was used in the analysis, the RMSEs for the mean total stem volume and basal area were about 3 percentage points higher. No differences in the mean height estimates were observed between the multispectral and panchromatic images. The most efficient predictor variables were the medians and the scale parameters of the Weibull distribution. The use of classified map information did not improve the results. The findings suggest that Ikonos satellite images can be used in to estimate forest stand characteristics giving an accuracy that corresponds to that achieved with aerial photographs.

The topographic effect on land surface reflectance is an important factor affecting quantitative analysis of remotely sensed data in mountainous regions. Different approaches have been developed to reduce topographical effects. Of the many methods, the Minnaert correction method is most frequently used for topographic correction, but a single global Minnaert value used in previous research cannot effectively reduce topographic effects on the remotely sensed data, especially in the areas with steep slopes. This paper explores the method to develop a pixel-based Minnaert coefficient image based on the established relationship between Minnaert coefficients and topographic slopes. A texture measure based on homogeneity is used to eva-luate the topographic correction result. This study has demonstrated promising in reducing topographic effects on the Landsat ETM+ image with the pixel-based Minnnaert correction method.

This paper explores the temporal composition of the main Land-use/Land-cover (LULC) trajectories, examines the spatial configuration of the trajectories, and derives the probabilities of transitions in the Northern Ecuadorian Amazon (NEA). This research uses a time-series of classified Landsat images that ranges from 1974 to 2002, and a set of spatial socioeconomic, demographic, and accessibility data assembled in a geographic information system. The LULC trajectories are analyzed for the Northern Intensive Study Area (NISA) using image algebra, and for the whole region, the NEA, using cluster analysis, landscape ecology principles, and spatial logistic regression models. In general, the trajectories are dominated (i.e., in terms of area) by recent transitions that contain forested classes (i.e., primary forest or succession), as well as the consistent representation of pasture through time. This exploratory analysis of LULC transitions suggests a set of clusters that form a "core and periphery" pattern in the NEA. This research shows how these clusters and probabilities of change can be used to characterize trajectories of LULC in the region.

In this article, we simulate forest fragmentation patterns by reference to the actual decision-making of the agents engaged in the fragmentation process itself. We take as our empirical case fragmentation in the Brazilian Amazon basin associated with road-building by loggers. Roads built by the private sector, particularly loggers, play a decisive role in the dynamics of frontier expansion in the Amazon. Our objective is to explain the manner in which logging roads manifest spatially, thereby creating fragmented landscapes in a small portion of the so-called “Terra do Meio,” a region of 300,000 km 2 in the heart of the Amazon basin. We combine geostatistical methods with GIS to replicate a common fragmentation pattern found in tropical forests known as dendritic. Such fragmentation has been identified as one of the three most common types observed in the Amazon basin. The model replicates the general dendritic pattern and many branching points of the network, although segments do not overlay precisely. The paper concludes with a discussion of steps necessary to develop a model that is fully effective in describing the spatial decision-making of loggers.

Many different types of geological hazards have seriously hindered sustainable development of society and its economy. It is critical to pay greater attention to effective geological hazard investigations, monitoring, early warning, mitigation, and prevention through advanced technologies of the Internet, Geographic Information Systems (GIS), Global Positioning System (GPS), and Remote Sensing (RS). In this paper, an information system to monitor geological hazards is designed and implemented on the basis of a WEBGIS platform and a large volume of multi-source remotely sensed data; its network structure and architecture are designed and presented. Detail is provided on the key technologies and function modules. Then, a concrete system implementation is introduced using a typical instance, namely, the geological hazard monitoring of the Three Gorges Reservoir Engineering Areas, followed by conclusions and directions for future work. The focus of our work is its provision of a highly efficient means of realtime geological hazard body monitoring. In addition, information WEB publishing, geological hazard assessment and decision-making for a geological department are also important targets.

This paper compares several stereo image interest point detectors with respect to their repeatability and information content through experimental analysis. The Harris-Laplace detector gives better results than other detectors in areas of good texture; however, in areas of poor texture, the HarrisLaplace detector may be not the best choice. A featurerelated filtering strategy is designed for the Harris-Laplace detector (as well as the standard Harris detector) to improve the repeatability and information content for imagery with both good and poor texture: (a) the local information entropy is computed to describe the local feature of the image; and (b) the redundant interest points are filtered according to the interest strength and the local information entropy. After the filtering process, the repeatability and information content of the final interest points are improved, and the mismatching then can be reduced. This conclusion is supported by experimental analysis with actual stereo images.

This paper reports on new geological mapping techniques using the rugged area of Moose Mountain, Alberta as a test site. First, we present a web-accessible photographic database that facilitates interactive visualization of multiple rock exposures for geology mapping, and second, the results of analysis of two readily available oblique photogrammetric methods, that have been previously applied to archeological and industrial surveys. These two techniques of high-resolution terrestrial oblique photogrammetry called "block bundle" and "terrain rendering" were tested using the web database, in order to evaluate their precision and accuracy as mapping techniques. The efficiency of the new techniques to support geological mapping is compared to other mapping techniques. The results show that in conditions where high-resolution digital elevation and imagery data are available, a web-based terrain rendering technique, combined with a web-accessible photographic database of rock exposures is advantageous to a wide range of geological analyses, including analog modeling of hydrocarbon reservoirs.

This paper examines the performance of a spatiospectral template on Ikonos imagery to automatically detect small recreational boats. The spatiospectral template is utilized and then enhanced through the use of a weighted Euclidean distance metric adapted from the Mahalanobis distance metric. The aim is to assist the Canadian Coast Guard in gathering data on recreational boating for the modeling of search and rescue incidence risk. To test the detection accuracy of the enhanced spatiospectral template, a dataset was created by gathering position and attribute data for 53 recreational vessel targets purposely moored for this research within Cadboro Bay, British Columbia, Canada. The Cadboro Bay study site containing the targets was imaged using Ikonos. Overall detection accuracy was 77%. Targets were broken down into 2 categories: 1) Category A-less than 6 m in length, and Category B-more than 6 m long. The detection rate for Category B targets was 100%, while the detection rate for Category A targets was 61%. It is important to note that some Category A targets were intentionally selected for their small size to test the detection limits of the enhanced spatiospectral template. The smallest target detected was 2.2 m long and 1.1 m wide. The analysis also revealed that the ability to detect targets between 2.2 and 6 m long was diminished if the target was dark in color.

This paper presents the potential of integrating radar data features with optical data to improve automatic land-cover mapping. For our study area of St. Louis, Missouri, Landsat ETMand Radarsat images are orthorectified and co-regis- tered to each other. A maximum likelihood classifier is utilized to determine different land-cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. In addition, the Landsat images are fused with several combinations of processed radar features. The classification accuracies from the Landsat and radar feature combinations are studied. Our research finds that fusion of multi-sensor data improves the classification accuracy over a single Landsat sensor, although different processing techniques on radar images are required to obtain the best results. In our study, fusion of Landsat images and Radarsat feature combinations from a 13 � 13 entropy window, 9 � 9 data range widow, and 19 � 19 mean filter window achieves the highest overall accuracy improvement (10 percent) over the Landsat images alone.

This paper describes a new object-oriented small footprint lidar algorithm in which the objects of interest are tree clusters. The algorithm first thresholds the lidar canopy height model (CHM) at two levels to produce tree cluster grids. Next, two metrics are calculated based on these grids. The metric values are used in a multiple regression equation to predict the forest parameter of interest. To set the two thresholds, an optimization algorithm is used in conjunction with training data consisting of subsets of the CHM in which the forest parameters are known through ground measurements. A test of the algorithm was performed using ground and lidar data from a non-intensively managed loblolly pine (Pinus taeda) plantation in Virginia. The accuracies of the lidar-based predictions of density (0.01 ≤ R 2 ≤ 0.80; 126 trees/ha ≤ RMSE ≤ 8,173 trees/ha) and biomass (0.04 ≤ R 2 ≤ 0.62; 12.4 t/ha ≤ RMSE ≤ 316.5 t/ha) depended on the combination of metrics used, whether trees with a diameter at breast height < 10 cm were excluded from the analysis, and the number of plots used for training and testing. However, the fit between the ground measurements and tree cluster-based predictions generally exceeded the fit between ground measurements and the output from an individual tree-based algorithm tested using the same data (100 percent of comparable cases when density was predicted, 85 percent of comparable cases when biomass was predicted, based on the coefficient of determination and RMSE).

In forestry, the availability of high spatial resolution (� 1 m/pixel) imagery from new earth observation satellites like Ikonos favours a shift in the image analysis paradigm from a pixel-based approach towards one dealing directly with the essential structuring element of such images: the individual tree crown (ITC). This paper gives an initial assessment of the effects of 1 m and 4 m/pixel spatial resolutions (panchromatic and multispectral bands, respectively) on the detection, delineation, and classification of the individual tree crowns seen in Ikonos images. Winter and summer Ikonos images of the Hudson plantation of the Petawawa Research Forest, Ontario, Canada were analyzed. The panchromatic images were resampled to 0.5 m/pixel and then smoothed using a 3 � 3 kernel mean filter. A valleyfollowing algorithm and rule-based isolation module were applied to delineate the individual tree crowns. Local maxima within a moving 3 � 3 window (i.e., Tree Tops) were also extracted from the smoothed images for comparison. Crown delineation and detection results were summarised and compared with field tree counts. Overall, the ITC delineation and the local maxima approaches led to tree counts that were on average 15 percent off for both seasons. Visual inspection reveals delineation of clusters of two or three crowns as a common source of error. Crown-based species spectral signatures were generated for six classes representing conifer species, plus a hardwood class and a shrub class. After the ITC-based classification, classification accuracy was ascertained using separate test areas of known species. The overall accuracy was 59 percent. Important confusion exists between red and white spruces, and mature versus immature white pines, but post-classification regroupings into single spruce and white pine classes led to an overall accuracy of 67 percent.

This paper puts forward a pixel-based and a region-based method for detecting land-use changes using a multitemporal pair of satellite KVR panchromatic and aerial Daedalus multispectral images. These methods, based on fuzzy logic, provide a confidence level of their post-classification change detection results. They were applied to support an early stage of mine clearance in Croatia, where mines laid during the latest conflict (1991 to 1995) cause disruption in the land-use evolution. They were used for locating the agricultural land that was abandoned during the war and is therefore considered as a mine contamination indicator. The mere location of the abandoned agricultural land is of limited use for supporting mine clearance. On the other hand, the integration of the location and spatial confidence level can help the mine experts in their decision making by evaluating both unlikelihood and likelihood of a plot of land to be abandoned.

Digital elevation models (DEMs) are representations of topography with inherent errors that constitute uncertainty. DEM data are often used in analyses without quantifying the effects of these errors. This paper describes a Monte Carlo methodology for evaluation of the effects of uncertainty on elevation and derived topographic parameters. Four methods for representing DEM uncertainty that utilize metadata and spatial characteristics of a DEM are presented. Seven statistics derived from simulation results were used to quantify the effect of DEM error. When uncertainty was quantified by the average relative absolute difference, elevation did not deviate. The range of deviation across the four methods for slope was 5 to 8 percent, 460 to 950 percent for derived catchment areas and 4 to 9 percent for the topographic index. This research demonstrates how application of this methodology can address DEM uncertainty, contributing to more responsible use of elevation and derived topographic parameters, and ultimately results obtained from their use.

In this paper, we compare the geometrical performance between the rigorous sensor model (RSM) and rational function model (RFM) in the sensor modeling of FORMOSAT-2 satellite images. For the RSM, we provide a least squares collocation procedure to determine the precise orbits. As for the RFM, we analyze the model errors when a large amount of quasi-control points, which are derived from the satellite ephemeris and attitude data, are employed. The model errors with respect to the length of the image strip are also demonstrated. Experimental results show that the RFM is well behaved, indicating that its positioning errors is similar to that of the RSM.

Laser scanning provides a good means to collect information on forest stands. This paper presents an approach to delineate single trees automatically in small footprint light detection and ranging (lidar) data in deciduous and mixed temperate forests. In rasterized laser data possible tree tops are detected with a local maximum filter. Afterwards the crowns are delineated with a combination of a pouring algorithm, knowledge-based assumptions on the shape of trees, and a final detection of the crown-edges by searching vectors starting from the trees’ tops. The segmentation results are assessed by comparison with terrestrial measured crown projections and with photogrammetrically delineated trees. The segmentation algorithm works well for coniferous stands. However, the implemented method tends to merge crowns in dense stands of deciduous trees.

USGS Digital Orthophoto Quadrangles (DOQs) are a form of inexpensive, high spatial resolution (1 m ground resolution) imagery available for most regions of the United States. Typically, DOQs have been used in the construction of “basemaps” or as training datasets. In this paper we present a technical approach that uses DOQs as the primary data source to map regional forest canopy cover. This approach, a form of advanced exploratory data analysis (AEDA), can separate areas of crown, shadow, and non-crown vegetation using a single value threshold and a “value range” threshold obtained by analyzing the statistical plots built on a multifractal model. By applying AEDA, we can distinguish the crown, crown boundary zone, shadow areas, and non-crown areas within a DOQ mosaic by their distinctive multifractal properties. Over a period of two years, we mapped canopy cover of 20,000 km 2 of ponderosa pine-dominated forest across northern Arizona using this technique. We used two hundred ground plot measurements from an independent source to assess the accuracy of the canopy cover map across an 8,000 km 2 region.

This paper compares different image processing routines to identify suitable remote sensing variables for urban classifi- cation in the Marion County, Indiana, USA, using a Landsat 7 Enhanced Thematic Mapper Plus (ETM� ) image. The ETMmultispectral, panchromatic, and thermal images are used. Incorporation of spectral signature, texture, and surface temperature is examined, as well as data fusion techniques for combining a higher spatial resolution image with lower spatial resolution multispectral images. Results indicate that incorporation of texture from lower spatial resolution images or of a temperature image cannot improve classification accuracies. However, incorporation of textures derived from a higher spatial resolution panchromatic image improves the classification accuracy. In particular, use of data fusion result and texture image yields the best classifi- cation accuracy with an overall accuracy of 78 percent and a kappa index of 0.73 for eleven land use and land cover classes.

Geographic Information Systems (GIS) have become a crucial support tool for governmental decision makers at many levels. Internet-based GIS applications provide unique services for the presentation of spatial data and mapping products to a broad range of audiences. Furthermore, compact and portable mapping devices provide new opportunities for the expansion of Internet-based GIS applications. In this paper, we present results of the research and development of a web-based, mobile, spatial system for coastal management, and decision-making that integrates these technologies. The developed system consists of an on-site mobile spatial subsystem, a web-based shoreline erosion awareness subsystem, and a coastal structures permit subsystem. The system is designed and developed for applications in the Lake Erie area and has the potential to be adapted and applied in other coastal areas.

Recent advances in technology and wider commercial applications have promoted the remote sensing and airborne mapping communities to take a closer look at digital imaging and its advantages over conventional film-based, large-format aerial photography. In this paper, we focus on the potential of high-resolution, digital color imagery acquired from an airborne small-format digital camera for rapid mapping of complex urban informal settlements. We discuss the example of automated building extraction using a novel color edge extractor developed with similarity-based color morphology and report on investigations into the use of this low-cost digital imaging option for rapid spatial data acquisition of informal settlements under dynamic conditions. Results from our work suggest that the approach taking advantage of color information to generate attributed edges to hypothesize shack roof outlines by exploiting the fuzzy similarity measure and color morphology is a potential new avenue to automatically extract buildings from high-resolution color digital imagery.

This paper addresses the separation of ground points from raw lidar data for bald ground digital elevation model (DEM) generation in urban areas. This task is considered to be a labeling process through which a lidar point is labeled as either a ground point or a non-ground point. Mathematical formulation is presented to define the ground. A new approach is proposed that conducts one-dimensional labeling in two opposite directions followed by a linear regression, both along the lidar scan line profile. The study shows that the one-dimensional characteristic makes the calculation efficient, and the reliability is assured by the bidirectional labeling process. Lidar data over suburban and downtown Baltimore (Maryland), Osaka (Japan), and Toronto (Canada) are used for the study. Quality assessment is designed and conducted to investigate the performance of the labeling approach by using manually-selected ground truth. It is shown that 2.7 percent ground points are wrongly labeled as building points, and 2.6 percent building points are mistakenly labeled as ground points over the four study areas. Detailed graphic and numerical results are provided to illustrate the proposed labeling approach and its performance for complex urban areas.