Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images

Accuracy measures for fuzzy classification in remote sensing

Over the last decades, many fuzzy classification algorithms have been proposed for image classification,and in particular to classify those images obtained by remote sensing. But relatively little effort has been done to evaluate goodness or effectiveness of such algorithms. Such a problem is most of the times solved by means of a subjective evaluation, meanwhile in the crisp case quality evaluation can be based upon an error matrix, in which the reference data set (the expert classi-fication) and crisp classifiers data set are been compared using specific accuracy measures. In this paper,some of these measures are translated into the fuzzy case, so that more general accuracy measures between fuzzy classifiers and the reference data set can be considered

Accuracy Assessment for Soft Classification Maps

An important topic in using maps derived from a statistical classifier is the accuracy assessment of the classification. Analysts usually need to compare various techniques, algorithms, or different approaches. As pointed out by Stehman and Czaplewski (1998), the accuracy assessment of classification maps generally involves three different steps: the sampling design, the response or measurement design to obtain the true classes for each sampling (usually requiring an expert), and the analysis of the data obtained. In the ..

Image

matching based on tracking matching paths in the similarity spac

Impacts of Predicted Sea-Level Rise and Extreme Storm Events on the Transportation Infrastructure in the San Francisco Bay Region

Literature concerning the potential effect of climate change (sea‐level rise inundation and 100‐year storm events) on the San Francisco Bay region’s transportation infrastructure is reviewed. Currently available geographical information system data is employed, and a review of how those datasets have been used in previous studies is reported. The second part of this paper presents methods. They include a higher‐resolution digital elevation model for the Bay Area; a new approach using a digital surface model is introduced to improve the surface elevations of features and better calculate the risk of over‐topping by sea level shifts and storm surges. A metric to assess change in the transportation infrastructure is introduced that calculates accessibility of first responders to the population at large. Sea level rise is incremented to the expected 1.4 meters in tandem with a 100‐year flood to analyze the extent to which transportation assets are at risk of inundation. The increased travel time from first responder locations to all neighborhoods in the region is measured for each iteration of the model. Local accessibility analysis for the entire San Francisco Bay region is performed to provide a synoptic view. Two localities are chosen to view in detail the impact on first‐responder accessibility caused by sea level rise and a 100‐year storm event. Next, the regional vulnerability of the transportation network to these events is assessed. This is accomplished by creating nodes that are the intersections of the major regional highways that surround the Bay. The loss of accessibility is measured by calculating the changes in travel time between these major nodes through iterations of our inundation model. Finally, the accessibility impacts to the hinterland from the major highway intersections for each peak water level iteration is determined, calculating the first and last 20 minutes of an origin‐destination journey

A comparison of photointerpretation and ground measurements of forest structure.

Traditional forest inventory methods are compared with photointerpreted results. The accuracy of photointerpretation for forest-type classification is assessed in test locations in northern California. If the accuracy of photointerpretation is not sufficiently high, then the traditional practice of comparing satellite classification to photointerpretation is not justified. If this hypothesis is true, it is speculated that spectral analysis of advanced digital satellite data (SPOT and TM) can be used in conjunction with ancillary ground data to produce forest classifications of the same or better accuracy than by traditional photointerpretation techniques. Results of the accuracy assessment of three levels of classification - species, size class, and density - are presented in tables

Impacts of Predicted Sea-Level Rise and Extreme Storm Events on the Transportation Infrastructure in the San Francisco Bay Region

Literature concerning the potential effect of climate change (sea‐level rise inundation and 100‐year storm events) on the San Francisco Bay region’s transportation infrastructure is reviewed. Currently available geographical information system data is employed, and a review of how those datasets have been used in previous studies is reported. The second part of this paper presents methods. They include a higher‐resolution digital elevation model for the Bay Area; a new approach using a digital surface model is introduced to improve the surface elevations of features and better calculate the risk of over‐topping by sea level shifts and storm surges. A metric to assess change in the transportation infrastructure is introduced that calculates accessibility of first responders to the population at large. Sea level rise is incremented to the expected 1.4 meters in tandem with a 100‐year flood to analyze the extent to which transportation assets are at risk of inundation. The increased travel time from first responder locations to all neighborhoods in the region is measured for each iteration of the model. Local accessibility analysis for the entire San Francisco Bay region is performed to provide a synoptic view. Two localities are chosen to view in detail the impact on first‐responder accessibility caused by sea level rise and a 100‐year storm event. Next, the regional vulnerability of the transportation network to these events is assessed. This is accomplished by creating nodes that are the intersections of the major regional highways that surround the Bay. The loss of accessibility is measured by calculating the changes in travel time between these major nodes through iterations of our inundation model. Finally, the accessibility impacts to the hinterland from the major highway intersections for each peak water level iteration is determined, calculating the first and last 20 minutes of an origin‐destination journey

Classifying pixels by means of fuzzy relations.

Most of the classification models assume, in a direct or indirect way, the possibility of a representation of the object to be classified within a good properties space, in which one is able to define some distances. Quite often this representation, which usually is the base of intuitive arguments made by the decision maker, is elaborated from a systematic comparison between different available options. The target of this article is to model a classification problem in the case that a comparative analysis is an essential part of the available information. This will be accomplished by using a description of the option set quality