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eDaRT: The Ecosystem Disturbance and Recovery Tracker system for monitoring landscape disturbances and their cumulative effects

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REMOTE SENSING OF ENVIRONMENT 2020年第0期238卷 111482-000页

作者： Koltunov, Alexander Ramirez, Carlos M. Ustin, Susan L. Slaton, Michele Haunreiter, Erik Univ Calif Davis Ctr Spatial Technol & Remote Sensing CSTARS One Shields Ave Davis CA 95616 USA US Forest Serv USDA Pacific Southwest Reg Remote Sensing Lab 3237 Peacekeeper WaySuite 201 Mcclellan CA 95652 USA

The worldwide demand for timely and accurate information about ecosystem dynamics at Landsat spatial scale is growing and as of today still exceeds the availability of information. The diversity of required disturbance metrics and trade-offs between sensitivity, reliability, timelines of information generation, and flexibility toward potential customizations suggests that a single system is not likely to fill such demand in the near future. To address this challenge, the scientific community has been developing and improving various Landsat-based algorithms for land change monitoring. We describe the Ecosystem Disturbance and Recovery Tracker (eDaRT) version 2.9 - a highly automated prototype system in continuous development, which has been operated since 2012 by the USDA Forest Service Pacific Southwest Region to generate most current disturbance maps at Landsat scale and provide customized information services and inputs to science and land management applications in the Region. The eDaRT processing system utilizes all three dimensions of dense Landsat image time series: spectral, temporal, and spatial. Two anomaly detection algorithms are sequentially applied, one estimating pixels' disturbance status metrics in every processed image and the other detecting disturbance events, the primary output of eDaRT. The first algorithm initially estimates change relative to a user-defined fixed baseline time period, using a stratified version of the dynamic detection model (DDM;Koltunov et al., 2009) applied to Landsat bands and vegetation indexes that reflect canopy greenness, abundance, and moisture content. Using the model residuals and a probabilistic context analysis, the detected anomalies are further classified as disturbed, cloud/snow, or recovered. The resulting residuals, classification maps, and the associated disturbance confidence values provide the most rapid preliminary snapshot of the current cumulative effect of disturbance and regeneration. The second algo

关键词： Remote sensing Landsat Forest disturbance Disturbance detection Change detection Time series Landscape monitoring dynamic detection model Ecosystem disturbance and recovery tracker eDaRT

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Early fire detection using non-linear multitemporal prediction of thermal imagery

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REMOTE SENSING OF ENVIRONMENT 2007年第1期110卷 18-28页

作者： Koltunov, A. Ustin, S. L. Univ Calif Davis Ctr Spatial Technol & Remote Sensing Ctr Excellence Calif Space Inst Davis CA 95616 USA

This paper presents a sub-pixel thermal anomaly detection method based on predicting background pixel intensities using a non-linear function of a plurality of past images of the inspected scene. At present, the multitemporal approach to thermal anomaly detection is in its early development stage. In case of space-borne surveillance the multitemporal detection is complicated by both spatial and temporal variability of background surface properties, weather influences, viewing geometries, sensor noise, residual misregistration, and other factors. We use the problem of fire detection and the MODIS data to demonstrate that advanced multitemporal detection methods can potentially outperform the operationally used optimized contextual algorithms both under morning and evening conditions. (c) 2007 Elsevier Inc. All rights reserved.

关键词： anomaly detection change detection target detection dynamic detection model multitemporal thermal infrared surveillance fire detection MODIS

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dynamic detection model and its application for perimeter security, intruder detection, and automated target recognition

Dynamic detection model and its application for perimeter se...

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Infrared Technology and Applications XXIX Conference

作者： Koltunov, J Koltunov, A Tel Aviv Univ Dept Geog & Human Environm IL-69978 Tel Aviv Israel

ISBN: (纸本)0819449334

Under unsteady weather conditions (gusty wind and partial cloudiness), the pixel intensities measured by infrared or optical imaging sensors may change considerably within even minutes. This makes a principal obstacle to automated target detection and recognition in real, outdoor settings. Currently existing automated recognition algorithms require strong similarity between the weather conditions of training and recognition. Empirical attempts to normalize image intensities do not lead to reliable detection in practice (e.g. for scenes with a complex relief). Also if the weather is relatively stable (weak wind, rare clouds), as short as 15-20 minutes delay between the training survey and the recognition survey may badly affect target recognition or detection, unless the targets are well separable from background. Thermal IR technologies based on invariants such as emissivity and thermal inertia are expensive and ineffective in making the recognition automated. Our approach to overcoming the problem is to take advantage of multitemporal prior surveying. It exploits the fact, that any new infrared or optical image of a scene can be accurately predicted based on sufficiently many scene images acquired previously. This removes the above severe constraints to variability of the weather conditions, whereas neither meteorological measurement nor radiometric calibration of the sensor are required. The present paper further generalizes the approach and addresses several points that are important for putting the ideas in practice. Two experimental examples: intruder detection and recognition of a suspicious target illustrate the potential of our method.

关键词： anomaly change detection target recognition dynamic detection model multitemporal infrared mine intruder learning classification surveillance weather variability

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Adaptive recognition under static and dynamic environment assumptions

Adaptive recognition under static and dynamic environment as...

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Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX

作者： Koltunov, A Koltunov, J Ben-Dor, E Tel Aviv Univ Dept Geog & Human Environm IL-69978 Tel Aviv Israel

ISBN: (纸本)0819449539

This paper presents two approaches to ATR* by trainable algorithms. The first approach assumes that the measurements coming from the objects remain unchanged for the time passed between the stages of learning and recognition. For outdoor scenes such an approach is viable when both learning and recognition can be completed within minutes, which is difficult to achieve in practice. More realistic is to acquire training image data short before surveying the scene of interest. Then computer-intensive or interactive learning algorithms can be applied. We exemplify this approach qualitatively by detecting buildings and asphalt roads in a typical urban scene from AISA hyperspectral sensor data. The second, new approach we derive takes into account the joint changes of all targets and backgrounds under dynamic external factors. This requires multitemporally surveying an area that is specially selected for training an ATR system. Then at the-future recognition stage the system can take advantage of the learning results in the real-time mode. Experimental verification of the new approach was performed using a fixed FLIR-type camera that surveyed the site containing more than 50 thermally different objects, whereas learning and recognition were spaced one week apart. The thermal joint prediction model proved working and was applied for detecting and identifying a scene anomaly an intruder.

关键词： anomaly change detection target recognition dynamic detection model multitemporal infrared learning hyperspectral classification urban

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