Simon Adar

A new approach for thresholding spectral change detection using multispectral and hyperspectral image data, a case study over Sokolov, Czech republic

Change detection and multitemporal analyses aim to detect changes occurring over a specific geographical area using two or more images acquired at two or more different times. In this article, we present a new thresholding approach for unsupervised change detection. This approach focuses on determining the threshold that discriminates between change and no-change pixels. The differences between pixels in the two images are associated with real changes or noise. We propose a thresholding scheme that separates the threshold into two parts: (1) a spectral domain threshold that accounts for errors related to sensor stability, atmospheric conditions, and data-processing variations, and (2) a spatial domain threshold associated with georectification errors. We demonstrate our method using both multispectral Landsat images and airborne imaging spectroscopy HyMap images. The results show that the spectral domain threshold gives high detection capabilities with moderate false-alarm rate. Adding the spatial domain threshold to the spectral domain threshold reduces the false-alarm rates while maintaining good detection capabilities.

Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor

Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor’s missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR) spectral region using the visible (VIS) spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS) sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic.

New approach for spectral change detection assessment using multistrip airborne hyperspectral data

Change detection of imaging spectroscopy data is widely used in many applications. Among them, environmental monitoring is of great importance. In this paper, we introduce a new automated method, termed spectral overlapping threshold (SOT), to derive a threshold to distinguish between “change” and “no change” areas. The method exploits the overlapping regions in multi-strip mosaic images, which are regarded as “no change” areas because they are acquired only a few minutes apart. The method consists of two steps. First, similarity measures are applied to the overlapping areas. Then, the histogram of the similarity values are computed and the thresholds for each land use land cover (LULC) category are determined. The method is independent of the underlying SM used to detect changes, and is demonstrated here for the spectral angle measure (SAM), spectral information divergence (SID), Euclidean distance (ED) and spectral correlation measure (SCM). This process is demonstrated for a mosaic of HyMap sensor data acquired in 2009 and 2010 over Sokolov mining area, Czech Republic.

EO-MINERS: Monitoring the environmental and societal impact of the extractive industry using Earth Observation

The growing demand for mineral and energy resources over the last decade has placed the extractive industry under increasing pressure to monitor and reduce the environmental and societal impact throughout the life-cycle of mining operations. Despite the mounting pressure, the industry is still facing the challenge of how to define targets for, and monitor, the impact of mining. In 2010, the EU-funded EO-MINERS project (Earth Observation for Monitoring and Observing Environmental and Societal Impacts of Mineral Resources Exploration and Exploitation) was set up in an effort to help address this issue, specifically through the application of Earth Observation (EO) data. Furthermore, the aim was to help facilitate and improve interaction and dialogue between the mineral extractive industry and society in view of its sustainable development, while improving its societal acceptability. One of the primary project objectives was to develop novel yet objective EO products contributing to a constructive “trialogue” involving stakeholders such as industrialists (mining companies), regulatory bodies and the civil society. EOMINERS is scheduled to run until October 2013.

Using Multispectral Remote Sensing in the TIR Region for Monitoring the Environment over Mining Area

Remote sensing techniques using VIS-NIR-SWIR-TIR sensors, offer a unique opportunity to collect necessary spatial parameters that play a key role for better assessments of mining related environmental impacts. The TIR HSR sensors data, as they are still not well investigated, may contribute to characterize the necessary parameters. An atmospheric correction of TIR (LWIR) data, taken with the AHS multispectral sensor over the Sokolov area in the Czech Republic, was performed. Surface kinetic temperature and emissivity values of the study area were calculated. Some important parameters such as Apparent Thermal Inertia and soil sand and clay content were derived from the corrected data set. The ongoing analysis of the TIR HSR sensors data, ground measurements data and laboratory studies will contribute an additional data layer to the mapping of mining related environmental impacts.