ELS2000 Version 2 takes advantage of the lossless uncompressed imagery source at 14.25m-resolution from USGS and NASA (file size ~4.3TB), and the processed result, in terms of both colours and spatial resolutions, is certainly one of the best!.....
Spectral Transformer Fc2Tc-CIR (PDF, ~300KB), a standalone utility program for converting popular colour infrared (CIR) imagery into eye-catching natural colour scenes, with six methods / colour templates included......
Remote sensing solutions provider DMC International Imaging Ltd (DMCii, UK) has signed a contract with Brazil’s National Institute for Space Research (INPE) to deliver near real-time satellite imagery to monitor forest clearing in the Amazon rainforest and target illegal logging as it happens.....
Landsat 5 recently captured and downlinked Multispectral Scanner (MSS) images for the first time in over a decade. An MSS sensor first flew on Landsat 1 in 1972 and was aboard each of the Landsats 1-5. The MSS was powered down on Landsat 5 in the late 1990's, but the USGS recently turned on the MSS to determine the current state of the sensor. The USGS successfully downlinked raw data from the MSS for several successful passes in the past two weeks. Due to the length of its inactivity, much analysis and testing still lie ahead to determine the quality of the data, and the ability of the USGS to collect, process, and distribute the data. Therefore, MSS data distribution is by no means a certainty.
Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination (R2) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797.
In a previous blog entry, we discussed how you can use Landsat image services in ArcMap to see the change over time. In this blog entry, we dive further into Landsat image services and describe how you can create thematic land cover maps which can then be used for analyses, such as land cover change detection.
The image classification process involves conversion of multi-band raster imagery into a single-band raster with a number of categorical classes that relate to different types of land cover.
There are two primary ways to classify a multi-band raster image; supervised and unsupervised classification. Using the supervised classification method, an image is classified using spectral signatures (i.e., reflectance values) obtained from training samples (polygons that represent distinct sample areas of the different land cover types to be classified). These samples are collected by you, the image analyst, to classify the image. With the unsupervised classification method, the software finds the spectral classes (or clusters) in the multi-band image without the analyst’s intervention, thus being unsupervised. Once the clusters are found, you then need to identify what the cluster represents (e.g., water, bare earth, dry soil, etc…)
In this blog entry, we explain how to use the Landsat image services with supervised classification method to create a land cover map. If you want to follow along, you can download the zip file we created for you to try this out yourself.
Envisat, the European Space Agency’s (ESA) earth observation satellite, has stopped functioning properly. Communication with the satellite was lost on April 8th when data was unable to be retrieved as it passed over the ground control station in Kiruna, Sweden. Efforts by members of the ESA mission control team have not been successful in reestablishing communications. ESA reports that the satellite remains in stable orbit around the earth.
Envisat recently celebrated its 10th year in orbit which marked twice the length of its intended lifespan. In October 2010, the orbit of Envisat was lowered from an altitude of 800 km to about 776 km in an effort to extend the lifespan of the satellite by three years.
TT Exelis Geospatial Systems has delivered GeoEye's next-generation commercial imaging system for the GeoEye-2 satellite to Lockheed Martin Space Systems Company in Sunnyvale, Calif. When operational in 2013, GeoEye-2 will deliver the highest resolution and most accurate color imagery to GeoEye's commercial, government and international customers.
The Exelis-built imaging payload for GeoEye-2 includes a telescope, sensor subsystem and outer barrel assembly and has the potential to capture panchromatic ground sample distance imagery of the Earth's surface at 0.34-meter, or 13.38-inch, ground resolution......
The US Navy has used their Fire Scout helicopter drone (seen here) to capture cocaine smugglers. Their next target is pirates operating from small boats. The new system will include the use of LIDAR to make the Fire Scout even more powerful.
“Infrared and visible cameras produce 2-D pictures, and objects in them can be difficult to automatically identify,” said Dean Cook, principal investigator for the MMSS program at the Naval Air Warfare Center Weapons Division. “With LADAR data, each pixel corresponds to a 3D point in space, so the automatic target recognition algorithm can calculate the dimensions of an object and compare them to those in a database.”
GeoEye-1 satellite image shows ice fields near Adelaide Island (on the west) which is a large, mainly ice-covered island, 75 miles (121 km) long and 20 miles (32 km) wide, lying at the north side of Marguerite Bay off the west coast of the Antarctic Peninsula. The island lies within the Argentine, British and Chilean Antarctic claims and is protected from commercial exploitation by the Antarctica Treaty implemented in 1959. Antarctica is perhaps the world’s greatest unspoiled and relatively unexplored wildernesses. About 98 percent of Antarctica is covered by ice that averages at least 1 mile (1.6 km) in thickness. According to news reports the Antarctic Peninsula is one of the fastest warming spots on the planet. This satellite image was collected from the GeoEye-1 satellite on April 18, 2012 while flying 423 miles (681 km) above the Earth at an average speed of 17,000 mph (four miles per second.).....
GIS chegou nos nuvens, com um novo serviço desenvolvido pela SourcePole.com agora é possivel publicar seus mapas na Web diretamente do seu desktop! A Sourcepole é uma empresa suiça especializada em Linux e Soluções Abertas, e criou um plugin (complemento) para publicar mapas na Web a partir do QGIS, e a hospedagem é gratuita.....
Since it travels at 26,000 km per hour and orbits the globe 16 times a day, the International Space Station is clearly the ideal tool for earth observation. Vancouver-based UrtheCast is taking steps to provide the live streaming of high-resolution video from space by installing HD cameras aboard the Space Station. By the end of 2012, the option of using this content to aid scientific research, education, research and news gathering will become a reality.
Our friends at Spatial Sustain recently covered this new endeavor and here’s what they had to say:
The company has recently signed a $4.2 million contract with MacDonald, Dettwiler and Associates to build some of the hardware and data compression for the project. The company is betting that this first HD live feed of the planet will aid scientific research, education, and international news.
mage registration determines the relative orientation between two images. As there are different techniques for image registration, it is important to compare these techniques to identify the advantages and disadvantages of each one. In this paper, a comparison between a fast Fourier transform (FFT)-based technique, a contour-based technique, a wavelet-based technique, a Harris–Pulse Coupled Neural Network (PCNN)-based technique and Harris–Moment-based technique is presented. The algorithms were tested on Landsat Thematic Mapper (TM) and SPOT remote sensing images and its performance were compared using the Root Mean Square Error (RMSE).
It has been concluded that the order of techniques with less RMSE is the PCNN, the moment, the contour, the wavelet and the FFT-based techniques, respectively. Whereas the order of techniques with the less running time is the contour, the wavelet, the moment, the FFT and the PCNN-based techniques, respectively. And finally the technique that detects the more control points in both images is the wavelet.
The following is a helpful section of python code to assist with calculating NDVI (Normalised Difference Vegetation Index) using the arcpy site package. The Image Analysis Window allows you to see this visually, but if you want the values in the raster itself, then you will need to use this code.
Thanks go to the Esri team for explaining this new notation in the Spatial Analyst module of arcpy (arcpy.sa). The arcpy.sa module calculates rasters more effectively than the Raster Calculator at 9.x, as it uses the number crunching power of Python. Also, dot notation for multiple bands no longer works in the Raster Calculator at 10, so this code is a optimised replacement for this loss in functionality.
We’re happy to report that the second batch of the large set (up to 50 million square kilometers) of high-resolution imagery to be added to the World_Imagery map service is fully processed and published to ArcGIS Online.
The World Imagery map was recently updated to include expanded coverage of 1m resolution GeoEye IKONOS imagery for Nigeria and parts of multiple countries in Southeast Asia (Viet Nam, Cambodia, and Thailand). This represents the second of what will be several updates over the next few months as additional IKONOS imagery is fully processed and published. Below are some sample images.
The Phase One iXA camera system is a fully integrated aerial camera system designed to meet the exacting needs of aerial photography, with features that rival large-format cameras at a fraction of their price.
Microwave remote sensing at wavelengths ranging from 1 cm to 1 m has gained a lot of importance over the last decade with the availability of active radar imaging systems for a wide range of scientific applications.......
“Malaria, once endemic in Canada, is now restricted to imported cases. Imported malaria in Canada has not been examined recently in the context of increased international mobility, which may influence incidence of imported and autochthonous cases. Surveillance of imported cases can highlight high-risk populations and help target prevention and control measures. To identify geographic and individual determinants of malaria incidence in Ontario, Canada, we conducted a descriptive spatial analysis. We then compared characteristics of case-patients and controls.