Remote Sensing News

In this image from NASA's Curiosity rover, a rock outcrop called Link pops out from a Martian surface that is elsewhere blanketed by reddish-brown dust. The fractured Link outcrop has blocks of exposed, clean surfaces. Rounded gravel fragments, or clasts, up to a couple inches (few centimeters) in size are in a matrix of white material. Many gravel-sized rocks have eroded out of the outcrop onto the surface, particularly in the left portion of the frame. The outcrop characteristics are consistent with a sedimentary conglomerate, or a rock that was formed by the deposition of water and is composed of many smaller rounded rocks cemented together. Water transport is the only process capable of producing the rounded shape of clasts of this size.

 

The Link outcrop was imaged with the 100-millimeter Mast Camera on Sept. 2, 2012, which was the 27th sol, or Martian day of operations.

 

The name Link is derived from a significant rock formation in the Northwest Territories of Canada, where there is also a lake with the same name.

 

Scientists enhanced the color in this version to show the Martian scene as it would appear under the lighting conditions we have on Earth, which helps in analyzing the terrain.

Image credit: NASA/JPL-Caltech/MSSS

Nitrogen (N) is one of the most important limiting nutrients for sugarcane production. Conventionally, sugarcane N concentration is examined using direct methods such as collecting leaf samples from the field followed by analytical assays in the laboratory. These methods do not offer real-time, quick, and non-destructive strategies for estimating sugarcane N concentration. Methods that take advantage of remote sensing, particularly hyperspectral data, can present reliable techniques for predicting sugarcane leaf N concentration. Hyperspectral data are extremely large and of high dimensionality. Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant spectral features. The aim of this study was to explore the potential of a random forest (RF) regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration. To achieve this, two Hyperion images were captured from fields of 6–7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used as a feature-selection and regression method to analyse the spectral data. Stepwise multiple linear (SML) regression was also examined to predict the concentration of sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The results showed that sugarcane leaf N concentration can be predicted using both non-linear RF regression (coefficient of determination, R 2 = 0.67; root mean square error of validation (RMSEV) = 0.15%; 8.44% of the mean) and SML regression models (R 2 = 0.71; RMSEV = 0.19%; 10.39% of the mean) derived from the first-order derivative of reflectance. It was concluded that the RF regression algorithm has potential for predicting sugarcane leaf N concentration using hyperspectral data.

The Interactive Histogram Stretch allows you to alter the minimum and maximum values for the red, green, and blue channels. You can also alter the line defining the stretch.

See a video about Interactive Stretch.  http://video.arcgis.com/watch/835/arcgis-10.1-new-feature-interactive-histogram-stretch

This study is focused on the integration of bare earth lidar (Light Detection and Ranging) data into unstructured (triangular) finite element meshes and the implications on simulating storm surge inundation using a shallow water equations model. A methodology is developed to compute root mean square error (RMSE) and the 95th percentile of vertical elevation errors using four different interpolation methods (linear, inverse distance weighted, natural neighbor, and cell averaging) to resample bare earth lidar and lidar-derived digital elevation models (DEMs) onto unstructured meshes at different resolutions. The results are consolidated into a table of optimal interpolation methods that minimize the vertical elevation error of an unstructured mesh for a given mesh node density. The cell area averaging method performed most accurate when DEM grid cells within 0.25 times the ratio of local element size and DEM cell size were averaged. The methodology is applied to simulate inundation extent and maximum water levels in southern Mississippi due to Hurricane Katrina, which illustrates that local changes in topography such as adjusting element size and interpolation method drastically alter simulated storm surge locally and non-locally. The methods and results presented have utility and implications to any modeling application that uses bare earth lidar.

This post describes how to use the image classification tools in Image Analysis for Supervised Classification.

 

What You Will Need and Other Assumptions
• A satellite image that has been properly prepared for input to an image classification (e.g.,atmospheric correction, terrain correction, etc.)
• Aerial photographs and other ancillary data for the same area, as well as good local knowledge of the terrain, vegetation and soils for the area
• The Supervised Classification tools in Image Analysis are very limited as compared to those available in ERDAS Imagine....

“Highlights

A 391 square kilometer urban light pollution map is produced with 1 m resolution.

Geospatial analysis of the map compares lighting to land use type.
Lighting associated with streets accounts for 1/3 of the total zenith uplight.

Land use types of differing areas are compared equivalently using mean brightness.

The utility of night aerial photography for light pollution studies is demonstrated.

“Aerial observations of light pollution can fill an important gap between ground based surveys and nighttime satellite data. Terrestrially bound surveys are labor intensive and are generally limited to a small spatial extent, and while existing satellite data cover the whole world, they are limited to coarse resolution. This paper describes the production of a high resolution (1 m) mosaic image of the city of Berlin, Germany at night. The dataset is spatially analyzed to identify the major sources of light pollution in the city based on urban land use data. An area-independent ‘brightness factor’ is introduced that allows direct comparison of the light emission from differently sized land use classes, and the percentage area with values above average brightness is calculated for each class. Using this methodology, lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%), although other land use classes have much higher average brightness. These results are compared with other urban light pollution quantification studies. The minimum resolution required for an analysis of this type is found to be near 10 m. Future applications of high resolution datasets such as this one could include: studies of the efficacy of light pollution mitigation measures, improved light pollution simulations, economic and energy use, the relationship between artificial light and ecological parameters (e.g. circadian rhythm, fitness, mate selection, species distributions, migration barriers and seasonal behavior), or the management of nightscapes. To encourage further scientific inquiry, the mosaic data is freely available at Pangaea: http://dx.doi.org/10.1594/PANGAEA.785492.”;

We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT) for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS), as introduced by the Open Geospatial Consortium (OGC), are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network) enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

Atop the "roof of the world," one of the world's longest glaciers moves imperceptibly down the mountain. Called the Fedchenko Glacier, seen in this false-color image taken by the Landsat 5 satellite on Oct. 2, 2011, it is 47 miles (77 kilometers) long.

 

Centered in eastern Tajikistan, the Pamir Mountains soar to heights of 24,000 feet (7,300 meters), according to a release from NASA. Covered in snow and thousands of glaciers, they are central Asia's water towers. Nearly 90 percent of people in central Eurasia depend on mountain water to drink and use in agriculture and energy.

 

The glaciers appear cyan due to the false-color of the image, which is meant to highlight water in the area.

 

The melt waters of Fedchenko Glacier feed into the Muksu, Vakhsh and Amu Darya rivers, which eventually make their way to the Aral Sea some 1,200 miles (2,000 km) away. Due to diversions of water from these rivers for drinking and agriculture, the Aral Sea has been shrinking for the last 50 years.

 

The glacier drops 2,500 meters (8,200 feet) in elevation from beginning to end. At the highest elevations, the glacier is covered in snow and ice, but as it flows downstream it picks up rocks that have fallen from above, which can be seen as reddish lines in the satellite image. These are called medial moraines.

 

Since 1933, Fedchenko Glacier has shrunk by 4,600 feet (1,400 meters) due to increased temperatures in the area, NASA said.

 

The area is home to snow leopards and other amazing animals like ibex.

 

The "roof of the world" is a term for the Himalayas and its surrounding peaks, which comprise the biggest, tallest mountain range on the planet.