Monthly Archives: July 2014
According to the newly released 2013 Journal Citation Reports®Science Edition (Thomson Reuters, 2014), the new Impact Factor for Remote Sensing is 2.623. The 5-Year Impact Factor is 2.729. Remote Sensing now ranks 6/27 (Q1) in the category ‘Remote Sensing’.
Evolution of the Remote Sensing Impact Factor since 2012:
Evolution of citations to Remote Sensing since 2009:
Presentation slides from the Esri User Conference technical workshops presented July 14–18, 2014 in San Diego, California. Offerings cover a wide variety of topics and levels of expertise.
The 3D Elevation Program (3DEP) initiative is being developed to respond to growing needs for high-quality topographic data and for a wide range of other three-dimensional representations of the Nation’s natural and constructed features. The primary goal of 3DEP is to systematically collect enhanced elevation data in the form of high-quality light detection and ranging (lidar) data over the conterminous United States, Hawaii, and the U.S. territories, with data acquired over an 8-year period. Interferometric synthetic aperture radar (ifsar) data will be collected over Alaska, where cloud cover and remote locations preclude the use of lidar over much of the State. The 3DEP initiative is based on the results of the National Enhanced Elevation Assessment.
Imagery and remote sensing has always been one of my areas of interest in GIS. As a support analyst at Esri Australia I get a large number of imagery-related questions and I often help clients learn how to process their geospatial imagery and LiDAR data in ArcGIS.
Lidar (or Light Detection and Ranging) technology has become very popular and accessible in recent years. Because it provides high resolution elevation data, it’s now extensively used in the GIS world for mapping, spatial analysis and 3D visualization.
Although Lidar data can be used in many of the ArcGIS Desktop software, it turns out that many users are not aware of some basic workflows that can be utilized to extract raster Digital Elevation Models from their LAS point clouds in ArcGIS.
The question “how do I create a DEM from my Lidar data” is one of the most frequently asked questions when it…
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Visualizing change over time is an effective way to analyze local or global trends and predict future scenarios.
In GIS, much information of this type—for example, sea-surface temperature, annual births, labour market statistics, vegetation type, land-use data—is commonly stored in raster format, and it’s very useful to see these data animated. So how do you make your rasters time-aware? An easy way to is to take advantage of two new features available in Version 10: time-enabled layers and the mosaic dataset.
Follow these steps to create an animation showing change in sea surface temperature over time, or vegetation change in South-West QLD. There’s a tonne of data to show change over time—and now we have an easy way to visualize it in ArcGIS!
Create the Mosaic dataset
1. Use the “Create Mosaic Dataset” tool to create a new mosaic dataset and load your rasters. Use the “Add Rasters To Moasic Dataset” tool to load your rasters. Make sure youbuild pyramids and calculate statistics (under advanced options). This increases display performance.
Python is a key tool for scripting geoprocessing functions and tasks in ArcGIS for Desktop, but many GIS professionals have not had the opportunity to learn it. New from Esri Press, GIS Tutorial for Python Scripting, by David W. Allen, is a workbook filled with hands-on programming exercises. It will help GIS users become comfortable working with Python, whether on-the-job or within an advanced GIS course. Python helps make workflows in ArcGIS for Desktop more efficient and saves countless hours by automating repetitive tasks.
GIS specialists who develop Python proficiency, particularly those who do not have a programming background, benefit by
- Saving time on many GIS tasks by using Python scripts to automate workflows.
- Customizing ArcGIS for Desktop to better match the work at hand with custom menus and add-ins.
- Easily sharing Python toolboxes and scripting tools with others in the organization.
To gain the most value from the book, some prior experience using ArcGIS for Desktop is required. Exercise data is provided and instructors can request supplemental resources for coursework.
Allen is the GIS manager for the City of Euless, Texas. He has taught at Tarrant County College since 1999, where he helped found one of the first GIS degree programs in Texas and establish a state standard for GIS degree programs. He is the author of GIS Tutorial 2: Spatial Analysis Workbook (Esri Press, 2013) and Getting to Know ArcGIS ModelBuilder (Esri Press, 2011) and the coauthor of GIS Tutorial 3: Advanced Workbook (Esri Press, 2011).
GIS Tutorial for Python Scripting is available in print (ISBN: 9781589483569, 288 pages, US$69.99) and e-book format (ISBN: 9781589483972, 288 pages, US$69.99). The book is available at online retailers worldwide, atesri.com/esripress, or by calling 1-800-447-9778. Outside the United States, visit esri.com/esripressorders for complete ordering options, or visit esri.com/distributors to contact your local Esri distributor. Interested retailers can contact Esri Press book distributor Ingram Publisher Services.
By Larisa Serbina and Holly M. Miller
The Landsat program has been collecting and archiving moderate resolution earth imagery since 1972. The number of Landsat users and uses has increased exponentially since the enactment of a free and open data policy in 2008, which made data available free of charge to all users. Benefits from the information Landsat data provides vary from improving environmental quality to protecting public health and safety and informing decision makers such as consumers and producers, government officials and the public at large. Although some studies have been conducted, little is known about the total benefit provided by open access Landsat imagery.
This report contains a set of case studies focused on the uses and benefits of Landsat imagery. The purpose of these is to shed more light on the benefits accrued from Landsat imagery and to gain a better understanding of the program’s value. The case studies tell a story of how Landsat imagery is used and what its value is to different private and public entities. Most of the case studies focus on the use of Landsat in water resource management, although some other content areas are included.
First posted June 26, 2014
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