Monthly Archives: December 2015
I just started exploring Sentinel-1 SAR data for my research on wetlands and water resources. Here are some useful resources I found:
About the Sentinel-1 mission:
- Working with Sentinel-1 data: searching and downloading images
- Working with Sentinel-1 data: pre-processing, georeferencing and exporting with SNAP
- How to Download Sentinel Satellite Data for Free
Advanced training course on the use of Sentinel-1 SAR data:
Sentinel-1 data analysis using PCI Geomatica:
- Download Sentinel-1 imagery for Free from ESA
- Sentinel 1: Basic Image Visualization and Analysis
- Sentinel-1: Dual channel (HH and HV) responses over Montreal, Canada
- Sentinel-1: Stacking imagery and visual change analysis
- Sentinel-1: Automated Change Detection
Synthetic Aperture Radar: Of Bats and Flying Pianos:
- An amusing introduction to radar remote sensing from satellites, with the concept of “range Doppler” image formation described using entertaining audio-video animations.
For those who are interested in using the Sentinel-1 and Sentinel-2 Satellite Data from the European Space Agency’s Copernicus Programme, please check out this blog at http://gisgeography.com/how-to-download-sentinel-satellite-data/. Note that Sentinel 2A multispectral data has a 10-m spatial resolution, which is much better than the Landsat 8 with 30-m resolution.
What are the Spectral Bands of Sentinel 2A and 2B?
The spectral and spatial resolution of Sentinel 2A are listed below. There are 13 bands in total. Four spectral bands have a 10 meter resolution. Six bands have a 20 meter resolution. And the remaining 3 have a spatial resolution of 60 meters.
Here are the spectral band details for Sentinel 2A:
Source: SENTINEL-2 Spatial Resolution
Each single satellite revisit time is 10 days. Because there are two satellites (Sentinel 2A and 2B), this means it has a combined constellation revisit of 5 days.
ESRI has released a new version of 3D LiDAR toolset, which was designed to extend the LiDAR capabilities of ArcGIS Desktop. It can be downloaded from : http://www.arcgis.com/home/item.html?id=fe221371b77940749ff96e90f2de3d10
- Classify ground*, building, vegetation, and noise points
- Extract building footprint approximations
- Clip LAS files*
- Improve QA/QC processes with lidar data:
- Evaluate LAS files for errors through the CheckLAS utility
- Export LAS file header information
- Define the spatial reference of LAS files with missing/incorrect information*
- Project LAS files to desired coordinate systems*
- Evaluate coverage of overlaps in lidar scans
- Rearrange LAS files to optimize data access I/O*
- Optimize lidar data for operational use and rapid access through the compressed ZLAS format
- Evaluate Z statistics with advanced height metrics*
- Analyze the proximity of LAS points to 3D features**
- Convert lidar data between various data formats
- Create tiled raster derivatives
- Correct the Z value of a multipatch model so that it “sits” on the ground
- Create a point skymap of sun positions for visualization and solar analysis workflows
- Simplify dense, 3D breaklines to support scalability in TIN-based surface modeling*
- Integrate a design surface, such as one created using the Grading tool, into a base TIN
- Export a TIN to LandXML for use in 3rd party applications
- Cross sections of a multipatch can be used with the Intersect 3D tool to:
- Generate contours in 3D space that capture cliff overhangs
- Determine a 3D model’s footprint at different heights
- Generate sightlines for visibility analysis
I just came across an interesting article: Analyzing 1.1 Billion NYC Taxi and Uber Trips, with a Vengeance. – An open-source exploration of the city’s neighborhoods, nightlife, airport traffic, and more, through the lens of publicly available taxi and Uber data.
Quoted from the author Todd W. Schneider :
“The New York City Taxi & Limousine Commission has released a staggeringly detailed historical dataset covering over 1.1 billion individual taxi trips in the city from January 2009 through June 2015. Taken as a whole, the detailed trip-level data is more than just a vast list of taxi pickup and drop off coordinates: it’s a story of New York. How bad is the rush hour traffic from Midtown to JFK? Where does the Bridge and Tunnel crowd hang out on Saturday nights? What time do investment bankers get to work? How has Uber changed the landscape for taxis? And could Bruce Willis and Samuel L. Jackson have made it from 72nd and Broadway to Wall Street in less than 30 minutes? The dataset addresses all of these questions and many more.
I mapped the coordinates of every trip to local census tracts and neighborhoods, then set about in an attempt to extract stories and meaning from the data. This post covers a lot, but for those who want to pursue more analysis on their own: everything in this post—the data, software, and code—is freely available. Full instructions to download and analyze the data for yourself are available on GitHub.”