Wednesday, February 25, 2015

Conference : Unmanned Aircraft Systems Technical Demonstration and Symposium 2015

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The second annual technical UAS symposium sponsored by the American Society for Photogrammetry and Remote Sensing (ASPRS) is scheduled for September 29-30, 2015 in Reno, Nevada. Expanding on the highly successful format and events of last year’s symposium this year’s event will include test flights, UAS data processing, and workshops.

Technical Demonstration and Symposium for Unmanned Aircraft Systems hosted by the American Society for Photogrammetry and Remote Sensing

Call for Speakers and Call for Workshops has also been announced! Details: http://uasreno.org/ 

Purpose: To assemble UAS developers and researchers, along with geospatial service providers and users of geospatial map data, to share information, showcase new technologies and demonstrate UAS systems in action (in flight). 
Mission: To advance knowledge and improve the understanding of UAS technologies and their safe and efficient introduction into our national airspace, government programs and business.

30 cm imagery from Digital Globe

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As the global leader in satellite imagery, DigitalGlobe is proud to once again push the boundaries of innovation by being the first company to delivery 30 cm resolution imagery.

This 5x improvement in resolution represents the definition of very-high resolution imagery.
30 cm imagery delivers clearer, richer images that empower better decision making through improved situational awareness. - See more at: Digital Globe 30 cm resolution imagery

Friday, November 22, 2013

Top 20 Research Institutes in Remote Sensing

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A recent study conducted by Yanhua Zhuang et al. (2013) reported the top 20 research institutes in remote sensing using bibliometric analysis on publications published during 1991-2010 in the remote sensing field. 

























Oho, Satellites are making RS research interesting!

Thursday, January 17, 2013

Automated Tools for Integrating Remote Sensing Data Into Spatial Epidemiology Research

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Satellite remote sensing provides valuable information that can be used to map infectious diseases and forecast future health risks. However, amassing and managing the geographic information from diverse datasets is difficult and time-consuming. Therefore, there is a need for a geoinformatics system that integrates the acquisition, processing, management, and analysis of geospatial data sets from various sources.

Here, we present our software model for automated data capture and processing of satellite remote sensing data for public health applications.  The system incorporates land surface temperature and vegetation indices from MODIS precipitation data from TRMM, and a novel measurement of actual evapotranspiration.

Sunday, January 13, 2013

Popular GIS Books

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Books Pro Cons
It provides solid guide to how geospatial analysis work, particularly with respect to GIS. The book emphasizes conceptual workflows and with basic math which is helpful for creating own code and also getting an understanding of what's happening under the hood in contemporary GIS. It is better to have an update because lots of changes in GIS software over last five years.
This book is for typical GIS user aspiring to design good maps. It is illustrating GIS map software and throughout with map samples in color which is especially useful for those who has little prior training or experience in map making. This is acceptable book for beginners but very little information of advanced users. It hardly touches on advanced cartographic representations.
This book explains the computational geometry and algorithms concisely and very readable. It emphasis on describing algorithms and data structures theoretically. It presents pseudo code with lots of figures that is very easy to understand and follow.

It's also worth reading for all computer scientists and mathematicians who are working on geometry.

This is good text/reference book for graduate course.
Focused on geometric computation and algorithm, very complicated for beginners, who does not have prior computer programming knowledge.

The various algorithms and concepts often used in this book are triangulation, indexing, calculating intersection, shortest paths etc.
The book illustrates the most common cartographic deceptions, and provides some excellent color guides. If you want to learn how to make influential maps for a cause, this is the book!. The reader can learn what to look for and how to avoid the inadvertent or unintentional 'lies'. Worth the effort! Basically, the book as an introduction to the science of cartography and targeted for prospective cartographer or decision making authority.
The book details the use of freely available open source mapping software and tools such as MapServer, GDAL, OpenEV, and PostGIS to create web gis and web maps.

Mostly focused on UMN Mapserver for web mapping and building web gis.
Not much technical discussion on how GPS databases work, how to decode GIS information.
The book is fairly shallow. It will give you a couple of basic examples of how to use some pieces of software, but for anything more complicated, you have to look elsewhere.

Friday, November 18, 2011

A 'mesmerizing' view of Earth from an orbiting space in HD

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Time lapse sequences of photographs taken with a special low-light 4K-camera (made in Japan) by the crew of expedition 28 & 29 onboard the International Space Station from August to October, 2011.

Shooting locations in order of appearance:
1. Aurora Borealis Pass over the United States at Night
2. Aurora Borealis and eastern United States at Night
3. Aurora Australis from Madagascar to southwest of Australia
4. Aurora Australis south of Australia
5. Northwest coast of United States to Central South America at Night
6. Aurora Australis from the Southern to the Northern Pacific Ocean
7. Halfway around the World
8. Night Pass over Central Africa and the Middle East
9. Evening Pass over the Sahara Desert and the Middle East
10. Pass over Canada and Central United States at Night
11. Pass over Southern California to Hudson Bay
12. Islands in the Philippine Sea at Night
13. Pass over Eastern Asia to Philippine Sea and Guam
14. Views of the Mideast at Night
15. Night Pass over Mediterranean Sea
16. Aurora Borealis and the United States at Night
17. Aurora Australis over Indian Ocean
18. Eastern Europe to Southeastern Asia at Night




Music: Carbon Based Lifeforms - Silent Running

Editing: Michael K├Ânig | koenigm.com

Image Courtesy of the Image Science & Analysis Laboratory,
NASA Johnson Space Center, The Gateway to Astronaut Photography of Earth
eol.jsc.nasa.gov


Enjoy the Images :)

Friday, June 3, 2011

875 Tornado hit USA in April 2011

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The U.S. experienced unprecedented tornado activity throughout the month of April 2011. The NOAA Storm Prediction Center received 875 tornado reports during that month alone; 625 have been confirmed as tornadoes, so far. Many of these storms were concentrated during 7 different major outbreaks, mostly in the Southern U.S. The largest of these outbreaks occurred during April 27-28, leaving over 300 people dead as over 180 storms were reported from Texas to Virginia.


This animation shows the GOES-East infrared imagery from April 1-30, along with the locations of each tornado that formed during the time (symbolized as red dots). Though tornadoes cannot actually be seen by GOES, these satellites are instrumental in being able to detect the conditions associated with their formation. As the resolution of GOES has increased with each successive satellite series, so have the warning times for tornadoes. The future GOES-R satellite will provide even higher resolution and storm prediction capability, especially with the use of the Geostationary Lightning Mapper sensor. The actual tornado locations are acquired from the Storm Prediction Center, which uses both NEXRAD radar and ground reports to generate a detailed database of tornadoes in the U.S.

Tuesday, May 3, 2011

NOAA releases aerial imagery of Tuscaloosa - Before & After

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NOAA releases the "before" and "after" shots for the damage caused by last week's F5 tornado in McFarland Boulevard in Tuscaloosa. Those images are captured from 5,000 ft high using special remote sensing equipment.

Tuesday, June 1, 2010

Getting MODIS Image Automatically From FTP in Python

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# This is a Python script that automatically downloads historical
# MODIS data from the LP DAAC FTP site
# This version should work for all of the tiled datasets
# It is currently hard-coded to downloaded specific MODIS tiles for
# the northern Great Plains & upper midwest

# Initailly historical date for Data transfer must be set on "lpdacc.txt".



import os, ftplib,sys,string


# Login information for accessing the LP DAAC FTP site
Hostname = "e4ftl01u.ecs.nasa.gov"
Username = "anonymous"
Password = "@anonymous"

# Get user inputs
# Base directory for the MODIS data

# Basedir = input("Enter the LP DAAC directory containing the dataset you want to download:")
Basedir="MOLT/MOD11A2.005"
print "The LP DAAC directory containing the dataset you want to download" +str(Basedir)


# Local directory for data storage
#Hdfdir = input("Enter the local directory where you want to store the hdf files:")
Hdfdir=r"H:\MODIS_LST_NDVI\MOD11A2\\"
print "The local directory where you want to store the hdf files" +str(Hdfdir)


# Empty lists for the collector functions
Dirlist = []
Filelist = []

mylog=open(r"H:\MODIS_LST_NDVI\MOD11A2\mylog.txt",'w',)

# Assigning Global ariables
i=0
k=0
flag=0

try:

# Define helper functions that are used to read files/subdirectories from the
# ftp site and store them as lists
def collector(line = ''):
global Dirlist
Dirlist.append(line)

def collector2(line = ''):
global Filelist
Filelist.append(line)



# Open ftp connection
ftp = ftplib.FTP(Hostname,Username,Password)

# Go to the directory containing the dataset of interest
ftp.cwd(Basedir)

# Involke the LIST ftp function, calling the collector function to store the
# results to Dirlist in list format
ftp.retrlines("LIST", collector)

# Get Directory listing only (Without including the sub directories)
mainDirlist=[]
myDirlist=[]
ftp.dir(mainDirlist.append)
myDirlist=mainDirlist[1:]
# parsing the Directory list
dirInfo=""


for mainDir in myDirlist:
#parsing the directory name only[2002.12.03]
mainDirname= mainDir[37:47]
dd=mainDirname[8:10]
mm=mainDirname[5:7]
yyyy=mainDirname[0:4]
#Extracging yyyy mm dd to compare with log file information
dirInfo=str(yyyy)+str(mm)+str(dd)
print mainDirname
print dirInfo

# Read the log file to retrive the information of latest downloaded data
logFileread=open(r"H:\MODIS_LST_NDVI\MOD11A2\lpdaac.txt",'r')
logFileread.seek(0)
logInfo=int(logFileread.read())
logFileread.close()

print"Local Drive Recent Log Dir # "+str(logInfo)
mylog.write("Local Drive Recent Log Dir # "+str(logInfo)+'\n')

# Coparing the logfile(already downloaded data) with recent datas in the ftp
if(int(logInfo) print "current path -->"+str(ftp.pwd())
if(flag==1): # if flag matches the criteria reset the counters
ftp.cwd("..")
k=0
flag=0
Filelist = []

# List all the files in the subdirectory
path=str(mainDirname)
ftp.cwd(path)
print "New path -->"+str(ftp.pwd())
# Filelist = ftp.dir()
##FTP Directory bhitra chire pni file ma chire ko chhina
ftp.retrlines("LIST", collector2)
#ftp.retrlines("LIST")
# Download data from the MODIS tiles that we are interested in
for Currow2 in Filelist:
Splitrow2 = Currow2.split()
Permissions = Splitrow2[0]

# Skip over the jpeg browse images - some of these cause problems
if Permissions[0:3] == "-rw":
Directories = Splitrow2[1]
Group = Splitrow2[2]
Size = Splitrow2[3]
Month = Splitrow2[4]
Date = Splitrow2[5]
Time = Splitrow2[6]
Filename = Splitrow2[7]
mylog.write(Filename)
LocalFile = Hdfdir + Filename
Splitfname = Filename.split(".")
# Split the header file name into its various components
Splitfname = Filename.split(".")
Mdataset = Splitfname[0]
Maqdate = Splitfname[1]
Mlocation = Splitfname[2]
Mprocdate = Splitfname[3]
Mext1 = Splitfname[4]
Mext2 = Splitfname[5]


# Pull out the horizontal and vertical tile numbers
Htile = Mlocation[1:3]
Vtile = Mlocation[4:6]


# Only retrieve data for the three tiles covering the NGP/Upper Midwest
if (((Htile == "09") & (Vtile == "04"))|((Htile == "10") & (Vtile == "04"))|((Htile == "11") & (Vtile == "04"))|((Htile == "12") & (Vtile == "04"))|((Htile == "09") & (Vtile == "05"))|((Htile == "10") & (Vtile == "05"))|((Htile == "11") & (Vtile == "05"))):
# Retrieve the hdf and xml files and place them in the local directory

ftp.retrbinary("RETR " + Filename, open(LocalFile, "wb").write)

# Write download information in the log file
logFwrite=open(r"H:\MODIS_LST_NDVI\MOD11A2\lpdaac.txt",'w')
logFwrite.seek(0)
logFwrite.write(dirInfo)
logFwrite.close()

k=k+1
if(k==14): # value of k should be no of tiles*2[for *.hdf and *.hdf.xml ]
flag=1

else:
print i
i=i+1
print "loginfor-->"+str(logInfo)
print "dirInfor-->"+str(dirInfo)
print "Dirname-->"+str(mainDirname)
print "Filename-->"+str(Filename)
mylog.write("loginfor-->"+str(logInfo)+'\n')
mylog.write("dirInfor-->"+str(dirInfo)+'\n')
mylog.write("Dirname-->"+str(mainDirname)+'\n')
mylog.write("Filename-->"+str(Filename)+'\n')



else:
print "Already downloaded in our local drive" +str(mainDirname)
mylog.write( "Already downloaded in our local drive" +str(mainDirname)+'\n')



finally:
ftp.quit()
ftp.close()
print "Closing FTP"
mylog.write("Closing FTP")
mylog.close()
 

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