Journal Citation Reports 2016 (JCR) Released

The Journal Citation Reports 2016 (JCR), with the Journal Impact Factors of 2015, have been released by Thomson Reuters. You can download the complete list here.

I sorted out some journals related to Remote Sensing, Geography, Hydrology, and Wetlands. You can download my sorted list here. Note that this is only my personal classification. My apologies if some of your preferred journals are not on the list here.

Keep in mind that journal impact factor is just one metrics, so don’t take it too seriously!

Remote Sensing Journals Total Cites  Impact Factor
Remote Sensing of Environment 36,252 5.881
Isprs Journal of Photogrammetry and Remote Sensing 5,125 4.188
International Journal of Applied Earth Observation and Geoinformation 3,638 3.798
IEEE Transactions on Geoscience and Remote Sensing 26,086 3.36
Remote Sensing 5,061 3.036
International Journal of Digital Earth 694 2.762
Giscience & Remote Sensing 638 2.482
IEEE Geoscience and Remote Sensing Letters 5,572 2.228
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3,033 2.145
Canadian Journal of Remote Sensing 1,634 1.976
International Journal of Remote Sensing 16,510 1.64
Remote Sensing Letters 638 1.487
Photogrammetric Engineering and Remote Sensing 5,570 1.288
European Journal of Remote Sensing 161 1.173
Journal of Applied Remote Sensing 1,189 0.937
Journal of the Indian Society of Remote Sensing 571 0.676

Remote Sensing Journals Google Scholar Ranking

Geography Journals – SCIE Total Cites Impact Factor
Nature Geoscience 14,574 12.508
Global Ecology and Biogeography 7,915 5.84
Geophysical Research Letters 77,712 4.212
Landscape and Urban Planning 8,923 3.654
Journal of Geophysical Research 198,092 3.318
Geomorphology 15,494 2.813
Progress in Physical Geography 2,966 2.728
Computers & Geosciences 7,567 2.474
International Journal of Health Geographics 1,596 2.27
International Journal of Geographical Information Science 3,556 2.065
Permafrost and Periglacial Processes 1,555 2
Journal of Geographical Sciences 1,276 1.923
Chinese Geographical Science 630 1.145
Physical Geography 778 0.875
Frontiers of Earth Science 207 0.76
Isprs International Journal of Geo-Information 133 0.651
Geography Journals – SSCI Total Cites Impact Factor
Nature Climate Change 9,526 17.184
Progress in Human Geography 4,360 5.162
Journal of Economic Geography 2,456 3.429
Geographical Journal 1,483 3.206
Transactions of the Institute of British Geographers 2,871 3.17
Economic Geography 1,880 2.824
Annals of the Association of American Geographers 4,439 2.756
Political Geography 2,052 2.733
Applied Geography 3,563 2.565
Journal of Transport Geography 3,067 2.09
International Journal of Geographical Information Science 3,556 2.065
Social & Cultural Geography 1,208 1.663
Geographical Analysis 1,750 1.571
Transactions in Gis 918 1.537
Professional Geographer 1,632 1.407
Geographical Research 445 1.353
Urban Geography 1,171 1.322
Journal of Geography 423 1.213
Australian Geographer 668 1.193
Journal of Geographical Systems 569 1.175
Moravian Geographical Reports 111 1.093
Singapore Journal of Tropical Geography 415 1.085
Journal of Geography in Higher Education 704 1.034
Canadian Geographer-Geographe Canadien 707 0.878
New Zealand Geographer 193 0.765
Geography 276 0.719
Scottish Geographical Journal 274 0.686
Geographical Review 1,170 0.5
South African Geographical Journal 128 0.423

Geography Journal Google Scholar Ranking

Hydrology Journals Total Cites Impact Factor
Water Research 61,285 5.991
Advances in Water Resources 8,156 4.349
Environmental Modelling & Software 8,255 4.207
Hydrology and Earth System Sciences 10,606 3.99
Water Resources Research 42,682 3.792
Journal of Hydrometeorology 6,766 3.511
Journal of Hydrology 37,044 3.043
Freshwater Biology 12,798 2.933
Hydrological Processes 16,884 2.768
Agricultural Water Management 8,901 2.603
Journal of Water Resources Planning and Management 3,692 2.521
Water Resources Management 6,400 2.437
Freshwater Science 957 2.433
Hydrological Sciences Journal-Journal Des Sciences Hydrologiques 4,664 2.182
Ecohydrology 1,507 2.138
Journal of Contaminant Hydrology 4,615 2.063
Hydrobiologia 21,166 2.051
Hydrogeology Journal 4,364 2.028
Journal of Hydro-Environment Research 481 1.971
Groundwater 5,078 1.947
Hydrology Research 653 1.779
Inland Waters 246 1.776
Journal of Soil and Water Conservation 3,037 1.752
Vadose Zone Journal 3,134 1.737
Water 1,035 1.687
Journal of the American Water Resources Association 4,644 1.659
Marine and Freshwater Research 4,207 1.583
Water Air and Soil Pollution 11,490 1.551
Journal of Hydrologic Engineering 3,231 1.53
Urban Water Journal 910 1.478
Journal of Hydrology and Hydromechanics 275 1.469
International Journal of Water Resources Development 898 1.463
International Review of Hydrobiology 1,047 1.459
Journal of Hydroinformatics 984 1.18
Water Science and Technology 16,933 1.064
Water International 1,093 1.04
Journal of Water and Health 1,320 1.025
Canadian Water Resources Journal 468 1.018
Water and Environment Journal 565 0.895
Paddy and Water Environment 450 0.871
Journal of Hydrodynamics 1,028 0.776
Journal of Water and Climate Change 154 0.775
Water Environment Research 2,455 0.659
Soil and Water Research 130 0.58

Hydrology Journals Google Scholar Ranking

Wetland Journals Total Cites Impact Factor
Landscape Ecology 6,478 3.657
Wetlands 3,660 1.504
Wetlands Ecology and Management 1,284 1.407
Advertisements

New article on sinkhole detection published in Geomorphology

My new peer-reviewed article titled “Automated delineation of karst sinkholes from LiDAR-derived digital elevation models” has been published in the latest issue of Geomorphology. You can download a free online copy using this link: http://authors.elsevier.com/a/1T3d9_,Oh6mAl8 (expires on July 8, 2016). In this paper, we present a localized contour tree method for automated extraction of sinkholes in karst landscapes. The study area was Fillmore County in southeastern Minnesota, USA. See some figures below:

Fig1_study_area

Fig. 1. Distribution of sinkhole inventory points in Fillmore County, Minnesota, USA.

Fig2_contour_tree

Fig. 3. Contour representation of a compound surface depression. (a) Contours overlain on DEM shaded relief. (b) Elevation profile of the transect A–B shown in (a).

Fig7_sinkhole_distriutions

Fig. 8. LiDAR DEM shaded relief (a) and examples of extracted sinkhole boundaries overlain on LiDAR DEM shaded relief (b) and color infrared aerial imagery (c).

Fig9_sinkhole_comparison

Fig 9. Sinkhole boundaries delineated using different methods. (a) The sink-filling method. (b) The localized contour tree method.

NASA and Japan make ASTER imagery available for free

Source: http://goo.gl/f9ECIG

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of the instruments on NASA’s Terra satellite. Although it is a NASA satellite, the instrument belongs to Japan’s Ministry of Economy, Trade and Industry (METI). The instrument was launched in 1999 and has captured more than 2.95 million individual scenes since then. On the first of April this year NASA announced that the full catalogue of imagery is being made available to the public at no cost. The instrument, amongst other things, takes stereoscopic images that enables it to calculate altitudes albeit rather low resolution. The elevation data has always been available to the public at no cost.

The most interesting images have been collected in a gallery found here. You can also see some of the more interesting images in this article and this one.


Mt. Etna, when it erupted in July 2001. The full resolution image and description can be found here.


This image shows the 3D capabilities of ASTER. The full resolution image and description can be found here.

To access the full database of imagery, you can use the MADAS (METI AIST Data Archive System). A really nice feature is that it allows you to download the images as network-linked KML files.

The imagery has a similar resolution to Landsat imagery (approximately 30 m per pixel), so is really only suitable for viewing large scale phenomena. As with Landsat imagery its best use would be to see current events before other satellite imagery becomes available. In December last year we used Landsat imagery to look at the scar made by a tornado near Holly Springs, Mississippi. We found it relatively easy to find an ASTER image of the same region captured on March 28th, 2016, and the scar is still visible. Download this KML file to view it in Google Earth.


The image only covers a small part of the tornado’s track.

Esri Releases Drone2Map for ArcGIS

Drone2Map for ArcGIS, released on February 24 by Esri, is a stand-alone desktop app for processing imagery collected by drones. Check out the Drone2Map FAQ and an interesting presentation (Working With Drone Data In ArcGIS) by Tony Mason of Esri. Interested users can visit esri.com/drone2map for more information.

Q: Is Drone2Map for ArcGIS going to be an ArcGIS Extension?
A: No. It is a stand-alone 64-bit Windows desktop app that will run alongside ArcMap and ArcGIS Pro.

Q: What does Drone2Map for ArcGIS do?
A: Drone2Map for ArcGIS is a desktop app that turns raw still imagery from drones into  stunning information products in ArcGIS. Now, with drone hardware becoming more accessible, you can create 2D and 3D maps of features and areas.

Q: Can Drone2Map for ArcGIS be used to make 3D models?
A: Yes, Drone2Map for ArcGIS will produce 3D colorized point clouds in LAS format as well as 3D textured meshes for use in ArcGIS Desktop and Web Apps.

Q: Does the Drone2Map for ArcGIS work only with a specific type of drone?
A: Drone2Map for ArcGIS is designed to be generic for all drones. What is important is that the drone collects certain types of metadata. At a very minimum, this metadata needs to include Latitude, Longitude, and Altitude. The addition of orientation, focal length and pixel size of the sensor will greatly improve results. Many commercially available drones have this capability and automatically add this information to the image metadata.

Snap3

Snap4

R script for updating student grades on Blackboard

This might be of interest to some of you teaching large enrollment courses and using scantrons for quizzes/exams. I developed a script using R programming language to automatically extract scores from ITS test scoring results and upload the grades to Blackboard.

The script needs two CSV format input files: the student info file from Blackboard (Full Grade Center -> Work Offline – Download) and the ITS test scoring results (convert the Excel file to CSV). It takes less than one second to get the results.

Feel free to let me know if you have any questions.

########################################

BBfile <- file.choose()  #”roster.csv”    ### The file downloaded from Blackboard
ITSfile <- file.choose()   #”result.csv”   ### The file received from ITS scantron results

# BBfile <- “roster.csv”
# ITSfile <- “result.csv”
output <- “score.csv”
scale.factor <- 1  ### scale factor multiplied by the scantron results.
### Extract students’ fullname from Blackboard roster
roster <- read.csv(BBfile,header = TRUE,stringsAsFactors = FALSE)
str(roster)
roster$firstname = as.character(lapply(strsplit(as.character(roster$First.Name), split=” “), “[“, 1))
roster$fullname <- tolower(paste(roster$Last.Name,roster$firstname,sep=””))
### read the ITS results
df <- read.csv(ITSfile,stringsAsFactors = FALSE)
df <- df[nchar(gsub(” “,””,df$X))>0,]
df <- df[!is.na(as.numeric(df$X.5)),c(“X”,”X.2″)]
colnames(df) <- c(“Name”,”Score”)
df$Score <- as.numeric(df$Score) * scale.factor
str(df)
### extract student names from ITS results

lastname <- as.character(lapply(strsplit(as.character(df$Name), split=” “), “[“, 1))
firstname <- as.character(lapply(strsplit(as.character(df$Name), split=” “), “[“, 2))
df$fullname  <- tolower(paste(lastname,firstname,sep = “”))
### match student names from Blackboard and ITS
m.x <- merge(roster,df,by = “fullname”,all.x = TRUE)
fix(m.x)
m.x$raw <- m.x$Score / scale.factor
### save the results to csv file
write.csv(m.x,output,na = “”,row.names = FALSE)
m.y <- merge(roster,df,by = “fullname”,all.x = TRUE,all.y = TRUE)
m.y.sub <- m.y[is.na(m.y$Last.Name), ]
score <- read.csv(output,header = TRUE,stringsAsFactors = FALSE)
summary(score)

########################################

15 Free Satellite Imagery Data Sources

Source: http://gisgeography.com/free-satellite-imagery-data-list/

If you want free satellite data, there’s no better way to do it then to follow this incredibly useful guide. Ranked from top to lower tier, here are your go-to free satellite imagery sources. Take a look at our list of eyes from the sky.

1 USGS Earth Explorer – Unlock the Power of Landsat and More

USGS Earth Explorer

Whether you live in the United States, in the Arctic circle or an obscure country like Transnistria, we can all appreciate the abundance of data theUSGS Earth Explorer has to offer.

We’ve relentlessly hyped USGS Earth Explorerhere, here and here… .and we’re about to do it again…

From no data to hyperspectral data, USGS is the undisputed world champion of free satellite data providers. Here’s why:

  • Access to Landsat satellite data – a legacy that goes unmatched. 40-years of history of our Earth with consistent spectral bands.
  • Vertically position yourself with NASA’s ASTER and Shuttle Radar Topography Missions global Digital Elevation Models.
  • Gain full access to NASA’s Land Data Products and Services including Hyperion’s hyperspectral data, MODIS & AVHRR land surface reflectance and disperse Radar data.

We sound like a broken record. But USGS Earth Explorer is a world class source of free satellite data. Regardless where you live, you NEED to look at the USGS Earth Explorer.

Read More: How to Download Free Landsat Imagery from the USGS Earth Explorer:

2 ESA’s Sentinel Mission – New Leader in Free High Resolution Data?

Read the rest of this entry

Tutorials for processing Sentinel-1data

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:

Blog posts:

Advanced training course on the use of Sentinel-1 SAR data:

Sentinel-1 data analysis using PCI Geomatica:

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.


How to Download Sentinel Satellite Data for Free

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.

How-To-Download-Sentinel-Satellite-Data

ProductDownload-Sentinel_SciDH

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.

ArcGIS 3D LiDAR Toolset

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

cwduyyhweaau4fdFunctionalities of the LiDAR toolset:
  • 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
Analysis & Data Management of 3D Features & Surfaces
  • 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

Analyzing 1.1 Billion NYC Taxi and Uber Trips

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.”

taxi_pickups_map

taxi_dropoffs_map

 

Alex Tereshenkov

Programming and managing GIS

REDD+ for the Guiana Shield

Technical Cooperation Project

LidarBlog.com

Dr. Qiusheng Wu @ SUNY Binghamton

Writing Science

How to write papers that get cited and proposals that get funded

GIS In Ecology

Providing Training, Advice And Consultation On The Use Of GIS In Ecology

metrhispanic

On cities, land, ...

GeoAcademy

Open GIS: No Bounds

Scientia Plus Conscientia

Thoughts on Science and Nature

r4hydrology

Learning hydrology with R

Karl Hennermann

GIS at the University of Manchester

GIS and Science

Applications of geospatial technology for scientific research and understanding.

Whitebox Geospatial Analysis Tools

Open-source GIS development and spatial analysis with Whitebox GAT

TopoToolbox

MATLAB-based software for topographic analysis

Anything Geospatial – AnyGeo

Dr. Qiusheng Wu @ SUNY Binghamton

GeospatialPython.com

Dr. Qiusheng Wu @ SUNY Binghamton