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|
|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|
|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|
|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|
|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|
|Transactions of the Institute of British Geographers||2,871||3.17|
|Annals of the Association of American Geographers||4,439||2.756|
|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|
|Transactions in Gis||918||1.537|
|Journal of Geography||423||1.213|
|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|
|Scottish Geographical Journal||274||0.686|
|South African Geographical Journal||128||0.423|
Geography Journal Google Scholar Ranking
|Hydrology Journals||Total Cites||Impact Factor|
|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|
|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|
|Hydrological Sciences Journal-Journal Des Sciences Hydrologiques||4,664||2.182|
|Journal of Contaminant Hydrology||4,615||2.063|
|Journal of Hydro-Environment Research||481||1.971|
|Journal of Soil and Water Conservation||3,037||1.752|
|Vadose Zone Journal||3,134||1.737|
|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|
|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|
|Wetlands Ecology and Management||1,284||1.407|
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:
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.
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.
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.
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)
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$Score) * scale.factor
### extract student names from ITS results
lastname <- as.character(lapply(strsplit(as.character(df$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)
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)
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
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.
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.
2 ESA’s Sentinel Mission – New Leader in Free High Resolution Data?
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.”