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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

6/3/2025

Rethinking the Reliability of Type 1a Supernovae   How do astronomers measure the universe? It all starts with distance. From gauging the size of a galaxy to calculating how fast the universe is expanding, measuring cosmic distances is essential to understanding everything in the sky. For nearby stars, astronomers use... Read More >

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

5/26/2025

Whether you’re new to remote sensing or a seasoned expert, there is no doubt that large language models (LLMs) like OpenAI’s ChatGPT or Google’s Gemini can be incredibly useful in many aspects of research. From exploring the electromagnetic spectrum to creating object detection models using the latest deep learning... Read More >

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

4/28/2025

When every second counts, the ability to process geospatial data rapidly and accurately isn’t just helpful, it’s critical. Geospatial Intelligence (GEOINT) has always played a pivotal role in defense, security, and disaster response. But in high-tempo operations, traditional workflows are no longer fast enough. Analysts are... Read More >

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

4/24/2025

This blog was written by Eli Dwek, Emeritus, NASA Goddard Space Flight Center, Greenbelt, MD and Research Fellow, Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA. It is the fifth blog in a series showcasing our IDL® Fellows program which supports passionate retired IDL users who may need support to continue their work... Read More >

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

2/25/2025

This blog was written in collaboration with Adam O’Connor from Wyvern.   As hyperspectral imaging (HSI) continues to grow in importance, access to high-quality satellite data is key to unlocking new insights in environmental monitoring, agriculture, forestry, mining, security, energy infrastructure management, and more.... Read More >

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Re-projection Over the 180th Meridian

Anonym

With certain data formats, especially those with large swaths like AVHRR, NPP VIIIRS, and MODIS, there are times when the spatial extent of the data crosses over the 180th meridian. When measuring longitude in degrees for a projection, this can present a problem since the longitude is not continuous as it goes from -180 to 180 degrees. There are a few ways to correct for this anomaly in the data using ENVI and IDL in order to re-project it properly. For the examples below, let's say that we have the variables "lat","lon", and "data" read from an AVHRR HDF4 file. One way to get these variables using IDL:

e = envi(/current)

hdf_id = hdf_sd_start('my_hdf_file.hdf')

hdf_sd_fileinfo, hdf_id, numData,atts

dataset_names = strarr(numData)

for i=0, numData-1 do begin

 hdf_sd_getinfo, hdf_sd_select(hdf_id, i),name=name

 dataset_names[i] = name

endfor

  

index = where(dataset_names eq 'avhrr_band1')

lon_index = where(dataset_names eq 'longitude')

lat_index = where(dataset_names eq 'latitude')

  

dataset_id=hdf_sd_select(hdf_id, index)

hdf_sd_getdata, dataset_id, data

hdf_sd_endaccess, dataset_id

  

lon_id=hdf_sd_select(hdf_id, lon_index)

hdf_sd_getdata, lon_id, lon

hdf_sd_endaccess, lon_id

  

lat_id=hdf_sd_select(hdf_id, lat_index)

hdf_sd_getdata, lat_id, lat

hdf_sd_endaccess, lat_id

; Close the file:

hdf_sd_end,hdf_id

 

How can I tell if my data crosses the 180th meridan?

Depending on your dataset, there are few ways to test if you will need to correct for the extent crossing over from -180 degrees to +180 degrees. If you are working in a certain area, the US and Hawaii for example, you can test to see if your longitude is over a certain threshold. If you know that you are looking at data in North America, you can test to see if you have any longitude values near 180 degrees by taking the maximum of your longitude grid:

if max(lon)gt 179 then ...

A more robust way to check if your data set crosses the180th meridian is to take the difference between the minimum and maximum longitude. If the data does cross over the 180th meridian, the max will be ~180 and the minimum will be ~-180. It is unlikely that your data will wrap around the entire globe, like in the case of data around the North and South Poles, but that I'll save for another entry. So we can assume that if the difference between the max and min longitude is close to 360 degrees, the 180th meridian has been crossed:

if (max(lon)- min(lon)) gt 359 then ...

 

What can I do to correct for this case?

If you want to use the GLT reprojection tool available in ENVI to get the data on a regular grid, and your data crosses the 180th meridian, you are in luck! The engineers at here at Exelis put in the time to correct for this case automatically. If your data crosses the 180th meridian, the resulting image will be on the right side (+180 degrees) of a standard WGS-84 world map. If you'd rather it be on the left side of a WGS-84 map (-180 degrees), just subtract 360 degrees from the "lon" variable before doing GLT reprojection:

lon -= 360

Here is an example display in ENVI – in one image I applied the 360 shift. In the other I left it as is.

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