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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!



Mapping Earthquake Deformation in Taiwan With ENVI

Mapping Earthquake Deformation in Taiwan With ENVI

12/15/2025

Unlocking Critical Insights With ENVI® Tools Taiwan sits at the junction of major tectonic plates and regularly experiences powerful earthquakes. Understanding how the ground moves during these events is essential for disaster preparedness, public safety, and building community resilience. But traditional approaches like field... Read More >

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... 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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