Why when i use from mosaic to new raster the created raster has some pixels with 0 value

Banishing Zero-Value Ghosts from Your Raster Mosaics

Ever stitched together a bunch of raster images, all excited to create one big, beautiful map, only to find it riddled with these weird, dark holes? I’m talking about those pesky pixels stubbornly stuck at a value of zero, even where you know there should be valid data. It’s a common head-scratcher when using tools like “Mosaic to New Raster,” but trust me, you’re not alone, and there are definitely ways to kick those zero-value ghosts to the curb.

So, what’s the deal? Why does this happen? Let’s break it down.

First off, think about “NoData” values. Rasters often use a specific number to represent areas where, well, there isn’t any data. Maybe it’s an area outside the sensor’s range, or a region intentionally masked out. A typical trick is assigning a value like -9999 to these spots. Now, if you don’t tell the mosaicking tool about these NoData values – if they’re not properly defined in your source rasters or in the mosaic dataset itself – the tool might mistakenly think those areas are perfectly valid spots with a value of zero. Imagine it like this: the tool sees a blank space and just assumes it’s supposed to be black, which translates to zero. This is especially true if those NoData areas show up as a black background; the tool just sees RGB (0,0,0) and runs with it.

But it’s not just about NoData. Sometimes, the problem lies in how the data itself is structured. The “Mosaic to New Raster” tool needs to know what kind of data it’s dealing with – the pixel type, as it’s called. This includes the bit depth and data type. If you pick the wrong one, things can go haywire. For example, if your raster has both positive and negative values (think elevation data that goes below sea level), and you tell the tool it’s only dealing with positive numbers (an unsigned integer pixel type), those negative values will get misinterpreted, often ending up as zero. I learned this the hard way once when trying to mosaic some bathymetric data – let’s just say the resulting map looked like a giant, flat plain!

And speaking of data types, make sure your chosen pixel type can actually represent your NoData values. If your original raster uses -9999 for NoData, but you select an 8-bit unsigned pixel type, those -9999s will get forced to zero, which isn’t what you want at all.

Then there’s the issue of alignment and coverage. If your input rasters don’t perfectly line up or if they don’t completely cover the area of your output raster, you’re likely to see those zero-value demons pop up. Areas not covered by any source raster might get filled with zero as a default, especially if you haven’t explicitly set a NoData value for the output. It’s like trying to tile a floor with pieces that don’t quite fit – you end up with gaps.

Cell alignment can also be a sneaky culprit. If the cells in your rasters aren’t perfectly aligned, the mosaicking process might resample the data. This resampling can cause slight shifts in pixel values, and in areas where data is sparse or NoData values are present, you can end up with zeros where you don’t expect them.

Finally, if you’re using a mosaic dataset, the mosaic operator setting can play a role. This setting tells the tool how to handle overlapping pixels. If it’s not set correctly, it might prioritize NoData values or assign zero in overlapping regions.

Okay, so that’s a lot of potential problems. But don’t despair! Here’s how to fight back:

Look, dealing with raster data can be tricky. But by understanding why those zero-value pixels appear and taking the right steps to prevent them, you can create seamless, accurate mosaics that would make any cartographer proud. Happy mapping!