I primarily do astrophotography of deep-sky objects, but I do use a Mac and can share what I use as some of it may apply to your needs.
In deep-sky astrophotography, the photographer collects lots and lots of frames of some deep-sky object such as galaxies, nebulae, etc. and each frame is meant to have more or less the identical field of view as all the other frames (unless they are shooting a mosaic or combining data shot by different cameras or different telescopes). But the main thing is that nothing in the images is moving enough to be noticeable over a period of a few hours (most things don't move enough to be noticeable even across many years.)
In landscape astrophotography, the photographer is trying to capture both the landscape on Earth as well as the star field in the sky. The "landscape" vs. the "sky" do move relative to each other second by second and that creates particular challenges unique to this type of astrophotography.
To deal with this, you'll need to capture a good clean "landscape" image (where you don't worry about the stars elongating in the sky) and then also capture many "sky" images for use in stacking. You stack the "sky" part of your data and then re-combine it with your "landscape" part of your data to create a final product.
The process for deep-sky astrophotography involves capturing quite a bit of data. This includes many regular exposures (you'll often see these referred to as "light frames" or just "lights" for short) as well as several other special types of exposures that are various forms of calibration exposures.
The calibration data includes bias frames, dark frames, and flat frames (if you're not familiar with these, I can provide more details ... but I'll skip the detail unless asked to get to your answer).
Often there are different exposure durations used during the capture sequence. Longer exposures help with faint objects where more time is needed to collect light. Meanwhile if the exposures are too long then things like the stars will blow out and instead of having color in stars you end up with just "white" stars because all three color channels clipped.
To sequence the data capture, capture software might be used.
On the Mac, the two programs I am familiar with are (1) Nebulosity and (2) AstroDSLR.
Nebulosity does both image capture and processing for deep-sky objects.
AstroDSLR is meant to control only the image acquisition process. It will control the camera to collect the sequence of all the different exposures you might want to collect and it can do this for hours on end if needed. AstroDSLR is available from CloudMakers.eu (it is also sold on the macOS App Store).
On a Windows PC I've previously used Backyard EOS (only supports Canon EOS cameras) and they now have a program called Backyard NIK (only supports specific Nikon cameras). AstroDSLR is similar to those except it runs on Mac and it supports quite a range of DSLR cameras. The same vendor makes something called AstroImager which is meant for dedicated astrophotography CCD or CMOS sensor cameras that are not DLSRs.
Other popular apps that PC astrophotographers use are things like Sequence Generator Pro (aka SGPro or SGP) and Maxim DL. These are not available on Mac.
Nebulosity is made by Stark-Labs.com. It runs on Mac and it does both image acquisition and image processing. But I find that the image acquisition isn't as full-featured as AstroDSLR and I also find that its image processing isn't nearly as full-featured as PixInsight. So rather than use one program that does both things... I use individual apps that specialize in the thing they do.
Once you've captured all your data (light frames plus all the calibration frames) you can start combining them. Nebulosity does have capabilities here, but I prefer a different application called PixInsight which offers quite a bit more.
PixInsight is €230. It does have a bit of a learning curve (it's a bit like trying to learn Photoshop). It will handle all the stacking (image calibration, registration, and integration process) and quite a bit more. One of the best learning resources for it is a website called IP4AP.com -- they have tutorials. The tutorials are part of a subscription service at about $10 month or $100 for a year (but you can sign up for just a month at a time - I don't think they force you to sign up for a long minimum term.)
PixInsight is VERY powerful in it's ability to handle the integration. It's the only application I've ever used where the images being stacked can be taken by completely different cameras, different lenses, different telescopes, at different rotation angles and different image scales and resolutions and it can STILL figure out how to stack them. If it can't automatically figure out how to align the frames (and usually it can) it lets you do a manual process where you pick on a common star visible in each frame, then pick a second star common in each frame and it will use that to figure out what the scale and rotation is.
But keep in mind that stacking software that uses star positions to align the frame is primarily meant for deep-sky astrophotography ... so it's not going to be good at dealing with your landscapes. You will likely have to mask out the landscapes and just stack the sky portion ... then manually recombine the foreground landscape frame to create the completed image.
You may want to consider getting a tracking head.
The Earth rotates from West to East at 15 arc-seconds of angular rotation per each second of clock time. This speed is referred to as sidereal speed (or sidereal rate). If you take long exposures using a camera on a stationary mount then you will eventually see the stars begin to elongate due to this rotation.
The guideline for this is called the 500 Rule. The rule is intended for use with full-frame cameras (so you would need to compensate for other sensor sizes). it suggests that if you divide 500 by the focal length of your lens then the result is the number of seconds you can expose without noticing elongation in stars. There are other formulas that yield a more precise value (by determining the angular field of view for your lens and dividing that by the camera resolution to work out the number of arc-seconds per pixel on your camera sensor & lens combination.) The 500 Rule is usually sufficient.
The tracking head lets you take much longer exposures than is possible without tracking. It has a motor which allows it to rotate at the same speed as the Earth ... but in the opposite direction.
The result is that if you align the tracking head so that the rotation axis is parallel to Earth's axis (they usually include an alignment aid) the rotation of the head exactly cancels the rotation of the Earth and you can take a very long exposure. You can still point your camera in any direction (it does not need to point toward the celestial pole.)
The caveat for "landscape" astrophotography is that while this works great for the stars, the landscape will now blur.
The tracking heads typically have several speed settings and one of them is typically a 1/2 sidereal speed option. This doubles the amount of time you can expose.
But if you use a tracking head to gather longer exposures and gather more data to do stacking, then you can add the landscape foreground to create a composite result.
Image integration (aka "stacking") doesn't collect more light, but it collects more samples of the same data. This allows the images to be statistically combined in a way that ends up improving the signal to noise ratio (SNR) and results in a much cleaner image with significantly reduced image noise.
If you have several frames, you can imagine aligning each frames so that the stars match up (this alignment process is called image registration). This is part of the workflow but it's a different step.
There is also a step called image calibration. The calibration step uses the data from the dark, flat, and bias frames to convert each light frame into a calibrated-light frame.
Once the frames have all been calibrated and registered, they can be integrated.
Integration via Averaging
The simplest form of integration is done via statistical averaging.
Suppose you have 10 frames (in reality you will likely have many more). You can imagine comparing any single pixel in one frame to it's corresponding pixel in all the other frames. The software can "average" the value of those pixels. If that pixel had a star in it, then every frame would have some brightness (luminosity) value from that star and the average value from all the frames is what would be used in the final result.
But suppose it's a pixel that is supposed to be the black background of the sky. It should have a very dark luminosity value. Hopefully in most of your frames it does. But if the occasional frame had a noisy pixel in that position, once you average all the pixels in every frame, your final pixel should hopefully be pretty dark. The Poisson relationship here is that the noise is reduced by the inverse square root of the number of samples provided... if you have 16 frames then the noise can be reduced to 1/4 of what the noise would have been in any single frame.
Integration by Sigma Clipping
It turns out you can do better than a simple average. If you only have 2 or 3 samples then you have no choice but to do simple averaging. But if you supply enough samples (say ... 10 or more) then you can do a statistical method called sigma clipping. Sigma clipping works based on a process similar to a mean & deviation from the mean.
Suppose that in just one of your frames an airplane flew through the photo. You've got a light trail in the image. If you use averaging, you can weaken the light trail but you cannot eliminate it. In Sigma Clipping you can actually make it vanish completely.
This method is similar to averaging except it calculates the statistical mean value of a pixel after comparing all the samples. But then it does a 2nd pass to determine how much the pixel in each frame deviates from the mean. You set a threshold. If the pixel in any frame deviates by too much, then that individual pixel will be rejected. It's basically as though all the frames "vote" on the value of the final pixel. So if we think of each pixel as a percentage of brightness where 0 = completely black and 100 = completely bright, suppose 19 out of 20 frames have a value of 10%. But suppose 1 frame has a value of 100 (where the airplane trail passed through the frame). This would work out a statistical mean of 14.5. Suppose we set a sigma clipping threshold of 10. On pass #2, any pixel that deviates from 14.5 by more than 10 will be rejected. This means the pixel that registered 100 will ignored -- it gets "voted off the island". The other pixels that registered as 10 will be retained. THOSE pixels then get averaged and the value of 10 is retained. The airplane trail disappears as though it was never there. This happens on a pixel by pixel basis... so it doesn't reject all of that 20th frame... just the pixels where the airplane passed through get rejected and the rest are kept. It's a wonderful thing.
PixInsight is very powerful. It does come with a script called "Batch PreProcessing" which makes it very easy to combine all your data to produce a master frame (you combine all the lights, darks, flats, and bias frames and let start running ... and it will ultimately produce a master integrated image.)
However PixInisght will also let you do each step of preprocessing separately and as you get better at learning the tool you can use this to your advantage by tuning the steps that it performs.
For example, I've had times where an object is low enough in the sky that the atmosphere behaves a bit like a lens and starts to create atmospheric dispersion (you can think of it as being much like chromatic aberration -- except this is caused but the atmosphere and not by the lens). But you get stars where you see a red-ring on one side and a blue ring on the other side. In PixInsight I can extra the full-color data into separate LRGB channels. I can then use the image registration process (the star alignment) to re-align the LRGB frames (re-register them) and then re-combine them back into a full-color image ... and it makes the dispersion problem go away.
It's a pretty amazing tool but it is optimized for astrophotography. (PixInsight isn't mac-specific ... it runs on Windows, Linux, & Mac).
Usually if someone uses PixInsight they also need to use at least one other tool (such as Photoshop, or Affinity Photo, or GIMP, etc.) because where as you can use a mouse to selectively apply adjustments/edits to a specific part of an image, in PixInsight you can't do that. All adjustments either apply to the entire image or can be applied to everything that isn't masked out. But it only supports two types of masks... a "star" mask (builds a mask based on the stars in the image -- as the name suggests) or as a "range" masks (selects an area based on the brightness range ... usually used to generate a mask based on objects in the image such as a nebulae or galaxy, etc.)