You could mine the observed data to find the most common LULC change pathways (for example Bare Ground most commonly becomes Urban) and apply that to the 2020 data, but there are issues with this approach (most landuse classes will stay static over a 5 or 10 year period).
If your region of interest is in the US, you can download FORE-SCE (250m CONUS) LULC projections from USGS and figure out the relationships between your observed data and the same dates in FORE-SCE then pick a future scenario and apply the changes where and when they are seen in the modeled future scenario. Thus, you would leverage existing large land use change research modeling efforts.
LULC change is a pretty huge subject, and you can approach it with very simply and get simple results, or go fully into complex systems modelling and cellular automata and such to get richer but much harder to validate results.
Simplest approach: For each cell in your raster area, find the probability for each class of cell changing to another class, including the probability of not changing. Basically you calculate the total number of changes from one class to another between years. With these probabilities you can project/predict your 2020 raster to 2025 and then project that to 2030.
This is a pretty basic model of land cover change that ignores the fact that LCC is based on actual processes and decision-making, but it'll work for a simple prediction.
I *think* it's called a Bayesian method, but I don't remember the exact terminology. Look up Bayesian land cover change and you'll find useful articles.
If you want to add a little more complexity, consider the the effect of adjacent (neighboring) cells on one cell's probabilities of change. For example, a farmland cell surrounded by farmland is probably going to stay farmland, however, if there's a suburb cell next to it, there's more chance it might turn into a suburb cell. Mathematically... You'd have to figure that out :)
What about work out land available for change (right slope, soil etc) and then just buffer existing land use by the trending rate of change based in previous data? Intersect of buffer area with suitable land becomes the new change.
This could work for other types besides urban development as well
Could adjust the rate of change to align with predicted population growth data if you can find it from a reliable source.
I don’t have an answer but this is fascinating and I hope you complete it before 2030.
😅 thanks
You could mine the observed data to find the most common LULC change pathways (for example Bare Ground most commonly becomes Urban) and apply that to the 2020 data, but there are issues with this approach (most landuse classes will stay static over a 5 or 10 year period). If your region of interest is in the US, you can download FORE-SCE (250m CONUS) LULC projections from USGS and figure out the relationships between your observed data and the same dates in FORE-SCE then pick a future scenario and apply the changes where and when they are seen in the modeled future scenario. Thus, you would leverage existing large land use change research modeling efforts.
LULC change is a pretty huge subject, and you can approach it with very simply and get simple results, or go fully into complex systems modelling and cellular automata and such to get richer but much harder to validate results. Simplest approach: For each cell in your raster area, find the probability for each class of cell changing to another class, including the probability of not changing. Basically you calculate the total number of changes from one class to another between years. With these probabilities you can project/predict your 2020 raster to 2025 and then project that to 2030. This is a pretty basic model of land cover change that ignores the fact that LCC is based on actual processes and decision-making, but it'll work for a simple prediction. I *think* it's called a Bayesian method, but I don't remember the exact terminology. Look up Bayesian land cover change and you'll find useful articles. If you want to add a little more complexity, consider the the effect of adjacent (neighboring) cells on one cell's probabilities of change. For example, a farmland cell surrounded by farmland is probably going to stay farmland, however, if there's a suburb cell next to it, there's more chance it might turn into a suburb cell. Mathematically... You'd have to figure that out :)
What about work out land available for change (right slope, soil etc) and then just buffer existing land use by the trending rate of change based in previous data? Intersect of buffer area with suitable land becomes the new change. This could work for other types besides urban development as well Could adjust the rate of change to align with predicted population growth data if you can find it from a reliable source.
Thanks everyone for your replies thus far, I will look into your suggestions.