Thank you for sharing. I'd like to ask, after I used the molusce plugin in QGIS for simulation, the overall accuracy is below 0.6, and the historical accuracy is 0.8. What could be the reason for this?
Hello! In current implementation of MOLUSCE predictor maps are static for the whole period, including forecast. It is one of current limitations. Usually it's better to use predictors data relevant for the latest known LULC date. Resolution of data could be different, depending on your study scale. In this video we used MODIS MCD12Q1 data with ~500 meters/pixel spatial resolution.
Hi there, I noticed that your predicted results for 2022 and 2027 are pretty similar at first glance despite the rigorous process. Almost unable to discern any changes from the full view between the 2 iterations. What do you think is the reason?
Hello! Actually hundreds of pixels were changed, so model was able to catch something. But in general hypothesis in this demo case was very weak, so I wouldn't count on results as something real. If we would take a look at transition potential maps for each pair of classes, we would notice that there many potential changes, but model wa not confident enough most of them, so it prefered to leave state unchanged.
It's very simple and doesn't have many things to be tuned. You should only set up the process of converting continuous data to discrete, selecting number of intervals for each factor.
@@akwijayanto Hi! First of all, the plugin was transited from the QGIS 2 engine to the QGIS 3 engine. It is a huge thing, a lot of internal mechanisms are changed. It also means that we are able to support Molusce again, which was nearly impossible before the update (too much outdated code was inside). Besides that: - Improved styles support. Now Molusce works with paletted/unique values render type, which is the actual standard for land cover rasters. In the previous version you had to use singleband pseudocolor, and in many cases (gaps in class numbers etc.) it wasn't convenient. - calculation efficiency of ANN method has been improved significantly, and stop button now works well (which is very important for fast experiments with training the model under different parameters) - some bugs and UI fixes
@user-md3tg8pe5h 0 seconds ago when i add slope map as a spatial variable to predict LULC by Weight of evidence i face a problem that the Rand min and Range max for the slope raster appers as (nan) how can i fix this problem, please?
If "range min" and "range max" values are invalid, it seems to be a problem with your input rasters and their metadata, so plugin is unable to take correct min/max values from it
Thank you sir for providing us Molusce 4 that works in QGIS 3.x, I'm glad to hear that. I'll try.
Thank you so much guysss for update it. Really thanks from my heart....... ❤❤❤❤
Very good!
Brazil
Finallyy!!
Sir.... Which version of qgis you used for molusce support
Thank you for sharing. I'd like to ask, after I used the molusce plugin in QGIS for simulation, the overall accuracy is below 0.6, and the historical accuracy is 0.8. What could be the reason for this?
What year should the predictor maps (road network, water bodies,etc.) be? And what is the resolution of your input data?
Hello! In current implementation of MOLUSCE predictor maps are static for the whole period, including forecast. It is one of current limitations. Usually it's better to use predictors data relevant for the latest known LULC date. Resolution of data could be different, depending on your study scale. In this video we used MODIS MCD12Q1 data with ~500 meters/pixel spatial resolution.
hello，I found the current validation kappa in my result was low (about 30), what should I do? Which part of my model has a problem?😭
Hi there, I noticed that your predicted results for 2022 and 2027 are pretty similar at first glance despite the rigorous process. Almost unable to discern any changes from the full view between the 2 iterations. What do you think is the reason?
Hello! Actually hundreds of pixels were changed, so model was able to catch something. But in general hypothesis in this demo case was very weak, so I wouldn't count on results as something real. If we would take a look at transition potential maps for each pair of classes, we would notice that there many potential changes, but model wa not confident enough most of them, so it prefered to leave state unchanged.
what about Weight of evidence method, could you teach us about it, please?
It's very simple and doesn't have many things to be tuned. You should only set up the process of converting continuous data to discrete, selecting number of intervals for each factor.
hi, is there any alternative if I dont have any applied style in my folder? how can I still colored it?
What is the main difference between this version and the previous version?
@@akwijayanto Hi! First of all, the plugin was transited from the QGIS 2 engine to the QGIS 3 engine. It is a huge thing, a lot of internal mechanisms are changed. It also means that we are able to support Molusce again, which was nearly impossible before the update (too much outdated code was inside).
Besides that:
- Improved styles support. Now Molusce works with paletted/unique values render type, which is the actual standard for land cover rasters. In the previous version you had to use singleband pseudocolor, and in many cases (gaps in class numbers etc.) it wasn't convenient.
- calculation efficiency of ANN method has been improved significantly, and stop button now works well (which is very important for fast experiments with training the model under different parameters)
- some bugs and UI fixes
Can it be installed in QGIS 3.2 version?
@@tahmidanam6391 hello, minimal QGIS version is 3.22
@user-md3tg8pe5h
0 seconds ago
when i add slope map as a spatial variable to predict LULC by Weight of evidence i face a problem that the Rand min and Range max for the slope raster appers as (nan) how can i fix this problem, please?
If "range min" and "range max" values are invalid, it seems to be a problem with your input rasters and their metadata, so plugin is unable to take correct min/max values from it