Locate the pixels which have the biggest standard deviation in a time-series layer stack raster using r [on hold]Calculating mean and standard deviation for raster values using whole pixels within a polygonR: raster stack (time series) loop pixelwise and write layer number of value occurence into a single output layerHow to store large time series of raster imagesPixel-wize trend analysis of an irregular time series stack of NDVI rasters in RAveraging MODIS 8-day LST for each pixel in a region to generate annual mean LSTUsing spei() function on time series from rasterstack in R?MODIS LST maximum value compositeFormatting h-axis of time series chart in Google Earth EngineDoes the MODIStsp package computes the QA's and eventually corrects the bad quality pixels?
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Locate the pixels which have the biggest standard deviation in a time-series layer stack raster using r [on hold]
Calculating mean and standard deviation for raster values using whole pixels within a polygonR: raster stack (time series) loop pixelwise and write layer number of value occurence into a single output layerHow to store large time series of raster imagesPixel-wize trend analysis of an irregular time series stack of NDVI rasters in RAveraging MODIS 8-day LST for each pixel in a region to generate annual mean LSTUsing spei() function on time series from rasterstack in R?MODIS LST maximum value compositeFormatting h-axis of time series chart in Google Earth EngineDoes the MODIStsp package computes the QA's and eventually corrects the bad quality pixels?
I have 30 monthly tiff images of MOD11A2, and I created a time-series layer stuck using the code
mod = timeStackMODIS(list, pattern = glob2rx("\.tif$"))
using the bfastspatial package. I want to check which pixels have the biggest standard deviation, so I can focus my analysis in those pixels. I have made an attempt using this code
modsd = (list.files(path = "my_dir", full.names = FALSE, pattern = "\.tif$" ))
modsdstack <- stack(modsd)
testmodsd = calc(modsdstack, fun = sd)
and as a result the code returned a single raster, which I was unable to explain his raster values (because I had 30 images and the code returned one). So I think thats not the correct way to proceed. What I can not understand is if I have to create 30 new raster (each for every original image) which would have the sd as pixel values or I can, somehow, calculate the sd using the time-series layer stack which I have.
raster r spatial-statistics modis time-series
asked 2 days ago
NikosGrNikosGr
317
put on hold as unclear what you're asking by Spacedman, Erik, csk, Andre Silva, wetland yesterday
Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
|
show 5 more comments
I have 30 monthly tiff images of MOD11A2, and I created a time-series layer stuck using the code
mod = timeStackMODIS(list, pattern = glob2rx("\.tif$"))
using the bfastspatial package. I want to check which pixels have the biggest standard deviation, so I can focus my analysis in those pixels. I have made an attempt using this code
modsd = (list.files(path = "my_dir", full.names = FALSE, pattern = "\.tif$" ))
modsdstack <- stack(modsd)
testmodsd = calc(modsdstack, fun = sd)
and as a result the code returned a single raster, which I was unable to explain his raster values (because I had 30 images and the code returned one). So I think thats not the correct way to proceed. What I can not understand is if I have to create 30 new raster (each for every original image) which would have the sd as pixel values or I can, somehow, calculate the sd using the time-series layer stack which I have.
raster r spatial-statistics modis time-series
asked 2 days ago
NikosGrNikosGr
317
put on hold as unclear what you're asking by Spacedman, Erik, csk, Andre Silva, wetland yesterday
Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
Yes, I want to compare each pixel in a raster for a specific year against the same pixel of the other 29 overlapping years/rasters. It's not what I expected because in order to check which pixels have the biggest sd I need to know the mean (average) of the time-series layer stack, no?
– NikosGr
2 days ago
1
When you runsdon a stack of 30 layers you get a single-band raster of thesdof each set of 30 values at each pixel. You're not going to get 30 layers. Its like adding the pixel values, or computing their mean. You get one layer out for all the layers put in.
– Spacedman
2 days ago
Thank you for your fast reply. What I can not understand is how will I know which pixels have a big value of sd if I don't know what is the mean (average) of each pixel in the overlapping images? What I mean is in order to locate the pixels with high sd values, firstly, I need to know the mean (average) of each pixel for every year, am I right?
– NikosGr
2 days ago
What I thought is this: from the 8day products of MOD11A2 which I had, I should create monthly products using the sd and after to utilize thecalcfunction to create a single image as I did before. Maybe in this way I will be able to understand the values.
– NikosGr
2 days ago
1
One should note that the sd is likely irrelevant in this case because this type of data is quite non-normal. If the skewness is high enough the sd could even take a value outside the observed distribution. Besides, you are not listing to the advice being presented and misunderstanding sd.
– Jeffrey Evans
2 days ago
|
show 5 more comments
I have 30 monthly tiff images of MOD11A2, and I created a time-series layer stuck using the code
mod = timeStackMODIS(list, pattern = glob2rx("\.tif$"))
using the bfastspatial package. I want to check which pixels have the biggest standard deviation, so I can focus my analysis in those pixels. I have made an attempt using this code
modsd = (list.files(path = "my_dir", full.names = FALSE, pattern = "\.tif$" ))
modsdstack <- stack(modsd)
testmodsd = calc(modsdstack, fun = sd)
and as a result the code returned a single raster, which I was unable to explain his raster values (because I had 30 images and the code returned one). So I think thats not the correct way to proceed. What I can not understand is if I have to create 30 new raster (each for every original image) which would have the sd as pixel values or I can, somehow, calculate the sd using the time-series layer stack which I have.
raster r spatial-statistics modis time-series
asked 2 days ago
NikosGrNikosGr
317
I have 30 monthly tiff images of MOD11A2, and I created a time-series layer stuck using the code
mod = timeStackMODIS(list, pattern = glob2rx("\.tif$"))
using the bfastspatial package. I want to check which pixels have the biggest standard deviation, so I can focus my analysis in those pixels. I have made an attempt using this code
modsd = (list.files(path = "my_dir", full.names = FALSE, pattern = "\.tif$" ))
modsdstack <- stack(modsd)
testmodsd = calc(modsdstack, fun = sd)
and as a result the code returned a single raster, which I was unable to explain his raster values (because I had 30 images and the code returned one). So I think thats not the correct way to proceed. What I can not understand is if I have to create 30 new raster (each for every original image) which would have the sd as pixel values or I can, somehow, calculate the sd using the time-series layer stack which I have.
raster r spatial-statistics modis time-series
raster r spatial-statistics modis time-series
asked 2 days ago
NikosGrNikosGr
317
asked 2 days ago
NikosGrNikosGr
317
edited 2 days ago
NikosGr
asked 2 days ago
NikosGrNikosGr
317
asked 2 days ago
NikosGrNikosGr
317
asked 2 days ago
NikosGrNikosGr
317
317
put on hold as unclear what you're asking by Spacedman, Erik, csk, Andre Silva, wetland yesterday
Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
put on hold as unclear what you're asking by Spacedman, Erik, csk, Andre Silva, wetland yesterday
Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
Yes, I want to compare each pixel in a raster for a specific year against the same pixel of the other 29 overlapping years/rasters. It's not what I expected because in order to check which pixels have the biggest sd I need to know the mean (average) of the time-series layer stack, no?
– NikosGr
2 days ago
1
When you runsdon a stack of 30 layers you get a single-band raster of thesdof each set of 30 values at each pixel. You're not going to get 30 layers. Its like adding the pixel values, or computing their mean. You get one layer out for all the layers put in.
– Spacedman
2 days ago
Thank you for your fast reply. What I can not understand is how will I know which pixels have a big value of sd if I don't know what is the mean (average) of each pixel in the overlapping images? What I mean is in order to locate the pixels with high sd values, firstly, I need to know the mean (average) of each pixel for every year, am I right?
– NikosGr
2 days ago
What I thought is this: from the 8day products of MOD11A2 which I had, I should create monthly products using the sd and after to utilize thecalcfunction to create a single image as I did before. Maybe in this way I will be able to understand the values.
– NikosGr
2 days ago
1
One should note that the sd is likely irrelevant in this case because this type of data is quite non-normal. If the skewness is high enough the sd could even take a value outside the observed distribution. Besides, you are not listing to the advice being presented and misunderstanding sd.
– Jeffrey Evans
2 days ago
|
show 5 more comments
Yes, I want to compare each pixel in a raster for a specific year against the same pixel of the other 29 overlapping years/rasters. It's not what I expected because in order to check which pixels have the biggest sd I need to know the mean (average) of the time-series layer stack, no?
– NikosGr
2 days ago
1
When you runsdon a stack of 30 layers you get a single-band raster of thesdof each set of 30 values at each pixel. You're not going to get 30 layers. Its like adding the pixel values, or computing their mean. You get one layer out for all the layers put in.
– Spacedman
2 days ago
Thank you for your fast reply. What I can not understand is how will I know which pixels have a big value of sd if I don't know what is the mean (average) of each pixel in the overlapping images? What I mean is in order to locate the pixels with high sd values, firstly, I need to know the mean (average) of each pixel for every year, am I right?
– NikosGr
2 days ago
What I thought is this: from the 8day products of MOD11A2 which I had, I should create monthly products using the sd and after to utilize thecalcfunction to create a single image as I did before. Maybe in this way I will be able to understand the values.
– NikosGr
2 days ago
1
One should note that the sd is likely irrelevant in this case because this type of data is quite non-normal. If the skewness is high enough the sd could even take a value outside the observed distribution. Besides, you are not listing to the advice being presented and misunderstanding sd.
– Jeffrey Evans
2 days ago
Yes, I want to compare each pixel in a raster for a specific year against the same pixel of the other 29 overlapping years/rasters. It's not what I expected because in order to check which pixels have the biggest sd I need to know the mean (average) of the time-series layer stack, no?
– NikosGr
2 days ago
Yes, I want to compare each pixel in a raster for a specific year against the same pixel of the other 29 overlapping years/rasters. It's not what I expected because in order to check which pixels have the biggest sd I need to know the mean (average) of the time-series layer stack, no?
– NikosGr
2 days ago
1
1
When you run
sd on a stack of 30 layers you get a single-band raster of the sd of each set of 30 values at each pixel. You're not going to get 30 layers. Its like adding the pixel values, or computing their mean. You get one layer out for all the layers put in.– Spacedman
2 days ago
When you run
sd on a stack of 30 layers you get a single-band raster of the sd of each set of 30 values at each pixel. You're not going to get 30 layers. Its like adding the pixel values, or computing their mean. You get one layer out for all the layers put in.– Spacedman
2 days ago
Thank you for your fast reply. What I can not understand is how will I know which pixels have a big value of sd if I don't know what is the mean (average) of each pixel in the overlapping images? What I mean is in order to locate the pixels with high sd values, firstly, I need to know the mean (average) of each pixel for every year, am I right?
– NikosGr
2 days ago
Thank you for your fast reply. What I can not understand is how will I know which pixels have a big value of sd if I don't know what is the mean (average) of each pixel in the overlapping images? What I mean is in order to locate the pixels with high sd values, firstly, I need to know the mean (average) of each pixel for every year, am I right?
– NikosGr
2 days ago
What I thought is this: from the 8day products of MOD11A2 which I had, I should create monthly products using the sd and after to utilize the
calc function to create a single image as I did before. Maybe in this way I will be able to understand the values.– NikosGr
2 days ago
What I thought is this: from the 8day products of MOD11A2 which I had, I should create monthly products using the sd and after to utilize the
calc function to create a single image as I did before. Maybe in this way I will be able to understand the values.– NikosGr
2 days ago
1
1
One should note that the sd is likely irrelevant in this case because this type of data is quite non-normal. If the skewness is high enough the sd could even take a value outside the observed distribution. Besides, you are not listing to the advice being presented and misunderstanding sd.
– Jeffrey Evans
2 days ago
One should note that the sd is likely irrelevant in this case because this type of data is quite non-normal. If the skewness is high enough the sd could even take a value outside the observed distribution. Besides, you are not listing to the advice being presented and misunderstanding sd.
– Jeffrey Evans
2 days ago
|
show 5 more comments
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Yes, I want to compare each pixel in a raster for a specific year against the same pixel of the other 29 overlapping years/rasters. It's not what I expected because in order to check which pixels have the biggest sd I need to know the mean (average) of the time-series layer stack, no?
– NikosGr
2 days ago
1
When you run
sdon a stack of 30 layers you get a single-band raster of thesdof each set of 30 values at each pixel. You're not going to get 30 layers. Its like adding the pixel values, or computing their mean. You get one layer out for all the layers put in.– Spacedman
2 days ago
Thank you for your fast reply. What I can not understand is how will I know which pixels have a big value of sd if I don't know what is the mean (average) of each pixel in the overlapping images? What I mean is in order to locate the pixels with high sd values, firstly, I need to know the mean (average) of each pixel for every year, am I right?
– NikosGr
2 days ago
What I thought is this: from the 8day products of MOD11A2 which I had, I should create monthly products using the sd and after to utilize the
calcfunction to create a single image as I did before. Maybe in this way I will be able to understand the values.– NikosGr
2 days ago
1
One should note that the sd is likely irrelevant in this case because this type of data is quite non-normal. If the skewness is high enough the sd could even take a value outside the observed distribution. Besides, you are not listing to the advice being presented and misunderstanding sd.
– Jeffrey Evans
2 days ago