Hello every one!!
im looking for the trend of LULC. suppose there is change in Agricultural land then how can i find the trend of it? in the agricultural land, suppose there is particular type of crop cultivation is also changing, how can i capture that trend? in the same line, i'm wondering what would be the resolution of image required to see the changes in particular crop cultivation area?
Good Morning!
you should first observe the scale of your study. Is it local? Regional? or national? How big is your study area?
What trends do you want to capture?
You need to answer these basic questions to arrive at an optimal resolution for your study.
Regards,
Paulo Barni
Hi
as far as I understand your question, you should use classification methods like Maximum or Minimum distance (in ENVI Software). The best resolution for extracting LU maps is lower than 10m, specially for small areas such as rivers or crop cultivation areas and The best sources for downloading high resolution images are Spot, IKONUS, IRIS and Sentinel...
I hope that works
the best
Azizian
Hi,
If you want to detect the change in the agricultural product pattern, RapidEye (5m) might be a good option.
If you want to detect LULC change on a more general scale, You can use Landsat satellite images (30-15m).
Hello Praveen,
First, you need time-series data (preferably remotely sensed images) to observe trends in LULC.
A good starting point in deciding the suitable image spatial resolution is to consider the landscape structure in terms of composition and spatial configuration.
And depending on the extent of your study area you may consider satellite or aerial images.
I assume you are familiar with your study area and have a fair idea about the rate of LULC changes. This will be vital in deciding the temporal resolution of the images (e.g seasonal, yearly, decadal etc.).
You can then apply supervised image classification methods and make area estimates of different LULC types (see FAO's OpenForis tool for area estimation) and analyze trends in LULC.
At the crop level, you may extract and test suitability of spectral (e.g. NDVI, EVI, spectral bands, tasseled cap variables etc.), textural (Homogeneity, entropy, variance etc.) and phenological (e.g. mean NDVI) variables from the remotely sensed images to characterize different crop types.
Select the most suitable variables and apply image differencing and thresholding methods.
Hope you find these useful.
Best regards,
Kwame
Good question.. Please share me the best answer might you get...
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