Dear Diego,

There are literally hundreds of indices, vegetation indices (VI's) being the most popular ones, Read the following paper to get some feeling of the literature comparing different VI's.

https://www.msu.edu/~vina/2011_RSE_GLAI.pdf

Even singular Red band reflectance gives you information which is invaluable. See:

https://www.researchgate.net/profile/Frank_Veroustraete

In the relatively new field of hyperspectral remote sensing new and pretty accurate multi-dimensional VI's and retrieval methods have been developed and described. See:

https://www.researchgate.net/profile/Frank_Veroustraete/contributions

https://www.researchgate.net/publication/262673837_On_the_Semi-Automatic_Retrieval_of_Biophysical_Parameters_Based_on_Spectral_Index_Optimization

Hence Diego, you are approaching the verge of an enormous field of R&D in Remote Sensing, which will take you in in a mirror palace of multi-dimensional spaces, viewing angles, bi-directionallity, anisotropic effects, hyperspectral data cube retrieval schemes  and many other aspects to be considered.

Beware, once you enter this field, you might never come out again. It is an enigmatic field of research with more problems to solve, than solutions found. In one word, a paradise for a scientist interested in Remote Sensing and its applications.

Concerning land cover change (LUCC) I would not advise to use VI's, but rather classification algorithms at landscape level resolution. Just an example of LUCC in the Northwest of China over the last 50 years:

https://www.researchgate.net/publication/235959317_Diachronic_Mapping_of_LUCC_in_the_Northwest_of_China_over_the_last_half_Century_Conversion_of_CORONA_Panchromatic_reflectance_into_LANDSAT_NDVI_Imagery

Good hunting and lots a luck and perseverance,

Frank

Article On the Semi-Automatic Retrieval of Biophysical Parameters Ba...

Article Diachronic mapping of LUCC in the northwest of China over th...

Dear Diego,

There are literally hundreds of indices, vegetation indices (VI's) being the most popular ones, Read the following paper to get some feeling of the literature comparing different VI's.

https://www.msu.edu/~vina/2011_RSE_GLAI.pdf

Even singular Red band reflectance gives you information which is invaluable. See:

https://www.researchgate.net/profile/Frank_Veroustraete

In the relatively new field of hyperspectral remote sensing new and pretty accurate multi-dimensional VI's and retrieval methods have been developed and described. See:

https://www.researchgate.net/profile/Frank_Veroustraete/contributions

https://www.researchgate.net/publication/262673837_On_the_Semi-Automatic_Retrieval_of_Biophysical_Parameters_Based_on_Spectral_Index_Optimization

Hence Diego, you are approaching the verge of an enormous field of R&D in Remote Sensing, which will take you in in a mirror palace of multi-dimensional spaces, viewing angles, bi-directionallity, anisotropic effects, hyperspectral data cube retrieval schemes  and many other aspects to be considered.

Beware, once you enter this field, you might never come out again. It is an enigmatic field of research with more problems to solve, than solutions found. In one word, a paradise for a scientist interested in Remote Sensing and its applications.

Concerning land cover change (LUCC) I would not advise to use VI's, but rather classification algorithms at landscape level resolution. Just an example of LUCC in the Northwest of China over the last 50 years:

https://www.researchgate.net/publication/235959317_Diachronic_Mapping_of_LUCC_in_the_Northwest_of_China_over_the_last_half_Century_Conversion_of_CORONA_Panchromatic_reflectance_into_LANDSAT_NDVI_Imagery

Good hunting and lots a luck and perseverance,

Frank

Article On the Semi-Automatic Retrieval of Biophysical Parameters Ba...

Article Diachronic mapping of LUCC in the northwest of China over th...

Dear Diego,

  Although I totally agree with Frank, I have a suggestion for you as I recommended this for another question made in RG. Why not try to find the best fit solution to your problem? Instead of using previously developed indices, you might be able to find one suitable for your purpose.

I provided my answer to Maria and it seems like she was happy with it.

https://www.researchgate.net/post/Is_there_any_reliable_techniques_for_remote_sensed_data_processing_on_images_obtained_with_low_sun_elevation_angle?_trid=55bf20c25dbbbd44308b463c_

Here is some parts of my answer to her. I hope you find it useful as well:

Here are two publications you may decide to take a look at them. One is about tuning the system to detect certain type of cloud and one may use it to tune it on other things.

The other article is about optimizing NDVI to better fit the requirements.

"Optimizing Cloud Detection Index for MODIS Imagery Using Genetic Algorithm"

https://www.researchgate.net/publication/224944583_Optimizing_Cloud_Detection_Index_for_MODIS_Imagery_Using_Genetic_Algorithm

"NDVI Optimization Using Genetic Algorithm"

https://www.researchgate.net/publication/261194640_NDVI_Optimization_Using_Genetic_Algorithm

Regards

Peyman

Is there any reliable techniques for remote sensed data processing on images obtained with low sun elevation angle? - ResearchGate. Available from: https://www.researchgate.net/post/Is_there_any_reliable_techniques_for_remote_sensed_data_processing_on_images_obtained_with_low_sun_elevation_angle [accessed Aug 3, 2015].

Conference Paper Optimizing Cloud Detection Index for MODIS Imagery Using Gen...

Conference Paper NDVI Optimization Using Genetic Algorithm

Dear Frank,

Thanks a lot for your detailed answer and the links. As a biologist  mainly focused in biodiversity research, I (still) don't have a deep knowledge about remote sensing analysis, so I think the information you provided will be really useful for my training.

Actually I thought to use a stardand analysis previously and/or typically used and apply it to my field interest, but the open doors you let me see with the needed research in RS encourages me to go on.

I hope you won't mind if I come back to your help in a couple of weeks, I'll be checking it.

Thanks a lot, once again.

Dear Peyman,

Thank you so much for your help and the articles. Thanks to your answer and Frank's one I'm also thinking of trying a new one that could adjust to achieve the objective of the research.

I also hope you won't mind if I come back to your help later, I'll be processing all this huge new information I have.

Best regards,

Diego