Hi Amjad.  Are you trying to do a change detection between what you observe in 2011 and 2016 RS data? Are you intending to use Landsat as the RS image?  If so, check the list of publications below.

https://www.researchgate.net/search.Search.html?query=change%20detection%20Landsat%20images&type=publication

Thanks alot for sharing the list. No doubt, they are very helpful. Please if possible send the papers using ArcGIS and TerrSet for vegetation analysis and climate change monitoring

 Please read this paper , may be helpfull .

Circumpolar vegetation dynamics product for global change study

Alemu Gonsamo, , Jing M. Chen

Show more

doi:10.1016/j.rse.2016.04.022

To start off, I should say that all models, including vegetation classification is wrong (or at least, can't perfectly represent the real world). It is our jobs as researchers to reduce this error by making the assumptions for the model as reasonable (and correct) as possible. Furthermore, if you are making conclusions regarding change over time, both of your models (e.g., 2011 and 2016) need to be as "correct" as possible, but also they must be developed using the same methods so that the biases innate to the models are identical.

If you are comparing new data to previously conducted classification analysis it is important to duplicate the previous methods as closely as possible. This includes both the initial classification (e.g., maximum likelihood classification) as well as the post classification image (e.g., nibble, boundary cleaning, etc.). So long as you are comparing data generated in the same (or as close-as-possible to the same) way you can then compare "apples to apples", looking at area and other metrics.

If it is not possible to determine the exact methods used in the prior 2011 work I would advise getting the scene(s) from the work done in 2011 as well as your 2016 data and processing both of them. This way, you can be sure that your classifications are at least equally biased by processing.

I hope this helps.

Regards,

Matt

Nice and good points for me to be considered, thanks alot Matthew Bolton

Maybe NDVI can help you ! https://en.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index

And maybe not...

Dear, Amjad

the words "to characterize vegetation" are too broad.

There are so many methods to "characterize vegetation". The best choice for you depend on the exact aims of your study, on the character of the study area (i.e. area size, climate type, relief, presence of water bodies, etc.).

You may use optical or radar imagery. In some cases thermal infrared imagery can be also applied. But what temporal/spatial resolution is accettable? Mapping not always mean classification in the usual sense.

Hi, from my view you doing change detection I hope the list of publications down will be very helpful to you

my best regards

Article Maximum Likelihood for Land-Use/Land-Cover Mapping and Chang...

Article Feature Extraction and Based Pixel Classification for Estima...

Two examples for your consideration are attached. Five years may too short, at least that was the case at our spatial resolution and our forest change rates. Both articles make also the next step, predictive modelling, after change detection. 

Article What Drives Conversion of Tropical Forest in Carrasco Provin...

Article Forecasting the pattern and pace of Fagus forest expansion i...