Hello everyone,
Does anybody know how to estimate defoliated forest stands using Landsat imagery?
As you know, NDVI may have a relationship with leaf area index (LAI). But, how about woody/defoliated area index? If somebody wants to estimate woody area index (for example in winter and for deciduous forests) what steps should be taken?
Thank you very much,
Regards,
Omid,
To distinguish dead trees from defoliated forests maybe an interannual comparison between summer seasons would work. On the other hand, during the winter maybe the thermal bands can help you recognize the woody areas. But I am not sure.
Good luck!
There is a lot of good literature out there on this, I suggest you look at some of these
http://scholar.google.ca/scholar?hl=en&q=landsat+defoliation&btnG=&as_sdt=1%2C5&as_sdtp=
For example...
Townsend, P. A., Singh, A., Foster, J. R., Rehberg, N. J., Kingdon, C. C., Eshleman, K. N., & Seagle, S. W. (2012). A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sensing of Environment, 119, 255-265.
Abstract
Defoliation by insect herbivores can be a persistent disturbance affecting ecosystem functioning. We developed an approach to map canopy defoliation due to gypsy moth based on site differences in Landsat vegetation index values between non-defoliation and defoliation dates. Using field data from two study areas in the U.S. central Appalachians and five different years (2000, 2001, 2006, 2007, and 2008), we fit a sigmoidal model predicting defoliation as a function of the difference in the vegetation index. We found that the normalized difference infrared index (NDII, [Band 4 − Band 5] / [Band 4 + Band 5]) and the moisture stress index (Band 5 / Band 4) worked better than visible-near infrared indices such as NDVI for mapping defoliation. We report a global 2-term fixed-effects model using all years that was at least as good as a mixed-effects model that varied the model coefficients by year. The final model was: proportion of foliage retained = 1 / (1 + exp(3.057 − 31.483 ∗ [NDIIbaseyear − NDIIdisturbanceyear]). Cross-validation by dropping each year of data and subsequently refitting the remaining data generated an RMS error estimate of 14.9% defoliation, a mean absolute error of 10.8% and a cross-validation R2 of 0.805. The results show that a robust, general model of percent defoliation can be developed to make continuous rather than categorical maps of defoliation across years and study sites based on field data collected using different sampling methods.
I would also suggest to compare summer/winter images as well as scenes from different years. If there is no Landsat scene for comparison sometimes google-earth images are by now often good enough for a rough comparison (of course without being able to compare different bands).
NDVI approach should work. For a defoliated stand you should expect NDVI smaller than 0.5 but you need to do some ground truthing.
All,
Thank you very much for the comments, and special thanks to " Steven L. Van Wilgenburg".
Md Abdul Halim: Thank you for the comments, but, the problem is that finding pure dead forest stands with a size of at least 30 m*30 m is very difficult in Zagros region as dead trees have been individually distributed.
Thanks,
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