Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the potential of BFAST for monitoring burned areas using
  multi-temporal Landsat-7 images   In this paper, we propose a semi-automatic approach to map burned areas and
assess burn severity that does not require prior knowledge of the fire date.
First, we apply BFAST to NDVI time series and estimate statistically abrupt
changes in NDVI trends. These estimated changes are then used as plausible fire
dates to calculate dNBR following a typical pre-post fire assessment. In
addition to its statistical guarantees, this method depends only on a tuning
parameter (the bandwidth of the test statistic for changes). This method was
applied to Landsat-7 images taken over La Primavera Flora and Fauna Protection
Area, in Jalisco, Mexico, from 2003 to 2016. We evaluated BFAST's ability to
estimate vegetation changes based on time series with significant observation
gaps. We discussed burn severity maps associated with massive wildfires (2005
and 2012) and another with smaller dimensions (2008) that might have been
excluded from official records. We validated our 2012 burned area map against a
high resolution burned area map obtained from RapidEye images; in zones with
moderate data quality, the overall accuracy of our map is 92%.

33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
1
The categories of this paper are: cs
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph