Resumen

TECUAPETLA-GOMEZ, Inder; CARBAJAL-DOMINGUEZ, Alfonso  y  MONTESINOS-CHICA, Valeria. Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020. Invest. Geog [online]. 2022, n.109, e60629.  Epub 29-Mayo-2023. ISSN 2448-7279.  https://doi.org/10.14350/rig.60629.

The Yucatan Peninsula (YP) is home to 32% of tropical forests of Mexico. Consequently, this area has a high cloudiness throughout the year, which represents a particular challenge for any mid- and long-term plant monitoring study based on satellite-image time series. This paper reports the results of a trend classification analysis of a time series (11 Landsat-7 ETM+ and 150 Landsat 8 OLI images) of the Normalized Difference Vegetation Index (NDVI) by soil and vegetation types in the eastern region of Escarcega, Campeche (YP) from 2014 to 2020.

We applied the bfast01 algorithm to classify pixels according to linear trends, either global (a line with a positive or negative slope through the study period) or local (two linear segments, each with a positive or negative slope). The analysis reveals that most of the study region has NDVI values with global linear trends (browning: 47%; greening: 15.39%) and, to a lesser degree, local linear trends (delayed browning: 20.66%; browning to greening: 6.04%; delayed greening: 5.26%; greening to browning: 3.88%)

We consider that generalized greening (which pools the greening, delayed greening, and browning to greening classes) and generalized browning (which pools the browning, delayed browning, and greening to browning classes) can be interpreted as dynamics with significant signs of recovery and degradation of the NDVI, respectively. These dynamics were identified mainly in the semi-evergreen medium tropical forest (generalized greening: 10.26%; generalized browning: 25.43%), semi-evergreen low thorny tropical forest (7.66 and 21.76) and the secondary tree vegetation of the medium tropical forest (3.26 and 10.93). The largest areas with any kind of linear local trend were identified in 2017 and 2018.

Palabras llave : NVDI; trend analysis; time series; gapfill; abrupts estimation.