Abstract

HURTADO-ABRIL, José Leonardo  and  LIZARAZO, Ivan. New Spectro-temporal index for the detection of forest loss in tropical forest areas. Colombian Amazon Case Study. Rev. cartogr. [online]. 2022, n.104, pp.11-35.  Epub Feb 28, 2022. ISSN 2663-3981.  https://doi.org/10.35424/rcarto.i104.1096.

Currently, one of the most serious problems at a global level is the accelerated rate of forest loss and the effects that have an impact on the delicate stability of the environment. Colombia has one of the largest extensions of natural forest on the planet, as well as a high rate of deforestation. There are several tools and methodologies for detecting loss of forest taking as input satellite images and products derived from them. However, at the time of starting the investigation, the identification of forest loss was carried out using two or more indices or spectral transformations as well as manual editing, which leads to longer time and propagation of errors. The present study aims to generate a spectral and temporal index with the ability to extract deforested areas in a range of time and area of interest defined by the user through Landsat satellite images. For this, a methodology based on the time series analysis using LandTrendr tool to identify spectral ranges that detect disturbance of forest and then propose an equation for calculating a disturbance indicator deforestation develops. The usefulness of the proposed index is tested in some areas of the Colombian Amazon in which there are official deforestation data for the period 2000 to 2017. The results of the tests show that the proposed index allows obtaining a thematic accuracy greater than 80%. The spectral index not only identifies the loss of the forest but its possible regeneration, which would allow obtaining the figures of various changes in the forest. Finally, the index can identify areas of disturbance in the forest with good results in its thematic accuracy.

Keywords : deforestation; LandTrendr; Spectro-temporal index; time series; forest.

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