Multitemporal distribution analysis of the Dodonaea viscosa (L. Jacq.) by remote sensing in Durango, Mexico

Abstract

Objective: To determine the distribution of D. viscosa at the Guadalupe Victoria dam for the years 1990, 2010 and 2017.

Design/methodology/approach: Landsat satellite images were processed in order to perform a supervised classification using an artificial neural network. The ground cover of pastures, crops, shrubs and oak forest was estimated from images for the years 1990, 2010 and 2017 at sites where the presence of D. viscosa had been recorded. These data were used to calculate the expansion of D. viscosa in the study area.

Results: Limitations on study/implications: The supervised classification of the artificial neural network was optimal after 400 iterations, obtaining the best overall precision, 84.5%, for 2017. This contrasted with 1990, where overall precision was low, at 45%, because there were few training sites (fewer than 100) recorded for each of the land cover classes.

Findings/conclusions: In 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, the potential distribution projected for D. viscosa shows it invading half the study area, occupying agricultural, forested and scrub areas.