A large-scale disturbance mapping ensemble through data-driven regionalization

ABSTRACT

Mapping and monitoring disturbances in vegetation over large areas demand reliable approaches and accurate end-user maps. Methods and algorithms have been developed to meet satisfactory disturbance map accuracies, and the combination of multiple approaches has shown promise as a reliable alternative to any single method. However, extracting meaningful disturbance information from these combined methods is still challenging. Data variance from environmental conditions and disturbance drivers leads to spatial-temporal heterogeneity in land surfaces over large areas, which results in mapping errors. We evaluated the effectiveness of ensemble classification and data-driven regionalization for mapping vegetation disturbances at a broad scale. Using Google’s Earth Engine cloud computing platform, our ensemble approach combines multispectral LandTrendr outputs reflecting preliminary disturbance information in a Random Forest model to map disturbances in Minas Gerais, Brazil. We then applied an unsupervised clustering technique to perform data-driven regionalization of our study area using several sources of environmental and anthropogenic information and analysed gains and losses in map accuracies. Our results indicated gains in accuracy by the ensemble method compared to non-ensemble methods of disturbance mapping, which ranged from 7.3 to 29.9% in overall accuracy at the 5% significance level. Data-driven regionalization addressed complexities arising from variability in vegetation types, local climate, and topography across our study area, identifying climate and seasonal metrics as important variables for reducing uncertainties in vegetation disturbance maps. The integration of these techniques has revealed significant potential for increasing map accuracy and has provided important insights into the development of disturbance mapping methods in heterogeneous environments.

KEYWORDS:

• Remote sensing
• Landsat time series
• LandTrendr
• Random Forest

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2023.2225711

Additional information

Funding

This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.