NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots

ABSTRACT

Land degradation in semi-arid natural environments is usually associated with climate vulnerability and anthropic pressure, leading to devastating social, economic and environmental impacts. In this sense, remotely sensed vegetation parameters, such as the Normalized Difference Vegetation Index (NDVI), are widely used in the monitoring and forecasting of vegetation patterns in regions at risk of desertification. Therefore, the objective of this study was to model NDVI time series at six desertification hotspots in the Brazilian semi-arid region and to verify the applicability of such models in forecasting vegetation dynamics. We used NDVI data obtained from the MOD13A2 product of the Moderate Resolution Imaging Spectroradiometer sensor, comprising 16-day composites time series of mean NDVI and NDVI variance for each hotspot during the 2000–2018 period. We also used rainfall measured by weather stations as an explanatory variable in some of the tested models. Firstly, we compared Holt-Winters with Box-Jenkins and Box-Jenkins-Tiao (BJT) models. In all hotspots the Box-Jenkins and BJT models performed slightly better than Holt-Winters models. Overall, model performance did not improve with the inclusion of rainfall as an exogenous explanatory variable. Mean NDVI series were modelled with a correlation of up to 0.94 and a minimum mean absolute percentage error of 5.1%. NDVI variance models performed slightly worse, with a correlation of up to 0.82 and a minimum mean absolute percentage error of 22.0%. After the selection of the best models, we combined mean NDVI and NDVI variance models in order to forecast mean-variance plots that represent vegetation state dynamics. The combined models performed better in representing dry and degraded vegetation states if compared to robust and heterogeneous vegetation during wet periods. The forecasts for one seasonal period ahead were satisfactory, indicating that such models could be used as tools for the monitoring of short-term vegetation states.

Acknowledgements

The authors are thankful to the Coordination for the Improvement of Higher Education Personnel (CAPES) for the scholarship granted to the first author (No 1765923) and to the National Council for Scientific and Technological Development (CNPq) for the research productivity grant of the second author (No 309165/2010-5). The authors are also thankful to the USGS for providing the MODIS NDVI data and to the INMET and FUNCEME for providing rainfall data. Finally, the authors would like to thank the two anonymous reviewers whose invaluable comments helped improve the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ [309165/2010-5]; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES [1765923].