ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data

Sanjiwana Arjasakusuma,Yasuhiro Hirano,Xiang Zhou,Yasushi Yamaguchi

doi:10.3390/ijgi7030103

Abstract

Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming years. Adequate mapping of regions with climate-sensitive vegetation and its associated time lag is required for appropriate mitigation planning to avoid potential negative ecological impacts towards vegetation. In this study, ENSO and climate indicator time series data, for example, Multivariate ENSO Index (MEI) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data for rainfall were linked with long-term time series vegetation proxies from remote sensing (RS proxies). ENSO- and rainfall-sensitive areas were identified from each RS proxy using the bivariate Granger test, and the areas identified by multiple RS proxies were taken to identify climate-sensitive regions in Indonesia. Of the biome types in Indonesia, savanna was the most sensitive, with approximately 53% of the total savanna area in Indonesia shown to be sensitive to ENSO and rainfall by two or more RS proxies. Rolling correlation analysis also found that the ENSO effect on the vegetation region after rainfall was positively correlated with the RS proxies with a time lag of +5 months. Therefore, rainfall can be taken as a proxy of the effects of ENSO on the temporal dynamics of sensitive vegetation regions in Indonesia.

Highlights

Greenhouse gas emissions have caused a substantial increase in the global average temperature
The regions sensitive to El Nino Southern Oscillation (ENSO) and rainfall were identified using the Granger test compiled in the Lmtest R package [50], with the assumption that the fluctuation in the time series remote sensing values could be predicted by past time series values of ENSO indices and rainfall intensity with a certain lag
The spatial distribution of sensitive areas obtained from multi-sensor remote sensing approaches showed the southern part of Indonesia, including some parts of Sumatera, Java Island, the southern part of KalimISaPnRtSaInnt.IJ.sGlaeon-Idnf,. 2t0h18e, 7i,sxlaFnORdsPEiEnRNREuVsIEaWTenggara, and the southern Part of Papua to be14EoNf 19SO- and rainfall-5s.eDnsisitciuvsesiaorneas

Summary

Introduction

Greenhouse gas emissions have caused a substantial increase in the global average temperature. The greenhouse gas-induced warming could potentially change the magnitude, frequency or characteristics of the El Nino Southern Oscillation (ENSO) [3,4], causing an amplified alteration of rainfall intensity, temperature, and air pressure. In a major ENSO event, such as in the 1997/1998 ENSO, it decreased the rainfall intensity and delayed the wet season [5]. Such impacts may cause socioeconomic effects, such as the decline of health quality [6] and ecological impacts, such as forest fires [7] and tree mortality [8,9] during the warmer phase of ENSO (El Nino). Assessment, and monitoring efforts are required to formulate proper mitigation actions in order to avoid the adverse effects of an intensified ENSO and to prevent any further negative ecological and social economics impacts

Objectives

Methods

Results

Conclusion