ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data
1
Earth and Environmental Systems Laboratory, Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan
2
Remote Sensing Laboratory, Geographic Information Science Department, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(3), 103; https://doi.org/10.3390/ijgi7030103
Received: 14 December 2017 / Revised: 23 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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.
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Keywords:
ENSO; rainfall; remote sensing; vegetation dynamics; Granger test
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Arjasakusuma, S.; Yamaguchi, Y.; Hirano, Y.; Zhou, X. ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data. ISPRS Int. J. Geo-Inf. 2018, 7, 103.
AMA Style
Arjasakusuma S, Yamaguchi Y, Hirano Y, Zhou X. ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data. ISPRS International Journal of Geo-Information. 2018; 7(3):103.
Chicago/Turabian StyleArjasakusuma, Sanjiwana; Yamaguchi, Yasushi; Hirano, Yasuhiro; Zhou, Xiang. 2018. "ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data" ISPRS Int. J. Geo-Inf. 7, no. 3: 103.
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