Greenhouse gas emissions have caused a substantial increase in the global average temperature. A recent calculation from Intergovernmental Panel on Climate Change (IPCC) revealed that the global temperature increased 0.85 °C (0.65–1.06 °C) from 1880 to 2012 and projected an increase of 0.3–0.7 °C for the period 2016–2035 [1
]. The current warming has triggered an increased frequency of extreme climate events such as warm nights and days, heat waves, heavy rainfalls, and droughts [1
]. In addition, the greenhouse gas-induced warming could potentially change the magnitude, frequency or characteristics of the El Nino Southern Oscillation (ENSO) [3
], 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
] during the warmer phase of ENSO (El Nino). Adequate identification, 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.
Indonesia is susceptible to the climate anomalies of ENSO due to its geographical location at the east side of the Pacific Ocean. Vegetation dynamics and associated biophysical properties are dependent on climate conditions. Therefore, changes in vegetation properties, such as phenology and declines in cover would be highly affected by ENSO anomalies [10
]. Many previous studies have identified the impact of ENSO in Indonesia on crop production [11
]. Previous studies of the ENSO impact on vegetation in Indonesia were benefited from the available long-term dataset of the Normalized Difference Vegetation Index (NDVI) collected from Advanced Very High-Resolution Radiometer (AVHRR) observations as the primary dataset [14
]. However, a problem remains with utilization of this specific index. With the development of remote sensing technologies, other vegetation indices and proxies have become available for use in vegetation monitoring. To fully understand the problem and to enable optimal application for climate and vegetation impact analysis, further exploration and comparisons involving multiple data are required.
Impact assessments using remote sensing are mainly carried out using NDVI as the proxy for photosynthetic activities and greenness levels [16
]. The simplicity of the NDVI calculation by red and near-infrared bands allows extending the data archive from the earliest era of remote sensing observations in the early 1980s, making it useful for long-term time series analysis. Early remote sensing sensors such as the AVHRR satellite, which was equipped with near-infrared and red bands, can provide long-term NDVI observations. Global Inventory Modeling and Mapping Studies (GIMMS), an AVHRR-based NDVI product [17
] has been widely employed for mapping vegetation dynamics in various regions [18
]. The major problems with the NDVI datasets are that measurement is profoundly affected by various factors such as sun-sensor geometry and saturation over high biomass areas [21
], impairing the reliability of NDVI observations, especially in high biomass regions where the value is saturated. With the development of remote sensing sensors and technologies, other vegetation proxies have become available and can be utilized to monitor vegetation dynamics.
The Enhanced Vegetation Index (EVI) is one of the alternative indices that offers a wider dynamic range with higher reliability for areas with high density vegetation [23
]. EVI calculation originally required a blue band in addition to red and near-infrared bands, hence, hindering the ability to be generated from the available long-term archives. However, the development of EVI2-a, which calculated from red and near-infrared bands, enabled the provision of long-term time series EVI data [24
]. Although both EVI and NDVI represent vegetation proxies, EVI is more sensitive to canopy structural variation due to its wide dynamic range of output values compared to NDVI, while NDVI indicates chlorophyll abundance based on the inclusion of the red band in the calculation [25
]. Therefore, the combination of these two observations linked with climate data can be used to comprehensively assess climate-related structural changes and photosynthetic activity of vegetation over time.
Development of a passive microwave remote sensing system also enabled the estimation of aboveground vegetation water content in the Vegetation Optical Depth (VOD) indicator developed by Owe et al. [26
]. The long wavelength microwave system used for VOD provides water content-sensitive information in leaf and tree structures with less interference due to clouds due to its penetration ability and also enhances the dynamic range of values over dense canopies [27
]. The development of long-term data is possible due to the strong correlation between VOD information collected by different passive microwave systems as demonstrated by Liu et al. [28
], which harmonized and extended the observation into July 1987. However, the drawback of low microwave emission observations is the coarse spatial resolution (25 km) of the data. Nevertheless, the distinctness of VOD observations for collecting biomass water content data can provide a different perspective mainly in vegetation dynamics monitoring when it is used to identify vegetation regions that are sensitive to ENSO and rainfall.
This study aims to identify ecosystems that are sensitive to ENSO and rainfall by employing Granger causality to various time series RS-based vegetation proxies (RS proxies), such as NDVI, EVI, and VOD over the period from 1993 to 2012. Further analysis is conducted to reveal the responses of vegetation during ENSO by examining the progression of cross-correlation coefficients between the Multivariate ENSO Index (MEI), rainfall, and RS proxies.
2. Study Area
Indonesia is an archipelagic country located in the South-East Asian region that is comprised of the five main islands of Sumatera, Java, Kalimantan, Sulawesi, and Papua. It shares borders with Malaysia (on Kalimantan Island), Papua New Guinea (on Papua Island), and Timor Leste (along East Nusa Tenggara Province). The country is situated between the Indian Ocean and Pacific Ocean, making it susceptible to the interannual oscillations of ENSO that affect rainfall variability and crop production [12
The vegetation cover in Indonesia is mainly dominated by forestland which represent 2% of the global forest area [30
]. There are three main classes of the forest types in Indonesia i.e., Evergreen Needleleaf Forest (ENF), Evergreen Broadleaved Forest (EBF) and Remnant Forest (IFL) (Figure 1
) that dominated the land cover in the island of Sumatera, Kalimantan, Sulawesi, and Papua. More complex land use and land cover can be found at Java Island as the most populated area so that most of the settlement area is centralized at this island (excluded from Figure 1
). In addition to settlement area, croplands (CRO), and arid-typical biome such as savanna (SAV) can also be found in the eastern part of Java Island. Another savanna cover is located at the arid environment of Indonesia such as in East Nusa Tenggara.
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 Kalimantan Island, the islands in Nusa Tenggara, and the southern Part of Papua to be ENSO- and rainfall-sensitive areas. This finding corroborates those of previous studies showing these regions to be the two climate regions sensitive to rainfall with seasonality heavily influenced by ENSO, which affects the area until the latter months of the year [53
]. The union of ENSO-sensitive areas from multi-sensor remote sensing data also corresponds with the same analysis in Erasmi et al. [15
], although the intersection of sensitive regions yielded different spatial patterns for ENSO- and rainfall-sensitive vegetation cover types. Eastern part of Java is one of the hotspot areas where most of the data agreed by more than three RS data which identified this region as the ENSO and rainfall sensitive region. The identification of East Java as an identified region is in line with a specific local study that identified this area for being massively affected by climate extremes and having experienced decreases in rainfall and prolonged droughts during ENSO [55
Identification of sensitive regions by combining multiple remote sensing datasets revealed a high similarity between NDVI3g and VOD measurements, while EVI detected fewer areas compared with previous datasets. The high agreement between NDVI3g and VOD is supported by a study showing that NDVI represents the vegetation and moisture condition [56
] which is similar to the findings presented in VOD. On the other hand, according to Pettorelli et al. [25
], EVI is more sensitive to structural change in vegetation canopy due to its wider dynamic range over NDVI-saturated areas. The combination of VOD, NDVI, and EVI led to identification of savanna as the vegetation type most influenced by ENSO and rainfall in Indonesia because drastic structural changes are observed in response to different climate conditions.
Savanna in Indonesia is a treeless biome in arid climate and covered with grass (Figure 10
). The stability of this biome depends on rainfall and fire cycles [57
]. The ENSO warm and cold phases offer a cycle of drought and intensified rainfall that controls the savanna dynamics, making this biome the most appropriate ecosystem to examine for a response to the ENSO effect. The intensified warm ENSOs with prolonged drought and increased temperature can triggered more frequent fire thus further reducing the tree density in savanna and impacting the ecosystem services and the biodiversity. In addition, similar reductions of tree density due to drought and fire can be expected from the shifting of tropical forest into savanna-like ecosystems where it may difficult to restore. The fact that some evergreen broadleaf forests in Sumatera were identified as an ecosystem that is sensitive to ENSO can also be attributed to the possible historical ENSO-related forest fires that are widely suspected to enhance the temporal shift of forest characteristics into a savanna-like environment [61
]. The ENSO- and rainfall-sensitivity in this study can be used to identify areas that require proper mitigation and for anticipating and possibly avoiding unrecoverable transformation of ecosystems.
The temporal graphics of CCF among the different vegetation cover types exposed the progress of how MEI-ENSO affects vegetation, especially in the ENSO- and rainfall-sensitive areas. MEI (ENSO) first affected rainfall at lag 0 months, as indicated with strong negative correlation and that ENSO-induced changes in rainfall affected vegetation (indicated by positive correlation) at around 5–6 months later in the graphs. The persistent effect up to four months after MEI to rainfall in the CCF results is similar to that in Hendon [62
]. It was also shown that stronger correlations were found in CCF between rainfall and RS proxies compared to CCF between MEI and RS proxies.
An in-depth look at the time lag spatial distribution of significant correlations revealed the influence of land cover and terrain characteristics. Figure 11
showed the lag distribution of negative correlations between rainfall to RS proxies and MEI ENSO to RS proxies with shaded relief by SRTM data in the background. The control of terrain configurations can be seen especially at the time lag between NDVI3g to rainfall. Topography differences controlled the distribution of the timing of lags and created a distinct spatial pattern of the lag time when strong negative correlation occurred. In NDVI3g lag to rainfall (Figure 11
), an area at high elevation experienced an earlier strong, negative correlation compared to a lowland area. The relationship between terrain characteristics and rainfall intensities during the ENSO period in Java Island have been discussed previously [63
]. Therefore, further studies of the effects of topography are needed to fully understand the vegetation and terrain relationships that control the impact of ENSO.