Focus on Land Use Cover Changes and Environmental Impacts in South/Southeast Asia

Understanding the impacts of land use cover changes (LUCC) on the environment is one of the most important scientific challenges in global change research. LUCC is one of the critical drivers of environmental change in South/Southeast Asia. Several studies suggest that LUCC in these countries is driven by population growth and economic development. In the region, LUCC is manifested in a variety of phenomena such as urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, etc. Documenting the LUCC and associated impacts gain significance as the results can aid improved land management. This editorial provides a summary and discussion of the nine different articles of the Focus issue, focusing on ground-based measurements, remote sensing, and modeling to quantify LUCC impacts. The results will be useful to academics, practitioners, government, and policymakers to quantify and address sustainable LUCC issues in South/Southeast Asian countries. The published papers add significant new science to the existing literature.

Land use/cover change (LUCC) has taken place since the 1990s in central Taiwan; however, its impacts on the local and regional hydroclimatology are not understood thoroughly. This study is grounded in a numerical experiment using the Weather Research and Forecasting (WRF) model and statistical assessments of continuous land cover and gridded precipitation data derived for central Taiwan. We incorporate survey-based land use data in 1995 and 2007 in driving WRF to simulate selective non-rainy and rainy (dry and wet) cases under weak synoptic forcings in July and August (JA). The two land-use conditions reveal changes in simulation fields on account of increased urban and built-up lands. Results averaged over the dry cases show increased (diminished) sensible heat fluxes and 2 m temperatures (latent heat fluxes and 2 m specific humidity) in 2007 compared to that in 1995. The wet-case simulation further identifies intensified precipitation over the downwind areas of urban and built-up lands, strongly subject to local topography and prevailing winds. Statistical assessments of the Landsat land cover and gridded precipitation data verify significant increasing trends in urbanization and the JA rainfall. Regression-based analysis that scales the effect of the LUCC on the change in precipitation corroborates the WRF simulation: LUCC has induced eastward, downwind association with the JA rainfall.

The continuous rise in the global demand for palm oil has resulted in large-scale expansion of industrial oil palm plantations—largely at the expense of primary and secondary forests. The potentially negative environmental impacts of these conversions have given rise to closer scrutiny. However, empirical data on the effects of conversion of forests to industrial oil palm plantations on soil organic carbon (SOC) stocks is scarce and patchy. We evaluated the changes in SOC stocks after conversion of tropical forest into oil palm plantations over the first and second rotation period in Sarawak, Malaysian Borneo. Soil samples were collected from three age classes of oil palm plantations converted from forest (49, 39 and 29 years ago respectively) with three replicate sites and four adjacent primary forest sites as reference. In each site under oil palm, the three management zones, namely weeded circle (WC), frond stacks (FS), and between palm (BP), were sampled separately. All soil samples were collected from five soil layers (0–5, 5–15, 15–30, 30–50 and 50–70 cm). Samples were analysed for SOC concentration, soil bulk density, pH and soil texture. Results showed SOC stocks declined by 42%, 24% and 18% after 29, 39 and 49 years of conversion respectively. Significant differences in SOC stocks were found among different management zones in the oil palm plantations, and the trend was similar for all age classes: FS > WC > BP, demonstrating the necessity of considering within-plantation variability when assessing soil C stocks. The largest differences between SOC stocks of the reference forest and converted plantations were found in the topsoil (0–15 cm depth) but differences were also found in the subsoil (>30 cm). Our results will contribute towards future modelling and life cycle accounting to calculate the carbon debt from the conversion of forest to oil palm plantations.

In order to map potential shifts of rubber (Hevea brasiliensis) cultivation as a consequence of the ongoing climate change in the Greater Mekong Subregion (GMS), we applied rule-based classifications to a selection of nine gridded climatic data projections (precipitation and temperature, and global circulation models (GCMs)). These projections were used to form an ensemble model set covering the representative concentration pathways (RCPs) 4.5 and 8.5 of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change at three future time sections: 2030, 2050 and 2070. We used a post classification ensemble formation technique based on a majority outcome of the classification to not only provide an ensemble projection but also to spatially track and weight the disagreements between the classified GCMs. A similar approach was used to form an ensemble model aggregating the involved climatic factors. The level of agreement between the ensemble projections and GCM products was assessed for each climatic factor separately, and also at the aggregate level. Shifting zones with high confidence were clustered based on their land use composition, physiographic attributes and proximity. Following the same ensemble formation technique and by setting a 28 °C threshold for annual mean temperature, we mapped areas prone to exposure to potentially excessive heat levels. Almost the entire shift projected with high certainty was in the form of expansion, associated with temperature components of climate and temporally limited to the 2030 time window where the total area conducive to rubber cultivation in the GMS is projected to exceed 50% by 2030 (from 44.3% at the turn of the century). The largest detected cluster (41% of the total shifting area), which also is the most ecologically degraded, corresponds to Northern Vietnam and Guangxi Autonomous Region of China. The area exposed to potentially excessive heat is projected to undergo a 25-fold increase under RCP4.5 by 2030 from 14568 km² at the baseline.

In September and October 2015, Equatorial Asia experienced the most intense biomass burning episodes over the past two decades. These events, mostly enhanced by the extremely dry weather associated with the occurrence of strong El Niño conditions, resulted in the transnational transport of hazardous pollutants from the originating sources in Indonesian Borneo and Sumatra to the highly populated Malaysian Peninsula. Quantifying the population exposure form this event is a major challenge, and only two model-based studies have been performed to date, with limited evaluation against measurements. This manuscript presents a new data set of 49 monitoring stations across Peninsular Malaysia and Malaysian Borneo active during the 2015 haze event, and performs the first comparative study of PM₁₀ (particulate matter with diameter < 10 µm) and carbon monoxide (CO) against the output of a state-of-the-art regional model (WRF-Chem). WRF-Chem presents high skills in describing the spatio-temporal patterns of both PM₁₀ and CO and thus was applied to estimate the impact of the 2015 wildfires on population exposure. This study showed that more than 60% of the population living in the highly populated region of the Greater Klang Valley was systematically exposed to unhealthy/hazardous air quality conditions associated with the increased pollutant concentrations from wildfires and that almost 40% of the Malaysian population was on average exposed to PM₁₀ concentrations higher than 100 µg m⁻³ during September and October 2015.

Forest and land fires in Riau province of Sumatera increase along with the rapid deforestation, land clearing, and are induced by dry climate. Forest and land fires, which occur routinely every year, cause trans-boundary air pollution up to Singapore. Economic losses were felt by Indonesia and Singapore as the affected country thus creates tensions among neighboring countries. A high concentration of aerosols are emitted from fire which degrade the local air quality and reduce visibility. This study aimed to analyze the impact of the June 2013 smoke haze event on the environment and air quality both in Riau and Singapore as well as to characterize the aerosol properties in Singapore during the fire period. Air quality parameters combine with aerosols from Aerosol Robotic Network (AERONET) data and some environmental parameters, i.e. rainfall, visibility, and hotspot numbers are investigated. There are significant relationships between aerosol and environmental parameters both in Riau and Singapore. From Hysplit modeling and a day lag correlation, smoke haze in Singapore is traced back to fire locations in Riau province after propagated one day. Aerosol characterization through aerosol optical depth (AOD), Ångstrom parameter and particle size distribution indicate the presence of fine aerosols in a great number in Singapore, which is characteristic of biomass burning aerosols. Fire and smoke haze even impaired economic activity both in Riau and Singapore, thus leaving some accounted economic losses as reported by some agencies.

In this study, an approach in determining effective mixing weight of soot aggregates from dust–soot aerosols is proposed to improve the accuracy of retrieving properties of polluted dusts by means of satellite remote sensing. Based on a pre-computed database containing several variables (such as wavelength, refractive index, soot mixing weight, surface reflectivity, observation geometries and aerosol optical depth (AOD)), the fan-shaped look-up tables can be drawn out accordingly for determining the mixing weights, AOD and single scattering albedo (SSA) of polluted dusts simultaneously with auxiliary regional dust properties and surface reflectivity. To validate the performance of the approach in this study, 6 cases study of polluted dusts (dust–soot aerosols) in Lower Egypt and Israel were examined with the ground-based measurements through AErosol RObotic NETwork (AERONET). The results show that the mean absolute differences could be reduced from 32.95% to 6.56% in AOD and from 2.67% to 0.83% in SSA retrievals for MODIS aerosol products when referenced to AERONET measurements, demonstrating the soundness of the proposed approach under different levels of dust loading, mixing weight and surface reflectivity. Furthermore, the developed algorithm is capable of providing the spatial distribution of the mixing weights and removing the requirement to assume that the dust plume properties are uniform. The case study further shows the spatially variant dust–soot mixing weight would improve the retrieval accuracy in AOD_mixture and SSA_mixture about 10.0% and 1.4% respectively.

Biomass burning plays a critical role not only in atmospheric emissions, but also in the deposition and redistribution of biologically important nutrients within tropical landscapes. We quantified the influence of fire on biogeochemical fluxes of nitrogen (N), phosphorus (P), and sulfur (S) in a 12 ha forested peatland in West Kalimantan, Indonesia. Total (inorganic + organic) N, ${{{\rm{NO}}}_{3}}^{-}$ –N, ${{{\rm{NH}}}_{4}}^{+}$ –N, total P, ${{{\rm{PO}}}_{4}}^{3-}$ –P, and ${{{\rm{SO}}}_{4}}^{2-}$ –S fluxes were measured in throughfall and bulk rainfall weekly from July 2013 to September 2014. To identify fire events, we used concentrations of particulate matter (PM₁₀) and MODIS Active Fire Product counts within 20 and 100 km radius buffers surrounding the site. Dominant sources of throughfall nutrient deposition were explored using cluster and back-trajectory analysis. Our findings show that this Bornean peatland receives some of the highest P (7.9 kg ${{{\rm{PO}}}_{4}}^{3-}$ –P ha⁻¹yr⁻¹) and S (42 kg ${{{\rm{SO}}}_{4}}^{2-}$ –S ha⁻¹yr⁻¹) deposition reported globally, and that N deposition (8.7 kg inorganic N ha⁻¹yr⁻¹) exceeds critical load limits suggested for tropical forests. Six major dry periods and associated fire events occurred during the study. Seventy-eight percent of fires within 20 km and 40% within 100 km of the site were detected within oil palm plantation leases (industrial agriculture) on peatlands. These fires had a disproportionate impact on below-canopy nutrient fluxes. Post-fire throughfall events contributed >30% of the total inorganic N (${{{\rm{NO}}}_{3}}^{-}$ –N + ${{{\rm{NH}}}_{4}}^{+}$ –N) and ${{{\rm{PO}}}_{4}}^{3-}$ –P flux to peatland soils during the study period. Our results indicate that biomass burning associated with agricultural peat fires is a major source of N, P, and S in throughfall and could rival industrial pollution as an input to these systems during major fire years. Given the sheer magnitude of fluxes reported here, fire-related redistribution of nutrients may have significant fertilizing or acidifying effects on a diversity of nutrient-limited ecosystems.

Accomplishing the objective of the current climate policies will require establishing carbon budget and flux estimates in each region and county of the globe by comparing and reconciling multiple estimates including the observations and the results of top-down atmospheric carbon dioxide (CO₂) inversions and bottom-up dynamic global vegetation models. With this in view, this study synthesizes the carbon source/sink due to net ecosystem productivity (NEP), land cover land use change (E_LUC), fires and fossil burning (E_FIRE) for the South Asia (SA), Southeast Asia (SEA) and South and Southeast Asia (SSEA = SA + SEA) and each country in these regions using the multiple top-down and bottom-up modeling results. The terrestrial net biome productivity (NBP = NEP – E_LUC – E_FIRE) calculated based on bottom-up models in combination with E_FIRE based on GFED4s data show net carbon sinks of 217 ± 147, 10 ± 55, and 227 ± 279 TgC yr⁻¹ for SA, SEA, and SSEA. The top-down models estimated NBP net carbon sinks were 20 ± 170, 4 ± 90 and 24 ± 180 TgC yr⁻¹. In comparison, regional emissions from the combustion of fossil fuels were 495, 275, and 770 TgC yr⁻¹, which are many times higher than the NBP sink estimates, suggesting that the contribution of the fossil fuel emissions to the carbon budget of SSEA results in a significant net carbon source during the 2000s. When considering both NBP and fossil fuel emissions for the individual countries within the regions, Bhutan and Laos were net carbon sinks and rest of the countries were net carbon source during the 2000s. The relative contributions of each of the fluxes (NBP, NEP, E_LUC, and E_FIRE, fossil fuel emissions) to a nation's net carbon flux varied greatly from country to country, suggesting a heterogeneous dominant carbon fluxes on the country-level throughout SSEA.

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg m⁻² (±0.033), 0.61 kg m⁻² (±0.076), and 0.88 kg m⁻² (±0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM_2.5, 36.54 Gg CO and 567.79 Gg CO₂ for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM_2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM_2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM_2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning.

Fires in Indonesia release excessive carbon and are exacerbated during drier El Niño years. The recent 2015 fires were affected by an extended drought caused by a strong El Niño event. This led to severe haze conditions across Southeast Asia, resulting in adverse socioeconomic and health impacts. Here, we evaluate the social and environmental factors that contributed to the 2015 extreme fires in Riau, Jambi and South Sumatra. We developed proxy variables for plausible drivers of fire which contribute either as a predisposing condition or as an ignition source for fires. We evaluated how these variables influenced fire count at an administrative regency-level and fire occurrence at a pixel-level (1 km²). We used generalized linear mixed effect models to model fire count at the regency-level and boosted regression trees to model fire occurrence at the pixel-level. Rainfall, slope and population density were the most important variables predicting fires at both levels. Economic variables such as the proportion of small-scale (<10 ha) and medium-scale (10–100 ha) plantation landholdings, and the reported use of fires to clear agricultural lands in villages were important in explaining fire count at the regency-level. At the pixel-level, distance from roads and the number of recorded burns over peatlands were important in explaining fire occurrence. The main influence of rain on fires corroborates with previous studies, and highlights the importance of establishing an early warning system for droughts to better prevent and manage future extreme fire events. Mitigation efforts for future fires, especially during El Niño years, can focus on identifying high-risk areas using environmental data on rainfall, slope, peatlands, and previously burnt peat areas, as well as social data related to population density, access to roads, extents of small- and medium-plantation landholdings, and village-level propensity to burn land for agriculture.