Search Results (10)

Rice fields and cultivation activities are sources of greenhouse gas (GHG) emissions. Therefore, quantification of the baseline emissions is necessary to discover and implement the appropriate mitigation options for the transition to low-carbon rice production in order to achieve the Sustainable Development Goals (SDGs) by 2030. This study aimed to track and estimate the baseline GHG emissions and the carbon footprint (C-footprint) from rice cultivation in three farmer communities in Thailand. The SMART GHG mobile application (SGA) was used to calculate the GHG emissions from many cultivation activities and calculate the C-footprint of paddy rice production. The field activity data were collected from 71 farmer households with 134 ha of harvested areas in Muang Chang (MJ) sub-district of Nan province, Suan Taeng (ST) sub-district of Suphan Buri province, and Na Kham (NK) sub-district of Nakorn Phanom province. The results from SGA showed that the total GHG emissions of MJ, ST and NK communities accounted for 7.5, 6.3 and 2.9 tCO₂e ha⁻¹, respectively. The mean of total GHG emissions from all communities accounted for 5.6 tCO₂e ha⁻¹. During the rice growing period with flooded fields, the emission of CH₄ contributed to 83.4% of the total GHG emissions. Whereas, the cultivation activities of fertilization, field preparation, harvest, and residue burning shared small emissions of 5.4, 4.4, 3.8 and 2.0%, respectively. The SGA also reported the C-footprint of paddy yields by 1.77, 1.10 and 1.09 kgCO₂e kg yield⁻¹ in MJ, NK and ST, respectively. This study indicated that the SGA can effectively demonstrate and track the GHG emissions and C-footprint, which can be developed into a baseline emission and mitigation for low-carbon rice production. This is a challenge for agriculture and rural community development in Thailand. Full article

Rice is an important economic crop in Thailand. However, paddy rice fields are one of the largest anthropogenic sources of methane (CH₄) emissions. Therefore, suitable crop management practice is necessary to reduce CH₄ emissions while rice grain yield is maintained. This study aimed to evaluate appropriate options of fertilizer and water management practices for Thai rice cultivation with regards to improving rice grain yield and reducing CH₄ emissions. The Denitrification–Decomposition (DNDC) model was used to simulate grain yield and the emission of CH₄ under the three fertilizer options (chemical fertilizer (F), manure (M) and chemical fertilizer + manure (F + M)) with three water management options (continuous flooding (CF), mid-season drainage (MD) and alternate wet and dry (AWD)) during the years 2011–2050. Rain-fed and irrigated rice cropping systems were used. A total of 24 sites distributed in 22 provinces were studied. The data sets of daily climate, soil properties, and rice management practices were required as inputs in the model. Model validation with observation data in a field experiment indicated that simulated grain yields (R² = 0.83, slope = 0.98, NRMES = 0.30) and cumulative seasonal CH₄ emissions (R² = 0.83, slope = 0.74, NRMES = 0.43) were significantly and positively correlated with the observation. At the end of the simulation period (2046–2050), fertilizer management options of F and F + M gave more grain yield than the M management option by 1–44% in rain-fed rice cropping and 104–190% in irrigated rice cropping system, respectively. Among options, the lower CH₄ emissions were found in AWD water management options. The appropriate options with regard to maintaining grain yield and reducing CH₄ emissions in the long term were suggested to be F + M with AWD for the rain-fed rice, and F with AWD for the irrigated rice cropping systems. Full article

Land use land cover (LULC) change is one of the main drivers contributing to global climate change. It alters surface hydrology and energy balance between the land surface and atmosphere. However, its impacts on surface air temperature have not been well understood in a dynamic region of LULC changes like Southeast Asia (SEA). This study quantitatively examined the contribution of LULC changes to temperature trends in Myanmar and Thailand as the typical parts of SEA during 1990–2019 using the “observation minus reanalysis” (OMR) method. Overall, the average maximum, mean, and minimum temperatures obtained from OMR trends indicate significant warming trends of 0.17 °C/10a, 0.20 °C/10a, and 0.42 °C/10a, respectively. The rates of minimum temperature increase were larger than maximum and mean temperatures. The decreases of forest land and cropland, and the expansions of settlements land fractions were strongly correlated with the observed warming trends. It was found that the effects of forest land converted to settlement land on warming were higher than forest conversion to cropland. A comprehensive discussion on this study could provide scientific information for the future development of more sustainable land use planning to mitigate and adapt to climate change at the local and national levels. Full article

The Himalayas, especially the Everest region, are highly sensitive to climate change. Although there are research works on this region related to cryospheric work, the ecological understandings of the alpine zone and climate impacts are limited. This study aimed to assess the changes in surface water including glacier lake and streamflow and the spatial and temporal changes in alpine vegetation and examine their relationships with climatic factors (temperature and precipitation) during 1995–2019 in the Everest region and the Dudh Koshi river basin. In this study, Landsat time-series data, European Commission’s Joint Research Center (JRC) surface water data, ECMWF Reanalysis 5th Generation (ERA5) reanalysis temperature data, and meteorological station data were used. It was found that the glacial lake area and volume are expanding at the rates of 0.0676 and 0.0198 km³/year, respectively; the average annual streamflow is decreasing at the rate of 2.73 m³/s/year. Similarly, the alpine vegetation greening as indicated by normalized difference vegetation index (NDVI) is increasing at the rate of 0.00352 units/year. On the other hand, the annual mean temperature shows an increasing trend of 0.0329 °C/year, and the annual precipitation also shows a significant negative monotonic trend. It was also found that annual NDVI is significantly correlated with annual temperature. Likewise, the glacial lake area expansion is strongly correlated with annual minimum temperature and annual precipitation. Overall, we found a significant alteration in the alpine ecosystem of the Everest region that could impact on the water–energy–food nexus of the Dudh Koshi river basin. Full article

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions. Full article

Secondary forest areas are increasing worldwide and understanding how these forests interact with climate change including frequent and extreme events becomes increasingly important. This study aims to investigate the effects of the strong 2015/2016 El Niño-induced drought on species-specific leaf phenology, dieback and tree mortality in a secondary dry dipterocarp forest (DDF) in western Thailand. During the 2015/2016 El Niño event, rainfall and soil water content were lower than 25 mm and 5% during 5–6 consecutive months. The dry season was 3–4 months longer during the El Niño than during non-El Niño events. We found that this prolonged drought induced the earlier shedding and a delay in leaf emergence of the DDF. The deciduousness period was also longer during the El Niño event (5 months instead of 2–3 months during non-El Niño event). We found that the DDF species showed different phenological responses and sensitivities to the El Niño-induced drought. The leaf phenology of stem succulent species Lannea coromandelica (Houtt.) Merr. and a complete deciduous species with low wood density. Sindora siamensis Teijsm. ex Miq. was only slightly affected by the El Niño-induced drought. Conversely, a semi-deciduous species such as Dipterocarpus obtusifolius Teijsm. ex Miq. showed a higher degree of deciduousness during the El Niño compared to non-El Niño events. Our results also highlight that dieback and mortality during El Niño were increased by 45 and 50%, respectively, compared to non-El Niño events, pointing at the importance of such events to shape DDF ecosystems. Full article

Projected increase in frequency and severity of extreme events are important threat brought by climate change. Thus, there is a need to understand the dynamics and magnitude of climate extreme at local and regional level. This study examines the patterns of annual trends and changes of extreme daily temperature and precipitation in Myanmar for the period of 1981 to 2015 using the RClimDex 1.1 software. The trends of maximum and minimum temperature show significant warming trends (p < 0.001) across Myanmar. From 2009 to 2015, the maximum temperature anomaly has continuously increased by 0.5 °C for all years except 2011. The larger rise in both maximum and minimum temperature observed after 2000 suggests that, overall, days and nights are becoming hotter for the entirety of Myanmar over this recent period. Furthermore, our works also show that the temperature extreme indices of warm days and warm nights have increased, whereas the frequency of cool days and cool nights have decreased. Our analysis also reveals that increasing trends in precipitation anomaly were not significant during 1981–2015. On the contrary, slight increasing trends towards wetter conditions were observed with a rate of 76.52 mm/decade during the study period. The other precipitation extreme indicators—namely, annual total precipitation (PRCPTOT), heavy precipitation days (R20mm), extreme wet days precipitation (R99p), and consecutive wet days (CWD)—are consistent with warming trends. Additionally, the relationship between inter-annual variability in the climate extremes indices and Oceanic Niño Index (ONI) patterns was also examined with a focus on the influence of the El Niño-Southern Oscillation (ENSO) phenomenon. Full article

This study investigated the spatiotemporal dynamics of tropical deciduous forest including dry dipterocarp forest (DDF) and mixed deciduous forest (MDF) and its phenological changes in responses to El Niño and La Niña during 2001–2016. Based on time series of Normalized Difference Vegetation Index (NDVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS), the start of growing season (SOS), the end of growing season (EOS), and length of growing season (LOS) were derived. In absence of climatic fluctuation, the SOS of DDF commonly started on 106 ± 7 DOY, delayed to 132 DOY in El Niño year (2010) and advanced to 87 DOY in La Niña year (2011). Thus, there was a delay of about 19 to 33 days in El Niño and an earlier onset of about 13 to 27 days in La Niña year. The SOS of MDF started almost same time as of DDF on the 107 ± 7 DOY during the neutral years and delayed to 127 DOY during El Niño, advanced to 92 DOY in La Niña year. The SOS of MDF was delayed by about 12 to 28 days in El Niño and was earlier about 8 to 22 days in La Niña. Corresponding to these shifts in SOS and LOS of both DDF and MDF were also induced by the El Niño–Southern Oscillation (ENSO). Full article

Deforestation, followed by abandonment and forest regeneration, has become one of the dominant types of land cover changes in the tropics. This study applied the eddy covariance (EC) technique to quantify the energy budget and evapotranspiration in a regenerated secondary dry dipterocarp forest in Western Thailand. The mean annual net radiation was 126.69, 129.61, and 125.65 W m⁻² day⁻¹ in 2009, 2010, and 2011, respectively. On average, fluxes of this energy were disaggregated into latent heat (61%), sensible heat (27%), and soil heat flux (1%). While the number of energy exchanges was not significantly different between these years, there were distinct seasonal patterns within a year. In the wet season, more than 79% of energy fluxes were in the form of latent heat, while during the dry season, this was in the form of sensible heat. The energy closure in this forest ecosystem was 86% and 85% in 2010 and 2011, respectively, and varied between 84–87% in the dry season and 83–84% in the wet season. The seasonality of these energy fluxes and energy closure can be explained by rainfall, soil moisture, and water vapor deficit. The rates of evapotranspiration also significantly varied between the wet (average 6.40 mm day⁻¹) and dry seasons (3.26 mm day⁻¹). Full article

The accurate mapping and monitoring of forests is essential for the sustainable management of forest ecosystems. Advancements in the Landsat satellite series have been very useful for various forest mapping applications. However, the topographic shadows of irregular mountains are major obstacles to accurate forest classification. In this paper, we test five topographic correction methods: improved cosine correction, Minnaert, C-correction, Statistical Empirical Correction (SEC) and Variable Empirical Coefficient Algorithm (VECA), with multisource digital elevation models (DEM) to reduce the topographic relief effect in mountainous terrain produced by the Landsat Thematic Mapper (TM)-5 and Operational Land Imager (OLI)-8 sensors. The effectiveness of the topographic correction methods are assessed by visual interpretation and the reduction in standard deviation (SD), by means of the coefficient of variation (CV). Results show that the SEC performs best with the Shuttle Radar Topographic Mission (SRTM) 30 m × 30 m DEM. The random forest (RF) classifier is used for forest classification, and the overall accuracy of forest classification is evaluated to compare the performances of the topographic corrections. Our results show that the C-correction, SEC and VECA corrected imagery were able to improve the forest classification accuracy of Landsat TM-5 from 78.41% to 81.50%, 82.38%, and 81.50%, respectively, and OLI-8 from 81.06% to 81.50%, 82.38%, and 81.94%, respectively. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method. Full article