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.

Alternate wetting and drying (AWD) is a water-saving irrigation technique in a paddy field that can reduce the emission of methane, a potent greenhouse gas (GHG). It is being adopted to Asian countries, but different results are reported in literatures on methane, nitrous oxide emission, and rice productivity under AWD. Therefore, the local feasibility needs to be investigated before its adoption by farmers. The current study carried out a 3-year experiment in an acid sulfate paddy field in Prachin Buri, Thailand. During five crops (3 dry and 2 wet seasons), three treatments of water management were compared: continuous flooding (CF), flooding whenever surface water level declined to 15 cm below the soil surface (AWD), and site-specific AWD (AWDS) that weakened the criteria of soil drying (AWDS). Methane and nitrous oxide emissions were measured by a closed chamber method. Rice grain yield did not significantly (p < 0.05) differ among the three treatments. The amount of total water use (irrigation + rainfall) was significantly reduced by AWD (by 42%) and AWDS (by 34%) compared to CF. There was a significant effect of treatment on the seasonal total methane emission; the mean methane emission in AWD was 49% smaller than that in CF. The seasonal total nitrous oxide emission and the global warming potential (GWP) of methane and nitrous oxide did not differ among treatments. The contribution of nitrous oxide to the GWP ranged 39–62% among three treatments in dry season whereas 3–13% in wet season. The results indicate that AWD is feasible in terms of GHG emission mitigation, rice productivity, and water saving in this site, especially in dry season.

Addition of straw is common practice in rice agriculture, but its effect on the path of microbial CH₄ production and the microbial community involved is not well known. Since straw from rice (C3 plant) and maize plants (C4 plant) exhibit different δ¹³C values, we compared the effect of these straw types using anoxic rice field soils from Italy and China, and also a soil from Thailand that had previously not been flooded. The temporal patterns of production of CH₄ and its major substrates H₂ and acetate, were slightly different between rice straw and maize straw. Addition of methyl fluoride, an inhibitor of acetoclastic methanogenesis, resulted in partial inhibition of acetate consumption and CH₄ production. The δ¹³C of the accumulated CH₄ and acetate reflected the different δ¹³C values of rice straw versus maize straw. However, the relative contribution of hydrogenotrophic methanogenesis to total CH₄ production exhibited a similar temporal change when scaled to CH₄ production irrespectively of whether rice straw or maize straw was applied. The composition of the methanogenic archaeal communities was characterized by terminal restriction fragment length polymorphism (T-RFLP) analysis and was quantified by quantitative PCR targeting archaeal 16S rRNA genes or methanogenic mcrA genes. The size of the methanogenic communities generally increased during incubation with straw, but the straw type had little effect. Instead, differences were found between the soils, with Methanosarcinaceae and Methanobacteriales dominating straw decomposition in Italian soil, Methanosarcinaceae, Methanocellales, and Methanobacteriale in China soil, and Methanosarcinaceae and Methanocellales in Thailand soil. The experiments showed that methanogenic degradation in different soils involved different methanogenic population dynamics. However, the path of CH₄ production was hardly different between degradation of rice straw versus maize straw and was also similar for the different soil types.

Three upland soils from Thailand, a natural forest, a 16-year-old reforested site, and an agricultural field, were studied with regard to methane uptake and the community composition of methanotrophic bacteria (MB). The methane uptake rates were similar to rates described previously for forest and farmland soils of the temperate zone. The rates were lower at the agricultural site than at the native forest and reforested sites. The sites also differed in the MB community composition, which was characterized by denaturing gradient gel electrophoresis (DGGE) of pmoA gene fragments (coding for a subunit of particulate methane monooxygenase) that were PCR amplified from total soil DNA extracts. Cluster analysis based on the DGGE banding patterns indicated that the MB communities at the forested and reforested sites were similar to each other but different from that at the farmland site. Sequence analysis of excised DGGE bands indicated that Methylobacter spp. and Methylocystis spp. were present. Sequences of the “forest soil cluster” or “upland soil cluster α,” which is postulated to represent organisms involved in atmospheric methane consumption in diverse soils, were detected only in samples from the native forest and reforested sites. Additional sequences that may represent uncultivated groups of MB in the Gammaproteobacteria were also detected.

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.

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.