Teak Tree-Ring Cellulose δ13C, δ18O, and Tree-Ring Width from Northwestern Thailand Capture Different Aspects of Asian Monsoon Variability

The inter-annual variability in tree-ring cellulose δ13C (δ13CTR, δ18OTR), and tree-ring chronology in teak (TRW) (Tectona grandis L.f.) trees from Northwestern Thailand during 1901–2009 AD was performed. The δ13CTR and δ18OTR have a positive correlation, significant at r =0.400, p <0.0001, and both of the stable isotopes were not significantly related to the TRW. The TRW is related to rainfall in the first half of the rainy season and has a strong relationship with the relative humidity. The δ18OTR captured moisture well throughout the rainy season, and the δ13CTR had a strong correlation with rainfall in the second half of the rainy season and had a high correlation with cloud fraction and vapor pressure. The δ13CTR and δ18OTR were associated with the stomata conductance response, but had no effect on photosynthesis. The three indices of the teak annual ring respond well to the variability in the Asian monsoon, and give us a better understanding of both the hydrological cycle and the factors that contribute to the growing of tropical broadleaf trees under changing climates

Factors Controlling Soil Organic Carbon Sequestration of Highland Agricultural Areas in the Mae Chaem Basin, Northern Thailand

Understanding the effect of the environment, crop types, and land management practices on the organic carbon sequestration of top soil is crucial for adopting management strategies in highland agricultural areas. The objectives of this study are: (1) to estimate top soil organic carbon density (SOCD) of different crop types and (2) to analyze the factors controlling top SOCD in highland agricultural areas. The top soil layers from 0 to 30 cm depths were collected from the Mae Chaem basin, Northern Thailand. The results showed that the highest top SOCD was found soil used for growing upland rice, which contained an average of 58.71 Mg C ha&minus;1. A significant difference between the top SOCD was detected between areas where minimum tillage and conventional tillage of various crops, with average of values 59.17 and 41.33 Mg C ha&minus;1, respectively, for areas growing strawberries; 61.14 and 37.58 Mg C ha&minus;1, respectively, for cabbage, and 71.15 and 39.55 Mg C ha&minus;1, respectively, for maize. At higher elevation, the top SOCD was high, which may be due to high clay content and low temperature. Increased use of chemical fertilizers lead to increases in top SOCD, resulting in increased crop yields. Elevation, bulk density, N and K2O fertilizers were the main factors controlling the top SOCD at all sites

Comparing Four Machine Learning Algorithms for Land Cover Classification in Gold Mining: A Case Study of Kyaukpahto Gold Mine, Northern Myanmar

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district

Growth Response of Thai Pine (<i>Pinus latteri</i>) to Climate Drivers in Tak Province of Northwestern Thailand

The long-term effects of climate change and climate extremes have been associated with changes in tree growth and forest productivity worldwide, and dendrochronological analyses are important tools that can be used to investigate the influence of climatic forces on tree growth at a particular site. In this study, a 180-year tree ring width chronology (spanning from 1843 to 2022) of living pine trees (Pinus latteri) in Tak province, northwestern Thailand, was developed. The analysis of the climate–tree growth relationship indicated the influences of the annual total rainfall (r = 0.60, p r = 0.47, p r = 0.94, p < 0.001) and the El Niño Southern Oscillation (ENSO). Our results confirm that rainfall and relative humidity are the main climatic factors regulating the radial growth of Thai pine. This finding could be an important contribution to further research on the effects of climate change and extreme weather events on the vulnerability of tropical and subtropical trees in this region

Teak Tree-Ring Cellulose δ13C, δ18O, and Tree-Ring Width from Northwestern Thailand Capture Different Aspects of Asian Monsoon Variability

The inter-annual variability in tree-ring cellulose δ13C (δ13CTR, δ18OTR), and tree-ring chronology in teak (TRW) (Tectona&nbsp;grandis L.f.) trees from Northwestern Thailand during 1901–2009 AD was performed. The δ13CTR and δ18OTR have a positive correlation, significant at r&nbsp;=0.400, p&nbsp;&lt;0.0001, and both of the stable isotopes were not significantly related to the TRW. The TRW is related to rainfall in the first half of the rainy season and has a strong relationship with the relative humidity. The δ18OTR captured moisture well throughout the rainy season, and the δ13CTR had a strong correlation with rainfall in the second half of the rainy season and had a high correlation with cloud fraction and vapor pressure. The δ13CTR and δ18OTR were associated with the stomata conductance response, but had no effect on photosynthesis. The three indices of the teak annual ring respond well to the variability in the Asian monsoon, and give us a better understanding of both the hydrological cycle and the factors that contribute to the growing of tropical broadleaf trees under changing climates