Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images.

Fruit tree identification that is quick and precise lays the groundwork for scientifically evaluating orchard yields and dynamically monitoring planting areas. This study aims to evaluate the applicability of time series Sentinel-1/2 satellite data for fruit tree classification and to provide a new method for accurately extracting fruit tree species. Therefore, the study area selected is the Tarim Basin, the most important fruit-growing region in northwest China. The main focus is on identifying several major fruit tree species in this region. Time series Sentinel-1/2 satellite images acquired from the Google Earth Engine (GEE) platform are used for the study. A multi-scale segmentation approach is applied, and six categories of features including spectral, phenological, texture, polarization, vegetation index, and red edge index features are constructed. A total of forth-four features are extracted and optimized using the Vi feature importance index to determine the best time phase. Based on this, an object-oriented (OO) segmentation combined with the Random Forest (RF) method is used to identify fruit tree species. To find the best method for fruit tree identification, the results are compared with three other widely used traditional machine learning algorithms: Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and Classification and Regression Tree (CART). The results show that: (1) the object-oriented segmentation method helps to improve the accuracy of fruit tree identification features, and September satellite images provide the best time window for fruit tree identification, with spectral, phenological, and texture features contributing the most to fruit tree species identification. (2) The RF model has higher accuracy in identifying fruit tree species than other machine learning models, with an overall accuracy (OA) and a kappa coefficient (KC) of 94.60% and 93.74% respectively, indicating that the combination of object-oriented segmentation and RF algorithm has great value and potential for fruit tree identification and classification. This method can be applied to large-scale fruit tree remote sensing classification and provides an effective technical means for monitoring fruit tree planting areas using medium-to-high-resolution remote sensing images.

Lowering of Reaction Rates by Energetically Favorable Hydrogen Bonding in the Transition State. Degradation of Biofuel Ketohydroperoxides by OH.

Ketohydroperoxides (KHPs) are oxygenates with carbonyl and hydroperoxy functional groups, and they are generated under combustion and atmospheric conditions. Their fate is crucial for secondary organic aerosol formation in the troposphere and for the ignition processes of biofuels in advanced combustion engines. We investigated the thermodynamics and kinetics of nine hydrogen abstraction reactions from four ether KHPs by OH. We find that the rate constants are strongly affected by entropic effects whose estimation requires a consideration of higher-energy conformers of the transition state. A density functional was selected for these reactions by comparison to coupled cluster calculations, and it was used for calculations by multistructural canonical transition-state theory with multidimensional tunneling over the temperature range of 200-2000 K. We find that the effect of multistructural torsional anharmonicity is very large and quite different for the various ether KHP reactions. A leading cause of the structural dependence is the dominance of entropic factors due to the lack of hydrogen bonding in some of the higher-energy conformers of the transition states. Four of the reactions involve abstraction from the α-carbon (the carbon vicinal to the hydroperoxide group); they exhibit nonmonotonic temperature dependence with complex fuel-specific dependence. The rate constants for abstraction from a non-α-carbon of a given KHP can be faster than the ones for abstraction from an α-carbon; in two cases, this is due to entropy, and in one case, the non-α-carbon abstraction has a lower energy barrier. Tunneling and recrossing effects are also found to be important.

Electronic structures and anisotropic carrier mobilities of monolayer ternary metal iodides MLaI5(M=Mg, Ca, Sr, Ba).

Exploiting two-dimensional (2D) materials with natural band gaps and anisotropic quasi-one-dimensional (quasi-1D) carrier transport character is essential in high-performance nanoscale transistors and photodetectors. Herein, the stabilities, electronic structures and carrier mobilities of 2D monolayer ternary metal iodides MLaI5(M = Mg, Ca, Sr, Ba) have been explored by utilizing first-principles calculations combined with numerical calculations. It is found that exfoliating MLaI5monolayers are feasible owing to low cleavage energy of 0.19-0.21 J m-2and MLaI5monolayers are thermodynamically stable based on phonon spectra. MLaI5monolayers are semiconductors with band gaps ranging from 2.08 eV for MgLaI5to 2.51 eV for BaLaI5. The carrier mobility is reasonably examined considering both acoustic deformation potential scattering and polar optical phonon scattering mechanisms. All MLaI5monolayers demonstrate superior anisotropic and quasi-1D carrier transport character due to the striped structures. In particular, the anisotropic ratios of electron and hole mobilities along different directions reach hundreds and tens for MLaI5monolayers, respectively. Thus, the effective electron-hole spatial separation could be actually achieved. Moreover, the absolute locations of band edges of MLaI5monolayers have been aligned. These results would provide fundamental insights for MLaI5monolayers applying in nano-electronic and optoelectronic devices.