Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.

Development of Desertification Indicators for Desertification Monitoring from Landsat Images Using Python Programming

Nowadays, desertification is one of the most serious environment socioeconomic issues and sand dune advances are a major threat that causes desertification. Wadi El-Rayan is one of the areas facing severe dune migration. Therefore, it's important to monitor desertification and study sand dune migration in this area. Image differencing for the years 2000 (Landsat ETM+) and 2019 (OLI images) and Bi-temporal layer stacking was performed. It was found that image differencing is a superior method to get changes of the study area compared to the visual method (Bi-temporal layer stacking). This research develops a quantitative technique for desertification assessment by developing indicators using Landsat images. Spatial distribution of the movement of sand dunes using some spectral indices (NDVI, BSI, LDI, and LST) was studied and a Python script was developed to calculate these indices. The results show that NDVI and BSI indices are the best indices in the identification and detection of vegetation. It was found that mobile sand dunes on the southern side of the lower Wadi El-Rayan Lake caused filling up of large part of the lower lake. The indices results show that sand movement decreased the size of the lower Wadi El-Rayan Lake and there are reclamation activities in the west of the lower lake. The results show that a good result could be achieved from the developed codes compared to ready-made software (ENVI 5).

Stacking-dependent exciton multiplicity in WSe2 bilayers

Twisted layers of atomically thin two-dimensional materials realize a broad range of novel quantum materials with engineered optical and transport phenomena arising from spin and valley degrees of freedom and strong electron correlations in hybridized interlayer bands. Here, we report experimental and theoretical studies of WSe2 homobilayers obtained in two stable configurations of 2H (60° twist) and 3R (0° twist) stackings by controlled chemical vapor synthesis of high-quality large-area crystals. Using optical absorption and photoluminescence spectroscopy at cryogenic temperatures, we uncover marked differences in the optical characteristics of 2H and 3R bilayer WSe2 which we explain on the basis of beyond-DFT theoretical calculations. Our results highlight the role of layer stacking for the spectral multiplicity of momentum-direct intralayer exciton transitions in absorption, and relate the multiplicity of phonon sidebands in the photoluminescence to momentum-indirect excitons with different spin valley and layer character. Our comprehensive study generalizes to other layered homobilayer and heterobilayer semiconductor systems and highlights the role of crystal symmetry and stacking for interlayer hybrid states.

Recent Progress on 3D NAND Flash Technologies

Since 3D NAND was introduced to the industry with 24 layers, the areal density has been successfully increased more than ten times, and has exceeded 10 Gb/mm2 with 176 layers. The physical scaling of XYZ dimensions including layer stacking and footprint scaling enabled the density scaling. Logical scaling has been successfully realized, too. TLC (triple-level cell, 3 bits per cell) is now the mainstream in 3D NAND, while QLC (quad-level cell, 4 bits per cell) is increasing the presence. Several attempts and partial demonstrations were made for PLC (penta-level cell, 5 bits per cell). CMOS under array (CuA) enabled the die size reduction and performance improvements. Program and erase schemes to address the technology challenges such as short-term data retention of the charge-trap cell and the large block size are being investigated.

Q-stacking prediction model considering wind speed error correction

In order to improve the accuracy of wind speed forecasting in wind farms, an ensemble-enhanced combined forecasting model is proposed considering error correction. First establish five independent base learners, build a two-layer Stacking ensemble model to fuse the prediction results of each base learner, and divide the input data by cross-validation to improve the generalization ability of the model. Then use the model-free learning framework Q learning selects the optimal model in the base learner to correct the preliminary prediction error and obtain the final prediction result. Select the actual wind farm measured data in different seasons to simulate the prediction effect of the model, and verify the prediction ability of the proposed model through comparative analysis. The results show that the model has high prediction accuracy with ε = 0.093.

Systematic Compounding of Ceramic Pastes in Stereolithographic Additive Manufacturing

In this paper, stereolithographic additive manufacturing of ceramic dental crowns is discussed and reviewed. The accuracy of parts in ceramic processing were optimized through smart computer-aided design, manufacturing, and evaluation. Then, viscous acrylic resin, including alumina particles, were successfully compounded. The closed packing of alumina particles in acrylic pastes was virtually simulated using the distinct element method. Multimodal distributions of particle diameters were systematically optimized at an 80% volume fraction, and an ultraviolet laser beam was scanned sterically. Fine spots were continuously joined by photochemical polymerization. The optical intensity distributions from focal spots were spatially simulated using the ray tracing method. Consequently, the lithographic conditions of the curing depths and dimensional tolerances were experimentally measured and effectively improved, where solid objects were freely processed by layer stacking and interlayer bonding. The composite precursors were dewaxed and sintered along effective heat treatment patterns. The results show that linear shrinkages were reduced as the particle volume fractions were increased. Anisotropic deformations in the horizontal and vertical directions were recursively resolved along numerical feedback for graphical design. Accordingly, dense microstructures without microcracks or pores were obtained. The mechanical properties were measured as practical levels for dental applications.

Analisis Kerapatan Vegetasi di Kabupaten Magelang Menggunakan Citra Landsat 8 Bermetode NDVI (Normalized Difference Vegetation Index)

Latar belakang: Magelang merupakan daerah di Provinsi Jawa Tengah yang diapit oleh 3 gunung besar, yaitu Gunung Sumbing, Gunung Merbabu dan Gunung Merapi. Tujuan penelitian: Tujuan penelitian ini yaitu untuk mengetahui penggunaan lahan yang ada di citra Magelang, melihat indeks kerapatan vegetasi di Magelang menggunakan metode NDVI, memberikan informasi tahapan pengolahan citra sesuai dengan ketentuan-ketentuan yang berlaku dan melakukan klasifikasi baik manual maupun otomatis. Metode penelitian: Metode yang digunakan untuk mengolah citra ini berupa layer stacking, subset data from ROIs, band math untuk NDVI dan kalibrasi radiometrik, koreksi radiometrik dan supervised class. Hasil penelitian: Hasil dari pengolahan citra menunjukkan keberadaan daerah Magelang yang diapit oleh tiga gunung, yaitu Gunung Sumbing (sebelah barat), Gunung Merapi (sebelah tenggara) dan Gunung Merbabu (sebelah timur). Daerah Magelang terlihat didominasi oleh tutupan vegetasi yang berada di kaki gunung dan daerah lembah. Namun, pada Gunung Merapi ditemukan dominasi keberadaan badan air yang dimanfaatkan untuk kepentingan pertanian dan keberadaannya tersebar secara acak pada lahan terbangun. Pusat Kota Magelang didominasi oleh klasifikasi berwarna merah muda, dimana hal tersebut menunjukkan lahan terbangun diikuti oleh badan-badan air yang mengairi lahan pertanian. Lalu, lahan terbuka yang ada di Magelang berada di puncak gunung. Kesimpulan: Magelang merupakan daerah yang diapit oleh tiga gunung, yaitu Gunung Sumbing. Merapi dan Merbabu. Citra Magelang yang diperoleh menunjukkan bahwa Gunung Merbabu (Timur) dan Gunung Sumbing (Barat) didominasi oleh tutupan vegetasi, menandakan kondisi daerah yang subur dan berbanding terbalik dengan Gunung Merapi (Tenggara) yang sudah mulai dipadati oleh lahan terbangun. Pusat Kota Magelang memiliki lahan yang datar, namun disekitarnya didominasi oleh daerah pegunungan. Hal ini menyebabkan kota menjadi lebih dipadati oleh manusia dibandingkan daerah pegunungan sekitar.

T1SEstacker: A tri-layer stacking model effectively predicts bacterial type 1 secreted proteins based on C-terminal non-RTX-motif sequence features

The proteins secreted through type 1 secretion systems often play important roles in pathogenicity of various gram-negative bacteria. However, the type 1 secretion mechanism remains unknown. In this research, we observed the sequence features of RTX proteins, a major class of type 1 secreted substrates. We found striking non-RTX-motif amino acid composition patterns at the C-termini, most typically exemplified by the enriched '[FLI][VAI]' at the most C-terminal two positions. Machine-learning models, including deep-learning models, were trained using these sequence-based non-RTX-motif features, and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a 5-fold cross-validated sensitivity of ~0.89 at the specificity of ~0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif type 1 secreted proteins, further suggesting their potential existence of common secretion signals. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C-termini, and developed a stacking model that can predict type 1 secreted proteins accurately.