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憤慨一下

Estimating Above-Ground Biomass Using Landsat 8 Imagery: A Case Study of Deciduous Broadleaf ForestB measurements were defined as the dependent variable. The statistical analysis results indicate a strong correlation between vegetation indices and measured AGB data, confirming the effectiveness of using Landsat 8 imagery for assessing AGB. The models show reasonably good performance, achieving R.

thalamus

hemorrhage

Evaluating Surface Water Salinity Indicators from Landsat-8 OLI Imagery Using Machine Learning,Random Forest, to select the salinity prediction model and evaluate the relative importance of salinity indicators. The results obtained from the XGBoost model indicate that 18 out of the 20 input variables in the first optimal model made a significant contribution. These variables include two coord

Constrain

Surface Displacement Monitoring Utilizing Sentinel-1 Time Series Images and Levelling Survey Data ioints are located at buildings, and the 12 remaining are situated at groundwater stations. The correlation coefficient (R.) between persistent scatterer (PS) points and levelling points is greater than 0.8, with a value of 0.847 in the building area and 0.859 in groundwater stations. Simultaneously,

使困惑

Analyzing Urban Expansion in Hanoi Using Machine Learning and Multi-Temporal Satellite Imagery, In the past 10?years, the built-up area has increased by approximately 11.56 square kilometres, of which 5.36 and 6.20 square kilometres were increased in the period 2013–2018 and 2018–2023, respectively. The results effectively contribute to urban planning and management, monitoring of environment

mercenary

Co-registration of PRISMA Hyperspectral Imagery for Accurate Land Cover Classification,tion process, and the closer the acquisition time of the reference image is to the acquisition time of the image to be co-registered, the higher the quality of the co-registration results. By integrating cutting-edge machine learning techniques, the proposed co-registration approach further enhances

粗魯?shù)娜?/a>

Relative Importance of Driving Factors for Aerosol Optical Depth in Hanoi Using Remotely Sensed Imahe AOD and CWV variables were first retrieved from the MODIS product. Landsat-9 OLI images were used to derive SAVI, NDBI, and MNDWI. The importance of driving factors of AOD variation was finally investigated using the MLP neural networks and Garson’s algorithm. Results show the high importance of

SLING

indoctrinate

tenuous

Characterization of Topographic Changes Due to Rainfall-Induced Slope Failure Using LiDAR Data,Convergence Index (CI) was higher due to the occurrence of slope failure and the valley topography becoming more developed. Change Vector Analysis (CVA) of geomorphological parameters revealed that it has become easier not only to identify the slope failure location but also to detect changes in the