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Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method
Conditioned Latin hypercube sampling Soil mapping Representativeness Sample randomness
2024/1/12
Sampling design plays an important role in soil survey and soil mapping. Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil m...
Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms
precision agriculture field crops machine learning deep learning image processing textural features
2023/12/21
The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging de...
Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms-Case Study of Evergreen Conifer Planted Forests in Japan
UAS stem volume TLS SAR random forest support vector multiple regression forest biophysical parameter
2023/12/21
The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing...
Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China
Water resources Remote sensing Total nitrogen Hyperspectral imagery Machine learning Bootstrap
2023/12/19
In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality paramete...
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches
land water segmentation remote sensing deep learning sparse labels
2023/12/5
Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife...
Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning
machine learning ratio-based indices orthogonal indices Koppen–Geiger climate regionalization landscape change remote sensing landcover
2023/12/4
Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural...
Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm
frequency ratio MCD64A1 Bayesian optimization support vector machine random forest extreme gradient boosting
2023/11/30
Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization abi...
Classification of Toona Sinensis Young Leaves Using Machine Learning And UAV-Borne Hyperspectral Imagery
Classification Toona Sinensis Young Leaves
2023/6/2
Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In th...
Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods
Spectrometric Classification Bamboo Shoot Machine Learning
2023/6/5
The nutrition and quality of bamboo shoots from different species have a large variation, and traditional methods used for the classification of different bamboo shoot species are expensive and time-c...