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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...
Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States
LiDAR airborne laser scanning enhanced forest inventory aboveground biomass forest carbon deep learning Maine New Hampshire Vermont Massachusetts Connecticut Rhode Island
2023/12/5
Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional sc...
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...
Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery
Forest Farm Fire Monitoring System Deep Learning
2023/12/1
Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire m...
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...
Learning through Experience: an interpretive trail design for Nasami Farm
Nasami Farm trail design
2014/12/8
Almost fifty years ago Freeman Tilden suggested that outdoor places have an ability to speak for themselves (1957). They each impart their own set of unique experiences for visitors, fostering the sen...
Learning from Worcester Union Station: An Istea Success Story
Istea Success Story Worcester Union Station:
2014/12/8
The passage of the Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 represented a major shift in federal transportation policy. ISTEA recognizes the interrelated nature of the country'...
Community Service Learning Through the Lens of Applied Field Studies in Guatemala, Central America
Community Service Learning Applied Field Studies Guatemala Central America
2014/11/19
How can landscape architects and planners apply the skills acquired through our academic training – and in the work we pursue in our careers – with the needs of underprivileged communities? This resea...
'Learning by doing': adaptive planning as a strategy to address uncertainty in planning
Learning by doing adaptive planning address uncertainty in planning
2014/11/19
Adaptive management, an established method in natural resource and ecosystem management, has not been widely applied to landscape planning due to the lack of an operational method that addresses the r...