[PDF] Top 20 Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data
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Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data
... satellite remote-sensing images as a data source to estimate various vegetation biomasses, such as grassland [3, 4], forest [5–8], crop- lands [9–11], and wetland [7, ...satellite remote ... See full document
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Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms
... Mangrove forests provide a wide range of ecological and socio-economic functions. One of their important roles is global climate change mitigation through carbon sequestration. Mangroves are well-known as highly ... See full document
163
Application of remote sensing and machine learning modeling to post-wildfire debris flow risks
... with ground truthing data and were mapped as debris flow ...statistical modeling approaches showed that both models performed well in isolating the high hazard basins, which is critical in ... See full document
85
UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax
... and approaches to help increase the rate of crop ...increase biomass of food and feed crops and that of bioenergy crops which reduce the need for fossil ...these approaches allows the flexibility to ... See full document
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Machine Learning Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery
... a machine learning based crop drought map- ping system is developed by integrating crop segmentation, feature engineering, Bayesian optimization and classifica- tion ...High-resolution UAV ... See full document
12
A Review of Machine Learning for Hyperspectral Image Applications
... from remote sensing. Remote sensing is defined as means of gathering information such that no physical contact is ...on remote site having numerous applications and usefulness in fields ... See full document
6
Black carbon radiative forcing in south Mexico City, 2015
... Data from the National Black Carbon Monitoring Network was obtained for the year 2015. This net- work was recently created as a collaborative effort between the National Autonomous University of Mexico (UNAM) and ... See full document
13
Road Recognition from Remote Sensing Imagery using Machine Learning
... In machine learning, an artificial neural network is a system of interconnected neurons that pass messages to each other. Neural networks are used to model complex functions and, in particular, as ... See full document
7
Kernel Feature Extraction Methods for Remote Sensing Data Analysis
... of remote sensing data analysis from a machine learning ...huge data volume acquired by these sensors –basically due to the increasing spatial, temporal and spectral resolution ... See full document
167
Remote Sensing and GIS Based Spectro Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia
... process based crop simulation ...involves data col- lection from stakeholders on predicted crop yield and comparing it with previous year’s yield as recorded by the Central Statistical Agency ...this ... See full document
11
Volcano remote sensing with ground-based spectroscopy
... are based upon measurements of the spectra of electromagnetic radiation that has been attenuated by passing through a volcanic plume (figure ...quantitatively, using the Beer–Lambert ... See full document
16
Change Detection in Remote Sensing Images Using Elitist Genetic Algorithm
... K-means (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. This nonhierarchical method initially takes the number of components of the population ... See full document
5
Evaluation of Machine Learning Algorithms for Lake Ice Classification from Optical Remote Sensing Data
... Vector Machine (SVM), back propagation neural network (BPNN), and Bayesian, with K nearest-neighbor (KNN) performing the worst (Shen et ...types using RADARSAT-2 imagery with above 86% classification ... See full document
94
Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia
... analyzing remote sensing data from a period of 30 years (1986 - 2016), the quantitative evidence of land use land cover change shows that cropland and grassland showed ...by using MCE model ... See full document
15
Change detection in rice area using remote sensing data
... statistical data is very vital for spatial planning, management and utilization of ...are using in the investigation of changes in land use and land cover as extremely useful ...employed using ... See full document
5
MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement
... models using machine-learning models as post- processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the cal- ibration ...a ... See full document
17
Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral-spatial classification of hyperspectral images
... extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral ... See full document
15
Are inventory based and remotely sensed above ground biomass estimates consistent?
... uncertain above-ground biomass ...that using more sophisticated methods of estimating above-ground biomass, which make use of remote sensing, will improve ... See full document
8
Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS
... As discussed earlier, a hazard map is one of the essential components of flood risk analysis. The assumption that rainfall is one of the primary triggering factors of flood occurrence over a study area, which results in ... See full document
24
Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia
... This study was carried out in Nyimba District (14°-15°S, 30°-31°E, ~1,000,000 ha), Eastern Province, Zambia (Fig. 1). Nyimba lies in the center of the miombo eco- region, a biome of diverse vegetation types dominated by ... See full document
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