scholarly journals Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires

2020 ◽  
Vol 12 (21) ◽  
pp. 3660
Author(s):  
Darío Domingo ◽  
Juan de la Riva ◽  
María Teresa Lamelas ◽  
Alberto García-Martín ◽  
Paloma Ibarra ◽  
...  
Keyword(s):  
Regional Scale ◽  
Structural Diversity ◽  
Classification Model ◽  
Support Vector ◽  
Mediterranean Forests ◽  
Fuel Type ◽  
Machine Method ◽  
Airborne Laser ◽  
Type Classification ◽  
Sentinel 2

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests.

scholarly journals Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping

2021 ◽  
Vol 13 (3) ◽  
pp. 342
Author(s):  
Sergio Revilla ◽  
María Teresa Lamelas ◽  
Darío Domingo ◽  
Juan de la Riva ◽  
Raquel Montorio ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Radiative Transfer ◽  
Laser Scanning ◽  
Diversity Index ◽  
Classification Model ◽  
Support Vector ◽  
Low Density ◽  
Fuel Type ◽  
Machine Method ◽  
Type Classification

Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.

hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

2008 ◽  
Vol 5 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Qiyuan Li ◽  
Flemming Steen Jørgensen ◽  
Tudor Oprea ◽  
Søren Brunak ◽  
Olivier Taboureau
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Model Based ◽  
Grind Descriptors

scholarly journals Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 48
Author(s):  
Muhammad Habibi ◽  
Puji Winar Cahyo
Keyword(s):  
Support Vector Machine ◽  
Cosine Similarity ◽  
Classification Model ◽  
Support Vector ◽  
Weighting Method ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Performance Accuracy ◽  
Similarity Algorithm ◽  
Journal Editorial

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.

scholarly journals ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM

2020 ◽  
Vol 9 (3) ◽  
pp. 376-390
Author(s):  
Nur Fitriyah ◽  
Budi Warsito ◽  
Di Asih I Maruddani
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Classification Model ◽  
Support Vector ◽  
Test Results ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Rbf Kernel ◽  
Negative Sentiment ◽  
Fold Cross Validation

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

scholarly journals An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2087 ◽  
Author(s):  
Jun Zhu ◽  
Ziwu Pan ◽  
Hang Wang ◽  
Peijie Huang ◽  
Jiulin Sun ◽  
...  
Keyword(s):  
Support Vector ◽  
Machine Method ◽  
Tea Plantation ◽  
Classification Feature ◽  
Feature Importance ◽  
Tea Plantations ◽  
Periodic Monitoring ◽  
Multi Temporal ◽  
Importance Analysis ◽  
Sentinel 2

As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed and tested a method for tea plantation identification based on multi-temporal Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. We used phenological patterns of tea cultivation in China’s Shihe District (such as the multiple annual growing, harvest, and pruning stages) to extracted multi-temporal Sentinel-2 MSI bands, their derived first spectral derivative, NDVI and textures, and topographic features. We then assessed feature importance using RF analysis; the optimal combination of features was used as the input variable for RF classification to extract tea plantations in the study area. A comparison of our results with those achieved using the Support Vector Machine method and statistical data from local government departments showed that our method had a higher producer’s accuracy (96.57%) and user’s accuracy (96.02%). These results demonstrate that: (1) multi-temporal and multi-feature classification can improve the accuracy of tea plantation recognition, (2) RF classification feature importance analysis can effectively reduce feature dimensions and improve classification efficiency, and (3) the combination of multi-temporal Sentinel-2 images and the RF algorithm improves our ability to identify and monitor tea plantations.

scholarly journals Monitoring the Damage of Armyworm in Summer Corn by Unmanned Aerial Vehicle Imaging

2020 ◽  
Author(s):  
mingzheng zhang ◽  
Xinsheng Wang ◽  
Jinghao Xue ◽  
Wei Su ◽  
Dehai Zhu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Regional Scale ◽  
Support Vector ◽  
Surface Model ◽  
Automated Identification ◽  
Aerial Vehicle ◽  
Corn Plants ◽  
Uav Images ◽  
Uav Image ◽  
Sentinel 2

Abstract Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately monitoring armyworm damage compared with Support Vector Machine (SVM), Multilayer Perceptron Classifier (MLPC) and Naive Bayes Classifier (NB) classifiers. Furthermore, the incorporation of an Unmanned Aerial Vehicle (UAV) image-generated digital surface model improved the performance of the RF classifier, increasing the F-score from 0.985 and 0.970 to 0.997 and 0.994, and increasing the Kappa coefficient from 0.955 to 0.990. In addition, we found that Band 3 (735 nm) of the UAV image and Band 6 (740 nm) of a coincident Sentinel-2 image were not sensitive to an armyworm infestation in this study. Conclusions: We developed an accurate algorithm for the automated identification of armyworm-damaged corn plants using UAV images at the field scale. The study also indicated the feasibility of the developed method for monitoring corn armyworm damage at regional scale when combined with Sentinel-2 images.

scholarly journals Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images

2021 ◽  
Vol 13 (20) ◽  
pp. 4067
Author(s):  
Zhenjiang Wu ◽  
Jiahua Zhang ◽  
Fan Deng ◽  
Sha Zhang ◽  
Da Zhang ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Large Scale ◽  
Regional Scale ◽  
Classification Model ◽  
Classification Method ◽  
Textural Features ◽  
Grassland Community ◽  
Community Classification ◽  
Grassland Communities ◽  
Sentinel 2

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.

scholarly journals Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network

2017 ◽  
Vol 11 (2) ◽  
pp. 187 ◽  
Author(s):  
Ira Zulfa ◽  
Edi Winarko
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Representation ◽  
Deep Belief Network ◽  
Training Data ◽  
Classification Model ◽  
Experimental Result ◽  
Support Vector ◽  
Machine Method ◽  
Belief Network

Sentiment analysis is a computational research of opinion sentiment and emotion which is expressed in textual mode. Twitter becomes the most popular communication device among internet users. Deep Learning is a new area of machine learning research. It aims to move machine learning closer to its main goal, artificial intelligence. The purpose of deep learning is to change the manual of engineering with learning. At its growth, deep learning has algorithms arrangement that focus on non-linear data representation. One of the machine learning methods is Deep Belief Network (DBN). Deep Belief Network (DBN), which is included in Deep Learning method, is a stack of several algorithms with some extraction features that optimally utilize all resources. This study has two points. First, it aims to classify positive, negative, and neutral sentiments towards the test data. Second, it determines the classification model accuracy by using Deep Belief Network method so it would be able to be applied into the tweet classification, to highlight the sentiment class of training data tweet in Bahasa Indonesia. Based on the experimental result, it can be concluded that the best method in managing tweet data is the DBN method with an accuracy of 93.31%, compared with  Naive Bayes method which has an accuracy of 79.10%, and SVM (Support Vector Machine) method with an accuracy of 92.18%.

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