scholarly journals RAPID IDENTIFICATION OF ABIOTIC STRESS (FROST) IN <i>IN-FILED</i> MAIZE CROP USING UAV REMOTE SENSING

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 467-471 ◽  
Author(s):  
J. Goswami ◽  
V. Sharma ◽  
B. U. Chaudhury ◽  
P. L. N. Raju
Keyword(s):  
Remote Sensing ◽  
Abiotic Stress ◽  
Computational Cost ◽  
Cost Effective ◽  
Rapid Identification ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Maize Crop ◽  
Multispectral Data

<p><strong>Abstract.</strong> Stress in the crop not only decreases the production but can also have devastating consequences for farmers whose life depends upon the healthy crops. In recent time (January 2018) a such abiotic stress event (hoar frost) was experienced at ICAR research complex experimental filed, Ri-Bhoi district of Meghalaya on standing Maize crop. Therefore, remote sensing (Multispectral UAV- Unmanned Aerial Vehicle) technology were used to detect the effect of frost on <i>in-filed</i> Maize crop. Two set of multispectral data (before frost and after frost) with four advanced machine learning techniques viz. Random Forest (RF), Random Committee (RC), Support Vector Machine (SVM) and Artificial Neural Network were employed for detection of stress free crop and stressed crop due to frost. Results revealed that all the four methods of classification could able to identify / detect stress-free vs. stressed crops at satisfactory level. However, among the classifiers RF achieved relatively higher overall accuracy (OA&amp;thinsp;=&amp;thinsp;86.47%) with Kappa Indexanalysis (KIA&amp;thinsp;=&amp;thinsp;0.80) and found very cost effective in context of computational cost (time complexity&amp;thinsp;=&amp;thinsp;0.08 Seconds) to train the model. In addition, we have also recorded the area of each classes and found that after frost stress-free area (36.01% of all over filed) is decreased by 11% in comparison of before frost (25.036% of all over filed). Based on the results we can suggest that the RF ensemble classification method can be used for further other crop classification in order to estimate the yield, detect the condition, monitoring the health etc.</p>

scholarly journals Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth

2021 ◽  
Vol 13 (23) ◽  
pp. 4829
Author(s):  
Bingquan Wang ◽  
Youhua Ran
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Thermal State ◽  
Reanalysis Data ◽  
Soil Freezing ◽  
Machine Learning Techniques ◽  
Tibet Plateau ◽  
Support Vector ◽  
Important Indicator ◽  
Freezing Depth

The maximum soil freezing depth (MSFD) is an important indicator of the thermal state of seasonally frozen ground. Its variation has important implications for the water cycle, ecological processes, climate and engineering stability. This study tested three aspects of data-driven predictions of MSFD in the Qinghai-Tibet Plateau (QTP), including comparison of three popular statistical/machine learning techniques, differences between remote sensing variables and reanalysis data as input conditions, and transportability of the model built by reanalysis data. The results show that support vector regression (SVR) performs better than random forest (RF), k-nearest neighbor (KNN) and the ensemble mean of the three models. Compared with the climate predictors, the remote sensing predictors are helpful for improving the simulation accuracy of the MSFD at both decadal and annual scales (at the annual and decadal scales, the root mean square error (RMSE) is reduced by 2.84 and 1.99 cm, respectively). The SVR model with climate predictor calibration using the in situ MSFD at the baseline period (2001–2010) can be used to simulate the MSFD over historical periods (1981–1990 and 1991–2000). This result indicates the good transferability of the well-trained machine learning model and its availability to simulate the MSFD of the past and the future when remote sensing predictors are not available.

scholarly journals A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1933 ◽  
Author(s):  
Tien Dat Pham ◽  
Junshi Xia ◽  
Nam Thang Ha ◽  
Dieu Tien Bui ◽  
Nga Nhu Le ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Salt Marshes ◽  
Vital Role ◽  
Blue Carbon ◽  
Aerial Photographs ◽  
Machine Learning Techniques ◽  
Coastal Vegetation ◽  
Multispectral Data ◽  
Sensing Applications ◽  
Optical Imagery

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

scholarly journals Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming

Telecom ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 255-270
Author(s):  
Saeid Pourroostaei Ardakani ◽  
Ali Cheshmehzangi
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Data Collection ◽  
Cost Effective ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Q Learning ◽  
Sensing Applications ◽  
Learning Technique ◽  
Target Locations

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

scholarly journals Detecting Land use Changes Affected by Human Activities using Remote Sensing (Case Study: Karkheh River Basin)

2015 ◽  
Vol 10 (2) ◽  
pp. 473-481 ◽  
Author(s):  
Saeid Maddah ◽  
Saeed Karimi ◽  
Hadi Rezai ◽  
Jabbar Khaledi
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood Method ◽  
Land Use Changes ◽  
Cost Effective ◽  
Well Being ◽  
Likelihood Method ◽  
Support Vector ◽  
Running Water ◽  
Land Water

Population growth and abundant activities in order to achieve maximum well-being has forced human to make a lot of changes in the nature. These changes will be cost-effective when they have the minimum damage on the landscape. One of the activities that human did for obtaining the water and preventing flood was making the dam in the track of running water. Since the dam is established until its impoundment and after impoundment, the condition of ecosystem and the appearance of the upstream and downstream of the dam will undergo changes. In this study, using satellite data and remote sensing, these changes have been studied and the landuse changes in vegetation, arid land, water level and residential and non-residential lands is measured in 1998 and 2014 using Maximum Likelihood method and support vector machine.

scholarly journals A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Binh Thai Pham ◽  
Manh Duc Nguyen ◽  
Nadhir Al-Ansari ◽  
Quoc Anh Tran ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Permeability Coefficient ◽  
Construction Projects ◽  
Statistical Error ◽  
Clay Content ◽  
Cost Effective ◽  
Liquid Limit ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Accurate Estimation ◽  
Support Vector

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.

scholarly journals Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007

2010 ◽  
Vol 10 (2) ◽  
pp. 305-317 ◽  
Author(s):  
G. P. Petropoulos ◽  
W. Knorr ◽  
M. Scholze ◽  
L. Boschetti ◽  
G. Karantounias
Keyword(s):  
Cost Effective ◽  
Support Vector ◽  
Svm Classifier ◽  
Wildland Fires ◽  
Burnt Area ◽  
Multispectral Data ◽  
Test Region ◽  
Vector Machines ◽  
Aster Image

Abstract. Remote sensing is increasingly being used as a cost-effective and practical solution for the rapid evaluation of impacts from wildland fires. The present study investigates the use of the support vector machine (SVM) classification method with multispectral data from the Advanced Spectral Emission and Reflection Radiometer (ASTER) for obtaining a rapid and cost effective post-fire assessment in a Mediterranean setting. A further objective is to perform a detailed intercomparison of available burnt area datasets for one of the most catastrophic forest fire events that occurred near the Greek capital during the summer of 2007. For this purpose, two ASTER scenes were acquired, one before and one closely after the fire episode. Cartography of the burnt area was obtained by classifying each multi-band ASTER image into a number of discrete classes using the SVM classifier supported by land use/cover information from the CORINE 2000 land nomenclature. Overall verification of the derived thematic maps based on the classification statistics yielded results with a mean overall accuracy of 94.6% and a mean Kappa coefficient of 0.93. In addition, the burnt area estimate derived from the post-fire ASTER image was found to have an average difference of 9.63% from those reported by other operationally-offered burnt area datasets available for the test region.

scholarly journals Canopy Height Estimation from Single Multispectral 2D Airborne Imagery Using Texture Analysis and Machine Learning in Structurally Rich Temperate Forests

2019 ◽  
Vol 11 (23) ◽  
pp. 2853
Author(s):  
Christos Boutsoukis ◽  
Ioannis Manakos ◽  
Marco Heurich ◽  
Anastasios Delopoulos
Keyword(s):  
Land Cover ◽  
Texture Analysis ◽  
Spatial Scales ◽  
Computational Cost ◽  
Texture Feature ◽  
Cost Effective ◽  
Canopy Height ◽  
Support Vector ◽  
Structural Parameter ◽  
Wide Range

Canopy height is a fundamental biophysical and structural parameter, crucial for biodiversity monitoring, forest inventory and management, and a number of ecological and environmental studies and applications. It is a determinant for linking the classification of land cover to habitat categories towards building one-to-one relationships. Light detection and ranging (LiDAR) or 3D Stereoscopy are the commonly used and most accurate remote sensing approaches to measure canopy height. However, both require significant time and budget resources. This study proposes a cost-effective methodology for canopy height approximation using texture analysis on a single 2D image. An object-oriented approach is followed using land cover (LC) map as segmentation vector layer to delineate landscape objects. Global texture feature descriptors are calculated for each land cover object and used as variables in a number of classifiers, including single and ensemble trees, and support vector machines. The aim of the analysis is the discrimination among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m). For that task, different spatial resolutions are tested, representing a range from airborne to spaceborne quality ones, as well as their combinations, forming a multiresolution training set. Multiple dataset alternatives are formed based on the missing data handling, outlier removal, and data normalization techniques. The approach was applied using orthomosaics from DMC II airborne images, and evaluated against a reference LiDAR-derived canopy height model (CHM). Results reached overall object-based accuracies of 67% with the percentage of total area correctly classified exceeding 88%. Sentinel-2 simulation and multiresolution analysis (MRA) experiments achieved even higher accuracies of up to 85% and 91%, respectively, at reduced computational cost, showing potential in terms of transferability of the framework to large spatial scales.

scholarly journals MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING

2016 ◽  
Vol XLI-B8 ◽  
pp. 1111-1113 ◽  
Author(s):  
H. Ben-Romdhane ◽  
P. R. Marpu ◽  
H. Ghedira ◽  
T. B. M. J. Ouarda
Keyword(s):  
Remote Sensing ◽  
United Arab Emirates ◽  
Benthic Communities ◽  
Arabian Gulf ◽  
Cost Effective ◽  
Support Vector ◽  
Reef Environment ◽  
Conservation Actions ◽  
Spatial Method ◽  
Multispectral Remote Sensing

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

scholarly journals Web-of-Service Software Reusability Prediction using Heterogenous Ensemble Classifier

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1276-1282
Keyword(s):  
Cost Effective ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Support Vector ◽  
Computing Environment ◽  
Software Reusability ◽  
Adaptive Regression ◽  
Decision Tress ◽  
Web Of Service ◽  
Performance Results

WoS computing environment is expected to have numerous parallel computing engines. Presently, software professionals or developers often want to reuse existing software components to exhibit a task with time-efficient and cost effective solutions. However, software component reusability in uncontrolled manner leads to failure, premature shutdown and software smells or aging. This paper develops a novel evolutionary computing assisted ensemble classification system for WoS software reusability prediction. This applies different base learners such asNaïve Bayes (NB), Linear Regression (LR), Decision Tress (DT),Logarithmic Regression (LOGR),and Support Vector Machine (SVM),Multivariate Adaptive Regression Spline (MARS). Once training the base learners, the outputs of each classifier have been processed with majority vote.The computation in conjunction with weighted sum enabled final labelling of each software class. The performance results affirmed that the present work ensemble classifier has better performance with respect to base classifiers.

scholarly journals Performance of Hybrid Ensemble Classification Techniques for Prevalence of Heart Disease Prediction

2019 ◽  
Vol 8 (10) ◽  
pp. 1875-1882
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Medical Science ◽  
The Body ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Classification Techniques

In medical science, heart disease is being considered as fatal problem and in every seconds most of the people dies due to this problem. In heart disease, typically heart stops blood supply to other parts of the body. Hence, proper functioning of body stopped and affected. In this way, timely and accurate prediction of heart disease is an important concern in medical science domain. Diagnosing of heart patients with previous medical history is not being considered as reliable in many aspects. However, machine learning techniques have mystery to classify heart disease data efficiently and effectively and provide reliable solutions. In the past, prediction of heart disease problem various machine learning tools and techniques have been adopted. In this study, hybrid ensemble classification techniques like bagging, boosting, Random Subspace Method (RSM) and Random Under Sampling (RUS) boost are proposed and performance is compared with simple base classification techniques like decision tree, logistic regression, Naive Bays, Support Vector Machine, k-Nearest Neighbor (KNN), Bays Net (BN) and Multi Layer Perceptron (MLP). The heart disease dataset from Kaggle data source containing 305 samples and Matlab R2017a machine learning tool are considered for performance evaluation. Finally, the experimental results stated that hybrid ensemble classification methods outperforms than simple base classification methods in terms of accuracy

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