Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data

Fu, Bolin; Liu, Man; He, Hongchang; Lan, Feiwu; He, Xu; Liu, Lilong; Huang, Liangke; Fan, Donglin; Zhao, Min; Jia, Zhenglei

doi:10.1016/j.jag.2021.102553

Cited by 18 publications

(13 citation statements)

References 45 publications

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“…UAV with multispectral cameras has high potential in mapping submerged marine fauna (Colefax et al, 2018 ). Environmental landscapes have been better studied and managed through applications of vegetation RS by UAVs, e.g., identifying the coverage degree of ground vegetation (Ghazal et al, 2015 ), mapping and monitoring non-submerged aquatic plants (Husson et al, 2016 ), classifying vegetation communities in the wetland (Fu et al, 2021 ), and estimate biomass of grass (Niko et al, 2018 ). As UAV technologies and associated methodologies have been improved with advanced development, they are more affordable and have been increasingly adopted in many research domains.…”

Section: Related Studiesmentioning

confidence: 99%

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

Zhao

Wang

et al. 2022

Front. Plant Sci.

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With the growth of the world population, cities expand and encroach on forests and plants, causing many environmental problems. Digital Twin, as the rapidly developing technique in recent years, provides the opportunity to implement the specific situation of forests and plants at present or in the future, which has great performance on predictive analysis and optimization. From the consideration of plants and forests, this study provides a comprehensive case study to research the relationship between urban development boundary and natural environment in a natural preserve in a coastal city. Multispectral data of the study area is collected by Unmanned Aerial Vehicle (UAV), combining satellite remote sensing (RS) historical data and geographic data to establish the digital twin model for plant identification. In conjunction with local Master planning of land use, the results of modeling are used to analyze the influences of urban construction on the natural environment, and the inappropriate aspects of the planning are discovered and summarized. In addition, 6 suggestions for effective management and planning strategies are presented. As plants and forests are effective factors of natural conditions, this study offered an objective assessment for the sustainability and rationality of urban planning with some guidance and bases.

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Section: Related Studiesmentioning

confidence: 99%

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

Zhao

Wang

et al. 2022

Front. Plant Sci.

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“…Random forests are widely used due to their versatility and ability to handle large data sets. 34,35 Support Vector Machine (SVM). SVM is a powerful supervised machine learning algorithm used for classification and regression tasks.…”

Section: ■ Introductionmentioning

confidence: 99%

Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters

Hassaan,

Mohamed,

Ibrahim

et al. 2024

ACS Omega

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The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (k) obtained through core analysis. Three machine learning models, namely, decision trees (DTs), random forest (RFs), and support vector machines (SVMs), were employed and evaluated for their effectiveness in predicting porosity and permeability. The results demonstrate promising performance across the different data sets. All three models achieved correlation coefficients (R) higher than 0.91 in predicting porosity. The RF model exhibited accurate predictions of permeability, achieving R values surpassing 0.92 in the various data sets. While the DT model displayed slightly lower performance, with the R-value decreasing to 0.88 in the testing data set, the SVM model suffered from overfitting, with R values dropping to 0.83 in the testing data set. The novelty of this work lies in the successful application of machine learning models to the real-time prediction of reservoir properties, providing a practical and efficient solution for the oil and gas industry. By achieving correlation coefficients exceeding 0.91 and showcasing the models' efficacy in a dynamic testing data set, this study paves the way for improved decision-making processes and enhanced exploration and production activities. The innovative aspect lies in the utilization of drilling parameters for timely and cost-effective estimation, transforming conventional reservoir evaluation methods.

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“…The large amount of detailed data embedded in UAV imagery requires the development and application of powerful advanced analysis programs for extracting information related to vegetation structure and biochemical composition to better understand relevant plant traits [29,30]. Bolin Fu et al [31] set the flight altitude of the UAV to 105 m uniformly and used an optimized Random Forest-Decision Tree (RF-DT) model to extract vegetation communities, which explored the optimal detection variables for various types of vegetation. Zhang et al [32] set the flight altitude to 100 m and used UAVbased hyperspectral images, combined with SVM and Edge-Preserving Filter (EPF), to automatically extract tree canopies damaged by Chinese pine caterpillars and perform more refined classification.…”

Section: Introductionmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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Background Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features. Results In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes. Conclusions In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

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Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data

Cited by 18 publications

References 45 publications

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

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