scholarly journals Spectral Discrimination of Six Dominant Seaweed Species in the Intertidal Zone of GouQi Island

2021 ◽  
Author(s):  
jianqu chen ◽  
xunmeng li ◽  
Kai Wang ◽  
Shouyu Zhang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Intertidal Zone ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Model Parameters ◽  
Seaweed Species ◽  
Discrimination Rate ◽  
Extreme Gradient Boosting ◽  
Input Parameters

Abstract Probing the coverage and biomass of seaweed is the basis for achieving sustainable utilization of nearshore seaweed resources. Unlike traditional sample surveys, remote sensing technology can realize dynamic monitoring on a large scale and for a long time. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions in the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Hizikia fusifarme. The different seaweed species were identified and analyzed by a combination of ANOVA and support vector machine (SVM). Fourteen common spectral parameters were used as input variables, and the input parameters were filtered by ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a 3:1 ratio for input into the SVM model. The results showed that when the input parameters were NDVI (Rg,Rr), RVI (Rg,Rr), Vre, Abe, Lbe, Lg, Lre, and Rr and the model parameters g=1.30 and c=2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.96%, and the highest accuracy was 93.94% when distinguishing different phyla of seaweed (g=6.85 and c=2.55). In addition, SVM is fused with XGBoost (eXtreme Gradient Boosting) by vote and further classified in combination with the selected parameters. The accuracy of the six seaweeds was 73.98% (vote mean score = 0.972). In this study, the spectral data of intertidal seaweed in different dry and wet states were classified for the first time to provide technical support for remote sensing monitoring of coastal zones and seaweed resource statistics.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

<p>Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R<sup>2</sup> = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R<sup>2</sup> = 0.75 and 0.78 respectively) while DL performs better in TDS (R<sup>2</sup> =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.</p>

scholarly journals Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors

2021 ◽  
Vol 13 (19) ◽  
pp. 3838
Author(s):  
Yan Liu ◽  
Sha Zhang ◽  
Jiahua Zhang ◽  
Lili Tang ◽  
Yun Bai
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Hybrid Model ◽  
Regional Scale ◽  
Hybrid Models ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Support Vector ◽  
Extreme Gradient Boosting ◽  
Monteith Equation

Accurate estimates of evapotranspiration (ET) over croplands on a regional scale can provide useful information for agricultural management. The hybrid ET model that combines the physical framework, namely the Penman-Monteith equation and machine learning (ML) algorithms, have proven to be effective in ET estimates. However, few studies compared the performances in estimating ET between multiple hybrid model versions using different ML algorithms. In this study, we constructed six different hybrid ET models based on six classical ML algorithms, namely the K nearest neighbor algorithm, random forest, support vector machine, extreme gradient boosting algorithm, artificial neural network (ANN) and long short-term memory (LSTM), using observed data of 17 eddy covariance flux sites of cropland over the globe. Each hybrid model was assessed to estimate ET with ten different input data combinations. In each hybrid model, the ML algorithm was used to model the stomatal conductance (Gs), and then ET was estimated using the Penman-Monteith equation, along with the ML-based Gs. The results showed that all hybrid models can reasonably reproduce ET of cropland with the models using two or more remote sensing (RS) factors. The results also showed that although including RS factors can remarkably contribute to improving ET estimates, hybrid models except for LSTM using three or more RS factors were only marginally better than those using two RS factors. We also evidenced that the ANN-based model exhibits the optimal performance among all ML-based models in modeling daily ET, as indicated by the lower root-mean-square error (RMSE, 18.67–21.23 W m−2) and higher correlations coefficient (r, 0.90–0.94). ANN are more suitable for modeling Gs as compared to other ML algorithms under investigation, being able to provide methodological support for accurate estimation of cropland ET on a regional scale.

scholarly journals Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway

2020 ◽  
Vol 12 (18) ◽  
pp. 2933
Author(s):  
Feng Qing ◽  
Yan Zhao ◽  
Xingmin Meng ◽  
Xiaojun Su ◽  
Tianjun Qi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Debris Flow ◽  
Debris Flows ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Safe Operation ◽  
Extreme Gradient Boosting ◽  
Karakoram Highway ◽  
Debris Flow Susceptibility

The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

scholarly journals An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6699
Author(s):  
Fei Sun ◽  
Fang Fang ◽  
Run Wang ◽  
Bo Wan ◽  
Qinghua Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Learning Strategy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Imbalanced Learning ◽  
Learning Methods ◽  
Minority Class ◽  
Imbalanced Classification ◽  
Extreme Gradient Boosting

Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.

scholarly journals Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhao Yang ◽  
Yifan Wang ◽  
Jie Li ◽  
Liming Liu ◽  
Jiyang Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Model Parameters ◽  
Neural Network Models ◽  
Flow Prediction ◽  
Extreme Gradient Boosting

This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.

scholarly journals Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 82
Author(s):  
Huaxin Liu ◽  
Qigang Jiang ◽  
Yue Ma ◽  
Qian Yang ◽  
Pengfei Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Classification Scheme ◽  
Land Cover Classification ◽  
Support Vector ◽  
Wetland Classification ◽  
Positive Contribution ◽  
Object Based ◽  
Extreme Gradient Boosting ◽  
Sentinel 2

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

2021 ◽  
Vol 13 (13) ◽  
pp. 2570
Author(s):  
Teng Li ◽  
Bozhong Zhu ◽  
Fei Cao ◽  
Hao Sun ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Eastern China ◽  
Water Environment ◽  
Relative Difference ◽  
Boreal Summer ◽  
Support Vector ◽  
Lake Area ◽  
The Past ◽  
Remote Sensing Reflectance

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

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