scholarly journals Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms

2021 ◽  
Vol 13 (22) ◽  
pp. 4662
Author(s):  
Zhi Qiao ◽  
Siyang Sun ◽  
Qun’ou Jiang ◽  
Ling Xiao ◽  
Yunqi Wang ◽  
...  
Keyword(s):  
Water Pollution ◽  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Total Phosphorus ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Retrieval Model ◽  
Miyun Reservoir

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

scholarly journals Machine learning method for quick identification of water quality index (WQI) based on sentinel-2 MSI data: Ebinur lake case study

2020 ◽  
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Nurmemet Ilyas
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Spectral Band ◽  
Surface Water Quality ◽  
Machine Learning Algorithms ◽  
Lake Basin ◽  
Spectral Modeling ◽  
Ebinur Lake

Abstract Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 WQPs were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, 4 water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index(DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. Calculate the evaluation and model the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through 4 machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and NIR bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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 Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 21 ◽  
Author(s):  
Francisco Rodríguez-Puerta ◽  
Rafael Alonso Ponce ◽  
Fernando Pérez-Rodríguez ◽  
Beatriz Águeda ◽  
Saray Martín-García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Lidar Data ◽  
Sensing Data ◽  
Sentinel 2

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

scholarly journals Using remote sensing data for environmental monitoring of water objects using GIS and machine learning

2021 ◽  
Vol 937 (2) ◽  
pp. 022051
Author(s):  
D Krivoguz ◽  
A Semenova ◽  
S Mal’ko
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Sensing Data ◽  
Water Ecosystems

Abstract The main way to understand variability of any spatial data using remote sensing is calculating spectral indices. For now, some difficulties have receiving water surface temperature due to specific properties for satellite sensors and low spatial resolution. The main sources of receiving salinity data are remote sensing data from ESA SMOS, NASA Aquarius and SMAP satellites. Using different machine learning algorithms, we can get models or equations, representing dependency between studied environmental variable and different spectral channels of remote monitoring data. After receiving and collecting remote sensing data in database this system uses machine learning algorithms to find dependency between collected field data and different spectral bands of the remote sensing data. Our goal was to form an analytical system based on remote sensors and machine learning algorithm to analyse, predict and evaluate water ecosystems for fisheries and environmental protection.

Sign in / Sign up

Export Citation Format

Share Document