scholarly journals A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2125 ◽  
Author(s):  
Lucas Silveira Kupssinskü ◽  
Tainá Thomassim Guimarães ◽  
Eniuce Menezes de Souza ◽  
Daniel C. Zanotta ◽  
Mauricio Roberto Veronez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Chlorophyll A ◽  
Suspended Solids ◽  
Analysis Data ◽  
Optically Active ◽  
Laboratory Analysis ◽  
Critical Parameters ◽  
Active Components ◽  
Sentinel 2

Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.

scholarly journals Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

2020 ◽  
Author(s):  
Jinhui Jeanne Huang ◽  
Hongwei Guo ◽  
Bowen Chen ◽  
Xiaolong Guo ◽  
Vijay P. Singh
Keyword(s):  
Water Management ◽  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Optically Active ◽  
Monitoring Strategy ◽  
Learning Models ◽  
Small Waterbodies ◽  
Machine Learning Models ◽  
Sentinel 2

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

scholarly journals Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

2022 ◽  
Vol 14 (1) ◽  
pp. 229
Author(s):  
Jiarui Shi ◽  
Qian Shen ◽  
Yue Yao ◽  
Junsheng Li ◽  
Fu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Water Bodies ◽  
Gradient Boosting ◽  
Learning Models ◽  
Small Water ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models ◽  
Sentinel 2 ◽  
Small Water Bodies

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

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 Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability

2020 ◽  
Vol 12 (10) ◽  
pp. 1620 ◽  
Author(s):  
Weichun Zhang ◽  
Hongbin Liu ◽  
Wei Wu ◽  
Linqing Zhan ◽  
Jing Wei
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rice Paddy ◽  
Temporal Data ◽  
Model Transferability ◽  
Local Classification ◽  
Multi Temporal ◽  
Sentinel 2

Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

scholarly journals Chlorophyll-a and total suspended solids retrieval and mapping using Sentinel-2A and machine learning for inland waters

2020 ◽  
Vol 113 ◽  
pp. 106236 ◽  
Author(s):  
Mohammadmehdi Saberioon ◽  
Jakub Brom ◽  
Václav Nedbal ◽  
Pavel Souc̆ek ◽  
Petr Císar̆
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Inland Waters ◽  
Sentinel 2A

A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery

2020 ◽  
Vol 42 (5) ◽  
pp. 1841-1866
Author(s):  
Hongwei Guo ◽  
Jinhui Jeanne Huang ◽  
Bowen Chen ◽  
Xiaolong Guo ◽  
Vijay P. Singh
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Quality Parameters ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Sentinel 2

scholarly journals Vertical Distribution of Bio-Optical Properties of the Azov – Black Sea Basin Waters in April – May, 2019

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
T. V. Efimova ◽  
T. Ya. Churilova ◽  
E. Yu. Skorokhod ◽  
N. A. Moiseeva ◽  
E. A. Zemlianskaia ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Light Absorption ◽  
Euphotic Zone ◽  
Optically Active ◽  
Colored Dissolved Organic Matter ◽  
Active Components ◽  
Absorption Coefficients ◽  
Chlorophyll A Concentration ◽  
Water Region ◽  
Total Light

Purpose. The work is aimed at investigating spatial distribution of the chlorophyll a concentration and the spectral light absorption coefficients by all optically active components in the Azov and Black seas in spring, when the seawater hydrophysical structure changes. Methods and Results. The data collected in the 106th scientific cruise of R/V Professor Vodyanitsky in April 19 – May 10, 2019 were used. The chlorophyll a concentration was measured by the spectrophotometric method. The spectral light absorption coefficients were determined in accordance with the NASA protocol 2018. The optical measurements were performed using the dual-beam spectrophotometer Lambda 35 (PerkinElmer). It was shown that in the surface layer of the Black Sea, the chlorophyll a concentration varied from 0.21 to 1.2 mg/m3. At some stations in the deep-water region, the increased values of this parameter were observed in the lower part of the euphotic zone that was associated with the beginning of seasonal water stratification due to the surface water heating. At these stations, the phytoplankton absorption spectra were more smoothed in the lower part of the euphotic zone than those in the upper layer. In the deep-water region, the non-algal particles contribution to the total particulate light absorption at wavelength 438 nm changed with depth from 40 ± 15 % at the surface to 29 ± 12 % near the bottom of the euphotic zone; whereas in the coastal waters this parameter was almost unchangeable within the water column (54 ± 11 %). No significant change of the colored dissolved organic matter contribution to the total light absorption with depth was revealed (69% on average). In the Sea of Azov, vertical distribution both of the chlorophyll a concentration (6.2 mg/m3 on average) and the spectral light absorption coefficients by all the optically active components was uniform. The non-algal particles contribution to the particulate light absorption was 40 ± 14 %, and the colored dissolved organic matter contribution to the total light absorption constituted 52 ± 6 %. Conclusions. New data on spatial distribution of the chlorophyll a concentration and the spectral light absorption coefficients by the optically active components in the Black and Azov seas were obtained for the spring period when the seawater hydrophysical characteristics changed.

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