scholarly journals Application of red edge band in remote sensing extraction of surface water body: a case study based on GF-6 WFV data in arid area

2021 ◽  
Author(s):  
Zhao Lu ◽  
Daqing Wang ◽  
Zhengdong Deng ◽  
Yue Shi ◽  
Zhibin Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Water Body ◽  
Arid Area ◽  
Single Band ◽  
Water Bodies ◽  
Urban Region ◽  
Threshold Method ◽  
Red Edge ◽  
Edge Band

Abstract This paper mainly researches the application method of red edge band in water body remote sensing extraction. Gaofen-6 (GF-6) WFV data were chosen for the high spatial resolution, more bands, and wide width. Two new methods were proposed: the single-band threshold method based on the red edge 2 band and the decision tree model method based on the combined operation of the green band, red band, near infrared band, red edge 1 band, and red edge 2 band. Four traditional methods were used for comparing the extraction accuracy. Two study areas with different characteristics were chosen to analyze the reliability of the proposed method, one mountainous and one urban region, both located in Minqin, Gansu, China, which is a typical arid area. The results showed that the two red edge bands of the GF-6 WFV data can be utilized to extract water body information. Kappa coefficients extracted from the single-band threshold method based on the red edge 2 band in water bodies in mountainous and urban areas reached 96.18% and 93.21%, respectively. The decision tree method has the best extraction effect. Kappa coefficients of this method in mountain and urban water bodies were 97.73% and 94.41%, respectively.

scholarly journals Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

2020 ◽  
Vol 12 (4) ◽  
pp. 716 ◽  
Author(s):  
Yelong Zhao ◽  
Qian Shen ◽  
Qian Wang ◽  
Fan Yang ◽  
Shenglei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Large Scale ◽  
Measured Data ◽  
Water Bodies ◽  
Polluted Water ◽  
Water Colour ◽  
Hue Angle ◽  
Anomalous Water ◽  
Sentinel 2

As polluted water bodies are often small in area and widely distributed, performing artificial field screening is difficult; however, remote-sensing-based screening has the advantages of being rapid, large-scale, and dynamic. Polluted water bodies often show anomalous water colours, such as black, grey, and red. Therefore, the large-scale recognition of suspected polluted water bodies through high-resolution remote-sensing images and water colour can improve the screening efficiency and narrow the screening scope. However, few studies have been conducted on such kinds of water bodies. The hue angle of a water body is a parameter used to describe colour in the International Commission on Illumination (CIE) colour space. Based on the measured data, the water body with a hue angle greater than 230.958° is defined as a water colour anomaly, which is recognised based on the Sentinel-2 image through the threshold set in this study. The results showed that the hue angle of the water body was extracted from the Sentinel-2 image, and the accuracy of the hue angle calculated by the in situ remote-sensing reflectance Rrs (λ) was evaluated, where the root mean square error (RMSE) and mean relative error (MRE) were 4.397° and 1.744%, respectively, proving that this method is feasible. The hue angle was calculated for a water colour anomaly and a general water body in Qiqihar. The water body was regarded as a water colour anomaly when the hue angle was >230.958° and as a general water body when the hue angle was ≤230.958°. High-quality Sentinel-2 images of Qiqihar taken from May 2016 to August 2019 were chosen, and the position of the water body remained unchanged; there was no error or omission, and the hue angle of the water colour anomaly changed obviously, indicating that this method had good stability. Additionally, the method proposed is only suitable for optical deep water, not for optical shallow water. When this method was applied to Xiong’an New Area, the results showed good recognition accuracy, demonstrating good universality of this method. In this study, taking Qiqihar as an example, a surface survey experiment was conducted from October 14 to 15, 2018, and the measured data of six general and four anomalous water sample points were obtained, including water quality terms such as Rrs (λ), transparency, water colour, water temperature, and turbidity.

scholarly journals Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries

2021 ◽  
Vol 11 (21) ◽  
pp. 10062
Author(s):  
Aimin Li ◽  
Meng Fan ◽  
Guangduo Qin ◽  
Youcheng Xu ◽  
Hailong Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Water Bodies ◽  
Support Vector ◽  
Landsat 8 ◽  
Transfer Performance ◽  
Remote Sensing Images

Monitoring open water bodies accurately is important for assessing the role of ecosystem services in the context of human survival and climate change. There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water index (NDWI), modified NDWI (MNDWI), and machine learning algorithms. Based on Landsat-8 remote sensing images, this study focuses on the effects of six machine learning algorithms and three threshold methods used to extract water bodies, evaluates the transfer performance of models applied to remote sensing images in different periods, and compares the differences among these models. The results are as follows. (1) Various algorithms require different numbers of samples to reach their optimal consequence. The logistic regression algorithm requires a minimum of 110 samples. As the number of samples increases, the order of the optimal model is support vector machine, neural network, random forest, decision tree, and XGBoost. (2) The accuracy evaluation performance of each machine learning on the test set cannot represent the local area performance. (3) When these models are directly applied to remote sensing images in different periods, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decrease range of 0.33–66.52%, and the differences among the different algorithm performances in the three areas are obvious. Generally, the decision tree algorithm has good transfer performance among the machine learning algorithms with area under curve (AUC) indexes of 0.790, 0.518, and 0.697 in the three areas, respectively, and the average value is 0.668. The Otsu threshold algorithm is the optimal among threshold methods, with AUC indexes of 0.970, 0.617, and 0.908 in the three regions respectively and an average AUC of 0.832.

scholarly journals Small Water Body Detection and Water Quality Variations with Changing Human Activity Intensity in Wuhan

2022 ◽  
Vol 14 (1) ◽  
pp. 200
Author(s):  
Lingjun Wang ◽  
Wanjuan Bie ◽  
Haocheng Li ◽  
Tanghong Liao ◽  
Xingxing Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Body ◽  
Human Activities ◽  
Water Bodies ◽  
Small Water ◽  
Small Water Body ◽  
Water Indices ◽  
Small Water Bodies

Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is important to properly identify and analyze them. In remote sensing technology, water body identification based on band analysis, image classification, and water indices are often designed for large, homogenous water bodies. Traditional water indices are often less accurate for small water bodies, which often contain submerged or floating plants or easily confused with hill shade. Water quality inversion commonly depends on establishing the relationship between the concentration of water constituents and the observed spectral reflectance. However, individual variation in water quality in small water bodies is enormous and often far beyond the range of existing water quality inversion models. In this study, we propose a method for small water body identification and water quality estimation and test its applicability in Wuhan. The kappa coefficient of small water body identification is over 0.95, and the coefficient of determination of the water quality inversion model is over 0.9. Our results show that the method proposed in this study can be employed to accurately monitor the dynamics of small water bodies. Due to the outbreak of the COVID-19 pandemic, the intensity of human activities decreased. As a response, significant changes in the water quality of small water bodies were observed. The results also suggest that the water quality of small water bodies under different production modes (intensive/casual) respond differently in spatial and temporal dimensions to the decrease in human activities. These results illustrate that effective remote sensing monitoring of small water bodies can provide valuable information on water quality.

scholarly journals Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

2020 ◽  
Vol 12 (6) ◽  
pp. 938 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Bei Cui ◽  
Yingying Dong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fusarium Wilt ◽  
Wilt Disease ◽  
Identification Accuracy ◽  
Validation Dataset ◽  
Multispectral Imagery ◽  
Red Edge ◽  
Fusarium Wilt Disease ◽  
Chlorophyll Index ◽  
Edge Band

Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

scholarly journals An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 431 ◽  
Author(s):  
Kelsey Herndon ◽  
Rebekke Muench ◽  
Emil Cherrington ◽  
Robert Griffin
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Decision Tree ◽  
Water Bodies ◽  
Spectral Indices ◽  
Dynamic Surface ◽  
Tree Methods ◽  
Global Surface ◽  
Decision Tree Methods ◽  
Semi Arid

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

scholarly journals A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

2020 ◽  
Vol 9 (4) ◽  
pp. 189 ◽  
Author(s):  
Hongxiang Guo ◽  
Guojin He ◽  
Wei Jiang ◽  
Ranyu Yin ◽  
Lei Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Water Body ◽  
Semantic Segmentation ◽  
Water Bodies ◽  
Water Extraction ◽  
Remote Sensing Images ◽  
Multi Scale

Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

scholarly journals Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 397 ◽  
Author(s):  
Shiran Song ◽  
Jianhua Liu ◽  
Yuan Liu ◽  
Guoqiang Feng ◽  
Hui Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Spatial Resolution ◽  
Water Body ◽  
High Spatial Resolution ◽  
Water Bodies ◽  
Top Down ◽  
Multi Temporal ◽  
Body Recognition

High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

scholarly journals Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1912
Author(s):  
Zhili Zhang ◽  
Meng Lu ◽  
Shunping Ji ◽  
Huafen Yu ◽  
Chenhui Nie
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Water Body ◽  
Feature Fusion ◽  
Feature Representation ◽  
Water Bodies ◽  
Feature Maps ◽  
Very High

Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.

scholarly journals A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 323-328 ◽  
Author(s):  
S. L. K. Reddy ◽  
C. V. Rao ◽  
P. R. Kumar ◽  
R. V. G. Anjaneyulu ◽  
B. G. Krishna
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Additional Information ◽  
Water Canal ◽  
Water Canals

<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>

scholarly journals Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7397
Author(s):  
Yanjun Wang ◽  
Shaochun Li ◽  
Yunhao Lin ◽  
Mengjie Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Water Resource ◽  
Spatial Resolution ◽  
Water Body ◽  
Water Bodies ◽  
Image Texture ◽  
Support Vector ◽  
Remote Sensing Images

Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning.

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