scholarly journals Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2

2018 ◽  
Vol 10 (10) ◽  
pp. 1643 ◽  
Author(s):  
Zifeng Wang ◽  
Junguo Liu ◽  
Jinbao Li ◽  
David Zhang
Keyword(s):  
Spatial Resolution ◽  
Spectral Band ◽  
Support Vector ◽  
High Threshold ◽  
Commission Error ◽  
Produce Water ◽  
Water Index ◽  
Water Mapping ◽  
Index Methods ◽  
Sentinel 2

Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.

scholarly journals Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

2018 ◽  
Vol 10 (11) ◽  
pp. 1704 ◽  
Author(s):  
Wei Wu ◽  
Qiangzi Li ◽  
Yuan Zhang ◽  
Xin Du ◽  
Hongyan Wang
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Urban Surface ◽  
Mapping Technique ◽  
Support Vector ◽  
Svm Classifier ◽  
P Value ◽  
Urban Water ◽  
Water Index ◽  
Water Mapping

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

scholarly journals Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253209
Author(s):  
Jianfeng Li ◽  
Biao Peng ◽  
Yulu Wei ◽  
Huping Ye
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Water Extraction ◽  
Practical Significance ◽  
Commission Error ◽  
Complex Environment ◽  
Visual Interpretation ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

scholarly journals Sentinel-2 Data for Land Cover/Use Mapping: A Review

2020 ◽  
Vol 12 (14) ◽  
pp. 2291 ◽  
Author(s):  
Darius Phiri ◽  
Matamyo Simwanda ◽  
Serajis Salekin ◽  
Vincent R. Nyirenda ◽  
Yuji Murayama ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Positive Impact ◽  
European Space Agency ◽  
Remotely Sensed ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Free Access ◽  
Remotely Sensed Data ◽  
Sentinel 2

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

scholarly journals Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 945
Author(s):  
Hao Jiang ◽  
Mo Wang ◽  
Hongda Hu ◽  
Jianhui Xu
Keyword(s):  
Noise Reduction ◽  
Spatial Resolution ◽  
Recall Rate ◽  
Optical Observations ◽  
Water Index ◽  
Mountainous Regions ◽  
Data Assessment ◽  
Normalized Difference Water Index ◽  
F Measure ◽  
Sentinel 2

Accurate waterbody mapping can support water-related environment monitoring and resource management. The Sentinel series satellites provide high-quality Synthetic Aperture Radar (SAR) and optical observations that are commonly used in waterbody mapping. However, owing to the 10-m spatial resolution of Sentinel data, previous studies mostly focused on the mapping of large waterbodies. In this work, we evaluated the performance of small waterbody mapping over urban and mountainous regions with two datasets, the average annual VH backscatter coefficients (VHavg), derived from the Sentinel-1A series, and the Modified Normalized Difference Water Index (MNDWI), derived from cloud-free Sentinel-2. A proven framework of waterbody mapping based on watershed segmentation and noise reduction was employed to assess the performance of the two datasets in waterbody identification. The validation was performed by comparing their results with 1-m spatial resolution reference waterbody data. Assessment metrics, including Precision, Recall, and F-measure, were employed. Results showed that: (1) the MNDWI outperformed the VHavg by 9 percentage points of the F-measure; (2) there was more room for results of VHavg to improve the accuracy through a combination with noise reduction; and (3) the potential smallest identifiable waterbody area (recall rate larger than 0.8) was larger than 104 m2.

scholarly journals Probabilistic River Water Mapping from Landsat-8 Using the Support Vector Machine Method

2020 ◽  
Vol 12 (9) ◽  
pp. 1374 ◽  
Author(s):  
Qihang Liu ◽  
Chang Huang ◽  
Zhuolin Shi ◽  
Shiqiang Zhang
Keyword(s):  
Support Vector Machine ◽  
River Water ◽  
Mapping Method ◽  
Water Bodies ◽  
Support Vector ◽  
Landsat 8 ◽  
Machine Method ◽  
Water Index ◽  
Mountainous Regions ◽  
Water Mapping

River water extent is essential for river hydrological surveys. Traditional methods for river water mapping often result in significant uncertainties. This paper proposes a support vector machine (SVM)-based river water mapping method that can quantify the extraction uncertainties simultaneously. Five specific bands of Landsat-8 Operational Land Imager (OLI) data were selected to construct the feature set. Considering the effect of terrain, a widely used terrain index called height above nearest drainage, calculated from the 1 arc-second Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), was also added into the feature set. With this feature set, a posterior probability SVM model was established to extract river water bodies and quantify the uncertainty with posterior probabilities. Three river sections in Northwestern China were selected as the case study areas, considering their different river characteristics and geographical environment. Then, the reliability and stability of the proposed method were evaluated through comparisons with the traditional Normalized Difference Water Index (NDWI) and modified NDWI (mNDWI) methods and validated with higher-resolution Sentinel-2 images. It was found that resultant probability maps obtained by the proposed SVM method achieved generally high accuracy with a weighted root mean square difference of less than 0.1. Other accuracy indices including the Kappa coefficient and critical success index also suggest that the proposed method outperformed the traditional water index methods in terms of river mapping accuracy and thresholding stability. Finally, the proposed method resulted in the ability to separate water bodies from hill shades more easily, ensuring more reliable river water mapping in mountainous regions.

scholarly journals Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

2020 ◽  
Vol 12 (9) ◽  
pp. 1406 ◽  
Author(s):  
Chris W. Cohrs ◽  
Rachel L. Cook ◽  
Josh M. Gray ◽  
Timothy J. Albaugh
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Resolution ◽  
Loblolly Pine ◽  
Southeastern United States ◽  
Model Fit ◽  
Support Vector ◽  
Area Index ◽  
Sentinel 2

Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.

scholarly journals EVALUATION OF MACHINE LEARNING CLASSIFIERS FOR MAPPING FALCATA PLANTATIONS IN SENTINEL-2 IMAGE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 103-108
Author(s):  
J. R. Santillan ◽  
J. L. E. Gesta
Keyword(s):  
Machine Learning ◽  
Maximum Likelihood ◽  
Spatial Resolution ◽  
The Other ◽  
Support Vector ◽  
Limited Information ◽  
Plantation Forest ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Sentinel 2

Abstract. Efficient and accurate mapping of forest and other industrial tree plantations (ITPs) is essential to ensure better monitoring and sustainable management of these plantations. In Caraga Region, Mindanao, Philippines, ITPs planted with Falcata (Paraserianthes falcataria (L.) Nielsen) are widespread and has contributed to more than 50% of the nationwide log production. At present, there is limited information on the location and extent of existing plantations. This provides an opportunity to evaluate satellite remote sensing approaches for mapping these plantations from images, particularly those provided by the Sentinel-2 mission. The objective of this study is to evaluate machine learning classifiers for mapping Falcata plantations in Sentinel-2 image using a 9 × 9 km2 study area in Caraga Region. It also aims to find the best classifier that can provide acceptable levels of accuracy by utilizing only the four bands of the 10-m spatial resolution and the 9 bands of the 20-m spatial resolution Level 2A Sentinel-2 image, respectively. The following classifiers and their variants were evaluated: Linear Support Vector Machine (SVM), Polynomial SVM, Radial Basis Function (RBF) SVM, Artificial Neural Network (Neural Net), Random Forest (RF), and Maximum Likelihood (ML). One or more of these classifiers have been successfully used in in natural and plantation forest mapping, including tree species classification from remotely sensed images. However, their performance and accuracy in detecting and discriminating Falcata plantations is yet to be evaluated. Results of the evaluation showed that the ML classifier has the highest overall accuracy (OA) of 90.90% and has more consistent values for Producer’s Accuracy (PA), and User’s Accuracy (UA) for Falcata and Non-Falcata classes, and hence, provides better Falcata classification results than the other classifiers when the 10-m spatial resolution Sentinel-2 image was used. The accuracy assessment of the 20-m subset classification provides relative different results from that of the 10-m subset, perhaps due to the inclusion of more bands. The highest OA was obtained by the Linear and RBF SVM classifiers at 92.05% each. The SVM classifiers have consistent performance and produce more accurate classification results than the other classifiers (i.e., more than 90% OA, PA, and UA). From these results, it can be concluded that Maximum Likelihood classifier is best to use for Falcata mapping using the 10-m spatial resolution Sentinel-2 image. For the 20-m resolution image, any of the two SVMs (linear or RBF) is more appropriate to use. However, it should be noted that these results are based on classifications where default parameters were used. Improvement in the classification accuracy may be achieved if these parameters were optimized.

scholarly journals DETECTION OF SHALLOW WATER AREA WITH MACHINE LEARNING ALGORITHMS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1269-1273 ◽  
Author(s):  
N. Yagmur ◽  
N. Musaoglu ◽  
G. Taskin
Keyword(s):  
Support Vector Machine ◽  
Shallow Water ◽  
Spatial Resolution ◽  
Water Area ◽  
Water Bodies ◽  
Support Vector ◽  
Spectral Bands ◽  
Shallow Water Area ◽  
Sentinel 2

<p><strong>Abstract.</strong> Remote sensing techniques has been widely used for detecting water bodies in especially wetlands. Different classification methods and water indices has used for this purpose and there are numerous studies for detecting water bodies. However, detecting shallow water area is difficult comparing with deep water bodies because of the mixed pixels. Akgol Wetland is chosen as study area to detect shallow water. For this purpose, Sentinel 2 satellite image, which gives more accurate results thanks to higher spatial resolution than the images having medium spatial resolution, is used. In this study, two classification approaches were applied on Sentinel 2 image to detect shallow water area. In the first approach, effectiveness of indices was determined and classification of spectral bands with indices shows higher accuracy than classification of only spectral bands by using support vector machine classification method. In the second approach, support vector machine recursive feature elimination method used for the most effective features in the first approach. Besides overall accuracy of only spectral bands is obtained as 88.10%, spectral bands and indices’ accuracy was obtained as 91.84%.</p>

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