Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan

Ning, Fang-Shii; Lee, Yu-Chan

doi:10.3390/w13192774

Cited by 9 publications

(3 citation statements)

References 21 publications

(28 reference statements)

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“…Ning and Lee [65] suggested that the various water indices differ in their strengths and weaknesses, and that combining indices (and morphology), depending on the environment in question, may provide solutions to river mapping. A simple spatial stratification with minimal inputs would be more efficient than a multitude of ecoregionally specific masking rulesets for global monitoring.…”

Section: Discussionmentioning

confidence: 99%

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

Aravena¹,

Lyons²,

Keith³

2023

Remote Sensing

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A remote sensing method that integrates virtual sampling from formalized visual interpretations is proposed to facilitate land cover mapping and enhance its accuracy, with an emphasis on spatial and temporal scalability. Indices are widely used for mapping and monitoring surface water across space and time; however, they typically display some kind of limitation across different environments and seasons. A decision matrix framework based on observations derived from interpretation keys was designed to compare the performance of existing indices alongside a set of newly developed indices. This comparison helped to shortlist indices that warranted further evaluation and accuracy assessment to identify effective indices for global inter-seasonal surface water extent mapping. Additional visual inspections were conducted for criteria that remained unresolved by the decision matrix to examine index consistency across the seasons in a wide range of geographic settings around the world, and further reduce the shortlist. An accuracy assessment was performed for three new shortlisted indices. On a global scale, CAWI (Comprehensive Automatic Water Index) was the best-performing index. Its distinct binary data distribution provides the possibility of regional automatic Otsu thresholding. CAWI was determined to be compatible for Sentinel-2 and Landsat 8 sensors, providing the highest possible spatial resolution as well as the longest time series for retrospective analyses with freely available multispectral imagery. Two alternative indices were identified for sensors limited to the visible and NIR bands. The first index, CATWIC (Clear and Turbid Water Index Combination), split the classification of water into two components, with one index for generally clear water and another index for turbid water. The second, NDCHRWI (Normalized Difference Colourimetric High Resolution Water Index), applied the hue angle from a normalized difference RGB. Masking indices based on modified HSV Saturation equations were developed to reduce misclassification due to other high reflectance features. The indices’ overall accuracies, respectively, were: 94.97%, 94.51%, and 94.85%. This study concludes with recommendations for the application of different indices for sensors possessing shortwave infrared bands and for sensors limited to the visible and NIR bands, with a simple stratification of six zones for Global Surface Water monitoring.

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Section: Discussionmentioning

confidence: 99%

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

Aravena¹,

Lyons²,

Keith³

2023

Remote Sensing

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“…In the red band, the spectral characteristics of the cropland show strong absorption, so that the difference in the reflectivity of the RCFs between the two periods has a small peak in this band. In the red-edge band and NIR band, the water has strong absorption, while the internal structure of the rice leaves in these bands show high reflectivity, rendering it easier to distinguish the RCFs in these two bands [47,48]. The vegetation index differences indicated by these different bands were introduced as classification features.…”

Section: Advantages Of the Btfdob Methodsmentioning

confidence: 99%

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

Wang

Yang

et al. 2023

Remote Sensing

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Rice–crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of the local crop cultivation structure and agricultural development. The single-temporal images of two phenological periods in the year were classified separately, and then the areas where the water disappeared were identified as RCFs in previous studies. However, due to the differences in the segmentation of lakes and rivers between the two images, the incorrect extraction of RCFs is unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed in order to map RCFs in Sihong County. We mapped RCFs by segmenting the bi-temporal images simultaneously based on the object-based method and selecting appropriate feature differences as the classification features. To evaluate the applicability, the classification results of the previous two years obtained using the single-temporal- and object-based (STOB) method were compared with the results of the BTFDOB method. The results suggested that spectral feature differences showed high feature importance, which could effectively distinguish the RCFs from non-RCFs. Our method worked well, with an overall accuracy (OA) of 96.77%. Compared with the STOB method, OA was improved by up to 2.18% across three years of data. The RCFs were concentrated in the low-lying eastern and southern regions, and the cultivation scale was expanded in Sihong. These findings indicate that the BTFDOB method can accurately identify RCFs, providing scientific support for the dynamic monitoring and rational management of the pattern.

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“…When water bodies are distinguished from other ground features using spectral characteristics, other methods can combine multiple characteristics to classify and identify them. Water body information extraction accuracy varies between algorithms [30,31]. For a given algorithm based on different data, the water body extraction accuracy will vary; therefore, using a combination of different algorithms and datasets will produce results with different accuracies [32].…”

Section: Introductionmentioning

confidence: 99%

Satellite Detection of Surface Water Extent: A Review of Methodology

Cao

et al. 2022

Water

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Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.

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Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan

Cited by 9 publications

References 21 publications

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

Satellite Detection of Surface Water Extent: A Review of Methodology

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