Open surface water index: a novel approach for surface water mapping and extraction using multispectral and multisensory data

Mishra, Vikash Kumar; Pant, Triloki

doi:10.1080/2150704x.2020.1804085

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Computed total inundated area agreed well between LandSat-8 and Sentinel-2 products for both water indices, similar to previously published analyses (e.g., [63,64]). As expected, the biggest differences occurred for dates with smaller inundation extents, when LandSat-8 estimates exceeded computed areas with Sentinel-2 images (Figure 7c-e).…”

Section: Off-channel Habitat Identification Using Spectral Indices Th...supporting

confidence: 87%

“…The equation coefficients were determined based on the analysis of the reflectance properties of different land cover types [58]. Due to the greater spectral response of water in the green and blue bands in respect to NIR and SWIR1 [63], water pixels should present large positive values. Then, NIR and SWIR2 bands help further differentiate water from other surfaces with similar reflectance characteristics by subtracting their value and forcing non-water pixels to present even larger negative values [58].…”

Section: Estimate Of Inundation Extentmentioning

confidence: 99%

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Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon (Oncorhynchus tshawytscha) along the Sacramento River, California, Using Remote Sensing Imagery

Lusardi

Lund

2022

Remote Sensing

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Off-channel areas are one of the most impacted aquatic habitats by humans globally, as extensive agricultural and urban development has limited them to roughly 10% of historical extent. This is also true for California’s Sacramento River Valley, where historically frequent widespread inundation has been reduced to a few off-channel water bodies along the mid-Sacramento River. This remaining shallow-water habitat provides crucial ecological benefits to multiple avian and fish species, but especially to floodplain-adapted species such as Chinook salmon (Oncorhynchus tshawytscha). Characterizing spatiotemporal off-channel dynamics, including inundation extent and residence time, is fundamental to better understanding the intrinsic value of such habitats and their potential to support recovery actions. Remote sensing techniques have been increasingly used to map surface water at regional and local scales, with improved resolutions. As such, this study maps off-channel inundation areas and describes their temporal dynamics by analyzing pixel-based time- series of multiple water indices, modified Normalized Difference Water Index (mNDWI) and the Automated Water Extraction Index (AWEI), generated from LandSat-8 and Sentinel-2 data between 2013–2021. Quantified off-channel area was similar with each water index and method used, but improved performance was associated with Sentinel-2 products and AWEI index to identify wetted areas under lower mainstem discharges. Results indicate an uneven distribution of off-channel habitat in the study area, with limited inundated areas in upstream reaches (<16% of total off-channel area for greater flows). In addition, much less habitat exists for flows under 400 m3/s, an important migration cue for endangered winter-run Chinook salmon, limiting juvenile access to areas with enhanced rearing conditions. Off-channel habitat residence times averaged between 7 and 16 days, primarily defined by the rate of receding flows, with rapid flow recession providing marginal off-channel habitat. This study shows reasonable performance of moderate resolution LandSat-8 and Sentinel-2 remote sensing imagery to characterize shallow-water inundated habitat in higher-order rivers, and as a method to inform restoration and native fish recovery efforts.

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Section: Off-channel Habitat Identification Using Spectral Indices Th...supporting

confidence: 87%

Section: Estimate Of Inundation Extentmentioning

confidence: 99%

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon (Oncorhynchus tshawytscha) along the Sacramento River, California, Using Remote Sensing Imagery

Lusardi

Lund

2022

Remote Sensing

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“…Monitoring indices constructed using remote sensing technology have become the primary means of the monitoring and evaluation of regional eco-environmental quality. For example, the normalized difference vegetation index (NDVI) has been widely used to evaluate vegetation growth and phenology (Erasmi et al, 2021;Zhang et al, 2021b), the water index has been applied to evaluate drought status or habitat suitability (Mishra and Pant, 2020;Al-Quraishi et al, 2021), and the land surface temperature (LST) has been used to evaluate the urban heat island effect (Lee and Park, 2020;Arshad et al, 2022). The ecological environment is composed of complex ecosystems and is affected by multiple factors; therefore, a remote sensing evaluation method based on a single monitoring index cannot comprehensively capture the systematic changes in eco-environmental quality (Wen et al, 2019;Zhou and Liu, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China

et al. 2023

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Eco-environmental quality is a measure of the suitability of the ecological environment for human survival and socioeconomic development. Understanding the spatial-temporal distribution and variation trend of eco-environmental quality is essential for environmental protection and ecological balance. The remote sensing ecological index (RSEI) can quickly and objectively quantify eco-environmental quality and has been extensively utilized in regional ecological environment assessment. In this paper, Moderate Resolution Imaging Spectroradiometer (MODIS) images during the growing period (July-September) from 2000 to 2020 were obtained from the Google Earth Engine (GEE) platform to calculate the RSEI in the three northern regions of China (the Three-North region). The Theil-Sen median trend method combined with the Mann-Kendall test was used to analyze the spatial-temporal variation trend of eco-environmental quality, and the Hurst exponent and the Theil-Sen median trend were superimposed to predict the future evolution trend of eco-environmental quality. In addition, ten variables from two categories of natural and anthropogenic factors were analyzed to determine the drivers of the spatial differentiation of eco-environmental quality by the geographical detector. The results showed that from 2000 to 2020, the RSEI in the Three-North region exhibited obvious regional characteristics: the RSEI values in Northwest China were generally between 0.2 and 0.4; the RSEI values in North China gradually increased from north to south, ranging from 0.2 to 0.8; and the RSEI values in Northeast China were mostly above 0.6. The average RSEI value in the Three-North region increased at an average growth rate of 0.0016/a, showing the spatial distribution characteristics of overall improvement and local degradation in eco-environmental quality, of which the areas with improved, basically stable and degraded eco-environmental quality accounted for 65.39%, 26.82% and 7.79% of the total study area, respectively. The Hurst exponent of the RSEI ranged from 0.20 to 0.76 and the future trend of eco-environmental quality was generally consistent with the trend over the past 21 years. However, the areas exhibiting an improvement trend in eco-environmental quality mainly had weak persistence, and there was a possibility of degradation in eco-environmental quality without strengthening ecological protection. Average relative humidity, accumulated precipitation and land use type were the dominant factors driving the spatial distribution of eco-environmental quality in the Three-North region, and two-factor interaction also had a greater influence on eco-environmental quality than single factors. The explanatory power of meteorological factors on the spatial distribution of eco-environmental quality was stronger than that of topographic factors. The effect of anthropogenic factors (such as population density and land use type) on eco-environmental quality gradually increased over time. This study can 232

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“…Open surface water index (OSWI) is used to delineate the water features by suppressing the other terrestrial features and expressed in the following equation: 18 OSWI=green−(SWIR1+SWIR2)/2green+(SWIR1+SWIR2)/2.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of tree canopy cover for the mapping of forest from the Sentinel-2 imagery

Mishra

Soni

Pant

et al. 2022

J. Appl. Rem. Sens.

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A forest is a piece of land dominated by trees; factors like tree density, tree height, canopy cover, land use, and ecological function discriminate different forest types. The canopy cover is instrumental in the identification of forest in the satellite imagery. Multispectral satellite imageries are very useful in feature highlighting. The combination of bands is a powerful tool not only to enhance some earth features but also to suppress others. In our study, a novel index is proposed for the delineation of tree canopy using Sentinel-2 multispectral imagery. A new simple vegetation ratio is proposed for the extraction of the vegetated areas including forest, farms, and other green lands, the existing normalized difference built-up index is modified, and an open surface water index is utilized to formulate a novel tree canopy cover index for the identification of tree canopy.

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Open surface water index: a novel approach for surface water mapping and extraction using multispectral and multisensory data

Cited by 6 publications

References 11 publications

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon (Oncorhynchus tshawytscha) along the Sacramento River, California, Using Remote Sensing Imagery

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon (Oncorhynchus tshawytscha) along the Sacramento River, California, Using Remote Sensing Imagery

Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China

Enhancement of tree canopy cover for the mapping of forest from the Sentinel-2 imagery

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