Use of NDVI and Landscape Metrics to Assess Effects of Riverine Inputs on Wetland Productivity and Stability

Suir, Glenn M.; Sasser, Charles E.

doi:10.1007/s13157-019-01132-3

Cited by 15 publications

(13 citation statements)

References 58 publications

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“…The NDVI was the most suitable proxy for the LAI among different VIs, and it was selected to evaluate the phenology from remote sensing using in situ spectra and multispectral images from four sensors (Landsat-8 OLI, Sentinel-2 MSI, PlanetScope, and RapidEye). The NDVI has been widely demonstrated to be a good descriptor of vegetation dynamics for many types of ecosystems, including wetlands [17,[63][64][65][66]. It is also widely used in satellite-based phenology monitoring, as it could be applied to almost any multispectral sensors on a wide range of platforms (in situ, drone, plane, satellite) [67,68].…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats

et al. 2022

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Intertidal vegetation provides important ecological functions, such as food and shelter for wildlife and ecological services with increased coastline protection from erosion. In cold temperate and subarctic environments, the short growing season has a significant impact on the phenological response of the different vegetation types, which must be considered for their mapping using satellite remote sensing technologies. This study focuses on the effect of the phenology of vegetation in the intertidal ecosystems on remote sensing outputs. The studied sites were dominated by eelgrass (Zostera marina L.), saltmarsh cordgrass (Spartina alterniflora), creeping saltbush (Atriplex prostrata), macroalgae (Ascophyllum nodosum, and Fucus vesiculosus) attached to scattered boulders. In situ data were collected on ten occasions from May through October 2019 and included biophysical properties (e.g., leaf area index) and hyperspectral reflectance spectra (Rrs(λ)). The results indicate that even when substantial vegetation growth is observed, the variation in Rrs(λ) is not significant at the beginning of the growing season, limiting the spectral separability using multispectral imagery. The spectral separability between vegetation types was maximum at the beginning of the season (early June) when the vegetation had not reached its maximum growth. Seasonal time series of the normalized difference vegetation index (NDVI) values were derived from multispectral sensors (Sentinel-2 multispectral instrument (MSI) and PlanetScope) and were validated using in situ-derived NDVI. The results indicate that the phenology of intertidal vegetation can be monitored by satellite if the number of observations obtained at a low tide is sufficient, which helps to discriminate plant species and, therefore, the mapping of vegetation. The optimal period for vegetation mapping was September for the study area.

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Section: Discussionmentioning

confidence: 99%

Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats

et al. 2022

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“…Remote sensing provides a means for classifying and monitoring wetland landscape features to assess the distribution and change of those features over time. Some practical remote sensing applications include assessing the areal extent of created wetland habitat (Suir et al 2020), evaluating changes and trends in wetland conditions (Suir and Sasser 2019b;Jiang et al 2020), assessing wetland elevation and shoreline change rates (Smith et al 2021), quantifying changes in hydrology (Suir et al 2014) and comparing wetland properties to reference wetlands or established targets.…”

Section: ® ®mentioning

confidence: 99%

“…Remote sensing is well equipped to monitor and quantify vegetation properties and project performance because the vegetation is typically easily observed with remote sensors. Remote monitoring of vegetation measures includes but are not limited to assessing changes in vegetation growth dynamics (i.e., shoot density, height) (Suir and Sasser 2019b;Jiang et al 2020), detecting and monitoring plant diversity and switching (e.g., invasive plants, species abundance, and competition) (Royimani et al 2019;Suir et al 2021), and plant health (e.g., dieback), resiliency, and recovery (Suir et al 2020).…”

Section: ® ®mentioning

confidence: 99%

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

Suir¹,

Reif²,

Saltus³

2022

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Engineering With Nature (EWN®) is a US Army Corps of Engineers (USACE) Initiative and Program that promotes more sustainable practices for delivering economic, environmental, and social benefits through collaborative processes. As the number and variety of EWN® projects continue to grow and evolve, there is an increasing opportunity to improve how to quantify their benefits and communicate them to the public. Recent advancements in remote sensing technologies are significant for EWN® because they can provide project-relevant detail across a large areal extent, in which traditional survey methods may be complex due to site access limitations. These technologies encompass a suite of spatial and temporal data collection and processing techniques used to characterize Earth's surface properties and conditions that would otherwise be difficult to assess. This document aims to describe the general underpinnings and utility of remote sensing technologies and applications for use: (1) in specific phases of the EWN® project life cycle; (2) with specific EWN® project types; and (3) in the quantification and assessment of project implementation, performance, and benefits.

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“…Therefore, the NDVI serves as a primary measure of vegetation condition, function, recovery, sustainability, and useful estimates of primary productivity, and wetland species distributions as well as resistance to, and recovery from, anthropogenic activities and episodic events (Carle 2013;Suir and Sasser 2017;Suir, Saltus, Reif 2018;Suir and Sasser 2019a, b). This investigation broadens the application of wetland classifications and NDVI analysis methods presented in previous EMRRP-funded research (Suir and Sasser 2019b) to assess baseline conditions in a forested wetland habitat using highresolution satellite imagery and NDVI as a measure of vegetation productivity and vigor.…”

Section: Introductionmentioning

confidence: 96%

Remotely sensed habitat assessment of bottomland hardwood and swamp habitat : West Shore Lake Pontchartrain Hurricane Storm Damage Risk Reduction System potential impact area

Saltus¹,

Suir²

2021

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This study used remote sensing techniques to identify and assess the current condition of bottomland hardwood (BLH) and swamp habitats within the West Shore Lake Pontchartrain (WSLP) hurricane storm-damage risk reduction system (HSDRRS) project area. This effort provides baseline knowledge of the location and quality of these habitats prior to the construction of the WSLP HSDRRS project. The resultant products will assist the USACE—New Orleans District (MVN) by informing ecosystem decision-making related to environmental assessments.

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Use of NDVI and Landscape Metrics to Assess Effects of Riverine Inputs on Wetland Productivity and Stability

Cited by 15 publications

References 58 publications

Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats

Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats

Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance

Remotely sensed habitat assessment of bottomland hardwood and swamp habitat : West Shore Lake Pontchartrain Hurricane Storm Damage Risk Reduction System potential impact area

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