Comparison of remote sensing indices for monitoring of desert cienegas

Wilson, Natalie R.; Norman, Laura M.; Villarreal, Miguel L.; Gass, Leila; Tiller, Ron; Salywon, Andrew M.

doi:10.1080/15324982.2016.1170076

Cited by 28 publications

(25 citation statements)

References 51 publications

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“…(5) Tributary: The riparian area of reaches that joined the downstream zone below the restoration zone. To observe the general trend in the study area, an additional (6) upland zone was created (Wilson et al 2016). To create the upland zone, 10 points were randomly distributed through the study area using ArcGIS Create Random Points tool.…”

Section: Zone Delineationmentioning

confidence: 99%

“…To examine spatiotemporal changes in vegetation greenness and water content, a zonebased analysis was performed (Weiss et al 2004;Wilson et al 2016). For linear regression analysis, pixels were considered separate samples, resulting in high sample sizes ( Table 1).…”

Section: Zonal Trend Analysismentioning

confidence: 99%

“…Specifically, remote sensing has been used in the region to evaluate vegetation dynamics (Bruce et al 2008;P. L. Nagler, Glenn, and Huete 2001), to assess the local effects of restoration on riparian habitat (L. Norman et al 2014;Wilson et al 2016), and to map changes in riparian vegetation (P. Nagler et al 2005;Bruce et al 2008;Villarreal, Van Leeuwen, and Romo-Leon 2012;Nguyen et al 2015). The Landsat series of satellite platforms offer mid-resolution (30 m) imagery over the longest period of time for any publically available imagery and has been used to evaluate spatial and temporal trends in vegetation-water dynamics (Henshaw et al 2013;L.…”

Section: Introductionmentioning

confidence: 99%

“…The normalized difference infrared index (NDII) developed by Hardisky et al (1984) correlates with vegetation water content and canopy water thickness (Serrano et al 2000;Jackson et al 2004;Hunt and Yilmaz 2007). Wilson et al (2016) considered the applicability of spectral indices, including NDVI and NDII, for mapping and monitoring cienega health and spatial extent through time using annual Landsat Thematic Mapper (TM) imagery. Results demonstrate that the NDII, calculated using Landsat TM band 5, outperforms the other indices, including the NDVI, normalized difference water index (Mcfeeters 1996), and NDII, calculated with Landsat TM band 7 (Chuvieco et al 2002), in differentiating cienegas from riparian and upland sites, and was the best means to analyze change in cienega vegetation in arid landscapes.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Wilson

Norman

2018

International Journal of Remote Sensing

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Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann-Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

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Section: Zone Delineationmentioning

confidence: 99%

Section: Zonal Trend Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Wilson

Norman

2018

International Journal of Remote Sensing

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“…TWI was positively correlated with both C3 and C4 AGB. The index determines the spatial variability in soil moisture conditions (Wilson et al, ), which boost vegetation cover, growth and productivity; this possibly explains it had a positive association with species AGB in this study. The influence of TWI on species cover and growth was also reported by Pei et al () and Seutloali, Dube, and Mutanga () in modelling erosion, using topographical derivatives.…”

Section: Discussionmentioning

confidence: 49%

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

2019

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Seasonal climate and topography influence C3 and C4 grass species aboveground biomass (AGB). Climate change further threatens these grasses AGB, thereby compromising their ability to provide ecosystem goods and services. This emphasises the need to monitor their AGB for well‐informed management. New‐generation sensors, with improved resolution capabilities present an opportunity to explore C3 and C4 AGB. This study therefore investigated the response of remotely sensed C3 and C4 grasses AGB to seasonal climate and topography. Overall, the spatial and temporal responses of AGB due to seasonal climate and topography were observed across the study area. For example, in March, a marked increase in C4 AGB was associated with an increase in rainfall, with the highest significant positive relationship (R2 = 0.82, p < 0.005). Elevation had very significant positive relationship (R2 = 0.84; p < 0.005) with C3 and highest negative (R2 = −0.77; p < 0.005) with C4 AGB. During the winter fall, AGB significantly decreased from averages of 2.592 and 1.101 kg/m2 in winter (May), to 0.718 and 0.469 kg/m2 in August, for C3 and C4 grasses, respectively. These findings provide a key step in monitoring rangelands and assessing management practices to boost productivity.

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The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

Soulard,

Walker,

Smith

et al. 2024

Earth Surf Processes Landf

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The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

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Comparison of remote sensing indices for monitoring of desert cienegas

Cited by 28 publications

References 51 publications

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

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