Remote Sensing Analysis to Quantify Change in Woodland Canopy Cover on the San Carlos Apache Reservation, Arizona, USA (1935 vs. 2017)

Middleton, Barry R.; Norman, Laura M.

doi:10.3390/land10040393

Cited by 4 publications

(3 citation statements)

References 20 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applications of remote sensing using both satellite-and aerialbased imagery sources have been widely used for the semicontinuous monitoring of the vegetation at a broad-scale on the San Carlos Apache Reservation (Petrakis et al, 2016;Wu et al, 2016;Norman et al, 2018;Middleton and Norman, 2021). However, riparian zones are extremely dynamic and complex systems structurally, and cover only a small portion of the landscape (Naiman and Décamps, 1997).…”

Section: Challenges and Benefits Of Remote Sensing Within Riparian Zonesmentioning

confidence: 99%

Advances in characterizing and monitoring land cover/use and associated ecosystem changes using remote sensing data

2024

Frontiers Research Topics

View full text Add to dashboard Cite

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

Section: Challenges and Benefits Of Remote Sensing Within Riparian Zonesmentioning

confidence: 99%

Advances in characterizing and monitoring land cover/use and associated ecosystem changes using remote sensing data

2024

Frontiers Research Topics

View full text Add to dashboard Cite

show abstract

“…Applications of remote sensing using both satellite-and aerialbased imagery sources have been widely used for the semicontinuous monitoring of the vegetation at a broad-scale on the San Carlos Apache Reservation (Petrakis et al, 2016;2018;Wu et al, 2016;Norman et al, 2018;Middleton and Norman, 2021). However, riparian zones are extremely dynamic and complex systems structurally, and cover only a small portion of the landscape (Naiman and Décamps, 1997).…”

Section: Challenges and Benefits Of Remote Sensing Within Riparian Zonesmentioning

confidence: 99%

“…We hypothesize that other micro-scale factors may also be impacting changes, such as climate variability (i.e., temperature and precipitation regimes), proximity to agriculture, and geomorphic-based shading effects we observed in the spatially explicit raster products. These detailed changes can be challenging when analyzing satellite imagery (Middleton and Norman, 2021). We largely generalized conditions across various reaches of the San Carlos and Gila Rivers.…”

Section: Challenges and Benefits Of Remote Sensing Within Riparian Zonesmentioning

confidence: 99%

Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: informing Tribal restoration priorities

Petrakis

Norman

Middleton

2023

Front. Environ. Sci.

View full text Add to dashboard Cite

Riparian systems across the Southwest United States are extremely valuable for the human and ecological communities that engage with them. However, they have experienced substantial changes and stresses over the past century, including non-native vegetation expansion, vegetation die-offs, and increased fire activity. Vegetation management approaches, such as ecological restoration, may address some of these stressors as well as reduce the risk of future impacts. We apply remote sensing to inform restoration priorities along the upper Gila River within the San Carlos Apache Reservation and Upper Gila River watershed. First, we develop a spatially and temporally explicit trend analysis across three observed climate periods (1985–1993, 1993–2014, 2014–2021) using the Landsat-derived indices to quantify changes in riparian vegetation conditions. These maps can be used to identify areas potentially more at risk for degradation. Second, we analyze changes in riparian vegetation within a climate framework to better understand trends and the potential effect of climate change. Vegetation greenness has largely increased throughout the watershed despite intensifying drought conditions across our study period, though areas within the lower watershed have shown increased stress and higher rates of wildfire and other disturbances over the past 5-years. Nevertheless, small-scale restoration activities appear to show improving vegetation conditions, suggesting efficacy of these restoration activities. Results from this study may be integrated with restoration objectives to develop a restoration plan that will help riparian vegetation communities adapt to change.

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

Remote Sensing Analysis to Quantify Change in Woodland Canopy Cover on the San Carlos Apache Reservation, Arizona, USA (1935 vs. 2017)

Cited by 4 publications

References 20 publications

Advances in characterizing and monitoring land cover/use and associated ecosystem changes using remote sensing data

Advances in characterizing and monitoring land cover/use and associated ecosystem changes using remote sensing data

Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: informing Tribal restoration priorities

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

Contact Info

Product

Resources

About