Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Waller, Eric K.; Villarreal, Miguel L.; Poitras, Travis B.; Nauman, Travis; Duniway, Michael C.

doi:10.1016/j.jag.2018.07.008

Cited by 28 publications

(29 citation statements)

References 54 publications

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“…GEE synchronously archives the Landsat data from USGS and effectively performs data processing by using the cloud computing technology through utilizing millions of servers worldwide. This technology shows potentials and prospects of the emerging GEE platforms in large regional and long temporal scales for land cover change research, and several studies have proved its feasibility [31,71,72]. Therefore, the data storage, processing, and analysis will be conducted by using the GEE platform in the next exploration.…”

Section: Discussionmentioning

confidence: 99%

“…The breaks detected in the trend component represent an ephemeral disturbance [24,25], while the breaks detected in the seasonal component represent a land cover change because of the seasonal pattern (phenology) difference between different land cover categories [25,26]. BFAST has been applied in many different land cover categories, such as wetland [24], wildlife nature reserve [25], city [26], tropical dry forest [27], vegetation [28,29], agriculture [30], and abandoned energy development sites [31]. Although BFAST can identify land cover changes (question 1), acquire the process information of land cover change (question 2), and distinguish land cover change from other change types (question 3), it still cannot provide the detailed "from what, to what" information (question 4).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Liu

et al. 2020

Remote Sensing

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Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an ‘anytime’ forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Liu

et al. 2020

Remote Sensing

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“…Similarly, time‐series remote sensing work in the same region suggests only about one‐third of the 388 pads analyzed had at least 50% recovery of vegetative cover after 5 yr (Waller et al. ).…”

Section: Energy Exploration and Developmentmentioning

confidence: 90%

“…, Waller et al. ). Trends in oil and gas development, coupled with low rates of reclamation success, suggest a potential role of these activities in regional wind erosion and increases in atmospheric dust, particularly in dry and erodible soils of the southwestern United States (Fig.…”

Section: Energy Exploration and Developmentmentioning

confidence: 98%

Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world

et al. 2019

Self Cite

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Erosion by wind is one of the principal processes associated with land degradation in drylands and is a significant concern to land managers and policymakers globally. In the drylands of North America, millions of tons of soil are lost to wind erosion annually. Of the 60 million ha in the United States identified as most vulnerable to wind erosion (arid and dominated by fine sandy soils), 64% are managed by federal agencies (37 million ha). Here we review the drivers and consequences of wind erosion and dust emissions on drylands in the United States, with an emphasis on actionable responses available to policymakers and practitioners. We find that while dryland soils are often relatively stable when intact, disturbances including fire, domestic livestock grazing, and off‐highway vehicles can increase horizontal eolian flux by an order of magnitude, in some cases as much as 40‐fold. A growing body of literature documents the large‐scale impacts of deposited dust changing the albedo of mountain snow cover and in some cases reducing regional water supplies by ~5%. Predicted future increases in aridity and extreme weather events, including drought, will likely increase wind erosion and consequent dust generation. Under a drier and more variable future climate, new and existing soil‐ and vegetation‐disturbing practices may interact in synergistic ways, with dire consequences for environments and society that are unforeseen to many but fairly predictable given current scientific understanding. Conventional restoration and reclamation approaches, which often entail surface disturbance and rely on adequate moisture to prevent erosion, also carry considerable erosion risk especially under drought conditions. Innovative approaches to dryland restoration that minimize surface disturbance may accomplish restoration or reclamation goals while limiting wind erosion risk. Finally, multidisciplinary and multijurisdictional approaches and perspectives are necessary to understand the complex processes driving dust emissions and provide timely, context‐specific information for mitigating the drivers and impacts of wind erosion and dust.

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“…Vegetation cover and greenness metrics based on remote sensing can provide complementary monitoring following land treatments and restoration activities where ground‐based plots are absent or sparse (Honey‐Rosés, Maurer, Ramírez, & Corbera, ; Johnston, Beever, Merkle, & Chong, ; Malmstrom et al., ; Meroni et al., ; Waller, Villarreal, Poitras, Nauman, & Duniway, ). In particular, the long‐term and global extent of readily‐accessible imagery from Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) could be widely used to understand restoration trends across unmonitored areas or timeperiods (Cordell et al., ), as they are currently being used to deliver cover of different plant functional types across the entire western US (Jones et al., ).…”

Section: Discussionmentioning

confidence: 99%

Influence of climate, post‐treatment weather extremes, and soil factors on vegetation recovery after restoration treatments in the southwestern US

Copeland

Munson

Bradford

et al. 2019

Applied Vegetation Science

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Aims:Understanding the conditions associated with dryland vegetation recovery after restoration treatments is challenging due to a lack of monitoring data and high environmental variability over time and space. Tracking recovery trajectories with satellite-based vegetation indices can strengthen predictions of restoration outcomes across broad areas with varying environmental conditions. Location: Southwestern United States. Methods:We quantified the recovery trajectories of spring and summer soil-adjusted total vegetation index (SATVI) for 5-10 year periods following post-wildfire seeding or prescribed burns for 241 treatment sites and related SATVI to ground-based vegetation cover. We modeled SATVI based on time since treatment, yearly temperature and precipitation, weather extremes following treatment, soil available water capacity, invasive species presence, and treatment type. We also tested for the effects of environmental variables on trajectories, by examining interactions with years post-treatment.Results: Ground-based vegetation cover and SATVI were highly correlated. Most treatment sites had positive recovery rates for spring (82%) and summer (85%) SATVI. Several environmental variables affected vegetation recovery trajectories as indi-

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Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Cited by 28 publications

References 54 publications

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world

Influence of climate, post‐treatment weather extremes, and soil factors on vegetation recovery after restoration treatments in the southwestern US

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