Feedback of coastal marshes to climate change: Long‐term phenological shifts

Abstract

Coastal marshes are important carbon sinks facing serious threats from climatic stressors. Current research reveals that the growth of individual marsh plants is susceptible to a changing climate, but the responses of different marsh systems at a landscape scale are less clear. Here, we document the multi‐decadal changes in the phenology and the area of the extensive coastal marshes in Louisiana, USA, a representative of coastal ecosystems around the world that currently experiencing sea‐level rise, temperature warming, and atmospheric CO2 increase. The phenological records are constructed using the longest continuous satellite‐based record of the Earth's ecosystems, the Landsat data, and an advanced modeling technique, the nonlinear mixed model. We find that the length of the growing seasons of the intermediate and brackish marshes increased concomitantly with the atmospheric CO2 concentration over the last 30 years, and predict that such changes will continue and accelerate in the future. These phenological changes suggest a potential increase in CO2 uptake and thus a negative feedback mechanism to climate change. The areas of the freshwater and intermediate marshes were stable over the period studied, but the areas of the brackish and saline marshes decreased substantially, suggesting ecosystem instability and carbon storage loss under the anticipated sea‐level rise. The marshes' phenological shifts portend their potentially critical role in climate mitigation, and the different responses among systems shed light on the underlying mechanisms of such changes.

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

“…Additionally, certain combinations of vegetation indices boost some crops' spectral traits while inhibiting others [16]. The wide use of RS imagery for monitoring land and environmental changes was proofed for soil sealing [17][18][19], human or natural factors that cause the loss of forests [20][21][22], effects of global warming [23,24], a wildfire's damage [25,26], and additional humanmade and natural dynamics. Particularly for SD investigations, which are frequently thought of as being over a lengthy period of time, data acquisition costs and time of coverage are the main limits of observation data such as SPOT [27,28] and Sentinel-2 [29,30].…”

Net land gain or loss for two Mississippi River diversions: Caernarvon and Davis Pond

“…The wetland area was estimated using the C version of the Function Mask (CFMask) with the Landsat CDRs. The overall accuracy of the CFMask to estimate the wetland area is 0.89 ± 0.04% (verified with the USGS Digital Orthophoto Quadrangle) (Mo et al ).…”

“…The first data set (Data 1) was primarily developed to measure seasonal changes in wetland greenness (revealing phenological changes; Mo et al ). We used 91 cloud‐free Landsat Climatic Data Records (CDRs) images (mosaics of Scenes of Path 22 Row 40 and Path 22 Row 39) collected from 1985 to 2014 to estimate the wetland area (range: 1–4 images/year).…”

“…The results from using the two data sets are classified herein as either land or water, but not wetland, because some pixels may be roads or structures, even though overwhelmingly composed of emergent wetland. The first data set (Data 1) was primarily developed to measure seasonal changes in wetland greenness (revealing phenological changes; Mo et al 2019). We used 91 cloud-free Landsat Climatic Data Records (CDRs) images (mosaics of Scenes of Path 22 Row 40 and Path 22 Row 39) collected from 1985 to 2014 to estimate the wetland area (range: 1-4 images/year).…”

Climate drivers and human impacts shape 35‐year trends of coastal wetland health and composition in an urban region

“…The Landsat satellite imagery archive offers global coverage and continuous 16-day image collections dating back to 1984, which allows for multidecadal change analyses over large geographic areas (Kennedy et al, 2014;Pasquarella et al, 2016;Wulder et al, 2012;Young et al, 2017). Landsat has been used to monitor wetland habitat distributions and zonation (Bunting et al, 2018;Kearney et al, 2002;Rogers et al, 2017), identify disturbance events (Steyer et al, 2013), estimate biomass and carbon storage (Byrd et al, 2014(Byrd et al, , 2018Doughty et al, 2021;Klemas, 2013;Mo et al, 2018), and to identify patterns and drivers of wetland biomass, health, phenology, and greenness in coastal ecosystems (Brooke et al, 2017;Buffington et al, 2018;Cavanaugh et al, 2013Cavanaugh et al, , 2018Hamilton & Casey, 2016;Kearney et al, 2002;Mo et al, 2015Mo et al, , 2019O'Donnell & Schalles, 2016;Wu et al, 2017). Trends in wetland greening or browning revealed by satellite-based vegetation indices can help uncover long-term changes to biological (plant productivity and growth) and physical (cover composition) properties in vegetated ecosystems (Myers-Smith et al, 2020;Sulla-Menashe et al, 2018).…”