scholarly journals The magnitude, direction, and tempo of forest change in Greater Yellowstone in a warmer world with more fire

2021 ◽  
Author(s):  
Monica G. Turner ◽  
Kristin H. Braziunas ◽  
Winslow D. Hansen ◽  
Tyler J. Hoecker ◽  
Werner Rammer ◽  
...  
2020 ◽  
Vol 12 (19) ◽  
pp. 3226
Author(s):  
Daniel Cunningham ◽  
Paul Cunningham ◽  
Matthew E. Fagan

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.


The Holocene ◽  
2021 ◽  
pp. 095968362110116
Author(s):  
Maegen L Rochner ◽  
Karen J Heeter ◽  
Grant L Harley ◽  
Matthew F Bekker ◽  
Sally P Horn

Paleoclimate reconstructions for the western US show spatial variability in the timing, duration, and magnitude of climate changes within the Medieval Climate Anomaly (MCA, ca. 900–1350 CE) and Little Ice Age (LIA, ca. 1350–1850 CE), indicating that additional data are needed to more completely characterize late-Holocene climate change in the region. Here, we use dendrochronology to investigate how climate changes during the MCA and LIA affected a treeline, whitebark pine ( Pinus albicaulis Engelm.) ecosystem in the Greater Yellowstone Ecoregion (GYE). We present two new millennial-length tree-ring chronologies and multiple lines of tree-ring evidence from living and remnant whitebark pine and Engelmann spruce ( Picea engelmannii Parry ex. Engelm.) trees, including patterns of establishment and mortality; changes in tree growth; frost rings; and blue-intensity-based, reconstructed summer temperatures, to highlight the terminus of the LIA as one of the coldest periods of the last millennium for the GYE. Patterns of tree establishment and mortality indicate conditions favorable to recruitment during the latter half of the MCA and climate-induced mortality of trees during the middle-to-late LIA. These patterns correspond with decreased growth, frost damage, and reconstructed cooler temperature anomalies for the 1800–1850 CE period. Results provide important insight into how past climate change affected important GYE ecosystems and highlight the value of using multiple lines of proxy evidence, along with climate reconstructions of high spatial resolution, to better describe spatial and temporal variability in MCA and LIA climate and the ecological influence of climate change.


2016 ◽  
Vol 173 ◽  
pp. 326-338 ◽  
Author(s):  
Christophe Sannier ◽  
Ronald E. McRoberts ◽  
Louis-Vincent Fichet

2011 ◽  
Vol 108 (32) ◽  
pp. 13165-13170 ◽  
Author(s):  
Anthony L. Westerling ◽  
Monica G. Turner ◽  
Erica A. H. Smithwick ◽  
William H. Romme ◽  
Michael G. Ryan

2021 ◽  
Vol 13 (9) ◽  
pp. 1743
Author(s):  
Daniel Paluba ◽  
Josef Laštovička ◽  
Antonios Mouratidis ◽  
Přemysl Štych

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.


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