Remnant midwestern oak savannas in the USA have been altered by fire suppression and the encroachment of woody evergreen trees and shrubs. The Gus Engeling Wildlife Management Area (GEWMA) near Palestine, Texas represents a relatively intact southern example of thickening and evergreen encroachment in oak savannas. In this study, 18 images from the CHRIS/PROBA (Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy) sensor were acquired between June 2009 and October 2010 and used to explore variation in canopy dynamics among deciduous and evergreen trees and shrubs, and savanna grassland in seasonal leaf-on and leaf-off conditions. Nadir CHRIS images from the 11 useable dates were processed to surface reflectance and a selection of vegetation indices (VIs) sensitive to pigments, photosynthetic efficiency, and canopy water content were calculated. An analysis of temporal VI phenology was undertaken using a fishnet polygon at 90 m resolution incorporating tree densities from a classified aerial photo and soil type polygons. The results showed that the major differences in spectral phenology were associated with deciduous tree density, the density of evergreen trees and shrubs—especially during deciduous leaf-off periods—broad vegetation types, and soil type interactions with elevation. The VIs were sensitive to high densities of evergreens during the leaf-off period and indicative of a photosynthetic advantage over deciduous trees. The largest differences in VI profiles were associated with high and low tree density, and soil types with the lowest and highest available soil water. The study showed how time series of hyperspectral data could be used to monitor the relative abundance and vigor of desirable and less desirable species in conservation lands.
Invasive annual grasses are a growing global concern because they facilitate larger and more frequent fires in historically fuel-limited ecosystems. Forests of the western United States have remained relatively resistant to invasion by annual grasses and their subsequent impacts. However, where forests are adjacent to invaded areas, increased fire spread across ecotones could alter fire behavior and ecosystem resilience. In the Inland Northwest, USA, recent invasion by the annual grass ventenata (Ventenata dubia) has increased fine fuel loads and continuity in nonforest patches embedded within the forested landscape. Despite ventenata's rapid spread across the American West and growing management concern, little is known regarding how invasion influences fire within invaded vegetation types or its potential to alter landscape-scale fire and management practices. Here, we examine how the ventenata invasion alters simulated fire across forest-mosaic landscapes of the 7 million ha Blue Mountains Ecoregion using the large fire simulator (FSim) with custom fuel landscapes: present-day invaded versus historic uninvaded. Invasion increased simulated mean fire size, burn probability, and flame lengths throughout the ecoregion, and the strength of these impacts varied by location and scale.Changes at the ecoregion scale were relatively modest given that fine fuels increased in only 2.8% of the ecoregion where ventenata invaded historically fuel-limited vegetation types. However, strong localized changes were simulated within invaded patches (primarily dwarf-shrublands) and where invasion facilitated fire spread into nearby forests. Within invaded patches, burn probabilities increased by 45%, and higher flame lengths required fire management strategies to shift from direct to indirect attack, requiring large machinery. Forests with 25% of their neighborhood invaded experienced a 28% increase in burn probability and 16% increase in the probability of experiencing flame lengths likely to
The climate of the United States Northern Great Plains region is highly variable. Modelling of agriculture in this region and similar locations depends on the availability and quality of satellite and ground data for agro-climate variables. We evaluated tropical rainfall measuring mission (TRMM) multi-satellite preparation analysis (TMPA) precipitation, atmospheric infrared sounder (AIRS) surface air temperature, and AIRS relative air humidity (RH). A significant bias was found within the temperature and RH products and no bias but an insufficient rain event detection skill in the precipitation product (probability of detection *0.3). A linear correction of the temperature product removed the bias as well as lowered the root mean square deviation (RMSD). The bias-corrections for RH led to increased RMSD or worse correlation. For precipitation, the correlation between the satellite product and ground data improved if cumulative precipitation or only precipitation during the growing season was used.
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