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This indicator characterizes the proportion of the Tahoe Region within the montane zone (below 7,000 feet in elevation) dominated by stands of old growth conifers. Old growth forests tend to be more structurally and biologically complex and resilient to natural disturbances (such as wildfire) than younger forests, due to tree spacing and fire resistance of bark on mature trees, especially pines. Soil conditions, aspect, hill slope position, drought frequency, direct sunlight, fire suppression, climate patterns, time, and natural disturbance influence the extent and distribution of large-diameter trees. TRPA and Environmental Improvement Program (EIP) partners have adopted several policies, ordinances and implementing programs designed to promote the conservation and protection of old growth forests.
- Status
- Considerably Worse Than Target
- Trend
- Little or No Change
- Confidence
- Low
- Late seral growth covers 2.3 percent of the upper montane zone, considerably worse than the standard of 48 percent.
- Attainment of this threshold is expected in the future. In absence of catastrophic wildfire, it is estimated attainment will take about 100 years.
Late Seral - Ecobject 2010
- This indicator characterizes the proportion of the Tahoe Region dominated by stands of old growth conifers. Old growth forests are valued because they add to Tahoe’s ecological integrity by providing a greater diversity of life forms, including a variety of unique lichen, fungi, insects, vegetation and wildlife. Old forests tend to be more structurally complex and resilient to natural disturbances (wildfire) than younger forests, due to tree spacing and fire resistance of bark on mature trees, especially pines. This indicator does not measure the relative condition of this vegetation type.
- Soil conditions, aspect, hill slope position, drought frequency, direct sunlight, fire suppression, climate patterns, time and natural disturbance influence the extent and distribution of large-diameter trees (Beardsley et al., 1999; Taylor, 2007; Taylor et al., 2014). Historical land uses, such as clear-cut logging in the late 1800s, dramatically reduced the overall extent of old growth forests in the Region (USDA, 2001). Current forest management emphasizes thinning of overstocked conifer stands, which could result in faster growth rates due to less competition for resources. Changing climate conditions and drought influence growth rates and can increase susceptibility of forest to insect and disease. The Southern Sierra is experiencing a massive die off due to bark beetle. Incidence and outbreak in the Tahoe Region could dramatically alter the conclusions of this evaluation and estimated timelines to attainment.
No related projects or programs defined for this indicator.
- Considerably worse than target. The status of each elevation zone was determined to be considerably worse than target. The LTBMU EcObject product represents a novel forest-wide existing vegetation dataset produced by Region 5 Remote Sensing Lab that incorporates Light Detection and Ranging (LiDAR) into several facets of the mapping process. It is created from a multi-resolution segmentation of LiDAR-derived tree approximate objects and a 1-m canopy height model, which were then aggregated by stand and tree-level ecologic relationships. The resulting segments were then populated with a collection of traditional and contemporary metrics at scales that benefit both project-level planning and large-landscape analysis. Different combinations of multi-dimensional datasets were used to estimate metrics and thus accuracies vary depending upon both the data used and workflows that were generated.
- Insufficient data to determine trend. The estimates for elevation class transition into old growth are based on average growth rates and the assumption that transition will occur evenly over the 40 to 80 years it is expected for the standard to be in attainment.
- High. Methods have been updated. Calculated by using the extracted trees and their estimated DBH to assess the central tendency of those diameters within a polygon and is considered more appropriate than arithmetic mean to characterize a group of trees. Compared to the arithmetic mean, QMD assigns greater weight to larger trees and is used to calculate several metrics within this dataset. Further, QMD is the most accurate measurement of this dataset when assessing tree size due to the strengths of the LiDAR dominant tree extraction algorithms.
- Low. Methods have changed since 2015 making data incomparable.
- Low. Overall confidence takes the lower of the two confidence determinations.
No photos available.
No documents available.
April 2017. EcObject Vegetation Map v2.1 Product Guide. USDA Forest Service, R5 Remote Sensing Lab.