Title:

Conifer seedling success likelihood maps derived from historical aerial photograph orthomosaic and vegetation land classification in the Mendocino National Forest

Author(s):

Drury, Stacy A.; Wright, Jamie L.

Publication Year:

2026

How to Cite:

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Drury, Stacy A.; Wright, Jamie L. 2026. Conifer seedling success likelihood maps derived from historical aerial photograph orthomosaic and vegetation land classification in the Mendocino National Forest. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2026-0045

Abstract:

We modeled conifer seedling success likelihood utilizing nine different scenarios for the Mendocino National Forest (MNF) in California as an effort to aid land managers in their decision-making process for post-fire replanting. The conifer seedling success likelihood maps were generated considering different land type designations as well as present and future conditions. The different land type designations were “Conifer with regions excluded,” “Conifer,” and “Entire MNF” in which three seedling likelihood maps were generated in each to consider two different future and one present scenario. For the “Conifer with regions excluded” models, the conifer layer - determined from a historical aerial photography orthomosaic (HAPO) - had the following regions removed: research areas, wilderness areas, private land, and meadows. All input layers were subsequently masked to this layer. The “Conifer” models did not exclude any land-uses but any regions that were not historically covered by conifers were excluded from these models. All input layers were subsequently masked to this layer. The “Entire MNF” models did not exclude any regions within the MNF boundary except for areas that had data gaps derived from missing aerial photographs. The input parameters included two different climate refugia datasets which were included as two options for future scenarios in the models. Additional input parameters were root zone water storage capacity, eight directional maximum wind speed layers, fire severity, aspect, slope, and climatic water deficit. The climate refugia layers differed by the number of GCMs included for the climate refugia consensus models as well as their input vegetation data. Climate refugia layers were ranked higher if there was more consensus on climate refugia areas - meaning that the different input GCMs agreed regarding if a pixel would not be impacted by climate change. Directional wind data were included since wind direction and wind speed contribute to increased fire behavior potentials. Areas with high modeled maximum wind speed were deemed less likely to support seedling survival to maturity due to potentially high intensity fire behavior. Fire severity data displayed the likelihood that an area will burn at high fire severity (high potential fire-caused mortality) which lowers the potential for planted seedlings to survive future wildfires. Aspect was used in conjunction with fire severity data from the August Complex Fire (2019) and Ranch Fire (2018). Aspects possessing more pixels that burned at high severity in the abovementioned fires were less desirable under the assumption they may burn again at high severity. Available water storage - a soil parameter- was used to determine the dryness of the soil which can contribute to plant drought stress and fire severity. Slope data were more relevant to replanting in that steeper slopes are observed as less favorable. Climatic water deficit data were included in all models in which there were separate data files for future versus present scenarios and provides insight on ecosystem drought stress and ultimately seedling survival. Root zone water storage capacity data were included in future models where deeper root water access was observed as favorable for seedling success. A land classification layer was only used as an input for models run for the “Entire MNF” to rank pixels classified as “Conifer” most favorable for planting. This data publication includes separate geodatabases for the three different land designations. A geodatabase that possesses auxiliary data is also included.

Keywords:

biota; planningCadastre; Climate change; Ecological adaptation; Ecology, Ecosystems, & Environment; Landscape ecology; Fire; Fire ecology; Forest & Plant Health; Climate effects; Natural Resource Management & Use; Forest management; historical vegetation; suitability; seedling success; California; Mendocino National Forest

Metrics:

Visit count : 16 0
Download count: 2
More details

Data Access:

• View metadata (HTML)
• View file index (HTML), which lists all files in this data publication and short description of their contents
• Download all files below for the complete publication:
  • RDS-2026-0045_Metadata_Fileindex.zip (44.56 KB; Checksum)
  • RDS-2026-0045.zip (835.34 MB; Checksum)

Need information about Using our Formats?