Assessment of volunteered geographic information for vegetation mapping.

Vegetation mapping requires extensive field data for training and validation. Volunteered geographic information in the form of geotagged photos of identified plants has the potential to serve as a supplemental data source for vegetation mapping projects. In this study, we compare the locations of specific taxa from the iNaturalist platform to locations identified on both a fine-scale vegetation map and high-resolution ortho-imagery in open-canopy shrubland in San Clemente Island, CA. Due to positional uncertainty associated with the iNaturalist observations, as well as the presence-only nature of the data, it was not possible to perform a traditional accuracy assessment. We instead measured the distance between the location recorded by an iNaturalist observer for a given taxon and the closest mapped individual of that taxon. This distance was within 10 m for a majority of the observations (64%). When comparing the iNaturalist location to the closest individual detected through image interpretation, 87% of the observations were within 10 m. The discrepancy in agreement between the vegetation map and imagery is likely due to mapping errors. While iNaturalist data come with important limitations, the platform is an excellent resource for supporting vegetation mapping and other ecological applications.

Use of ultra-high spatial resolution aerial imagery in the estimation of chaparral wildfire fuel loads.

Development of methods that more accurately estimate spatial distributions of fuel loads in shrublands allows for improved understanding of ecological processes such as wildfire behavior and postburn recovery. The goal of this study is to develop and test remote sensing methods to upscale field estimates of shrubland fuel to broader-scale biomass estimates using ultra-high spatial resolution imagery captured by a light-sport aircraft. The study is conducted on chaparral shrublands located in eastern San Diego County, CA, USA. We measured the fuel load in the field using a regression relationship between basal area and aboveground biomass of shrubs and estimated ground areal coverage of individual shrub species by using ultra-high spatial resolution imagery and image processing routines. Study results show a strong relationship between image-derived shrub coverage and field-measured fuel loads in three even-age stands that have regrown approximately 7, 28, and 68 years since last wildfire. We conducted ordinary least square analysis using ground coverage as the independent variable regressed against biomass. The analysis yielded R 2 values ranging from 0.80 to 0.96 in the older stands for the live shrub species, while R 2 values for species in the younger stands ranged from 0.32 to 0.89. Pooling species-based data into larger sample sizes consisting of a functional group and all-shrub classes while obtaining suitable linear regression models supports the potential for these methods to be used for upscaling fuel estimates to broader areal extents, without having to classify and map shrubland vegetation at the species level.

Perspectives on disconnects between scientific information and management decisions on post-fire recovery in Western US.

Environmental regulations frequently mandate the use of "best available" science, but ensuring that it is used in decisions around the use and protection of natural resources is often challenging. In the Western US, this relationship between science and management is at the forefront of post-fire land management decisions. Recent fires, post-fire threats (e.g. flooding, erosion), and the role of fire in ecosystem health combine to make post-fire management highly visible and often controversial. This paper uses post-fire management to present a framework for understanding why disconnects between science and management decisions may occur. We argue that attributes of agencies, such as their political or financial incentives, can limit how effectively science is incorporated into decision-making. At the other end of the spectrum, the lack of synthesis or limited data in science can result in disconnects between science-based analysis of post-fire effects and agency policy and decisions. Disconnects also occur because of the interaction between the attributes of agencies and the attributes of science, such as their different spatial and temporal scales of interest. After offering examples of these disconnects in post-fire treatment, the paper concludes with recommendations to reduce disconnects by improving monitoring, increasing synthesis of scientific findings, and directing social-science research toward identifying and deepening understanding of these disconnects.