Tamzen K. Stringham

Assessing vegetation conditions in expansive rangeland ecosystems has long been challenging. Recent advancements in remote sensing technologies have provided new opportunities for improving this assessment process. In this study conducted at two ranch sites in Nevada, we integrated existing line-point-intercept monitoring data with additional field observations to evaluate the vegetation condition or ”State” at various monitoring points. We then used machine learning techniques to classify gridded raster datasets, aligning them with existing state-and-transition models (STMs) specific to each study area. Leveraging Landsat-derived fractional cover datasets and climate and soil predictors, we aimed to predict vegetation State within specific Vegetation Great Groups (VGGs). The resulting vegetation State maps were then combined to generate a cohesive representation of vegetation conditions across the study sites. Our analysis revealed that relative functional group cover emerged as the strongest predictor of vegetation state and ecological processes at the site level. However, we encountered variations in state mapping accuracy ranging from approximately 14% to 44% error. These discrepancies were influenced by factors such as study location, landscape heterogeneity, availability of training data, and species-specific challenges, all of which complicate the accurate classification of remote sensing datasets.

Canopy interception loss is an important component of the water budget for many ecosystems, and may be particularly influential in semi‐arid shrublands where water is limiting. In this experiment, we quantified interception loss by mountain big sagebrush (Artemisia tridentata spp. vaseyana)using simulated rainfall events in the field. Various levels of canopy cover and rainfall intensities were tested to measure their effects on net precipitation and interception loss. Additionally, the comparisons were made between three methods of measuring shrub cover to enhance the scalability of results. Stands of sagebrush from 20% to 50% canopy cover intercepted 18.5 ± 12.5% of incoming precipitation. Percent interception loss differed by 13.7% between the high and low cover classes, indicating that net precipitation may be significantly reduced under mature, dense stands of sagebrush. Hemispherical photography was a viable method for estimating canopy cover in this vegetation type. Interception loss by sagebrush was 14% lower on average than pinyon and juniper measured in the same watershed. Results from this study quantified the effects of sagebrush canopy on rainfall interception and improved understanding of vegetation dynamics in the sagebrush steppe.

Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m², while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm² or area averaged regions can be analyzed with scales up to 1 km². For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson’s correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.