Matching Items (2)

Filtering by

Clear all filters

150960-Thumbnail Image.png

Quantifying percent-cover in Prescott National Forest, Arizona through the integration of Landsat imagery, vegetation Indices, and spatial transformations

Description

Accurate characterization of forest canopy cover from satellite imagery hinges on the development of a model that considers the level of detail achieved by field methods. With the improved precision

Accurate characterization of forest canopy cover from satellite imagery hinges on the development of a model that considers the level of detail achieved by field methods. With the improved precision of both optical sensors and various spatial techniques, models built to extract forest structure attributes have become increasingly robust, yet many still fail to address some of the most important characteristics of a forest stand's intricate make-up. The objective of this study, therefore, was to address canopy cover from the ground, up. To assess canopy cover in the field, a vertical densitometer was used to acquire a total of 2,160 percent-cover readings from 30 randomly located triangular plots within a 6.94 km2 study area in the central highlands of the Bradshaw Ranger District, Prescott National Forest, Arizona. Categorized by species with the largest overall percentage of cover observations (Pinus ponderosa, Populus tremuloides, and Quercus gambelii), three datasets were created to assess the predictability of coniferous, deciduous, and mixed (coniferous and deciduous) canopies. Landsat-TM 5 imagery was processed using six spectral enhancement algorithms (PCA, TCT, NDVI, EVI, RVI, SAVI) and three local windows (3x3, 5x5, 7x7) to extract and assess the various ways in which these data were expressed in the imagery, and from those expressions, develop a model that predicted percent-cover for the entire study area. Generally, modeled cover estimates exceeded actual cover, over predicting percent-cover by a margin of 9-13%. Models predicted percent-cover more accurately when treated with a 3x3 local window than those treated with 5x5 and 7x7 local windows. In addition, the performance of models defined by the principal components of three vegetation indices (NDVI, EVI, RVI) were superior to those defined by the principal components of all four (NDVI, EVI, RVI, SAVI), as well as the principal and tasseled cap components of all multispectral bands (bands 123457). Models designed to predict mixed and coniferous percent-cover were more accurate than deciduous models.

Contributors

Agent

Created

Date Created
  • 2012

156753-Thumbnail Image.png

Monitoring algal abundance and water quality in Arizona reservoirs through field sampling and remote sensing

Description

Safe, readily available, and reliable sources of water are an essential component of any municipality’s infrastructure. Phoenix, Arizona, a southwestern city, has among the highest per capita water use in

Safe, readily available, and reliable sources of water are an essential component of any municipality’s infrastructure. Phoenix, Arizona, a southwestern city, has among the highest per capita water use in the United States, making it essential to carefully manage its reservoirs. Generally, municipal water bodies are monitored through field sampling. However, this approach is limited spatially and temporally in addition to being costly. In this study, the application of remotely sensed reflectance data from Landsat 7’s Enhanced Thematic Mapper Plus (ETM+) and Landsat 8’s Operational Land Imager (OLI) along with data generated through field-sampling is used to gain a better understanding of the seasonal development of algal communities and levels of suspended particulates in the three main terminal reservoirs supplying water to the Phoenix metro area: Bartlett Lake, Lake Pleasant, and Saguaro Lake. Algal abundances, particularly the abundance of filamentous cyanobacteria, increased with warmer temperatures in all three reservoirs and reached the highest comparative abundance in Bartlett Lake. Prymnesiophytes (the class of algae to which the toxin-producing golden algae belong) tended to peak between June and August, with one notable peak occurring in Saguaro Lake in August 2017 during which time a fish-kill was observed. In the cooler months algal abundance was comparatively lower in all three lakes, with a more even distribution of abundance across algae classes. In-situ data from March 2017 to March 2018 were compared with algal communities sampled approximately ten years ago in each reservoir to understand any possible long-term changes. The findings show that the algal communities in the reservoirs are relatively stable, particularly those of the filamentous cyanobacteria, chlorophytes, and prymnesiophytes with some notable exceptions, such as the abundance of diatoms, which increased in Bartlett Lake and Lake Pleasant. When in-situ data were compared with Landsat-derived reflectance data, two-band combinations were found to be the best-estimators of chlorophyll-a concentration (as a proxy for algal biomass) and total suspended sediment concentration. The ratio of the reflectance value of the red band and the blue band produced reasonable estimates for the in-situ parameters in Bartlett Lake. The ratio of the reflectance value of the green band and the blue band produced reasonable estimates for the in-situ parameters in Saguaro Lake. However, even the best performing two-band algorithm did not produce any significant correlation between reflectance and in-situ data in Lake Pleasant. Overall, remotely-sensed observations can significantly improve our understanding of the water quality as measured by algae abundance and particulate loading in Arizona Reservoirs, especially when applied over long timescales.

Contributors

Agent

Created

Date Created
  • 2018