Geology topics

John Dwyer

Publications and source records attributed to John Dwyer.

4 recordsLinked to original sources

USGS2014

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

California↗

USGS2011

Terrestrial essential climate variables (ECVs) at a glance

The Global Terrestrial Observing System, Global Climate Observing System, World Meteorological Organization, and Committee on Earth Observation Satellites all support consistent global land observations and measurements. To accomplish this goal, the Global Terrestrial Observing System defined 'essential climate variables' as measurements of atmosphere, oceans, and land that are technically and economically feasible for systematic observation and that are needed to meet the United Nations Framework Convention on Climate Change and requirements of the Intergovernmental Panel on Climate Change. The following are the climate variables defined by the Global Terrestrial Observing System that relate to terrestrial measurements. Several of them are currently measured most appropriately by in-place observations, whereas others are suitable for measurement by remote sensing technologies. The U.S. Geological Survey is the steward of the Landsat archive, satellite imagery collected from 1972 to the present, that provides a potential basis for deriving long-term, global-scale, accurate, timely and consistent measurements of many of these essential climate variables.

Scientific Investigations Map↗

USGS2011

Developing climate data records and essential climate variables from landsat data

The series of Landsat missions has compiled the longest record of satellite observation of the Earth’s land surface, extending for more than 38 years for most areas of the globe. Landsat data are particularly important as long term climate data records because the scale of observation is sufficient to differentiate between natural and human drivers of land cover change. The USGS has established consistent radiometric calibration across the Landsat TM and ETM+ sensors, and have extended the calibration back to the earlier MSS sensors. The USGS is developing capabilities to create fundamental climate data records (FCDRs), thematic climate data records (TCDRs), and essential climate variables (ECVs) from the Landsat data archive. Two high priority TCDRs were identified: surface reflectance and land surface temperature because they have direct application or are required as input to the generation of ECVs. We will focus development on a few of the terrestrial ECVs that have a high potential for being derived from Landsat data, that include land cover, albedo, fire disturbance, surface water, snow and ice, and leaf area index (LAI). We are collaborating with scientists who have demonstrated successful algorithm development and application of these science products to develop a framework of processing capabilities to support research projects and land management applications, along with an independent strategy for product validation. Our goal is to scale the creation and validation of these products from specific sites in the conterminous U.S. and Alaska, for extension to continental and global scales.

Conference Paper↗

USGS1997

Validation of behave fire behavior predictions in oak savannas

Prescribed fire is a valuable tool in the restoration and management of oak savannas. BEHAVE, a fire behavior prediction system developed by the United States Forest Service, can be a useful tool when managing oak savannas with prescribed fire. BEHAVE predictions of fire rate-of-spread and flame length were validated using four standardized fuel models: Fuel Model 1 (short grass), Fuel Model 2 (timber and grass), Fuel Model 3 (tall grass), and Fuel Model 9 (hardwood litter). Also, a customized oak savanna fuel model (COSFM) was created and validated. Results indicate that standardized fuel model 2 and the COSFM reliably estimate mean rate-of-spread (MROS). The COSFM did not appreciably reduce MROS variation when compared to fuel model 2. Fuel models 1, 3, and 9 did not reliably predict MROS. Neither the standardized fuel models nor the COSFM adequately predicted flame lengths. We concluded that standardized fuel model 2 should be used with BEHAVE when predicting fire rates-of-spread in established oak savannas.

General Technical Report↗