Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States

2021 ◽  
Vol 260 ◽  
pp. 112445
Author(s):  
Yanhua Xie ◽  
Tyler J. Lark
Keyword(s):  
United States ◽  
Environmental Variables ◽  
Landsat Imagery

scholarly journals Technical Note: Development of a Landsat Time Series for Application in Forest Status Assessment in the Inland Northwest United States

2008 ◽  
Vol 23 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Russell N. Beck ◽  
Paul E. Gessler
Keyword(s):  
United States ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Satellite Image ◽  
Temporal Trends ◽  
Monitoring Cost ◽  
Small Pilot Study ◽  
Inland Northwest

Abstract The Inland Northwest United States contains extensive areas of complex, inaccessible terrain requiring significant resource expenditure for forest inventory, assessment, and monitoring. Cost-effective methods are necessary for annual broad-scale assessment of forest condition over complex terrain. Proficiency in the use of timely satellite image products along with spatial analysis tools such as geographic information systems can assist natural resource managers to understand regional dynamics and change within these landscapes. Satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI) can effectively assess and monitor vegetation dynamics of large remote areas. This article presents a newly developed archive and example methods for monitoring forest dynamics through the creation of NDVI departure maps. The NDVI products were generated from a time series of Landsat imagery (1989–2004) to derive both density distributions and a long-term departure from average map for any year or series of years within the time series archive. A preliminary application of the data is demonstrated showing temporal trends of vegetation dynamics relating to harvesting and management within two small pilot study areas in north Idaho.

A Cycle of Organizational Strategy: The Adaptation Process in U.S. Regulatory Agencies

1988 ◽  
Vol 9 (4) ◽  
pp. 573-597 ◽  
Author(s):  
Sirinimal Withane
Keyword(s):  
United States ◽  
Longitudinal Data ◽  
Conceptual Framework ◽  
Environmental Variables ◽  
Organizational Strategy ◽  
Regulatory Agencies ◽  
Adaptation Process ◽  
Growth Strategies ◽  
Strategic Changes ◽  
Strategy Making

This paper proposes a conceptual framework and examines the pattern of strategy- making in regulatory organizations. It analyzes longitudinal data on eight United States agencies. The paper suggests that strategic changes are triggered by various strategic, organizational and environmental variables, and concludes that there is a distinct cycle of strategies in American regulatory agencies. This cycle is progressive, moving from defensive through analytical to growth strategies.

A Climatology and Comparison of Parameters for Significant Tornado Events in the United States

2012 ◽  
Vol 27 (1) ◽  
pp. 106-123 ◽  
Author(s):  
Jeremy S. Grams ◽  
Richard L. Thompson ◽  
Darren V. Snively ◽  
Jayson A. Prentice ◽  
Gina M. Hodges ◽  
...  
Keyword(s):  
United States ◽  
Environmental Variables ◽  
The United States ◽  
Kinematic Parameters ◽  
Convective Systems ◽  
The Past ◽  
Standard Pressure ◽  
Wind Events ◽  
Convective Mode

Abstract A sample of 448 significant tornado events was collected, representing a population of 1072 individual tornadoes across the contiguous United States from 2000 to 2008. Classification of convective mode was assessed from radar mosaics for each event with the majority classified as discrete cells compared to quasi-linear convective systems and clusters. These events were further stratified by season and region and compared with a null-tornado database of 911 significant hail and wind events that occurred without nearby tornadoes. These comparisons involved 1) environmental variables that have been used through the past 25–50 yr as part of the approach to tornado forecasting, 2) recent sounding-based parameter evaluations, and 3) convective mode. The results show that composite and kinematic parameters (whether at standard pressure levels or sounding derived), along with convective mode, provide greater discrimination than thermodynamic parameters between significant tornado versus either significant hail or wind events that occurred in the absence of nearby tornadoes.

scholarly journals Utilizing Multi-Sensor Fire Detections to Map Fires in the United States

2014 ◽  
Vol XL-1 ◽  
pp. 161-166 ◽  
Author(s):  
S. M. Howard ◽  
J. J. Picotte ◽  
M. J. Coan
Keyword(s):  
United States ◽  
Landsat Imagery ◽  
Local Level ◽  
The United States ◽  
Burn Severity ◽  
Burned Area ◽  
Fire Occurrence ◽  
Cooperative Effort ◽  
The Us ◽  
Occurrence Records

In 2006, the Monitoring Trends in Burn Severity (MTBS) project began a cooperative effort between the US Forest Service (USFS) and the U.S.Geological Survey (USGS) to map and assess burn severity all large fires that have occurred in the United States since 1984. Using Landsat imagery, MTBS is mandated to map wildfire and prescribed fire that meet specific size criteria: greater than 1000 acres in the west and 500 acres in the east, regardless of ownership. Relying mostly on federal and state fire occurrence records, over 15,300 individual fires have been mapped. While mapping recorded fires, an additional 2,700 "unknown" or undocumented fires were discovered and assessed. It has become apparent that there are perhaps thousands of undocumented fires in the US that are yet to be mapped. Fire occurrence records alone are inadequate if MTBS is to provide a comprehensive accounting of fire across the US. Additionally, the sheer number of fires to assess has overwhelmed current manual procedures. To address these problems, the National Aeronautics and Space Administration (NASA) Applied Sciences Program is helping to fund the efforts of the USGS and its MTBS partners (USFS, National Park Service) to develop, and implement a system to automatically identify fires using satellite data. In near real time, USGS will combine active fire satellite detections from MODIS, AVHRR and GOES satellites with Landsat acquisitions. Newly acquired Landsat imagery will be routinely scanned to identify freshly burned area pixels, derive an initial perimeter and tag the burned area with the satellite date and time of detection. Landsat imagery from the early archive will be scanned to identify undocumented fires. Additional automated fire assessment processes will be developed. The USGS will develop these processes using open source software packages in order to provide freely available tools to local land managers providing them with the capability to assess fires at the local level.

scholarly journals Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots

2021 ◽  
Vol 13 (10) ◽  
pp. 1935
Author(s):  
Flavie Pelletier ◽  
Bianca N.I. Eskelson ◽  
Vicente J. Monleon ◽  
Yi-Chin Tseng
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Landsat Imagery ◽  
The United States ◽  
Remotely Sensed ◽  
National Forest Inventory ◽  
Burn Severity ◽  
National Forest ◽  
Remotely Sensed Data ◽  
National Inventory

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.

Sign in / Sign up

Export Citation Format

Share Document