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USGS2013

Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska

Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0–2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101 cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes.

Alaska↗

USGS2002

Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land across the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a waterquality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres). This information was also compared to a similar data set based on 1980 imagery. The 1980 data classified 13.7 million acres as irrigated. Although the change in the amount of irrigated land between the two times was not substantial, the location of the irrigated land did shift from areas where there were large ground-water-level declines to other areas where ground-water levels were static or rising.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗

USGS1980

A limnological study of 43 selected Maine lakes

Federal and State legislation require the trophic clas sification of lakes and ponds in the State of Maine as part of a lake management program. In 1974, the State of Maine Department of Environmental Protection (DEP) adopted a pre liminary set of procedures for establishing an index of lake trophic status. Also in 1974, this three-year study of selected Maine lakes was initiated to evaluate and improve this trophic classification system. Analyses of project results led to the following rec-commendations for modifications in Maine's lake-management program. Lake trophic state should be established using a mean annual value of twice monthly observations of chlorophyll a in epilimnetic waters during the open-water season. Actual Trophic State Index (TSI) values for lakes are calculated using the formula: TSI = 77.7 - 27.3 (log annual mean chlorophyll a, in mg/L). In lakes with colors less than or equal to TO platinum-cobalt units, mean annual Secchi disk transparencies may be used for calculation of lake TSI using the formula: TSI = 40.0 + 33.0 (log annual10mean Secchi disk, in meters). Supplemental information recommended for inclusion in the lake management program should include: morphological, hydrologic, and geologic data; baseline profile water-quality surveys; and benthic invertebrate, vegetation, and cultural surveys. Another recommendation is to change DEP's method of calculating the mean annual runoff for a lake drainage basin to an empirical formula based on drainage area characteristics. This resulted in more accurate estimations of mean annual hydraulic retention times for lakes. Many of these parameters recommended for inclusion in the lake management program will allow comparisons with future surveys to determine the impacts of cultural development on Maine lakes.

Maine↗

USGS2026

Habitat-based predictions of bridle shiner ( Notropis bifrenatus ) in the northeastern U.S.

We sought to assess bridle shiner ( Notropis bifrenatus ) habitat associations at local and regional scales across southern Maine and New Hampshire. We used local habitat data at 95 Maine sites to predict occupancy with classification and regression trees (CART). We then used ensemble species distribution models (SDMs) to model the historical (1898–2008) and current (2009–2022) ranges of the species. We used the BIOMOD platform to model the association between 35 environmental variables and bridle shiner presence during both time periods and at fine (pseudo-HUC14) and coarse (HUC12) spatial scales. We then calculated the change in predicted occupied drainages to estimate the change in the species' distribution at both scales. Within a site, bridle shiners were associated with submerged aquatic vegetation, organic substrate, and watermilfoil ( Myriophyllum spp.). SDMs revealed an association with Appalachian (Hemlock-)Northern Hardwood Forest, sand substrate, and low-elevation terrain (at both spatial scales). Ensemble fine-scale SDMs suggest a substantial loss of historical bridle shiner habitat in both Maine (36% of drainages) and New Hampshire (16%), with comparable described losses (of 21% and 14%) at a coarse scale. Our local and regional models may be used to focus surveys on areas with high predicted habitat suitability or to inform habitat restoration efforts.

Maine, New Hampshire↗

USGS2012

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (∼80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (k hat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (k hat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (∼90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ∼30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms . The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/ ). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.

Remote Sensing↗

USGS2012

Classifications for Coastal Wetlands Planning, Protection and Restoration Act site-specific projects: 2008 and 2009

The Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) funds over 100 wetland restoration projects across Louisiana. Integral to the success of CWPPRA is its long-term monitoring program, which enables State and Federal agencies to determine the effectiveness of each restoration effort. One component of this monitoring program is the analysis of high-resolution, color-infrared aerial photography at the U.S. Geological Survey's National Wetlands Research Center in Lafayette, Louisiana. Color-infrared aerial photography (9- by 9-inch) is obtained before project construction and several times after construction. Each frame is scanned on a photogrametric scanner that produces a high-resolution image in Tagged Image File Format (TIFF). By using image-processing software, these TIFF files are then orthorectified and mosaicked to produce a seamless image of a project area and its associated reference area (a control site near the project that has common environmental features, such as marsh type, soil types, and water salinities.) The project and reference areas are then classified according to pixel value into two distinct classes, land and water. After initial land and water ratios have been established by using photography obtained before and after project construction, subsequent comparisons can be made over time to determine land-water change. Several challenges are associated with the land-water interpretation process. Primarily, land-water classifications are often complicated by the presence of floating aquatic vegetation that occurs throughout the freshwater systems of coastal Louisiana and that is sometimes difficult to differentiate from emergent marsh. Other challenges include tidal fluctuations and water movement from strong winds, which may result in flooding and inundation of emergent marsh during certain conditions. Compensating for these events is difficult but possible by using other sources of imagery to verify marsh conditions for other dates in time.

Louisiana↗

USGS2003

Survey methods for assessing land cover map accuracy

The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.

Environmental and Ecological Statistics↗

USGS2010

Terrestrial ecosystems: Surficial lithology of the conterminous United States

As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey (USGS) has generated a new classification of the lithology of surficial materials to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States. The ecosystems classification used in this effort was developed by NatureServe. A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. This ecosystem mapping methodology is transparent, replicable, and rigorous. Surficial lithology strongly influences the differentiation and distribution of terrestrial ecosystems, and is one of the key input layers in this biophysical stratification. These surficial lithology classes were derived from the USGS map 'Surficial Materials in the Conterminous United States,' which was based on texture, internal structure, thickness, and environment of deposition or formation of materials. This original map was produced from a compilation of regional surficial and bedrock geology source maps using broadly defined common map units for the purpose of providing an overview of the existing data and knowledge. For the terrestrial ecosystem effort, the 28 lithology classes of Soller and Reheis (2004) were generalized and then reclassified into a set of 17 lithologies that typically control or influence the distribution of vegetation types.

Conterminous United States↗

USGS1993

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Pecora 12 Symposium↗

USGS2012

Mapping plant species ranges in the Hawaiian Islands: developing a methodology and associated GIS layers

This report documents a methodology for projecting the geographic ranges of plant species in the Hawaiian Islands. The methodology consists primarily of the creation of several geographic information system (GIS) data layers depicting attributes related to the geographic ranges of plant species. The most important spatial-data layer generated here is an objectively defined classification of climate as it pertains to the distribution of plant species. By examining previous zonal-vegetation classifications in light of spatially detailed climate data, broad zones of climate relevant to contemporary concepts of vegetation in the Hawaiian Islands can be explicitly defined. Other spatial-data layers presented here include the following: substrate age, as large areas of the island of Hawai'i, in particular, are covered by very young lava flows inimical to the growth of many plant species; biogeographic regions of the larger islands that are composites of multiple volcanoes, as many of their species are restricted to a given topographically isolated mountain or a specified group of them; and human impact, which can reduce the range of many species relative to where they formerly were found. Other factors influencing the geographic ranges of species that are discussed here but not developed further, owing to limitations in rendering them spatially, include topography, soils, and disturbance. A method is described for analyzing these layers in a GIS, in conjunction with a database of species distributions, to project the ranges of plant species, which include both the potential range prior to human disturbance and the projected present range. Examples of range maps for several species are given as case studies that demonstrate different spatial characteristics of range. Several potential applications of species-range maps are discussed, including facilitating field surveys, informing restoration efforts, studying range size and rarity, studying biodiversity, managing invasive species, and planning of conservation efforts.

Hawai'i↗

USGS2016

Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System

Introduction/Background The U.S. Geological Survey (USGS)-Upper Midwest Environmental Sciences Center (UMESC) was responsible for development of several land cover/land use (LCU) systemic datasets of the Upper Mississippi River System (UMRS). These efforts (1989 and 2000) were funded by the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program (UMRR) Long Term Resource Monitoring (LTRM) element. Development of systemic datasets includes the acquisition, processing, and serving of high-resolution aerial photography and land cover/land use spatial datasets ( http://www.umesc.usgs.gov/data_library/land_cover_use/land_cover_use_data.html ). In 2008, the UMRR reached a collaborative agreement with the U.S. Fish and Wildlife Service-Region 3 to collect high-resolution digital imagery of the entire UMRS floodplain during 2010–11 for LTRM. The UMESC helped acquire, process, and serve this imagery, as well as produce and serve the 2010–11 LCU systemic dataset of the UMRS floodplain. Digital imagery for Pools 13, 26, La Grange, and Open River South was collected using an Applanix DSS 439 digital sensor system with a 40 millimeter lens and Color Infrared (CIR) filter. The imagery was collected at a resolution of 20 centimeters/pixel (8 inches/pixel) for Pool 13 and 40 centimeters/pixel (16 inches/pixel) for Pools 26, Open River South, and La Grange. All imagery was projected to Universal Transverse Mercator (UTM) Zone 15, North American Datum of 1983 (NAD 83). The General Wetland Vegetation Classification (GWVC) system used for mapping is hierarchical, and its 31 classes can be collapsed into broader categories using either a 15- or 7-class level. While the 1989 and 2000 LCU systemic datasets have not gone through a traditional thematic accuracy assessment (AA) in the past, nor have they undergone a validation analysis, the end products are of high quality. For each systemic dataset produced (1989, 2000, 2010–11), extensive field reconnaissance is performed before photointerpretation. The intent of this field reconnaissance is to learn, test, and verify image signatures as they relate to vegetation types. Questionable areas on the imagery are visited, and the plants or land features observed in the area are recorded for reference. This procedure verifies vegetation signatures on the imagery with those on the ground. In addition, once the photointerpretation is complete, the final LCU dataset undergoes extensive quality assurance/quality control to ensure the imagery is mapped correctly. Since the 2000 LCU systemic dataset was developed, there has been a growing interest in completing thematic AAs for the LTRM LCU spatial datasets. The objective of an AA is to measure the probability that a particular location has been assigned its correct vegetation class. An AA estimates thematic (map class) errors in the data, giving users information needed to determine data suitability for a particular application. At the same time, data producers are able to learn more about the nature of errors in the data. Thus, the two attributes of an AA are “producers’ accuracy,” which is the probability that an AA point has been mapped correctly (also referred to as an error of omission); and “users’ accuracy,” which is the probability that the map actually represents what was found on the ground (also referred to as error of commission). Producers’ and users’ accuracies can be obtained from the same set of data by using different analyses. Accuracy assessment is an extensive effort that requires seasonal field personnel and equipment, data entry, analyses, and post processing—tasks that are costly and time consuming. The geospatial team at the UMESC has suggested a validation process for understanding the accuracy of the spatial datasets, which will be tested on at least some areas of the UMRS. Validation is not a true verification of map-class type in the field; however, it can provide the user of the map with useful information that is similar to a field AA. Similar to an AA, validation involves generating random points based on the total area for each map class. However, instead of collecting field data, two or three individuals not involved with the photo-interpretative mapping separately review each of the points onscreen and record a best-fit vegetation type(s) for each site. Once the individual analyses are complete, results are joined together and a comparative analysis is performed. The objective of this initial analysis is to identify areas where the validation results were in agreement (matches) and areas where validation results were in disagreement (mismatches). The two or three individuals then perform an analysis, looking at each mismatched site, and agree upon a final validation class. (If two vegetation types at a specific site appear to be equally prevalent, the validation team is permitted to assign the site two best-fit vegetation types.) Following the validation team’s comparative analysis of vegetation assignments, the data are entered into a database and compared to the mappers’ vegetation assignments. Agreements and disagreements between the map and validation classes are identified, and a contingency table is produced. This document presents the AA processes/results for Pools 13 and La Grange, as well as the validation process/results for Pools 13 and 26 and Open River South.

Iowa, Illinois, Minnesota, Missouri, Wisconsin↗

USGS1986

Evapotranspiration estimates using remote-sensing data, Parker and Palo Verde Valleys, Arizona and California

In 1981 the U.S. Geological Survey established an experimental project to assess the possible and practical use of remote sensing data to estimate evapotranspiration as an approximation of consumptive use in the lower Colorado River flood plain. The project area was in Parker Valley, Arizona. The approach selected was to measure the areas covered by each type of vegetation using remote sensing data in various types of analyses and to multiply each area by a predetermined water use rate. Two calibration and six remote sensing methods of classifying crop types were compared for cost, accuracy, consistency, and labor requirements. Included were one method each for field reconnaissance using 1982 data, low altitude (< than 5,000 ft) aerial photography using 1982 data, and visual photointerpretation of Landsat satellite images using 1981 and 1982 data; two methods for medium-altitude (15,000-18,000 ft) aerial photography using 1982 data; and three methods for digital Landsat satellite images using 1981 data. A test of the most promising digital processing method, which used three image dates, was made in part of Palo Verde Valley, California, where 1981 crop data were more complete than in Parker Valley. Of the eight methods studied, the three-date digital processing method was the most consistent and least labor-intensive; visual photointerpretation of Landsat images was the least expensive. Evapotranspiration estimates from crop classifications by all methods differed by a maximum of 6%. Total evapotranspiration calculated from crop data and phreatophyte maps in 1981 ranged from 12% lower in Palo Verde Valley to 17% lower in Parker Valley than consumptive use calculated by water budgets. The difference was greater in Parker Valley because the winter crop data were not included. (Author 's abstract)

Arizona, California↗

USGS1989

Evapotranspiration estimates using remote-sensing data, Parker and Palo Verde valleys, Arizona and California

In 1981 the U.S. Geological Survey established an experimental project to assess the possible and practical use of remote-sensing data to estimate evapotranspiration as an approximation of consumptive use of water in the lower Colorado River flood plain. The project area was in Parker Valley, Arizona. The approach selected was to measure the areas covered by each type of vegetation, using remote-sensing data in various types of analyses, and to multiply each area by a predetermined water-use rate. Two calibration and six remote-sensing methods of classifying crop types were compared for cost, accuracy, consistency, and labor requirements. Included were one method each for field reconnaissance using 1982 data, low-altitude (less than 5,000 feet) aerial photography using 1982 data, and visual photointerpretation of Landsat satellite images using 1981 and 1982 data; two methods for medium-altitude (15,000-18,000 feet) aerial photography using 1982 data; and three methods for digital Landsat satellite images using 1981 data. A test of the most promising digital-processing method, which used three image dates, was made in part of Palo Verde Valley, California, where 1981 crop data were more complete than in Parker Valley. Of the eight methods studied, the two-date digital-processing method was the most consistent and least labor intensive for identifying two or three major crops; visual photointerpretation of Landsat images was the least expensive. Evapotranspiration estimates from crop classifications by all methods differed by a maximum of 6 percent. Total evapotranspiration calculated from crop data and phreatophyte maps in 1981 ranged from 11 percent lower in Palo Verde Valley to 17 percent lower in Parker Valley than consumptive use calculated by water budgets. The difference was greater in Parker Valley because the winter crop data were not included.

Water Supply Paper↗

USGS1998

North American landscape characterization project: The production of a continental scale three-decade Landsat data set

The North American Landscape Characterization (NALC) project is a component of the National Aeronautics and Space Administration (NASA) Landsat Pathfinder program. Pathfinder projects are focused on the investigation of global change utilizing current remote sensing technologies. The NALC project is a cooperative effort between the U. S. Environmental Protection Agency (EPA), the U.S. Geological Survey (USGS), and NASA to make Landsat data available to the widest possible user community for scientific research and general public interest. The NALC project is principally funded by the EPA Office of Research and Development and the USGS's Earth Resources Observation Systems (EROS) Data Center (EDC). The objectives of the NALC project are to produce standardized remote sensing data sets, develop standardized analysis methods, and derive standardized land cover change products for a large portion of the North American continent (the conterminous United States and Mexico) (Lunetta and Sturdevant, 1993). The standard product is the NALC “triplicate”;, consisting of co‐registered Landsat multispectral scanner data for the years 1973, 1986, and 1991 (plus or minus one year), plus co‐registered 3 arcsecond digital terrain elevation data. Processing began with the 1986 scene, which was precision corrected (with full terrain correction) to a 60 meter Universal Transverse Mercator base. Automated cross‐correlation procedures were used to co‐register the 1970's and 1990's data to the 1980's base, and independent verifications of registration quality were performed on all triplicate components. The pertinent metadata were compiled in a relational database, which includes WRS2 path/rows, scene ID's, image dates, solar azimuth and elevation, verification RMSE's, and the number of verification control points. NALC triplicate data sets are being used for a number of applications, including the analysis of urbanization patterns, dynamics of climatic fluctuations, deforestation studies, and vegetation classification and mapping. These data are being distributed through the Earth Observing System Data and Information System (EOSDIS) Information Management System (IMS) at a cost of $15(U.S.) for each triplicate.

Geocarto International↗

USGS1981

Digital elevation data as an aid to land use and land cover classification

Elevation data is generally associated with topographic maps and expressed by contours and spot elevations. However, elevation data is also essential to the proper classification of land use and land cover by remote sensing techniques. Absolute elevation governs various types of vegetative growth as does the degree and direction of slope. However, the effect of terrain aspect (slope and direction) on reflectance is of even greater significance. The angular relationship of a surface to the sun can significantly effect the radiometric response. For flat areas this effect is contract but she slopes are involved they must be considered if automated classification is to be applied. To overcome the terrain aspect effect, rating of multi-spectral responses is used in an attempt to eliminate the terrain aspect. However, this is only partially effective and when the terrain aspect controls group cover it is of little or no value. The proper solution to the aspect problem is to obtain and utilize suitable digital elevation data in conjunction with remote sensor response. The relationship of the aspect to the response can be developed by theory of empirical test and when properly applied can neutralize the aspect response. Then the classification of land use and land cover can proceed without the unwanted radiometric anomalies of terrain aspect. In relatively well mapped areas such as the United States and Europe, digital data can be developed from topographic maps or from the stereo aerial photographic movie. For poorer mapped areas (which involved most of the world's land areas), a satellite designed to obtain stereo data offers the best hope for a digital elevation database. Such a satellite, known as Mapsat, has been defined by the U.S. Geological Survey. Utilizing modern solid state technology, there is no reason why such stereo data cannot be acquired simultaneously with the multispectral response, thus simplifying the overall problem of land use and land cover classification.

Pecora VII Symposium↗

USGS2010

Landsat Thematic Mapper Image Mosaic of Colorado

The U.S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC) produced a seamless, cloud-minimized remotely-sensed image spanning the State of Colorado. Multiple orthorectified Landsat 5 Thematic Mapper (TM) scenes collected during 2006-2008 were spectrally normalized via reflectance transformation and linear regression based upon pseudo-invariant features (PIFS) following the removal of clouds. Individual Landsat scenes were then mosaicked to form a six-band image composite spanning the visible to shortwave infrared spectrum. This image mosaic, presented here, will also be used to create a conifer health classification for Colorado in Scientific Investigations Map 3103. An archive of past and current Landsat imagery exists and is available to the scientific community (http://glovis.usgs.gov/), but significant pre-processing was required to produce a statewide mosaic from this information. Much of the data contained perennial cloud cover that complicated analysis and classification efforts. Existing Landsat mosaic products, typically three band image composites, did not include the full suite of multispectral information necessary to produce this assessment, and were derived using data collected in 2001 or earlier. A six-band image mosaic covering Colorado was produced. This mosaic includes blue (band 1), green (band 2), red (band 3), near infrared (band 4), and shortwave infrared information (bands 5 and 7). The image composite shown here displays three of the Landsat bands (7, 4, and 2), which are sensitive to the shortwave infrared, near infrared, and green ranges of the electromagnetic spectrum. Vegetation appears green in this image, while water looks black, and unforested areas appear pink. The lines that may be visible in the on-screen version of the PDF are an artifact of the export methods used to create this file. The file should be viewed at 150 percent zoom or greater for optimum viewing.

Scientific Investigations Map↗

USGS1999

A GIS modeling method applied to predicting forest songbird habitat

We have developed an approach for using “presence” data to construct habitat models. Presence data are those that indicate locations where the target organism is observed to occur, but that cannot be used to define locations where the organism does not occur. Surveys of highly mobile vertebrates often yield these kinds of data. Models developed through our approach yield predictions of the amount and the spatial distribution of good-quality habitat for the target species. This approach was developed primarily for use in a GIS context; thus, the models are spatially explicit and have the potential to be applied over large areas. Our method consists of two primary steps. In the first step, we identify an optimal range of values for each habitat variable to be used as a predictor in the model. To find these ranges, we employ the concept of maximizing the difference between cumulative distribution functions of (1) the values of a habitat variable at the observed presence locations of the target organism, and (2) the values of that habitat variable for all locations across a study area. In the second step, multivariate models of good habitat are constructed by combining these ranges of values, using the Boolean operators “and” and “or.” We use an approach similar to forward stepwise regression to select the best overall model. We demonstrate the use of this method by developing species-specific habitat models for nine forest-breeding songbirds (e.g., Cerulean Warbler, Scarlet Tanager, Wood Thrush) studied in southern Ohio. These models are based on species’ microhabitat preferences for moisture and vegetation characteristics that can be predicted primarily through the use of abiotic variables. We use slope, land surface morphology, land surface curvature, water flow accumulation downhill, and an integrated moisture index, in conjunction with a land-cover classification that identifies forest/nonforest, to develop these models. The performance of these models was evaluated with an independent data set. Our tests showed that the models performed better than random at identifying where the birds occurred and provided useful information for predicting the amount and spatial distribution of good habitat for the birds we studied. In addition, we generally found positive correlations between the amount of habitat, as predicted by the models, and the number of territories within a given area. This added component provides the possibility, ultimately, of being able to estimate population sizes. Our models represent useful tools for resource managers who are interested in assessing the impacts of alternative management plans that could alter or remove habitat for these birds.

Ecological Applications↗

USGS2007

Preliminary Surficial Geology of the Dove Spring Off-Highway Vehicle Open Area, Mojave Desert, California

Introduction As part of a U.S. Geological Survey (USGS) monitoring plan to evaluate the environmental impact of off-highway vehicle (OHV) use on Bureau of Land Management (BLM) land in California, this report presents results of geologic studies in the Dove Spring OHV Open Area. This study produced baseline data, which when combined with historic and current patterns of land use, forms the basis for vegetation and wildlife monitoring designed to address the following questions: 1. Is the density and length of OHV routes increasing? 2. Are there cumulative effects of past and current OHV use associated with changes in the environmental integrity of soils, plants, and wildlife? 3. Is the spread of invasive species associated with levels of OHV use? 4. Is there a threshold of OHV impact that might be translated to management action by the BLM? The monitoring studies will be used to collect baseline environmental information to determine levels of environmental impact of OHV use. This approach will use a low-impact area as a proxy for pre-impact conditions (substituting space for time) to determine thresholds of OHV impacts beyond which environmental integrity is affected. Indicators of environmental integrity will emphasize factors that are fundamental to ecosystem structure and function and likely to be sensitive to OHV impacts. Surficial geology is studied because material properties such as texture and chemistry strongly control soil moisture and nutrient availability and therefore affect plant growth and distribution. An understanding of surficial geology can be used to predict and extrapolate soil properties and improve understanding of vegetation assemblages and their distribution. In the present study, vegetation associations may be examined as a function of surficial geology as well as other environmental variables such as slope, aspect, NRCS (National Resources Conservation Service) soil classification, elevation, and land-use history. Ground measurements of vegetation, biological soil crusts, compaction, and other information may be correlated with land use to identify possible ecological thresholds in OHV use that require monitoring. Surficial geology is relevant for several other studies of OHV impact, such as soil compaction, dust emissions, and acceleration of erosion. Compaction, reduced infiltration, and accelerated erosion have been documented in Dove Spring Canyon because of OHV use (Snyder and others, 1976) and elsewhere in the Mojave Desert (e.g., Webb, 1983; Langdon, 2000). A surficial geologic map enables the use of geomorphic process models, which when combined with measured soil properties, such as texture, nutrient chemistry, and bulk density, allows spatial extrapolation of the properties. Maps can be produced that predict compaction susceptibility, moisture conditions, dust emissions, flood hazards, and erodibility, among other applications.

Open-File Report↗