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At least 109 records · Page 6Linked to original sources
USGS2022

Drought and water management in ancient Maya society

Paleoclimate research in the Maya region of Mesoamerica provides compelling evidence of drought during key periods of cultural transition in Maya society. These include the transition from the Preclassic to the Classic, and from Classic to the Postclassic. Previous research emphasized a causal relationship between drought and cultural change, or so-called “collapse” in the Maya region. Recent advances in the range and precision of climate-sensitive proxies and the development of new archives have enabled quantitative reconstructions of past hydroclimate, as well as providing evidence of high impact, short-duration events, such as tropical cyclones. Simultaneously, archaeological research has unearthed widespread evidence of technologies used by the Maya to exert control over water resources in urban, rural, and agricultural settings. Evidence suggests that many of these water features were in use for multiple generations, possibly centuries, and many were constructed during the Terminal Preclassic and Terminal Classic periods. We suggest that, given the availability of new archaeological and paleoclimate records, these data can be combined to identify the full complexity of Maya adaptation to hydroclimate variability to emphasize adaptation and resilience to both water scarcity and over-abundance (e.g., flooding). Such syntheses, which can offer lessons for present-day efforts to grapple with regional climate change, will benefit from additional studies in data-poor zones of the Maya region, as well as public archiving of paleoclimate and archaeological data.

Progress in Physical Geography: Earth and Environm↗
USGS2023

Crop water productivity from cloud-Based landsat helps assess California’s water savings

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m 3 ), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ET a ; mm/d) to determine Crop Water Use (CWU; m 3 /m 2 ), and 2. Crop Productivity (CP; kg/m 2 ) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m 2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m 3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant.

California↗
USGS2009

The South Florida Ecosystem Portfolio Model - A Map-Based Multicriteria Ecological, Economic, and Community Land-Use Planning Tool

The South Florida Ecosystem Portfolio Model (EPM) prototype is a regional land-use planning Web tool that integrates ecological, economic, and social information and values of relevance to decision-makers and stakeholders. The EPM uses a multicriteria evaluation framework that builds on geographic information system-based (GIS) analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to regional land-use/land-cover (LULC) change. The EPM uses both economics (monetized) and multiattribute utility (nonmonetized) approaches to valuing these endpoints and consequences. This hybrid approach represents a methodological middle ground between rigorous economic and ecological/ environmental scientific approaches. The EPM sacrifices some degree of economic- and ecological-forecasting precision to gain methodological transparency, spatial explicitness, and transferability, while maintaining credibility. After all, even small steps in the direction of including ecosystem services evaluation are an improvement over current land-use planning practice (Boyd and Wainger, 2003). There are many participants involved in land-use decision-making in South Florida, including local, regional, State, and Federal agencies, developers, environmental groups, agricultural groups, and other stakeholders (South Florida Regional Planning Council, 2003, 2004). The EPM's multicriteria evaluation framework is designed to cut across the objectives and knowledge bases of all of these participants. This approach places fundamental importance on social equity and stakeholder participation in land-use decision-making, but makes no attempt to determine normative socially 'optimal' land-use plans. The EPM is thus a map-based set of evaluation tools for planners and stakeholders to use in their deliberations of what is 'best', considering a balancing of disparate interests within a regional perspective. Although issues of regional ecological sustainability can be explored with the EPM (for example, changes in biodiversity potential and regional habitat fragmentation), it does not attempt to define or evaluate long-term ecological sustainability as such. Instead, the EPM is intended to provide transparent first-order indications of the direction of ecological, economic, and community change, not to make detailed predictions of ecological, economic, and social outcomes. In short, the EPM is an attempt to widen the perspectives of its users by integrating natural and social scientific information in a framework that recognizes the diversity of values at stake in South Florida land-use planning. For terrestrial ecosystems, land-cover change is one of the most important direct drivers of changes in ecosystem services (Hassan and others, 2005). More specifically, the fragmentation of habitat from expanding low-density development across landscapes appears to be a major driver of terrestrial species decline and the impairment of terrestrial ecosystem integrity, in some cases causing irreversible impairment from a land-use planning perspective (Brody, 2008; Peck, 1998). Many resource managers and land-use planners have come to realize that evaluating land-use conversions on a parcel-by-parcel basis leads to a fragmented and narrow view of the regional effects of natural land-cover loss to development (Marsh and Lallas, 1995). The EPM is an attempt to integrate important aspects of the coupled natural-system/human-system view from a regional planning perspective. The EPM evaluates proposed land-use changes, both conversion and intensification, in terms of relevant ecological, economic, and social criteria that combine information about probable land-use outcomes, based on ecological and environmental models, as well as value judgments, as expressed in user-modifiable preference models. Based on on-going meetings and interviews with stakeholders and potential tool users we foc

Scientific Investigations Report↗
USGS2020

Advancements towards selective barrier passage by automatic species identification: Applications of deep convolutional neural networks on images of dewatered fish

Invasive species negatively affect enterprises such as fisheries, agriculture, and international trade. In the Laurentian Great Lakes Basin, threats include invasive sea lamprey ( Petromyzon marinus ) and the four major Chinese carps. Barriers have proven to be an effective mechanism for managing invasive species but are detrimental in that they also limit the migration of desirable, native species. Fish passage technologies that selectively pass desirable species while blocking undesirable species are needed. Key to an automated selective barrier passage system is a high precision fish classifier to assign fish to be passed or blocked. Presented is an evaluation of two classifiers developed using images of partially dewatered fish captured from a commercial, high-speed camera array. For a lamprey vs. non-lamprey classification task, an ensemble prediction approach achieved near perfect accuracy on both a validation and test dataset. For a species classification task for 13 species found in the Great Lakes region, an ensemble prediction approach achieved accuracies of 96% and 97% on a validation and test dataset, respectively. Both prediction approaches were based on deep convolutional neural networks constructed using transfer learning and image augmentation. The study provides an important proof-of-concept for the viability in fully automated, selective fish passage systems.

Illinois, Michigan,Ohio↗
USGS2003

True metabolizable energy for wood ducks from acorns compared to other waterfowl foods

Acorns of bottomland red oaks (Quercus spp.) are an important food of North American wood ducks (Aix sponsa). Barras et al. (1996) demonstrated that female wood ducks selected willow oak (Q. phellos) acorns over other species. We measured true metabolizable energy (TME) derived by captive, wild-strain, adult female wood ducks from acorns of willow oak, water oak (Q. nigra), cherrybark oak (Q. pagoda), and pin oak (Q. palustris) to determine whether female wood ducks ' preference for willow oak acorns was related to TME. Estimates of TME within acorn species were relatively precise, yet we did not detect variation in TME among acorn species (P=0.31); hence, we estimated TME across species (2.76±0.033 [SE] kcal/g dry mass; n=34). We concluded that TME apparently did not explain female wood ducks ' preference for willow oak acorns and hypothesized that morphological characteristics of willow oak acorns may be proximate cues related to selection by wood ducks . We also summarized known TME estimates for acorns fed to wood ducks and mallards (Anas platyrhynchos), and natural and agricultural foods fed to mallards, northern pintails (A. acuta), blue-winged teal (A. discors), and Canada geese (Branta canadensis). We found that acorns and moist-soil plant seeds and tubers provided, on average, about 76% of the TME in agricultural seeds. Thus, bottomland-hardwood and moist-soil habitats have potential to provide significant amounts of dietary energy , as well as greater diversity of foods and nutrients than croplands. Researchers should continue to determine TME of common foods (plant and animal) of waterfowl , and use TME in estimating waterfowl habitat carrying capacity (e.g., Reinecke et al. 1989). Additionally, large-scale, reliable estimates of plant and animal food availability in bottomland-hardwood and moist-soil habitats are needed to evaluate carrying capacity of landscapes important to waterfowl , such as the Mississippi Alluvial Valley (MAV).

Journal of Wildlife Management↗
USGS2024

The LTAR Grazing Land Common Experiment at the Jornada Experimental Range: Old genetics, new precision technologies, and adaptive value chains

Rangelands and the supply chains connected to them are central to the agrifood systems of the Southwestern United States. Local ranchers are simultaneously arid lands managers, herd managers, and marketing managers. To stay in business, they must constantly adapt to unpredictable forage resources and markets while conserving soils and vegetation resources for the long term. As climate warming and drying exacerbate the complexity and difficulty of day-to-day production, producers and policy-makers are seeking alternatives to “business as usual”. To meet this need, the LTAR-Jornada team has developed a package of strategies to help producers adapt to the local and inter-regional challenges. The package includes heritage cattle, precision ranching systems, and adaptive value chains. Five ranches across the Southwest have adopted different combinations of the strategies and are partnering to measure their benefits and drawbacks in real-world conditions. Opportunities for controlled experimentation differ among the ranches, so we use LTAR’s indicator system to assess and compare results. Even as we invest in co-producing knowledge about these three strategies, we recognize that progressive aridification and urbanization of Southwestern rangelands create challenges for which a single “silver bullet” agricultural innovation is unlikely to provide durable solutions. We are learning from our customers and stakeholders about ways to adjust the development of new options.

New Mexico↗
USGS2022

The effect of scent lures on detection is not equitable among sympatric species

Context: Camera trapping is an effective tool for cost-efficient monitoring of species over large temporal and spatial scales and it is becoming an increasingly popular method for investigating wildlife communities and trophic interactions. However, camera trapping targeting rare and elusive species can be hampered by low detection rates, which can decrease the accuracy and precision of results from common analytical approaches (e.g., occupancy modeling, capture-recapture). Consequently, researchers often employ attractants to increase detection without accounting for how attractants influence detection of species among trophic levels. Aims: We aimed to evaluate the influences of a commonly used non-species-specific olfactory lure (i.e. sardines) and sampling design on detection of four species (i.e. bobcat [ Lynx rufus ], coyote [ Canis latrans ], raccoon [ Procyon lotor ], and eastern cottontail [ Sylvilagus floridanus ]) that represented a range of foraging guilds in an agricultural landscape. Methods: We set 180 camera stations, each for ∼28 days, during the summer of 2019. We set cameras with one of three lure treatments: (1) olfactory lure, (2) no olfactory lure, or (3) olfactory lure only during the latter half of the survey. We evaluated the influence of the lure at three temporal scales of detection (i.e. daily probability of detection, independent sequences per daily detection, and triggers per independent sequence). Key results: The lure tended to positively influence detection of coyotes and raccoons but negatively influenced detection of bobcats and eastern cottontails. The influence of the lure varied among temporal scales of detection. Conclusions: Scent lures can differentially influence detection of species within or among tropic levels, and the influence of a scent lure may vary among temporal scales. Implications: Our results demonstrate the importance of evaluating the influence of an attractant for each focal species when using camera data to conduct multi-species or community analyses, accounting for variation in sampling strategies across cameras, and identifying the appropriate species-specific temporal resolution for assessing variation in detection data. Furthermore, we highlight that care should be taken when using camera data as an index of relative abundance (e.g. as is commonly done with prey species) when there is variation in the use of lures across cameras.

Wildlife Research↗
USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗
USGS2013

Predicting thermal reference conditions for USA streams and rivers

Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r 2 = 0.84–0.94, residual mean square error [RMSE] = 1.2–2.0 ° C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r 2 = 0.87–0.95, RMSE = 1.1–1.9 ° C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most DMs, setting SWAs to 0 resulted in biased estimates of thermal reference condition.

Freshwater Science↗
USGS2009

Remote sensing of global croplands for food security: Way forward

This book opens a new pathway for global mapping that is focused on a specific land use theme, such as irrigated or rain-fed croplands and classes within these themes. Since croplands use most of the water consumed by humans, specific knowledge of irrigated and rain-fed croplands will be critical for precise estimates of water use. At present and in the coming decades, irrigated and rain-fed cropland area mapping is crucial for food security studies. Throughout this book, various subjects pertaining to global croplands are discussed comprehensively.

Book chapter↗
USGS2006

A robust design mark-resight abundance estimator allowing heterogeneity in resighting probabilities

This article introduces the beta-binomial estimator (BBE), a closed-population abundance mark-resight model combining the favorable qualities of maximum likelihood theory and the allowance of individual heterogeneity in sighting probability (p). The model may be parameterized for a robust sampling design consisting of multiple primary sampling occasions where closure need not be met between primary occasions. We applied the model to brown bear data from three study areas in Alaska and compared its performance to the joint hypergeometric estimator (JHE) and Bowden's estimator (BOWE). BBE estimates suggest heterogeneity levels were non-negligible and discourage the use of JHE for these data. Compared to JHE and BOWE, confidence intervals were considerably shorter for the AICc model-averaged BBE. To evaluate the properties of BBE relative to JHE and BOWE when sample sizes are small, simulations were performed with data from three primary occasions generated under both individual heterogeneity and temporal variation in p. All models remained consistent regardless of levels of variation in p. In terms of precision, the AICc model-averaged BBE showed advantages over JHE and BOWE when heterogeneity was present and mean sighting probabilities were similar between primary occasions. Based on the conditions examined, BBE is a reliable alternative to JHE or BOWE and provides a framework for further advances in mark-resight abundance estimation. ?? 2006 American Statistical Association and the International Biometric Society.

Journal of Agricultural, Biological, and Environme↗
USGS1999

Use of generalized linear models and digital data in a forest inventory of northern Utah

Forest inventories, like those conducted by the Forest Service's Forest Inventory and Analysis Program (FIA) in the Rocky Mountain Region, are under increased pressure to produce better information at reduced costs. Here we describe our efforts in Utah to merge satellite-based information with forest inventory data for the purposes of reducing the costs of estimates of forest population totals and providing spatial depiction of forest resources. We illustrate how generalized linear models can be used to construct approximately unbiased and efficient estimates of population totals while providing a mechanism for prediction in space for mapping of forest structure. We model forest type and timber volume of five tree species groups as functions of a variety of predictor variables in the northern Utah mountains. Predictor variables include elevation, aspect, slope, geographic coordinates, as well as vegetation cover types based on satellite data from both the Advanced Very High Resolution Radiometer (AVHRR) and Thematic Mapper (TM) platforms. We examine the relative precision of estimates of area by forest type and mean cubic-foot volumes under six different models, including the traditional double sampling for stratification strategy. Only very small gains in precision were realized through the use of expensive photointerpreted or TM-based data for stratification, while models based on topography and spatial coordinates alone were competitive. We also compare the predictive capability of the models through various map accuracy measures. The models including the TM-based vegetation performed best overall, while topography and spatial coordinates alone provided substantial information at very low cost.

Utah↗
USGS1966

The promise of precise borehole gravimetry in petroleum exploration and exploitation

This report provides comprehensive information on the geology, geohydrology, and mineral resources of Boone and Winnebago Counties for use in resource-based land-use planning and development. Data on the composition, thickness, and regional distribution of glacial drift and bedrock materials were used to construct maps of: geologic materials to a depth of 20 ft; bedrock topography; drift thickness; major terrains; and glacial drift aquifers. Because contamination of aquifers is a serious concern in some areas of the two counties, a major focus of this study is on interpreting data critical to the selection of suitable areas for municipal waste disposal and prevention of contamination from existing municipal landfills and septic systems. Interpretive maps accompanying the text: (1) rate geological sequences on their capacity to protect aquifers and surface water from contamination by land burial of municipal wastes, septic system disposal, and surface spreading of wastes and agricultural chemicals; (2) geologic sequences on their suitability for general construction; and (3) delineate resources of sand, gravel, peat, and dolomite. Areas in which aquifer contamination from waste disposal and other land-use practices is most likely to occur are those in which sand and gravel and/or permeable creviced bedrock are at or near the surface. Areas having the lowest contamination potential are underlain by thick (20 ft or more) deposits of fine-grained glacial till. Areas most favorable for general construction are well-drained locations in major river valleys and terrace outwash plains; areas least favorable for construction are scattered throughout both counties on poorly drained land having low bearing capacities and on uplands and slopes in northwestern Winnebago County where the drift is thin over the bedrock. Extensive deposits of sand and gravel occur in the major bedrock valleys. Dolomite deposits underlie all the uplands of Winnebago County and most of Boone County, but extraction costs are prohibitive where overburden is thick. High-grade dolomite with relatively thin overburden is found in eastern Winnebago County and southwestern Boone County. (Author 's abstract)

Circular↗
USGS2009

RESIS-II: An Updated Version of the Original Reservoir Sedimentation Survey Information System (RESIS) Database

The Reservoir Sedimentation Survey Information System (RESIS) database, originally compiled by the Soil Conservation Service (now the Natural Resources Conservation Service) in collaboration with the Texas Agricultural Experiment Station, is the most comprehensive compilation of data from reservoir sedimentation surveys throughout the conterminous United States (U.S.). The database is a cumulative historical archive that includes data from as early as 1755 and as late as 1993. The 1,823 reservoirs included in the database range in size from farm ponds to the largest U.S. reservoirs (such as Lake Mead). Results from 6,617 bathymetric surveys are available in the database. This Data Series provides an improved version of the original RESIS database, termed RESIS-II, and a report describing RESIS-II. The RESIS-II relational database is stored in Microsoft Access and includes more precise location coordinates for most of the reservoirs than the original database but excludes information on reservoir ownership. RESIS-II is anticipated to be a template for further improvements in the database.

Data Series↗
USGS2020

Positional accuracy assessment of lidar point cloud from NAIP/3DEP pilot project

The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy the U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation Program (3DEP) and the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor has the potential to meet current and future 3DEP topographic lidar collection requirements. We performed an accuracy analysis and assessment on the lidar point cloud produced from CountryMapper. The boresighting calibration and co-registration by georeferencing correction based on ground control points are assumed to be performed by the data provider. The scope of the accuracy assessment is to apply the following variety of ways to measure the accuracy of the delivered point cloud to obtain the error statistics. Intraswath uncertainty from a flat surface was computed to evaluate the point cloud precision. Intraswath difference between opposite scan directions and the interswath overlap difference were evaluated to find boresighting or any systematic errors. Absolute vertical accuracy over vegetated and non-vegetated areas were also assessed. Both horizontal and vertical absolute errors were assessed using the 3D absolute error analysis methodology of comparing conjugate points derived from geometric features. A three-plane feature makes a single unique intersection point. Intersection points were computed from ground-based lidar and airborne lidar point clouds for comparison. The difference between two intersection points form one error vector. The geometric feature-based error analysis was applied to intraswath, interswath, and absolute error analysis. The CountryMapper pilot data appear to satisfy the accuracy requirements suggested by the USGS lidar specification, based upon the error analysis results. The focus of this research was to demonstrate various conventional accuracy measures and novel 3D accuracy techniques using two different error computation methods on the CountryMapper airborne lidar point cloud.

Remote Sensing↗
USGS2024

Joint spatial modeling bridges the gap between disparate disease surveillance and population monitoring efforts informing conservation of at-risk bat species

White-Nose Syndrome (WNS) is a wildlife disease that has decimated hibernating bats since its introduction in North America in 2006. As the disease spreads westward, assessing the potentially differential impact of the disease on western bat species is an urgent conservation need. The statistical challenge is that the disease surveillance and species response monitoring data are not co-located, available at different spatial resolutions, non-Gaussian, and subject to observation error requiring a novel extension to spatially misaligned regression models for analysis. Previous work motivated by epidemiology applications has proposed two-step approaches that overcome the spatial misalignment while intentionally preventing the human health outcome from informing estimation of exposure. In our application, the impacted animals contribute to spreading the fungus that causes WNS, motivating development of a joint framework that exploits the known biological relationship. We introduce a Bayesian, joint spatial modeling framework that provides inferences about the impact of WNS on measures of relative bat activity and accounts for the uncertainty in estimation of WNS presence at non-surveyed locations. Our simulations demonstrate that the joint model produced more precise estimates of disease occurrence and unbiased estimates of the association between disease presence and the count response relative to competing two-step approaches. Our statistical framework provides a solution that leverages disparate monitoring activities and informs species conservation across large landscapes. Stan code and documentation are provided to facilitate access and adaptation for other wildlife disease applications.

Journal of Agricultural, Biological and Environmen↗
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↗
USGS1989

Water quality and supply on Cortina Rancheria, Colusa County, California

Cortina Rancheria covers an area of 1 sq mi in Colusa County, California, near the western edge of the Sacramento Valley. Local sources of water for residents of the rancheria are of poor quality or limited availability. Domestic needs are presently met by water from a hand-dug well and from a drilled well with a potential yield of 15 gal/min. Water from both wells fails to meet California State drinking-water standards, primarily because of high concentrations of chloride and dissolved solids. High concentrations of sodium and boron pose additional problems for agricultural use of the water. The dissolved ions originate in Upper Cretaceous marine sediments of the Cortina Formation, which occurs at or near land surface throughout the rancheria. Small quantities of fresh groundwater may occur locally in the Tehama Formation which overlies the Cortina Formation in the eastern part of the rancheria. Canyon Creek, the largest stream on the rancheria, flows only during winter and spring. Water from one of the rancheria 's three springs meet drinking water standards, but it almost stops flowing in summer. The generally poor quality of ground and surface water on the rancheria is typical of areas along the west side of the Sacramento Valley. Additional hydrologic information could indicate more precisely the quantity and quality of surface and groundwater on Cortina Rancheria. Principal features of a possible data-collection program would include monitoring of discharge and water quality in three springs and in Canyon Creek, electromagntic terrain conductivity surveys, and monitoring of water levels and quality in two existing wells and several proposed test wells. (USGS)

Water-Resources Investigations Report↗