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At least 1,423 records · Page 79Linked to original sources

USGS2018

Efficacy of using data from angler-caught Burbot to estimate population rate functions

The effective management of a fish population depends on the collection of accurate demographic data from that population. Since demographic data are often expensive and difficult to obtain, developing cost‐effective and efficient collection methods is a high priority. This research evaluates the efficacy of using angler‐supplied data to monitor a nonnative population of Burbot Lota lota . Age and growth estimates were compared between Burbot collected by anglers and those collected in trammel nets from two Wyoming reservoirs. Collection methods produced different length‐frequency distributions, but no difference was observed in age‐frequency distributions. Mean back‐calculated lengths at age revealed that netted Burbot grew faster than angled Burbot in Fontenelle Reservoir. In contrast, angled Burbot grew slightly faster than netted Burbot in Flaming Gorge Reservoir. Von Bertalanffy growth models differed between collection methods, but differences in parameter estimates were minor. Estimates of total annual mortality ( A ) of Burbot in Fontenelle Reservoir were comparable between angled ( A = 35.4%) and netted fish (33.9%); similar results were observed in Flaming Gorge Reservoir for angled (29.3%) and netted fish (30.5%). Beverton–Holt yield‐per‐recruit models were fit using data from both collection methods. Estimated yield differed by less than 15% between data sources and reservoir. Spawning potential ratios indicated that an exploitation rate of 20% would be required to induce recruitment overfishing in either reservoir, regardless of data source. Results of this study suggest that angler‐supplied data are useful for monitoring Burbot population dynamics in Wyoming and may be an option to efficiently monitor other fish populations in North America.

Wyoming↗

USGS2025

A framework for analyzing wild turkey summer sighting data.

Wildlife agencies collect data on productivity (e.g., proportion of hens with poults and number of poults per hen) of wild turkey ( Meleagris gallopavo ) to monitor population status and trends. However, sampling protocols to collect productivity data rely on opportunistic observations reported by wildlife agency personnel and the public and have changed over time and differed among agencies. A protocol to standardize data collection was adopted by most state wildlife agencies in 2019, but long-term historical datasets exist that cannot be analyzed readily to make inferences about spatial and temporal patterns in wild turkey productivity. We developed statistical models to allow comparisons and model trends in productivity among and within states even though data collection protocols changed over time and differed among states. We found greater spatial variation in the proportion of hens with poults than the number of poults per brood, which may reflect how environmental factors influence wild turkey productivity. Our models can also provide inferences about productivity when data are limited or temporally discontinuous for some spatial units. Additionally, we found that temporal and spatial variation in data collection, even under the new protocol, can affect inferences about trends in productivity. The statistical models we developed address the uncontrolled nature of when and where data are collected and offer the ability to investigate long-term patterns of productivity in relation to factors such as changing climate or habitat conditions.

Maryland, New Jersey, New York, Ohio, Pennsylvania↗

USGS2026

Comparison of data handling techniques for modeling bat acoustic activity

With the proliferation of acoustic sampling to investigate bat distribution and ecology, researchers have implemented a myriad of statistical modeling approaches to interpret findings. Bats are taxa of high conservation concern; therefore, ensuring the accuracy of species-level habitat association models is critical for informing management. We sought to determine prediction differences among statistical approaches to modeling counts of acoustic detections, using generalized linear mixed models with 8 acoustic data-handling techniques. We applied each approach or combination of approaches to a rare species, the northern long-eared bat ( Myotis septentrionalis ), and a common species, the eastern red bat ( Lasiurus borealis ), from summer survey results on a landscape in south-central Pennsylvania, USA, 2024. We evaluated the accuracy of habitat association models of bat acoustic activity at the species level using cross-validation and compared resulting predictions of models using spatial correlations. We determined that filtering data by automated identification software (Kaleidoscope Pro), the maximum likelihood estimate (MLE) P -value thresholds reduced relative mean absolute error (rMAE) in cross-validation of northern long-eared bat models. Using the MLE-retained data produced the most accurate predictions over using raw data or the overly conservative match ratio data. However, for the eastern red bat, the results from the most conservative approach of only retaining data with at least a 90% match ratio from software development training sets had the lowest rMAE. We have provided evidence that the current standard of filtering data by nightly MLE can result in more accurate and informative habitat-use acoustic activity models for rare bat species.

Pennsylvania↗

USGS2011

The use of U.S. Geological Survey digital geospatial data products for science research

The development of geographic information system (GIS) transformed the practice of geographic science research. The availability of low-cost, reliable data by the U.S. Geological Survey (USGS) supported the advance of GIS in the early stages of the transition to digital technology. To estimate the extent of the scientific use of USGS digital geospatial data products, a search of science literature databases yielded numbers of articles citing USGS products. Though this method requires careful consideration to avoid false positives, these citation numbers of three types of products (vector, land-use/land-cover, and elevation data) were graphed, and the frequency trends were examined. Trends indicated that the use of several, but not all, products increased with time. The use of some products declined and reasons for these declines are offered. To better understand how these data affected the design and outcomes of research projects, the study begins to build a context for the data by discussing digital cartographic research preceding the production of mass-produced products. The data distribution methods used various media for different system types and were supported by instructional material. The findings are an initial assessment of the affect of USGS products on GIS-enabled science research. A brief examination of the specific papers indicates that USGS data were used for science and GIS conceptual research, advanced education, and problem analysis and solution applications.

Book chapter↗

USGS1993

An objective replacement method for censored geochemical data

Geochemical data are commonly censored, that is, concentrations for some samples are reported as "less than" or "greater than" some value. Censored data hampers statistical analysis because certain computational techniques used in statistical analysis require a complete set of uncensored data. We show that the simple substitution method for creating an uncensored dataset, e.g., replacement by 3/4 times the detection limit, has serious flaws, and we present an objective method to determine the replacement value. Our basic premise is that the replacement value should equal the mean of the actual values represented by the qualified data. We adapt the maximum likelihood approach (Cohen, 1961) to estimate this mean. This method reproduces the mean and skewness as well or better than a simple substitution method using 3/4 of the lower detection limit or 3/4 of the upper detection limit. For a small proportion of "less than" substitutions, a simple-substitution replacement factor of 0.55 is preferable to 3/4; for a small proportion of "greater than" substitutions, a simple-substitution replacement factor of 1.7 is preferable to 4/3, provided the resulting replacement value does not exceed 100%. For more than 10% replacement, a mean empirical factor may be used. However, empirically determined simple-substitution replacement factors usually vary among different data sets and are less reliable with more replacements. Therefore, a maximum likelihood method is superior in general. Theoretical and empirical analyses show that true replacement factors for "less thans" decrease in magnitude with more replacements and larger standard deviation; those for "greater thans" increase in magnitude with more replacements and larger standard deviation. In contrast to any simple substitution method, the maximum likelihood method reproduces these variations. Using the maximum likelihood method for replacing "less thans" in our sample data set, correlation coefficients were reasonably accurately estimated in 90% of the cases for as much as 40% replacement and in 60% of the cases for 80% replacement. These results suggest that censored data can be utilized more than is commonly realized. ?? 1993 International Association for Mathematical Geology.

Mathematical Geology↗

USGS1976

Resource data bases-Resource assessment

The U.S. Geological Survey's Office of Resource Analysis is developing computer methods for the handling of mineral-resources data in order to provide improved means for addressing and manipulating data. These methods include: computerized data files and predictive resource models. Data files contain the raw or disaggregated information on mineral deposits and commodities. One operational data file is CRIB (Computerized Resources Information Bank) which is a general purpose inventory and reference file on metallic and nonmetallic mineral deposits. A computer file on resources should contain detailed information on the following main categories: record identification, name and location, description of deposit, analytical data, and production/reserves. A resource model employs postulates and inferences in conjunction with the data to make predictions about resources-as key variables concerning a mineral commodity are changed. The objective is to estimate the availability of minerals including: geological availability (occurrence models), technological availability (exploration and beneficiation models), and economic availability (economics models). ?? 1976.

Computers & Geosciences↗

USGS1976

The national coal-resources data system of the U.S. geological survey

The National Coal Resources Data System (NCRDS) was designed by the U.S. Geological Survey (USGS) to meet the increasing demands for rapid retrieval of information on coal location, quantity, quality, and accessibility. An interactive conversational query system devised by the USGS retrieves information from the data bank through a standard computer terminal. The system is being developed in two phases. Phase I, which currently is available on a limited basis, contains published areal resource and chemical data. The primary objective of this phase is to retrieve, calculate, and tabulate coal-resource data by area on a local, regional, or national scale. Factors available for retrieval include: state, county, quadrangle, township, coal field, coal bed, formation, geologic age, source and reliability of data, and coal-bed rank, thickness, overburden, and tonnage, or any combinations of variables. In addition, the chemical data items include individual values for proximate and ultimate analyses, BTU value, and several other physical and chemical tests. Information will be validated and deleted or updated as needed. Phase II is being developed to store, retrieve, and manipulate basic point source coal data (e.g., field observations, drill-hole logs), including geodetic location; bed thickness; depth of burial; moisture; ash; sulfur; major-, minor-, and trace-element content; heat value; and characteristics of overburden, roof rocks, and floor rocks. The computer system may be used to generate interactively structure-contour or isoline maps of the physical and chemical characteristics of a coal bed or to calculate coal resources. ?? 1976.

Computers & Geosciences↗

USGS2020

Estimating population persistence for at-risk species using citizen science data

Population persistence probability is valuable for characterizing risk to species and informing listing and conservation decisions but is challenging to estimate through traditional methods for rare, data-limited species. Modeling approaches have used citizen science data to mitigate data limitations of focal species and better estimate parameters such as occupancy and detection, but their use to estimate persistence and inform conservation decisions is limited. We developed an approach to estimate persistence using only occurrence records of the target species and citizen science occurrence data of non-target species to account for search effort and imperfect detection. We applied the approach to a highly cryptic and data-limited species, the southern hognose snake ( Heterodon simus ), as part of its USFWS Species Status Assessment, and estimated current (in 2018) and future persistence under plausible scenarios of varying levels of urbanization, sea level rise, and management. Of 222 known populations, 133 (60%) are likely extirpated currently (persistence probability < 50%), and 165 (74%) populations are likely to be extirpated by 2080 with no additional management. Future management scenarios that included strategies to acquire and improve habitat on currently unprotected lands with existing populations lessened the estimated rate of population declines. These results can directly inform listing decisions and conservation planning for the southern hognose snake by Federal, State, and other partners. Our approach – using occurrence records and auxiliary data from non-target species to estimate population persistence – is applicable across rare and at-risk species for evaluating extinction risk with limited data and prioritizing management actions.

Biological Conservation↗

USGS2022

gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring

gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.

Computers & Geosciences↗

USGS2007

PVTx properties of the CO 2 -H 2 O and CO 2 -H 2 O-NaCl systems below 647 K: assessment of experimental data and thermodynamic models

Evaluation of CO 2 sequestration in formation brine or in seawater needs highly accurate experimental data or models of pressure–volume–temperature-composition ( PVTx ) properties for the CO 2 –H 2 O and CO 2 –H 2 O–NaCl systems. This paper presents a comprehensive review of the experimental PVTx properties and the thermodynamic models of these two systems. The following conclusions are drawn from the review: (1) About two-thirds of experimental data are consistent with each other, where the uncertainty in liquid volumes is within 0.5%, and that in gas volumes within 2%. However, this accuracy is not sufficient for assessing CO 2 sequestration. Among the data sets for liquids, only a few are available for accurate modeling of CO 2 sequestration. These data have an error of about 0.1% on average, roughly covering from 273 to 642 K and from 1 to 35 MPa; (2) There is a shortage of volumetric data of saturated vapor phase. (3) There are only a few data sets for the ternary liquids, and they are inconsistent with each other, where only a couple of data sets can be used to test a predictive density model for CO 2 sequestration; (4) Although there are a few models with accuracy close to that of experiments, none of them is accurate enough for CO 2 sequestration modeling, which normally needs an accuracy of density better than 0.1%. Some calculations are made available on www.geochem-model.org .

Chemical Geology↗

USGS2017

Sparse targets in hydroacoustic surveys: Balancing quantity and quality of in situ target strength data

Hydroacoustic sampling of low-density fish in shallow water can lead to low sample sizes of naturally variable target strength (TS) estimates, resulting in both sparse and variable data. Increasing maximum beam compensation (BC) beyond conventional values (i.e., 3 dB beam width) can recover more targets during data analysis; however, data quality decreases near the acoustic beam edges. We identified the optimal balance between data quantity and quality with increasing BC using a standard sphere calibration, and we quantified the effect of BC on fish track variability, size structure, and density estimates of Lake Erie walleye ( Sander vitreus ). Standard sphere mean TS estimates were consistent with theoretical values (−39.6 dB) up to 18-dB BC, while estimates decreased at greater BC values. Natural sources (i.e., residual and mean TS) dominated total fish track variation, while contributions from measurement related error (i.e., number of single echo detections (SEDs) and BC) were proportionally low. Increasing BC led to more fish encounters and SEDs per fish, while stability in size structure and density were observed at intermediate values (e.g., 18 dB). Detection of medium to large fish (i.e., age-2+ walleye) benefited most from increasing BC, as proportional changes in size structure and density were greatest in these size categories. Therefore, when TS data are sparse and variable, increasing BC to an optimal value (here 18 dB) will maximize the TS data quantity while limiting lower-quality data near the beam edges.

Ohio↗

USGS2019

The effects of topographic surveying technique and data resolution on the detection and interpretation of geomorphic change

Change detection of high resolution topographic data is commonly used in river valleys to quantify reach- and site-scale sediment budgets by estimating the erosion/deposition volume, and to interpret the geomorphic processes driving erosion and deposition. Field survey data are typically collected as point clouds that are often converted to gridded raster datasets and the ultimate choice of grid resolution is left to the user. This choice may have important implications for both the quantification and interpretation of geomorphic change. Here we used concurrent topographic data collected by terrestrial laser scanning (TLS) and structure-from-motion (SfM) photogrammetry to quantify the influence of grid resolution and sampling technique on (a) the sediment budget and (b) the presence and role of geomorphic processes (i.e., alluvial, colluvial, aeolian, and fluvial transport) driving topographic change at four sites along the Colorado River in Grand Canyon, Arizona, USA. We found that while both techniques produced similar estimates for site-scale sediment budgets, the magnitude of detected topographic change was dampened at coarser pixel resolutions. An overall decrease in the areal extent of erosion and deposition were observed, respectively, when coarsening pixel size from 5 cm to 1 m among all sites. Coarser resolution data tended to affect interpretation of landscape change along the margins of river valleys. For example, when changing from 5 cm to 1 m pixel resolution, the inferred contribution of aeolian changes to total site-scale geomorphic change increased in area by 7.9%, whereas the inferred contribution of alluvial and colluvial processes decreased in area by 97.9% and 88.2%, respectively. More generally, we found that coarsening pixel sizes disproportionately attributed geomorphic change to one or more of the most common processes operating at a site. We also found that coarsening pixel resolution amplified the net sediment imbalance at the site scale, driving the imbalance at erosional sites further into erosion and vice versa for depositional sites. Our results have implications both for point cloud data collection and for raster dataset processing. We argue that selecting the finest obtainable resolution is not always warranted to accurately quantify and interpret geomorphic change, because remote sensing technique, topographic data resolution, and analysis procedure can be optimized to capture the spatial scale of those processes driving landscape change. However, in landscapes at or near sediment equilibrium (i.e., equal amounts of erosion and deposition), the finest obtainable topographic data resolution is warranted to avoid amplifying sediment imbalance and erroneously inferring that sites are trending toward erosion or deposition.

Arizona↗

USGS2016

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4–8. Training data were selected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2018

Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA

The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-x-30 m) of the Landsat Thematic Mapper satellite. These data are potentially valuable to environmental managers and stakeholders because of the utility of %IC as an indicator of watershed and aquatic condition, but lack an accuracy assessment because of the absence of suitable reference data. Recently developed 1 m2 land cover data for the Chesapeake Bay region makes it possible to assess NLCD %IC accuracy for a 262,000 km2 region based on a census rather than a sample of reference data. We report agreement between the two %IC datasets for watersheds and the riparian zones within watersheds and four additional square units. The areas of the six assessment units were 40 ha cell, 433 ha (riparian unit average), 2,756 ha cell, 5,626 ha cell, 8,569 ha (watershed unit average) and 22,500 ha cell. Mean Absolute Deviation (MAD) was ≤ 1.6% for each of the six assessment units and Mean Deviation (MD) was only slightly less, indicating NLCD reliably reproduced %IC from the 1 m2 data with a small (≤ 1.6%) and consistent tendency for underestimation. Results were sensitive to assessment unit choice. The results for the four largest assessment units had very similar regression parameters, R2 values, and patterns of bias. Results for the riparian assessment were different from those for the watershed unit and the other three larger units. MAD was about 50% less for the riparian zones than it was for the watersheds, the direction of bias was less consistent, and NLCD %IC was uniformly higher than 1 m2 %IC in urbanized riparian zones. For the smallest unit, bias patterns were more similar to the riparian unit and regression results were more similar to the four larger units. MAD and MD were also sensitive to the amount of urbanization, increasing as NLCD %IC increased. The low overall bias and positive relationship between bias and level of urbanization suggest that the benefits of obtaining 1 m2 IC data outside of urban areas may not outweigh the costs of obtaining such data.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2021

Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data

We use combined teleseismic and strong motion data sets to investigate finite-fault slip models for a double of earthquakes that occurred on August 11, 2012, in northwestern Iran near the cities of Ahar and Varzaghan. The data include teleseismic P-waveforms retrieved from broadband seismic stations located between 30°–94° from the earthquakes and local strong motion data recorded by the Iran Strong Motion Network, installed and operated by the Building and Housing Research Centre. We first invert teleseismic P-waveforms and local strong motion data separately. For the first event (12:23 UTC), the teleseismic broadband inversion yields a somewhat deeper and simpler distribution of slip than the local strong motion inversion. The strong motion inversion results in a more complex distribution because of higher frequency content but can also be influenced by complexities in the propagation path. For the second event (12:34 UTC), the slip distribution from strong motion data is more similar to the teleseismic result and shows a simple slip area with a small relative movement to the west. To resolve the differences between the results of these two data sets and obtain a better constrained slip model, we perform a joint inversion of teleseismic broadband and local strong motion data. The joint inversion for the first event shows two asperities with a maximum slip of 3.9 m up- dip from the hypocenter and extending to the west between depths of 1 and 5 km. A second narrower high-slip area is seen just above the hypocenter from 6 to 10 km depth. The total moment for this earthquake is calculated to be M o = 3.8 × 10 25 dyn-cm (3.8 × 10 18 N.m) (M w 6.4). For the second event, the results of the joint inversion show a simple slip distribution that is mainly confined in a single patch around the hypocenter with a depth range from about 10 to 13 km and maximum slip of 1.9 m. We compute a total seismic moment of M o = 1.6 × 10 25 dyn-cm (1.6 × 10 18 N.m) (M w 6.1) for the second event. The largest stress drops for the first event occur above the hypocenter with an average stress drop over the rupture area of 120 bar (12 Mpa). For the second event, the maximum stress drop occurs at the reported focal depth with an average stress drop over the rupture area of 80 bar (8 Mpa).

East Anatolian Fault↗

USGS2018

Interoperability in planetary research for geospatial data analysis

For more than a decade there has been a push in the planetary science community to support interoperable methods for accessing and working with geospatial data. Common geospatial data products for planetary research include image mosaics, digital elevation or terrain models, geologic maps, geographic location databases (e.g., craters, volcanoes) or any data that can be tied to the surface of a planetary body (including moons, comets or asteroids). Several U.S. and international cartographic research institutions have converged on mapping standards that embrace standardized geospatial image formats, geologic mapping conventions, U.S. Federal Geographic Data Committee (FGDC) cartographic and metadata standards, and notably on-line mapping services as defined by the Open Geospatial Consortium (OGC). The latter includes defined standards such as the OGC Web Mapping Services (simple image maps), Web Map Tile Services (cached image tiles), Web Feature Services (feature streaming), Web Coverage Services (rich scientific data streaming), and Catalog Services for the Web (data searching and discoverability). While these standards were developed for application to Earth-based data, they can be just as valuable for planetary domain. Another initiative, called VESPA (Virtual European Solar and Planetary Access), will marry several of the above geoscience standards and astronomy-based standards as defined by International Virtual Observatory Alliance (IVOA). This work outlines the current state of interoperability initiatives in use or in the process of being researched within the planetary geospatial community.

Planetary and Space Science↗

USGS2004

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine ( Pinus ponderosa ) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI e ) ( r 2 =0.55, p <0.001) and with the lidar-derived estimate of tree occurrence ( r 2 =0.79, p <0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response ( r 2 =0.30, r =−0.55 and r 2 =0.41, r =−0.64, respectively; p <0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction ( r 2 =0.99, p <0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures ( r 2 =0.77, p <0.001) than at 4 m resolution ( r 2 =0.58, p <0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data ( r 2 =0.66, p <0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.

Remote Sensing of Environment↗

USGS2017

Mapping burned areas using dense time-series of Landsat data

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.

continental United States↗