The giant gartersnake (Thamnophis gigas) is a semi-aquatic species of snake precinctive to the Central Valley of California. Because the Central Valley has experienced a substantial loss of wetland habitat, giant gartersnake populations are largely found in aquatic habitats associated with rice agriculture. In dry years, less water may be available for rice agriculture, resulting in less aquatic habitat, which could have cascading effects on giant gartersnake populations. We present 2 years of data intended to examine how the demography of giant gartersnakes is affected by the availability of aquatic habitat on the landscape (2016–17), along with 2 years of (sparse) preliminary data (2014–15) collected as part of an earlier radio-telemetry study on giant gartersnake movement behavior. We sampled agricultural canals near rice fields for giant gartersnakes at 8 sites distributed throughout the Sacramento Valley. Five sites were sampled from 2014–17, and 3 sites were sampled from 2015–17. In total, we made 2,995 captures of 1,011 snakes from 2014–17. We used these capture data to fit a multi-site Jolly-Seber model to estimate the abundance of giant gartersnakes as well as the daily and annual probability of capture at each site. We used remotely sensed Landsat data to characterize the extent of flooded rice fields surrounding each site in each year. In addition, we collected 175 females from 2014–17 and delivered them to the Sacramento Zoo for health assessments and reproductive exams.
The abundance of giant gartersnakes varied among sites, and abundance estimates were more precise in 2016 and 2017 when sampling effort was greatest. The probability of a giant gartersnake being captured at least once in a year was higher in 2016 and 2017 than 2014 and 2015, and recaptures of snakes marked the previous year were highest in 2016 and 2017 as well. Mean annual apparent survival was estimated to be 0.40 but varied among sites from a low of 0.14 to a high of 0.63. Five sites had diverse size distributions that included abundant sub-adult and large adult female snakes. One site had a truncated size distribution with few large adult female snakes, and 2 sites had mostly large adult-sized snakes and few small individuals. Both the probability a female was gravid and a female’s litter size were positively related to the female’s snout-vent length. Somatic growth rates varied more among years than among sites, and females grew faster (in millimeters per day) than male snakes.
The proportion of the landscape around each site under active rice cultivation fluctuated over time (generally between 60–90 percent of the landscape was active rice growing, although this proportion was lower for some sites in some years), and variation in rice growing was asynchronous among sites. This study demonstrates that intensive demographic sampling enables estimation of several key demographic variables at each study site. Continued sampling would allow for investigating potential relationships between the amount of rice growing at a site and demographic parameters such as growth, survival, and reproduction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181114","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Rose, J.P., Ersan, J.S.M., Reyes, G.A., Gustafson, K.B., Fulton, A.M., Fouts, K.J., Wack, R.F., Wylie, G.D., Casazza, M.L., and Halstead, B.J., 2018, Findings from a preliminary investigation of the effects of aquatic habitat (water) availability on giant gartersnake (Thamnophis gigas) demography in the Sacramento Valley, California, 2014–17: U.S. Geological Survey Open-File Report 2018–1114, 48 p., https://doi.org/10.3133/ofr20181114.","productDescription":"vi, 48 p.","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-097068","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":355890,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1114/coverthb.jpg"},{"id":355891,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1114/ofr20181114.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1114"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.33,\n 38.8333\n ],\n [\n -121.5833,\n 38.8333\n ],\n [\n -121.5833,\n 39.6667\n ],\n [\n -122.33,\n 39.6667\n ],\n [\n -122.33,\n 38.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000 km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.
","language":"English","publisher":"SPIE","doi":"10.1117/1.JRS.12.026017","usgsCitation":"Norman, L.M., Middleton, B.R., and Wilson, N.R., 2018, Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area: Journal of Applied Remote Sensing, v. 12, no. 2, p. 1-19, https://doi.org/10.1117/1.JRS.12.026017.","productDescription":"Article 026017; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-093007","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468713,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.12.026017","text":"Publisher Index Page"},{"id":437886,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OCZ17X","text":"USGS data release","linkHelpText":"Vegetation Survey of the San Carlos Apache Reservation, Arizona and Surrounding Area (September to November 2017)."},{"id":354725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 32.5\n ],\n [\n -109,\n 32.5\n ],\n [\n -109,\n 34\n ],\n [\n -111,\n 34\n ],\n [\n -111,\n 32.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1a5","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":737279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":737281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":5770,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":737280,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200911,"text":"70200911 - 2018 - Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems","interactions":[],"lastModifiedDate":"2018-12-13T09:13:22","indexId":"70200911","displayToPublicDate":"2018-05-19T11:09:07","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems","docAbstract":"Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.
","language":"English","publisher":"MDPI","doi":"10.3390/rs10050791","usgsCitation":"Pastick, N.J., Wylie, B.K., and Wu, Z., 2018, Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems: Remote Sensing, v. 10, no. 5, 15 p., https://doi.org/10.3390/rs10050791.","productDescription":"15 p.","ipdsId":"IP-097826","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468743,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050791","text":"Publisher Index Page"},{"id":359420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-19","publicationStatus":"PW","scienceBaseUri":"5bed4274e4b0b3fc5cf91c92","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":751236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":751237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":751238,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272980,"text":"70272980 - 2018 - Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico","interactions":[],"lastModifiedDate":"2025-12-11T16:57:04.855099","indexId":"70272980","displayToPublicDate":"2018-05-13T10:52:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico","docAbstract":"The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R2 = 0.27) and riparian vegetation (R2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.
","language":"English","publisher":"MDPI","doi":"10.3390/s18051546","usgsCitation":"Jarchow, C., Didan, K., Barreto-Muñoz, A., Nagler, P.L., and Glenn, E., 2018, Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico: Sensors, v. 18, no. 5, 1546, 17 p., https://doi.org/10.3390/s18051546.","productDescription":"1546, 17 p.","ipdsId":"IP-086846","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":497383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s18051546","text":"Publisher Index Page"},{"id":497335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.39760654188758,\n 32.8908327506181\n ],\n [\n -115.33226880384278,\n 32.8908327506181\n ],\n [\n -115.33226880384278,\n 31.59336803534171\n ],\n [\n -114.39760654188758,\n 31.59336803534171\n ],\n [\n -114.39760654188758,\n 32.8908327506181\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":951980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":951981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":951982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":951983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":951984,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202574,"text":"70202574 - 2018 - An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries","interactions":[],"lastModifiedDate":"2019-03-12T10:27:17","indexId":"70202574","displayToPublicDate":"2018-05-10T10:27:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries","docAbstract":"The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1471545","usgsCitation":"Schaeffer, B.A., Iiames, J., Dwyer, J.L., Urquhart, E., Salls, W., Rover, J., and Seegers, B., 2018, An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries: International Journal of Remote Sensing, v. 39, no. 22, p. 7789-7805, https://doi.org/10.1080/01431161.2018.1471545.","productDescription":"17 p.","startPage":"7789","endPage":"7805","ipdsId":"IP-096965","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1471545","text":"Publisher Index Page"},{"id":362002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"39","issue":"22","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iiames, John","contributorId":214110,"corporation":false,"usgs":false,"family":"Iiames","given":"John","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":759164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin","contributorId":214111,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salls, Wilson","contributorId":214112,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seegers, Bridget","contributorId":214113,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":759170,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271969,"text":"70271969 - 2018 - Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction","interactions":[],"lastModifiedDate":"2025-09-29T14:56:13.497511","indexId":"70271969","displayToPublicDate":"2018-05-09T09:50:05","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction","docAbstract":"To protect ecosystem services and the increasing wildland urban interface in a world with fire, comprehensive maps of wildland fuels are needed to predict fire behavior and effects. Traditionally, fuels have been categorized into a classification scheme whereby a single metric represents vegetation composition and structure, which can then be parameterized based on variable vegetation amount and condition. Remote sensing has been used to extrapolate between known field plots across the landscape, however until recently, those technologies have had limited ability to characterize fuels (e.g., composition, horizontal and vertical connectivity). Using new technologies (imaging spectroscopy and LIDAR), the objectives of this study are to assess: 1) how fuel characteristics observed from remote sensing affect categorical fuel classifications, and 2) how fuel characteristics affect landscape-scale fire behavior (spread rate, areal extent and perimeter). The analysis was conducted over the 2014 California King Fire that burned ~40,000 ha over lands with varying use and history and has unique remote sensing observations from before and after the fire. This analysis compares fuel classifications from a synergistic field, model, and Landsat approach (LANDFIRE) and products derived from the Airborne Visible/Infrared Imaging Spectrometer and LIDAR (MapFUELS). Each classification focuses on different fuel characteristics, which were then used to compare differences in a fire simulation model (CAWFE) and actual fire behavior. The results show that fuel characteristic inputs such as horizontal connectivity or fuel type and vertical structure affect fire spread rate and final fire extent (respectively). These results present the opportunity for future integration of fuel characteristics observed at coarser resolutions (900 m2) into predictions of fire behavior a similar spatial resolutions (as opposed to the current standard based on empirical relationships between fuel and fire behavior at ~12 m2 resolution).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2018.04.010","usgsCitation":"Stavros, E.N., Coen, J., Peterson, B., Singh, H., Kennedy, K., Ramirez, C., and Schimel, D., 2018, Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction: Remote Sensing Applications: Society and Environment, v. 11, p. 41-50, https://doi.org/10.1016/j.rsase.2018.04.010.","productDescription":"10 p.","startPage":"41","endPage":"50","ipdsId":"IP-097303","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":496224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stavros, E. Natasha","contributorId":361822,"corporation":false,"usgs":false,"family":"Stavros","given":"E.","middleInitial":"Natasha","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":949522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coen, Janice","contributorId":361823,"corporation":false,"usgs":false,"family":"Coen","given":"Janice","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":949523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Birgit 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":192353,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":949524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singh, Harshvardhan","contributorId":361826,"corporation":false,"usgs":false,"family":"Singh","given":"Harshvardhan","affiliations":[{"id":86363,"text":"Indian Institute of Space Science and Technology","active":true,"usgs":false}],"preferred":false,"id":949525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Kama","contributorId":361827,"corporation":false,"usgs":false,"family":"Kennedy","given":"Kama","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":949526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ramirez, Carlos","contributorId":177061,"corporation":false,"usgs":false,"family":"Ramirez","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":949527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schimel, David","contributorId":146637,"corporation":false,"usgs":false,"family":"Schimel","given":"David","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":949528,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198081,"text":"70198081 - 2018 - A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices","interactions":[],"lastModifiedDate":"2018-07-16T11:25:35","indexId":"70198081","displayToPublicDate":"2018-05-04T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices","docAbstract":"Context
Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers.
Objectives
We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes.
Methods
Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass (Bromus tectorum) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability.
Results
Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ.
Conclusions
Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology.
Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2017.1382065","usgsCitation":"Boyte, S.P., Wylie, B.K., Rigge, M.B., and Dahal, D., 2018, Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA: GIScience and Remote Sensing, v. 55, no. 3, p. 376-399, https://doi.org/10.1080/15481603.2017.1382065.","productDescription":"24 p.","startPage":"376","endPage":"399","ipdsId":"IP-087872","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499993,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/d0da5ee1cd9c49fab95dfe363f4d48a7","text":"External Repository"},{"id":437930,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R20ZVX","text":"USGS data release","linkHelpText":"Downscaled 30 m weekly MODIS NDVI for the Central Great Basin"},{"id":354284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin rangelands","volume":"55","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-28","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdfc","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":734937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":734938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":734939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":734940,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196821,"text":"70196821 - 2018 - Reduced arctic tundra productivity linked with landform and climate change interactions","interactions":[],"lastModifiedDate":"2018-05-03T13:48:20","indexId":"70196821","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Reduced arctic tundra productivity linked with landform and climate change interactions","docAbstract":"Arctic tundra ecosystems have experienced unprecedented change associated with climate warming over recent decades. Across the Pan-Arctic, vegetation productivity and surface greenness have trended positively over the period of satellite observation. However, since 2011 these trends have slowed considerably, showing signs of browning in many regions. It is unclear what factors are driving this change and which regions/landforms will be most sensitive to future browning. Here we provide evidence linking decadal patterns in arctic greening and browning with regional climate change and local permafrost-driven landscape heterogeneity. We analyzed the spatial variability of decadal-scale trends in surface greenness across the Arctic Coastal Plain of northern Alaska (~60,000 km²) using the Landsat archive (1999–2014), in combination with novel 30 m classifications of polygonal tundra and regional watersheds, finding landscape heterogeneity and regional climate change to be the most important factors controlling historical greenness trends. Browning was linked to increased temperature and precipitation, with the exception of young landforms (developed following lake drainage), which will likely continue to green. Spatiotemporal model forecasting suggests carbon uptake potential to be reduced in response to warmer and/or wetter climatic conditions, potentially increasing the net loss of carbon to the atmosphere, at a greater degree than previously expected.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-018-20692-8","usgsCitation":"Lara, M.J., Nitze, I., Grosse, G., Martin, P., and McGuire, A.D., 2018, Reduced arctic tundra productivity linked with landform and climate change interactions: Scientific Reports, v. 8, Article 2345; 10 p., https://doi.org/10.1038/s41598-018-20692-8.","productDescription":"Article 2345; 10 p.","ipdsId":"IP-085871","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-20692-8","text":"Publisher Index Page"},{"id":353942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-05","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbe02","contributors":{"authors":[{"text":"Lara, Mark J.","contributorId":194640,"corporation":false,"usgs":false,"family":"Lara","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":734605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nitze, Ingmar","contributorId":191057,"corporation":false,"usgs":false,"family":"Nitze","given":"Ingmar","affiliations":[],"preferred":false,"id":734606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":734607,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Philip","contributorId":204661,"corporation":false,"usgs":false,"family":"Martin","given":"Philip","affiliations":[{"id":27594,"text":"Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":734608,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":734604,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195023,"text":"sir20185024 - 2018 - Evaluating the potential for near-shore bathymetry on the Majuro Atoll, Republic of the Marshall Islands, using Landsat 8 and WorldView-3 imagery","interactions":[],"lastModifiedDate":"2019-12-30T11:31:34","indexId":"sir20185024","displayToPublicDate":"2018-04-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5024","title":"Evaluating the potential for near-shore bathymetry on the Majuro Atoll, Republic of the Marshall Islands, using Landsat 8 and WorldView-3 imagery","docAbstract":"Satellite-derived near-shore bathymetry (SDB) is becoming an increasingly important method for assessing vulnerability to climate change and natural hazards in low-lying atolls of the northern tropical Pacific Ocean. Satellite imagery has become a cost-effective means for mapping near-shore bathymetry because ships cannot collect soundings safely while operating close to the shore. Also, green laser light detection and ranging (lidar) acquisitions are expensive in remote locations. Previous research has demonstrated that spectral band ratio-based techniques, commonly called the natural logarithm approach, may lead to more precise measurements and modeling of bathymetry because of the phenomenon that different substrates at the same depth have approximately equal ratio values. The goal of this research was to apply the band ratio technique to Landsat 8 at-sensor radiance imagery and WorldView-3 atmospherically corrected imagery in the coastal waters surrounding the Majuro Atoll, Republic of the Marshall Islands, to derive near-shore bathymetry that could be incorporated into a seamless topobathymetric digital elevation model of Majuro. Attenuation of light within the water column was characterized by measuring at-sensor radiance and reflectance at different depths and calculating an attenuation coefficient. Bathymetric lidar data, collected by the U.S. Naval Oceanographic Office in 2006, were used to calibrate the SDB results. The bathymetric lidar yielded a strong linear relation with water depths. The Landsat 8-derived SDB estimates derived from the blue/green band ratio exhibited a water attenuation extinction depth of 6 meters with a coefficient of determination R2=0.9324. Estimates derived from the coastal/red band ratio had an R2=0.9597. At the same extinction depth, SDB estimates derived from WorldView-3 imagery exhibited an R2=0.9574. Because highly dynamic coastal shorelines can be affected by erosion, wetland loss, hurricanes, sea-level rise, urban development, and population growth, consistent bathymetric data are needed to better understand sensitive coastal land/water interfaces in areas subject to coastal disasters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185024","usgsCitation":"Poppenga, S.K., Palaseanu-Lovejoy, M., Gesch, D.B., Danielson, J.J., and Tyler, D.J., 2018, Evaluating the potential for near-shore bathymetry on the Majuro Atoll, Republic of the Marshall Islands, using Landsat 8 and WorldView-3 imagery: U.S. Geological Survey Scientific Investigations Report 2018–5024, 14 p., https://doi.org/10.3133/sir20185024.","productDescription":"Report: vii, 14 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092726","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":353367,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7416VXX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"One Meter Topobathymetric Digital Elevation Model for Majuro Atoll, Republic of the Marshall Islands, 1944 to 2016"},{"id":353365,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5024/coverthb2.jpg"},{"id":353366,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5024/sir20185024.pdf","text":"Report","size":"3.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5024"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Majuro Atoll","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD
Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m2, 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2018.03.019","usgsCitation":"Byrd, K.B., Ballanti, L., Thomas, N., Nguyen, D., Holmquist, J.R., Simard, M., and Windham-Myers, L., 2018, A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States: ISPRS Journal of Photogrammetry and Remote Sensing, v. 139, p. 255-271, https://doi.org/10.1016/j.isprsjprs.2018.03.019.","productDescription":"17 p.","startPage":"255","endPage":"271","ipdsId":"IP-091200","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468833,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2018.03.019","text":"Publisher Index Page"},{"id":437949,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90PG34S","text":"USGS data release","linkHelpText":"Tidal marsh biomass field plot and remote sensing datasets for six regions in the conterminous United States (ver. 2.0, June 2020)"},{"id":353396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6e3e4b0da30c1bfbed8","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":733142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ballanti, Laurel 0000-0002-6478-8322 lballanti@usgs.gov","orcid":"https://orcid.org/0000-0002-6478-8322","contributorId":198603,"corporation":false,"usgs":true,"family":"Ballanti","given":"Laurel","email":"lballanti@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":733143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Nathan","contributorId":204124,"corporation":false,"usgs":false,"family":"Thomas","given":"Nathan","affiliations":[{"id":33580,"text":"NASA-JPL","active":true,"usgs":false}],"preferred":false,"id":733144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nguyen, Dung","contributorId":204125,"corporation":false,"usgs":false,"family":"Nguyen","given":"Dung","email":"","affiliations":[],"preferred":false,"id":733145,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holmquist, James R.","contributorId":173462,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":733146,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Simard, Marc","contributorId":19036,"corporation":false,"usgs":true,"family":"Simard","given":"Marc","email":"","affiliations":[],"preferred":false,"id":733147,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":733148,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196965,"text":"70196965 - 2018 - Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska","interactions":[],"lastModifiedDate":"2018-05-15T16:50:33","indexId":"70196965","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska","docAbstract":"Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2018.58","usgsCitation":"Lara, M.J., Nitze, I., Grosse, G., and McGuire, A.D., 2018, Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska: Scientific Data, v. 5, p. 1-10, https://doi.org/10.1038/sdata.2018.58.","productDescription":"Article number: 180058; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-088497","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468870,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2018.58","text":"Publisher Index Page"},{"id":354201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","volume":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5afee6ece4b0da30c1bfbf73","contributors":{"authors":[{"text":"Lara, Mark J.","contributorId":194640,"corporation":false,"usgs":false,"family":"Lara","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":735152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nitze, Ingmar","contributorId":191057,"corporation":false,"usgs":false,"family":"Nitze","given":"Ingmar","affiliations":[],"preferred":false,"id":735153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":735154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":735151,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196146,"text":"sir20185047 - 2018 - One-meter topobathymetric digital elevation model for Majuro Atoll, Republic of the Marshall Islands, 1944 to 2016","interactions":[],"lastModifiedDate":"2022-04-22T16:49:51.433887","indexId":"sir20185047","displayToPublicDate":"2018-03-30T11:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5047","title":"One-meter topobathymetric digital elevation model for Majuro Atoll, Republic of the Marshall Islands, 1944 to 2016","docAbstract":"Atoll and island coastal communities are highly exposed to sea-level rise, tsunamis, storm surges, rogue waves, king tides, and the occasional combination of multiple factors, such as high regional sea levels, extreme high local tides, and unusually strong wave set-up. The elevation of most of these atolls averages just under 3 meters (m), with many areas roughly at sea level. The lack of high-resolution topographic data has been identified as a critical data gap for hazard vulnerability and adaptation efforts and for high-resolution inundation modeling for atoll nations. Modern topographic survey equipment and airborne lidar surveys can be very difficult and costly to deploy. Therefore, unmanned aircraft systems (UAS) were investigated for collecting overlapping imagery to generate topographic digital elevation models (DEMs). Medium- and high-resolution satellite imagery (Landsat 8 and WorldView-3) was investigated to derive nearshore bathymetry.
The Republic of the Marshall Islands is associated with the United States through a Compact of Free Association, and Majuro Atoll is home to the capital city of Majuro and the largest population of the Republic of the Marshall Islands. The only elevation datasets currently available for the entire Majuro Atoll are the Shuttle Radar Topography Mission and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 elevation data, which have a 30-m grid-cell spacing and a 8-m vertical root mean square error (RMSE). Both these datasets have inadequate spatial resolution and vertical accuracy for inundation modeling.
The final topobathymetric DEM (TBDEM) developed for Majuro Atoll is derived from various data sources including charts, soundings, acoustic sonar, and UAS and satellite imagery spanning over 70 years of data collection (1944 to 2016) on different sections of the atoll. The RMSE of the TBDEM over the land area is 0.197 m using over 70,000 Global Navigation Satellite System real-time kinematic survey points for validation, and 1.066 m for Landsat 8 and 1.112 m for WorldView-3 derived bathymetry using over 16,000 and 9,000 lidar bathymetry points, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185047","usgsCitation":"Palaseanu-Lovejoy, M., Poppenga, S.K., Danielson, J.J., Tyler, D.J., Gesch, D.B., Kottermair, M., Jalandoni, A., Carlson, E., Thatcher, C.A., and Barbee, M.M., 2018, One-meter topobathymetric digital elevation model for Majuro Atoll, Republic of the Marshall Islands, 1944 to 2016: U.S. Geological Survey Scientific Investigations Report 2018–5047, 16 p., https://doi.org/10.3133/sir20185047.","productDescription":"vii, 16 p.","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090429","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":352868,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5047/sir20185047.pdf","text":"Report","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5047"},{"id":352867,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5047/coverthb.jpg"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Majuro Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 170.96923828125,\n 7.009578865370235\n ],\n [\n 171.42654418945312,\n 7.009578865370235\n ],\n [\n 171.42654418945312,\n 7.261669781279355\n ],\n [\n 170.96923828125,\n 7.261669781279355\n ],\n [\n 170.96923828125,\n 7.009578865370235\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geographic Science Center
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Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2017.12.008","usgsCitation":"Poitras, T.B., Villarreal, M.L., Waller, E.K., Nauman, T.W., Miller, M.E., and Duniway, M.C., 2018, Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands: Journal of Arid Environments, v. 153, p. 76-87, https://doi.org/10.1016/j.jaridenv.2017.12.008.","productDescription":"12 p.","startPage":"76","endPage":"87","ipdsId":"IP-084812","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468897,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2017.12.008","text":"Publisher Index Page"},{"id":437977,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SWDWLS","text":"USGS data release","linkHelpText":"Grassland State and Transition Map of Canyonlands National Park Needles District and Indian Creek Grazing Allotment"},{"id":352759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.20797729492188,\n 37.81737834565083\n ],\n [\n -109.58862304687499,\n 37.81737834565083\n ],\n [\n -109.58862304687499,\n 38.494443887725055\n ],\n [\n -110.20797729492188,\n 38.494443887725055\n ],\n [\n -110.20797729492188,\n 37.81737834565083\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f7e4b0da30c1bfbfe0","contributors":{"authors":[{"text":"Poitras, Travis B. 0000-0001-8677-1743 tpoitras@usgs.gov","orcid":"https://orcid.org/0000-0001-8677-1743","contributorId":195168,"corporation":false,"usgs":true,"family":"Poitras","given":"Travis","email":"tpoitras@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Eric K. 0000-0002-9169-9210","orcid":"https://orcid.org/0000-0002-9169-9210","contributorId":203496,"corporation":false,"usgs":true,"family":"Waller","given":"Eric","email":"","middleInitial":"K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":true,"id":731645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":731648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196137,"text":"70196137 - 2018 - Wetlands inform how climate extremes influence surface water expansion and contraction","interactions":[],"lastModifiedDate":"2018-03-21T13:22:39","indexId":"70196137","displayToPublicDate":"2018-03-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Wetlands inform how climate extremes influence surface water expansion and contraction","docAbstract":"Effective monitoring and prediction of flood and drought events requires an improved understanding of how and why surface water expansion and contraction in response to climate varies across space. This paper sought to (1) quantify how interannual patterns of surface water expansion and contraction vary spatially across the Prairie Pothole Region (PPR) and adjacent Northern Prairie (NP) in the United States, and (2) explore how landscape characteristics influence the relationship between climate inputs and surface water dynamics. Due to differences in glacial history, the PPR and NP show distinct patterns in regards to drainage development and wetland density, together providing a diversity of conditions to examine surface water dynamics. We used Landsat imagery to characterize variability in surface water extent across 11 Landsat path/rows representing the PPR and NP (images spanned 1985–2015). The PPR not only experienced a 2.6-fold greater surface water extent under median conditions relative to the NP, but also showed a 3.4-fold greater change in surface water extent between drought and deluge conditions. The relationship between surface water extent and accumulated water availability (precipitation minus potential evapotranspiration) was quantified per watershed and statistically related to variables representing hydrology-related landscape characteristics (e.g., infiltration capacity, surface storage capacity, stream density). To investigate the influence stream connectivity has on the rate at which surface water leaves a given location, we modeled stream-connected and stream-disconnected surface water separately. Stream-connected surface water showed a greater expansion with wetter climatic conditions in landscapes with greater total wetland area, but lower total wetland density. Disconnected surface water showed a greater expansion with wetter climatic conditions in landscapes with higher wetland density, lower infiltration and less anthropogenic drainage. From these findings, we can expect that shifts in precipitation and evaporative demand will have uneven effects on surface water quantity. Accurate predictions regarding the effect of climate change on surface water quantity will require consideration of hydrology-related landscape characteristics including wetland storage and arrangement.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-22-1851-2018","usgsCitation":"Vanderhoof, M.K., Lane, C., McManus, M.L., Alexander, L.C., and Christensen, J.R., 2018, Wetlands inform how climate extremes influence surface water expansion and contraction: Hydrology and Earth System Sciences, v. 22, p. 1851-1873, https://doi.org/10.5194/hess-22-1851-2018.","productDescription":"23 p.","startPage":"1851","endPage":"1873","ipdsId":"IP-090618","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":468912,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-1851-2018","text":"Publisher Index Page"},{"id":352699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Prairie Pothole Regino","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109,\n 39.198205348894795\n ],\n [\n -91.0986328125,\n 39.198205348894795\n ],\n [\n -91.0986328125,\n 48.980216985374994\n ],\n [\n -109,\n 48.980216985374994\n ],\n [\n -109,\n 39.198205348894795\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-15","publicationStatus":"PW","scienceBaseUri":"5afee6fde4b0da30c1bfc026","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":731498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lane, Charles R.","contributorId":138991,"corporation":false,"usgs":false,"family":"Lane","given":"Charles R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":731499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McManus, Michael L.","contributorId":189612,"corporation":false,"usgs":false,"family":"McManus","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":731500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Laurie C.","contributorId":196285,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":731501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, Jay R.","contributorId":179361,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":731502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196799,"text":"70196799 - 2018 - Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes","interactions":[],"lastModifiedDate":"2018-05-01T16:01:37","indexId":"70196799","displayToPublicDate":"2018-03-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes","docAbstract":"The interannual variability of tidal marsh plant phenology is largely unknown and may have important ecological consequences. Marsh plants are critical to the biogeomorphic feedback processes that build estuarine soils, maintain marsh elevation relative to sea level, and sequester carbon. We calculated Tasseled Cap Greenness, a metric of plant biomass, using remotely sensed data available in the Landsat archive to assess how recent climate variation has affected biomass production and plant phenology across three maritime tidal marshes in the Pacific Northwest of the United States. First, we used clipped vegetation plots at one of our sites to confirm that tasseled cap greenness provided a useful measure of aboveground biomass (r2 = 0.72). We then used multiple measures of biomass each growing season over 20–25 years per study site and developed models to test how peak biomass and the date of peak biomass varied with 94 climate and sea-level metrics using generalized linear models and Akaike Information Criterion (AIC) model selection. Peak biomass was positively related to total annual precipitation, while the best predictor for date of peak biomass was average growing season temperature, with the peak 7.2 days earlier per degree C. Our study provides insight into how plants in maritime tidal marshes respond to interannual climate variation and demonstrates the utility of time-series remote sensing data to assess ecological responses to climate stressors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2018.01.006","usgsCitation":"Buffington, K., Dugger, B.D., and Thorne, K., 2018, Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes: Estuarine, Coastal and Shelf Science, v. 202, p. 212-221, https://doi.org/10.1016/j.ecss.2018.01.006.","productDescription":"10 p.","startPage":"212","endPage":"221","ipdsId":"IP-093014","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437990,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7F18XZR","text":"USGS data release","linkHelpText":"Data for climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes"},{"id":353900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.8486328125,\n 43.068887774169625\n ],\n [\n -122.51953124999999,\n 43.068887774169625\n ],\n [\n -122.51953124999999,\n 47.18971246448421\n ],\n [\n -124.8486328125,\n 47.18971246448421\n ],\n [\n -124.8486328125,\n 43.068887774169625\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"202","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70de4b0da30c1bfc084","contributors":{"authors":[{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":734453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Bruce D.","contributorId":176167,"corporation":false,"usgs":false,"family":"Dugger","given":"Bruce","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":734454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":734452,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195451,"text":"70195451 - 2018 - 2017 Landsat Science Team Summer Meeting Summary","interactions":[],"lastModifiedDate":"2018-03-28T15:55:02","indexId":"70195451","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3555,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":10}},"title":"2017 Landsat Science Team Summer Meeting Summary","docAbstract":"The summer meeting of the U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) was held June 11-13, 2017, at the USGS’s Earth Resources Observation and Science (EROS) Center near Sioux Falls, SD. This was the final meeting of the Second (2012-2017) LST.1 Frank Kelly [EROS—Center Director] welcomed the attendees and expressed his thanks to the LST members for their contributions. He then introduced video-recorded messages from South Dakota’s U.S. senators, John Thune and Mike Rounds, in which they acknowledged the efforts of the team in advancing the societal impacts of the Landsat Program.
","language":"English","publisher":"NASA","usgsCitation":"Crawford, C.J., Loveland, T.R., Wulder, M.A., and Irons, J.R., 2018, 2017 Landsat Science Team Summer Meeting Summary: The Earth Observer, v. 30, no. 1, p. 21-25.","productDescription":"5 p.","startPage":"21","endPage":"25","ipdsId":"IP-092816","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":352880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":351675,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/Jan_Feb_2018_color508_0.pdf#page=21"}],"volume":"30","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0d4","contributors":{"authors":[{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":202517,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher","email":"cjcrawford@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":728669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":728670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Michael A.","contributorId":189990,"corporation":false,"usgs":false,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":731967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":731968,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195463,"text":"70195463 - 2018 - Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)","interactions":[],"lastModifiedDate":"2018-02-16T10:33:21","indexId":"70195463","displayToPublicDate":"2018-02-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)","docAbstract":"Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1437297","usgsCitation":"Wilson, N., and Norman, L., 2018, Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI): International Journal of Remote Sensing, v. 39, no. 10, p. 3243-3274, https://doi.org/10.1080/01431161.2018.1437297.","productDescription":"30 p.","startPage":"3243","endPage":"3274","ipdsId":"IP-087663","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468994,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1437297","text":"Publisher Index Page"},{"id":438011,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F798867T","text":"USGS data release","linkHelpText":"Data Release for Analysis of Vegetation Recovery Surrounding a Restored Wetland using the Normalized Difference Infrared Index (NDII) and Normalized Difference Vegetation Index (NDVI)"},{"id":351692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Cuenca Los Ojos, San Bernardino National Wildlife Refuge, San Bernadino Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.51995849609375,\n 31.049404461655996\n ],\n [\n -108.93630981445312,\n 31.049404461655996\n ],\n [\n -108.93630981445312,\n 31.468496379205966\n ],\n [\n -109.51995849609375,\n 31.468496379205966\n ],\n [\n -109.51995849609375,\n 31.049404461655996\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-12","publicationStatus":"PW","scienceBaseUri":"5afee72be4b0da30c1bfc16e","contributors":{"authors":[{"text":"Wilson, Natalie R. 0000-0001-5145-1221","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":202534,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":728707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura","contributorId":202535,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":728708,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195402,"text":"ofr20171166 - 2018 - Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region","interactions":[],"lastModifiedDate":"2022-04-22T16:28:36.224437","indexId":"ofr20171166","displayToPublicDate":"2018-02-15T16:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1166","title":"Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region","docAbstract":"To improve understanding of the distribution of ecologically important, ephemeral wetland habitats across the Great Plains, the occurrence and distribution of surface water in playa wetland complexes were documented for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. This information is important because it informs land and wildlife managers about the timing and location of habitat availability. Data with an accurate timestamp that indicate the presence of water, the percent of the area inundated with water, and the spatial distribution of playa wetlands with water are needed for a host of resource inventory, monitoring, and research applications. For example, the distribution of inundated wetlands forms the spatial pattern of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface waters that recharge the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Documentation of these spatially and temporally intricate processes, here, provides data required to assess connections between inundation and multiple environmental drivers, such as climate, land use, soil, and topography. Climate drivers are understood to interact with land cover, land use and soil attributes in determining the amount of water that flows overland into playa wetlands. Results indicated significant spatial variability represented by differences in the percent of playas inundated among States within the GPLCC. Further, analysis-of-variance comparison of differences in inundation between years showed significant differences in all cases. Although some connections with seasonal moisture patterns may be observed, the complex spatial-temporal gradients of precipitation, temperature, soils, and land use need to be combined as covariates in multivariate models to effectively account for these patterns. We demonstrate the feasibility of using classification of Landsat satellite imagery to describe playa-wetland inundation across years and seasons. Evaluating classifications representing only 4 years of imagery, we found significant year-to-year and state-to-state differences in inundation rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171166","collaboration":"Prepared in cooperation with the Great Plains Landscape Conservation Cooperative, U.S. Fish and Wildlife Service, Albuquerque, New Mexico","usgsCitation":"Manier, D.J., and Rover, J.R., 2018, Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region: U.S. Geological Survey Open-File Report 2017–1166, 20 p., https://doi.org/10.3133/ofr20171166.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-073569","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438012,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MW2GCN","text":"USGS data release","linkHelpText":"Landsat classification of surface water for multiple seasons to monitor inundation of playa wetlands"},{"id":351569,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1166/ofr20171166.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1166"},{"id":351568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1166/coverthb.jpg"}],"country":"United States","otherGeospatial":"Great Plains Landscape Conservation Cooperative","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 30\n ],\n [\n -96,\n 30\n ],\n [\n -96,\n 44\n ],\n [\n -106,\n 44\n ],\n [\n -106,\n 30\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-aconcentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R2 value of 0.66; root mean square error value of 8.9 µg l−1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1430912","usgsCitation":"Keith, D., Rover, J., Green, J., Zalewsky, B., Charpentier, M., Hursby, G., and Bishop, J., 2018, Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager: International Journal of Remote Sensing, v. 39, no. 9, p. 2818-2846, https://doi.org/10.1080/01431161.2018.1430912.","productDescription":"29 p.","startPage":"2818","endPage":"2846","ipdsId":"IP-096964","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469076,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6020680","text":"External Repository"},{"id":355407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.2,\n 35.6333\n ],\n [\n -78.8333,\n 35.6333\n ],\n [\n -78.8333,\n 35.8833\n ],\n [\n -79.2,\n 35.8833\n ],\n [\n -79.2,\n 35.6333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-29","publicationStatus":"PW","scienceBaseUri":"5b46e5d2e4b060350a15d218","contributors":{"authors":[{"text":"Keith, Darryl","contributorId":206068,"corporation":false,"usgs":false,"family":"Keith","given":"Darryl","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":739317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":26162,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":739316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Jason","contributorId":206069,"corporation":false,"usgs":false,"family":"Green","given":"Jason","email":"","affiliations":[{"id":37231,"text":"NC DENR","active":true,"usgs":false}],"preferred":false,"id":739318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zalewsky, Brian","contributorId":206070,"corporation":false,"usgs":false,"family":"Zalewsky","given":"Brian","email":"","affiliations":[{"id":37232,"text":"RI DEMP","active":true,"usgs":false}],"preferred":false,"id":739319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Charpentier, Mike","contributorId":206071,"corporation":false,"usgs":false,"family":"Charpentier","given":"Mike","email":"","affiliations":[{"id":37233,"text":"Raytheon Company","active":true,"usgs":false}],"preferred":false,"id":739320,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hursby, Glen","contributorId":206072,"corporation":false,"usgs":false,"family":"Hursby","given":"Glen","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":739321,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bishop, Joseph","contributorId":206073,"corporation":false,"usgs":false,"family":"Bishop","given":"Joseph","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":739322,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196845,"text":"70196845 - 2018 - Estimating wetland connectivity to streams in the Prairie Pothole Region: An isotopic and remote sensing approach","interactions":[],"lastModifiedDate":"2018-05-04T10:36:11","indexId":"70196845","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Estimating wetland connectivity to streams in the Prairie Pothole Region: An isotopic and remote sensing approach","docAbstract":"Understanding hydrologic connectivity between wetlands and perennial streams is critical to understanding the reliance of stream flow on inputs from wetlands. We used the isotopic evaporation signal in water and remote sensing to examine wetland‐stream hydrologic connectivity within the Pipestem Creek watershed, North Dakota, a watershed dominated by prairie‐pothole wetlands. Pipestem Creek exhibited an evaporated‐water signal that had approximately half the isotopic‐enrichment signal found in most evaporatively enriched prairie‐pothole wetlands. Groundwater adjacent to Pipestem Creek had isotopic values that indicated recharge from winter precipitation and had no significant evaporative enrichment, indicating that enriched surface water did not contribute significantly to groundwater discharging into Pipestem Creek. The estimated surface water area necessary to generate the evaporation signal within Pipestem Creek was highly dynamic, varied primarily with the amount of discharge, and was typically greater than the immediate Pipestem Creek surface water area, indicating that surficial flow from wetlands contributed to stream flow throughout the summer. We propose a dynamic range of spilling thresholds for prairie‐pothole wetlands across the watershed allowing for wetland inputs even during low‐flow periods. Combining Landsat estimates with the isotopic approach allowed determination of potential (Landsat) and actual (isotope) contributing areas in wetland‐dominated systems. This combined approach can give insights into the changes in location and magnitude of surface water and groundwater pathways over time. This approach can be used in other areas where evaporation from wetlands results in a sufficient evaporative isotopic signal.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR021016","usgsCitation":"Brooks, J.R., Mushet, D.M., Vanderhoof, M.K., Leibowitz, S.G., Christensen, J.R., Neff, B., Rosenberry, D.O., Rugh, W.D., and Alexander, L., 2018, Estimating wetland connectivity to streams in the Prairie Pothole Region: An isotopic and remote sensing approach: Water Resources Research, v. 54, no. 2, p. 955-977, https://doi.org/10.1002/2017WR021016.","productDescription":"23 p.","startPage":"955","endPage":"977","ipdsId":"IP-086197","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469082,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5903587","text":"Publisher Index Page"},{"id":353957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","volume":"54","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-09","publicationStatus":"PW","scienceBaseUri":"5afee740e4b0da30c1bfc1cb","contributors":{"authors":[{"text":"Brooks, J. 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For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.
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