Remote Sensing of Aerosols from Satellites: Why Has It Been Do Difficult to Quantify Aerosol-Cloud Interactions for Climate Assessment, and How Can We Make Progress?

NASA Technical Reports Server (NTRS)

Kahn, Ralph A.

2015-01-01

The organizers of the National Academy of Sciences Arthur M. Sackler Colloquia Series on Improving Our Fundamental Understanding of the Role of Aerosol-Cloud Interactions in the Climate System would like to post Ralph Kahn's presentation entitled Remote Sensing of Aerosols from Satellites: Why has it been so difficult to quantify aerosol-cloud interactions for climate assessment, and how can we make progress? to their public website.

Assessing Wetland Hydroperiod and Soil Moisture with Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Assist with the evaluation and measuring of wetlands hydroperiod at the Plum Brook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: (1) Show the relative length of hydroperiod using available remote sensing datasets, (2) Date linked table of wetlands extent over time for all feasible non-forested wetlands, (3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables (4), A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment; and (5) A MTRI style report summarizing year 2 results.

Data needs and data bases for climate studies

NASA Technical Reports Server (NTRS)

Matthews, Elaine

1986-01-01

Two complementary global digital data bases of vegetation and land use, compiled at 1 deg resolution from published sources for use in climate studies, are discussed. The data bases were implemented, in several individually tailored formulations, in a series of climate related applications including: land-surface prescriptions in three-dimensional general circulation models, global biogeochemical cycles (CO2, methane), critical-area mapping for satellite monitoring of land-cover change, and large-scale remote sensing of surface reflectance. The climate applications are discussed with reference to data needs, and data availability from traditional and remote sensing sources.

Remote Sensing of Aerosol and Aerosol Radiative Forcing of Climate from EOS Terra MODIS Instrument

NASA Technical Reports Server (NTRS)

Kaufman, Yoram; Tanre, Didier; Remer, Lorraine; Einaudi, Franco (Technical Monitor)

2000-01-01

The recent launch of EOS-Terra into polar orbit has begun to revolutionize remote sensing of aerosol and their effect on climate. Terra has five instruments, two of them,Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectro-Radiometer (MISR) are designed to monitor global aerosol in two different complementary ways. Here we shall discuss the use of the multispectral measurements of MODIS to derive: (1) the global distribution of aerosol load (and optical thickness) over ocean and land; (2) to measure the impact of aerosol on reflection of sunlight to space; and (3) to measure the ability of aerosol to absorb solar radiation. These measurements have direct applications on the understanding of the effect of aerosol on climate, the ability to predict climate change, and on the monitoring of dust episodes and man-made pollution. Principles of remote sensing of aerosol from MODIS will be discussed and first examples of measurements from MODIS will be provided.

The Role of Remote Sensing Displays in Earth Climate and Planetary Atmospheric Research

NASA Technical Reports Server (NTRS)

DelGenio, Anthony D.; Hansen, James E. (Technical Monitor)

2001-01-01

The communities of scientists who study the Earth's climate and the atmospheres of the other planets barely overlap, but the types of questions they pose and the resulting implications for the use and interpretation of remote sensing data sets have much in common. Both seek to determine the characteristic behavior of three-dimensional fluids that also evolve in time. Climate researchers want to know how and why the general patterns that define our climate today might be different in the next century. Planetary scientists try to understand why circulation patterns and clouds on Mars, Venus, or Jupiter are different from those on Earth. Both disciplines must aggregate large amounts of data covering long time periods and several altitudes to have a representative picture of the rapidly changing atmosphere they are studying. This emphasis separates climate scientists from weather forecasters, who focus at any one time on a limited number of images. Likewise, it separates planetary atmosphere researchers from planetary geologists, who rely primarily on single images (or mosaics of images covering the globe) to study two-dimensional planetary surfaces that are mostly static over the duration of a spacecraft mission yet reveal dynamic processes acting over thousands to millions of years. Remote sensing displays are usually two-dimensional projections that capture an atmosphere at an instant in time. How scientists manipulate and display such data, how they interpret what they see, and how they thereby understand the physical processes that cause what they see, are the challenges I discuss in this chapter. I begin by discussing differences in how novices and experts in the field relate displays of data to the real world. This leads to a discussion of the use and abuse of image enhancement and color in remote sensing displays. I then show some examples of techniques used by scientists in climate and planetary research to both convey information and design research strategies using remote sensing displays.

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501

Use of Remote Sensing for Decision Support in Africa

NASA Technical Reports Server (NTRS)

Policelli, Frederick S.

2007-01-01

Over the past 30 years, the scientific community has learned a great deal about the Earth as an integrated system. Much of this research has been enabled by the development of remote sensing technologies and their operation from space. Decision makers in many nations have begun to make use of remote sensing data for resource management, policy making, and sustainable development planning. This paper makes an attempt to provide a survey of the current state of the requirements and use of remote sensing for sustainable development in Africa. This activity has shown that there are not many climate data ready decision support tools already functioning in Africa. There are, however, endusers with known requirements who could benefit from remote sensing data.

Merlin: an integrated path differential absorption (IPDA) lidar for global methane remote sensing

NASA Astrophysics Data System (ADS)

Bode, M.; Alpers, M.; Millet, B.; Ehret, G.; Flamant, P.

2017-11-01

The Methane Remote Sensing LIDAR Mission (MERLIN) is a joint French-German cooperation on the development, launch and operation of a climate monitoring satellite, executed by the French Space Agency CNES and the German Space Administration DLR.

Merlin: an integrated path differential absorption (IPDA) lidar for global methane remote sensing

NASA Astrophysics Data System (ADS)

Bode, M.; Wührer, C.; Alpers, M.; Millet, B.; Ehret, G.; Bousquet, P.

2017-09-01

The Methane Remote Sensing LIDAR Mission (MERLIN) is a joint French-German cooperation on the development, launch and operation of a climate monitoring satellite, executed by the French Space Agency CNES and the German Space Administration DLR.

Incorporating Student Activities into Climate Change Education

NASA Astrophysics Data System (ADS)

Steele, H.; Kelly, K.; Klein, D.; Cadavid, A. C.

2013-12-01

Under a NASA grant, Mathematical and Geospatial Pathways to Climate Change Education, students at California State University, Northridge integrated Geographic Information Systems (GIS), remote sensing, satellite data technologies, and climate modelling into the study of global climate change under a Pathway for studying the Mathematics of Climate Change (PMCC). The PMCC, which is an interdisciplinary option within the BS in Applied Mathematical Sciences, consists of courses offered by the departments of Mathematics, Physics, and Geography and is designed to prepare students for careers and Ph.D. programs in technical fields relevant to global climate change. Under this option students are exposed to the science, mathematics, and applications of climate change science through a variety of methods including hands-on experience with computer modeling and image processing software. In the Geography component of the program, ESRI's ArcGIS and ERDAS Imagine mapping, spatial analysis and image processing software were used to explore NASA satellite data to examine the earth's atmosphere, hydrosphere and biosphere in areas that are affected by climate change or affect climate. These technology tools were incorporated into climate change and remote sensing courses to enhance students' knowledge and understanding of climate change through hands-on application of image processing techniques to NASA data. Several sets of exercises were developed with specific learning objectives in mind. These were (1) to increase student understanding of climate change and climate change processes; (2) to develop student skills in understanding, downloading and processing satellite data; (3) to teach remote sensing technology and GIS through applications to climate change; (4) to expose students to climate data and methods they can apply to solve real world problems and incorporate in future research projects. In the Math and Physics components of the course, students learned about atmospheric circulation with applications of the Lorenz model, explored the land-sea breeze problem with the Dynamics and Thermodynamics Circulation Model (DTDM), and developed simple radiative transfer models. Class projects explored the effects of varying the content of CO2 and CH4 in the atmosphere, as well as the properties of paleoclimates in atmospheric simulations using EdGCM. Initial assessment of student knowledge, attitudes, and behaviors associated with these activities, particularly about climate change, was measured. Pre- and post-course surveys provided student perspectives about the courses and their learning about remote sensing and climate change concepts. Student performance on the tutorials and course projects evaluated students' ability to learn and apply their knowledge about climate change and skills with remote sensing to assigned problems or proposed projects of their choice. Survey and performance data illustrated that the exercises were successful in meeting their intended learning objectives as well as opportunities for further refinement and expansion.

Remote Sensing of Aerosol and their Radiative Forcing of Climate

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Tanre, Didier; Remer, Lorraine A.

1999-01-01

Remote sensing of aerosol and aerosol radiative forcing of climate is going through a major transformation. The launch in next few years of new satellites designed specifically for remote sensing of aerosol is expected to further revolutionized aerosol measurements: until five years ago satellites were not designed for remote sensing of aerosol. Aerosol optical thickness was derived as a by product, only over the oceans using one AVHRR channel with errors of approx. 50%. However it already revealed a very important first global picture of the distribution and sources of aerosol. In the last 5 years we saw the introduction of polarization and multi-view observations (POLDER and ATSR) for satellite remote sensing of aerosol over land and ocean. Better products are derived from AVHRR using its two channels. The new TOMS aerosol index shows the location and transport of aerosol over land and ocean. Now we anticipate the launch of EOS-Terra with MODIS, MISR and CERES on board for multi-view, multi-spectral remote sensing of aerosol and its radiative forcing. This will allow application of new techniques, e.g. using a wide spectral range (0.55-2.2 microns) to derive precise optical thickness, particle size and mass loading. Aerosol is transparent in the 2.2 microns channel, therefore this channel can be used to detect surface features that in turn are used to derive the aerosol optical thickness in the visible part of the spectrum. New techniques are developed to derive the aerosol single scattering albedo, a measure of absorption of sunlight, and techniques to derive directly the aerosol forcing at the top of the atmosphere. In the last 5 years a global network of sun/sky radiometers was formed, designed to communicate in real time the spectral optical thickness from 50-80 locations every day, every 15 minutes. The sky angular and spectral information is also measured and used to retrieve the aerosol size distribution, refractive index, single scattering albedo and the spectral flux reaching the surface. Effort to introduce remote sensing from lidars will literally additional dimension to aerosol remote sensing. The vertical dimension is a critical link between the global satellite observations and modeling of aerosol transport. Lidars are also critical to study aerosol impact on cloud microphysics and reflectance. Both lidar ground networks and satellite systems are in development. This new capability is expected to put remote sensing in the forefront of aerosol and climate studies. Together with field experiments, chemical analysis and chemical transport models we anticipate, in the next decade, to be able to resolve some of the outstanding questions regarding the role of aerosol in climate, in atmospheric chemistry and its influence on human health and life on this planet.

Satellite Remote Sensing of Ozone Change, Air Quality and Climate

NASA Technical Reports Server (NTRS)

Hilsenrath, Ernest; Bhartia, Pawan K. (Technical Monitor)

2001-01-01

To date satellite remote sensing of ozone depletion has been very successful. Data sets have been validated and measured trends are in agreement with model calculations. Technology developed for sensing the stratosphere is now being employed to study air quality and climate with promising results. These new data show that air quality is a transcontinental issue, but that better instrumentation is needed. Recent data show a connection between the stratosphere, troposphere and climate, which will require new technology to quantify these relationships. NASA and NOAA (National Oceanic and Atmospheric Administration) are planning and developing new missions. Recent results from TOMS (Total Ozone Mapping Spectrometer), SeaWiffs, and Terra will be discussed and upcoming missions to study atmospheric chemistry will be discussed.

Environmental and Landscape Remote Sensing Using Free and Open Source Image Processing Tools

EPA Science Inventory

As global climate change and human activities impact the environment, there is a growing need for scientific tools to monitor and measure environmental conditions that support human and ecological health. Remotely sensed imagery from satellite and airborne platforms provides a g...

Application of Geographic Information System (GIS) in Student Experiential Learning on Climate Change and Sustainability

NASA Astrophysics Data System (ADS)

Ozbay, G.; Sriharan, S.; Fan, C.; Prakash, A.; San Juan, F.

2016-12-01

Consortium of minority serving institutions including Delaware State University, Virginia State University, Morgan State University, University of Alaska Fairbanks, and Elizabeth City State University have collaborated on various student experiential learning programs to expand the technology-based education by incorporating Geographic Information System (GIS) technique to promote student learning on climate change and sustainability. Specific objectives of this collaborative programs are to: (i) develop new or enhance existing courses of Introduction to Geographic Information System (GIS) and Introduction to Remote Sensing, (ii) enhance teaching and research capabilities through faculty professional development workshops, (iii) engage minority undergraduates in GIS and remote sensing research via experiential learning activities including summer internship, workshop, and work study experience. Ultimate goal is to prepare pipeline of minority task force with skills in GIS and remote sensing application in climate sciences. Various research projects were conducted on topics such as carbon footprint, atmospheric CO2, wildlife diversity, ocean circulation, wild fires, geothermal exploration, etc. Students taking GIS and remote sensing courses often express interests to be involved in research projects to enhance their knowledge and obtain research skills. Of about 400 students trained, approximately 30% of these students were involved in research experience in our programs since 2004. The summer undergraduate research experiences (REU) have offered hands-on research experience to the students on climate change and sustainability. Previous studies indicate that students who are previously exposed to environmental science only by a single field trip or an introductory course could be still at risk of dropping out of this field in their early years of the college. The research experience, especially at early college years, would significantly increase the participation and retention of students in climate sciences and sustainability by creating and maintaining interest in these areas. These programs promoted active recruitment of faculty, staff, and students, fostered the development of partnerships, and enhanced related skill sets among students in GIS and remote sensing.

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

NASA Technical Reports Server (NTRS)

Zhang, Xiaodong; Kirilenko, Andrei; Lim, Howe; Teng, Williams

2010-01-01

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Using Remotely Sensed Data for Climate Change Mitigation and Adaptation: A Collaborative Effort Between the Climate Change Adaptation Science Investigators Workgroup (CASI), NASA Johnson Space Center, and Jacobs Technology

NASA Technical Reports Server (NTRS)

Jagge, Amy

2016-01-01

With ever changing landscapes and environmental conditions due to human induced climate change, adaptability is imperative for the long-term success of facilities and Federal agency missions. To mitigate the effects of climate change, indicators such as above-ground biomass change must be identified to establish a comprehensive monitoring effort. Researching the varying effects of climate change on ecosystems can provide a scientific framework that will help produce informative, strategic and tactical policies for environmental adaptation. As a proactive approach to climate change mitigation, NASA tasked the Climate Change Adaptation Science Investigators Workgroup (CASI) to provide climate change expertise and data to Center facility managers and planners in order to ensure sustainability based on predictive models and current research. Generation of historical datasets that will be used in an agency-wide effort to establish strategies for climate change mitigation and adaptation at NASA facilities is part of the CASI strategy. Using time series of historical remotely sensed data is well-established means of measuring change over time. CASI investigators have acquired multispectral and hyperspectral optical and LiDAR remotely sensed datasets from NASA Earth Observation Satellites (including the International Space Station), airborne sensors, and astronaut photography using hand held digital cameras to create a historical dataset for the Johnson Space Center, as well as the Houston and Galveston area. The raster imagery within each dataset has been georectified, and the multispectral and hyperspectral imagery has been atmospherically corrected. Using ArcGIS for Server, the CASI-Regional Remote Sensing data has been published as an image service, and can be visualized through a basic web mapping application. Future work will include a customized web mapping application created using a JavaScript Application Programming Interface (API), and inclusion of the CASI data for the NASA Johnson Space Center into a NASA-Wide GIS Institutional Portal.

Constraining the dynamics of the water budget at high spatial resolution in the world's water towers using models and remote sensing data; Snake River Basin, USA

NASA Astrophysics Data System (ADS)

Watson, K. A.; Masarik, M. T.; Flores, A. N.

2016-12-01

Mountainous, snow-dominated basins are often referred to as the water towers of the world because they store precipitation in seasonal snowpacks, which gradually melt and provide water supplies to downstream communities. Yet significant uncertainties remain in terms of quantifying the stores and fluxes of water in these regions as well as the associated energy exchanges. Constraining these stores and fluxes is crucial for advancing process understanding and managing these water resources in a changing climate. Remote sensing data are particularly important to these efforts due to the remoteness of these landscapes and high spatial variability in water budget components. We have developed a high resolution regional climate dataset extending from 1986 to the present for the Snake River Basin in the northwestern USA. The Snake River Basin is the largest tributary of the Columbia River by volume and a critically important basin for regional economies and communities. The core of the dataset was developed using a regional climate model, forced by reanalysis data. Specifically the Weather Research and Forecasting (WRF) model was used to dynamically downscale the North American Regional Reanalysis (NARR) over the region at 3 km horizontal resolution for the period of interest. A suite of satellite remote sensing products provide independent, albeit uncertain, constraint on a number of components of the water and energy budgets for the region across a range of spatial and temporal scales. For example, GRACE data are used to constrain basinwide terrestrial water storage and MODIS products are used to constrain the spatial and temporal evolution of evapotranspiration and snow cover. The joint use of both models and remote sensing products allows for both better understanding of water cycle dynamics and associated hydrometeorologic processes, and identification of limitations in both the remote sensing products and regional climate simulations.

Section summary: Remote sensing

Treesearch

Belinda Arunarwati Margono

2013-01-01

Remote sensing is an important data source for monitoring the change of forest cover, in terms of both total removal of forest cover (deforestation), and change of canopy cover, structure and forest ecosystem services that result in forest degradation. In the context of Intergovernmental Panel on Climate Change (IPCC), forest degradation monitoring requires information...

Remote Sensing the Thermal and Humidity Structure of the Earth's Atmosphere Using the GPS Radio Occultation Technique: Applications in Climate Studies

NASA Astrophysics Data System (ADS)

Vergados, P.; Mannucci, A. J.; Ao, C. O.; Verkhoglyadova, O. P.; Iijima, B.

2017-12-01

This presentation introduces the fundamentals of the Global Positioning System radio occultation (GPS RO) remote sensing technique in retrieving atmospheric temperature and humidity information and presents the use of these observations in climate research. Our objective is to demonstrate and establish the GPS RO remote sensing technique as a complementary data set to existing state-of-the-art space-based platforms for climate studies. We show how GPS RO measurements at 1.2-1.6 GHz frequency band can be used to infer the upper tropospheric water vapor and temperature feedbacks and we present a decade-long specific humidity (SH) record from January 2007 until December 2015. We cross-compare the GPS RO-estimated climate feedbacks and the SH long-record with independent data sets from the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim), and the Atmospheric Infrared Sounder (AIRS) instrument. These cross-comparisons serve as a performance guide for the GPS-RO observations with respect to other data sets by providing an independent measure of climate feedbacks and humidity short-term trends.

A new simple concept for ocean colour remote sensing using parallel polarisation radiance

PubMed Central

He, Xianqiang; Pan, Delu; Bai, Yan; Wang, Difeng; Hao, Zengzhou

2014-01-01

Ocean colour remote sensing has supported research on subjects ranging from marine ecosystems to climate change for almost 35 years. However, as the framework for ocean colour remote sensing is based on the radiation intensity at the top-of-atmosphere (TOA), the polarisation of the radiation, which contains additional information on atmospheric and water optical properties, has largely been neglected. In this study, we propose a new simple concept to ocean colour remote sensing that uses parallel polarisation radiance (PPR) instead of the traditional radiation intensity. We use vector radiative transfer simulation and polarimetric satellite sensing data to demonstrate that using PPR has two significant advantages in that it effectively diminishes the sun glint contamination and enhances the ocean colour signal at the TOA. This concept may open new doors for ocean colour remote sensing. We suggest that the next generation of ocean colour sensors should measure PPR to enhance observational capability. PMID:24434904

Satellites as Shared Resources for Caribbean Climate and Health Studies

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2002-01-01

Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring, risk mapping, and surveillance of parameters useful to such problems as vector-borne and infectious diseases, air and water quality, harmful algal blooms, UV (ultraviolet) radiation, contaminant and pathogen transport in air and water, and thermal stress. Remote sensing, geographic information systems (GIS), global positioning systems (GPS), improved computational capabilities, and interdisciplinary research between the Earth and health science communities are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global health issues. Collaborative efforts among scientists from health and Earth sciences together with local decision-makers are enabling increased understanding of the relationships between changes in temperature, rainfall, wind, soil moisture, solar radiation, vegetation, and the patterns of extreme weather events and the occurrence and patterns of diseases (especially, infectious and vector-borne diseases) and other health problems. This increased understanding through improved information and data sharing, in turn, empowers local health and environmental officials to better predict health problems, take preventive measure, and improve response actions. This paper summarizes the remote sensing systems most useful for climate, environment and health studies of the Caribbean region and provides several examples of interdisciplinary research projects in the Caribbean currently using remote sensing technologies. These summaries include the use of remote sensing of algal blooms, pollution transport, coral reef monitoring, vectorborne disease studies, and potential health effects of African dust on Trinidad and Barbados.

Assessing Climate-Induced Change in River Flow Using Satellite Remote Sensing and Process Modeling in High Mountain Asia

NASA Astrophysics Data System (ADS)

McDonald, K. C.

2017-12-01

Snow- and glacier-fed river systems originating from High Mountain Asia (HMA) support diverse ecosystems and provide the basis for food and energy production for more than a billion people living downstream. Climate-driven changes in the melting of snow and glaciers and in precipitation patterns are expected to significantly alter the flow of the rivers in the HMA region at various temporal scales, which in turn could heavily affect the socioeconomics of the region. Hence, climate change effects on seasonal and long-term hydrological conditions may have far reaching economic impact annually and over the century. We are developing a decision support tool utilizing integrated microwave remote sensing datasets, process modeling and economic models to inform water resource management decisions and ecosystem sustainability as related to the High Mountain Asia (HMA) region's response to climate change. The availability of consistent time-series microwave remote sensing datasets from Earth-orbiting scatterometers, radiometers and synthetic aperture radar (SAR) imagery provides the basis for the observational framework of this monitoring system. We discuss the assembly, processing and application of scatterometer and SAR data sets from the Advanced Scatterometer (ASCAT) and Sentinal-1 SARs, and the enlistment of these data to monitor seasonal melt and thaw status of glacier-dominated and surrounding regions. We present current status and future plans for this effort. Our team's study emphasizes processes and economic modeling within the Trishuli basin; our remote sensing analysis supports analyses across the HiMAT domain.

Remote sensing of aerosols in the Arctic for an evaluation of global climate model simulations

PubMed Central

Glantz, Paul; Bourassa, Adam; Herber, Andreas; Iversen, Trond; Karlsson, Johannes; Kirkevåg, Alf; Maturilli, Marion; Seland, Øyvind; Stebel, Kerstin; Struthers, Hamish; Tesche, Matthias; Thomason, Larry

2014-01-01

In this study Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua retrievals of aerosol optical thickness (AOT) at 555 nm are compared to Sun photometer measurements from Svalbard for a period of 9 years. For the 642 daily coincident measurements that were obtained, MODIS AOT generally varies within the predicted uncertainty of the retrieval over ocean (ΔAOT = ±0.03 ± 0.05 · AOT). The results from the remote sensing have been used to examine the accuracy in estimates of aerosol optical properties in the Arctic, generated by global climate models and from in situ measurements at the Zeppelin station, Svalbard. AOT simulated with the Norwegian Earth System Model/Community Atmosphere Model version 4 Oslo global climate model does not reproduce the observed seasonal variability of the Arctic aerosol. The model overestimates clear-sky AOT by nearly a factor of 2 for the background summer season, while tending to underestimate the values in the spring season. Furthermore, large differences in all-sky AOT of up to 1 order of magnitude are found for the Coupled Model Intercomparison Project phase 5 model ensemble for the spring and summer seasons. Large differences between satellite/ground-based remote sensing of AOT and AOT estimated from dry and humidified scattering coefficients are found for the subarctic marine boundary layer in summer. Key Points Remote sensing of AOT is very useful in validation of climate models PMID:25821664

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

USDA-ARS?s Scientific Manuscript database

Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variabi...

Satellite Calibration and Verification of Remotely Sensed Cloud and Radiation Properties Using ARM UAV Data

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Charlock, Thomas P.

1998-01-01

The work proposed under this agreement was designed to validate and improve remote sensing of cloud and radiation properties in the atmosphere for climate studies with special emphasis on the use of satellites for monitoring these parameters to further the goals of the Atmospheric Radiation Measurement (ARM) Program.

Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems

Treesearch

Alistair M. S. Smith; Crystal A. Kolden; Wade T. Tinkham; Alan F. Talhelm; John D. Marshall; Andrew T. Hudak; Luigi Boschetti; Michael J. Falkowski; Jonathan A. Greenberg; John W. Anderson; Andrew Kliskey; Lilian Alessa; Robert F. Keefe; James R. Gosz

2014-01-01

Climate change is altering the species composition, structure, and function of vegetation in natural terrestrial ecosystems. These changes can also impact the essential ecosystem goods and services derived from these ecosystems. Following disturbances, remote-sensing datasets have been used to monitor the disturbance and describe antecedent conditions as a means of...

Quantifying early-seral forest composition with remote sensing

Treesearch

Rayma A. Cooley; Peter T. Wolter; Brian R. Sturtevant

2016-01-01

Spatially explicit modeling of recovering forest structure within two years following wildfire disturbance has not been attempted, yet such knowledge is critical for determining successional pathways. We used remote sensing and field data, along with digital climate and terrain data, to model and map early-seral aspen structure and vegetation species richness following...

Remote Sensing in Archeology: Classifying Bajos of the Paten, Guatemala

NASA Technical Reports Server (NTRS)

Lowry, James D., Jr.

1998-01-01

This project focuses on the adaptation of human populations to their environments from prehistoric times to the present. It emphasizes interdisciplinary research to develop ecological baselines through the use of remotely sensed imagery, in situ field work, and the modeling of human population dynamics. It utilizes cultural and biological data from dated archaeological sites to assess the subsistence and settlement patterns of human societies in response to changing climatic and environmental conditions. The utilization of remote sensing techniques in archaeology is relatively new, exciting, and opens many doors.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2007-01-01

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Advancing Technologies for Climate Observation

NASA Technical Reports Server (NTRS)

Wu, D.; Esper, J.; Ehsan, N.; Johnson, T.; Mast, W.; Piepmeier, J.; Racette, P.

2014-01-01

Climate research needs Accurate global cloud ice measurements Cloud ice properties are fundamental controlling variables of radiative transfer and precipitation Cost-effective, sensitive instruments for diurnal and wide-swath coverage Mature technology for space remote sensing IceCube objectivesDevelop and validate a flight-qualified 883 GHz receiver for future use in ice cloud radiometer missions Raise TRL (57) of 883 GHz receiver technology Reduce instrument cost and risk by developing path to space for COTS sub-mm-wave receiver systems Enable remote sensing of global cloud ice with advanced technologies and techniques

Maniac Talk - Michael Mishchenko

NASA Image and Video Library

2015-01-26

Michael Mishchenko Maniac Lecture, January 26, 2015 NASA climate scientist Dr. Michael I. Mishchenko presented a Maniac Talk entitled "How much first-principle physics do we need in remote-sensing and atmospheric-radiation research." Michael explained his skepticism and how it has shaped his contributions to the disciplines of electromagnetic scattering, radiative transfer, and remote sensing, which have found widespread use.

Technology transfer of remote sensing technology

NASA Technical Reports Server (NTRS)

Smith, A. D.

1980-01-01

The basic philosophy and some current activities of MSFC Technology Transfer with regard to remote sensing technology are briefly reviewed. Among the problems that may be alleviated through such technology transfer are the scarcity of energy and mineral resources, the alteration of the environment by man, unpredictable natural disasters, and the effect of unanticipated climatic change on agricultural productivity.

[Application of hyperspectral remote sensing in research on ecological boundary in north farming-pasturing transition in China].

PubMed

Wang, Hong-Mei; Wang, Kun; Xie, Ying-Zhong

2009-06-01

Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e. g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics, soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.

Spatial analysis of vector-borne infectious diseases and ecological indicators using GIS and remote sensing

NASA Astrophysics Data System (ADS)

Anh, N. K.; Liou, Y. A.

2017-12-01

Ecological and climate indicators play a vital role in defining patterns of human activities and behaviors, such as seasonal features, migration, winter-summer lifestyles, which in turn might be associated with vector-borne disease habitats and transmission risks. Remote sensing has been instrumental in deriving environmental variables and indicators. GIS is shown to be a powerful tool in spatiotemporal visualization and distribution of vector-borne diseases and for analysis of associations between environmental conditions and characteristics of vector-borne habitats. Vietnam is in the sub-tropical climate zone with high humidity and abundant precipitation, while the distribution of precipitation is uneven leading to frequently annual occurrence of drought and flood disasters. Moreover, urban heat island effect is significantly enhanced in urbanized areas in recent years. The increase in the frequency and magnitude of severity of weather extremes that are potentially linked to climate change and anthropogenic processes have highlighted the demand of research into health risk assessment and adaptive capacity. This research focuses on the analysis of physical features of environmental indicators and its association with vector-borne diseases as well as adaptive capacity. The study illustrates how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, promising possibilities of allowing disease early-warning systems with citizen participation platform will be proposed. Keywords: Vector-borne diseases; environmental indicators; remote sensing; GIS; Vietnam.

Geomorphic Processes and Remote Sensing Signatures of Alluvial Fans in the Kun Lun Mountains, China

NASA Technical Reports Server (NTRS)

Farr, Tom G.; Chadwick, Oliver A.

1996-01-01

The timing of alluvial deposition in arid and semiarid areas is tied to land-surface instability caused by regional climate changes. The distribution pattern of dated deposits provides maps of regional land-surface response to past climate change. Sensitivity to differences in surface roughness and composition makes remote sensing techniques useful for regional mapping of alluvial deposits. Radar images from the Spaceborne Radar Laboratory and visible wavelength images from the French SPOT satellite were used to determine remote sensing signatures of alluvial fan units for an area in the Kun Lun Mountains of northwestern China. These data were combined with field observations to compare surface processes and their effects on remote sensing signatures in northwestern China and the southwestern United States. Geomorphic processes affecting alluvial fans in the two areas include aeolian deposition, desert varnish, and fluvial dissection. However, salt weathering is a much more important process in the Kun Lun than in the southwestern United States. This slows the formation of desert varnish and prevents desert pavement from forming. Thus the Kun Lun signatures are characteristic of the dominance of salt weathering, while signatures from the southwestern United States are characteristic of the dominance of desert varnish and pavement processes. Remote sensing signatures are consistent enough in these two regions to be used for mapping fan units over large areas.

7 CFR 3800.3 - Functions.

Code of Federal Regulations, 2013 CFR

2013-01-01

.... (c) Weather and climate. (1) Serve as a focal point within the Department for coordination of weather, climate, and related crop monitoring activities. (d) Remote sensing. (1) Provide technical assistance...

7 CFR 3800.3 - Functions.

Code of Federal Regulations, 2011 CFR

2011-01-01

.... (c) Weather and climate. (1) Serve as a focal point within the Department for coordination of weather, climate, and related crop monitoring activities. (d) Remote sensing. (1) Provide technical assistance...

7 CFR 3800.3 - Functions.

Code of Federal Regulations, 2014 CFR

2014-01-01

.... (c) Weather and climate. (1) Serve as a focal point within the Department for coordination of weather, climate, and related crop monitoring activities. (d) Remote sensing. (1) Provide technical assistance...

7 CFR 3800.3 - Functions.

Code of Federal Regulations, 2012 CFR

2012-01-01

.... (c) Weather and climate. (1) Serve as a focal point within the Department for coordination of weather, climate, and related crop monitoring activities. (d) Remote sensing. (1) Provide technical assistance...

The Use of Remote Sensing to Resolve the Aerosol Radiative Forcing

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Tanre, D.; Remer, Lorraine

1999-01-01

Satellites are used for remote sensing of aerosol optical thickness and optical properties in order to derive the aerosol direct and indirect radiative forcing of climate. Accuracy of the derived aerosol optical thickness is used as a measure of the accuracy in deriving the aerosol radiative forcing. Several questions can be asked to challenge this concept. Is the accuracy of the satellite-derived aerosol direct forcing limited to the accuracy of the measured optical thickness? What are the spectral bands needed to derive the total aerosol forcing? Does most of the direct or indirect aerosol forcing of climate originate from regions with aerosol concentrations that are high enough to be detected from space? What should be the synergism ground-based and space-borne remote sensing to solve the problem? We shall try to answer some of these questions, using AVIRIS airborne measurements and simulations.

Simple and Multiple Endmember Mixture Analysis in the Boreal Forest

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Gamon, John A.; Qiu, Hong-Lie

2000-01-01

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS Follow-on program was concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales.

Development of sea ice monitoring with aerial remote sensing technology

NASA Astrophysics Data System (ADS)

Jiang, Xuhui; Han, Lei; Dong, Liang; Cui, Lulu; Bie, Jun; Fan, Xuewei

2014-11-01

In the north China Sea district, sea ice disaster is very serious every winter, which brings a lot of adverse effects to shipping transportation, offshore oil exploitation, and coastal engineering. In recent years, along with the changing of global climate, the sea ice situation becomes too critical. The monitoring of sea ice is playing a very important role in keeping human life and properties in safety, and undertaking of marine scientific research. The methods to monitor sea ice mainly include: first, shore observation; second, icebreaker monitoring; third, satellite remote sensing; and then aerial remote sensing monitoring. The marine station staffs use relevant equipments to monitor the sea ice in the shore observation. The icebreaker monitoring means: the workers complete the test of the properties of sea ice, such as density, salinity and mechanical properties. MODIS data and NOAA data are processed to get sea ice charts in the satellite remote sensing means. Besides, artificial visual monitoring method and some airborne remote sensors are adopted in the aerial remote sensing to monitor sea ice. Aerial remote sensing is an important means in sea ice monitoring because of its strong maneuverability, wide watching scale, and high resolution. In this paper, several methods in the sea ice monitoring using aerial remote sensing technology are discussed.

Remote sensing of water quality in reservoirs and lakes in semi-arid climates

NASA Technical Reports Server (NTRS)

Anderson, H. M.; Horne, A. J.

1975-01-01

Overlake measurements using aerial cameras (remote sensing) combined with water truth collected from boats most economically provided wide-band photographs rather than precise spectra. With use of false color infrared film (400-950 nm), the reflected spectral signatures seen from hundreds to thousands of meters above the lake merged to produce various color tones. Such colors were easily and inexpensively obtained and could be recognized by lake management personnel without any prior training. The characteristic spectral signatures of various algal types were also recognizable in part by the color tone produced by remote sensing.

Boundary Layer Remote Sensing with Combined Active and Passive Techniques: GPS Radio Occultation and High-Resolution Stereo Imaging (WindCam) Small Satellite Concept

NASA Technical Reports Server (NTRS)

Mannucci, A.J.; Wu, D.L.; Teixeira, J.; Ao, C.O.; Xie, F.; Diner, D.J.; Wood, R.; Turk, Joe

2012-01-01

Objective: significant progress in understanding low-cloud boundary layer processes. This is the Single largest uncertainty in climate projections. Radio occultation has unique features suited to boundary layer remote sensing (1) Cloud penetrating (2) Very high vertical resolution (approximately 50m-100m) (3) Sensitivity to thermodynamic variables

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Diasters Caused by Large Scale Rift Valley fever Outbreaks

USDA-ARS?s Scientific Manuscript database

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of vector-borne diseases. We show that episodic outbreaks of Rift Valley fever are influen...

An Examination of Intertidal Temperatures Through Remotely Sensed Satellite Observations

NASA Astrophysics Data System (ADS)

Lakshmi, V.

2010-12-01

MODIS Aqua and Terra satellites produce both land surface temperatures and sea surface temperatures using calibrated algorithms. In this study, the land surface temperatures were retrieved during clear-sky (non-cloudy) conditions at a 1 km2 resolution (overpass time at 10:30 am) whereas the sea surface temperatures are also retrieved during clear-sky conditions at approximately 4 km resolution (overpass time at 1:30 pm). The purpose of this research was to examine remotely sensed sea surface (SST), intertidal (IST), and land surface temperatures (LST), in conjunction with observed in situ mussel body temperatures, as well as associated weather and tidal data. In Strawberry Hill, Oregon, it was determined that intertidal surface temperatures are similar to but distinctly different from land surface temperatures although influenced by sea surface temperatures. The air temperature and differential heating throughout the day, as well as location in relation to the shore, can greatly influence the remotely sensed surface temperatures. Therefore, remotely sensed satellite data is a very useful tool in examining intertidal temperatures for regional climatic changes over long time periods and may eventually help researchers forecast expected climate changes and help determine associated biological implications.

Using NASA Remote Sensing Data to Reduce Uncertainty of Land-use Transitions in Global Carbon-Climate Models

NASA Astrophysics Data System (ADS)

Chini, L. P.; Hurtt, G. C.; Frolking, S. E.; Sahajpal, R.; Potapov, P.; Hansen, M.; Fisk, J.

2016-12-01

For the 5th IPCC Assessment almost all Earth System Models (ESMs) incorporated new gridded products of land-use and land-use change that were harmonized to ensure a continuous transition from historical to future data in a consistent format for all models. However, these Land-Use Harmonization (LUH) data products are estimates, constrained with data where available, and with modeling assumptions, and the remaining challenge is to quantify, and reduce, the uncertainty in these products. At the same time, satellite remote sensing of the terrestrial biosphere has also evolved. Global-scale land cover extent and change monitoring is now possible given systematically acquired earth observation data sets, advanced characterization algorithms and data intensive computing capabilities. Here we consider: how can satellite remote sensing products be used to generate (and reduce uncertainty in) new gridded maps of land-use transitions for use in coupled carbon-climate simulations? As part of the international effort to develop the next generation of land-use datasets (LUH2), new NASA remote-sensing-based maps of global forest extent and change (Hansen et al. 2013) were used as both an added constraint and diagnostic in the LUH process. Harmonizing this remote sensing data with the LUH data was a major computational challenge involving 143 billion 30m Landsat pixels, and the simulation of over 20 billion LUH unknowns. Our approach involved first harmonizing the definitions of forest loss between the observed and simulated data for the years 2000-2012. Next, new spatial patterns of historical wood harvest were calculated to match the observed forest loss transitions while simultaneously meeting all other constraints of the model, and ensuring consistency throughout the historical time-period. After reconciling definitions and developing new wood harvest patterns the LUH2 global forest loss for the period 2000-2012 was reduced from over 8.3 million km2 to 1.78 million km2 (compared with the remote-sensing-based forest loss of 2.03 million km2). Next steps are to evaluate the ability of these land-use transitions to improve the representation of land-use-related climate forcings in ESM experiments, and to then build upon the LUH framework to incorporate additional remote-sensing data constraints.

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

Climate and climate change and infectious disease risk in Thailand: A spatial study of dengue hemorrhagic fever using GIS and remotely-sensed imagery

NASA Astrophysics Data System (ADS)

Kuzera, Kristopher

The scientific community has widely accepted that climate plays a key role in the sustainability and transmission of many infectious diseases. Global climate change can potentially trigger the spread of disease into new regions and increase the intensity of disease in regions where it is endemic. This study explores the association between monthly conditions of climate change to changes in disease risk, emphasizing the potential spread of dengue fever due to climate change in Thailand. This study also develops techniques new to GIS and remote sensing that generate surfaces of daily minimum temperature toward identifying areas at greater transmission risk. Dengue fever expansion due to global warming is a serious concern for Thailand where warming temperatures may increase the size of the habitat of the disease-spreading vector, Aedes aegypti, particularly during cooler months when transmission is limited by environmental conditions. In this study, first, the association between past dengue hemorrhagic fever (DHF) and climate in Thailand is determined. Second, evidence of recent climate change is related to changes in DHF rates. Third, daily minimum temperature is derived from remote sensing toward identifying the spatial and temporal limitations of potential transmission risk. The results indicate that minimum temperature has recently experienced a rapid increase, particularly in the winter months when transmission is low. This is associated with a recent rise in winter DHF cases. As increasing minimum temperatures in these regions are anticipated to continue, we can expect dengue transmission rates to also increase throughout the year.

Leveraging this Golden Age of Remote Sensing and Modeling of Terrestrial Hydrology to Understand Water Cycling in the Water Availability Grand Challenge for North America

NASA Astrophysics Data System (ADS)

Painter, T. H.; Famiglietti, J. S.; Stephens, G. L.

2016-12-01

We live in a time of increasing strains on our global fresh water availability due to increasing population, warming climate, changes in precipitation, and extensive depletion of groundwater supplies. At the same time, we have seen enormous growth in capabilities to remotely sense the regional to global water cycle and model complex systems with physically based frameworks. The GEWEX Water Availability Grand Challenge for North America is poised to leverage this convergence of remote sensing and modeling capabilities to answer fundamental questions on the water cycle. In particular, we envision an experiment that targets the complex and resource-critical Western US from California to just into the Great Plains, constraining physically-based hydrologic modeling with the US and international remote sensing capabilities. In particular, the last decade has seen the implementation or soon-to-be launch of water cycle missions such as GRACE and GRACE-FO for groundwater, SMAP for soil moisture, GPM for precipitation, SWOT for terrestrial surface water, and the Airborne Snow Observatory for snowpack. With the advent of convection-resolving mesoscale climate and water cycle modeling (e.g. WRF, WRF-Hydro) and mesoscale models capable of quantitative assimilation of remotely sensed data (e.g. the JPL Western States Water Mission), we can now begin to test hypotheses on the nature and changes in the water cycle of the Western US from a physical standpoint. In turn, by fusing water cycle science, water management, and ecosystem management while addressing these hypotheses, this golden age of remote sensing and modeling can bring all fields into a markedly less uncertain state of present knowledge and decadal scale forecasts.

An investigation of satellite sounding products for the remote sensing of the surface energy balance and soil moisture

NASA Technical Reports Server (NTRS)

Diak, George R.

1989-01-01

Improved techniques for the remote sensing of the land surface energy balance (SEB) and soil moisture would greatly improve prediction of climate and weather as well as be of benefit to agriculture, hydrology and many associated fields. Most of the satellite remote sensing methods which were researched to date rely upon satellite-measured infrared surface temperatures or their time changes as a remote sensing signal. Optimistically, only four or five levels of information (wet to dry) in surface heating/evaporation are discernable by surface temperature methods and a good understanding of atmospheric conditions is necessary to bring them to this accuracy level. Skin temperature methods were researched as well as begun work on several new methods for the remote sensing of the SEB, some elements of which are applicable to current and retrospective data sources and some which will rely on instrumentation from the Earth Observing System (EOS) program in the 1990s.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Ten ways remote sensing can contribute to conservation.

PubMed

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

What Models and Satellites Tell Us (and Don't Tell Us) About Arctic Sea Ice Melt Season Length

NASA Astrophysics Data System (ADS)

Ahlert, A.; Jahn, A.

2017-12-01

Melt season length—the difference between the sea ice melt onset date and the sea ice freeze onset date—plays an important role in the radiation balance of the Arctic and the predictability of the sea ice cover. However, there are multiple possible definitions for sea ice melt and freeze onset in climate models, and none of them exactly correspond to the remote sensing definition. Using the CESM Large Ensemble model simulations, we show how this mismatch between model and remote sensing definitions of melt and freeze onset limits the utility of melt season remote sensing data for bias detection in models. It also opens up new questions about the precise physical meaning of the melt season remote sensing data. Despite these challenges, we find that the increase in melt season length in the CESM is not as large as that derived from remote sensing data, even when we account for internal variability and different definitions. At the same time, we find that the CESM ensemble members that have the largest trend in sea ice extent over the period 1979-2014 also have the largest melt season trend, driven primarily by the trend towards later freeze onsets. This might be an indication that an underestimation of the melt season length trend is one factor contributing to the generally underestimated sea ice loss within the CESM, and potentially climate models in general.

Development of a Cost-Effective Airborne Remote Sensing System for Coastal Monitoring

PubMed Central

Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael

2015-01-01

Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge. PMID:26437413

A change detection method for remote sensing image based on LBP and SURF feature

NASA Astrophysics Data System (ADS)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

Development of a Cost-Effective Airborne Remote Sensing System for Coastal Monitoring.

PubMed

Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael

2015-09-30

Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge.

Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.

Water Column Correction for Coral Reef Studies by Remote Sensing

PubMed Central

Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

2014-01-01

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application. PMID:25215941

Water column correction for coral reef studies by remote sensing.

PubMed

Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

2014-09-11

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

FOREWORD: Satellite Remote Sensing Beyond 2015

NASA Technical Reports Server (NTRS)

Tucker, Compton J.

2017-01-01

Satellite remote sensing has progressed tremendously since the first Landsat was launched on June 23, 1972. Since the 1970s, satellite remote sensing and associated airborne and in situ measurements have resulted in vital and indispensable observations for understanding our planet through time. These observations have also led to dramatic improvements in numerical simulation models of the coupled atmosphere-land-ocean systems at increasing accuracies and predictive capability. The same observations document the Earth's climate and are driving the consensus that Homo sapiens is changing our climate through greenhouse gas emissions. These accomplishments are the combined work of many scientists from many countries and a dedicated cadre of engineers who build the instruments and satellites that collect Earth observation data from satellites, all working toward the goal of improving our understanding of the Earth. This edition of the Remote Sensing Handbook (Vol. I, II, and III) is a compendium of information for many research areas of our Planet that have contributed to our substantial progress since the 1970s. Remote sensing community is now using multiple sources of satellite and in situ data to advance our studies, what ever they might be. In the following paragraphs, I will illustrate how valuable and pivotal role satellite remote sensing has played in climate system study over last five decades, The Chapters in the Remote Sensing Handbook (Vol. I, II, and III) provides many other specific studies on land, water, and other applications using EO data of last five decades, The Landsat system of Earth-observing satellites has led the way in pioneering sustained observations of our planet. From 1972 to the present, at least one and sometimes two Landsat satellites have been in operation. Starting with the launch of the first NOAA-NASA Polar Orbiting Environmental Satellites NOAA-6 in 1978, improved imaging of land, clouds, and oceans and atmospheric soundings of temperature were accomplished. The NOAA system of polar-orbiting meteorological satellites has continued uninterrupted since that time, providing vital observations for numerical weather prediction. These same satellites are also responsible for the remarkable records of sea surface temperature and land vegetation index from the Advanced Very High Resolution Radiometers (AVHRR) that now span more than 33 years, although no one anticipated these valuable climate records from this instrument before the launch of NOAA-7 in 1981. The success of data from the AVHRR led to the design of the MODIS instruments on NASA's Earth Observing System of satellite platforms that improved substantially upon the AVHRR. The first of the EOS platforms, Terra, was launched in 2000 and the second of these platforms, Aqua, was launched in 2002.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nitschke, Kim

The ARM Climate Research Facility, a DOE scientific user facility, provides the climate research community with strategically located in situ and remote sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface.

On the division of contribution of the atmosphere and ocean in the radiation of the earth for the tasks of remote sensing and climate

NASA Astrophysics Data System (ADS)

Sushkevich, T. A.; Strelkov, S. A.; Maksakova, S. V.

2017-11-01

We are talking about the national achievements of the world level in theory of radiation transfer in the system atmosphere-oceans and about the modern scientific potential developing in Russia, which adequately provides a methodological basis for theoretical and computational studies of radiation processes and radiation fields in the natural environments with the use of supercomputers and massively parallel processing for problems of remote sensing and the climate of Earth. A model of the radiation field in system "clouds cover the atmosphere-ocean" to the separation of the contributions of clouds, atmosphere and ocean.

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

PubMed Central

Kwan, Paul; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops. PMID:28875085

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

PubMed

Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

Physical Characteristics of Arctic Clouds from Ground-based Remote-sensing with a Polarized Micro-Pulse Lidar and a 95-GHz Cloud Radar in Ny-Ålesund, Svalbard

NASA Astrophysics Data System (ADS)

Shiobara, M.; Takano, T.; Okamoto, H.; Yabuki, M.

2015-12-01

Clouds and aerosols are key elements having a potential to change climate by their radiative effects on the energy balance in the global climate system. In the Arctic, we have been continuing ground-based remote-sensing measurements for clouds and aerosols using a sky-radiometer, a micro-pulse lidar (MPL) and an all-sky camera in Ny-Ålesund (78.9N, 11.9E), Svalbard since early 2000's. In addition to such regular operations, several new measurements have been performed with a polarization MPL since August 2013, a 95GHz Doppler cloud radar since September 2013, and a dual frequency microwave radiometer since June 2014. An intensive field experiment for cloud-aerosol-radiation interaction study named A-CARE (PI: J. Ukita) was conducted for water clouds in the period of 23 June - 13 July 2014 and for mixed phase clouds in the period of 30 March - 23 April 2015 in Ny-Alesund. The experiment consisted of ground-based remote-sensing and in-situ cloud microphysics measurements. In this paper, preliminary results from these remote-sensing measurements will be presented, particularly in regard to physical characteristics of Arctic clouds based on radar-lidar collocated observation in Ny-Ålesund.

Validation of Remote Sensing Retrieval Products using Data from a Wireless Sensor-Based Online Monitoring in Antarctica

PubMed Central

Li, Xiuhong; Cheng, Xiao; Yang, Rongjin; Liu, Qiang; Qiu, Yubao; Zhang, Jialin; Cai, Erli; Zhao, Long

2016-01-01

Of the modern technologies in polar-region monitoring, the remote sensing technology that can instantaneously form large-scale images has become much more important in helping acquire parameters such as the freezing and melting of ice as well as the surface temperature, which can be used in the research of global climate change, Antarctic ice sheet responses, and cap formation and evolution. However, the acquirement of those parameters is impacted remarkably by the climate and satellite transit time which makes it almost impossible to have timely and continuous observation data. In this research, a wireless sensor-based online monitoring platform (WSOOP) for the extreme polar environment is applied to obtain a long-term series of data which is site-specific and continuous in time. Those data are compared and validated with the data from a weather station at Zhongshan Station Antarctica and the result shows an obvious correlation. Then those data are used to validate the remote sensing products of the freezing and melting of ice and the surface temperature and the result also indicated a similar correlation. The experiment in Antarctica has proven that WSOOP is an effective system to validate remotely sensed data in the polar region. PMID:27869668

Validation of Remote Sensing Retrieval Products using Data from a Wireless Sensor-Based Online Monitoring in Antarctica.

PubMed

Li, Xiuhong; Cheng, Xiao; Yang, Rongjin; Liu, Qiang; Qiu, Yubao; Zhang, Jialin; Cai, Erli; Zhao, Long

2016-11-17

Of the modern technologies in polar-region monitoring, the remote sensing technology that can instantaneously form large-scale images has become much more important in helping acquire parameters such as the freezing and melting of ice as well as the surface temperature, which can be used in the research of global climate change, Antarctic ice sheet responses, and cap formation and evolution. However, the acquirement of those parameters is impacted remarkably by the climate and satellite transit time which makes it almost impossible to have timely and continuous observation data. In this research, a wireless sensor-based online monitoring platform (WSOOP) for the extreme polar environment is applied to obtain a long-term series of data which is site-specific and continuous in time. Those data are compared and validated with the data from a weather station at Zhongshan Station Antarctica and the result shows an obvious correlation. Then those data are used to validate the remote sensing products of the freezing and melting of ice and the surface temperature and the result also indicated a similar correlation. The experiment in Antarctica has proven that WSOOP is an effective system to validate remotely sensed data in the polar region.

Building hydrologic information systems to promote climate resilience in the Blue Nile/Abay higlands

USDA-ARS?s Scientific Manuscript database

Climate adaptation requires information about climate and land-surface conditions – spatially distributed, and at scales of human influence (the field scale). This article describes a project aimed at combining meteorological data, satellite remote sensing, hydrologic modeling, and downscaled clima...

Land Cover Applications, Landscape Dynamics, and Global Change

USGS Publications Warehouse

Tieszen, Larry L.

2007-01-01

The Land Cover Applications, Landscape Dynamics, and Global Change project at U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) seeks to integrate remote sensing and simulation models to better understand and seek solutions to national and global issues. Modeling processes related to population impacts, natural resource management, climate change, invasive species, land use changes, energy development, and climate mitigation all pose significant scientific opportunities. The project activities use remotely sensed data to support spatial monitoring, provide sensitivity analyses across landscapes and large regions, and make the data and results available on the Internet with data access and distribution, decision support systems, and on-line modeling. Applications support sustainable natural resource use, carbon cycle science, biodiversity conservation, climate change mitigation, and robust simulation modeling approaches that evaluate ecosystem and landscape dynamics.

China national space remote sensing infrastructure and its application

NASA Astrophysics Data System (ADS)

Li, Ming

2016-07-01

Space Infrastructure is a space system that provides communication, navigation and remote sensing service for broad users. China National Space Remote Sensing Infrastructure includes remote sensing satellites, ground system and related systems. According to the principle of multiple-function on one satellite, multiple satellites in one constellation and collaboration between constellations, series of land observation, ocean observation and atmosphere observation satellites have been suggested to have high, middle and low resolution and fly on different orbits and with different means of payloads to achieve a high ability for global synthetically observation. With such an infrastructure, we can carry out the research on climate change, geophysics global surveying and mapping, water resources management, safety and emergency management, and so on. I This paper gives a detailed introduction about the planning of this infrastructure and its application in different area, especially the international cooperation potential in the so called One Belt and One Road space information corridor.

Transitioning Earth Remote Sensing Data to Benefit Society: A Paradigm for a Center of Excellence

NASA Technical Reports Server (NTRS)

Jedlovec, Gary; Bjorgo, Einar; Burn, Anthony

2015-01-01

Over the past decade there has been a substantial increase in the number of Earth remote sensing satellites launched for research and operational usage and numerous others planned by the international community. These satellites have been used to varying degrees by their supporting agencies for weather and environmental monitoring, climate studies, disaster monitoring and response, and other humanitarian activities. While there are success stories on useful applications of remote sensing data, the broader use of these satellite assets by other organizations and entities has been limited for a number of reasons including lack of data services, data dissemination issues, and a general failure to engage the broader end user community with useful data access and knowledge of how to use the data and products. This paper describes some of these current limitations on the broader use of Earth remote sensing data by the international community and describes the concept of a general "Center of Excellence" to facilitate the development, transition, and utilization of these Earth remote sensing observations by the broader international community.

Satellite Remote Sensing for Coastal Management: A Review of Successful Applications

NASA Astrophysics Data System (ADS)

McCarthy, Matthew J.; Colna, Kaitlyn E.; El-Mezayen, Mahmoud M.; Laureano-Rosario, Abdiel E.; Méndez-Lázaro, Pablo; Otis, Daniel B.; Toro-Farmer, Gerardo; Vega-Rodriguez, Maria; Muller-Karger, Frank E.

2017-08-01

Management of coastal and marine natural resources presents a number of challenges as a growing global population and a changing climate require us to find better strategies to conserve the resources on which our health, economy, and overall well-being depend. To evaluate the status and trends in changing coastal resources over larger areas, managers in government agencies and private stakeholders around the world have increasingly turned to remote sensing technologies. A surge in collaborative and innovative efforts between resource managers, academic researchers, and industry partners is becoming increasingly vital to keep pace with evolving changes of our natural resources. Synoptic capabilities of remote sensing techniques allow assessments that are impossible to do with traditional methods. Sixty years of remote sensing research have paved the way for resource management applications, but uncertainties regarding the use of this technology have hampered its use in management fields. Here we review examples of remote sensing applications in the sectors of coral reefs, wetlands, water quality, public health, and fisheries and aquaculture that have successfully contributed to management and decision-making goals.

Satellite Remote Sensing for Coastal Management: A Review of Successful Applications.

PubMed

McCarthy, Matthew J; Colna, Kaitlyn E; El-Mezayen, Mahmoud M; Laureano-Rosario, Abdiel E; Méndez-Lázaro, Pablo; Otis, Daniel B; Toro-Farmer, Gerardo; Vega-Rodriguez, Maria; Muller-Karger, Frank E

2017-08-01

Management of coastal and marine natural resources presents a number of challenges as a growing global population and a changing climate require us to find better strategies to conserve the resources on which our health, economy, and overall well-being depend. To evaluate the status and trends in changing coastal resources over larger areas, managers in government agencies and private stakeholders around the world have increasingly turned to remote sensing technologies. A surge in collaborative and innovative efforts between resource managers, academic researchers, and industry partners is becoming increasingly vital to keep pace with evolving changes of our natural resources. Synoptic capabilities of remote sensing techniques allow assessments that are impossible to do with traditional methods. Sixty years of remote sensing research have paved the way for resource management applications, but uncertainties regarding the use of this technology have hampered its use in management fields. Here we review examples of remote sensing applications in the sectors of coral reefs, wetlands, water quality, public health, and fisheries and aquaculture that have successfully contributed to management and decision-making goals.

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Estimating the future agriculture freight transportation network needs due to climate change using remote sensing and regional climate models.

DOT National Transportation Integrated Search

2016-12-01

A reoccurring challenge with increasing fuel prices is optimization of multi- and inter-modal freight transport to move products most efficiently. Projections for the future of agriculture in the United States (U.S.) combined with regional climate mo...

The Increasing Use of Remote Sensing Data in Studying the Climatological Impacts on Public Health

NASA Technical Reports Server (NTRS)

Kempler, Steven; Benedict, Karl; Ceccato, Pietro; Golden, Meredith; Maxwell, Susan; Morian, Stan; Soebiyanto, Radina; Tong, Daniel

2011-01-01

One of the more fortunate outcomes of the capture and transformation of remote sensing data into applied information is their usefulness and impacts to better understanding climatological impacts on public health. Today, with petabytes of remote sensing data providing global coverage of climatological parameters, public health research and policy decision makers have an unprecedented (and growing) data record that relates the effects of climatic parameters, such as rainfall, heat, soil moisture, etc. to incidences and spread of disease, as well as predictive modeling. In addition, tools and services that specifically serve public health researchers and respondents have grown in response to needs of the these information users.

Biodiversity and Climate Modeling Workshop Series: Identifying gaps and needs for improving large-scale biodiversity models

NASA Astrophysics Data System (ADS)

Weiskopf, S. R.; Myers, B.; Beard, T. D.; Jackson, S. T.; Tittensor, D.; Harfoot, M.; Senay, G. B.

2017-12-01

At the global scale, well-accepted global circulation models and agreed-upon scenarios for future climate from the Intergovernmental Panel on Climate Change (IPCC) are available. In contrast, biodiversity modeling at the global scale lacks analogous tools. While there is great interest in development of similar bodies and efforts for international monitoring and modelling of biodiversity at the global scale, equivalent modelling tools are in their infancy. This lack of global biodiversity models compared to the extensive array of general circulation models provides a unique opportunity to bring together climate, ecosystem, and biodiversity modeling experts to promote development of integrated approaches in modeling global biodiversity. Improved models are needed to understand how we are progressing towards the Aichi Biodiversity Targets, many of which are not on track to meet the 2020 goal, threatening global biodiversity conservation, monitoring, and sustainable use. We brought together biodiversity, climate, and remote sensing experts to try to 1) identify lessons learned from the climate community that can be used to improve global biodiversity models; 2) explore how NASA and other remote sensing products could be better integrated into global biodiversity models and 3) advance global biodiversity modeling, prediction, and forecasting to inform the Aichi Biodiversity Targets, the 2030 Sustainable Development Goals, and the Intergovernmental Platform on Biodiversity and Ecosystem Services Global Assessment of Biodiversity and Ecosystem Services. The 1st In-Person meeting focused on determining a roadmap for effective assessment of biodiversity model projections and forecasts by 2030 while integrating and assimilating remote sensing data and applying lessons learned, when appropriate, from climate modeling. Here, we present the outcomes and lessons learned from our first E-discussion and in-person meeting and discuss the next steps for future meetings.

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Review of Remote Sensing Needs and Applications in Africa

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2007-01-01

Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The Regional Remote Sensing Unit (RRSU) in Gaborone, Botswana, began work in June 1988 and operates under the Agriculture Information Management System (AIMS), as part of the Food, Agriculture and Natural Resources (FANR) Directorate, based at the Southern Africa Development Community (SADC) Secretariat.

The photochemical reflectance index provides an optical indicator of spring photosynthetic activation in evergreen conifers.

PubMed

Wong, Christopher Y S; Gamon, John A

2015-04-01

In evergreens, the seasonal down-regulation and reactivation of photosynthesis is largely invisible and difficult to assess with remote sensing. This invisible phenology may be changing as a result of climate change. To better understand the mechanism and timing of these hidden physiological transitions, we explored several assays and optical indicators of spring photosynthetic activation in conifers exposed to a boreal climate. The photochemical reflectance index (PRI), chlorophyll fluorescence, and leaf pigments for evergreen conifer seedlings were monitored over 1 yr of a boreal climate with the addition of gas exchange during the spring. PRI, electron transport rate, pigment levels, light-use efficiency and photosynthesis all exhibited striking seasonal changes, with varying kinetics and strengths of correlation, which were used to evaluate the mechanisms and timing of spring activation. PRI and pigment pools were closely timed with photosynthetic reactivation measured by gas exchange. The PRI provided a clear optical indicator of spring photosynthetic activation that was detectable at leaf and stand scales in conifers. We propose that PRI might provide a useful metric of effective growing season length amenable to remote sensing and could improve remote-sensing-driven models of carbon uptake in evergreen ecosystems. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Using NOAA/AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh

NASA Astrophysics Data System (ADS)

Nizamuddin, Mohammad; Akhand, Kawsar; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch

2015-06-01

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

The Future of Remote Sensing from Space: Civilian Satellite Systems and Applications.

DTIC Science & Technology

1993-07-01

image shows abundant (dark green) vegetation across the Amazon of South America, while lack of vegetation (black areas) is seen across the Sahara Desert...primarily through the space shuttle and space station Freedom programs.25 Hence, if NASA’s overall budget remains flat or includes only modest growth... remain the primary collector of satellite remote sensing data for both meteorolog- ical and climate monitoring efforts through the decade of the 1990s

Impacts of a Changing Climate and Land Use on Reindeer Pastoralism: Indigenous Knowledge and Remote Sensing

NASA Technical Reports Server (NTRS)

Maynard, N. G.; Oskal, A.; Turi, A.; Mathiesen, J. M.; Eira, S. D.; Yurchak, I. M. G.; Etylin, B.; Gebelein, J.

2009-01-01

The Arctic is home to many indigenous peoples, including those who depend on reindeer herding for their livelihood, in one of the harshest environments in the world. For the largely nomadic peoples, reindeer not only form a substantial part of the Arctic food base and economy, but they are also culturally important, shaping their way of life, mythologies, festivals and ceremonies. Reindeer pastoralism or husbandry has been practiced by numerous peoples all across Eurasia for thousands of years and involves moving herds of reindeer, which are very docile animals, from pasture to pasture depending on the season. Thus, herders must adapt on a daily basis to find optimal conditions for their herds according to the constantly changing conditions. Climate change and variability plus rapid development are increasingly creating major changes in the physical environment, ecology, and cultures of these indigenous reindeer herder communities in the North, and climate changes are occurring significantly faster in the Arctic than the rest of the globe, with correspondingly dramatic impacts (Oskal, 2008). In response to these changes, Eurasian reindeer herders have created the EALAT project, a comprehensive new initiative to study these impacts and to develop local adaptation strategies based upon their traditional knowledge of the land and its uses - in targeted partnership with the science and remote sensing community - involving extensive collaborations and coproduction of knowledge to minimize the impacts of the various changes. This chapter provides background on climate and development challenges to reindeer husbandry across the Arctic and an overview of the EALAT initiative, with an emphasis on indigenous knowledge, remote sensing, Geographic Information Systems (GIS), and other scientific data to 'co-produce' datasets for use by herders for improved decision-making and herd management. It also provides a description of the EALAT monitoring data integration and sharing system and portal being developed for reindeer pastoralism. In addition, the chapter provides some preliminary results from the EALAT Project, including some early remote sensing research results.

Biogeochemical cycling in terrestrial ecosystems - Modeling, measurement, and remote sensing

NASA Technical Reports Server (NTRS)

Peterson, D. L.; Matson, P. A.; Lawless, J. G.; Aber, J. D.; Vitousek, P. M.

1985-01-01

The use of modeling, remote sensing, and measurements to characterize the pathways and to measure the rate of biogeochemical cycling in forest ecosystems is described. The application of the process-level model to predict processes in intact forests and ecosystems response to disturbance is examined. The selection of research areas from contrasting climate regimes and sites having a fertility gradient in that regime is discussed, and the sites studied are listed. The use of remote sensing in determining leaf area index and canopy biochemistry is analyzed. Nitrous oxide emission is investigated by using a gas measurement instrument. Future research projects, which include studying the influence of changes on nutrient cycling in ecosystems and the effect of pollutants on the ecosystems, are discussed.

Novel Technique and Technologies for Active Optical Remote Sensing of Greenhouse Gases

NASA Technical Reports Server (NTRS)

Singh, Upendra N.; Refaat, Tamer F.; Petros, Mulugeta

2017-01-01

The societal benefits of understanding climate change through identification of global carbon dioxide sources and sinks led to the desired NASA's active sensing of carbon dioxide emissions over nights, days, and seasons (ASCENDS) space-based missions of global carbon dioxide measurements. For more than 15 years, NASA Langley Research Center (LaRC) have developed several carbon dioxide active remote sensors using the differential absorption lidar (DIAL) technique operating at the two-micron wavelength. Currently, an airborne two-micron triple-pulse integrated path differential absorption (IPDA) lidar is under development. This IPDA lidar measures carbon dioxide as well as water vapor, the dominant interfering molecule on carbon dioxide remote sensing. Advancement of this triple-pulse IPDA lidar development is presented.

Assessment of polar climate change using satellite technology

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1988-01-01

Using results of selected studies, this paper highlights some of the problems that exist in the remote sensing of snow and ice, and demonstrates the importance of remote sensing for the study of snow and ice in determining the effect of temperature increase, due to the atmospheric CO2 increase, on the cryospheric features. Evidence obtained from NOAA, Nimbus, and other satellites, that may already indicate a global or at least a regional warming, includes an increase in permafrost temperature in northern Alaska and the retreat of many of the world's small glaciers in the last 100 years. It is emphasized that remote sensing is of major importance as the method of obtaining data for monitoring future changes in cryospheric features.

The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review.

PubMed

Palaniyandi, M

2012-12-01

There have been several attempts made to the appreciation of remote sensing and GIS for the study of vectors, biodiversity, vector presence, vector abundance and the vector-borne diseases with respect to space and time. This study was made for reviewing and appraising the potential use of remote sensing and GIS applications for spatial prediction of vector-borne diseases transmission. The nature of the presence and the abundance of vectors and vector-borne diseases, disease infection and the disease transmission are not ubiquitous and are confined with geographical, environmental and climatic factors, and are localized. The presence of vectors and vector-borne diseases is most complex in nature, however, it is confined and fueled by the geographical, climatic and environmental factors including man-made factors. The usefulness of the present day availability of the information derived from the satellite data including vegetation indices of canopy cover and its density, soil types, soil moisture, soil texture, soil depth, etc. is integrating the information in the expert GIS engine for the spatial analysis of other geoclimatic and geoenvironmental variables. The present study gives the detailed information on the classical studies of the past and present, and the future role of remote sensing and GIS for the vector-borne diseases control. The ecological modeling directly gives us the relevant information to understand the spatial variation of the vector biodiversity, vector presence, vector abundance and the vector-borne diseases in association with geoclimatic and the environmental variables. The probability map of the geographical distribution and seasonal variations of horizontal and vertical distribution of vector abundance and its association with vector -borne diseases can be obtained with low cost remote sensing and GIS tool with reliable data and speed.

Driving terrestrial ecosystem models from space

NASA Technical Reports Server (NTRS)

Waring, R. H.

1993-01-01

Regional air pollution, land-use conversion, and projected climate change all affect ecosystem processes at large scales. Changes in vegetation cover and growth dynamics can impact the functioning of ecosystems, carbon fluxes, and climate. As a result, there is a need to assess and monitor vegetation structure and function comprehensively at regional to global scales. To provide a test of our present understanding of how ecosystems operate at large scales we can compare model predictions of CO2, O2, and methane exchange with the atmosphere against regional measurements of interannual variation in the atmospheric concentration of these gases. Recent advances in remote sensing of the Earth's surface are beginning to provide methods for estimating important ecosystem variables at large scales. Ecologists attempting to generalize across landscapes have made extensive use of models and remote sensing technology. The success of such ventures is dependent on merging insights and expertise from two distinct fields. Ecologists must provide the understanding of how well models emulate important biological variables and their interactions; experts in remote sensing must provide the biophysical interpretation of complex optical reflectance and radar backscatter data.

A New Way to Measure Cirrus Ice Water Content by Using Ice Raman Scatter with Raman Lidar

NASA Technical Reports Server (NTRS)

Wang, Zhien; Whiteman, David N.; Demoz, Belay; Veselovskii, Igor

2004-01-01

High and cold cirrus clouds mainly contain irregular ice crystals, such as, columns, hexagonal plates, bullet rosettes, and dendrites, and have different impacts on the climate system than low-level clouds, such as stratus, stratocumulus, and cumulus. The radiative effects of cirrus clouds on the current and future climate depend strongly on cirrus cloud microphysical properties including ice water content (IWC) and ice crystal sizes, which are mostly an unknown aspect of cinus clouds. Because of the natural complexity of cirrus clouds and their high locations, it is a challenging task to get them accurately by both remote sensing and in situ sampling. This study presents a new method to remotely sense cirrus microphysical properties by using ice Raman scatter with a Raman lidar. The intensity of Raman scattering is fundamentally proportional to the number of molecules involved. Therefore, ice Raman scattering signal provides a more direct way to measure IWC than other remote sensing methods. Case studies show that this method has the potential to provide essential information of cirrus microphysical properties to study cloud physical processes in cirrus clouds.

Global Validation of MODIS Atmospheric Profile-Derived Near-Surface Air Temperature and Dew Point Estimates

NASA Astrophysics Data System (ADS)

Famiglietti, C.; Fisher, J.; Halverson, G. H.

2017-12-01

This study validates a method of remote sensing near-surface meteorology that vertically interpolates MODIS atmospheric profiles to surface pressure level. The extraction of air temperature and dew point observations at a two-meter reference height from 2001 to 2014 yields global moderate- to fine-resolution near-surface temperature distributions that are compared to geographically and temporally corresponding measurements from 114 ground meteorological stations distributed worldwide. This analysis is the first robust, large-scale validation of the MODIS-derived near-surface air temperature and dew point estimates, both of which serve as key inputs in models of energy, water, and carbon exchange between the land surface and the atmosphere. Results show strong linear correlations between remotely sensed and in-situ near-surface air temperature measurements (R2 = 0.89), as well as between dew point observations (R2 = 0.77). Performance is relatively uniform across climate zones. The extension of mean climate-wise percent errors to the entire remote sensing dataset allows for the determination of MODIS air temperature and dew point uncertainties on a global scale.

Bangladesh Agro-Climatic Environmental Monitoring Project

NASA Technical Reports Server (NTRS)

Vermillion, C.; Maurer, H.; Williams, M.; Kamowski, J.; Moore, T.; Maksimovich, W.; Obler, H.; Gilbert, E.

1988-01-01

The Agro-Climatic Environmental Monitoring Project (ACEMP) is based on a Participating Agency Service Agreement (PASA) between the Agency for International Development (AID) and the National Oceanic and Atmospheric Administration (NOAA). In FY80, the Asia Bureau and Office of Federal Disaster Assistance (OFDA), worked closely to develop a funding mechanism which would meet Bangladesh's needs both for flood and cyclone warning capability and for application of remote sensing data to development problems. In FY90, OFDA provided for a High Resolution Picture Transmission (HRPT) receiving capability to improve their forecasting accuracy for cyclones, flooding and storm surges. That equipment is primarily intended as a disaster prediction and preparedness measure. The ACEM Project was designed to focus on the development applications of remote sensing technology. Through this Project, AID provided to the Bangladesh Government (BDG) the equipment, technical assistance, and training necessary to collect and employ remote sensing data made available by satellites as well as hydrological data obtained from data collection platforms placed in major rivers. The data collected will enable the BDG to improve the management of its natural resources.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles: REMOTE SENSING OF THERMODYNAMIC PROFILES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wulfmeyer, Volker; Hardesty, R. Michael; Turner, David D.

A review of remote sensing technology for lower tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer—usually characterized by an inversion—and the lowermore » troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global navigation satellite system, as well as water vapor and temperature Raman lidar and water vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.« less

Famine Early Warning Systems and Their Use of Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Essam, Timothy; Leonard, Kenneth

2011-01-01

Famine early warning organizations have experience that has much to contribute to efforts to incorporate climate and weather information into economic and political systems. Food security crises are now caused almost exclusively by problems of food access, not absolute food availability, but the role of monitoring agricultural production both locally and globally remains central. The price of food important to the understanding of food security in any region, but it needs to be understood in the context of local production. Thus remote sensing is still at the center of much food security analysis, along with an examination of markets, trade and economic policies during food security analyses. Technology including satellite remote sensing, earth science models, databases of food production and yield, and modem telecommunication systems contributed to improved food production information. Here we present an econometric approach focused on bringing together satellite remote sensing and market analysis into food security assessment in the context of early warning.

Research on Remote Sensing Geological Information Extraction Based on Object Oriented Classification

NASA Astrophysics Data System (ADS)

Gao, Hui

2018-04-01

The northern Tibet belongs to the Sub cold arid climate zone in the plateau. It is rarely visited by people. The geological working conditions are very poor. However, the stratum exposures are good and human interference is very small. Therefore, the research on the automatic classification and extraction of remote sensing geological information has typical significance and good application prospect. Based on the object-oriented classification in Northern Tibet, using the Worldview2 high-resolution remote sensing data, combined with the tectonic information and image enhancement, the lithological spectral features, shape features, spatial locations and topological relations of various geological information are excavated. By setting the threshold, based on the hierarchical classification, eight kinds of geological information were classified and extracted. Compared with the existing geological maps, the accuracy analysis shows that the overall accuracy reached 87.8561 %, indicating that the classification-oriented method is effective and feasible for this study area and provides a new idea for the automatic extraction of remote sensing geological information.

[Contribution of remote sensing to malaria control].

PubMed

Machault, V; Pages, F; Rogier, C

2009-04-01

Despite national and international efforts, malaria remains a major public health problem and the fight to control the disease is confronted by numerous hurdles. Study of space and time dynamics of malaria is necessary as a basis for making appropriate decision and prioritizing intervention including in areas where field data are rare and sanitary information systems are inadequate. Evaluation of malarial risk should also help anticipate the risk of epidemics as a basis for early warning systems. Since 1960-70 civilian satellites launched for earth observation have been providing information for the measuring or evaluating geo-climatic and anthropogenic factors related to malaria transmission and burden. Remotely sensed data gathered for several civilian or military studies have allowed setup of entomological, parasitological, and epidemiological risk models and maps for rural and urban areas. Mapping of human populations at risk has also benefited from remotely sensing. The results of the published studies show that remote sensing is a suitable tool for optimizing planning, efficacy and efficiency of malaria control.

An assessment of aerosol optical properties from remote-sensing observations and regional chemistry-climate coupled models over Europe

NASA Astrophysics Data System (ADS)

Palacios-Peña, Laura; Baró, Rocío; Baklanov, Alexander; Balzarini, Alessandra; Brunner, Dominik; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; María López-Romero, José; Montávez, Juan Pedro; Pérez, Juan Luis; Pirovano, Guido; San José, Roberto; Schröder, Wolfram; Werhahn, Johannes; Wolke, Ralf; Žabkar, Rahela; Jiménez-Guerrero, Pedro

2018-04-01

Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry-climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol-radiation (ARI) or/and aerosol-cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality-climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in the aerosol representation. The modelling results showed better skills when ARI+ACI interactions were included; hence this improvement in the representation of AOD (above 30 % in the model error) and AE (between 20 and 75 %) is important to provide a better description of aerosol-radiation-cloud interactions in regional climate models.

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

NASA Astrophysics Data System (ADS)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the decrease in the available energy balances the decrease in the sensible heat flux. These datasets are useful for benchmarking climate models and LSM output at the global annual scale and the regional scale subject to the regional uncertainties and performance. Future work should improve the input data, particularly the temperature gradient and Zilitinkevich empirical constant, to reduce uncertainties.

Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

2013-03-01

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

Linking Satellite Remote Sensing Based Environmental Predictors to Disease: AN Application to the Spatiotemporal Modelling of Schistosomiasis in Ghana

NASA Astrophysics Data System (ADS)

Wrable, M.; Liss, A.; Kulinkina, A.; Koch, M.; Biritwum, N. K.; Ofosu, A.; Kosinski, K. C.; Gute, D. M.; Naumova, E. N.

2016-06-01

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

Satellite and Ground-based Radiometers Reveal Much Lower Dust Absorption of Sunlight than Used in Climate Models

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Tanre, D.; Dubovik, O.; Karnieli, A.; Remer, L. A.; Einaudi, Franco (Technical Monitor)

2000-01-01

The ability of dust to absorb solar radiation and heat the atmosphere is one of the main uncertainties in climate modeling and the prediction of climate change. Dust absorption is not well known due to limitations of in situ measurements. New techniques to measure dust absorption are needed in order to assess the impact of dust on climate. Here we report two new independent remote sensing techniques that provide sensitive measurements of dust absorption. Both are based on remote sensing. One uses satellite spectral measurements, the second uses ground based sky measurements from the AERONET network. Both techniques demonstrate that Saharan dust absorption of solar radiation is several times smaller than the current international standards. Dust cooling of the earth system in the solar spectrum is therefore significantly stronger than recent calculations indicate. We shall also address the issue of the effects of dust non-sphericity on the aerosol optical properties.

Working Toward a Healthy Planet

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2003-01-01

Using information from NASA s Earth Science Public Health Applications Program, Dr. Maynard will address how remote sensing data and associated technologies can be used toward a better understanding of the links among human health, the environment and weather/climate - and, how this increased understanding plus improved information sharing can empower local health and environmental decision-makers to better predict climate-related health problems, take preventive measures, and improve response actions. Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring and surveillance of parameters useful to such problems as infectious and vector-borne diseases, air and water quality, harmful algal blooms, UV radiation, contaminant and pathogen transport in air and water, and thermal stress. NASA s multi-disciplinary scientific team is demonstrating how satellites from their unique vantage point in space can serve as sentinels for weather, climate, and health problems through studies on asthma, malaria, Rift Valley Fever, Asian and African dust, and West Nile Virus

Remote Sensing Terminology in a Global and Knowledge-Based World

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana

The paper is devoted to terminology issues related to all aspects of remote sensing research and applications. Terminology is the basis for a better understanding among people. It is crucial to keep up with the latest developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have ever extending applications in various domains of science and human activities. Remote sensing terminology issues are directly relevant to the contemporary worldwide policies on information accessibility, dissemination and utilization of research results in support of solutions to global environmental challenges and sustainable development goals. Remote sensing and spatial information technologies are an integral part of the international strategies for cooperation in scientific, research and application areas with a particular accent on environmental monitoring, ecological problems natural resources management, climate modeling, weather forecasts, disaster mitigation and many others to which remote sensing data can be put. Remote sensing researchers, professionals, students and decision makers of different counties and nationalities should fully understand, interpret and translate into their native language any term, definition or acronym found in papers, books, proceedings, specifications, documentation, and etc. The importance of the correct use, precise definition and unification of remote sensing terms refers not only to people working in this field but also to experts in a variety of disciplines who handle remote sensing data and information products. In this paper, we draw the attention on the specifics, peculiarities and recent needs of compiling specialized dictionaries in the area of remote sensing focusing on Earth observations and the integration of remote sensing with other geoinformation technologies such as photogrammetry, geodesy, GIS, etc. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. The work on an English-Bulgarian Dictionary of Remote Sensing Terms is described including considerations on its scope, structure, information content, sellection of terms, and etc. The vision builds upon previous national and international experience and makes use of ongoing activities on the subject. Any interest in cooperation and initiating suchlike collaborative projects is welcome and highly appreciated.

Remote Sensing and Geographic Information Systems as Decision Support Tools for Malaria Control in the Republic of Korea

DTIC Science & Technology

2001-05-30

larviciding due to the larger number of at- risk personnel and the smaller size of the vector habitats. This study demonstrates the usefulness of remote sensing...climate and demonstrates both short and long incubation periods. Long incubation periods increase the risk of re-introducing malaria to the continental...United States, making malaria control in the ROK even more important (Walter Reed Army Institute of Research 1998). This increased risk is from U.S

Predictions of avian Plasmodium expansion under climate change.

PubMed

Loiseau, Claire; Harrigan, Ryan J; Bichet, Coraline; Julliard, Romain; Garnier, Stéphane; Lendvai, Adám Z; Chastel, Olivier; Sorci, Gabriele

2013-01-01

Vector-borne diseases are particularly responsive to changing environmental conditions. Diurnal temperature variation has been identified as a particularly important factor for the development of malaria parasites within vectors. Here, we conducted a survey across France, screening populations of the house sparrow (Passer domesticus) for malaria (Plasmodium relictum). We investigated whether variation in remotely-sensed environmental variables accounted for the spatial variation observed in prevalence and parasitemia. While prevalence was highly correlated to diurnal temperature range and other measures of temperature variation, environmental conditions could not predict spatial variation in parasitemia. Based on our empirical data, we mapped malaria distribution under climate change scenarios and predicted that Plasmodium occurrence will spread to regions in northern France, and that prevalence levels are likely to increase in locations where transmission already occurs. Our findings, based on remote sensing tools coupled with empirical data suggest that climatic change will significantly alter transmission of malaria parasites.

Estimation of rainfall using remote sensing for Riyadh climate, KSA

NASA Astrophysics Data System (ADS)

AlHassoun, Saleh A.

2013-05-01

Rainfall data constitute an important parameter for studying water resources-related problems. Remote sensing techniques could provide rapid and comprehensive overview of the rainfall distribution in a given area. Thus, the infrared data from the LandSat satellite in conjunction with the Scofield-oliver method were used to monitor and model rainfall in Riyadh area as a resemble of any area in the Kingdom of Saudi Arabia(KSA). Four convective clouds that covered two rain gage stations were analyzed. Good estimation of rainfall was obtained from satellite images. The results showed that the satellite rainfall estimations were well correlated to rain gage measurements. The satellite climate data appear to be useful for monitoring and modeling rainfall at any area where no rain gage is available.

Spaceborne studies of ocean circulation

NASA Technical Reports Server (NTRS)

Patzert, W. C.

1984-01-01

The history and near-term future of ocean remote sensing to study ocean circulation are examined. Seasat provided the first-ever global data sets of sea surface topography (altimeter) and marine winds (scatterometer) and laid the foundation for the next generation of satellite missions planned for the late 1980s. The future missions are the next generation of altimeter and scatterometer to be flown aboard TOPEX (TOPography EXperiment) and NROSS (Navy Remote Sensing System), respectively. The data from these satellites will be coordinated with measurements made at sea to determine the driving forces of ocean circulation and to study the oceans' role in climate variability. The significance of such studies to such matters as climatic changes, fisheries, commerce, waste disposal, and national defense is noted.

Remote Sensing and the Kyoto Protocol: A Workshop Summary

NASA Technical Reports Server (NTRS)

Rosenqvist, Ake; Imhoff, Marc; Milne, Anthony; Dobson, Craig

2000-01-01

The Kyoto Protocol to the United Nations Framework Convention on Climate Change contains quantified, legally binding commitments to limit or reduce greenhouse gas emissions to 1990 levels and allows carbon emissions to be balanced by carbon sinks represented by vegetation. The issue of using vegetation cover as an emission offset raises a debate about the adequacy of current remote sensing systems and data archives to both assess carbon stocks/sinks at 1990 levels, and monitor the current and future global status of those stocks. These concerns and the potential ratification of the Protocol among participating countries is stimulating policy debates and underscoring a need for the exchange of information between the international legal community and the remote sensing community. On October 20-22 1999, two working groups of the International Society for Photogrammetry and Remote Sensing (ISPRS) joined with the University of Michigan (Michigan, USA) to convene discussions on how remote sensing technology could contribute to the information requirements raised by implementation of, and compliance with, the Kyoto Protocol. The meeting originated as a joint effort between the Global Monitoring Working Group and the Radar Applications Working Group in Commission VII of the ISPRS, co-sponsored by the University of Michigan. Tile meeting was attended by representatives from national government agencies and international organizations and academic institutions. Some of the key themes addressed were: (1) legal aspects of transnational remote sensing in the context of the Kyoto Protocol; (2) a review of the current and future and remote sensing technologies that could be applied to the Kyoto Protocol; (3) identification of areas where additional research is needed in order to advance and align remote sensing technology with the requirements and expectations of the Protocol; and 94) the bureaucratic and research management approaches needed to align the remote sensing community with both the science and policy communities.

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review.

PubMed

Lees, K J; Quaife, T; Artz, R R E; Khomik, M; Clark, J M

2018-02-15

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Assessment of variations in taxonomic diversity, forest structure, and aboveground biomass using remote sensing along an altitudinal gradient in tropical montane forest of Costa Rica

NASA Astrophysics Data System (ADS)

Robinson, C. M.; Saatchi, S. S.; Clark, D.; Fricker, G. A.; Wolf, J.; Gillespie, T. W.; Rovzar, C. M.; Andelman, S.

2012-12-01

This research sought to understand how alpha and beta diversity of plants vary and relate to the three-dimensional vegetation structure and aboveground biomass along environmental gradients in the tropical montane forests of Braulio Carrillo National Park in Costa Rica. There is growing evidence that ecosystem structure plays an important role in defining patterns of species diversity and along with abiotic factors (climate and edaphic) control the phenotypic and functional variations across landscapes. It is well documented that strong subdivisions at local and regional scales are found mainly on geologic or climate gradients. These general determinants of biodiversity are best demonstrated in regions with natural gradients such as tropical montane forests. Altitudinal gradients provide a landscape scale changes through variations in topography, climate, and edaphic conditions on which we tested several theoretical and biological hypotheses regarding drivers of biodiversity. The study was performed by using forest inventory and botanical data from nine 1-ha plots ranging from 100 m to 2800 m above sea level and remote sensing data from airborne lidar and radar sensors to quantify variations in forest structure. In this study we report on the effectiveness of relating patterns of tree taxonomic alpha diversity to three-dimensional structure of a tropical montane forest using lidar and radar observations of forest structure and biomass. We assessed alpha and beta diversity at the species, genus, and family levels utilizing datasets provided by the Terrestrial Ecology Assessment and Monitoring (TEAM) Network. Through the comparison to active remote sensing imagery, our results show that there is a strong relationship between forest 3D-structure, and alpha and beta diversity controlled by variations in abiotic factors along the altitudinal gradient. Using spatial analysis with the aid of remote sensing data, we find distinct patterns along the environmental gradients defining species turnover and changes in functional diversity. The study will provide novel approaches to use detailed spatial information from remote sensing data to study relations between functional and taxonomic dimensions of diversity.

Reliability analysis of airship remote sensing system

NASA Astrophysics Data System (ADS)

Qin, Jun

1998-08-01

Airship Remote Sensing System (ARSS) for obtain the dynamic or real time images in the remote sensing of the catastrophe and the environment, is a mixed complex system. Its sensor platform is a remote control airship. The achievement of a remote sensing mission depends on a series of factors. For this reason, it is very important for us to analyze reliability of ARSS. In first place, the system model was simplified form multi-stage system to two-state system on the basis of the result of the failure mode and effect analysis and the failure tree failure mode effect and criticality analysis. The failure tree was created after analyzing all factors and their interrelations. This failure tree includes four branches, e.g. engine subsystem, remote control subsystem, airship construction subsystem, flying metrology and climate subsystem. By way of failure tree analysis and basic-events classing, the weak links were discovered. The result of test running shown no difference in comparison with theory analysis. In accordance with the above conclusions, a plan of the reliability growth and reliability maintenance were posed. System's reliability are raised from 89 percent to 92 percent with the reformation of the man-machine interactive interface, the augmentation of the secondary better-groupie and the secondary remote control equipment.

Remote sensing of atmosphere and oceans; Proceedings of Symposium 1 and of the Topical Meeting of the 27th COSPAR Plenary Meeting, Espoo, Finland, July 18-29, 1988

NASA Technical Reports Server (NTRS)

Raschke, E. (Editor); Ghazi, A. (Editor); Gower, J. F. R. (Editor); Mccormick, P. (Editor); Gruber, A. (Editor); Hasler, A. F. (Editor)

1989-01-01

Papers are presented on the contribution of space remote sensing observations to the World Climate Research Program and the Global Change Program, covering topics such as space observations for global environmental monitoring, experiments related to land surface fluxes, studies of atmospheric composition, structure, motions, and precipitation, and remote sensing for oceanography, observational studies of the atmosphere, clouds, and the earth radiation budget. Also, papers are given on results from space observations for meteorology, oceanography, and mesoscale atmospheric and ocean processes. The topics include vertical atmospheric soundings, surface water temperature determination, sea level variability, data on the prehurricane atmosphere, linear and circular mesoscale convective systems, Karman vortex clouds, and temporal patterns of phytoplankton abundance.

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Changes in Landscape Greenness and Climatic Factors over 25 Years (1989–2013) in the USA

EPA Science Inventory

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g. climate change) versus direct and ...

Biotic regulation of CO2 uptake-climate responses: Links to vegetation properties

USDA-ARS?s Scientific Manuscript database

Background/Question/Methods Primary productivity is regulated by climatic factors, but also depends on which plant species are present and, more specifically, on how plant traits that control photosynthesis and its response to the environment are distributed in the community. We used remotely-sense...

Several thoughts for using new satellite remote sensing and global modeling for aerosol and cloud climate studies

NASA Astrophysics Data System (ADS)

Nakajima, Teruyuki; Hashimoto, Makiko; Takenaka, Hideaki; Goto, Daisuke; Oikawa, Eiji; Suzuki, Kentaroh; Uchida, Junya; Dai, Tie; Shi, Chong

2017-04-01

The rapid growth of satellite remote sensing technologies in the last two decades widened the utility of satellite data for understanding climate impacts of aerosols and clouds. The climate modeling community also has received the benefit of the earth observation and nowadays closed-collaboration of the two communities make us possible to challenge various applications for societal problems, such as for global warming and global-scale air pollution and others. I like to give several thoughts of new algorithm developments, model use of satellite data for climate impact studies and societal applications related with aerosols and clouds. Important issues are 1) Better aerosol detection and solar energy application using expanded observation ability of the third generation geostationary satellites, i.e. Himawari-8, GOES-R and future MTG, 2) Various observation functions by directional, polarimetric, and high resolution near-UV band by MISR, POLDER&PARASOL, GOSAT/CAI and future GOSAT2/CAI2, 3) Various applications of general purpose-imagers, MODIS, VIIRS and future GCOM-C/SGLI, and 4) Climate studies of aerosol and cloud stratification and convection with active and passive sensors, especially climate impact of BC aerosols using CLOUDSAT&CALIPSO and future Earth Explorer/EarthCARE.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Decision Support Systems To Manage Water Resources At Irrigation District Level In Southern Italy Using Remote Sensing Information. An Integrated Project (AQUATER)

NASA Astrophysics Data System (ADS)

Rinaldi, M.; Castrignanò, A.; Mastrorilli, M.; Rana, G.; Ventrella, D.; Acutis, M.; D'Urso, G.; Mattia, F.

2006-08-01

An efficient management of water resources is crucial point for Italy and in particular for southern areas characterized by Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. A three-year Project (2005-2008) has been funded by the Italian Ministry of Agriculture and Forestry Policies; it involves four Italian research institutions: the Agricultural Research Council (ISA, Bari), the National Research Council (ISSIA, Bari) and two Universities (Federico II-Naples and Milan). It is focused on the remote sensing, the plant and the climate and, for interdisciplinary relationships, the project working group consists of agronomists, engineers and physicists. The aims of the Project are: a) to produce a Decision Support System (DSS) combining remote sensing information, spatial data and simulation models to manage water resources in irrigation districts; b) to simulate irrigation scenarios to evaluate the effects of water stress on crop yield using agro-ecological indicators; c) to identify the most sensitive areas to drought risk in Southern Italy. The tools used in this Project will be: 1. Remote sensing images, topographic maps, soil and land use maps; 2. Geographic Information Systems; 3. Geostatistic methodologies; 4. Ground truth measurements (land use, canopy and soil temperatures, soil and plant water status, Normalized Difference Vegetation Index, Crop Water Stress Index, Leaf Area Index, actual evapotranspiration, crop coefficients, crop yield, agro-ecological indicators); 5. Crop simulation models. The Project is structured in four work packages with specific objectives, high degree of interaction and information exchange: 1) Remote Sensing and Image Analysis; 2) Cropping Systems; 3) Modelling and Softwares Development; 4) Stakeholders. The final product will be a DSS with the purpose of integrating remote sensing images, to estimate crop and soil variables related to drought, to assimilate these variables into a simulation model at district scale and, finally, to estimate evapotranspiration, plant water status and drought indicators. A project Web home page, a technical course about DSS for the employers of irrigation authorities and dissemination of results (meetings, publications, reports), are also planned.

Hyperspectral Remote Sensing of Foliar Nitrogen Content

NASA Technical Reports Server (NTRS)

Knyazikhin, Yuri; Schull, Mitchell A.; Stenberg, Pauline; Moettus, Matti; Rautiainen, Miina; Yang, Yan; Marshak, Alexander; Carmona, Pedro Latorre; Kaufmann, Robert K.; Lewis, Philip;

Measuring grassland structure for recovery of grassland species at risk

NASA Astrophysics Data System (ADS)

Guo, Xulin; Gao, Wei; Wilmshurst, John

2005-09-01

An action plan for recovering species at risk (SAR) depends on an understanding of the plant community distribution, vegetation structure, quality of the food source and the impact of environmental factors such as climate change at large scale and disturbance at small scale, as these are fundamental factors for SAR habitat. Therefore, it is essential to advance our knowledge of understanding the SAR habitat distribution, habitat quality and dynamics, as well as developing an effective tool for measuring and monitoring SAR habitat changes. Using the advantages of non-destructive, low cost, and high efficient land surface vegetation biophysical parameter characterization, remote sensing is a potential tool for helping SAR recovery action. The main objective of this paper is to assess the most suitable techniques for using hyperspectral remote sensing to quantify grassland biophysical characteristics. The challenge of applying remote sensing in semi-arid and arid regions exists simply due to the lower biomass vegetation and high soil exposure. In conservation grasslands, this problem is enhanced because of the presence of senescent vegetation. Results from this study demonstrated that hyperspectral remote sensing could be the solution for semi-arid grassland remote sensing applications. Narrow band raw data and derived spectral vegetation indices showed stronger relationships with biophysical variables compared to the simulated broad band vegetation indices.

New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data

PubMed Central

Yang, Bao; He, Minhui; Shishov, Vladimir; Tychkov, Ivan; Vaganov, Eugene; Rossi, Sergio; Ljungqvist, Fredrik Charpentier; Bräuning, Achim; Grießinger, Jussi

2017-01-01

Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world’s largest high-elevation region. This study provides a perspective on vegetation phenology shifts during 1960–2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960–2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982–2014. No significant changes in SOS or EOS were observed during 1960–1981. April–June and August–September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April–June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale. PMID:28630302

Climate- and remote sensing-based tools for drought management application in North and South Korea

NASA Astrophysics Data System (ADS)

Nam, W.; Wardlow, B.; Hayes, M. J.; Tadesse, T.; Svoboda, M.; Fuchs, B.; Wilhite, D. A.

2015-12-01

North and South Korea have experienced more frequent and extreme droughts since the late 1990s. In recent years, severe droughts in 2000-2001, 2012, and 2015 have led to widespread agricultural and environmental impacts, and resulted in water shortages and large reductions in crop yields. This has been particularly problematic in the agricultural sector of North Korea, which has a high-level of vulnerability due to variations of climate and this, in turn, results in food security issues. This vulnerability is exacerbated by North Korea's relatively small area of arable land, most of which is not very productive. The objective of this study was to develop a drought management application using climate- and remote sensing-based tools for North and South Korea. These tools are essential for improving drought planning and preparedness in this area. In this study, various drought indicators derived from climate and remote sensing data (SPI, SC-PDSI, SPEI, and VegDRI-Korea) were investigated to monitor the current drought condition and evaluate their ability to characterize agricultural and meteorological drought events and their potential impacts. Results from this study can be used to develop or improve the national-level drought management application for these countries. The goal is to provide improved and more timely information on both the spatial and temporal dimensions of drought conditions and provide a tool to identify both past and present drought events in order to make more informed management decisions and reduce the impacts of current droughts and reduce the risk to future events.

Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data

NASA Astrophysics Data System (ADS)

Salmon, J. Meghan; Friedl, Mark A.; Frolking, Steve; Wisser, Dominik; Douglas, Ellen M.

2015-06-01

Irrigation accounts for 70% of global water use by humans and 33-40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.

New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data

NASA Astrophysics Data System (ADS)

Yang, Bao; He, Minhui; Shishov, Vladimir; Tychkov, Ivan; Vaganov, Eugene; Rossi, Sergio; Charpentier Ljungqvist, Fredrik; Bräuning, Achim; Grießinger, Jussi

2017-07-01

Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world’s largest high-elevation region. This study provides a perspective on vegetation phenology shifts during 1960-2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960-2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982-2014. No significant changes in SOS or EOS were observed during 1960-1981. April-June and August-September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April-June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale.

A review of the remote sensing of lower-tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles

DOE PAGES

Wulfmeyer, Volker; Hardesty, Mike; Turner, David D.; ...

2015-07-08

A review of remote sensing technology for lower-tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land-surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer – usually characterized by an inversion – andmore » the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global positioning system as well as water-vapor and temperature Raman lidar and water-vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.« less

A review of the remote sensing of lower-tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wulfmeyer, Volker; Hardesty, Mike; Turner, David D.

A review of remote sensing technology for lower-tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land-surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer – usually characterized by an inversion – andmore » the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global positioning system as well as water-vapor and temperature Raman lidar and water-vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.« less

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa.

PubMed

Ardö, Jonas

2015-12-01

Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.

Methods of satellite oceanography

NASA Technical Reports Server (NTRS)

Stewart, R. H.

1985-01-01

The theoretical basis for remote sensing measurements of climate and ocean dynamics is examined. Consideration is given to: the absorption of electromagnetic radiation in the atmosphere; scattering in the atmosphere; and satellite observations using visible light. Consideration is also given to: the theory of radio scatter from the sea; scatter of centimeter waves from the sea; and the theory of operation of synthetic aperture radars. Additional topics include: the coordinate systems of satellite orbits for oceanographic remote sensing applications; the operating features of the major U.S. satellite systems for viewing the ocean; and satellite altimetry.

Interfacing remote sensing and geographic information systems for global environmental change research

NASA Technical Reports Server (NTRS)

Lee, Jae K.; Randolph, J. C.; Lulla, Kamlesh P.; Helfert, Michael R.

1993-01-01

Because changes in the Earth's environment have become major global issues, continuous, longterm scientific information is required to assess global problems such as deforestation, desertification, greenhouse effects and climate variations. Global change studies require understanding of interactions of complex processes regulating the Earth system. Space-based Earth observation is an essential element in global change research for documenting changes in Earth environment. It provides synoptic data for conceptual predictive modeling of future environmental change. This paper provides a brief overview of remote sensing technology from the perspective of global change research.

Best Practice Guidelines for Pre-Launch Characterization and Calibration of Instruments for Passive Optical Remote Sensing1

PubMed Central

Datla, R. U.; Rice, J. P.; Lykke, K. R.; Johnson, B. C.; Butler, J. J.; Xiong, X.

2011-01-01

The pre-launch characterization and calibration of remote sensing instruments should be planned and carried out in conjunction with their design and development to meet the mission requirements. The onboard calibrators such as blackbodies and the sensors such as spectral radiometers should be characterized and calibrated using SI traceable standards. In the case of earth remote sensing, this allows inter-comparison and intercalibration of different sensors in space to create global time series of climate records of high accuracy where some inevitable data gaps can be easily bridged. The recommended best practice guidelines for this pre-launch effort is presented based on experience gained at National Institute of Standards and Technology (NIST), National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) programs over the past two decades. The currently available radiometric standards and calibration facilities at NIST serving the remote sensing community are described. Examples of best practice calibrations and intercomparisons to build SI (international System of Units) traceable uncertainty budget in the instrumentation used for preflight satellite sensor calibration and validation are presented. PMID:26989588

Best Practice Guidelines for Pre-Launch Characterization and Calibration of Instruments for Passive Optical Remote Sensing.

PubMed

Datla, R U; Rice, J P; Lykke, K R; Johnson, B C; Butler, J J; Xiong, X

2011-01-01

The pre-launch characterization and calibration of remote sensing instruments should be planned and carried out in conjunction with their design and development to meet the mission requirements. The onboard calibrators such as blackbodies and the sensors such as spectral radiometers should be characterized and calibrated using SI traceable standards. In the case of earth remote sensing, this allows inter-comparison and intercalibration of different sensors in space to create global time series of climate records of high accuracy where some inevitable data gaps can be easily bridged. The recommended best practice guidelines for this pre-launch effort is presented based on experience gained at National Institute of Standards and Technology (NIST), National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) programs over the past two decades. The currently available radiometric standards and calibration facilities at NIST serving the remote sensing community are described. Examples of best practice calibrations and intercomparisons to build SI (international System of Units) traceable uncertainty budget in the instrumentation used for preflight satellite sensor calibration and validation are presented.

Remote-sensing supported monitoring of global biodiversity change

NASA Astrophysics Data System (ADS)

Jetz, W.; Tuanmu, M. N.; W, A.; Melton, F. S.; Parmentier, B.; Amatulli, G.; Guzman, A.

2016-12-01

Remote sensing combined with biodiversity observation offers an unrivalled tool for understanding and predicting species distributions and their changes at the planetary scale. I will illustrate recently developed high-resolution remote-sensing based layers targeted for spatiotemporal biodiversity modeling, addressing climate, environment, topography, and habitat heterogeneity. In particular, I will illustrate the development and use of global MODIS-derived environmental layers for biodiversity assessment and change monitoring. Remote-sensing based capture of these putative predictors of biodiversity dynamics provides more a reliable signal than spatially interpolated layers and avoids inflated spatial autocorrelation. The layers result in more accurate models of species occurrence and are more readily able to address the scale of processes underpinning species distributions, e.g. when combined with emerging hierarchical, cross-scale models. I illustrate the multiple ways in which this type of information, based on continuously collected data, supports the prediction of not just spatial but also temporal variation in biodiversity. Using implementations in the Map of Life infrastructure I will showcase new indicators of species distribution and change that demonstrate these new opportunities.

Extracting Forest Canopy Characteristics from Remote Sensing Imagery: Implications for Sentinel-2 Mission

NASA Astrophysics Data System (ADS)

Gholizadeh, Asa; Kopaekova, Veronika; Rogass, Christian; Mielke, Christian; Misurec, Jan

2016-08-01

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change. Remote sensing has been introduced as a time and cost- efficient way to carry out large scale monitoring of vegetation parameters. Red-Edge Position (REP) is a hyperspectrally detectable parameter which is sensitive to vegetation Chl. In the current study, REP was modelled for the Norway spruce forest canopy resampled to HyMap and Sentinel-2 spectral resolution as well as calculated from the real HyMap and Sentinel-2 simulated data. Different REP extraction methods (4PLI, PF, LE, 4PLIH and 4PLIS) were assessed. The study showed the way for effective utilization of the forthcoming hyper and superspectral remote sensing sensors from orbit to monitor vegetation attributes.

The promise of remote sensing in the atmospheric sciences

NASA Technical Reports Server (NTRS)

Atlas, D.

1981-01-01

The applications and advances in remote sensing technology for weather prediction, mesoscale meteorology, severe storms, and climate studies are discussed. Doppler radar permits tracking of the three-dimensional field of motion within storms, thereby increasing the accuracy of convective storm modeling. Single Doppler units are also employed for detecting mesoscale storm vortices and tornado vortex signatures with lead times of 30 min. Clear air radar in pulsed and high resolution FM-CW forms reveals boundary layer convection, Kelvin-Helmoltz waves, shear layer turbulence, and wave motions. Lidar is successfully employed for stratospheric aerosol measurements, while Doppler lidar provides data on winds from the ground and can be based in space. Sodar is useful for determining the structure of the PBL. Details and techniques of satellite-based remote sensing are presented, and results from the GWE and FGGE experiments are discussed.

Remote Sensing Proxies for Vector-borne Disease Risk Assessment (Invited)

NASA Astrophysics Data System (ADS)

Anyamba, A.

2010-12-01

The spread of re-emerging vector-borne diseases such Rift Valley fever (RVF) and Chikungunya (CHIK) is a major issue of global public health concern. This combined with a variable climate regime has opened an avenue for satellite remote sensing to contribute towards a comprehensive understanding of some of the drivers influencing such vector-borne disease outbreaks. Satellite derived measurements such as vegetation indices, rainfall estimates, and land-surface temperature; can be used to infer the complex mosaic of factors that influence ecology and habitat suitability, emergence and population dynamics of disease vectors. However, there are still some gaps in application including appropriate temporal resolution of remote sensing measurements, the complexity of the virus-vector-disease-ecology system and human components that contribute to disease risk that need to be addressed. Geographic Distribution of Recent Rift Valley fever oubreaks

Remote Sensing of Terrestrial Snow and Ice for Global Change Studies

NASA Technical Reports Server (NTRS)

Kelly, Richard; Hall, Dorothy K.

2007-01-01

Snow and ice play a significant role in the Earth's water cycle and are sensitive and informative indicators climate change. Significant changes in terrestrial snow and ice water storage are forecast, and while evidence of large-scale changes is emerging, in situ measurements alone are insufficient to help us understand and explain these changes. Imaging remote sensing systems are capable of successfully observing snow and ice in the cryosphere. This chapter examines how those remote sensing sensors, that now have more than 35 years of observation records, are capable of providing information about snow cover, snow water equivalent, snow melt, ice sheet temperature and ice sheet albedo. While significant progress has been made, especially in the last five years, a better understanding is required of the records of satellite observations of these cryospheric variables.

Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement

NASA Technical Reports Server (NTRS)

Lobitz, B.; Beck, L.; Huq, A.; Wood, B.; Fuchs, G.; Faruque, A. S.; Colwell, R.

2000-01-01

It has long been known that cholera outbreaks can be initiated when Vibrio cholerae, the bacterium that causes cholera, is present in drinking water in sufficient numbers to constitute an infective dose, if ingested by humans. Outbreaks associated with drinking or bathing in unpurified river or brackish water may directly or indirectly depend on such conditions as water temperature, nutrient concentration, and plankton production that may be favorable for growth and reproduction of the bacterium. Although these environmental parameters have routinely been measured by using water samples collected aboard research ships, the available data sets are sparse and infrequent. Furthermore, shipboard data acquisition is both expensive and time-consuming. Interpolation to regional scales can also be problematic. Although the bacterium, V. cholerae, cannot be sensed directly, remotely sensed data can be used to infer its presence. In the study reported here, satellite data were used to monitor the timing and spread of cholera. Public domain remote sensing data for the Bay of Bengal were compared directly with cholera case data collected in Bangladesh from 1992-1995. The remote sensing data included sea surface temperature and sea surface height. It was discovered that sea surface temperature shows an annual cycle similar to the cholera case data. Sea surface height may be an indicator of incursion of plankton-laden water inland, e.g., tidal rivers, because it was also found to be correlated with cholera outbreaks. The extensive studies accomplished during the past 25 years, confirming the hypothesis that V. cholerae is autochthonous to the aquatic environment and is a commensal of zooplankton, i.e., copepods, when combined with the findings of the satellite data analyses, provide strong evidence that cholera epidemics are climate-linked.

Remote Sensing Techniques for Rapid Assessment of Forest Damage Caused by Catastrophic Climatic Events, NA-TP-01-01

Treesearch

William Ciesla; William Frament; Margaret Miller-Weeks

2001-01-01

Catastrophic climatic events such as hurricanes, tornadoes, and ice storms can cause billions of dollars in damage to infrastructure and personal property, loss of lives, and damage to natural resources. Forests are especially susceptible to these events. The following is a list of recent climatic events in North America that have had devastating effects on forest...

Atmospheric Effects and Potential Climatic Impact of the 1980 Eruptions of Mount St. Helens

NASA Technical Reports Server (NTRS)

Deepak, A. (Editor)

1982-01-01

Measurements and studies of the 1980 Mount St. Helens volcanic eruptions and their atmospheric effects and climatic impact are addressed. Specific areas discussed include: (1) nature and impact of volcanic eruptions; (2) in situ measurements of effluents; (3) remote sensing measurements; (4) transport and dispersion of volcanic effluents; (5) chemistry of volcanic effluents; and (6) weather and potential climate impact.

The Atmospheric Infrared Sounder (AIRS) on Aqua: instrument stability and data products for climate observations

NASA Technical Reports Server (NTRS)

Pagano, Thomas S.; Chahine, M.; Aumann, H.; Strow, L.; Broberg, S.; Gaiser, S.

2003-01-01

30th International Symposium on Remote Sensing of the Environment (ISRSE) NASA Honolulu, Hawaii, USAThis paper discusses the stability of the AIRS instrument as measured pre-flight and in-orbit. In order differentiate instrument related changes with true changes in climate observations, the instrument stability must be demonstrated.

NEON terrestrial field observations: designing continental scale, standardized sampling

Treesearch

R. H. Kao; C.M. Gibson; R. E. Gallery; C. L. Meier; D. T. Barnett; K. M. Docherty; K. K. Blevins; P. D. Travers; E. Azuaje; Y. P. Springer; K. M. Thibault; V. J. McKenzie; M. Keller; L. F. Alves; E. L. S. Hinckley; J. Parnell; D. Schimel

2012-01-01

Rapid changes in climate and land use and the resulting shifts in species distributions and ecosystem functions have motivated the development of the National Ecological Observatory Network (NEON). Integrating across spatial scales from ground sampling to remote sensing, NEON will provide data for users to address ecological responses to changes in climate, land use,...

A remote sensing tool to monitor and predict epidemiologic outbreaks of Hanta virus infections and Lyme disease

NASA Astrophysics Data System (ADS)

Barrios, J. M.

2009-04-01

Lyme disease and Hanta virus infection are the result of the conjunction of several climatic and ecological conditions. Although both affections have different causal agents, they share an important characteristic which is the fact that rodents play an important role in the contagium. One of the most important agents in the dispersion of these diseases is the bank vole (Clethrionomys glareoulus). The bank vole is a common host for both, the Borrelia bacteria which via the ticks (Ixodes ricinus) reaches the human body and causes the Lyme disease, and the Nephropatia epidemica which is caused by Puumala Hantavirus and affects kidneys in humans. The prefered habitat of bank voles is broad-leaf forests with an important presence of beeches (Fagus sylvatica) and oaks (Quercus sp.) and a relatively dense low vegetation layer. These vegetation systems are common in West-Europe and their dynamics have a great influence in the bank voles population and, therefore, in the spreading of the infections this study is concerned about. The fact that the annual seed production is not stable in time has an important effect in bank voles population and, as it has been described in other studies, in the number of reported cases of Hanta virus infections and Lyme disease. The years in which an abundant production of seeds is observed are referred to as mast years which are believed to obey to cyclic patterns and to certain climatological characteristics of the preceding years. Statistical analysis have confirmed the correlation in the behaviour of the number of infected cases and the presence of mast years. This project aims at the design of a remote sensing based system (INFOPRESS - INFectious disease Outbreak Prediction REmote Sensing based System) that should enable local and national health care instances to predict and locate the occurrence of infection outbreaks and design policies to counteract undesired effects. The predictive capabilities of the system are based on the understanding and modelling of the interactions between relevant climatic parameters (temperature, humidity, precipitation) and the main features of vegetation systems which host the vectors and determine the survival and infectious potential of the causal agents. Among the most important study subjects in this research initiative one can mention the time series analysis of vegetation parameters derived from satellite remote sensing and its relatation to climatic time series and historical records of infected cases; with special attention to the assessment of remotely sensed evidences of the mast phenomenon. These analysis will constitute important buildind bricks in the construction of the INFOPRESS system in what concerns the assessment of the potentials of satellite remote sensing as information source for the prediction of infection outbreaks. The bank voles habitat description will also be supported by on-gound remote sensing techniques, specially Lidar technology and soil humidity modelling. These measurements are to be coupled to bank voles and ticks epidemiologic features obtained from field capturing and lab analysis.

Remote Sensing of Aerosol in the Terrestrial Atmosphere from Space: New Missions

NASA Technical Reports Server (NTRS)

Milinevsky, G.; Yatskiv, Ya.; Degtyaryov, O.; Syniavskyi, I.; Ivanov, Yu.; Bovchaliuk, A.; Mishchenko, M.; Danylevsky, V.; Sosonkin, M.; Bovchaliuk, V.

2015-01-01

The distribution and properties of atmospheric aerosols on a global scale are not well known in terms of determination of their effects on climate. This mostly is due to extreme variability of aerosol concentrations, properties, sources, and types. Aerosol climate impact is comparable to the effect of greenhouse gases, but its influence is more difficult to measure, especially with respect to aerosol microphysical properties and the evaluation of anthropogenic aerosol effect. There are many satellite missions studying aerosol distribution in the terrestrial atmosphere, such as MISR/Terra, OMI/Aura, AVHHR, MODIS/Terra and Aqua, CALIOP/CALIPSO. To improve the quality of data and climate models, and to reduce aerosol climate forcing uncertainties, several new missions are planned. The gap in orbital instruments for studying aerosol microphysics has arisen after the Glory mission failed during launch in 2011. In this review paper, we describe several planned aerosol space missions, including the Ukrainian project Aerosol-UA that obtains data using a multi-channel scanning polarimeter and wide-angle polarimetric camera. The project is designed for remote sensing of the aerosol microphysics and cloud properties on a global scale.

Remote sensing of vegetation structure using computer vision

NASA Astrophysics Data System (ADS)

Dandois, Jonathan P.

High-spatial resolution measurements of vegetation structure are needed for improving understanding of ecosystem carbon, water and nutrient dynamics, the response of ecosystems to a changing climate, and for biodiversity mapping and conservation, among many research areas. Our ability to make such measurements has been greatly enhanced by continuing developments in remote sensing technology---allowing researchers the ability to measure numerous forest traits at varying spatial and temporal scales and over large spatial extents with minimal to no field work, which is costly for large spatial areas or logistically difficult in some locations. Despite these advances, there remain several research challenges related to the methods by which three-dimensional (3D) and spectral datasets are joined (remote sensing fusion) and the availability and portability of systems for frequent data collections at small scale sampling locations. Recent advances in the areas of computer vision structure from motion (SFM) and consumer unmanned aerial systems (UAS) offer the potential to address these challenges by enabling repeatable measurements of vegetation structural and spectral traits at the scale of individual trees. However, the potential advances offered by computer vision remote sensing also present unique challenges and questions that need to be addressed before this approach can be used to improve understanding of forest ecosystems. For computer vision remote sensing to be a valuable tool for studying forests, bounding information about the characteristics of the data produced by the system will help researchers understand and interpret results in the context of the forest being studied and of other remote sensing techniques. This research advances understanding of how forest canopy and tree 3D structure and color are accurately measured by a relatively low-cost and portable computer vision personal remote sensing system: 'Ecosynth'. Recommendations are made for optimal conditions under which forest structure measurements should be obtained with UAS-SFM remote sensing. Ultimately remote sensing of vegetation by computer vision offers the potential to provide an 'ecologist's eye view', capturing not only canopy 3D and spectral properties, but also seeing the trees in the forest and the leaves on the trees.

A Citizen Science Campaign to Validate Snow Remote-Sensing Products

NASA Astrophysics Data System (ADS)

Wikstrom Jones, K.; Wolken, G. J.; Arendt, A. A.; Hill, D. F.; Crumley, R. L.; Setiawan, L.; Markle, B.

2017-12-01

The ability to quantify seasonal water retention and storage in mountain snow packs has implications for an array of important topics, including ecosystem function, water resources, hazard mitigation, validation of remote sensing products, climate modeling, and the economy. Runoff simulation models, which typically rely on gridded climate data and snow remote sensing products, would be greatly improved if uncertainties in estimates of snow depth distribution in high-elevation complex terrain could be reduced. This requires an increase in the spatial and temporal coverage of observational snow data in high-elevation data-poor regions. To this end, we launched Community Snow Observations (CSO). Participating citizen scientists use Mountain Hub, a multi-platform mobile and web-based crowdsourcing application that allows users to record, submit, and instantly share geo-located snow depth, snow water equivalence (SWE) measurements, measurement location photos, and snow grain information with project scientists and other citizen scientists. The snow observations are used to validate remote sensing products and modeled snow depth distribution. The project's prototype phase focused on Thompson Pass in south-central Alaska, an important infrastructure corridor that includes avalanche terrain and the Lowe River drainage and is essential to the City of Valdez and the fisheries of Prince William Sound. This year's efforts included website development, expansion of the Mountain Hub tool, and recruitment of citizen scientists through a combination of social media outreach, community presentations, and targeted recruitment of local avalanche professionals. We also conducted two intensive field data collection campaigns that coincided with an aerial photogrammetric survey. With more than 400 snow depth observations, we have generated a new snow remote-sensing product that better matches actual SWE quantities for Thompson Pass. In the next phase of the citizen science portion of this project we will focus on expanding our group of participants to a larger geographic area in Alaska, further develop our partnership with Mountain Hub, and build relationships in new communities as we conduct a photogrammetric survey in a different region next year.

Using Remote Sensing Mapping and Growth Response to Environmental Variability to Aide Aquatic Invasive Plant Management

NASA Technical Reports Server (NTRS)

Bubenheim, David L.; Schlick, Greg; Genovese, Vanessa; Wilson, Kenneth D.

2018-01-01

Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for floating and submerged aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used map (area coverage and biomass density), direct operations, and assess management impacts on plant communities. Archived satellite records enable review of results following previous climate and management events and aide in developing long-term strategies. Examples of remote sensing aiding effectiveness of aquatic weed management will be discussed as well as areas for potential technological improvement. Modeling at local and watershed scales using the SWAT modeling tool provides insight into land-use effects on water quality (described by Zhang in same Symposium). Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), nitrogen, phosphorus, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology. Initial utilization of remote sensing tools developed for mapping of aquatic invasive plants improved operational efficiency in management practices. These assessment methods provide a comprehensive and quantitative view of aquatic invasive plants communities in the California Delta.

Hyperspectral remote sensing and long term monitoring reveal watershed-estuary ecosystem interactions

NASA Astrophysics Data System (ADS)

Hestir, E. L.; Schoellhamer, D. H.; Santos, M. J.; Greenberg, J. A.; Morgan-King, T.; Khanna, S.; Ustin, S.

2016-02-01

Estuarine ecosystems and their biogeochemical processes are extremely vulnerable to climate and environmental changes, and are threatened by sea level rise and upstream activities such as land use/land cover and hydrological changes. Despite the recognized threat to estuaries, most aspects of how change will affect estuaries are not well understood due to the poorly resolved understanding of the complex physical, chemical and biological processes and their interactions in estuarine systems. Remote sensing technologies such as high spectral resolution optical systems enable measurements of key environmental parameters needed to establish baseline conditions and improve modeling efforts. The San Francisco Bay-Delta is a highly modified estuary system in a state of ecological crisis due to the numerous threats to its sustainability. In this study, we used a combination of hyperspectral remote sensing and long-term in situ monitoring records to investigate how water clarity has been responding to extreme climatic events, anthropogenic watershed disturbances, and submerged aquatic vegetation (SAV) invasions. From the long-term turbidity monitoring record, we found that water clarity underwent significant increasing step changes associated with sediment depletion and El Nino-extreme run-off events. Hyperspectral remote sensing data revealed that invasive submerged aquatic pant species have facultative C3 and C4-like photosynthetic pathways that give them a competitive advantage under the changing water clarity conditions of the Bay-Delta system. We postulate that this adaptation facilitated the rapid expansion of SAV following the significant step changes in increasing water clarity caused by watershed disturbances and the 1982-1983 El Nino events. Using SAV maps from hyperspectral remote sensing, we estimate that SAV-water clarity feedbacks were responsible for 20-70% of the increasing water clarity trend in the Bay-Delta. Ongoing and future developments in airborne and global mapping hyperspectral satellite missions will enable full canopy-to-benthos characterization of estuarine ecosystems. When coupled with synoptic watershed measurements, these will improve understanding of watershed-estuary interactions for improved sustainable management.

Developing Remote Sensing Products for Monitoring and Modeling Great Lakes Coastal Wetland Vulnerability to Climate Change and Land Use

NASA Astrophysics Data System (ADS)

Bourgeau-Chavez, L. L.; Miller, M. E.; Battaglia, M.; Banda, E.; Endres, S.; Currie, W. S.; Elgersma, K. J.; French, N. H. F.; Goldberg, D. E.; Hyndman, D. W.

2014-12-01

Spread of invasive plant species in the coastal wetlands of the Great Lakes is degrading wetland habitat, decreasing biodiversity, and decreasing ecosystem services. An understanding of the mechanisms of invasion is crucial to gaining control of this growing threat. To better understand the effects of land use and climatic drivers on the vulnerability of coastal zones to invasion, as well as to develop an understanding of the mechanisms of invasion, research is being conducted that integrates field studies, process-based ecosystem and hydrological models, and remote sensing. Spatial data from remote sensing is needed to parameterize the hydrological model and to test the outputs of the linked models. We will present several new remote sensing products that are providing important physiological, biochemical, and landscape information to parameterize and verify models. This includes a novel hybrid radar-optical technique to delineate stands of invasives, as well as natural wetland cover types; using radar to map seasonally inundated areas not hydrologically connected; and developing new algorithms to estimate leaf area index (LAI) using Landsat. A coastal map delineating wetland types including monocultures of the invaders (Typha spp. and Phragmites austrailis) was created using satellite radar (ALOS PALSAR, 20 m resolution) and optical data (Landsat 5, 30 m resolution) fusion from multiple dates in a Random Forests classifier. These maps provide verification of the integrated model showing areas at high risk of invasion. For parameterizing the hydrological model, maps of seasonal wetness are being developed using spring (wet) imagery and differencing that with summer (dry) imagery to detect the seasonally wet areas. Finally, development of LAI remote sensing high resolution algorithms for uplands and wetlands is underway. LAI algorithms for wetlands have not been previously developed due to the difficulty of a water background. These products are being used to improve the hydrological model through higher resolution products and parameterization of variables that have previously been largely unknown.

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system.

PubMed

Seinfeld, John H; Bretherton, Christopher; Carslaw, Kenneth S; Coe, Hugh; DeMott, Paul J; Dunlea, Edward J; Feingold, Graham; Ghan, Steven; Guenther, Alex B; Kahn, Ralph; Kraucunas, Ian; Kreidenweis, Sonia M; Molina, Mario J; Nenes, Athanasios; Penner, Joyce E; Prather, Kimberly A; Ramanathan, V; Ramaswamy, Venkatachalam; Rasch, Philip J; Ravishankara, A R; Rosenfeld, Daniel; Stephens, Graeme; Wood, Robert

2016-05-24

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

Improving Our Fundamental Understanding of the Role of Aerosol Cloud Interactions in the Climate System

NASA Technical Reports Server (NTRS)

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; Coe, Hugh; DeMott, Paul J.; Dunlea, Edward J.; Feingold, Graham; Ghan, Steven; Guenther, Alex B.; Kahn, Ralph;

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system

DOE PAGES

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; ...

2016-05-24

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from pre-industrial time. General Circulation Models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions but significant challengesmore » exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. Lastly, we suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.« less

Satellites as Sentinels for Climate and Health

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2003-01-01

Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring, risk mapping, and surveillance of parameters useful to such problems as vector- borne and infectious diseases, air and water quality,. harmful algal blooms, W radiation, contaminant and pathogen transport in air and water, and thermal stress. Remote sensing, geographic information systems (GIs), global positioning systems (GPS), improved computation capabilities, and interdisciplinary research between the Earth and health science communities, together with local knowledge, are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global climate and health issues. These collaborative efforts are enabling increased understanding of the relationships among changes in temperature, rainfall, wind, soil moisture, solar radiation, vegetation, and the patterns of extreme weather events and health issues. This increased understanding and improved information and data sharing, in turn, empowers local health and environmental decision-makers to better predict climate-related health problems, decrease vulnerability, take preventive measures, and improve response actions. This paper provides a number of recent examples of how satellites - from their unique vantage point in space - can serve as sentinels for climate and health.

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

PubMed Central

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; Coe, Hugh; DeMott, Paul J.; Dunlea, Edward J.; Feingold, Graham; Ghan, Steven; Guenther, Alex B.; Kraucunas, Ian; Molina, Mario J.; Nenes, Athanasios; Penner, Joyce E.; Prather, Kimberly A.; Ramanathan, V.; Ramaswamy, Venkatachalam; Rasch, Philip J.; Ravishankara, A. R.; Rosenfeld, Daniel; Stephens, Graeme; Wood, Robert

2016-01-01

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty. PMID:27222566

Evaluation of Landscape Structure Using AVIRIS Quicklooks and Ancillary Data

NASA Technical Reports Server (NTRS)

Sanderson, Eric W.; Ustin, Susan L.

1998-01-01

Currently the best tool for examining landscape structure is remote sensing, because remotely sensed data provide complete and repeatable coverage over landscapes in many climatic regimes. Many sensors, with a variety of spatial scales and temporal repeat cycles, are available. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has imaged over 4000 scenes from over 100 different sites throughout North America. For each of these scenes, one-band "quicklook" images have been produced for review by AVIRIS investigators. These quicklooks are free, publicly available over the Internet, and provide the most complete set of landscape structure data yet produced. This paper describes the methodologies used to evaluate the landscape structure of quicklooks and generate corresponding datasets for climate, topography and land use. A brief discussion of preliminary results is included at the end. Since quicklooks correspond exactly to their parent AVIRIS scenes, the methods used to derive climate, topography and land use data should be applicable to any AVIRIS analysis.

Integrating Remote Sensing, Field Observations, and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cohen, Warren

2014-07-03

As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 inmore » the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance.« less

Integrating Remote Sensing, Field Observations, and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U.S., Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beverly E. Law

2011-10-05

As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 inmore » the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance.« less

Improving evaluation of climate change impacts on the water cycle by remote sensing ET-retrieval

NASA Astrophysics Data System (ADS)

García Galiano, S. G.; Olmos Giménez, P.; Ángel Martínez Pérez, J.; Diego Giraldo Osorio, J.

2015-05-01

Population growth and intense consumptive water uses are generating pressures on water resources in the southeast of Spain. Improving the knowledge of the climate change impacts on water cycle processes at the basin scale is a step to building adaptive capacity. In this work, regional climate model (RCM) ensembles are considered as an input to the hydrological model, for improving the reliability of hydroclimatic projections. To build the RCMs ensembles, the work focuses on probability density function (PDF)-based evaluation of the ability of RCMs to simulate of rainfall and temperature at the basin scale. To improve the spatial calibration of the continuous hydrological model used, an algorithm for remote sensing actual evapotranspiration (AET) retrieval was applied. From the results, a clear decrease in runoff is expected for 2050 in the headwater basin studied. The plausible future scenario of water shortage will produce negative impacts on the regional economy, where the main activity is irrigated agriculture.

Portable Laser Spectrometer for Airborne and Ground-Based Remote Sensing of Geological CO2 Emissions

NASA Technical Reports Server (NTRS)

Queisser, Manuel; Burton, Mike; Allan, Graham R.; Chiarugi, Antonio

2017-01-01

A 24 kilogram, suitcase-sized, CW (Continuous Wave) Laser Remote Sensing Spectrometer (LARSS) with an approximately 2-kilometer range has been developed. It has demonstrated its flexibility in measuring both atmospheric CO2 from an airborne platform and terrestrial emission of CO2 from a remote mud volcano, Bledug Kuwu, Indonesia, from a ground-based sight. This system scans the CO2 absorption line with 20 discrete wavelengths, as opposed to the typical two-wavelength online-offline instrument. This multi-wavelength approach offers an effective quality control, bias control, and confidence estimate of measured CO2 concentrations via spectral fitting. The simplicity, ruggedness, and flexibility in the design allow for easy transportation and use on different platforms with a quick setup in some of the most challenging climatic conditions. While more refinement is needed, the results represent a stepping stone towards widespread use of active one-sided gas remote sensing in the earth sciences.

Portable laser spectrometer for airborne and ground-based remote sensing of geological CO₂ emissions.

PubMed

Queisser, Manuel; Burton, Mike; Allan, Graham R; Chiarugi, Antonio

2017-07-15

A 24 kg, suitcase sized, CW laser remote sensing spectrometer (LARSS) with a ~2 km range has been developed. It has demonstrated its flexibility in measuring both atmospheric CO₂ from an airborne platform and terrestrial emission of CO₂ from a remote mud volcano, Bledug Kuwu, Indonesia, from a ground-based sight. This system scans the CO₂ absorption line with 20 discrete wavelengths, as opposed to the typical two-wavelength online offline instrument. This multi-wavelength approach offers an effective quality control, bias control, and confidence estimate of measured CO₂ concentrations via spectral fitting. The simplicity, ruggedness, and flexibility in the design allow for easy transportation and use on different platforms with a quick setup in some of the most challenging climatic conditions. While more refinement is needed, the results represent a stepping stone towards widespread use of active one-sided gas remote sensing in the earth sciences.

Evaluating Coupled Human-Hydrologic Systems in High Altitude Regions: A Case Study of the Arun Watershed, Eastern Nepal

NASA Astrophysics Data System (ADS)

Voss, K.; Bookhagen, B.; Tague, C.; Lopez-Carr, D.

2014-12-01

The Himalaya exhibit dynamic ecological, hydrological, and climatic extremes that magnify the variability and extent of natural hazards, resulting in destruction to both physical and human landscapes. Coupled with poverty, these factors intensify local communities' vulnerability to climate change. This study highlights the Arun watershed in eastern Nepal as a case study to evaluate how local communities in high altitude regions are managing their water for domestic and agricultural needs while coping with extreme events, such as floods and landslides. Remotely-sensed precipitation, snowpack and glacial extent data from the past decade are combined with preliminary results from extensive field-based community surveys in the Arun watershed. The analysis of remotely-sensed data will describe seasonal trends in water availability, glacial lake growth, and the spatial variation of these trends within the basin. These hydrologic changes will be linked to the human survey analysis, which will provide an understanding of locals' perceptions of water challenges and the current water management strategies within the basin. Particular attention will be given to a comparison between the eastern and western tributaries of the Arun River, where the catchments are mainly rain-fed (eastern) versus glacial-fed (western). This contrast will highlight how different hydrologic scenarios evidenced from remote-sensing data motivate diverse human water management responses as defined in field surveys. A particular focus will be given to management decisions related to agriculture expansion and hydropower development. This synthesis of remote-sensing and social research methodologies provides a valuable perspective on coupled human-hydrologic systems.

Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

NASA Astrophysics Data System (ADS)

Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

2016-12-01

satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

MISR ARCTAS Products

Atmospheric Science Data Center

2016-11-25

... Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport) campaigns for the International Polar Year, is aimed at learning more about the state of the Arctic atmosphere and reporting on the changes that have occurred ...

Remote sensing applications of wildland fire and air quality in China

Treesearch

John J. Qu; Xianjun Hao; Yongqiang Liu; Allen R. Riebau; Haoruo Yi; Xianlin Qin

2009-01-01

As one of the most populous and geographically largest countries, China faces many problems including industrial growth, economic sustainability, food security, climate change, and air pollution. Interwoven with these challenges,...

Identifying Spatiotemporal Changes In Irrigated Area Across Southwestern Michigan, USA, Using Remote Sensing and Climate Data

NASA Astrophysics Data System (ADS)

Xu, T.; Deines, J. M.; Kendall, A. D.; Hyndman, D. W.

2017-12-01

Irrigation, which has become more common in humid regions, is the largest consumptive water use across the US and the globe. In southwestern Michigan, there has been a dramatic expansion in irrigation water use for row crops (primarily corn and soybean) in the past decade, mostly from groundwater pumping. The rapid expansion of irrigated row crops has potentially profound implications for terrestrial water balances, food production, and local to regional climate. Detailed maps of spatio-temporal changes in irrigation are essential to better understand irrigation impacts. However, accurate monitoring of irrigation area can be difficult in humid regions using remotely sensed methods due to the similarity in greenness between non-irrigated and irrigated areas in most years. Here, we use remote sensing to create annual, 30m-resolution maps of irrigated cropland by integrating Landsat and MODIS satellite products along with the PRISM climate dataset. From these data we developed spatial time series of vegetation and extreme weather indices, including novel indices we developed specifically to maximize detection of irrigation. Using these input data, machine learning classification was then performed over the region to identify irrigated crop area for each year. The resulting annual irrigation maps suggest that total irrigated area in southwestern Michigan increased by 160% from 2000 to 2017. The accuracy of the maps is assessed relative to maps created for an arid region using the same method. The maps can be integrated into hydrologic models to quantify irrigation impacts and support water resources management.

Inroads of remote sensing into hydrologic science during the WRR era

NASA Astrophysics Data System (ADS)

Lettenmaier, Dennis P.; Alsdorf, Doug; Dozier, Jeff; Huffman, George J.; Pan, Ming; Wood, Eric F.

2015-09-01

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community.

Remote sensing aides studies of climate and wildlife in the Arctic-on land, at sea, and in the air (Invited)

NASA Astrophysics Data System (ADS)

Douglas, D. C.; Durner, G. M.; Gill, R. E.; Griffith, B.; Schmutz, J. A.

2013-12-01

Every day a variety of remote sensing technologies collects large volumes of data that are supporting new analyses and new interpretations about how weather and climate influence the status and distribution of wildlife populations worldwide. Understanding how climate presently affects wildlife is crucial for projecting how climate change could affect wildlife in the future. This talk highlights climate-related wildlife studies by the US Geological Survey in the Arctic. The Arctic is experiencing some of the most pronounced climate changes on earth, raising concerns for species that have evolved seasonal migration strategies tuned to habitat availability and quality. On land, large herbivores such as caribou select concentrated calving areas with high abundance of rapidly growing vegetation and calf survival increases with earlier green-up and with the quantity of food available to cows at peak lactation. Geese time their migrations and reproductive efforts to coincide with optimal plant phenology and peak nutrient availability and departures from this synchrony can influence the survival of goslings. At sea, the habitats of polar bears and other sea-ice-dependent species have dramatically changed over just the past two decades. The ice pack is comprised of younger ice that melts much more extensively during summer-a trend projected to continue by all general circulation models under all but the most aggressive greenhouse gas mitigation scenarios. Studies show that by mid-century optimal polar bear habitats will be so reduced that the species may become extirpated from some regions of the Arctic. In the air, a variety of shorebird species make non-stop endurance flights between northern and southern hemispheres. The bar-tailed godwit undertakes a trans-Pacific flight between Alaska and Australasia that lasts more than seven days and spans more than 10,000 km. Studies show that godwits time their flights to coincide with favorable wind conditions, but stochastic weather events en route can impose energetic costs that affect reproduction and possibly survival. As more is learned about how climate and climate change affect species and ecosystems, better adaptive management decisions can be made. Remote sensing will continue to play an essential role.

Remote sensing of Essential Biodiversity Variables: new measurements linking ecosystem structure, function and composition

NASA Astrophysics Data System (ADS)

Schimel, D.; Pavlick, R.; Stavros, E. N.; Townsend, P. A.; Ustin, S.; Thompson, D. R.

2017-12-01

Remote sensing can inform a wide variety of essential biodiversity variables, including measurements that define primary productivity, forest structure, biome distribution, plant communities, land use-land cover change and climate drivers of change. Emerging remote sensing technologies can add significantly to remote sensing of EBVs, providing new, large scale insights on plant and habitat diversity itself, as well as causes and consequences of biodiversity change. All current biodiversity assessments identify major data gaps, with insufficient coverage in critical regions, limited observations to monitor change over time, with very limited revisit of sample locations, as well as taxon-specific biased biases. Remote sensing cannot fill many of the gaps in global biodiversity observations, but spectroscopic measurements in terrestrial and marine environments can aid in assessing plant/phytoplankton functional diversity and efficiently reveal patterns in space, as well as changes over time, and, by making use of chlorophyll fluorescence, reveal associated patterns in photosynthesis. LIDAR and RADAR measurements quantify ecosystem structure, and can precisely define changes due to growth, disturbance and land use. Current satellite-based EBVs have taken advantage of the extraordinary time series from LANDSAT and MODIS, but new measurements more directly reveal ecosystem structure, function and composition. We will present results from pre-space airborne studies showing the synergistic ability of a suite of new remote observation techniques to quantify biodiversity and ecosystem function and show how it changes during major disturbance events.

The added value of remote sensing products in constraining hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Almeida, Susana; Pechlivanidis, Ilias; Capell, René; Gustafsson, David; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; Sleziak, Patrik; Parajka, Juraj; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model's ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.

NASA/GSFC Research Activities for the Global Ocean Carbon Cycle: A Prospectus for the 21st Century

NASA Technical Reports Server (NTRS)

Gregg, W. W.; Behrenfield, M. J.; Hoge, F. E.; Esaias, W. E.; Huang, N. E.; Long, S. R.; McClain, C. R.

2000-01-01

There are increasing concerns that anthropogenic inputs of carbon dioxide into the Earth system have the potential for climate change. In response to these concerns, the GSFC Laboratory for Hydrospheric Processes has formed the Ocean Carbon Science Team (OCST) to contribute to greater understanding of the global ocean carbon cycle. The overall goals of the OCST are to: 1) detect changes in biological components of the ocean carbon cycle through remote sensing of biooptical properties, 2) refine understanding of ocean carbon uptake and sequestration through application of basic research results, new satellite algorithms, and improved model parameterizations, 3) develop and implement new sensors providing critical missing environmental information related to the oceanic carbon cycle and the flux of CO2 across the air-sea interface. The specific objectives of the OCST are to: 1) establish a 20-year time series of ocean color, 2) develop new remote sensing technologies, 3) validate ocean remote sensing observations, 4) conduct ocean carbon cycle scientific investigations directly related to remote sensing data, emphasizing physiological, empirical and coupled physical/biological models, satellite algorithm development and improvement, and analysis of satellite data sets. These research and mission objectives are intended to improve our understanding of global ocean carbon cycling and contribute to national goals by maximizing the use of remote sensing data.

Teaching global and local environmental change through Remote Sensing

NASA Astrophysics Data System (ADS)

Mauri, Emanuela Paola; Rossi, Giovanni

2013-04-01

Human beings perceive the world primarily through their sense of sight. This can explain why the use of images is so important and common in educational materials, in particular for scientific subjects. The development of modern technologies for visualizing the scientific features of the Earth has provided new opportunities for communicating the increasing complexity of science both to the public and in school education. In particular, the use of Earth observation satellites for civil purposes, which started in the 70s, has opened new perspectives in the study of natural phenomena and human impact on the environment; this is particularly relevant for those processes developing on a long term period and on a global scale. Instruments for Remote Sensing increase the power of human sight, giving access to additional information about the physical world, which the human eye could not otherwise perceive. The possibility to observe from a remote perspective significant processes like climate change, ozone depletion, desertification, urban development, makes it possible for observers to better appreciate and experience the complexity of environment. Remote Sensing reveals the impact of human activities on ecosystems: this allows students to understand important concepts like global and local change in much more depth. This poster describes the role and effectiveness of Remote Sensing imagery in scientific education, and its importance towards a better global environmental awareness.

Canadian SAR remote sensing for the Terrestrial Wetland Global Change Research Network (TWGCRN)

USGS Publications Warehouse

Kaya, Shannon; Brisco, Brian; Cull, Andrew; Gallant, Alisa L.; Sadinski, Walter J.; Thompson, Dean

2010-01-01

The Canada Centre for Remote Sensing (CCRS) has more than 30 years of experience investigating the use of SAR remote sensing for many applications related to terrestrial water resources. Recently, CCRS scientists began contributing to the Terrestrial Wetland Global Change Research Network (TWGCRN), a bi-national research network dedicated to assessing impacts of global change on interconnected wetland-upland landscapes across a vital portion of North America. CCRS scientists are applying SAR remote sensing to characterize wetland components of these landscapes in three ways. First, they are using a comprehensive set of RADARSAT-2 SAR data collected during April to September 2009 to extract multi-temporal surface water information for key TWGCRN study landscapes in North America. Second, they are analyzing polarimetric RADARSAT-2 data to determine areas where double-bounce represents the primary scattering mechanism and is indicative of flooded vegetation in these landscapes. Third, they are testing advanced interferometric SAR techniques to estimate water levels with RADARSAT-2 Fine Quad polarimetric image pairs. The combined information from these three SAR analysis activities will provide TWGCRN scientists with an integrated view and monitoring capability for these dynamic wetland-upland landscapes. These data are being used in conjunction with other remote sensing and field data to study interactions between landscape and animal (birds and amphibians) responses to climate/global change.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

Remote sensing of snow using bistatic radar reflectometry

NASA Astrophysics Data System (ADS)

Komanduru, Abi

Snow and ice processes are a critical part of the Earth's hydrological and climate cycles. These processes can serve as an important source of fresh water as well as a cause of flooding. Various missions have been proposed by NASA and ESA for the purpose of remote sensing of snow. This research looks at applying bistatic radar reflectometry to the remote sensing of snow water equivalent. The resulting phase offset from changes in optical path length due to reflection through snow are the primary measurements made. The research uses data from a field campaign in Fraser, CO, involving an instrument collecting direct and reflected from S band during Jan 2015 - Apr 2015. Phase measurements from the field data are made from the two signals and compared to theoretical phase computed from a forward model using in situ data. A moderate correlation (>0.6) is found between the measured and modeled phase.

Characterizing water resources of the Nile Basin using remotely sensed data

NASA Astrophysics Data System (ADS)

Mekonnen, Z. T.; Gebremichael, M.; Demissie, S. S.

2015-12-01

The Nile is one of the largest river basin in the world with a rich biodiversity as well supporting the lives of 450 million people residing within the 11 riparian countries. This vital resource is under a growing stress due to population growth, rapid development and climate change. In this work, we explore the use of the latest various remote sensing products to capture the water resource of the basin: rainfall from GPM and TRMM, soil moisture from SMAP and SMOS, evapotranspiration from MODIS and EUMETSAT LSA-SAF, and total water storage variations from GRACE. The satellite estimates were supplemented and checked by ground measurements whenever possible. Our results show that spatiotemporal variations of the basin's water resources characteristics are well captured by remote sensing products rather than the scarce point measurements that currently exist. Several aspects of our results will be presented and discussed.

Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate

NASA Astrophysics Data System (ADS)

Shidiq, I. P. A.; Ismail, M. H.; Kamarudin, N.

2014-02-01

The preservation and sustainable management of forest and other land cover ecosystems such as rubber trees will help addressing two major recent issues: climate change and bio-resource energy. The rubber trees are dominantly distributed in the Negeri Sembilan and Kedah on the west coast side of Peninsular Malaysia. This study is aimed to analyse the spatial distribution and biomass of rubber trees in Peninsular Malaysia with special emphasis in Negeri Sembilan State. Geospatial data from remote sensors are used to tackle the time and labour consuming problem due to the large spatial coverage and the need of continuous temporal data. Remote sensing imagery used in this study is a Landsat 5 TM. The image from optical sensor was used to sense the rubber trees and further classified rubber tree by different age.

The Scientific and Societal Need for Accurate Global Remote Sensing of Marine Suspended Sediments

NASA Technical Reports Server (NTRS)

Acker, James G.

2006-01-01

Population pressure, commercial development, and climate change are expected to cause continuing alteration of the vital oceanic coastal zone environment. These pressures will influence both the geology and biology of the littoral, nearshore, and continental shelf regions. A pressing need for global observation of coastal change processes is an accurate remotely-sensed data product for marine suspended sediments. The concentration, delivery, transport, and deposition of sediments is strongly relevant to coastal primary production, inland and coastal hydrology, coastal erosion, and loss of fragile wetland and island habitats. Sediment transport and deposition is also related to anthropogenic activities including agriculture, fisheries, aquaculture, harbor and port commerce, and military operations. Because accurate estimation of marine suspended sediment concentrations requires advanced ocean optical analysis, a focused collaborative program of algorithm development and assessment is recommended, following the successful experience of data refinement for remotely-sensed global ocean chlorophyll concentrations.

Bringing an ecological view of change to Landsat-based remote sensing

USGS Publications Warehouse

Kennedy, Robert E.; Andrefouet, Serge; Cohen, Warren; Gomez, Cristina; Griffiths, Patrick; Hais, Martin; Healey, Sean; Helmer, Eileen H.; Hostert, Patrick; Lyons, Mitchell; Meigs, Garrett; Pflugmacher, Dirk; Phinn, Stuart; Powell, Scott; Scarth, Peter; Susmita, Sen; Schroeder, Todd A.; Schneider, Annemarie; Sonnenschein, Ruth; Vogelmann, James; Wulder, Michael A.; Zhu, Zhe

2014-01-01

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Convergence of microclimate in residential landscapes across diverse cities in the United States

Treesearch

Sharon J. Hall; J. Learned; B. Ruddell; K.L. Larson; J. Cavender-Bares; N. Bettez; P.M. Groffman; Morgan Grove; J.B. Heffernan; S.E. Hobbie; J.L. Morse; C. Neill; K.C. Nelson; Jarlath O' Neil-Dunne; L. Ogden; D.E. Pataki; W.D. Pearse; C. Polsky; R. Roy Chowdhury; M.K. Steele; T.L.E. Trammell

2016-01-01

The urban heat island (UHI) is a well-documented pattern of warming in cities relative to rural areas. Most UHI research utilizes remote sensing methods at large scales, or climate sensors in single cities surrounded by standardized land cover. Relatively few studies have explored continental-scale climatic patterns within common urban microenvironments such as...

Direct measurements of meltwater runoff on the Greenland ice sheet surface

NASA Astrophysics Data System (ADS)

Smith, Laurence C.; Yang, Kang; Pitcher, Lincoln H.; Overstreet, Brandon T.; Chu, Vena W.; Rennermalm, Åsa K.; Ryan, Jonathan C.; Cooper, Matthew G.; Gleason, Colin J.; Tedesco, Marco; Jeyaratnam, Jeyavinoth; van As, Dirk; van den Broeke, Michiel R.; van de Berg, Willem Jan; Noël, Brice; Langen, Peter L.; Cullather, Richard I.; Zhao, Bin; Willis, Michael J.; Hubbard, Alun; Box, Jason E.; Jenner, Brittany A.; Behar, Alberto E.

2017-12-01

Meltwater runoff from the Greenland ice sheet surface influences surface mass balance (SMB), ice dynamics, and global sea level rise, but is estimated with climate models and thus difficult to validate. We present a way to measure ice surface runoff directly, from hourly in situ supraglacial river discharge measurements and simultaneous high-resolution satellite/drone remote sensing of upstream fluvial catchment area. A first 72-h trial for a 63.1-km2 moulin-terminating internally drained catchment (IDC) on Greenland's midelevation (1,207–1,381 m above sea level) ablation zone is compared with melt and runoff simulations from HIRHAM5, MAR3.6, RACMO2.3, MERRA-2, and SEB climate/SMB models. Current models cannot reproduce peak discharges or timing of runoff entering moulins but are improved using synthetic unit hydrograph (SUH) theory. Retroactive SUH applications to two older field studies reproduce their findings, signifying that remotely sensed IDC area, shape, and supraglacial river length are useful for predicting delays in peak runoff delivery to moulins. Applying SUH to HIRHAM5, MAR3.6, and RACMO2.3 gridded melt products for 799 surrounding IDCs suggests their terminal moulins receive lower peak discharges, less diurnal variability, and asynchronous runoff timing relative to climate/SMB model output alone. Conversely, large IDCs produce high moulin discharges, even at high elevations where melt rates are low. During this particular field experiment, models overestimated runoff by +21 to +58%, linked to overestimated surface ablation and possible meltwater retention in bare, porous, low-density ice. Direct measurements of ice surface runoff will improve climate/SMB models, and incorporating remotely sensed IDCs will aid coupling of SMB with ice dynamics and subglacial systems.

Direct measurements of meltwater runoff on the Greenland ice sheet surface.

PubMed

Smith, Laurence C; Yang, Kang; Pitcher, Lincoln H; Overstreet, Brandon T; Chu, Vena W; Rennermalm, Åsa K; Ryan, Jonathan C; Cooper, Matthew G; Gleason, Colin J; Tedesco, Marco; Jeyaratnam, Jeyavinoth; van As, Dirk; van den Broeke, Michiel R; van de Berg, Willem Jan; Noël, Brice; Langen, Peter L; Cullather, Richard I; Zhao, Bin; Willis, Michael J; Hubbard, Alun; Box, Jason E; Jenner, Brittany A; Behar, Alberto E

2017-12-12

Meltwater runoff from the Greenland ice sheet surface influences surface mass balance (SMB), ice dynamics, and global sea level rise, but is estimated with climate models and thus difficult to validate. We present a way to measure ice surface runoff directly, from hourly in situ supraglacial river discharge measurements and simultaneous high-resolution satellite/drone remote sensing of upstream fluvial catchment area. A first 72-h trial for a 63.1-km 2 moulin-terminating internally drained catchment (IDC) on Greenland's midelevation (1,207-1,381 m above sea level) ablation zone is compared with melt and runoff simulations from HIRHAM5, MAR3.6, RACMO2.3, MERRA-2, and SEB climate/SMB models. Current models cannot reproduce peak discharges or timing of runoff entering moulins but are improved using synthetic unit hydrograph (SUH) theory. Retroactive SUH applications to two older field studies reproduce their findings, signifying that remotely sensed IDC area, shape, and supraglacial river length are useful for predicting delays in peak runoff delivery to moulins. Applying SUH to HIRHAM5, MAR3.6, and RACMO2.3 gridded melt products for 799 surrounding IDCs suggests their terminal moulins receive lower peak discharges, less diurnal variability, and asynchronous runoff timing relative to climate/SMB model output alone. Conversely, large IDCs produce high moulin discharges, even at high elevations where melt rates are low. During this particular field experiment, models overestimated runoff by +21 to +58%, linked to overestimated surface ablation and possible meltwater retention in bare, porous, low-density ice. Direct measurements of ice surface runoff will improve climate/SMB models, and incorporating remotely sensed IDCs will aid coupling of SMB with ice dynamics and subglacial systems. Copyright © 2017 the Author(s). Published by PNAS.

Direct measurements of meltwater runoff on the Greenland ice sheet surface

PubMed Central

Smith, Laurence C.; Yang, Kang; Pitcher, Lincoln H; Overstreet, Brandon T.; Chu, Vena W.; Rennermalm, Åsa K.; Ryan, Jonathan C.; Cooper, Matthew G.; Gleason, Colin J.; Tedesco, Marco; Jeyaratnam, Jeyavinoth; van As, Dirk; van den Broeke, Michiel R.; van de Berg, Willem Jan; Noël, Brice; Langen, Peter L.; Cullather, Richard I.; Zhao, Bin; Hubbard, Alun; Box, Jason E.; Jenner, Brittany A.; Behar, Alberto E.

2017-01-01

Meltwater runoff from the Greenland ice sheet surface influences surface mass balance (SMB), ice dynamics, and global sea level rise, but is estimated with climate models and thus difficult to validate. We present a way to measure ice surface runoff directly, from hourly in situ supraglacial river discharge measurements and simultaneous high-resolution satellite/drone remote sensing of upstream fluvial catchment area. A first 72-h trial for a 63.1-km2 moulin-terminating internally drained catchment (IDC) on Greenland’s midelevation (1,207–1,381 m above sea level) ablation zone is compared with melt and runoff simulations from HIRHAM5, MAR3.6, RACMO2.3, MERRA-2, and SEB climate/SMB models. Current models cannot reproduce peak discharges or timing of runoff entering moulins but are improved using synthetic unit hydrograph (SUH) theory. Retroactive SUH applications to two older field studies reproduce their findings, signifying that remotely sensed IDC area, shape, and supraglacial river length are useful for predicting delays in peak runoff delivery to moulins. Applying SUH to HIRHAM5, MAR3.6, and RACMO2.3 gridded melt products for 799 surrounding IDCs suggests their terminal moulins receive lower peak discharges, less diurnal variability, and asynchronous runoff timing relative to climate/SMB model output alone. Conversely, large IDCs produce high moulin discharges, even at high elevations where melt rates are low. During this particular field experiment, models overestimated runoff by +21 to +58%, linked to overestimated surface ablation and possible meltwater retention in bare, porous, low-density ice. Direct measurements of ice surface runoff will improve climate/SMB models, and incorporating remotely sensed IDCs will aid coupling of SMB with ice dynamics and subglacial systems. PMID:29208716

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity.

PubMed

Bustamante, Mercedes M C; Roitman, Iris; Aide, T Mitchell; Alencar, Ane; Anderson, Liana O; Aragão, Luiz; Asner, Gregory P; Barlow, Jos; Berenguer, Erika; Chambers, Jeffrey; Costa, Marcos H; Fanin, Thierry; Ferreira, Laerte G; Ferreira, Joice; Keller, Michael; Magnusson, William E; Morales-Barquero, Lucia; Morton, Douglas; Ometto, Jean P H B; Palace, Michael; Peres, Carlos A; Silvério, Divino; Trumbore, Susan; Vieira, Ima C G

2016-01-01

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation. © 2015 John Wiley & Sons Ltd.

Determining Greenland Ice Sheet Accumulation Rates from Radar Remote Sensing

NASA Technical Reports Server (NTRS)

Jezek, Kenneth C.

2001-01-01

An important component of NASA's Program for Arctic Regional Climate Assessment (PARCA) is a mass balance investigation of the Greenland Ice Sheet. The mass balance is calculated by taking the difference between the snow accumulation and the ice discharge of the ice sheet. Uncertainties in this calculation include the snow accumulation rate, which has traditionally been determined by interpolating data from ice core samples taken throughout the ice sheet. The sparse data associated with ice cores, coupled with the high spatial and temporal resolution provided by remote sensing, have motivated scientists to investigate relationships between accumulation rate and microwave observations.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Temporal Forest Change Detection and Forest Health Assessment using Remote Sensing

NASA Astrophysics Data System (ADS)

Ya'acob, Norsuzila; Mohd Azize, Aziean Binti; Anis Mahmon, Nur; Laily Yusof, Azita; Farhana Azmi, Nor; Mustafa, Norfazira

2014-03-01

This paper presents the detection of Angsi and Berembun Reserve Forest change for years 1996 and 2013. Forest is an important part of our ecosystem. The main function is to absorb carbon oxide and produce oxygen in their cycle of photosynthesis to maintain a balance and healthy atmosphere. However, forest changes as time changes. Some changes are necessary as to give way for economic growth. Nevertheless, it is important to monitor forest change so that deforestation and development can be planned and the balance of ecosystem is still preserved. It is important because there are number of unfavorable effects of deforestation that include environmental and economic such as erosion of soil, loss of biodiversity and climate change. The forest change detection can be studied with reference of several satellite images using remote sensing application. Forest change detection is best done with remote sensing due to large and remote study area. The objective of this project is to detect forest change over time and to compare forest health indicated by Normalized Difference Vegetation Index (NDVI) using remote sensing and image processing. The forest under study shows depletion of forest area by 12% and 100% increment of deforestation activities. The NDVI value which is associated with the forest health also shows 13% of reduction.

The role of remotely-sensed evapotranspiration data in watershed water resources management

NASA Astrophysics Data System (ADS)

Shuster, W.; Carroll, M.; Zhang, Y.

2006-12-01

Evapotranspiration (ET) is an important component of the watershed hydrologic cycle and a key factor to consider in water resource planning. Partly due to the loss of evaporation pans from the national network in the 1980s because of budget cuts, ET values are not available in many locations in the US and practitioners often have to rely on the climatically averaged regional estimates instead. Several new approaches have been developed for estimating ET via remote sensing. In this study we employ one established approach that allows us to derive ET estimates on 1 km2 resolution on the basis of AVHRR brightness temperature. By applying this method to southwestern Ohio we obtain ET estimates for a 2 km2 partially suburban watershed near Cincinnati, OH. Along with precipitation and surface discharge measurements, these remotely-sensed ET estimates form the basis for determining both long and short term water budgets for this watershed. These ET estimates are next compared with regional climatic values on a seasonal basis to examine the potential differences that can be introduced to our conceptualization of the watershed processes by considering area- specific ET values. We then discuss implications of this work for more widespread application to watershed management imperatives (e.g., stream ecological health).

Vegetation shifts observed in arctic tundra 17 years after fire

USGS Publications Warehouse

Barrett, Kirsten; Rocha, Adrian V.; van de Weg, Martine Janet; Shaver, Gaius

2012-01-01

With anticipated climate change, tundra fires are expected to occur more frequently in the future, but data on the long-term effects of fire on tundra vegetation composition are scarce. This study addresses changes in vegetation structure that have persisted for 17 years after a tundra fire on the North Slope of Alaska. Fire-related shifts in vegetation composition were assessed from remote-sensing imagery and ground observations of the burn scar and an adjacent control site. Early-season remotely sensed imagery from the burn scar exhibits a low vegetation index compared with the control site, whereas the late-season signal is slightly higher. The range and maximum vegetation index are greater in the burn scar, although the mean annual values do not differ among the sites. Ground observations revealed a greater abundance of moss in the unburned site, which may account for the high early growing season normalized difference vegetation index (NDVI) anomaly relative to the burn. The abundance of graminoid species and an absence of Betula nana in the post-fire tundra sites may also be responsible for the spectral differences observed in the remotely sensed imagery. The partial replacement of tundra by graminoid-dominated ecosystems has been predicted by the ALFRESCO model of disturbance, climate and vegetation succession.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades

USGS Publications Warehouse

Zhu, Qiuan; Peng, Changhui; Liu, Jinxun; Jiang, Hong; Fang, Xiuqin; Chen, Huai; Niu, Zhichun; Gong, Peng; Lin, Guanghui; Wang, Meng; Yang, Yanzheng; Chang, Jie; Ge, Ying; Xiang, Wenhua; Deng, Xiangwen; He, Jin-Sheng

2016-01-01

Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH4) emissions in China is important for improving our knowledge on CH4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH4 model to quantify the human and climate change induced contributions to natural wetland CH4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH4 emissions reduction (0.92 TgCH4), and climate change contributed 20.4% to the CH4 emissions increase (0.31 TgCH4), suggesting that decreasing CH4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH4 emissions estimation.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades

PubMed Central

Zhu, Qiuan; Peng, Changhui; Liu, Jinxun; Jiang, Hong; Fang, Xiuqin; Chen, Huai; Niu, Zhenguo; Gong, Peng; Lin, Guanghui; Wang, Meng; Wang, Han; Yang, Yanzheng; Chang, Jie; Ge, Ying; Xiang, Wenhua; Deng, Xiangwen; He, Jin-Sheng

2016-01-01

Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH4) emissions in China is important for improving our knowledge on CH4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH4 model to quantify the human and climate change induced contributions to natural wetland CH4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH4 emissions reduction (0.92 TgCH4), and climate change contributed 20.4% to the CH4 emissions increase (0.31 TgCH4), suggesting that decreasing CH4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH4 emissions estimation. PMID:27892535

Topographic Signatures in Aquarius Radiometer/Scatterometer Response: Initial Results

NASA Technical Reports Server (NTRS)

Utku, C.; LeVine, D. M.

2012-01-01

The effect of topography on remote sensing at L-band is examined using the co-located Aquarius radiometer and scatterometer observations over land. A correlation with slope standard deviation is demonstrated for both the radiometer and scatterometer at topographic scales. Although the goal of Aquarius is remote sensing of sea surface salinity, the radiometer and scatterometer are on continuously and collect data for remote sensing research over land. Research is reported here using the data over land to determine if topography could have impact on the passive remote sensing at L-band. In this study, we report observations from two study regions: North Africa between 15 deg and 30 deg Northern latitudes and Australia less the Tasmania Island. Common to these two regions are the semi-arid climate and low population density; both favorable conditions to isolate the effect of topography from other sources of scatter and emission such as vegetation and urban areas. Over these study regions, topographic scale slopes within each Aquarius pixel are computed and their standard deviations are compared with Aquarius scatterometer and radiometer observations over a 36 day period between days 275 and 311 of 2011.

A Review of Wetland Remote Sensing.

PubMed

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-04-05

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

A Review of Wetland Remote Sensing

PubMed Central

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-01-01

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers. PMID:28379174

Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

NASA Technical Reports Server (NTRS)

Ransom, K. J.; Sun, G.; Kharuk, V. I.; Howl, J.

2011-01-01

The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas.

The Green Sahara: Climate Change, Hydrologic History and Human Occupation

NASA Technical Reports Server (NTRS)

Blom, Ronald G.; Farr, Tom G.; Feynmann, Joan; Ruzmaikin, Alexander; Paillou, Philippe

2009-01-01

Archaeology can provide insight into interactions of climate change and human activities in sensitive areas such as the Sahara, to the benefit of both disciplines. Such analyses can help set bounds on climate change projections, perhaps identify elements of tipping points, and provide constraints on models. The opportunity exists to more precisely constrain the relationship of natural solar and climate interactions, improving understanding of present and future anthropogenic forcing. We are beginning to explore the relationship of human occupation of the Sahara and long-term solar irradiance variations synergetic with changes in atmospheric-ocean circulation patterns. Archaeological and climate records for the last 12 K years are gaining adequate precision to make such comparisons possible. We employ a range of climate records taken over the globe (e.g. Antarctica, Greenland, Cariaco Basin, West African Ocean cores, records from caves) to identify the timing and spatial patterns affecting Saharan climate to compare with archaeological records. We see correlation in changing ocean temperature patterns approx. contemporaneous with drying of the Sahara approx. 6K years BP. The role of radar images and other remote sensing in this work includes providing a geographically comprehensive geomorphic overview of this key area. Such coverage is becoming available from the Japanese PALSAR radar system, which can guide field work to collect archaeological and climatic data to further constrain the climate change chronology and link to models. Our initial remote sensing efforts concentrate on the Gilf Kebir area of Egypt.

Integrating Indigenous Traditional, Local and Scientific Knowledge for Improved Management, Policy and Decision-Making in Reindeer Husbandry in the Russian Arctic

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.; Yurchak, Boris; Turi, Johan Mathis; Mathiesen, Svein D.; Aissi-Wespi, Rita L.

2004-01-01

As scientists and policy-makers from both indigenous and non-indigenous communities begin to build closer partnerships to address common sustainability issues such as the health impacts of climate change and anthropogenic activities, it becomes increasingly important to create shared information management systems which integrate all relevant factors for optimal information sharing and decision-making. This paper describes a new GIs-based system being designed to bring local and indigenous traditional knowledge together with scientific data and information, remote sensing, and information technologies to address health-related environment, weather, climate, pollution and land use change issues for improved decision/policy-making for reindeer husbandry. The system is building an easily-accessible archive of relevant current and historical, traditional, local and remotely-sensed and other data and observations for shared analysis, measuring, and monitoring parameters of interest. Protection of indigenous culturally sensitive information will be respected through appropriate data protocols. A mechanism which enables easy information sharing among all participants, which is real time and geo-referenced and which allows interconnectivity with remote sites is also being designed into the system for maximum communication among partners. A preliminary version of our system will be described for a Russian reindeer test site, which will include a combination of indigenous knowledge about local conditions and issues, remote sensing and ground-based data on such parameters as the vegetation state and distribution, snow cover, temperature, ice condition, and infrastructure.

A remote sensing tool to monitor and predict epidemiologic outbreaks of Hanta virus infections and Lyme disease

NASA Astrophysics Data System (ADS)

Barrios, M.; Verstraeten, W. W.; Amipour, S.; Wambacq, J.; Aerts, J.-M.; Maes, P.; Berckmans, D.; Lagrou, K.; van Ranst, M.; Coppin, P.

2009-04-01

Lyme disease and Hanta virus infection are the result of the conjunction of several climatic and ecological conditions. Although both affections have different causal agents, they share an important characteristic which is the fact that rodents play an important role in the contagion. One of the most important agents in the dispersion of these diseases is the bank vole (Clethrionomys glareoulus). The bank vole is a common host for both, the Borrelia bacteria which via the ticks (Ixodes ricinus) reaches the human body and causes the Lyme disease, and the Nephropatia epidemica which is caused by Puumala Hantavirus and affects kidneys in humans. The prefered habitat of bank voles is broad-leaf forests with an important presence of beeches (Fagus sylvatica) and oaks (Quercus sp.) and a relatively dense low vegetation layer. These vegetation systems are common in West-Europe and their dynamics have a great influence in the bank voles population and, therefore, in the spreading of the infections this study is concerned about. The fact that the annual seed production is not stable in time has an important effect in bank voles population and, as it has been described in other studies, in the number of reported cases of Hanta virus infections and Lyme disease. The years in which an abundant production of seeds is observed are referred to as mast years which are believed to obey to cyclic patterns and to certain climatologically characteristics of the preceding years. Statistical analysis have confirmed the correlation in the behaviour of the number of infected cases and the presence of mast years. This project aims at the design of a remote sensing based system (INFOPRESS - INFectious disease Outbreak Prediction REmote Sensing based System) that should enable local and national health care instances to predict and locate the occurrence of infection outbreaks and design policies to counteract undesired effects. The predictive capabilities of the system are based on the understanding and modelling of the interactions between relevant climatic parameters (temperature, humidity, precipitation) and the main features of vegetation systems which host the vectors and determine the survival and infectious potential of the causal agents. Among the most important study subjects in this research initiative one can mention the time series analysis of vegetation parameters derived from satellite remote sensing and its relation to climatic time series and historical records of infected cases; with special attention to the assessment of remotely sensed evidences of the mast phenomenon. This analysis will constitute important buildind bricks in the construction of the INFOPRESS system in what concerns the assessment of the potentials of satellite remote sensing as information source for the prediction of infection outbreaks. The bank voles habitat description will also be supported by on-ground remote sensing techniques, specially LiDAR technology and soil humidity modelling. These measurements are to be coupled to bank voles epidemiologic features obtained from field capturing and lab analysis in which the presence of Hanta virus will be assessed.

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large Scale Rift Valley Fever Outbreaks

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Linthicum, Kenneth J.; Small, Jennifer; Britch, S. C.; Tucker, C. J.

2012-01-01

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever are directly influenced by large scale flooding associated with the El Ni o/Southern Oscillation. This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential Rift Valley fever outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

Shared Knowledge for Decision-making on Environment and Health Issues in the Arctic

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2003-01-01

This paper will describe a remote sensing and GIs-based system to bring indigenous traditional knowledge together with contemporary scientific knowledge to address impacts resulting from changes in climate, environment, weather and pollution in the Arctic. As scientists and policy-makers from both indigenous and non-indigenous communities continue to build closer partnerships to address common sustainability issues such as the health impacts of climate change and anthropogenic activities, it becomes increasingly important to create shared information management systems which integrate all relevant factors for optimal information sharing and decision-making. This system is being designed to bring together remotely sensed, indigenous and other data and observations for analysis, measuring, and monitoring parameters of interest (e.g., snow cover, rainfall, temperature, ice conditions, vegetation, infrastructure, fires). A description of the system and its components as well as a preliminary application of the system in the Arctic will be presented.

Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project.

PubMed

Fuentes, Màrius V

2006-11-01

Fasciolosis caused by Fasciola hepatica in various South American countries located on the slopes of the Andes has been recognized as an important public health problem. However, the importance of this zoonotic hepatic parasite was neglected until the last decade. Countries such as Peru and Bolivia are considered to be hyperendemic areas for human and animal fasciolosis, and other countries such as Chile, Ecuador, Colombia and Venezuela are also affected. At the beginning of the 1990s a multidisciplinary project was launched with the aim to shed light on the problems related to this parasitic disease in the Northern Bolivian Altiplano. A few years later, a geographic information system (GIS) was incorporated into this multidisciplinary project analysing the epidemiology of human and animal fasciolosis in this South American Andean region. Various GIS projects were developed in some Andean regions using climatic data, climatic forecast indices and remote sensing data. Step by step, all these GIS projects concerning the forecast of the fasciolosis transmission risk in the Andean mountain range were revised and in some cases updated taking into account new data. The first of these projects was developed on a regional scale for the central Chilean regions and the proposed model was validated on a local scale in the Northern Bolivian Altiplano. This validated mixed model, based on both fasciolosis climatic forecast indices and normalized difference vegetation index values from Advanced Very High Resolution Radiometer satellite sensor, was extrapolated to other human and/or animal endemic areas of Peru and Ecuador. The resulting fasciolosis risk maps make it possible to show the known human endemic areas of, mainly, the Peruvian Altiplano, Cajamarca and Mantaro Peruvian valleys, and some valleys of the Ecuadorian Cotopaxi province. Nevertheless, more climate and remote sensing data, as well as more accurate epidemiological reports, have to be incorporated into these GIS projects, which should be considered the key in understanding fasciolosis transmission in the Andes.

Accessing and Utilizing Remote Sensing Data for Vectorborne Infectious Diseases Surveillance and Modeling

NASA Technical Reports Server (NTRS)

Kiang, Richard; Adimi, Farida; Kempler, Steven

2008-01-01

Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.

Crosscutting Airborne Remote Sensing Technologies for Oil and Gas and Earth Science Applications

NASA Technical Reports Server (NTRS)

Aubrey, A. D.; Frankenberg, C.; Green, R. O.; Eastwood, M. L.; Thompson, D. R.; Thorpe, A. K.

2015-01-01

Airborne imaging spectroscopy has evolved dramatically since the 1980s as a robust remote sensing technique used to generate 2-dimensional maps of surface properties over large spatial areas. Traditional applications for passive airborne imaging spectroscopy include interrogation of surface composition, such as mapping of vegetation diversity and surface geological composition. Two recent applications are particularly relevant to the needs of both the oil and gas as well as government sectors: quantification of surficial hydrocarbon thickness in aquatic environments and mapping atmospheric greenhouse gas components. These techniques provide valuable capabilities for petroleum seepage in addition to detection and quantification of fugitive emissions. New empirical data that provides insight into the source strength of anthropogenic methane will be reviewed, with particular emphasis on the evolving constraints enabled by new methane remote sensing techniques. Contemporary studies attribute high-strength point sources as significantly contributing to the national methane inventory and underscore the need for high performance remote sensing technologies that provide quantitative leak detection. Imaging sensors that map spatial distributions of methane anomalies provide effective techniques to detect, localize, and quantify fugitive leaks. Airborne remote sensing instruments provide the unique combination of high spatial resolution (<1 m) and large coverage required to directly attribute methane emissions to individual emission sources. This capability cannot currently be achieved using spaceborne sensors. In this study, results from recent NASA remote sensing field experiments focused on point-source leak detection, will be highlighted. This includes existing quantitative capabilities for oil and methane using state-of-the-art airborne remote sensing instruments. While these capabilities are of interest to NASA for assessment of environmental impact and global climate change, industry similarly seeks to detect and localize leaks of both oil and methane across operating fields. In some cases, higher sensitivities desired for upstream and downstream applications can only be provided by new airborne remote sensing instruments tailored specifically for a given application. There exists a unique opportunity for alignment of efforts between commercial and government sectors to advance the next generation of instruments to provide more sensitive leak detection capabilities, including those for quantitative source strength determination.

The NASA CYGNSS mission: a pathfinder for GNSS scatterometry remote sensing applications

NASA Astrophysics Data System (ADS)

Rose, Randy; Gleason, Scott; Ruf, Chris

2014-10-01

Global Navigation Satellite System (GNSS) based scatterometry offers breakthrough opportunities for wave, wind, ice, and soil moisture remote sensing. Recent developments in electronics and nano-satellite technologies combined with modeling techniques developed over the past 20 years are enabling a new class of remote sensing capabilities that present more cost effective solutions to existing problems while opening new applications of Earth remote sensing. Key information about the ocean and global climate is hidden from existing space borne observatories because of the frequency band in which they operate. Using GNSS-based bi-static scatterometry performed by a constellation of microsatellites offers remote sensing of ocean wave, wind, and ice data with unprecedented temporal resolution and spatial coverage across the full dynamic range of ocean wind speeds in all precipitating conditions. The NASA Cyclone Global Navigation Satellite System (CYGNSS) is a space borne mission being developed to study tropical cyclone inner core processes. CYGNSS consists of 8 GPS bi-static radar receivers to be deployed on separate micro-satellites in October 2016. CYGNSS will provide data to address what are thought to be the principle deficiencies with current tropical cyclone intensity forecasts: inadequate observations and modeling of the inner core. The inadequacy in observations results from two causes: 1) Much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands. 2) The rapidly evolving (genesis and intensification) stages of the tropical cyclone life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. It is anticipated that numerous additional Earth science applications can also benefit from the cost effective high spatial and temporal sampling capabilities of GNSS remote sensing. These applications include monitoring of rough and dangerous sea states, global observations of sea ice cover and extent, meso-scale ocean circulation studies, and near surface soil moisture observations. This presentation provides a primer for GNSS based scatterometry, an overview of NASA's CYGNSS mission and its expected performance, as well as a summary of possible other GNSS based remote sensing applications.

Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

2017-12-01

Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

Hillslope characterization: Identifying key controls on local-scale plant communities' distribution using remote sensing and subsurface data fusion.

NASA Astrophysics Data System (ADS)

Falco, N.; Wainwright, H. M.; Dafflon, B.; Leger, E.; Peterson, J.; Steltzer, H.; Wilmer, C.; Williams, K. H.; Hubbard, S. S.

2017-12-01

Mountainous watershed systems are characterized by extreme heterogeneity in hydrological and pedological properties that influence biotic activities, plant communities and their dynamics. To gain predictive understanding of how ecosystem and watershed system evolve under climate change, it is critical to capture such heterogeneity and to quantify the effect of key environmental variables such as topography, and soil properties. In this study, we exploit advanced geophysical and remote sensing techniques - coupled with machine learning - to better characterize and quantify the interactions between plant communities' distribution and subsurface properties. First, we have developed a remote sensing data fusion framework based on the random forest (RF) classification algorithm to estimate the spatial distribution of plant communities. The framework allows the integration of both plant spectral and structural information, which are derived from multispectral satellite images and airborne LiDAR data. We then use the RF method to evaluate the estimated plant community map, exploiting the subsurface properties (such as bedrock depth, soil moisture and other properties) and geomorphological parameters (such as slope, curvature) as predictors. Datasets include high-resolution geophysical data (electrical resistivity tomography) and LiDAR digital elevation maps. We demonstrate our approach on a mountain hillslope and meadow within the East River watershed in Colorado, which is considered to be a representative headwater catchment in the Upper Colorado Basin. The obtained results show the existence of co-evolution between above and below-ground processes; in particular, dominant shrub communities in wet and flat areas. We show that successful integration of remote sensing data with geophysical measurements allows identifying and quantifying the key environmental controls on plant communities' distribution, and provides insights into their potential changes in the future climate conditions.

Essential climatic variables estimation with satellite imagery

NASA Astrophysics Data System (ADS)

Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Friedl, Mark A.; Schaaf, Crystal B.

2006-12-01

In the last two decades the availability of global remote sensing data sets has provided a new means of studying global patterns and dynamics in vegetation. The vast majority of previous work in this domain has used data from the Advanced Very High Resolution Radiometer, which until recently was the primary source of global land remote sensing data. In recent years, however, a number of new remote sensing data sources have become available that have significantly improved the capability of remote sensing to monitor global ecosystem dynamics. In this paper, we describe recent results using data from NASA's Moderate Resolution Imaging Spectroradiometer to study global vegetation phenology. Using a novel new method based on fitting piecewise logistic models to time series data from MODIS, key transition dates in the annual cycle(s) of vegetation growth can be estimated in an ecologically realistic fashion. Using this method we have produced global maps of seven phenological metrics at 1-km spatial resolution for all ecosystems exhibiting identifiable annual phenologies. These metrics include the date of year for (1) the onset of greenness increase (greenup), (2) the onset of greenness maximum (maturity), (3) the onset of greenness decrease (senescence), and (4) the onset of greenness minimum (dormancy). The three remaining metrics are the growing season minimum, maximum, and summation of the enhanced vegetation index derived from MODIS. Comparison of vegetation phenology retrieved from MODIS with in situ measurements shows that these metrics provide realistic estimates of the four transition dates identified above. More generally, the spatial distribution of phenological metrics estimated from MODIS data is qualitatively realistic, and exhibits strong correspondence with temperature patterns in mid- and high-latitude climates, with rainfall seasonality in seasonally dry climates, and with cropping patterns in agricultural areas.

Implication of relationship between natural impacts and land use/land cover (LULC) changes of urban area in Mongolia

NASA Astrophysics Data System (ADS)

Gantumur, Byambakhuu; Wu, Falin; Zhao, Yan; Vandansambuu, Battsengel; Dalaibaatar, Enkhjargal; Itiritiphan, Fareda; Shaimurat, Dauryenbyek

2017-10-01

Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.

Strategic positioning of the ERATOSTHENES Research Centre for atmospheric remote sensing research in the Eastern Mediterranean and Middle East region

NASA Astrophysics Data System (ADS)

Mamouri, Rodanthi-Elisavet; Ansmann, Albert; Hadjimitsis, Diofantos G.; Nisantzi, Argyro; Bühl, Johannes; Michaelides, Silas; Seifert, Patric; Engelmann, Ronny; Wandinger, Ulla; Kontoes, Charalampos; Schreier, Gunter; Komodromos, Georgios; Themistocleous, Kyriacos

2017-10-01

The aim of this article is to present the importance of a permanent state-of-the-art atmospheric remote sensing ground based station in the region of the Eastern Mediterranean and Middle East (EMME). The ERATOSTHENES Research Centre (ERC) with the vision to become a Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment (EXCELSIOR H2020: Teaming project) already operates (within Phase 1) a fully established EARLINETt-Cloudnet supersite at Limassol, Cyprus, for a period of 2 years, in close collaboration with the German Leibniz Institute for Tropospheric Research (TROPOS), The scientific aspects of this prototype-like field campaign CyCARE (Cyprus Cloud Aerosol and Rain Experiment) - a common initiative between the Cyprus University of Technology (CUT), Limassol and TROPOS- are presented in this paper. Cy-CARE has been designed by TROPOS and CUT to fill a gap in the understanding of aerosol-cloud interaction in one of the key regions of climate change and how precipitation formation is influenced by varying aerosol/pollution and meteorological conditions The guiding questions are: How may rain patterns change in future and what may be the consequences of climate change in arid regions such as EMME. EXCELSIOR is a team effort between CUT (acting as the coordinator), the German Aerospace Centre (DLR), the Institute for Astronomy and Astrophysics Space Applications and Remote Sensing of the National Observatory of Athens (NOA), TROPOS and the Cyprus Department of Electronic Communications of the Ministry of Transport, Communications and Works (DEC-MTCW) who will work together to improve the network structures significantly, resulting in Cyprus being regarded as a cornerstone of a European Network of active remote sensing of the atmosphere.

Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing.

PubMed

Duncan, John M A; Dash, Jadunandan; Atkinson, Peter M

2015-04-01

Remote sensing-derived wheat crop yield-climate models were developed to highlight the impact of temperature variation during thermo-sensitive periods (anthesis and grain-filling; TSP) of wheat crop development. Specific questions addressed are: can the impact of temperature variation occurring during the TSP on wheat crop yield be detected using remote sensing data and what is the impact? Do crop critical temperature thresholds during TSP exist in real world cropping landscapes? These questions are tested in one of the world's major wheat breadbaskets of Punjab and Haryana, north-west India. Warming average minimum temperatures during the TSP had a greater negative impact on wheat crop yield than warming maximum temperatures. Warming minimum and maximum temperatures during the TSP explain a greater amount of variation in wheat crop yield than average growing season temperature. In complex real world cereal croplands there was a variable yield response to critical temperature threshold exceedance, specifically a more pronounced negative impact on wheat yield with increased warming events above 35 °C. The negative impact of warming increases with a later start-of-season suggesting earlier sowing can reduce wheat crop exposure harmful temperatures. However, even earlier sown wheat experienced temperature-induced yield losses, which, when viewed in the context of projected warming up to 2100 indicates adaptive responses should focus on increasing wheat tolerance to heat. This study shows it is possible to capture the impacts of temperature variation during the TSP on wheat crop yield in real world cropping landscapes using remote sensing data; this has important implications for monitoring the impact of climate change, variation and heat extremes on wheat croplands. © 2014 John Wiley & Sons Ltd.

FLUXNET to MODIS: Connecting the dots to capture heterogenious biosphere metabolism

NASA Astrophysics Data System (ADS)

Woods, K. D.; Schwalm, C.; Huntzinger, D. N.; Massey, R.; Poulter, B.; Kolb, T.

2015-12-01

Eddy co-variance flux towers provide our most widely distributed network of direct observations for land-atmosphere carbon exchange. Carbon flux sensitivity analysis is a method that uses in situ networks to understand how ecosystems respond to changes in climatic variables. Flux towers concurrently observe key ecosystem metabolic processes (e..g. gross primary productivity) and micrometeorological variation, but only over small footprints. Remotely sensed vegetation indices from MODIS offer continuous observations of the vegetated land surface, but are less direct, as they are based on light use efficiency algorithms, and not on the ground observations. The marriage of these two data products offers an opportunity to validate remotely sensed indices with in situ observations and translate information derived from tower sites to globally gridded products. Here we provide correlations between Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and MODIS gross primary production with FLUXNET derived estimates of gross primary production, respiration and net ecosystem exchange. We demonstrate remotely sensed vegetation products which have been transformed to gridded estimates of terrestrial biosphere metabolism on a regional-to-global scale. We demonstrate anomalies in gross primary production, respiration, and net ecosystem exchange as predicted by both MODIS-carbon flux sensitivities and meteorological driver-carbon flux sensitivities. We apply these sensitivities to recent extreme climatic events and demonstrate both our ability to capture changes in biosphere metabolism, and differences in the calculation of carbon flux anomalies based on method. The quantification of co-variation in these two methods of observation is important as it informs both how remotely sensed vegetation indices are correlated with on the ground tower observations, and with what certainty we can expand these observations and relationships.

Disturbance and climate effects on carbon stocks and fluxes across western Oregon USA.

Treesearch

B.E. Law; D. Turner; J. Campbell; O.J. Sun; S. Van Tuyl; W.D. Ritts; W.B. Cohen

2004-01-01

We used a spatially nested hierarchy of field and remote-sensing observations and a process model, Biome-BGC, to produce a carbon budget for the forested region of Oregon, and to determine the relative influence of differences in climate and disturbance among the ecoregions on carbon stocks and fluxes. The simulations suggest that annual net uptake (net ecosystem...

A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Cressie, N.; Teixeira, J.

2010-12-01

Like other scientific and engineering problems that involve physical modeling of complex systems, climate models can be evaluated and diagnosed by comparing their output to observations of similar quantities. Though the global remote sensing data record is relatively short by climate research standards, these data offer opportunities to evaluate model predictions in new ways. For example, remote sensing data are spatially and temporally dense enough to provide distributional information that goes beyond simple moments to allow quantification of temporal and spatial dependence structures. In this talk, we propose a new method for exploiting these rich data sets using a Bayesian paradigm. For a collection of climate models, we calculate posterior probabilities its members best represent the physical system each seeks to reproduce. The posterior probability is based on the likelihood that a chosen summary statistic, computed from observations, would be obtained when the model's output is considered as a realization from a stochastic process. By exploring how posterior probabilities change with different statistics, we may paint a more quantitative and complete picture of the strengths and weaknesses of the models relative to the observations. We demonstrate our method using model output from the CMIP archive, and observations from NASA's Atmospheric Infrared Sounder.

The Increasing Use of Remote Sensing Data in Studying the Climatological Impacts on Public Health

NASA Astrophysics Data System (ADS)

Kempler, S.; Benedict, K. K.; Ceccato, P.; Golden, M.; Maxwell, S.; Morain, S.; Soebiyanto, R.; Tong, D.

2011-12-01

One of the most fortunate outcomes of the capture and transformation of remote sensing data into applied information is their usefulness and impacts to better understanding climatological impacts on public health. Today, with petabytes of remote sensing data providing global coverage of climatological parameters, public health research and policy decision makers have an unprecedented (and growing) data record that relates the effects of climatic parameters, such as rainfall, heat, soil moisture, etc. to incidences and spread of disease, as well as predictive modeling. In addition, tools and services that specifically serve public health researchers and respondents have grown in response to the needs of the these information users. This presentation provides: A perspective of the use of remote sensing data in public health research; NASA funded systems developed to facilitate specific public health decision and public support services, and: Insights on remote sensing data and information services that are available for public health studies and decision making. After providing a review of the use of remote sensing data, the following specific services will be discussed: - Rainfall, Vegetation and Water Bodies Monitoring for Malaria Surveillance - Heat Evaluation and Assessment - Multi-resolution Nested Dust Forecast - Socioeconomic Data and Application Center (SEDAC) Health Related Data and Services - Goddard Earth Sciences Data and Information Services Center (GES DISC) Health Related Data and Services The purpose of this presentation is to provide a (strong) flavor of the data and information services available to public health research and decision making, to invoke new ways of thinking about how public health work can be accomplished, and stimulate new ideas on how information services can be further utilized.

Predicting Near-Term Water Quality from Satellite Observations of Watershed Conditions

NASA Astrophysics Data System (ADS)

Weiss, W. J.; Wang, L.; Hoffman, K.; West, D.; Mehta, A. V.; Lee, C.

2017-12-01

Despite the strong influence of watershed conditions on source water quality, most water utilities and water resource agencies do not currently have the capability to monitor watershed sources of contamination with great temporal or spatial detail. Typically, knowledge of source water quality is limited to periodic grab sampling; automated monitoring of a limited number of parameters at a few select locations; and/or monitoring relevant constituents at a treatment plant intake. While important, such observations are not sufficient to inform proactive watershed or source water management at a monthly or seasonal scale. Satellite remote sensing data on the other hand can provide a snapshot of an entire watershed at regular, sub-monthly intervals, helping analysts characterize watershed conditions and identify trends that could signal changes in source water quality. Accordingly, the authors are investigating correlations between satellite remote sensing observations of watersheds and source water quality, at a variety of spatial and temporal scales and lags. While correlations between remote sensing observations and direct in situ measurements of water quality have been well described in the literature, there are few studies that link remote sensing observations across a watershed with near-term predictions of water quality. In this presentation, the authors will describe results of statistical analyses and discuss how these results are being used to inform development of a desktop decision support tool to support predictive application of remote sensing data. Predictor variables under evaluation include parameters that describe vegetative conditions; parameters that describe climate/weather conditions; and non-remote sensing, in situ measurements. Water quality parameters under investigation include nitrogen, phosphorus, organic carbon, chlorophyll-a, and turbidity.

An evaluation of uncertainty in the aerosol optical properties as represented by satellites and an ensemble of chemistry-climate coupled models over Europe

NASA Astrophysics Data System (ADS)

Palacios-Peña, Laura; Baró, Rocío; Jiménez-Guerrero, Pedro

2016-04-01

The changes in Earth's climate are produced by forcing agents such as greenhouse gases, clouds and atmospheric aerosols. The latter modify the Earth's radiative budget due to their optical, microphysical and chemical properties, and are considered to be the most uncertain forcing agent. There are two main approaches to the study of aerosols: (1) ground-based and remote sensing observations and (2) atmospheric modelling. With the aim of characterizing the uncertainties associated with these approaches, and estimating the radiative forcing caused by aerosols, the main objective of this work is to assess the representation of aerosol optical properties by different remote sensing sensors and online-coupled chemistry-climate models and to determine whether the inclusion of aerosol radiative feedbacks in this type of models improves the modelling outputs over Europe. Two case studies have been selected under the framework of the EuMetChem COST Action ES1004, when important aerosol episodes during 2010 over Europe took place: a Russian wildfires episode and a Saharan desert dust outbreak covering most of Europe. Model data comes from an ensemble of regional air quality-climate simulations performed by the working group 2 of EuMetChem, that investigates the importance of different processes and feedbacks in on-line coupled chemistry-climate models. These simulations are run for three different configurations for each model, differing in the inclusion (or not) of aerosol-radiation and aerosol-cloud interactions. The remote sensing data comes from three different sensors, MODIS (Moderate Resolution Imaging Spectroradiometer), OMI (Ozone Monitoring Instrument) and SeaWIFS (Sea-viewing Wide Field-of-view Sensor). The evaluation has been performed by using classical statistical metrics, comparing modelled and remotely sensed data versus a ground-based instrument network (AERONET). The evaluated variables are aerosol optical depth (AOD) and the Angström exponent (AE) at different wavelengths. Regarding the uncertainty in satellite representation of AOD, MODIS appears to have the best agreement with AERONET observations when compared to other satellite AOD observations. Focusing on the comparison between model output and MODIS and AERONET, results indicate a general slight improvement of AOD in the case of including the aerosol radiative effects in the model and a slight worsening for the Angström exponent for some stations and regions. Regarding the correlation coefficient, both episodes show similar values of this metric, which are higher for AOD. Generally, for the Angström exponent, models tend to underestimate the variability of this variable. Despite this , the improvement in the representation by on-line coupled chemistry-climate models of AOD reflected here may be of essential importance for a better description of aerosol-radiation-cloud interactions in regional climate models. On the other hand, the differences found between remote sensing sensors (which is of the same order of magnitude as the differences between the different members of the model ensemble) point out the uncertainty in the measurements and observations that have to be taken into account when the models are evaluated. Acknowledgments: the funding from REPAIR-CGL2014-59677-R projects (Spanish Ministry of Economy and Innovation, funded by the FEDER programme of the European Union). Special thanks to the EuMetChem COST ACTION ES1004.

Impact of Climate Change on Lake Erie Drinking Water Quality—An Initial Assessment with Remote Sensing

NASA Astrophysics Data System (ADS)

Liou, L.

2012-12-01

A changing climate in the Lake Erie region appears to be having direct impacts on the quality of Lake Erie's drinking water. A dramatic increase in the size and duration of harmful algal blooms (HABs), changes in chlorophyll (Chl) levels and related primary production (PP), prominent sediment plumes, and nearshore production of submerged aquatic vegetation (SAV) are likely being impacted by warmer winters, more intense storms, and reduced ice extent, amongst other meteorological factors. Hypoxia, another major drinking water issue in the lake, is exacerbated by HABs and nearshore SAV. A Michigan Tech research team (Shuchman, Sayers, Brooks) has recently been developing algorithms to derive HAB extents, Chl levels, PP, sediment plume extents, and nearshore SAV maps for the Great Lakes. Inputs have primarily been derived from MODIS Aqua imagery from the NASA Oceancolor website; investigations in the capability of VIIRS imagery to provide the same critical data are being pursued. Remote sensing-derived ice extent and thickness spatial data are also being analyzed. Working with Liou and Lekki of the NASA Glenn Research Center, the study team is deriving algorithms specifically for Lake Erie and integrating them into an analysis of the lake's changing trends over the last 10 years (2002-2012) to improve understanding of how they are impacting the area's water quality, especially for customers dependent on Lake Erie drinking water. This analysis is tying these remote sensing-derived products to climate-driven meteorological factors to enable an initial assessment of how future changes could continue to impact the region's drinking water quality.

Synthesis of Remote Sensing and Field Observations to Model and Understand Disturbance and Climate Effects on the Carbon Balance of Oregon & Northern California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beverly Law; David Turner; Warren Cohen

2008-05-22

The goal is to quantify and explain the carbon (C) budget for Oregon and N. California. The research compares "bottom -up" and "top-down" methods, and develops prototype analytical systems for regional analysis of the carbon balance that are potentially applicable to other continental regions, and that can be used to explore climate, disturbance and land-use effects on the carbon cycle. Objectives are: 1) Improve, test and apply a bottom up approach that synthesizes a spatially nested hierarchy of observations (multispectral remote sensing, inventories, flux and extensive sites), and the Biome-BGC model to quantify the C balance across the region; 2)more » Improve, test and apply a top down approach for regional and global C flux modeling that uses a model-data fusion scheme (MODIS products, AmeriFlux, atmospheric CO2 concentration network), and a boundary layer model to estimate net ecosystem production (NEP) across the region and partition it among GPP, R(a) and R(h). 3) Provide critical understanding of the controls on regional C balance (how NEP and carbon stocks are influenced by disturbance from fire and management, land use, and interannual climate variation). The key science questions are, "What are the magnitudes and distributions of C sources and sinks on seasonal to decadal time scales, and what processes are controlling their dynamics? What are regional spatial and temporal variations of C sources and sinks? What are the errors and uncertainties in the data products and results (i.e., in situ observations, remote sensing, models)?« less

[Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices.

PubMed

Zuo, Lu; Wang, Huan Jiong; Liu, Rong Gao; Liu, Yang; Shang, Rong

2018-02-01

Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear. We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands. The results showed that there were significant differences between NDVI-phenology and SR-phenology. SOS derived from NDVI averaged 18.9 days earlier than that from SR. EOS derived from NDVI averaged 19.0 days later than from SR. NDVI-phenology had a longer growing season. There were significant differences in the inter-annual variation of phenology from NDVI and SR. More than 20% of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend. These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR. The observed data source of NDVI and SR were completely consistent, only the mathematical expressions were different, but phenology results were significantly different. Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index. How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.

The hydrological cycle in the high Pamir Mountains: how temperature and seasonal precipitation distribution influence stream flow in the Gunt catchment, Tajikistan

NASA Astrophysics Data System (ADS)

Pohl, E.; Knoche, M.; Gloaguen, R.; Andermann, C.; Krause, P.

2014-12-01

Complex climatic interactions control hydrological processes in high mountains that in their turn regulate the erosive forces shaping the relief. To unravel the hydrological cycle of a glaciated watershed (Gunt River) considered representative of the Pamirs' hydrologic regime we developed a remote sensing-based approach. At the boundary between two distinct climatic zones dominated by Westerlies and Indian summer monsoon, the Pamir is poorly instrumented and only a few in situ meteorological and hydrological data are available. We adapted a suitable conceptual distributed hydrological model (J2000g). Interpolations of the few available in situ data are inadequate due to strong, relief induced, spatial heterogeneities. Instead we use raster data, preferably from remote sensing sources depending on availability and validation. We evaluate remote sensing-based precipitation and temperature products. MODIS MOD11 surface temperatures show good agreement with in situ data, perform better than other products and represent a good proxy for air temperatures. For precipitation we tested remote sensing products as well as the HAR10 climate model data and the interpolation-based APHRODITE dataset. All products show substantial differences both in intensity and seasonal distribution with in-situ data. Despite low resolutions, the datasets are able to sustain high model efficiencies (NSE ≥0.85). In contrast to neighbouring regions in the Himalayas or the Hindukush, discharge is dominantly the product of snow and glacier melt and thus temperature is the essential controlling factor. 80% of annual precipitation is provided as snow in winter and spring contrasting peak discharges during summer. Hence, precipitation and discharge are negatively correlated and display complex hysteresis effects that allow to infer the effect of inter-annual climatic variability on river flow. We infer the existence of two subsurface reservoirs. The groundwater reservoir (providing 40% of annual discharge) recharges in spring and summer and releases slowly during fall and winter. A not fully constrained shallow reservoir with very rapid retention times buffers melt waters during spring and summer. This study highlights the importance of a better understanding of the hydrologic cycle to constrain natural hazards such as floods and landslides as well as water availability in the downstream areas. The negative glacier mass balance (-0.6 m w.e. yr-1) indicates glacier retreat, that will effect the currently 30% contribution of glacier melt to stream flow.

Using CHIRPS Rainfall Dataset to detect rainfall trends in West Africa

NASA Astrophysics Data System (ADS)

Blakeley, S. L.; Husak, G. J.

2016-12-01

In West Africa, agriculture is often rain-fed, subjecting agricultural productivity and food availability to climate variability. Agricultural conditions will change as warming temperatures increase evaporative demand, and with a growing population dependent on the food supply, farmers will become more reliant on improved adaptation strategies. Development of such adaptation strategies will need to consider West African rainfall trends to remain relevant in a changing climate. Here, using the CHIRPS rainfall product (provided by the Climate Hazards Group at UC Santa Barbara), I examine trends in West African rainfall variability. My analysis will focus on seasonal rainfall totals, the structure of the rainy season, and the distribution of rainfall. I then use farmer-identified drought years to take an in-depth analysis of intra-seasonal rainfall irregularities. I will also examine other datasets such as potential evapotranspiration (PET) data, other remotely sensed rainfall data, rain gauge data in specific locations, and remotely sensed vegetation data. Farmer bad year data will also be used to isolate "bad" year markers in these additional datasets to provide benchmarks for identification in the future of problematic rainy seasons.

Remotely Sensed Northern Vegetation Response to Changing Climate: Growing Season and Productivity Perspective

NASA Technical Reports Server (NTRS)

Ganguly, S.; Park, Taejin; Choi, Sungho; Bi, Jian; Knyazikhin, Yuri; Myneni, Ranga

2016-01-01

Vegetation growing season and maximum photosynthetic state determine spatiotemporal variability of seasonal total gross primary productivity of vegetation. Recent warming induced impacts accelerate shifts on growing season and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we first investigate how vegetation growing season and maximum photosynthesis state are evolved and how such components contribute on inter-annual variation of seasonal total gross primary productivity. Furthermore, seasonally different response of northern vegetation to changing temperature and water availability is also investigated. We utilized both long-term remotely sensed data to extract larger scale growing season metrics (growing season start, end and duration) and productivity (i.e., growing season summed vegetation index, GSSVI) for answering these questions. We find that regionally diverged growing season shift and maximum photosynthetic state contribute differently characterized productivity inter-annual variability and trend. Also seasonally different response of vegetation gives different view of spatially varying interaction between vegetation and climate. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation to changing climate.

Linking Arctic plant biodiversity measurements with landscape heterogeneity

NASA Astrophysics Data System (ADS)

Gerber, F.; Schaepman-Strub, G.; Furrer, R.

2016-12-01

Climate warming in the Arctic region triggers changes in the vegetation productivity and species composition of the tundra. To investigate these changes and their feedback to climate, we consider species richness and abundance data of the International Tundra EXperiment (ITEX). As this information is very sparse in time and space, we aim to upscale available records to climatically relevant scales with a remote sensing based characterization of the study sites. More precisely, we relate species richness and evenness derived from the ITEX data to summary statistics describing the landscape heterogeneity, which are derived from an elevation model (ASTER GDEM) and spectral satellite observations (LANDSAT 5 and 7). Preliminary results from the statistical analysis using generalized linear mixed models show that no remote sensing based landscape characterization does significantly explain species richness. Reasons could be a mismatch of the spatial scales, an inappropriate characterization of the test sites through the satellite measurements, incomparable plot measurements from the different test sites and/or too few plot measurements. We are looking forward to presenting our results and getting your inputs.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, B.C.; Tieszen, L.T.

2003-01-01

Time integrated normalized difference vegetation index (??NDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989-1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ??NDVI and the ??NDVI coefficient of variation (CV ??NDVI) used as a proxy for interranual climate variability is analysed. Results suggest that the differences in the long-term climate control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primary C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ??NDVI values.

Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery

PubMed Central

Scottá, Fernando C.; da Fonseca, Eliana L.

2015-01-01

This study aimed to evaluate changes in the aboveground net primary productivity (ANPP) of grasslands in the Pampa biome by using experimental plots and changes in the spectral responses of similar vegetation communities obtained by remote sensing and to compare both datasets with meteorological variations to validate the transition scales of the datasets. Two different geographic scales were considered in this study. At the local scale, an analysis of the climate and its direct influences on grassland ANPP was performed using data from a long-term experiment. At the regional scale, the influences of climate on the grassland reflectance patterns were determined using vegetation sensor imagery data. Overall, the monthly variations of vegetation canopy growth analysed using environmental changes (air temperature, total rainfall and total evapotranspiration) were similar. The results from the ANPP data and the NDVI data showed the that variations in grassland growth were similar and independent of the analysis scale, which indicated that local data and the relationships of local data with climate can be considered at the regional scale in the Pampa biome by using remote sensing. PMID:26197320

Climate impacts on environmental risks evaluated from space: a conceptual approach to the case of Rift Valley Fever in Senegal.

PubMed

Tourre, Yves M; Lacaux, Jean-Pierre; Vignolles, Cécile; Lafaye, Murielle

2009-11-11

Climate and environment vary across many spatio-temporal scales, including the concept of climate change, which impact on ecosystems, vector-borne diseases and public health worldwide. To develop a conceptual approach by mapping climatic and environmental conditions from space and studying their linkages with Rift Valley Fever (RVF) epidemics in Senegal. Ponds in which mosquitoes could thrive were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on pond dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localisation of vulnerable hosts such as penned cattle (from QuickBird satellite) were also used. Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds' dynamics. While Zones Potentially Occupied by Mosquitoes are mapped, detailed risk areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of areas where cattle are potentially exposed to mosquitoes' bites. This new conceptual approach, using precise remote-sensing techniques, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of operational early warning systems for RVF based on both natural and anthropogenic climatic and environmental changes. In a climate change context, this approach could also be applied to other vector-borne diseases and places worldwide.

The Role of Combination Techniques in Maximizing the Utility of Precipitation Estimates from Several Multi-Purpose Remote-Sensing Systems

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Curtis, Scott; Einaudi, Franco (Technical Monitor)

2001-01-01

Multi-purpose remote-sensing products from various satellites have proved crucial in developing global estimates of precipitation. Examples of these products include low-earth-orbit and geosynchronous-orbit infrared (leo- and geo-IR), Outgoing Longwave Radiation (OLR), Television Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) data, and passive microwave data such as that from the Special Sensor Microwave/ Imager (SSM/I). Each of these datasets has served as the basis for at least one useful quasi-global precipitation estimation algorithm; however, the quality of estimates varies tremendously among the algorithms for the different climatic regions around the globe.

Spectral Resolution and Coverage Impact on Advanced Sounder Information Content

NASA Technical Reports Server (NTRS)

Larar, Allen M.; Liu, Xu; Zhou, Daniel K.; Smith, William L.

2010-01-01

Advanced satellite sensors are tasked with improving global measurements of the Earth s atmosphere, clouds, and surface to enable enhancements in weather prediction, climate monitoring capability, and environmental change detection. Achieving such measurement improvements requires instrument system advancements. This presentation focuses on the impact of spectral resolution and coverage changes on remote sensing system information content, with a specific emphasis on thermodynamic state and trace species variables obtainable from advanced atmospheric sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) systems on the MetOp and NPP/NPOESS series of satellites. Key words: remote sensing, advanced sounders, information content, IASI, CrIS

The use of remotely sensed soil moisture data in large-scale models of the hydrological cycle

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Gurney, R. J.; Schmugge, T. J.

1985-01-01

Manabe (1982) has reviewed numerical simulations of the atmosphere which provided a framework within which an examination of the dynamics of the hydrological cycle could be conducted. It was found that the climate is sensitive to soil moisture variability in space and time. The challenge arises now to improve the observations of soil moisture so as to provide up-dated boundary condition inputs to large scale models including the hydrological cycle. Attention is given to details regarding the significance of understanding soil moisture variations, soil moisture estimation using remote sensing, and energy and moisture balance modeling.

Headlines: Planet Earth: Improving Climate Literacy with Short Format News Videos

NASA Astrophysics Data System (ADS)

Tenenbaum, L. F.; Kulikov, A.; Jackson, R.

2012-12-01

One of the challenges of communicating climate science is the sense that climate change is remote and unconnected to daily life--something that's happening to someone else or in the future. To help face this challenge, NASA's Global Climate Change website http://climate.nasa.gov has launched a new video series, "Headlines: Planet Earth," which focuses on current climate news events. This rapid-response video series uses 3D video visualization technology combined with real-time satellite data and images, to throw a spotlight on real-world events.. The "Headlines: Planet Earth" news video products will be deployed frequently, ensuring timeliness. NASA's Global Climate Change Website makes extensive use of interactive media, immersive visualizations, ground-based and remote images, narrated and time-lapse videos, time-series animations, and real-time scientific data, plus maps and user-friendly graphics that make the scientific content both accessible and engaging to the public. The site has also won two consecutive Webby Awards for Best Science Website. Connecting climate science to current real-world events will contribute to improving climate literacy by making climate science relevant to everyday life.

Grassland Aboveground Biomass in Inner Mongolia: Dynamics (2001-2016) and Driving force

NASA Astrophysics Data System (ADS)

Li, F.; Zeng, Y.; Chen, J.; Wu, B.

2017-12-01

Plant biomass is the most critical measure of carbon stored in an ecosystem, yet it remains imprecisely modeled for many terrestrial biomes. This lack of modeling capacity for biomass and its change through time and space has impeded scientists from making headway concerning issues in the geographic and social sciences. Satellite remote sensing techniques excel at detecting changes in the Earth's surface; however, accurate estimates of biomass for the heterogeneous biome landscapes based on remote sensing techniques are few and far between, which has led to many repetitive studies. Here, we argued that our ability to assess biomass in a heterogeneous landscape using satellite remote sensing techniques would be effectively enhanced through a stratification of landscapes, i.e homogenizing landscapes. Specifically, above-ground biomass (AGB) for an extended heterogeneous grassland biome over the entirety of Inner Mongolia during the past 16 years (2001-2016) was explored using remote sensing time series data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Massive and extensive in-situ measurement AGB data and pure vegetation index (PVI) models, developed from normal remote sensing vegetation indices such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were highlighted in the accomplishment of this study. Taking into full consideration the landscape heterogeneity for the grassland biome over Inner Mongolia, we achieved a series of AGB models with high R2 (>0.85) and low RMSE ( 20.85 g/m2). The total average amount of fresh AGB for the entirety of Inner Mongolia grasslands over the past 16 years was estimated as 87 Tg with an inter-annual standard deviation of 9 Tg. Overall, the grassland AGB for Inner Mongolia increased sporadically. We found that the dynamics of AGB in the grassland biome of Inner Mongolia were substantially dominated by variation in precipitation despite the accommodation of a huge population of livestock in this area over the past few decades. Concerning the production of grassland AGB for the future, we emphasized that the impacts of the frequently warming-drying climate associated with climate change across the Mongolia Plateau should be paid more attention.

Characterization and analysis of pasture degradation in Rondonia using remote sensing

NASA Astrophysics Data System (ADS)

Numata, Izaya

2006-04-01

Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. This dissertation evaluated pasture degradation as it varied due to environmental and human factors across different scales by combining field measures, ancillary data, and remote sensing. To better understand the link between pasture nutrients and soil chemistry, samples were analyzed in the laboratory demonstrating that pasture soil fertility and grass nutrients varied significantly according to soil order. Pastures established on Alfisols, nutrient-rich soils, had higher levels of Phosphorus in soil and grass compared to pastures established on Oxisols and Ultisols. To evaluate remote sensing measures of pasture biophysical properties related to pasture degradation, remote sensing analysis focused on a variety of sensors that provide a range in spatial, spectral and temporal scales, including Landsat Thematic Mapper (TM), a field spectrometer, Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). Of the measures derived from Landsat, degraded pastures were best characterized by high non-photosynthetic vegetation (NPV) and low shade fractions, while pastures with high biomass were characterized by high green vegetation and low NPV fractions. Absorption features calculated from hyperspectral spectra collected in the field, including water and ligno-cellulose absorption depth and area, provided the best estimates of field grass measures. Temporal MODIS Normalized Difference Vegetation Index (NDVI) data were used to characterize changes in pasture quality across the region and through time. Degraded pastures were characterized by low temporal NDVI variation and occurred in dry or very wet climate conditions and on nutrient poor soils. Productive pastures were characterized by high temporal NDVI variation, were predominantly found more in the central part of the state, and were located in areas with milder climate conditions and relatively more fertile soils. As a general trend of regional pasture change in Rondonia, the proportions of productive pastures decreased and degraded pastures increased as pastures aged. The results obtained in this dissertation will contribute to understanding pasture sustainability needs for the future of Rondonia and provide the first step in monitoring pasture degradation in the Amazon using remote sensing.

Experimental philosophy leading to a small scale digital data base of the conterminous United States for designing experiments with remotely sensed data

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J.; Broderick, P. W.; Garman, T. R.; Ludwig, R. W.; Beltran, G. N.; Heyman, P. J.; Hooker, L. K.

1983-01-01

Research using satellite remotely sensed data, even within any single scientific discipline, often lacked a unifying principle or strategy with which to plan or integrate studies conducted over an area so large that exhaustive examination is infeasible, e.g., the U.S.A. However, such a series of studies would seem to be at the heart of what makes satellite remote sensing unique, that is the ability to select for study from among remotely sensed data sets distributed widely over the U.S., over time, where the resources do not exist to examine all of them. Using this philosophical underpinning and the concept of a unifying principle, an operational procedure for developing a sampling strategy and formal testable hypotheses was constructed. The procedure is applicable across disciplines, when the investigator restates the research question in symbolic form, i.e., quantifies it. The procedure is set within the statistical framework of general linear models. The dependent variable is any arbitrary function of remotely sensed data and the independent variables are values or levels of factors which represent regional climatic conditions and/or properties of the Earth's surface. These factors are operationally defined as maps from the U.S. National Atlas (U.S.G.S., 1970). Eighty-five maps from the National Atlas, representing climatic and surface attributes, were automated by point counting at an effective resolution of one observation every 17.6 km (11 miles) yielding 22,505 observations per map. The maps were registered to one another in a two step procedure producing a coarse, then fine scale registration. After registration, the maps were iteratively checked for errors using manual and automated procedures. The error free maps were annotated with identification and legend information and then stored as card images, one map to a file. A sampling design will be accomplished through a regionalization analysis of the National Atlas data base (presently being conducted). From this analysis a map of homogeneous regions of the U.S.A. will be created and samples (LANDSAT scenes) assigned by region.

Integrating Climate and Risk-Informed Science to Support Critical Decisions

ScienceCinema

None

2018-01-16

The PNNL Environmental Health and Remediation Sector stewards several decision support capabilities to integrate climate- and risk-informed science to support critical decisions. Utilizing our expertise in risk and decision analysis, integrated Earth systems modeling, and remote sensing and geoinformatics, PNNL is influencing the way science informs high level decisions at national, regional and local scales to protect and preserve our most critical assets.

Integrating Climate and Risk-Informed Science to Support Critical Decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

2016-07-27

The PNNL Environmental Health and Remediation Sector stewards several decision support capabilities to integrate climate- and risk-informed science to support critical decisions. Utilizing our expertise in risk and decision analysis, integrated Earth systems modeling, and remote sensing and geoinformatics, PNNL is influencing the way science informs high level decisions at national, regional and local scales to protect and preserve our most critical assets.

Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

USDA-ARS?s Scientific Manuscript database

Surface soil moisture is critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purpo...

Solid State Laser Technology Development for Atmospheric Sensing Applications

NASA Technical Reports Server (NTRS)

Barnes, James C.

1998-01-01

NASA atmospheric scientists are currently planning active remote sensing missions that will enable global monitoring of atmospheric ozone, water vapor, aerosols and clouds as well as global wind velocity. The measurements of these elements and parameters are important because of the effects they have on climate change, atmospheric chemistry and dynamics, atmospheric transport and, in general, the health of the planet. NASA will make use of Differential Absorption Lidar (DIAL) and backscatter lidar techniques for active remote sensing of molecular constituents and atmospheric phenomena from advanced high-altitude aircraft and space platforms. This paper provides an overview of NASA Langley Research Center's (LaRC's) development of advanced solid state lasers, harmonic generators, and wave mixing techniques aimed at providing the broad range of wavelengths necessary to meet measurement goals of NASA's Earth Science Enterprise.

Great Basin vegetation response to groundwater fluctuation, climate variability, and previous land cultivation: The application of multitemporal measurements from remote sensing data to regional vegetation dynamics

NASA Astrophysics Data System (ADS)

Elmore, Andrew James

The conversion of large natural basins to managed watersheds for the purpose of providing water to urban centers has had a negative impact on semiarid ecosystems, worldwide. We view semiarid plant communities as being adapted to short, regular periods of drought. However, human induced changes in the water balance often remove these systems from the range of natural variability that has been historically established. This thesis explores vegetation changes over a 13-yr period for Owens Valley, in eastern California. Using remotely sensed measurements of vegetation cover, an extensive vegetation survey, field data and observations, precipitation records, and data on water table depth, I identify the key modes of response of xeric, phreatophytic, and exotic Great Basin plant communities. Three specific advancements were reached as a result of this work. (1) A change classification technique was developed that was used to separate regions of land-cover that were dependent on precipitation from regions dependent on groundwater. This technique utilized Spectral Mixture Analysis of annually acquired Landsat Thematic Mapper remote sensing data, to retrieve regional estimates of percent vegetation cover. (2) A threshold response related to depth-to-water dependence was identified for phreatophytic Alkali Meadow communities. Plant communities that were subject to groundwater depths below this threshold exhibited greater invasion by precipitation sensitive plants. (3) The floristic differences between previously cultivated and uncultivated land were found to account for an increased sensitivity of plant communities to precipitation variability. Through (2) and (3), two human influences (groundwater decline and previous land cultivation) were shown to alter land cover such that the land became more sensitive to precipitation change. Climate change predictions include a component of increased climate variability for the western United States; therefore, these results place serious doubt on the sustainability of human activities in this region. The results from this work broadly cover topics from remote sensing techniques to the ecology of Great Basin plant communities and are applicable wherever large regions of land are being managed in an era of changing environmental conditions.

Climate Change and Algal Blooms =

NASA Astrophysics Data System (ADS)

Lin, Shengpan

Algal blooms are new emerging hazards that have had important social impacts in recent years. However, it was not very clear whether future climate change causing warming waters and stronger storm events would exacerbate the algal bloom problem. The goal of this dissertation was to evaluate the sensitivity of algal biomass to climate change in the continental United States. Long-term large-scale observations of algal biomass in inland lakes are challenging, but are necessary to relate climate change to algal blooms. To get observations at this scale, this dissertation applied machine-learning algorithms including boosted regression trees (BRT) in remote sensing of chlorophyll-a with Landsat TM/ETM+. The results show that the BRT algorithm improved model accuracy by 15%, compared to traditional linear regression. The remote sensing model explained 46% of the total variance of the ground-measured chlorophyll- a in the first National Lake Assessment conducted by the US Environmental Protection Agency. That accuracy was ecologically meaningful to study climate change impacts on algal blooms. Moreover, the BRT algorithm for chlorophyll- a would not have systematic bias that is introduced by sediments and colored dissolved organic matter, both of which might change concurrently with climate change and algal blooms. This dissertation shows that the existing atmospheric corrections for Landsat TM/ETM+ imagery might not be good enough to improve the remote sensing of chlorophyll-a in inland lakes. After deriving long-term algal biomass estimates from Landsat TM/ETM+, time series analysis was used to study the relations of climate change and algal biomass in four Missouri reservoirs. The results show that neither temperature nor precipitation was the only factor that controlled temporal variation of algal biomass. Different reservoirs, even different zones within the same reservoir, responded differently to temperature and precipitation changes. These findings were further tested in 1157 lakes across the continental United States. The results show that mean annual algal biomass generally increased with annual temperature. Greater increase was found in lakes with more nutrients. Mean annual algal biomass generally decreased with annual total precipitation. In both the "low" and the "high" greenhouse-gas emission scenarios, mean annual algal biomass in lakes generally increased with climate change, and greater increases are predicted from the high emission scenario.

Modeling Global Urbanization Supported by Nighttime Light Remote Sensing

NASA Astrophysics Data System (ADS)

Zhou, Y.

2015-12-01

Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering carbon cycling and climate. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the nighttime light remote sensing data, extended this method to the global domain by developing a computational method (parameterization) to estimate the key parameters in the cluster-based method, and built a consistent 20-year global urban map series to evaluate the time-reactive nature of global urbanization (e.g. 2000 in Fig. 1). Supported by urban maps derived from nightlights remote sensing data and socio-economic drivers, we developed an integrated modeling framework to project future urban expansion by integrating a top-down macro-scale statistical model with a bottom-up urban growth model. With the models calibrated and validated using historical data, we explored urban growth at the grid level (1-km) over the next two decades under a number of socio-economic scenarios. The derived spatiotemporal information of historical and potential future urbanization will be of great value with practical implications for developing adaptation and risk management measures for urban infrastructure, transportation, energy, and water systems when considered together with other factors such as climate variability and change, and high impact weather events.

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

A new climatic classification of afforestation in Three-North regions of China with multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Zheng, Xiao; Zhu, Jiaojun

2017-01-01

Afforestation and reforestation activities achieve high attention at the policy agenda as measures for carbon sequestration in order to mitigate climate change. The Three-North Shelter Forest Program, the largest ecological afforestation program worldwide, was launched in 1978 and will last until 2050 in the Three-North regions (accounting for 42.4 % of China's territory). Shelter forests of the Three-North Shelter Forest Program have exhibited severe decline after planting in 1978 due to lack of detailed climatic classification. Besides, a comprehensive assessment of climate adaptation for the current shelter forests was lacking. In this study, the aridity index determined by precipitation and reference evapotranspiration was employed to classify climatic zones for the afforestation program. The precipitation and reference evapotranspiration with 1-km resolution were estimated based on data from the tropical rainfall measuring mission and moderate resolution imaging spectroradiometer, respectively. Then, the detailed climatic classification for the afforestation program was obtained based on the relationship between the different vegetation types and the aridity index. The shelter forests in 2008 were derived from Landsat TM in the Three-North regions. In addition, climatic zones and shelter forests were corrected by comparing with natural vegetation map and field surveys. By overlaying the shelter forests on the climatic zones, we found that 16.30 % coniferous forests, 8.21 % broadleaved forests, 2.03 % mixed conifer-broadleaved forests, and 10.86 % shrubs were not in strict accordance with the climate conditions. These results open new perspectives for potential use of remote sensing techniques for afforestation management.

Remote sensing detection of atmospheric pollutants using lidar, sodar and correlation with air quality data in an industrial area

NASA Astrophysics Data System (ADS)

Steffens, Juliana; da Costa, Renata F.; Landulfo, Eduardo; Guardani, Roberto; Moreira, Paulo F., Jr.; Held, Gerhard

2011-11-01

Optical remote sensing techniques have obvious advantages for monitoring gas and aerosol emissions, since they enable the operation over large distances, far from hostile environments, and fast processing of the measured signal. In this study two remote sensing devices, namely a Lidar (Light Detection and Ranging) for monitoring the vertical profile of backscattered light intensity, and a Sodar (Acoustic Radar, Sound Detection and Ranging) for monitoring the vertical profile of the wind vector were operated during specific periods. The acquired data were processed and compared with data of air quality obtained from ground level monitoring stations, in order to verify the possibility of using the remote sensing techniques to monitor industrial emissions. The campaigns were carried out in the area of the Environmental Research Center (Cepema) of the University of Sao Paulo, in the city of Cubatao, Brazil, a large industrial site, where numerous different industries are located, including an oil refinery, a steel plant, as well as fertilizer, cement and chemical/petrochemical plants. The local environmental problems caused by the industrial activities are aggravated by the climate and topography of the site, unfavorable to pollutant dispersion. Results of a campaign are presented for a 24- hour period, showing data of a Lidar, an air quality monitoring station and a Sodar.

AccuRT: A versatile tool for radiative transfer simulations in the coupled atmosphere-ocean system

NASA Astrophysics Data System (ADS)

Hamre, Børge; Stamnes, Snorre; Stamnes, Knut; Stamnes, Jakob

2017-02-01

Reliable, accurate, and efficient modeling of the transport of electromagnetic radiation in turbid media has important applications in the study of the Earth's climate by remote sensing. For example, such modeling is needed to develop forward-inverse methods used to quantify types and concentrations of aerosol and cloud particles in the atmosphere, the dissolved organic and particulate biogeochemical matter in lakes, rivers, coastal, and open-ocean waters. It is also needed to simulate the performance of remote sensing detectors deployed on aircraft, balloons, and satellites as well as radiometric detectors deployed on buoys, gliders and other aquatic observing systems. Accurate radiative transfer modeling is also required to compute irradiances and scalar irradiances that are used to compute warming/cooling and photolysis rates in the atmosphere and primary production and warming/cooling rates in the water column. AccuRT is a radiative transfer model for the coupled atmosphere-water system that is designed to be a versatile tool for researchers in the ocean optics and remote sensing communities. It addresses the needs of researchers interested in analyzing irradiance and radiance measurements in the field and laboratory as well as those interested in making simulations of the top-of-the-atmosphere radiance in support of remote sensing algorithm development.

The Impact of Iron Limitation on Remote Sensing Reflectance in Phaeocystis antarctica

NASA Astrophysics Data System (ADS)

Tagliabue, A.; van Dijken, G. L.; Arrigo, K. R.

2006-12-01

The iron limited Southern Ocean is an important controller of the global carbon cycle and is predicted to be heavily impacted by future changes in climate. Such remote regions are heavily reliant on acquiring data from remotely sensed satellite observations of pigment concentrations, via algorithms that utilize bio-optical properties to estimate chlorophyll a concentrations. The haptophyte Phaeocystis antarctica is a key phytoplankton functional group across the Southern Ocean and dominates phytoplankton biomass in the highly productive southwestern Ross Sea. In this study, we examine absorption spectra obtained from laboratory cultures of P. antarctica grown under iron sufficient and deficient conditions. We then utilize a semi-analytical reflectance model, alongside data collected from the Ross Sea, to compare remote sensing reflectance (Rrs) derived from absorption spectra to chlorophyll a. We find that Rrs(490):Rrs(555) per unit chlorophyll a for iron sufficient P. antarctica is consistent with the existing Ross Sea algorithm. However, the increased chlorophyll specific absorption at 490 and 555 nm of iron deficient P. antarctica results in a reduction in Rrs(490):Rrs(555) per unit chlorophyll a. Therefore, remotely sensed chlorophyll a concentrations based on Rrs(490):Rrs(555) will be overestimated when waters dominated by P. antarctica experience iron stress. If remotely sensed chlorophyll a concentrations are erroneously high when P. antarctica is iron limited, then both the magnitude and duration of P. antarctica blooms might have been overestimated. We suggest that an in situ investigation of the P. antarctica Rrs to chlorophyll a relationship is necessary during the onset of iron limitation. The likely causes and broader implications of these conclusions will also be discussed.

Remote Sensing, Modeling, and In-Situ Measurements to Study the Spring and Summer Thermal Regime of the Kuparuk River, Northern Alaska

NASA Astrophysics Data System (ADS)

Floyd, A.; Liljedahl, A. K.; Gens, R.; Prakash, A.; Mann, D. H.

2011-12-01

A combined use of remote sensing techniques, modeling and in-situ measurements is a pragmatic approach to study arctic hydrology, given the vastness, complexity, and logistical challenges posed by most arctic watersheds. Remote sensing techniques can provide tools to assess the geospatial variations that form the integrated response of a river system and therefore provide important details to study climate change effects on the remote arctic environment. The proposed study tests the applicability of remote sensing and modeling techniques to map, monitor and compare river temperatures and river break-up in the coastal and foothill sections of the Kuparak River, which is an intensely studied watershed. We co-registered about hundred synthetic aperture radar (SAR) images from RADARSAT-1, ERS-1 and ERS-2 satellites, acquired during the months of May through July for a period between 1999 and 2010. Co-registration involved a Fast Fourier Transform (FFT) match of amplitude images. The offsets were then applied to the radiometrically corrected SAR images, converted to dB values, to generate an image stack. We applied a mask to extract pixels representing only the river, and used an adaptive threshold to delineate open water from frozen areas. The variation in river break-up can be bracketed by defining open vs. frozen river conditions. Summer river surface water temperatures will be simulated through the well-established HEC-RAS hydrologic software package and validated with field measurements. The three-pronged approach of using remote sensing, modeling and field measurements demonstrated in this study can be adapted to work for other watersheds across the Arctic.

Development of the Metropolitan Water Availability Index (MWAI) and Short-term Assessment with Multi-scale Remote Sensing Technologies

EPA Science Inventory

Global climate change will change environmental conditions including temperature, precipitation, surface radiation, humidity, soil moisture, and sea level, and impact significantly the regional-scale hydrologic processes such as evapotranspiration (ET), runoff, groundwater levels...

Satellite mapping of crop water demand in California

USDA-ARS?s Scientific Manuscript database

Surface delivery of irrigation water in the San Joaquin Valley is becoming increasingly restricted due to urbanization and environmental regulation, and the strain is projected to worsen under most climate change scenarios. Remote sensing technology offers the potential to monitor crop evapotranspi...

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

Spatial and Temporal Scaling of Thermal Infrared Remote Sensing Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Goel, Narendra S.

1995-01-01

Although remote sensing has a central role to play in the acquisition of synoptic data obtained at multiple spatial and temporal scales to facilitate our understanding of local and regional processes as they influence the global climate, the use of thermal infrared (TIR) remote sensing data in this capacity has received only minimal attention. This results from some fundamental challenges that are associated with employing TIR data collected at different space and time scales, either with the same or different sensing systems, and also from other problems that arise in applying a multiple scaled approach to the measurement of surface temperatures. In this paper, we describe some of the more important problems associated with using TIR remote sensing data obtained at different spatial and temporal scales, examine why these problems appear as impediments to using multiple scaled TIR data, and provide some suggestions for future research activities that may address these problems. We elucidate the fundamental concept of scale as it relates to remote sensing and explore how space and time relationships affect TIR data from a problem-dependency perspective. We also describe how linearity and non-linearity observation versus parameter relationships affect the quantitative analysis of TIR data. Some insight is given on how the atmosphere between target and sensor influences the accurate measurement of surface temperatures and how these effects will be compounded in analyzing multiple scaled TIR data. Last, we describe some of the challenges in modeling TIR data obtained at different space and time scales and discuss how multiple scaled TIR data can be used to provide new and important information for measuring and modeling land-atmosphere energy balance processes.

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments.

PubMed

Tigges, Jan; Lakes, Tobia

2017-10-04

Urban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany. Recent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified, especially due to increasing climate change effects. This is important for calibrating and validating recent prognoses of urban forest carbon offset, which have so far scarcely addressed longer timeframes. Additionally, higher resolution remote sensing of urban forest carbon estimates can improve upscaling approaches, which should be extended to reach a more precise global estimate for the first time. Urban forest carbon offset can be made more relevant by making more standardized assessments available for science and professional practitioners, and the increasing availability of high resolution remote sensing data and the progress in data processing allows for precisely that.

Using High Resolution Remotely Sensed Data to Predict Territory Occupancy and Mircrorefugia for a Habitat Specialist, the American Pika (Ochotona princeps)

NASA Astrophysics Data System (ADS)

Beers, A.; Ray, C.

2015-12-01

Climate change is likely to affect mountainous areas unevenly due to the complex interactions between topography, vegetation, and the accumulation of snow and ice. This heterogeneity will complicate relationships between species presence and large-scale drivers such as precipitation and make predicting habitat extent and connectivity much more difficult. We studied the potential for fine-scale variation in climate and habitat use throughout the year in the American pika (Ochotona princeps), a talus specialist of mountainous western North America known for strong microhabitat affiliation. Not all areas of talus are likely to be equally hospitable, which may reduce connectivity more than predicted by large-scale occupancy drivers. We used high resolution remotely sensed data to create metrics of the terrain and land cover in the Niwot Ridge (NWT) LTER site in Colorado. We hypothesized that pikas preferentially use heterogeneous terrain, as it might foster greater snow accumulation, and used radio telemetry to test this with radio-collared pikas. Pikas use heterogeneous terrain during snow covered periods and less heterogeneous area during the summer. This suggests that not all areas of talus habitat are equally suitable as shelter from extreme conditions but that pikas need more than just shelter from winter cold. With those results we created a predictive map using the same habitat metrics to model the extent of suitable habitat across the NWT area. These strong effects of terrain on pika habitat use and territory occupancy show the great utility that high resolution remotely sensed data can have in ecological applications. With increasing effects of climate change in mountainous regions, this modeling approach is crucial for quantifying habitat connectivity at both small and large scales and to identify potential refugia for threatened or isolated species.

Shrub sensitivity to recent warming across Arctic Alaska from dendrochronological and remote sensing records

NASA Astrophysics Data System (ADS)

Andreu-Hayles, Laia; Gaglioti, Benjamin V.; D'Arrigo, Rosanne; Anchukaitis, Kevin J.; Goetz, Scott

2017-04-01

Shrub expansion into Arctic and alpine tundra ecosystems has been documented during the last several decades based on repeat aerial photography, remote sensing, and ground-truthed estimates of vegetation cover. Today, summer temperatures limit the northern limit of Arctic shrubs, and warmer summers have been shown to have higher NDVI in shrub tundra zones. Although global warming has been considered the main driver of shrub expansion, soil types, shrub species and non-linear responses can moderate how sensitive shrub growth is to climate warming. Here, we assess the sensitivity of shrub growth to inter-annual climate variability using a newly generated network of 18 shrub ring-width chronologies in the tundra regions of the North Slope of Alaska. We then test whether the dendroclimatic patterns we observe at individual sites are representative of the broader region using remotely sensed productivity data (NDVI). The common period of both satellite and shrub ring data from all sites was 1982 to 2010. Instrumental daily data from Toolik Lake and interpolated products was compared to detrended growth rates of Salix spp. (willow) and Alnus sp. (alder), located on and to the west of the Dalton Highway ( 68-70°N 148°W). Whereas summer temperatures were found to enhance shrub growth, warm temperatures outside the core of the growing season have the inverse effect in some chronologies. All tundra shrub chronologies shared a common strong positive response to summer temperatures despite growing in heterogeneous site conditions and belonging to different species. In this work we will discuss shrub climate sensitive across Alaska and how NDVI data compared to the shrub ring-width network.

Climate Change, Nutrition and Food Security in Sub-Saharan Africa

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2010-01-01

Food security and nutrition in sub-Saharan Africa have long been affected by variations in the weather. Vulnerability to these hazards, along with economic shocks and an adverse political environment, is often uneven in a community. Some individuals and households are more susceptible to emergencies or crises than others, and thus determining who is most vulnerable are and how they are responding to a shock or crises is essential to understand the impact on food security. Daily, quantitative and global observations derived from satellite remote sensing instruments can contribute to understanding how food production has declined due to drought, flood or other weather-related hazard, but it can say nothing about the likelihood that the people living in that area are suffering food insecurity as a result. As Amartya Sen argued, a famine can occur even when there is an absolute surplus of food in a region. Thus organizations like the US Agency for International Development's Famine Early Warning Systems Network (FEWS NET) work to integrate biophysical and socio-economic indicators together with on-the ground assessments to estimate the food security consequences of a variety of events. Climate change is likely to restructure local, regional and global agricultural systems and commodity markets. Although remote sensing information has been used to identify seasonal production declines for the past two decades, new ways of using the data will need to be developed in order to understand, document and respond to the impact of climate change on food security as it is manifested in shorter term shocks. In this article, the contribution of remote sensing is explained, along with the other factors that affect food security

New Directions in Land Remote Sensing Policy and International Cooperation

NASA Astrophysics Data System (ADS)

Stryker, Timothy

2010-12-01

Recent changes to land remote sensing satellite data policies in Brazil and the United States have led to the phenomenal growth in the delivery of land imagery to users worldwide. These new policies, which provide free and unrestricted access to land remote sensing data over a standard electronic interface, are expected to provide significant benefits to scientific and operational users, and open up new areas of Earth system science research and environmental monitoring. Freely-available data sets from the China-Brazil Earth Resources Satellites (CBERS), the U.S. Landsat satellites, and other satellite missions provide essential information for land surface monitoring, ecosystems management, disaster mitigation, and climate change research. These missions are making important contributions to the goals and objectives of regional and global terrestrial research and monitoring programs. These programs are in turn providing significant support to the goals and objectives of the United Nations Framework Convention on Climate Change (UN FCCC), the Global Earth Observation System of Systems (GEOSS), and the UN Reduction in Emissions from Deforestation and Degradation (REDD) program. These data policies are well-aligned with the "Data Democracy" initiative undertaken by the international Committee on Earth Observation Satellites (CEOS), through its current Chair, Brazil's National Institute for Space Research (Instituto Nacional de Pesquisas Espaciais, or INPE), and its former chairs, South Africa's Council for Scientific and Industrial Research (CSIR) and Thailand's Geo Informatics and Space Technology Development Agency (GISTDA). Comparable policies for land imaging data are under consideration within Europe and Canada. Collectively, these initiatives have the potential to accelerate and improve international mission collaboration, and greatly enhance the access, use, and application of land surface imagery for environmental monitoring and societal adaption to changing climate conditions.

UC Irvine CHRS RainSphere - a new user friendly tool for analyzing global remotely sensed rainfall estimates

NASA Astrophysics Data System (ADS)

Nguyen, P.; Sorooshian, S.; Hsu, K. L.; Gao, X.; AghaKouchak, A.; Braithwaite, D.; Thorstensen, A. R.; Ashouri, H.; Tran, H.; Huynh, P.; Palacios, T.

2016-12-01

Center for Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine has recently developed the CHRS RainSphere (hosted at http://rainsphere.eng.uci.edu) for scientific studies and applications using the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR, Ashouri et al. 2015). PERSIANN-CDR is a long-term (33+ years) high-resolution (daily, 0.25 degree) global satellite precipitation dataset which is useful for climatological studies and water resources applications. CHRS RainSphere has functionalities allowing users to visualize and query spatiotemporal statistics of global daily satellite precipitation for the past three decades. With a couple of mouse-clicks, users can easily obtain a report of time series, spatial plots, and basic trend analysis of rainfall for various spatial domains of interest such as location, watershed, basin, political division and country for yearly, monthly, monthly by year or daily. Mann-Kendall test is implemented on CHRS RainSphere for statistically investigating whether there is a significant increasing/decreasing rainfall trend at a location or over a specific spatial domain. CHRS RainSphere has a range of capabilities and should appeal to a broad spectrum of users including climate scientists, water resources managers and planners, and engineers. CHRS RainSphere can also be a useful educational tool for the general public to investigate climate change and variability. The video tutorial on CHRS RainSphere is available at https://www.youtube.com/watch?v=eI2-f88iGlY&feature=youtu.be. A demonstration of CHRS RainSphere will be included in the presentation.

Monitoring Effects of Climatic stresses on a Papyrus Wetland System in Eastern Uganda Using Times Series of Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Kayendeke, Ellen; French, Helen K.; Kansiime, Frank; Bamutaze, Yazidhi

2017-04-01

Papyrus wetlands predominant in southern, central and eastern Africa; are important in supporting community livelihoods since they provide land for agriculture, materials for building and craft making, as well as services of water purification and water storage. Papyrus wetlands are dominated by a sedge Cyperus papyrus, which is rooted at wetland edges but floats in open water with the help of a root mat composed of intermingled roots and rhizomes. The hypothesis is that the papyrus mat structure reduces flow velocity and increases storage volume during storm events, which not only helps to mitigate flood events but aids in storage of excess water that can be utilised during the dry seasons. However, due to sparse gauging there is inadequate meteorological and hydrological data for continuous monitoring of the hydrological functioning of papyrus systems. The objective of this study was to assess the potential of utilising freely available remote sensing data (MODIS, Landsat, and Sentinel-1) for cost effective monitoring of papyrus wetland systems, and their response to climatic stresses. This was done through segmentation of MODIS NDVI and Landsat derived NDWI datasets; as well as classification of Sentinel-1 images taken in wet and dry seasons of 2015 and 2016. The classified maps were used as proxies for changes in hydrological conditions with time. The preliminary results show that it is possible to monitor changes in biomass, wetland inundation extent, flooded areas, as well as changes in moisture content in surrounding agricultural areas in the different seasons. Therefore, we propose that remote sensing data, when complemented with available meteorological data, is a useful resource for monitoring changes in the papyrus wetland systems as a result of climatic and human induced stresses.

Remote sensing for studying atmospheric aerosols in Malaysia

NASA Astrophysics Data System (ADS)

Kanniah, Kasturi D.; Kamarul Zaman, Nurul A. F.

2015-10-01

The aerosol system is Southeast Asia is complex and the high concentrations are due to population growth, rapid urbanization and development of SEA countries. Nevertheless, only a few studies have been carried out especially at large spatial extent and on a continuous basis to study atmospheric aerosols in Malaysia. In this review paper we report the use of remote sensing data to study atmospheric aerosols in Malaysia and document gaps and recommend further studies to bridge the gaps. Satellite data have been used to study the spatial and seasonal patterns of aerosol optical depth (AOD) in Malaysia. Satellite data combined with AERONET data were used to delineate different types and sizes of aerosols and to identify the sources of aerosols in Malaysia. Most of the aerosol studies performed in Malaysia was based on station-based PM10 data that have limited spatial coverage. Thus, satellite data have been used to extrapolate and retrieve PM10 data over large areas by correlating remotely sensed AOD with ground-based PM10. Realising the critical role of aerosols on radiative forcing numerous studies have been conducted worldwide to assess the aerosol radiative forcing (ARF). Such studies are yet to be conducted in Malaysia. Although the only source of aerosol data covering large region in Malaysia is remote sensing, satellite observations are limited by cloud cover, orbital gaps of satellite track, etc. In addition, relatively less understanding is achieved on how the atmospheric aerosol interacts with the regional climate system. These gaps can be bridged by conducting more studies using integrated approach of remote sensing, AERONET and ground based measurements.

Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis

NASA Astrophysics Data System (ADS)

Welle, Paul

Policy analyses of agricultural and environmental systems are often limited due to data constraints. Measurement campaigns can be costly, especially when the area of interest includes oceans, forests, agricultural regions or other dispersed spatial domains. Satellite based remote sensing offers a way to increase the spatial and temporal resolution of policy analysis concerning these systems. However, there are key limitations to the implementation of satellite data. Uncertainty in data derived from remote-sensing can be significant, and traditional methods of policy analysis for managing uncertainty on large datasets can be computationally expensive. Moreover, while satellite data can increasingly offer estimates of some parameters such as weather or crop use, other information regarding demographic or economic data is unlikely to be estimated using these techniques. Managing these challenges in practical policy analysis remains a challenge. In this dissertation, I conduct five case studies which rely heavily on data sourced from orbital sensors. First, I assess the magnitude of climate and anthropogenic stress on coral reef ecosystems. Second, I conduct an impact assessment of soil salinity on California agriculture. Third, I measure the propensity of growers to adapt their cropping practices to soil salinization in agriculture. Fourth, I analyze whether small-scale desalination units could be applied on farms in California in order mitigate the effects of drought and salinization as well as prevent agricultural drainage from entering vulnerable ecosystems. And fifth, I assess the feasibility of satellite-based remote sensing for salinity measurement at global scale. Through these case studies, I confront both the challenges and benefits associated with implementing satellite based-remote sensing for improved policy analysis.

Controlling Malaria and Other Diseases Using Remote Sensing

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Wharton, Stephen W. (Technical Monitor)

2001-01-01

Remote sensing offers the vantage of monitoring a vast area of the Earth continuously. Once developed and launched, a satellite gives years of service in collecting data from the land, the oceans, and the atmosphere. Since the 1980s, attempts have been made to relate disease occurrence with remotely sensed environmental and geophysical parameters, using data from Landsat, SPOT, AVHRR, and other satellites. With higher spatial resolution, the recent satellite sensors provide a new outlook for disease control. At sub-meter to I 10m resolution, surface types associated with disease carriers can be identified more accurately. The Ikonos panchromatic sensor with I m resolution, and the Advanced Land Imager with 1 Om resolution on the newly launched Earth Observing-1, both have displayed remarkable mapping capabilities. In addition, an entire array of geophysical parameters can now be measured or inferred from various satellites. Airborne remote sensing, with less concerns on instrument weight, size, and power consumption, also offers a low-cost alternative for regional applications. NASA/GSFC began to collaborate with the Mahidol University on malaria and filariasis control using remote sensing in late 2000. The objectives are: (1) To map the breeding sites for the major vector species; (2) To identify the potential sites for larvicide and insecticide applications; (3) To explore the linkage of vector population and transmission intensity to environmental variables; (4) To monitor the impact of climate change and human activities on vector population and transmission; and (5) To develop a predictive model for disease distribution. Field studies are being conducted in several provinces in Thailand. Data analyses will soon begin. Malaria data in South Korea are being used as surrogates for developing classification techniques. GIS has been shown to be invaluable in making the voluminous remote sensing data more readily understandable. It will be used throughout this study to clearly demonstrate the spatial relationship between the disease intensities, geophysical variables, and socioeconomic parameters. Asides from malaria and filariasis, application of remote sensing to the control of other diseases have been vigorously pursued by NASA's Environment and Health Initiative. The current program includes projects on Rift Valley fever, St. Louis encephalitis, dengue fever, ebola, African dust and diseases, meningitis, asthma, bartonellosis, cholera, and urban health concerns. Results from these projects indicate that remote sensing will play an increasingly important role in disease control in the future.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

Treesearch

Heather L. Kimball; Paul C. Selmants; Alvaro Moreno; Steve W. Running; Christian P. Giardina; Benjamin Poulter

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the...

Using Remotely Sensed Data and Hydrologic Models to Evaluate the Effects of Climate Change on Shallow Aquatic Ecosystems in the Mobile Bay, AL Estuary

NASA Astrophysics Data System (ADS)

Estes, M. G.; Al-Hamdan, M. Z.; Thom, R.; Judd, C.; Ellis, J.; Woodruff, D.; Quattrochi, D.; Rose, K.; Swann, R.

2012-12-01

Coastal systems in the northern Gulf of Mexico, including the Mobile Bay, AL estuary, are subject to increasing pressure from a variety of activities including climate change. Climate changes have a direct effect on the discharge of rivers that drain into Mobile Bay and adjacent coastal water bodies. The outflows change water quality (temperature, salinity, and sediment concentrations) in the shallow aquatic areas and affect ecosystem functioning. Mobile Bay is a vital ecosystem that provides habitat for many species of fauna and flora. Historically, submerged aquatic vegetation (SAV) and seagrasses were found in this area of the northern Gulf of Mexico; however the extent of vegetation has significantly decreased over the last 60 years. The objectives of this research are to determine: how climate changes affect runoff and water quality in the estuary and how these changes will affect habitat suitability for SAV and seagrasses. Our approach is to use watershed and hydrodynamic modeling to evaluate the impact of climate change on shallow water aquatic ecosystems in Mobile Bay and adjacent coastal areas. Remotely sensed Landsat data were used for current land cover land use (LCLU) model input and the data provided by Intergovernmental Panel on Climate Change (IPCC) of the future changes in temperature, precipitation, and sea level rise were used to create the climate scenarios for the 2025 and 2050 model simulations. Project results are being shared with Gulf coast stakeholders through the Gulf of Mexico Data Atlas to benefit coastal policy and climate change adaptation strategies.

Using Remotely Sensed Data and Hydrologic Models to Evaluate the Effects of Climate Change on Shallow Aquatic Ecosystems in the Mobile Bay, AL Estuary

NASA Technical Reports Server (NTRS)

Estes, M. G.; Al-Hamdan, M. Z.; Thom, R.; Judd, C.; Woodruff, D.; Ellis, J. T.; Quattrochi, D.; Swann, R.

2012-01-01

Coastal systems in the northern Gulf of Mexico, including the Mobile Bay, AL estuary, are subject to increasing pressure from a variety of activities including climate change. Climate changes have a direct effect on the discharge of rivers that drain into Mobile Bay and adjacent coastal water bodies. The outflows change water quality (temperature, salinity, and sediment concentrations) in the shallow aquatic areas and affect ecosystem functioning. Mobile Bay is a vital ecosystem that provides habitat for many species of fauna and flora. Historically, submerged aquatic vegetation (SAV) and seagrasses were found in this area of the northern Gulf of Mexico; however the extent of vegetation has significantly decreased over the last 60 years. The objectives of this research are to determine: how climate changes affect runoff and water quality in the estuary and how these changes will affect habitat suitability for SAV and seagrasses. Our approach is to use watershed and hydrodynamic modeling to evaluate the impact of climate change on shallow water aquatic ecosystems in Mobile Bay and adjacent coastal areas. Remotely sensed Landsat data were used for current land cover land use (LCLU) model input and the data provided by Intergovernmental Panel on Climate Change (IPCC) of the future changes in temperature, precipitation, and sea level rise were used to create the climate scenarios for the 2025 and 2050 model simulations. Project results are being shared with Gulf coast stakeholders through the Gulf of Mexico Data Atlas to benefit coastal policy and climate change adaptation strategies.

Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes

NASA Astrophysics Data System (ADS)

Buffington, Kevin J.; Dugger, Bruce D.; Thorne, Karen M.

2018-03-01

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.

Accessing Both Halves of the Brain to Make Climate Decisions: How Community-Sourced Media, Earth Remote Sensing Data, and Creative Placemaking Art Can Cultivate Change

NASA Astrophysics Data System (ADS)

Drapkin, J. K.; Wagner, L.

2017-12-01

Decision-making, science tells us, accesses multiple parts of the brain: both logic and data as well as memory and emotion. It is this mix of signals that propels individuals and communities to act. Founded in 2012, ISeeChange is the nation's first community crowdsourced climate and weather journal that empowers users to document environmental changes with others and discuss the impacts over time. Our neighborhood investigation methodology includes residents documenting their personal experiences alongside collected data, Earth remote sensing data, and local artists interpreting community questions and experiences into place-based public art in the neighborhood to inspire a culture of resilience and climate literacy. ISeeChange connects the public with national media, scientists, and data tools that support community dialogue and enable collaborative science and journalism investigations about our changing environment. Our groundbreaking environmental reporting platform—available online and through a mobile app—personalizes and tracks climate change from the perspective of every day experiences, bringing Eearth science home and into the placesspaces people know best and trust most- their own communities Our session will focus on our newest neighborhood pilot program in New Orleans, furthering the climate resilience, green infrastructure, and creative placemaking efforts of the Trust for Public Land, the City of New Orleans, and other resilience community partners.

"A remote sensing approach to determining susceptibility of national park forest areas to forecasted changes in precipitation and temperature"

NASA Astrophysics Data System (ADS)

Finley, T.; Griffin, R.

2016-12-01

The United States designates 59 protected areas around the country as national parks, totaling around 51.9 million acres. With the exception of a few, the majority of these parks feature forested areas of biological and/or historical importance. Depending on their location, these forested areas are threatened by climate change in the form of decreasing precipitation and/or increasing temperatures, which can result in significant drying resulting in increased susceptibility to threats and resultant tree mortality. This study aims to survey the forested areas of America's national parks and determine their susceptibility to climate-induced drying. Land cover derived from remotely sensed multispectral data was used to characterize forested areas within national parks. Multiple climate change scenarios to end of century were taken from the NASA Earth Exchange Downscaled Climate Projections (DEX _DCP30) dataset and were compared with the forested areas. Forests projected to experience both an increase in temperature and decrease in precipitation were considered most at risk. A susceptibility analysis was performed to develop an index that would identify these areas most prone to negative effects from climate change in low (B1), medium (A1B), and high (A2) emissions scenarios. With this information, park officials can better focus efforts to monitor and preserve their forested areas.

Enhancing a Remote-Sensing Method for Soil Moisture by Accounting for Regional Soil, Vegetation, and Climatic Characteristics

NASA Astrophysics Data System (ADS)

Sahaar, A. S.; Niemann, J. D.

2016-12-01

Accurate knowledge of root-zone soil moisture is critical for understanding the perpetuation of droughts and managing agricultural water systems. A remote-sensing method based on optical and thermal satellite imagery has been previously proposed to estimate fine-resolution (30 m) root-zone soil moisture over large regions. This method uses Landsat imagery to calculate all the components of the surface energy balance and then calculates the evaporative fraction (Λ) as the ratio of the latent heat flux to the sum of the sensible and latent heat fluxes. Root-zone soil moisture (θ) is then estimated from an empirical relationship with Λ. A similar approach has also been proposed to estimate the degree of saturation. Previous testing of this method for a semiarid region of southeastern Colorado has shown that a single relationship between θ and Λ does not apply universally. The primary objective of this study is to evaluate the impact of regional soil, vegetation, and climatic conditions on the form and strength of the Λ- θ relationship. To accomplish this goal, a global sensitivity analysis is performed using the Extended Fourier Amplitude Sensitivity Test (FAST) and a physically-based model (Hydrus-1D) that simulates both the land-surface energy balance and soil moisture dynamics. The modeling results show that, within a given climatic region, soil characteristics are very important in determining the shape of the Λ-θ relationship, while vegetation characteristics have the largest effect on the strength of the relationship. The modeling results also indicate that the annual average rainfall, which helps determine the climatic region, has a strong effect on both the form and strength of the relationship. From this analysis, the constants that define the Λ-θ relationships are estimated using regional characteristics. This approach allows the remote-sensing method to be adapted to local conditions and has the potential to greatly improve its performance.

Geosphere-biosphere interactions in European Protected Areas: a view from the H2020 ECOPOTENTIAL Project

NASA Astrophysics Data System (ADS)

Provenzale, Antonello; Beierkuhnlein, Carl; Karnieli, Arnon; Marangi, Carmela; Giamberini, Mariasilvia; Imperio, Simona

2017-04-01

The large H2020 project ECOPOTENTIAL (2015-2019, 47 partners, contributing to GEO and GEOSS - http://www.ecopotential-project.eu/) is devoted to making best use of remote sensing and in situ data to improve future ecosystem benefits, adopting the view of ecosystems as one physical system with their environment, focusing on geosphere-biosphere interactions, Earth Critical Zone dynamics, Macrosystem Ecology and cross-scale interactions, the effect of extreme events and using Essential (Climate, Biodiversity and Ocean) Variables as descriptors of change. In ECOPOTENTIAL, remote sensing and in situ data are collected, processed and used for a better understanding of the ecosystem dynamics, analysing and modelling the effects of global changes on ecosystem functions and services, over an array of different ecosystem types, including mountain, marine, coastal, arid and semi-arid ecosystems. The project focuses on a network of Protected Areas of international relevance, that is representative of the range of environmental and biogeographical conditions characterizing Europe. Some of the activities of the project are devoted to detect and quantify the changes taking place in the Protected Areas, through the analysis of remote sensing observations, in-situ data and gridded climatic datasets. Likewise, the project aims at providing estimates of the future ecosystem conditions in different climate and environmental change scenarios. In all such endeavours, one is faced with cross-scale issues: downscaling of climate information to drive ecosystem response, and upscaling of local ecosystem changes to larger scales. So far, the analysis has been conducted mainly by using traditional methods, but there is wide room for improvement by using more refined approaches. In particular, a crucial question is how to upscale the information gained at single-site scale to larger, regional or continental scale, an issue that could benefit from using, for example, complex network analysis.

Wind Streaks on Earth; Exploration and Interpretation

NASA Astrophysics Data System (ADS)

Cohen-Zada, Aviv Lee; Blumberg, Dan G.; Maman, Shimrit

2015-04-01

Wind streaks, one of the most common aeolian features on planetary surfaces, are observable on the surface of the planets Earth, Mars and Venus. Due to their reflectance properties, wind streaks are distinguishable from their surroundings, and they have thus been widely studied by remote sensing since the early 1970s, particularly on Mars. In imagery, these streaks are interpreted as the presence - or lack thereof - of small loose particles on the surface deposited or eroded by wind. The existence of wind streaks serves as evidence for past or present active aeolian processes. Therefore, wind streaks are thought to represent integrative climate processes. As opposed to the comprehensive and global studies of wind streaks on Mars and Venus, wind streaks on Earth are understudied and poorly investigated, both geomorphologically and by remote sensing. The aim of this study is, thus, to fill the knowledge gap about the wind streaks on Earth by: generating a global map of Earth wind streaks from modern high-resolution remotely sensed imagery; incorporating the streaks in a geographic information system (GIS); and overlaying the GIS layers with boundary layer wind data from general circulation models (GCMs) and data from the ECMWF Reanalysis Interim project. The study defines wind streaks (and thereby distinguishes them from other aeolian features) based not only on their appearance in imagery but more importantly on their surface appearance. This effort is complemented by a focused field investigation to study wind streaks on the ground and from a variety of remotely sensed images (both optical and radar). In this way, we provide a better definition of the physical and geomorphic characteristics of wind streaks and acquire a deeper knowledge of terrestrial wind streaks as a means to better understand global and planetary climate and climate change. In a preliminary study, we detected and mapped over 2,900 wind streaks in the desert regions of Earth distributed in approximately 500 sites. Most terrestrial wind streaks are formed on a relatively young geological surface and are concentrated along the equator (± 30°). They are categorized by the combination of their planform and reflectance; with linear-bright and dark are the most common. A site-specific examination of remote-sensing effects on wind streaks identification has been conducted. The results thus far, indicate that in images with varying spatial and spectral specifications some wind streaks are actually composed of other aeolian bedforms, especially dunes. Specific regions of the Earth were then compared qualitatively to surface wind data extracted from a general circulation model. Understanding the mechanism and spatial and temporal distribution of wind streak formation is important not only for understanding surface modifications in the geomorphological context but also for shedding light on past and present climatic processes and atmospheric circulation on Earth. This study yields an explanation for wind streaks as a geomorphological feature. Moreover, it is in this planet-wide geomorphological research ability to lay down the foundations for comparative planetary research.

Shifting relative importance of climatic constraints on land surface phenology

NASA Astrophysics Data System (ADS)

Garonna, Irene; de Jong, Rogier; Stöckli, Reto; Schmid, Bernhard; Schenkel, David; Schimel, David; Schaepman, Michael E.

2018-02-01

Land surface phenology (LSP), the study of seasonal dynamics of vegetated land surfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood at global scale. Understanding the climatic constraints that underlie LSP is crucial for explaining climate change effects on global vegetation phenology. We used a combination of modelled and remotely-sensed vegetation activity records to quantify the interplay of three climatic constraints on land surface phenology (namely minimum temperature, moisture availability, and photoperiod), as well as the dynamic nature of these constraints. Our study examined trends and the relative importance of the three constrains at the start and the end of the growing season over eight global environmental zones, for the past three decades. Our analysis revealed widespread shifts in the relative importance of climatic constraints in the temperate and boreal biomes during the 1982-2011 period. These changes in the relative importance of the three climatic constraints, which ranged up to 8% since 1982 levels, varied with latitude and between start and end of the growing season. We found a reduced influence of minimum temperature on start and end of season in all environmental zones considered, with a biome-dependent effect on moisture and photoperiod constraints. For the end of season, we report that the influence of moisture has on average increased for both the temperate and boreal biomes over 8.99 million km2. A shifting relative importance of climatic constraints on LSP has implications both for understanding changes and for improving how they may be modelled at large scales.

Tools and Data Services from the NASA Earth Satellite Observations for Remote Sensing Commercial Applications

NASA Technical Reports Server (NTRS)

Vicente, Gilberto

2005-01-01

Several commercial applications of remote sensing data, such as water resources management, environmental monitoring, climate prediction, agriculture, forestry, preparation for and migration of extreme weather events, require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation of data reduction, combination and data product production. The time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions.

Remote sensing of global croplands for food security

USGS Publications Warehouse

Thenkabail, Prasad S.; Biradar, Chandrashekhar M.; Turral, Hugh; Lyon, John G.

2009-01-01

Increases in populations have created an increasing demand for food crops while increases in demand for biofuels have created an increase in demand for fuel crops. What has not increased is the amount of croplands and their productivity. These and many other factors such as decreasing water resources in a changing climate have created a crisis like situation in global food security. Decision makers in these situations need accurate information based on science. Remote Sensing of Global Croplands for Food Security provides a comprehensive knowledge base in use of satellite sensor-based maps and statistics that can be used to develop strategies for croplands (irrigated and rainfed) and their water use for food security.

Global geochemical problems

NASA Technical Reports Server (NTRS)

Harriss, R. C.

1980-01-01

Application of remote sensing techniques to the solution of geochemical problems is considered with emphasis on the 'carbon-cycle'. The problem of carbon dioxide sinks and the areal extent of coral reefs are treated. In order to assess the problems cited it is suggested that remote sensing techniques be utilized to: (1)monitor globally the carbonate and bicarbonate concentrations in surface waters of the world ocean; (2)monitor the freshwater and oceanic biomass and associated dissolved organic carbon; (3) inventory the coral reef areas and types and the associated oceanographic climatic conditions; and (4)measure the heavy metal fluxes from forested and vegetated areas, from volcanos, from different types of crustal rocks, from soils, and from sea surfaces.

The Role of Aerospace Technology in Agriculture. The 1977 Summer Faculty Fellowship Program in Engineering Systems Design

NASA Technical Reports Server (NTRS)

1977-01-01

Possibilities were examined for improving agricultural productivity through the application of aerospace technology. An overview of agriculture and of the problems of feeding a growing world population are presented. The present state of agriculture, of plant and animal culture, and agri-business are reviewed. Also analyzed are the various systems for remote sensing, particularly applications to agriculture. The report recommends additional research and technology in the areas of aerial application of chemicals, of remote sensing systems, of weather and climate investigations, and of air vehicle design. Also considered in detail are the social, legal, economic, and political results of intensification of technical applications to agriculture.

Mapping the Risks of Malaria, Dengue and Influenza Using Satellite Data

NASA Astrophysics Data System (ADS)

Kiang, R. K.; Soebiyanto, R. P.

2012-07-01

It has long been recognized that environment and climate may affect the transmission of infectious diseases. The effects are most obvious for vector-borne infectious diseases, such as malaria and dengue, but less so for airborne and contact diseases, such as seasonal influenza. In this paper, we examined the meteorological and environmental parameters that influence the transmission of malaria, dengue and seasonal influenza. Remotely sensed parameters that provide such parameters were discussed. Both statistical and biologically inspired, processed based models can be used to model the transmission of these diseases utilizing the remotely sensed parameters as input. Examples were given for modelling malaria in Thailand, dengue in Indonesia, and seasonal influenza in Hong Kong.

Remote sensing is a viable tool for mapping soil salinity in agricultural lands

USDA-ARS?s Scientific Manuscript database

Soil salinity negatively impacts the productivity and profitability of western San Joaquin Valley (WSJV) farmland. Drought, climate change, reduced water allocations, and land use changes are among many current phenomena that could potentially worsen salinity conditions in agricultural lands. Monito...

Agricultural greenhouse gas flux determination via remote sensing and modeling

USDA-ARS?s Scientific Manuscript database

Serious concerns have been raised about increasing levels of atmospheric greenhouse gases (GHGs) and associated climate change. For every degree in global temperature increase, grain production yields are expected to decrease 10%, while the global human population continues to increase by roughly 8...

Vertical profiles of aerosol mass concentration derived by unmanned airborne in situ and remote sensing instruments during dust events

NASA Astrophysics Data System (ADS)

Mamali, Dimitra; Marinou, Eleni; Sciare, Jean; Pikridas, Michael; Kokkalis, Panagiotis; Kottas, Michael; Binietoglou, Ioannis; Tsekeri, Alexandra; Keleshis, Christos; Engelmann, Ronny; Baars, Holger; Ansmann, Albert; Amiridis, Vassilis; Russchenberg, Herman; Biskos, George

2018-05-01

In situ measurements using unmanned aerial vehicles (UAVs) and remote sensing observations can independently provide dense vertically resolved measurements of atmospheric aerosols, information which is strongly required in climate models. In both cases, inverting the recorded signals to useful information requires assumptions and constraints, and this can make the comparison of the results difficult. Here we compare, for the first time, vertical profiles of the aerosol mass concentration derived from light detection and ranging (lidar) observations and in situ measurements using an optical particle counter on board a UAV during moderate and weak Saharan dust episodes. Agreement between the two measurement methods was within experimental uncertainty for the coarse mode (i.e. particles having radii > 0.5 µm), where the properties of dust particles can be assumed with good accuracy. This result proves that the two techniques can be used interchangeably for determining the vertical profiles of aerosol concentrations, bringing them a step closer towards their systematic exploitation in climate models.

Exploring Remote Sensing Products Online with Giovanni for Studying Urbanization

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina; Kempler, Steve

2012-01-01

Recently, a Large amount of MODIS land products at multi-spatial resolutions have been integrated into the online system, Giovanni, to support studies on land cover and land use changes focused on Northern Eurasia and Monsoon Asia regions. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC) providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data. The customized Giovanni Web portals (Giovanni-NEESPI and Giovanni-MAIRS) are created to integrate land, atmospheric, cryospheric, and social products, that enable researchers to do quick exploration and basic analyses of land surface changes and their relationships to climate at global and regional scales. This presentation documents MODIS land surface products in Giovanni system. As examples, images and statistical analysis results on land surface and local climate changes associated with urbanization over Yangtze River Delta region, China, using data in Giovanni are shown.

Climate impacts on environmental risks evaluated from space: a conceptual approach to the case of Rift Valley Fever in Senegal

PubMed Central

Tourre, Yves M.; Lacaux, Jean-Pierre; Vignolles, Cécile; Lafaye, Murielle

2009-01-01

Background Climate and environment vary across many spatio-temporal scales, including the concept of climate change, which impact on ecosystems, vector-borne diseases and public health worldwide. Objectives To develop a conceptual approach by mapping climatic and environmental conditions from space and studying their linkages with Rift Valley Fever (RVF) epidemics in Senegal. Design Ponds in which mosquitoes could thrive were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on pond dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localisation of vulnerable hosts such as penned cattle (from QuickBird satellite) were also used. Results Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds’ dynamics. While Zones Potentially Occupied by Mosquitoes are mapped, detailed risk areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of areas where cattle are potentially exposed to mosquitoes’ bites. Conclusions This new conceptual approach, using precise remote-sensing techniques, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of operational early warning systems for RVF based on both natural and anthropogenic climatic and environmental changes. In a climate change context, this approach could also be applied to other vector-borne diseases and places worldwide. PMID:20052381

Potential reciprocal effect between land use / land cover change and climate change

NASA Astrophysics Data System (ADS)

Daham, Afrah; Han, Dawei; Rico-Ramirez, Miguel

2016-04-01

Land use/land cover (LULC) activity influences climate change and one way to explore climate change is to analyse the change in LULC patterns. Modelling the Spatio-temporal pattern of LULC change requires the use of satellite remote sensing data and aerial photographs with different pre-processing steps. The aim of this research is to analyse the reciprocal effects of LUCC (Land Use and Cover Change) and the climate change on each other in the study area which covers part of Bristol, South Gloucestershire, Bath and Somerset in England for the period (1975-2015). LUCC is assessed using remote sensing data. Three sets of remotely sensed data, LanSAT-1 Multispectral Scanner (MSS) data obtained in (1975 and 1976), LanSAT-5 Thematic Mapper (TM) data obtained in (1984 and 1997), and LandSAT-7 Enhanced Thematic Mapper Plus (ETM+) acquired in (2003 and 2015), with a time span of forty years were used in the study. One of the most common problems in the satellite images is the presence of cloud covers. In this study, the cloud cover problem is handled using a novel algorithm, which is capable of reducing the cloud coverage in the classified images significantly. This study also examines a suite of possible photogrammetry techniques applicable to detect the change in LULC. At the moment photogrammertic techniques are used to derive the ground truth for supervised classification from the high resolution aerial photos which were provided by Ordnance Survey (contract number: 240215) and global mapper for the years in (2001 and 2014). After obtaining the classified images almost free of clouds, accuracy assessment is implemented with the derived classified images using confusion matrix at some ground truth points. Eight classes (Improved grassland, Built up areas and gardens, Arable and horticulture, Broad-leaved / mixed woodland, Coniferous woodland, Oceanic seas, Standing open water and reservoir, and Mountain; heath; bog) have been classified in the chosen study area. Also, CORINE Land Cover (CLC) maps are used to study the environmental changes and to validate the obtained maps from remote sensing and photogrammetry data. On climate change, different sources of climate data were used in this research. Three rainfall datasets from the Global Precipitation Climatology Centre (GPCC), the Climate Research Unit (CRU) and Gridded Estimates of daily Areal Rainfall (CEH-GEAR) in the study area were compared at a resolution of 0.5 degrees. The dataset were available for the operational period 1975-2015. The historically observed rainfall datasets for the study area were obtained from the Met Office Integrated Data Archive System (MIDAS) Land and Marine downloaded through the British Atmospheric Data Centre (BADC) website, which includes the rainfall and the temperature, are collected from all the weather stations in the UK in the last 40 years. Only four gauging stations were available to represent the spatial variability of rainfall within and around the study area. The monthly rainfall time series were evaluated against a dataset based on four rain gauges. These data are processed and analysed statistically to find the changes in climate of the study area in the last 40 years. The potential reciprocal effect between the LULC change and the climate change is done by finding the correlation between LUCC and the variables Rainfall and Temperature. In addition, The Soil and Water Assessment Tool (SWAT) model is used to study the impact of LULC change on the water system and climate.

Remote sensing supported surveillance and characterization of tailings behavior at a gold mine site, Finland.

NASA Astrophysics Data System (ADS)

Rauhala, Anssi; Tuomela, Anne; Rossi, Pekka M.; Davids, Corine

2017-04-01

The management of vast amounts of tailings produced is one of the key issues in mining operations. The effective and economic disposal of the waste requires knowledge concerning both basic physical properties of the tailings as well as more complex aspects such as consolidation behavior. The behavior of tailings in itself is a very complex issue that can be affected by flocculation, sedimentation, consolidation, segregation, deposition, freeze-thaw, and desiccation phenomena. The utilization of remote sensing in an impoundment-scale monitoring of tailings could benefit the management of tailings, and improve our knowledge on tailings behavior. In order to gain better knowledge of tailings behavior in cold climate, we have utilized both modern remote sensing techniques and more traditional in situ and laboratory measurements in characterizing thickened gold tailings behavior at a Finnish gold mine site, where the production has been halted due to low gold prices. The remote sensing measurements consisted of elevation datasets collected from unmanned aerial vehicles during summers 2015 and 2016, and a further campaign is planned for the summer 2017. The ongoing traditional measurements include for example particle-size distribution, frost heave, frost depth, water retention, temperature profile, and rheological measurements. Initial results from the remote sensing indicated larger than expected settlements on parts of the tailings impoundment, and also highlighted some of the complexities related to data processing. The interpretation of the results and characterization of the behavior is in this case complicated by possible freeze-thaw effects and potential settlement of the impoundment bottom structure consisting of natural peat. Experiments with remote sensing and unmanned aerial vehicles indicate that they could offer potential benefits in frequent mine site monitoring, but there is a need towards more robust and streamlined data acquisition and processing. The gathered data and obtained results form the basis for further modelling efforts which aim at better management of tailings storage facilities.

Intercomparison of Remotely Sensed Vegetation Indices, Ground Spectroscopy, and Foliar Chemistry Data from NEON

NASA Astrophysics Data System (ADS)

Hulslander, D.; Warren, J. N.; Weintraub, S. R.

2017-12-01

Hyperspectral imaging systems can be used to produce spectral reflectance curves giving rich information about composition, relative abundances of materials, mixes and combinations. Indices based on just a few spectral bands have been used for over 40 years to study vegetation health, mineral abundance, and more. These indices are much simpler to visualize and use than a full hyperspectral data set which may contain over 400 bands. Yet historically, it has been difficult to directly relate remotely sensed spectral indices to quantitative biophysical properties significant to forest ecology such as canopy nitrogen, lignin, and chlorophyll. This linkage is a critical piece in enabling the detection of high value ecological information, usually only available from labor-intensive canopy foliar chemistry sampling, to the geographic and temporal coverage available via remote sensing. Previous studies have shown some promising results linking ground-based data and remotely sensed indices, but are consistently limited in time, geographic extent, and land cover type. Moreover, previous studies are often focused on tuning linkage algorithms for the purpose of achieving good results for only one study site or one type of vegetation, precluding development of more generalized algorithms. The National Ecological Observatory Network (NEON) is a unique system of 47 terrestrial sites covering all of the major eco-climatic domains of the US, including AK, HI, and Puerto Rico. These sites are regularly monitored and sampled using uniform instrumentation and protocols, including both foliar chemistry sampling and remote sensing flights for high resolution hyperspectral, LiDAR, and digital camera data acquisition. In this study we compare the results of foliar chemistry analysis to the remote sensing vegetation indices and investigate possible sources for variance and difference through the use of the larger hyperspectral dataset as well as ground based spectrometer measurements of samples subsequently analyzed for foliar chemistry.

Detecting urban warming signals in climate records

NASA Astrophysics Data System (ADS)

He, Yuting; Jia, Gensuo; Hu, Yonghong; Zhou, Zijiang

2013-07-01

Determining whether air temperatures recorded at meteorological stations have been contaminated by the urbanization process is still a controversial issue at the global scale. With support of historical remote sensing data, this study examined the impacts of urban expansion on the trends of air temperature at 69 meteorological stations in Beijing, Tianjin, and Hebei Province over the last three decades. There were significant positive relations between the two factors at all stations. Stronger warming was detected at the meteorological stations that experienced greater urbanization, i.e., those with a higher urbanization rate. While the total urban area affects the absolute temperature values, the change of the urban area (urbanization rate) likely affects the temperature trend. Increases of approximately 10% in urban area around the meteorological stations likely contributed to the 0.13°C rise in air temperature records in addition to regional climate warming. This study also provides a new approach to selecting reference stations based on remotely sensed urban fractions. Generally, the urbanization-induced warming contributed to approximately 44.1% of the overall warming trends in the plain region of study area during the past 30 years, and the regional climate warming was 0.30°C (10 yr)-1 in the last three decades.

Analyzing the Effects of Climate Change on Sea Surface Temperature in Monitoring Coral Reef Health in the Florida Keys Using Sea Surface Temperature Data

NASA Technical Reports Server (NTRS)

Jones, Jason; Burbank, Renane; Billiot, Amanda; Schultz, Logan

2011-01-01

This presentation discusses use of 4 kilometer satellite-based sea surface temperature (SST) data to monitor and assess coral reef areas of the Florida Keys. There are growing concerns about the impacts of climate change on coral reef systems throughout the world. Satellite remote sensing technology is being used for monitoring coral reef areas with the goal of understanding the climatic and oceanic changes that can lead to coral bleaching events. Elevated SST is a well-documented cause of coral bleaching events. Some coral monitoring studies have used 50 km data from the Advanced Very High Resolution Radiometer (AVHRR) to study the relationships of sea surface temperature anomalies to bleaching events. In partnership with NOAA's Office of National Marine Sanctuaries and the University of South Florida's Institute for Marine Remote Sensing, this project utilized higher resolution SST data from the Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR. SST data for 2000-2010 was employed to compute sea surface temperature anomalies within the study area. The 4 km SST anomaly products enabled visualization of SST levels for known coral bleaching events from 2000-2010.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, Bradley C.; Tieszen, Larry L.

2003-01-01

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

Application of Satellite Remote Sensing to Identify Climatic and Anthropogenic Changes Related to Water and Health Conditions in Emerging Megacities

NASA Astrophysics Data System (ADS)

Akanda, A. S.; Serman, E. A.; Jutla, A.

2014-12-01

By 2050, more than 70% of the world's population is expected to be living in a city. In many of the urbanizing regions in Asia and Africa, most new development is taking place without adequate urban or regional planning, and a majority population is crowded into densely populated unplanned settlements, also known as slums. During the same period, precipitation and temperature patterns are likely to see significant changes in many of these regions while coastal megacities will have to accommodate sea-level rise in their ecosystems. The rapid increase in population is usually observed in fringes of the urban sprawl without adequate water or sanitation facilities or access to other municipal amenities (such as utilities, healthcare, and education). Collectively, these issues make the ever increasing slum dwellers in emerging megacities significantly vulnerable to a combination of climatic and anthropogenic threats. However, how the growth of unplanned urban and peri-urban sprawl and simultaneous change in climatic patterns have impacted public health in the emerging megacities remain largely unexplored due to lack of readily available and usable data. We employ a number of Remote Sensing products (GRACE, LANDSAT, MODIS) to bridge above knowledge gaps and to identify relevant hydrologic and anthropogenic changes in emerging megacities that are most vulnerable due to the climate-water-health nexus. We explore one of the largest and the fastest growing megacities in the world - Dhaka, Bangladesh - on identifying and investigating the changes in the water environment and growth of slum areas, and impact on water services and health outcomes. The hydroclimatology of South Asia is highly seasonal and the asymmetric availability of water affects vast areas of Bangladesh differently in space and time, exposing the population of Dhaka region to both droughts and floods and periodic spring-fall outbreaks of diarrheal diseases, such as cholera and rotavirus. This research focuses on stretching this understanding to water and health implications in this growing megacity and adjoining slum areas, and how satellite remote sensing data products and derived knowledge can inform urban planning, water management, and public health sectors to adapt to these climatic and anthropogenic changes for the benefit of societies.

Carbon stock and carbon turnover in boreal and temperate forests - Integration of remote sensing data and global vegetation models

NASA Astrophysics Data System (ADS)

Thurner, Martin; Beer, Christian; Carvalhais, Nuno; Forkel, Matthias; Tito Rademacher, Tim; Santoro, Maurizio; Tum, Markus; Schmullius, Christiane

2016-04-01

Long-term vegetation dynamics are one of the key uncertainties of the carbon cycle. There are large differences in simulated vegetation carbon stocks and fluxes including productivity, respiration and carbon turnover between global vegetation models. Especially the implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current models and their importance at global scale is highly uncertain. These shortcomings have been due to the lack of spatially extensive information on vegetation carbon stocks, which cannot be provided by inventory data alone. Instead, we recently have been able to estimate northern boreal and temperate forest carbon stocks based on radar remote sensing data. Our spatially explicit product (0.01° resolution) shows strong agreement to inventory-based estimates at a regional scale and allows for a spatial evaluation of carbon stocks and dynamics simulated by global vegetation models. By combining this state-of-the-art biomass product and NPP datasets originating from remote sensing, we are able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests along spatial gradients. We observe an increasing turnover rate with colder winter temperatures and longer winters in boreal forests, suggesting frost damage and the trade-off between frost adaptation and growth being important mortality processes in this ecosystem. In contrast, turnover rate increases with climatic conditions favouring drought and insect outbreaks in temperate forests. Investigated global vegetation models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce observation-based spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well in terms of NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in models and estimating their impact on the land carbon balance.

Improving the Representation of Land in Climate Models by Application of EOS Observations

NASA Technical Reports Server (NTRS)

2004-01-01

The PI's IDS current and previous investigation has focused on the applications of the land data toward the improvement of climate models. The previous IDS research identified the key factors limiting the accuracy of climate models to be the representation of albedos, land cover, fraction of landscape covered by vegetation, roughness lengths, surface skin temperature and canopy properties such as leaf area index (LAI) and average stomatal conductance. Therefore, we assembled a team uniquely situated to focus on these key variables and incorporate the remotely sensed measures of these variables into the next generation of climate models.

Airborne Remote Observations of L-Band Radio Frequency Interference and Implications for Satellite Missions

NASA Technical Reports Server (NTRS)

Laymon, Charles; Srinivasan, Karthik; Limaye, Ashutosh

2011-01-01

Passive remote sensing of the Earth s surface and atmosphere from space has significant importance in operational and research environmental studies, in particular for the scientific understanding, monitoring and prediction of climate change and its impacts. Passive remote sensing requires the measurement of naturally occurring radiations, usually of very low power levels, which contain essential information on the physical process under investigation. As such, these sensed radio frequency bands are a unique natural resource enabling space borne passive sensing of the atmosphere and the Earth s surface that deserves adequate allocation to the Earth Exploration Satellite Service and absolute protection from interference. Unfortunately, radio frequency interference (RFI) is an increasing problem for Earth remote sensing, particularly for passive observations of natural emissions. Because these natural signals tend to be very weak, even low levels of interference received by a passive sensor may degrade the fidelity of scientific data. The characteristics of RFI (low-level interference and radar-pulse noise) are not well known because there has been no systematic surveillance, spectrum inventory or mapping of RFI. While conducting a flight experiment over central Tennessee in May 2010, RFI, a concern for any instrument operating in the passive L band frequency, was observed across 16 subbands between 1402-1427 MHz. Such a survey provides rare characterization data from which to further develop mitigation technologies as well as to identify bandwidths to avoid in future sensor formulation.

Fostering Climate Change Literacy Through Rural-Urban Collaborations and GIS

NASA Astrophysics Data System (ADS)

Boger, R. A.; Low, R.; Gorokhovich, Y.; Mandryk, C.

2012-12-01

Three universities, University of Nebraska-Lincoln (UNL), Brooklyn College, and Lehman College, shared expertise and resources to expand the spectrum of climate change topics offered at these institutions. Through this collaboration, four independent but linked modules that incorporate geographic information systems (GIS) and remote sensing desktop and web-based tools and resources (e.g., NASA, NOAA, USGS, and a variety of universities and organizations) have been developed for use by instructors in all three institutions. Module 1 theme is an introduction to sustainability, climate, with an introduction to remote sensing and online GIS tools. The theme for Module 2 is water resources while Module 3 explores local meteorological data and global climate change models. The last module focuses on food production and independent research building on the urban farm movement in New York City and the agricultural stronghold of Nebraska. The hybrid online and face-face course, Global Climate Change, Food Security, and Local Sustainability, was piloted Fall 2012 in a jointly-taught course offered through UNL and Brooklyn College. The online portion was offered through the CAMEL Climate Change website to foster interactions between the rural Nebraska and urban New York City students. A major objective of the course materials is to foster rural-urban student exchanges while motivating students to make connections between climate change and the potential impacts on health, food, and water in their local communities, the nation and around the world. The research component of the project focuses on understanding the importance of spatial literacy in climate change understanding, and is supported by assessment instruments designed specifically for this course. In addition, the formal evaluation will determine whether our rural-urban, local-global approach will empower students to better understand the causes and impacts of climate change.

Study of erosion processes in the Tinto salt-marshes with remote sensing images.

DOT National Transportation Integrated Search

2016-01-01

Both climatic factors and the sea wave energy are two important factors to study the tidal wetlands. One of the most important wetlands in the Southwest of the Iberian Peninsula is the Tinto salt-marshes, the third largest wetland in Andalusia after ...

Uncoupling the complexity of forest soil variation: influence of terrain attributes, spectral indices, and spatial variability

EPA Science Inventory

Growing concern over climate and management induced changes to soil nutrient status has prompted interest in understanding the spatial distribution of forest soil properties. Recent advancements in remotely sensed geospatial technologies are providing an increasing array of data...

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Nellis, M. Duane

1996-01-01

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

Soil moisture variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar

NASA Astrophysics Data System (ADS)

Shrivastava, Sourabh; Kar, Sarat C.; Sharma, Anu Rani

2017-07-01

Variation of soil moisture during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of soil moisture are few or not available, researchers use data derived from remote sensing satellites or global reanalysis. This study documents the intercomparison of soil moisture from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. Soil moisture data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against soil moisture data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002-2011. The ESA soil moisture correlates rather well with observed gridded rainfall. The ESA data indicates that soil moisture increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the soil moisture over India, while the CFSR soil moisture agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate soil moisture values and the ERA-I soil moisture does not show much variability from year to year. Day-to-day variations of soil moisture in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR soil moisture values are reduced up to 0.1 m3/m3 compared to climatological values of more than 0.35 m3/m3. This reduction is not seen in the ERA-I data. Therefore, soil moisture from the CFSR is closer to the ESA observed soil moisture than that from the ERA-I during weak phases of monsoon in the study region.

Tools and Techniques to Collaborate and Connect with At-Risk Climate Communities UsingSensors, Remote Sensing Data, and Media

NASA Astrophysics Data System (ADS)

Drapkin, J. K.; Ramamurthy, P.; Vant-Hull, B.; Yuen, K.; Glenn, A.; Jusino, C.; Corbin, C.; Schuerman, M.; Keefe, J.; Brooke, H.

2016-12-01

Those most at risk during heat waves and floods are often the socio-economically vulnerable. Yet very few studies exist of indoor temperatures during heat waves or of standing water events at the neighborhood level during extreme events. ISeeChange, a community weather and climate journal, is developing tools and testing techniques in a series of community pilots in Harlem and New Orleans to assess if a combination of citizen science, remote sensing, and journalism can bridge the gap. Our consortium of media (WNYC,Adapt NYC, ISeeChange), scientists (CUNY, CoCoRaHS, NASAJPL), and community partners (WE ACT for Environmental Justice, tenant, and neighborhood associations) are collaborating to engage with residents, report radio stories, as well as develop scientifically valuableinformation for decision-making. Community volunteers place temperature and humidity sensors inside residences (Harlem) or photograph standing water using specific methodologies (New Orleans). Sensordata, photographs, and text documenting the impacts of extreme weather on residents are posted on the ISeeChange platform via mobile app or community ambassadors and compared to other remote sensing data products (surface temperature, precipitation, subsidence) Preliminary results of the Harlem pilot show that indoor temperatures are far more stable than outdoor temperatures, so can be both cooler during the day but warmer at night; preliminary work on the New Orleans pilot is set to begin in fall 2016. A full analysis of the Harlem pilot will be presented along with preliminary results of the New Orleans pilot.

Reconstructing time series water volumes of drying lakes in Central Asia with ZY-3 stereo remote sensing data

NASA Astrophysics Data System (ADS)

Li, J.; Warner, T.; Bao, A.

2017-12-01

Central Asia is one of the world most vulnerable areas responding to global change. Lakes in arid regions of Central Asia remain sensitive to climatic change and fluctuate with temperature and precipitation variations. Study showed that some central asian inland lakes in showed a trend of area shrinkage or extinct in the last decades. Quantitative analysis of lake volume changes in spatio-temporal processes will improve our understanding water resource utilization in arid regions and their responses to regional climate change. However, due to the lack of lake bathmetry or observation data, the volumes of these lakes remain unknown. In this paper, three lakes, such as Chaiwopu lake, Alik Lake and Selectyteniz Lake in Central Asia are used to reconstruct lake volume changes. Firstly, stereo mapping technologies derived from ZY-3 high resolution data are used to map the high-precision 3-D lake bathmetry, so as to create "Area-Level-Volume" based on contours of lake bathmetry. Secondly, time series lake areas in the last 50 years are mapped with multi-source and multi-temporal remote sensing images. Based on lake storage curves and time series lake areas, lake volumes in the last 5 decades can be reconstructed, and the spatio-temporal characteristics of lake volume changes and their mechanisms are also analyzed. The results showed that the high-precision lake hydrological elements are reconstructed on arid drying lakes through the application of stereo mapping technology in remote sensing.

LEARNERS: Interdisciplinary Learning Technology Projects Provide Visualizations and Simulations for Use of Geospatial Data in the Classroom

NASA Astrophysics Data System (ADS)

Farrell, N.; Hoban, S.

2001-05-01

The NASA Leading Educators to Applications, Research and NASA-related Educational Resources in Science (LEARNERS) initiative supports seven projects for enhancing kindergarten-to-high school science, geography, technology and mathematics education through Internet-based products derived from content on NASA's mission. Topics incorporated in LEARNERS projects include remote sensing of the Earth for agriculture and weather/climate studies, virtual exploration of remote worlds using robotics and digital imagery. Learners are engaged in inquiry or problem-based learning, often assuming the role of an expert scientist as part of an interdisciplinary science team, to study and explain practical problems using real-time NASA data. The presentation/poster will demonstrate novel uses of remote sensing data for K-12 and Post-Secondary students. This will include the use of visualizations, tools for educators, datasets, and classroom scenarios.

Non-Topographic Space-Based Laser Remote Sensing

NASA Technical Reports Server (NTRS)

Yu, Anthony W.; Abshire, James B.; Riris, Haris; Purucker, Michael; Janches, Diego; Getty, Stephanie; Krainak, Michael A.; Stephen, Mark A.; Chen, Jeffrey R.; Li, Steve X.;

Lidar remote sensing of laser-induced incandescence on light absorbing particles in the atmosphere.

PubMed

Miffre, Alain; Anselmo, Christophe; Geffroy, Sylvain; Fréjafon, Emeric; Rairoux, Patrick

2015-02-09

Carbon aerosol is now recognized as a major uncertainty on climate change and public health, and specific instruments are required to address the time and space evolution of this aerosol, which efficiently absorbs light. In this paper, we report an experiment, based on coupling lidar remote sensing with Laser-Induced-Incandescence (LII), which allows, in agreement with Planck's law, to retrieve the vertical profile of very low thermal radiation emitted by light-absorbing particles in an urban atmosphere over several hundred meters altitude. Accordingly, we set the LII-lidar formalism and equation and addressed the main features of LII-lidar in the atmosphere by numerically simulating the LII-lidar signal. We believe atmospheric LII-lidar to be a promising tool for radiative transfer, especially when combined with elastic backscattering lidar, as it may then allow a remote partitioning between strong/less light absorbing carbon aerosols.

Inland and coastal waters

NASA Astrophysics Data System (ADS)

Mouw, Colleen; Greb, Steven

2012-09-01

Workshop for Remote Sensing of Coastal and Inland Waters;Madison, Wisconsin, 20-22 June 2012 Coastal and inland water bodies, which have great value for recreation, food supply, commerce, transportation, and human health, have been experiencing external pressure from direct human activities and climate change. Given their societal and economic value, understanding issues of water quality, water quantity, and the impact of environmental change on the ecological and biogeochemical functioning of these water bodies is of interest to a broad range of communities. Remote sensing offers one of the most spatially and temporally comprehensive tools for observing these waters. While there has been some success with remotely observing these water bodies, many challenges still remain, including algorithm performance, atmospheric correction, the relationships between optical properties and biogeochemical parameters, sufficient spatial and spectral resolution, and a lack of uncertainty estimates over the wide range of environmental conditions encountered across these coastal and inland water bodies.

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Remote Sensing of Vegetation Recovery from Disturbance in Drylands

NASA Astrophysics Data System (ADS)

Poitras, T. B.; Villarreal, M. L.; Waller, E.; Duniway, M.; Nauman, T.

2016-12-01

Characteristics of dryland ecosystems such as climatic extremes and water limitations render semi-arid regions vulnerable to disturbance and slow to recover. Land surface monitoring over time through the use of remote sensing may have potential for identifying dryland ecosystem recovery after anthropogenic and natural disturbance. However, semi-arid vegetation cover is challenging to measure using remote sensing techniques due to low vegetation cover and confusion between bright and variable soils and non-photosynthetic vegetation (NPV). We therefore evaluated the ability of various multispectral indices to distinguish bare ground from total vegetation cover, in order to determine those that can detect changes over time in heavily disturbed sites. We calculated nine spectral indices from Landsat TM using Google Earth Engine (March through October, 2006 through 2008) and tested relationships between index values and ground measurements from long-term monitoring data collected in and around Canyonlands National Park in Utah. We also tested multivariate models, with some showing improvement under cross-validation. We found that indices that included shortwave infrared bands and soil brightness were important for capturing gradients in bare ground, and vegetation cover was best quantified with near-infrared bands. These results will be used to help assess the landscape-scale impacts of oil and gas development in dryland ecosystems and to measure response to restoration efforts. Keywords: remote sensing, landsat, drylands

Evaluating ESA CCI soil moisture in East Africa.

PubMed

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P

2016-06-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar

NASA Astrophysics Data System (ADS)

Chen, Qi

2015-08-01

Estimating tree aboveground biomass (AGB) and carbon (C) stocks using remote sensing is a critical component for understanding the global C cycle and mitigating climate change. However, the importance of allometry for remote sensing of AGB has not been recognized until recently. The overarching goals of this study are to understand the differences and relationships among three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and to examine the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models. Airborne lidar data from three study sites in the Pacific Northwest, USA were used to predict woody AGB estimated from the different allometric methods. It was found that the CRM and Jenkins estimates of woody AGB are related via the CRM adjustment factor. In terms of lidar-biomass modeling, CRM had the smallest model errors, while the Jenkins method had the largest ones and the regional method was between. The best model fitting from CRM is attributed to its inclusion of tree height in calculating merchantable stem volume and the strong dependence of non-merchantable stem biomass on merchantable stem biomass. This study also argues that it is important to characterize the allometric model errors for gaining a complete understanding of the remotely-sensed AGB prediction errors.

Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

NASA Astrophysics Data System (ADS)

Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

2017-07-01

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

Assessing the impacts of climate change in Mediterranean catchments under conditions of data scarcity - The Gaza case study

NASA Astrophysics Data System (ADS)

Gampe, David; Ludwig, Ralf

2013-04-01

According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. While there is scientific consensus that climate induced changes on the hydrology of Mediterranean regions are presently occurring and are projected to amplify in the future, very little knowledge is available about the quantification of these changes, which is hampered by a lack of suitable and cost effective hydrological monitoring and modeling systems. The European FP7-project CLIMB is aiming to analyze climate induced changes on the hydrology of the Mediterranean Basins by investigating seven test sites located in the countries Italy, France, Turkey, Tunisia, Gaza and Egypt. CLIMB employs a combination of novel geophysical field monitoring concepts, remote sensing techniques and integrated hydrologic modeling to improve process descriptions and understanding and to quantify existing uncertainties in climate change impact analysis. One of those seven sites is the Gaza Strip, located in the Eastern Mediterranean and part of the Palestinian Autonomous Area, covers an area of 365km² with a length of 35km and 6 to 12km in width. Elevation ranges from sea level up to 104m in the East of the test site. Mean annual precipitation varies from 235mm in the South to 420mm in the North of the area. The inter annual variability of rainfall and the rapid population growth in an highly agricultural used area represent the major challenges in this area. The physically based Water Simulation Model WaSiM Vers. 2 (Schulla & Jasper (1999)) is setup to model current and projected future hydrological conditions. The availability of measured meteorological and hydrological data is poor as common to many Mediterranean catchments. The lack of available measured input data hampers the calibration of the model setup and the validation of model outputs. WaSiM was driven with meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. State of the art remote sensing techniques and field measuring techniques were applied to improve the quality of hydrological input parameters. For the parameterization of the vegetation the Leaf Area Index (LAI) is a crucial component. However, the LAI is difficult to access at field scale, hence a simple remote sensing approach, using the Normalized Difference Vegetation Index (NDVI) and MODIS LAI information, was applied for the parameterization in WaSiM. As no permanent streams, hence no discharge measurements, exist in the Gaza Strip, the actual evapotranspiration (ETact) outputs of the model were used for model validation. Landsat TM images were applied to calculate the actual monthly mean ETact rates using the triangle method (Jiang and Islam, 1999). Simulated spatial ETact patterns and those derived from remote sensing show a good fit especially for the growing season.

Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand.

PubMed

Kiang, Richard; Adimi, Farida; Soika, Valerii; Nigro, Joseph; Singhasivanon, Pratap; Sirichaisinthop, Jeeraphat; Leemingsawat, Somjai; Apiwathnasorn, Chamnarn; Looareesuwan, Sornchai

2006-11-01

In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world's malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible.

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

Future opportunities and challenges in remote sensing of drought

USGS Publications Warehouse

Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt; Wardlow, Brian D.; Anderson, Martha C.; Verdin, James P.

2012-01-01

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Future Opportunities and Challenges in Remote Sensing of Drought

NASA Technical Reports Server (NTRS)

Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt

2011-01-01

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)

Unique Offerings of the ISS as an Earth Observing Platform

NASA Technical Reports Server (NTRS)

Cooley, Victor M.

2013-01-01

The International Space Station offers unique capabilities for earth remote sensing. An established Earth orbiting platform with abundant power, data and commanding infrastructure, the ISS has been in operation for twelve years as a crew occupied science laboratory and offers low cost and expedited concept-to-operation paths for new sensing technologies. Plug in modularity on external platforms equipped with structural, power and data interfaces standardizes and streamlines integration and minimizes risk and start up difficulties. Data dissemination is also standardized. Emerging sensor technologies and instruments tailored for sensing of regional dynamics may not be worthy of dedicated platforms and launch vehicles, but may well be worthy of ISS deployment, hitching a ride on one of a variety of government or commercial visiting vehicles. As global acceptance of the urgent need for understanding Climate Change continues to grow, the value of ISS, orbiting in Low Earth Orbit, in complementing airborne, sun synchronous polar, geosynchronous and other platform remote sensing will also grow.

High resolution remote sensing of densely urbanised regions: a case study of Hong Kong.

PubMed

Nichol, Janet E; Wong, Man Sing

2009-01-01

Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21(st) century.

[On-Orbit Multispectral Sensor Characterization Based on Spectral Tarps].

PubMed

Li, Xin; Zhang, Li-ming; Chen, Hong-yao; Xu, Wei-wei

2016-03-01

The multispectral remote sensing technology has been a primary means in the research of biomass monitoring, climate change, disaster prediction and etc. The spectral sensitivity is essential in the quantitative analysis of remote sensing data. When the sensor is running in the space, it will be influenced by cosmic radiation, severe change of temperature, chemical molecular contamination, cosmic dust and etc. As a result, the spectral sensitivity will degrade by time, which has great implication on the accuracy and consistency of the physical measurements. This paper presents a characterization method of the degradation based on man-made spectral targets. Firstly, a degradation model is established in the paper. Then, combined with equivalent reflectance of spectral targets measured and inverted from image, the degradation characterization can be achieved. The simulation and on orbit experiment results showed that, using the proposed method, the change of center wavelength and band width can be monotored. The method proposed in the paper has great significance for improving the accuracy of long time series remote sensing data product and comprehensive utilization level of multi sensor data products.

Multi-decadal Arctic sea ice roughness.

NASA Astrophysics Data System (ADS)

Tsamados, M.; Stroeve, J.; Kharbouche, S.; Muller, J. P., , Prof; Nolin, A. W.; Petty, A.; Haas, C.; Girard-Ardhuin, F.; Landy, J.

2017-12-01

The transformation of Arctic sea ice from mainly perennial, multi-year ice to a seasonal, first-year ice is believed to have been accompanied by a reduction of the roughness of the ice cover surface. This smoothening effect has been shown to (i) modify the momentum and heat transfer between the atmosphere and ocean, (ii) to alter the ice thickness distribution which in turn controls the snow and melt pond repartition over the ice cover, and (iii) to bias airborne and satellite remote sensing measurements that depend on the scattering and reflective characteristics over the sea ice surface topography. We will review existing and novel remote sensing methodologies proposed to estimate sea ice roughness, ranging from airborne LIDAR measurement (ie Operation IceBridge), to backscatter coefficients from scatterometers (ASCAT, QUICKSCAT), to multi angle maging spectroradiometer (MISR), and to laser (Icesat) and radar altimeters (Envisat, Cryosat, Altika, Sentinel-3). We will show that by comparing and cross-calibrating these different products we can offer a consistent multi-mission, multi-decadal view of the declining sea ice roughness. Implications for sea ice physics, climate and remote sensing will also be discussed.

Volcano remote sensing with ground-based spectroscopy.

PubMed

McGonigle, Andrew J S

2005-12-15

The chemical compositions and emission rates of volcanic gases carry important information about underground magmatic and hydrothermal conditions, with application in eruption forecasting. Volcanic plumes are also studied because of their impacts upon the atmosphere, climate and human health. Remote sensing techniques are being increasingly used in this field because they provide real-time data and can be applied at safe distances from the target, even throughout violent eruptive episodes. However, notwithstanding the many scientific insights into volcanic behaviour already achieved with these approaches, technological limitations have placed firm restrictions upon the utility of the acquired data. For instance, volcanic SO(2) emission rate measurements are typically inaccurate (errors can be greater than 100%) and have poor time resolution (ca once per week). Volcanic gas geochemistry is currently being revolutionized by the recent implementation of a new generation of remote sensing tools, which are overcoming the above limitations and are providing degassing data of unprecedented quality. In this article, I review this field at this exciting point of transition, covering the techniques used and the insights thereby obtained, and I speculate upon the breakthroughs that are now tantalizingly close.

Using Tree-Rings and Remote Sensing to Investigate Forest Productivity Response to Landscape Fragmentation in Northeastern Algeria

NASA Astrophysics Data System (ADS)

Rouini, N.; Lepley, K. S.; Messaoudene, M.

2017-12-01

Remote sensing and dendrochronology are valuable tools in the face of climate change and land use change, yet the connection between these resources remains largely unexploited. Research on forest fragmentation is mainly focused on animal groups, while our work focuses on tree communities. We link tree-rings and remotely-sensed Normalized Difference Vegetation Index (NDVI) using seasonal correlation analysis to investigate forest primary productivity response to fragmentation. Tree core samples from Quercus afares have been taken from two sites within the Guerrouche Forest in northeastern Algeria. The first site is located within a very fragmented area while the second site is intact. Fragmentation is estimated to have occurred with the construction of a road in 1930. We find raw tree-ring width chronologies from each site reveal growth release in the disturbed site after 1930. The means of each chronology for the 1930 to 2016 period are statistically different (p < 0.01). Based on these preliminary results we hypothesize that reconstructed primary productivity (NDVI) will be higher in the fragmented site after fragmentation took place.

Some insights on grassland health assessment based on remote sensing.

PubMed

Xu, Dandan; Guo, Xulin

2015-01-29

Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment.

Some Insights on Grassland Health Assessment Based on Remote Sensing

PubMed Central

Xu, Dandan; Guo, Xulin

2015-01-01

Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment. PMID:25643060

High Resolution Remote Sensing of Densely Urbanised Regions: a Case Study of Hong Kong

PubMed Central

Nichol, Janet E.; Wong, Man Sing

2009-01-01

Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21st century. PMID:22408549

Remote Sensing of Cryosphere: Estimation of Mass Balance Change in Himalayan Glaciers

NASA Astrophysics Data System (ADS)

Ambinakudige, Shrinidhi; Joshi, Kabindra

2012-07-01

Glacial changes are an important indicator of climate change. Our understanding mass balance change in Himalayan glaciers is limited. This study estimates mass balance of some major glaciers in the Sagarmatha National Park (SNP) in Nepal using remote sensing applications. Remote sensing technique to measure mass balance of glaciers is an important methodological advance in the highly rugged Himalayan terrain. This study uses ASTER VNIR, 3N (nadir view) and 3B (backward view) bands to generate Digital Elevation Models (DEMs) for the SNP area for the years 2002, 2003, 2004 and 2005. Glacier boundaries were delineated using combination of boundaries available in the Global land ice measurement (GLIMS) database and various band ratios derived from ASTER images. Elevation differences, glacial area, and ice densities were used to estimate the change in mass balance. The results indicated that the rate of glacier mass balance change was not uniform across glaciers. While there was a decrease in mass balance of some glaciers, some showed increase. This paper discusses how each glacier in the SNP area varied in its annual mass balance measurement during the study period.

Sustainable Biosphere Initiative Project

NASA Technical Reports Server (NTRS)

1997-01-01

The goal of the Advanced Technology in Ecological Sciences project is to gain broad participation within the environmental scientific community in developing a research agenda addressing the development and refinement of technologies instrumental to research that responds to these challenges (e.g. global climate change, unsustainable resource use, and threats to biological diversity). The following activities have been completed: (1) A listserve 'eco-tech was set up to serve as a clearinghouse of information about activities and events relating to advanced technologies; (2) A series of conference calls were organized on specific topics including data visualization and spatial analysis, and remote sensing; and (3) Two meetings were organized at the 19% ESA Annual Meeting in Providence, Rhode Island. Topics covered included concerns about tool and data sharing; interest in expanded development of ground-based remote sensing technologies for monitoring; issues involved in training for using new technologies and increasing data streams, and- associated implications of data processing capabilities; questions about how to develop appropriate standards (i.e. surface morphology classification standards) that facilitate the exchange and comparison of analytical results; and some thoughts about remote sensing platforms and vehicles.

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

NASA Astrophysics Data System (ADS)

Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng

2014-11-01

Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.

A Changing Number of Alternative States in the Boreal Biome: Reproducibility Risks of Replacing Remote Sensing Products.

PubMed

Xu, Chi; Holmgren, Milena; Van Nes, Egbert H; Hirota, Marina; Chapin, F Stuart; Scheffer, Marten

2015-01-01

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct 'savanna-like' state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available.

Remote sensing, paleoecology, and the archaeology of human migration during the Pleistocene in central Asia and western China

NASA Astrophysics Data System (ADS)

Glantz, Michelle M.; Todd, Lawrence

2003-07-01

Remote sensing used in the context of global information systems has enormous applications within archaeology. This technology enables the discovery of new archaeological features and promotes an understanding of the relationship between ecosystem and cultural dynamics. Archaeologists are able to add a time dimension to 'creeping environmental changes' that other areas of scientific inquiry concerned with climate change often lack. Remote sensing and other aerial prospecting has been used successfully to model land use and population expansions during relatively recent archaeological eras, such as the Bronze and Iron Ages. Although satellite image databases exist for numerous areas of the New and Old World, very little research has been conducted in Central Asia or western China. This region is historically significant because of its position along the important trading route called the Silk Road. The purpose of the present research is to investigate another poorly understood period of human history that would benefit from the application of remote sensing and associated ground truthing techniques. The migration of hominids out of Africa during the late Pliocene/early Pleistocene and their subsequent colonization of north-central, east, and south-east Asia is relatively well documented in the archaeological record and marks the beginning of the long-term process of human impacts on the region. However, the trajectory of dispersal of Homo erectus, Neandertals, and early modern humans and the ways by which ecosystem vagaries affected this dispersal across Eurasia is unknown. Our purpose is to summarize what is currently known about the geological indicators of ecosystem changes that remote sensing techniques provide and how ecosystem variables may allow us to model human migration as that of an invasive species through this important geographic crossroads of the Old World.

Monitoring Freeze-Thaw States in the Pan-Arctic: Application of Microwave Remote Sensing to Monitoring Hydrologic and Ecological Processes

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Kimball, J. S.

2004-12-01

The transition of the landscape between predominantly frozen and non-frozen conditions in seasonally frozen environments impacts climate, hydrological, ecological and biogeochemical processes profoundly. Satellite microwave remote sensing is uniquely capable of detecting and monitoring a range of related biophysical processes associated with the measurement of landscape freeze/thaw status. We present the development, physical basis, current techniques and selected hydrological applications of satellite-borne microwave remote sensing of landscape freeze/thaw states for the terrestrial cryosphere. Major landscape hydrological processes embracing the remotely-sensed freeze/thaw signal include timing and spatial dynamics of seasonal snowmelt and associated soil thaw, runoff generation and flooding, ice breakup in large rivers and lakes, and timing and length of vegetation growing seasons and associated productivity and trace gas exchange. Employing both active and passive microwave sensors, we apply a selection of temporal change classification algorithms to examine a variety of hydrologic processes. We investigate contemporaneous and retrospective applications of the QuikSCAT scatterometer, and the SSM/I and SMMR radiometers to this end. Results illustrate the strong correspondence between regional thawing, seasonal ice break up for rivers, and the springtime pulse in river flow. We present the physical principles of microwave sensitivity to landscape freeze/thaw state, recent progress in applying these principles toward satellite remote sensing of freeze/thaw processes over broad regions, and potential for future global monitoring of this significant phenomenon of the global cryosphere. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, and at the University of Montana, Missoula, under contract to the National Aeronautics and Space Administration.

Vulnerability and feedbacks of permafrost to climate change

Treesearch

Guido Grosse; Vladimir Romanovsky; Torre Jorgenson; Katey Walter Anthony; Jerry Brown; Pier Paul Overduin; Alfred Wegener

2011-01-01

The effects of permafrost degradation on terrestrial and offshore environments in polar regions and on the Earth's atmosphere are significant. Field-based observations, remote sensing, and modeling document regional warming and thawing of permafrost. However, major research questions regarding vulnerability of permafrost to thawing, the projected decline in...

Ecoregional analysis of nearshore sea-surface temperature in the North Pacific

EPA Science Inventory

Aim Sea surface temperature (SST) has been a parameter widely-identified to be useful to the investigation of marine species distribution, migration, and invasion, especially as SSTs are predicted to be affected by climate change. Here we use a remotely-sensed dataset to focus on...

Thermal remote sensing of crop water status: pros and cons of two different approaches

USDA-ARS?s Scientific Manuscript database

Recent climate change has lead, in many places around the world, to a decrease in the availability of water resources. This limited water availability is decreasing the cost-effectiveness of irrigated agricultural crops, and practices reducing the required amount of water without decreasing the qua...

Thermal remote sensing of crop water status: pros and cons of two different approaches

USDA-ARS?s Scientific Manuscript database

Recent climate change has lead, in many places around the world, to a decrease in the availability of fresh water resources. This limited water availability decreases the cost-effectiveness of irrigated agricultural crops, and increases the desirability of practices that reduce applied water withou...

Remote sensing of leaf, canopy and vegetation water contents for satellite climate data records

USDA-ARS?s Scientific Manuscript database

Foliar water content is a dynamic quantity depending on water losses from transpiration and water uptake from the soil. Absorption of shortwave radiation by water is determined by various frequency overtones of fundamental bending and stretching molecular transitions. Leaf water potential and rela...

Protocols for vegetation and habitat monitoring with unmanned aerial vehicles: linking research to management on US public lands

USDA-ARS?s Scientific Manuscript database

Background/Question/Methods: Monitoring of the condition and trend of natural resources is critical for determining effectiveness of management actions and understanding ecosystem responses to broad-scale processes like climate change. While broad-scale remote sensing has generally improved the abi...

Analysis of the spatial and temporal variability of terrestrial water storage and snowpack in the Pacific Northwestern United States

EPA Science Inventory

The spatial and temporal variability of terrestrial water storage and snowpack in the Pacific Northwest (PNW) was analyzed for water years 2001–2010 using measurements from the Gravity Recovery and Climate Experiment (GRACE) instrument. GRACE provides remotely-sensed measurements...

Staff - Jacquelyn R. Overbeck | Alaska Division of Geological & Geophysical

Science.gov Websites

Tidal Datum Portal Climate and Cryosphere Hazards Coastal Hazards Program Guide to Geologic Hazards in main content Jacquelyn R. Overbeck Jacquelyn R. Overbeck Geomorphology, coastal hazards, remote sensing University, Environmental Science Projects and/or Research Interests As the project manager for the Coastal

Flexibility on storage-release based distributed hydrologic modeling with object-oriented approach

USDA-ARS?s Scientific Manuscript database

With the availability of advanced hydrologic data in the public domain such as remotely sensed and climate change scenario data, there is a need for a modeling framework that is capable of using these data to simulate and extend hydrologic processes with multidisciplinary approaches for sustainable ...

The error structure of the SMAP single and dual channel soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

Knowledge of the temporal error structure for remotely-sensed surface soil moisture retrievals can improve our ability to exploit them for hydrology and climate studies. This study employs a triple collocation type analysis to investigate both the total variance and temporal auto-correlation of erro...

Role of subsurface physics in the assimilation of surface soil moisture observations

USDA-ARS?s Scientific Manuscript database

Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sense...

Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA

EPA Science Inventory

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can potentially detect large-scale, slow changes (e.g., climate change), as well as more local and rapid changes (e.g., fire, land development). A useful indicator for detecting change i...

Remote sensing applications for estimating changes on crop evapotranspiration of the most water intensive crops, due to climate change in Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Neocleous, D.; Stylianou, A.; Markou, M.; Kountios, G.; Hadjimitsis, D.

2016-08-01

Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural process. Deficit or excess water irrigation quantities could create either crop health related problems or water over-consumption situation which lead to stored water reduction and toxic material depletion to deeper ground layers, respectively. In this context, and under the current conditions, where Cyprus is facing effects of climate changes, purpose of this study is basically to estimate the needed crop water requirements of the past (1995-2004) and the corresponding ones of the present (2005-2015) in order to test if there were any significant changes regarding the crop water requirements of the most water intensive trees in Cyprus. Mediterranean region has been identified as the region that will suffer the most from climate change. Thus the paper refers to effects of climate changes on crop evapotranspiration (ETc) using remotely sensed data from Landsat TM/ ETM+ / OLI employing a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL). Though the general feeling is that of changes on climate will consequently affect ETc, the results have indicated that there is no significant effect of climate change on crop evapotranspiration, despite the fact that some climatic factors have changed. Applying Student's T-test, the mean values for the most water intensive trees in Cyprus of the 1994-2004 decade have shown no statistical difference from the mean values of 2005-2015 decade's for all the cases, concluding that the climate change taking place the last decades in Cyprus have either not affected the crop evapotranspiration or the crops have manage to adapt into the new environmental conditions through time.

Response of Vegetation Dynamics to Projected Climate Change based on NDVI Simulations using Stepwise Cluster Analysis in the Three-River Headwaters Region of China

NASA Astrophysics Data System (ADS)

Zheng, Y.; Lv, E.; Huang, Y.

2016-12-01

Located in the hinterland of the Qinghai-Tibetan Plateau, the Three-River Headwaters region (THR) features unique eco-environmental conditions and fragile ecosystems, and is very vulnerable to climate change. To investigate the effects of climate change on the ecosystem, the Normalized Difference Vegetation Index (NDVI) was employed as an indicator to reflect the vegetation dynamics in response to climate change. This study proposed a model based on Stepwise-cluster analysis to predict the temporal and spatial distributions of NDVI values for five future years according to Global Circulation Models (GCMs) climate projections under the RCP4.5 scenario. The obtained spatial results showed very good agreements between simulations and remote sensing observations of the NDVI value for both training and validation, and the developed model demonstrated its capability of predicting the monthly changes of NDVI through representing the relationships between it and various climatic factors, including remote sensed precipitation and temperature with no, 1 and 2-month lag period. The monthly average precipitation with one-month lag period was further found to be the most important climatic factor that drives the changes of NDVI in the THR. Compared with the values of NDVI in 2000 - 2013, the predicting results indicate the values of NDVI for the THR in growing season (May to October) will decrease by 15.74% in the next 100 years, suggesting that the THR is going to experience an environmental degradation. The results also show that precipitation is the primary driving factor relative to temperature, especially the one-month-lag precipitation. Findings from this study would help policy makers draw up effective water resource and eco-environmental management strategies for adapting to climate change in the THR.

Monitoring boreal ecosystem phenology with integrated active/passive microwave remote sensing

NASA Technical Reports Server (NTRS)

McDonald, K. C.; Njoku, E.; Kimball, J.; Running, S.; Thompson, C.; Lee, J. K.

2002-01-01

The important role of the high latitudes in the functioning of global processes is becoming well established. The size and remoteness of arctic and boreal ecosystems, however, pose a challenge to quantification of both terrestrial ecosystem processes and their feedbacks to regional and global climate conditions. Boreal and arctic regions form a complex land cover mosaic where vegetation structure, condition and distribution are strongly regulated by environmental factors such as moisture availability, permafrost, growing season length, disturbance and soil nutrients.

Application of Remote Sensing to Assess the Impact of Short Term Climate Variability on Coastal Sedimentation

NASA Technical Reports Server (NTRS)

Menzel, W. Paul; Huh, Oscar K.; Walker, Nan

2004-01-01

The purpose of this joint University of Wisconsin (UW) and Louisiana State University (LSU) project has been to relate short term climate variation to response in the coastal zone of Louisiana in an attempt to better understand how the coastal zone is shaped by climate variation. Climate variation in this case largely refers to variation in surface wind conditions that affect wave action and water currents in the coastal zone. The primary region of focus was the Atchafalaya Bay and surrounding bays in the central coastal region of Louisiana. Suspended solids in the water column show response to wind systems both in quantity (through resuspension) and in the pattern of dispersement or transport. Wind systems associated with cold fronts are influenced by short term climate variation. Wind energy was used as the primary signature of climate variation in this study because winds are a significant influence on sediment transport in the micro-tidal Gilf of Mexico coastal zone. Using case studies, the project has been able to investigate the influence of short term climate variation on sediment transport. Wind energy data, collected daily for National Weather Service (NWS) stations at Lake Charles and New Orleans, LA, were used as an indicator of short term climate variation influence on seasonal time scales. A goal was to relate wind energy to coastal impact through sediment transport. This goal was partially accomplished by combining remote sensing and wind energy data. Daily high resolution remote sensing observations are needed to monitor the complex coastal zone environment, where winds, tides, and water level all interact to influence sediment transport. The NASA Earth Observing System (EOS) era brings hope for documenting and revealing response of the complex coastal transport mosaic through regular high spatial resolution observations from the Moderate resolution Imaging Spectrometer (MODIS) instrument. MODIS observations were sampled in this project for information content and should continue to be viewed as a resource for coastal zone monitoring. The project initialized the effort to transfer a suspended sediment concentration (SSC) algorithm to the MODIS platform for case 2 waters. MODIS enables monitoring of turbid coastal zones around the globe. The MODIS SSC algorithm requires refinements in the atmospheric aerosol contribution, sun glint influence, and designation of the sediment inherent optical properties (IOPs); the framework for continued development is in place with a plan to release the algorithm to the MODIS direct broadcast community.

Cloud Optical Depth Measured with Ground-Based, Uncooled Infrared Imagers

NASA Technical Reports Server (NTRS)

Shaw, Joseph A.; Nugent, Paul W.; Pust, Nathan J.; Redman, Brian J.; Piazzolla, Sabino

2012-01-01

Recent advances in uncooled, low-cost, long-wave infrared imagers provide excellent opportunities for remotely deployed ground-based remote sensing systems. However, the use of these imagers in demanding atmospheric sensing applications requires that careful attention be paid to characterizing and calibrating the system. We have developed and are using several versions of the ground-based "Infrared Cloud Imager (ICI)" instrument to measure spatial and temporal statistics of clouds and cloud optical depth or attenuation for both climate research and Earth-space optical communications path characterization. In this paper we summarize the ICI instruments and calibration methodology, then show ICI-derived cloud optical depths that are validated using a dual-polarization cloud lidar system for thin clouds (optical depth of approximately 4 or less).

Food Security, Decision Making and the Use of Remote Sensing in Famine Early Warning Systems

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2008-01-01

Famine early warning systems use remote sensing in combination with socio-economic and household food economy analysis to provide timely and rigorous information on emerging food security crises. The Famine Early Warning Systems Network (FEWS NET) is the US Agency for International Development's decision support system in 20 African countries, as well as in Guatemala, Haiti and Afghanistan. FEWS NET provides early and actionable policy guidance for the US Government and its humanitarian aid partners. As we move into an era of climate change where weather hazards will become more frequent and severe, understanding how to provide quantitative and actionable scientific information for policy makers using biophysical data is critical for an appropriate and effective response.

Integrated remote sensing for multi-temporal analysis of urban land cover-climate interactions

NASA Astrophysics Data System (ADS)

Savastru, Dan M.; Zoran, Maria A.; Savastru, Roxana S.

2016-08-01

Climate change is considered to be the biggest environmental threat in the future in the South- Eastern part of Europe. In frame of predicted global warming, urban climate is an important issue in scientific research. Surface energy processes have an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables in Bucharest metropolitan area of Romania. Remote sensing data from Landsat TM/ETM+ and time series MODIS Terra/Aqua sensors have been used to assess urban land cover- climate interactions over period between 2000 and 2015 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Determining the relative importance of climatic drivers on spring phenology in grassland ecosystems of semi-arid areas.

PubMed

Zhu, Likai; Meng, Jijun

2015-02-01

Understanding climate controls on spring phenology in grassland ecosystems is critically important in predicting the impacts of future climate change on grassland productivity and carbon storage. The third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) data were applied to derive the start of the growing season (SOS) from 1982-2010 in grassland ecosystems of Ordos, a typical semi-arid area in China. Then, the conditional Granger causality method was utilized to quantify the directed functional connectivity between key climatic drivers and the SOS. The results show that the asymmetric Gaussian (AG) function is better in reducing noise of NDVI time series than the double logistic (DL) function within our study area. The southeastern Ordos has earlier occurrence and lower variability of the SOS, whereas the northwestern Ordos has later occurrence and higher variability of the SOS. The research also reveals that spring precipitation has stronger causal connectivity with the SOS than other climatic factors over different grassland ecosystem types. There is no statistically significant trend across the study area, while the similar pattern is observed for spring precipitation. Our study highlights the link of spring phenology with different grassland types, and the use of coupling remote sensing and econometric tools. With the dramatic increase in global change research, Granger causality method augurs well for further development and application of time-series modeling of complex social-ecological systems at the intersection of remote sensing and landscape changes.

Climate impacts on environmental risks evaluated from space: a contribution to social benefits within the GEOSS Health Area: The case of Rift Valley Fever in Senegal

NASA Astrophysics Data System (ADS)

Tourre, Y. M.

2009-12-01

Climate and environment vary on many spatio-temporal scales, including climate change, with impacts on ecosystems, vector-borne diseases and public health worldwide. This study is to enable societal benefits from a conceptual approach by mapping climatic and environmental conditions from space and understanding the mechanisms within the Health Social Benefit GEOSS area. The case study is for Rift Valley Fever (RVF) epidemics in Senegal is presented. Ponds contributing to mosquitoes’ thriving, were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on ponds’ dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localization of vulnerable hosts such as parked cattle (from QuickBird satellite) are also used. Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds’ dynamics. While Zones Potentially Occupied by Mosquitoes (ZPOM) are mapped, detailed risks areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of parks where cattle is potentially exposed to mosquitoes’ bites. This new conceptual approach, using remote-sensing techniques belonging to GEOSS, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of integrated operational early warning system within the health application communities since climatic and environmental conditions (both natural and anthropogenic) are changing rapidly.

Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes

USGS Publications Warehouse

Buffington, Kevin J.; Dugger, Bruce D.; Thorne, Karen M.

2018-01-01

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.

Atmospheric Radiation Measurement (ARM) Climate Research Facility Management Plan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mather, James

2016-04-01

Mission and Vision Statements for the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility Mission The ARM Climate Research Facility, a DOE scientific user facility, provides the climate research community with strategically located in situ and remote-sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface. Vision To provide a detailed and accurate description of the Earth atmosphere in diverse climate regimes to resolve the uncertainties in climate and Earth system models toward the development ofmore » sustainable solutions for the nation's energy and environmental challenges.« less

Forecasting climate change impacts on plant populations over large spatial extents

DOE PAGES

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; ...

2016-10-24

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; Homer, Collin G.; Kleinhesselink, Andrew R.; Adler, Peter B.

2016-01-01

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

The footprint of Alaskan tundra fires during the past half-century: implications for surface properties and radiative forcing

USGS Publications Warehouse

Rocha, Adrian V.; Loranty, Michael M.; Higuera, Phil E.; Mack, Michelle C.; Hu, Feng Sheng; Jones, Benjamin M.; Breen, Amy L.; Rastetter, Edward B.; Goetz, Scott J.; Shaver, Gus R.

2012-01-01

Recent large and frequent fires above the Alaskan arctic circle have forced a reassessment of the ecological and climatological importance of fire in arctic tundra ecosystems. Here we provide a general overview of the occurrence, distribution, and ecological and climate implications of Alaskan tundra fires over the past half-century using spatially explicit climate, fire, vegetation and remote sensing datasets for Alaska. Our analyses highlight the importance of vegetation biomass and environmental conditions in regulating tundra burning, and demonstrate that most tundra ecosystems are susceptible to burn, providing the environmental conditions are right. Over the past two decades, fire perimeters above the arctic circle have increased in size and importance, especially on the North Slope, indicating that future wildfire projections should account for fire regime changes in these regions. Remote sensing data and a literature review of thaw depths indicate that tundra fires have both positive and negative implications for climatic feedbacks including a decadal increase in albedo radiative forcing immediately after a fire, a stimulation of surface greenness and a persistent long-term (>10 year) increase in thaw depth. In order to address the future impact of tundra fires on climate, a better understanding of the control of tundra fire occurrence as well as the long-term impacts on ecosystem carbon cycling will be required.

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites

PubMed Central

Karl, Jason W.

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches. PMID:28414731

Advancing High Spatial and Spectral Resolution Remote Sensing for Observing Plant Community Response to Environmental Variability and Change in the Alaskan Arctic

NASA Astrophysics Data System (ADS)

Vargas Zesati, Sergio A.

The Arctic is being impacted by climate change more than any other region on Earth. Impacts to terrestrial ecosystems have the potential to manifest through feedbacks with other components of the Earth System. Of particular concern is the potential for the massive store of soil organic carbon to be released from arctic permafrost to the atmosphere where it could exacerbate greenhouse warming and impact global climate and biogeochemical cycles. Even though substantial gains to our understanding of the changing Arctic have been made, especially over the past decade, linking research results from plot to regional scales remains a challenge due to the lack of adequate low/mid-altitude sampling platforms, logistic constraints, and the lack of cross-scale validation of research methodologies. The prime motivation of this study is to advance observational capacities suitable for documenting multi-scale environmental change in arctic terrestrial landscapes through the development and testing of novel ground-based and low altitude remote sensing methods. Specifically this study addressed the following questions: • How well can low-cost kite aerial photography and advanced computer vision techniques model the microtopographic heterogeneity of changing tundra surfaces? • How does imagery from kite aerial photography and fixed time-lapse digital cameras (pheno-cams) compare in their capacity to monitor plot-level phenological dynamics of arctic vegetation communities? • Can the use of multi-scale digital imaging systems be scaled to improve measurements of ecosystem properties and processes at the landscape level? • How do results from ground-based and low altitude digital remote sensing of the spatiotemporal variability in ecosystem processes compare with those from satellite remote sensing platforms? Key findings from this study suggest that cost-effective alternative digital imaging and remote sensing methods are suitable for monitoring and quantifying plot to landscape level ecosystem structure and phenological dynamics at multiple temporal scales. Overall, this study has furthered our knowledge of how tundra ecosystems in the Arctic change seasonally and how such change could impact remote sensing studies conducted from multiple platforms and across multiple spatial scales. Additionally, this study also highlights the urgent need for research into the validation of satellite products in order to better understand the causes and consequences of the changing Arctic and its potential effects on global processes. This study focused on sites located in northern Alaska and was formed in collaboration with Florida International University (FIU) and Grand Valley State University (GVSU) as a contribution to the US Arctic Observing Network (AON). All efforts were supported through the National Science Foundation (NSF), the Cyber-ShARE Center of Excellence, and the International Tundra Experiment (ITEX).

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

PubMed

Maynard, Jonathan J; Karl, Jason W

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Thermokarst Characteristics and Distribution in a Transitional Arctic Biome: New Discoveries and Possible Monitoring Directions in a Climate Change Scenario

NASA Astrophysics Data System (ADS)

Balser, A. W.; Gooseff, M. N.; Jones, J. B.; Bowden, W. B.; Sanzone, D. M.; Allen, A.; Larouche, J. R.

2006-12-01

In arctic regions, climate warming is leading to permafrost melting and wide-scale ecosystem alteration. A prominent pathway of permafrost loss is through thermokarst, which includes the catastrophic loss of soil structure and rapid subsidence. The regional-scale distribution of thermokarst is poorly documented throughout arctic regions. Remote landscapes and a lack of reliable, regional-scale detection techniques severely hamper our understanding of past prevalence and present distribution patterns. Intensive field campaigns are providing key data to bolster our understanding of the distribution and the characteristics of thermokarst formation, and enabling comprehensive method studies to develop remotely-sensed detection techniques. The Noatak Valley in northwestern Alaska's Brooks Range mountains harbors a transitional landscape from arctic and alpine tundra to boreal forest, all contained in a single 7,000,000 acre watershed. Preliminary field investigations augmented by photogrammetric measurements in 2006 revealed consistent patterns in the distribution of classifiable thermokarst feature types in a 2300 square-mile study area in the middle Noatak basin. Four distinct classes of thermokarst show remarkably tight relationships with ambient slope and local landcover. These investigations tie to larger efforts to document past and present regional distribution, testing remotely sensed data analysis techniques for baseline metrics and a future monitoring scheme.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Modeling and dynamic monitoring of ecosystem performance in the Yukon River Basin

USGS Publications Warehouse

Wylie, Bruce K.; Zhang, L.; Ji, Lei; Tieszen, Larry L.; Bliss, N.B.

2008-01-01

Central Alaska is ecologically sensitive and experiencing stress in response to marked regional warming. Resource managers would benefit from an improved ability to monitor ecosystem processes in response to climate change, fire, insect damage, and management policies and to predict responses to future climate scenarios. We have developed a method for analyzing ecosystem performance as represented by the growing season integral of normalized difference vegetation index (NDVI), which is a measure of greenness that can be interpreted in terms of plant growth or photosynthetic activity (gross primary productivity). The approach illustrates the status and trends of ecosystem changes and separates the influences of climate and local site conditions from the influences of disturbances and land management.We emphasize the ability to quantify ecosystem processes, not simply changes in land cover, across the entire period of the remote sensing archive (Wylie and others, 2008). The method builds upon remotely sensed measures of vegetation greenness for each growing season. By itself, however, a time series of greenness often reflects annual climate variations in temperature and precipitation. Our method seeks to remove the influence of climate so that changes in underlying ecological conditions are identified and quantified. We define an "expected ecosystem performance" to represent the greenness response expected in a particular year given the climate of that year. We distinguish "performance anomalies" as cases where the ecosystem response is significantly different from the expected ecosystem performance. Maps of the performance anomalies (fig. 1) and trends in the anomalies give valuable information on the ecosystems for land managers and policy makers at a resolution of 1 km to 250 m.

Large-Scale Variation in Forest Carbon Turnover Rate and its Relation to Climate - Remote Sensing vs. Global Vegetation Models

NASA Astrophysics Data System (ADS)

Carvalhais, N.; Thurner, M.; Beer, C.; Forkel, M.; Rademacher, T. T.; Santoro, M.; Tum, M.; Schmullius, C.

2015-12-01

While vegetation productivity is known to be strongly correlated to climate, there is a need for an improved understanding of the underlying processes of vegetation carbon turnover and their importance at a global scale. This shortcoming has been due to the lack of spatially extensive information on vegetation carbon stocks, which we recently have been able to overcome by a biomass dataset covering northern boreal and temperate forests originating from radar remote sensing. Based on state-of-the-art products on biomass and NPP, we are for the first time able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests. The implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current global vegetation models. In contrast to our observation-based findings, investigated models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well to observation-based NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in global vegetation models and estimating their impact on the land carbon balance.

Climate variability drives recent tree mortality in Europe.

PubMed

Neumann, Mathias; Mues, Volker; Moreno, Adam; Hasenauer, Hubert; Seidl, Rupert

2017-11-01

Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental-scale patterns of recent tree mortality from the only consistent pan-European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on interannual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold-induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite-derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large-scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state-of-the-art remote sensing to anticipate an increased likelihood of tree mortality in space and time. © 2017 John Wiley & Sons Ltd.

Effects of Global Change on U.S. Urban Areas: Vulnerabilities, Impacts, and Adaptation

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Wilbanks, Thomas J.; Kirshen, Paul; Romero-Lankao, Patricia; Rosenzweig, Cynthia; Ruth, Mattias; Solecki, William; Tarr, Joel

2008-01-01

This slide presentation reviews some of the effects that global change has on urban areas in the United States and how the growth of urban areas will affect the environment. It presents the elements of our Synthesis and Assessment Report (SAP) report that relate to what vulnerabilities and impacts will occur, what adaptation responses may take place, and what possible effects on settlement patterns and characteristics will potentially arise, on human settlements in the U.S. as a result of climate change and climate variability. We will also present some recommendations about what should be done to further research on how climate change and variability will impact human settlements in the U.S., as well as how to engage government officials, policy and decision makers, and the general public in understanding the implications of climate change and variability on the local and regional levels. Additionally, we wish to explore how technology such as remote sensing data coupled with modeling, can be employed as synthesis tools for deriving insight across a spectrum of impacts (e.g. public health, urban planning for mitigation strategies) on how cities can cope and adapt to climate change and variability. This latter point parallels the concepts and ideas presented in the U.S. National Academy of Sciences, Decadal Survey report on "Earth Science Applications from Space: National Imperatives for the Next Decade and Beyond" wherein the analysis of the impacts of climate change and variability, human health, and land use change are listed as key areas for development of future Earth observing remote sensing systems.

Redefining climate regions in the United States of America using satellite remote sensing and machine learning for public health applications.

PubMed

Liss, Alexander; Koch, Magaly; Naumova, Elena N

2014-12-01

Existing climate classification has not been designed for an efficient handling of public health scenarios. This work aims to design an objective spatial climate regionalization method for assessing health risks in response to extreme weather. Specific climate regions for the conterminous United States of America (USA) were defined using satellite remote sensing (RS) data and compared with the conventional Köppen-Geiger (KG) divisions. Using the nationwide database of hospitalisations among the elderly (≥65 year olds), we examined the utility of a RS-based climate regionalization to assess public health risk due to extreme weather, by comparing the rate of hospitalisations in response to thermal extremes across climatic regions. Satellite image composites from 2002-2012 were aggregated, masked and compiled into a multi-dimensional dataset. The conterminous USA was classified into 8 distinct regions using a stepwise regionalization approach to limit noise and collinearity (LKN), which exhibited a high degree of consistency with the KG regions and a well-defined regional delineation by annual and seasonal temperature and precipitation values. The most populous was a temperate wet region (10.9 million), while the highest rate of hospitalisations due to exposure to heat and cold (9.6 and 17.7 cases per 100,000 persons at risk, respectively) was observed in the relatively warm and humid south-eastern region. RS-based regionalization demonstrates strong potential for assessing the adverse effects of severe weather on human health and for decision support. Its utility in forecasting and mitigating these effects has to be further explored.

Joint Conference on Sensing of Environmental Pollutants, 4th, New Orleans, La., November 6-11, 1977, Proceedings

NASA Technical Reports Server (NTRS)

1978-01-01

Papers are presented on such topics as environmental chemistry, the effects of sulfur compounds on air quality, the prediction and monitoring of biological effects caused by environmental pollutants, environmental indicators, the satellite remote sensing of air pollution, weather and climate modification by pollution, and the monitoring and assessment of radioactive pollutants. Consideration is also given to empirical and quantitative modeling of air quality, disposal of hazardous and nontoxic materials, sensing and assessment of water quality, pollution source monitoring, and assessment of some environmental impacts of fossil and nuclear fuels.

NASA's Sentinels Monitoring Weather and Climate: Past, Present, and Future

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Herring, David; Gutro, Rob; Huffman, George; Halverson, Jeff

2002-01-01

Weatherwise is probably the most popular newstand magazine focusing on the subject of weather. It is published six times per year and includes features on weather, climate, and technology. This article (to appear in the January/February Issue) provides a comprehensive review of NASA s past, present, and future contributions in satellite remote sensing for weather and climate processes. The article spans the historical strides of the TIROS program through the scientific and technological innovation of Earth Observer-3 and Global Precipitation Measurement (GPM). It is one of the most thorough reviews of NASA s weather and climate satellite efforts to appear in the popular literature.

Effects of land cover and regional climate variations on long-term spatiotemporal changes in sagebrush ecosystems

USGS Publications Warehouse

Xian, George Z.; Homer, Collin G.; Aldridge, Cameron L.

2012-01-01

This research investigated the effects of climate and land cover change on variation in sagebrush ecosystems. We combined information of multi-year sagebrush distribution derived from multitemporal remote sensing imagery and climate data to study the variation patterns of sagebrush ecosystems under different potential disturbances. We found that less than 40% of sagebrush ecosystem changes involved abrupt changes directly caused by landscape transformations and over 60% of the variations involved gradual changes directly related to climatic perturbations. The primary increases in bare ground and declines in sagebrush vegetation abundance were significantly correlated with the 1996-2006 decreasing trend in annual precipitation.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

NASA Astrophysics Data System (ADS)

Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

2015-04-01

Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in order to improve planning, business strategies but also to target the drought relief in case of major drought event. This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248, project supported by Czech National Agency of Agricultural Research No. QJ1310123 "Crop modelling as a tool for increasing the production potential and food security of the Czech Republic under Climate Change".

Ground and surface temperature variability for remote sensing of soil moisture in a heterogeneous landscape

USDA-ARS?s Scientific Manuscript database

At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture-Agriculture Research Service (USDA-ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To ...

Mapping the Risk of Rift Valley fever re-emergence in Southern Africa using remote sensing data

USDA-ARS?s Scientific Manuscript database

Rift Valley fever is a viral disease of animals and humans that occurs throughout sub-Saharan Africa, Egypt and the Arabian Peninsula. Outbreaks of the disease are episodic and closely linked to climate variability, especially widespread elevated rainfall that facilitates Rift Valley fever virus tra...

Remote sensing data assimilation for a prognostic phenology model

Treesearch

R. Stockli; T. Rutishauser; D. Dragoni; J. O' Keefe; P. E. Thornton; M. Jolly; L. Lu; A. S. Denning

2008-01-01

Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active...

A remote-sensing/GIS application for analysis of sea surface temperature off the western coast of North America

EPA Science Inventory

Recent work reports a warming trend in Pacific Ocean temperatures over the last 50 years. Coastal regions along western North America are particularly sensitive to climatic change, an important indicator of which is sea surface temperature (SST). In situ SST measurements (typica...

Sources and Impacts of Atmospheric NH3: Current Understanding and Frontiers for Modeling, Measurements, and Remote Sensing in North America

EPA Science Inventory

Ammonia (NH3) contributes to widespread adverse health impacts, affects the climate forcing of ambient aerosols, and is a significant component of reactive nitrogen, deposition of which threatens many sensitive ecosystems. Historically, the scarcity of in situ measurements and th...

Validation of the GCOM-W SCA and JAXA soil moisture algorithms

USDA-ARS?s Scientific Manuscript database

Satellite-based remote sensing of soil moisture has matured over the past decade as a result of the Global Climate Observing Mission-Water (GCOM-W) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...

Reading the Ice: Using Remote Sensing to Analyze Radar Data

ERIC Educational Resources Information Center

Gillette, Brandon; Leinmiller-Renick, Kelsey; Foga, Steve

2013-01-01

Understanding the behavior of ice sheets (thick, continent-size ice masses) and glaciers (smaller, flowing masses of ice) is increasingly important as our climate changes, particularly in the Polar Regions. This article describes two lessons, based on the 5E (engage, explore, explain, elaborate, and evaluate) model, that help students practice…

Data Mining in Earth System Science (DMESS 2011)

Treesearch

Forrest M. Hoffman; J. Walter Larson; Richard Tran Mills; Bhorn-Gustaf Brooks; Auroop R. Ganguly; William Hargrove; et al

2011-01-01

From field-scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize, and interpret. Data mining, information theoretic, and machine learning techniques—such as cluster analysis, singular value decomposition, block entropy, Fourier and...

Modelling soil salinity in Oued El Abid watershed, Morocco

NASA Astrophysics Data System (ADS)

Mouatassime Sabri, El; Boukdir, Ahmed; Karaoui, Ismail; Arioua, Abdelkrim; Messlouhi, Rachid; El Amrani Idrissi, Abdelkhalek

2018-05-01

Soil salinisation is a phenomenon considered to be a real threat to natural resources in semi-arid climates. The phenomenon is controlled by soil (texture, depth, slope etc.), anthropogenic factors (drainage system, irrigation, crops types, etc.), and climate factors. This study was conducted in the watershed of Oued El Abid in the region of Beni Mellal-Khenifra, aimed at localising saline soil using remote sensing and a regression model. The spectral indices were extracted from Landsat imagery (30 m resolution). A linear correlation of electrical conductivity, which was calculated based on soil samples (ECs), and the values extracted based on spectral bands showed a high accuracy with an R2 (Root square) of 0.80. This study proposes a new spectral salinity index using Landsat bands B1 and B4. This hydro-chemical and statistical study, based on a yearlong survey, showed a moderate amount of salinity, which threatens dam water quality. The results present an improved ability to use remote sensing and regression model integration to detect soil salinity with high accuracy and low cost, and permit intervention at an early stage of salinisation.

Environmental factor analysis of cholera in China using remote sensing and geographical information systems.

PubMed

Xu, M; Cao, C X; Wang, D C; Kan, B; Xu, Y F; Ni, X L; Zhu, Z C

2016-04-01

Cholera is one of a number of infectious diseases that appears to be influenced by climate, geography and other natural environments. This study analysed the environmental factors of the spatial distribution of cholera in China. It shows that temperature, precipitation, elevation, and distance to the coastline have significant impact on the distribution of cholera. It also reveals the oceanic environmental factors associated with cholera in Zhejiang, which is a coastal province of China, using both remote sensing (RS) and geographical information systems (GIS). The analysis has validated the correlation between indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local number of cholera cases based on 8-year monthly data from 2001 to 2008. The results show the number of cholera cases has been strongly affected by the variables of SST, SSH and OCC. Utilizing this information, a cholera prediction model has been established based on the oceanic and climatic environmental factors. The model indicates that RS and GIS have great potential for designing an early warning system for cholera.

A Spatiotemporal Analysis of Extreme Heat Vulnerability Across the United States using Geospatial Techniques

NASA Astrophysics Data System (ADS)

Schoessow, F. S.; Li, Y.; Howe, P. D.

2016-12-01

Extreme heat events are the deadliest natural hazard in the United States and are expected to increase in both severity and frequency in the coming years due to the effects of climate change. The risks of climate change and weather-related events such as heat waves to a population can be more comprehensively assessed by coupling the traditional examination of natural hazards using remote sensing and geospatial analysis techniques with human vulnerability factors and individual perceptions of hazards. By analyzing remote-sensed and empirical survey data alongside national hazards advisories, this study endeavors to establish a nationally-representative baseline quantifying the spatiotemporal variation of individual heat vulnerabilities at multiple scales and between disparate population groups affected by their unique socioenvironmental factors. This is of immediate academic interest because the study of heat waves risk perceptions remains relatively unexplored - despite the intensification of extreme heat events. The use of "human sensors", georeferenced & timestamped individual response data, provides invaluable contextualized data at a high spatial resolution, which will enable policy-makers to more effectively implement targeted strategies for risk prevention, mitigation, and communication. As climate change risks are further defined, this cognizance will help identify vulnerable populations and enhance national hazard preparedness and recovery frameworks.

Land surface and climate parameters and malaria features in Vietnam

NASA Astrophysics Data System (ADS)

Liou, Y. A.; Anh, N. K.

2017-12-01

Land surface parameters may affect local microclimate, which in turn alters the development of mosquito habitats and transmission risks (soil-vegetation-atmosphere-vector borne diseases). Forest malaria is a chromic issue in Southeast Asian countries, in particular, such as Vietnam (in 1991, approximate 2 million cases and 4,646 deaths were reported (https://sites.path.org)). Vietnam has lowlands, sub-tropical high humidity, and dense forests, resulting in wide-scale distribution and high biting rate of mosquitos in Vietnam, becoming a challenging and out of control scenario, especially in Vietnamese Central Highland region. It is known that Vietnam's economy mainly relies on agriculture and malaria is commonly associated with poverty. There is a strong demand to investigate the relationship between land surface parameters (land cover, soil moisture, land surface temperature, etc.) and climatic variables (precipitation, humidity, evapotranspiration, etc.) in association with malaria distribution. GIS and remote sensing have been proven their powerful potentials in supporting environmental and health studies. The objective of this study aims to analyze physical attributes of land surface and climate parameters and their links with malaria features. The outcomes are expected to illustrate how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, a platform with promising possibilities of allowing disease early-warning systems with citizen participation will be proposed.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Climate Change in the US: Potential Consequences for Human Health

NASA Technical Reports Server (NTRS)

Maynard, Nancy G.

2001-01-01

The U.S. National Assessment identified five major areas of consequences of climate change in the United States: temperature-related illnesses and deaths, health effects related to extreme weather events, air pollution-related health effects, water- and food-borne diseases, and insect-, tick-, and rodent-borne diseases. The U.S. National Assessment final conclusions about these potential health effects will be described. In addition, a summary of some of the new tools for studying human health aspects of climate change as well as environment-health linkages through remotely sensed data and observations will be provided.

Using data of remote sensing to retrieve surface's evapotranspiration in northwest of China

NASA Astrophysics Data System (ADS)

Yu, Xinwen; Zheng, Youfei; Guo, Jianmao; He, Jinhai

2005-08-01

Northwestern China is a semi-arid or arid area in China. Ningnan (or South Ningxia) district is located south of Ningxia Province belong to Northwestern part of China. Climate in this region is more dry and lack of precipitation. Because global climate have been changing, temperature has been increasing and rainfall has been decreasing in South Ningxia. The ecology has been deteriorating, such as vegetation cover destroying, water losing and soil erosion. Therefore, the people who live in South Ningxia have been poor. Recently, Chinese government put into effect on strategy of "great development of Chinese northwest", aiming to improve environmental and ecological conditions and rise people's living standard. South Ningxia district was defined as area of emigration where the measurements of returning land for farming to forestry were taken into account . How to evaluate the plans and measurements is very important to continue to improving local environmental and ecological conditions further. The basic index of evaluation is soil water profit and loss statement while evapotranspiration (ET) is an important component in statement income and outcome of soil water. It is a very complicated problem to estimate evapotranspiration (ET) over large area of natural surface. In this paper, the natural surface was classified as 5 categories based on information from remote sensing, each categories being dealt with special way. Using data of remote sensing and weather stations, the result of regional evapotranspiration over Ningnan(South Ningxia) was given out, and verified and discussing are also made out. The work helps to assess whether or not improve environmental and ecological conditions.

AVIRIS Land-Surface Mapping in Support of the Boreal Ecosystem-Atmosphere Study (BOREAS)

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Gamon, John; Keightley, Keir; Prentiss, Dylan; Reith, Ernest; Green, Robert

2001-01-01

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely-sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS follow-on program is concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has the potential of contributing to BOREAS through: (1) accurate retrieved apparent surface reflectance; (2) improved landcover classification; and (3) direct assessment of biochemical/biophysical information such as canopy liquid water and chlorophyll concentration through pigment fits. In this paper, we present initial products for major flux tower sites including: (1) surface reflectance of dominant cover types; (2) a land-cover classification developed using spectral mixture analysis (SMA) and Multiple Endmember Spectral Mixture Analysis (MESMA); and (3) liquid water maps. Our goal is to compare these land-cover maps to existing maps and to incorporate AVIRIS image products into models of photosynthetic flux.

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods.

PubMed

Goetz, Scott J; Baccini, Alessandro; Laporte, Nadine T; Johns, Tracy; Walker, Wayne; Kellndorfer, Josef; Houghton, Richard A; Sun, Mindy

2009-03-25

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.

Evaluating ESA CCI Soil Moisture in East Africa

NASA Technical Reports Server (NTRS)

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.

2016-01-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

NIST activities in support of space-based radiometric remote sensing

NASA Astrophysics Data System (ADS)

Rice, Joseph P.; Johnson, B. Carol

2001-06-01

We provide an historical overview of NIST research and development in radiometry for space-based remote sensing. The applications in this field can be generally divided into two areas: environmental and defense. In the environmental remote sensing area, NIST has had programs with agencies such as the National Aeronautical and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) to verify and improve traceability of the radiometric calibration of sensors that fly on board Earth-observing satellites. These produce data used in climate models and weather prediction. Over the years, the scope of activities has expanded from existing routine calibration services for artifacts such as lamps, diffusers, and filters, to development and off-site deployment of portable radiometers for radiance- and irradiance-scale intercomparisons. In the defense remote sensing area, NIST has had programs with agencies such as the Department of Defense (DOD) for support of calibration of small, low-level infrared sources in a low infrared background. These are used by the aerospace industry to simulate ballistic missiles in a cold space background. Activities have evolved from calibration of point-source cryogenic blackbodies at NIST to measurement of irradiance in off-site calibration chambers by a portable vacuum/cryogenic radiometer. Both areas of application required measurements on the cutting edge of what was technically feasible, thus compelling NIST to develop a state-of-the-art radiometric measurement infrastructure to meet the needs. This infrastructure has led to improved dissemination of the NIST spectroradiometric quantities.

Monitoring crop coefficient of orange orchards using energy balance and the remote sensed NDVI

NASA Astrophysics Data System (ADS)

Consoli, Simona; Cirelli, Giuseppe Luigi; Toscano, Attilio

2006-09-01

The structure of vegetation is paramount in regulating the exchange of mass and energy across the biosphereatmosphere interface. In particular, changes in vegetation density affected the partitioning of incoming solar energy into sensible and latent heat fluxes that may result in persistent drought through reductions in agricultural productivity and in the water resources availability. Limited research with citrus orchards has shown improvements to irrigation scheduling due to better water-use estimation and more appropriate timing of irrigation when crop coefficient (Kc) estimate, derived from remotely sensed multispectral vegetation indices (VIs), are incorporated into irrigation-scheduling algorithms. The purpose of this article is the application of an empirical reflectance-based model for the estimation of Kc and evapotranspiration fluxes (ET) using ground observations on climatic data and high-resolution VIs from ASTER TERRA satellite imagery. The remote sensed Kc data were used in developing the relationship with the normalized difference vegetation index (NDVI) for orange orchards during summer periods. Validation of remote sensed data on ET, Kc and vegetation features was deal through ground data observations and the resolution of the energy balance to derive latent heat flux density (λE), using measures of net radiation (Rn) and soil heat flux density (G) and estimate of sensible heat flux density (H) from high frequency temperature measurements (Surface Renewal technique). The chosen case study is that of an irrigation area covered by orange orchards located in Eastern Sicily, Italy) during the irrigation seasons 2005 and 2006.

Discharge prediction in the Upper Senegal River using remote sensing data

NASA Astrophysics Data System (ADS)

Ceccarini, Iacopo; Raso, Luciano; Steele-Dunne, Susan; Hrachowitz, Markus; Nijzink, Remko; Bodian, Ansoumana; Claps, Pierluigi

2017-04-01

The Upper Senegal River, West Africa, is a poorly gauged basin. Nevertheless, discharge predictions are required in this river for the optimal operation of the downstream Manantali reservoir, flood forecasting, development plans for the entire basin and studies for adaptation to climate change. Despite the need for reliable discharge predictions, currently available rainfall-runoff models for this basin provide only poor performances, particularly during extreme regimes, both low-flow and high-flow. In this research we develop a rainfall-runoff model that combines remote-sensing input data and a-priori knowledge on catchment physical characteristics. This semi-distributed model, is based on conceptual numerical descriptions of hydrological processes at the catchment scale. Because of the lack of reliable input data from ground observations, we use the Tropical Rainfall Measuring Mission (TRMM) remote-sensing data for precipitation and the Global Land Evaporation Amsterdam Model (GLEAM) for the terrestrial potential evaporation. The model parameters are selected by a combination of calibration, by match of observed output and considering a large set of hydrological signatures, as well as a-priori knowledge on the catchment. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to choose the most likely range in which the parameter sets belong. Analysis of different experiments enhances our understanding on the added value of distributed remote-sensing data and a-priori information in rainfall-runoff modelling. Results of this research will be used for decision making at different scales, contributing to a rational use of water resources in this river.

Remote Sensing Training for Middle School through the Center of Excellence in Remote Sensing Education

NASA Astrophysics Data System (ADS)

Hayden, L. B.; Johnson, D.; Baltrop, J.

2012-12-01

Remote sensing has steadily become an integral part of multiple disciplines, research, and education. Remote sensing can be defined as the process of acquiring information about an object or area of interest without physical contact. As remote sensing becomes a necessity in solving real world problems and scientific questions an important question to consider is why remote sensing training is significant to education and is it relevant to training students in this discipline. What has been discovered is the interest in Science, Technology, Engineering and Mathematics (STEM) fields, specifically remote sensing, has declined in our youth. The Center of Excellence in Remote Sensing Education and Research (CERSER) continuously strives to provide education and research opportunities on ice sheet, coastal, ocean, and marine science. One of those continued outreach efforts are Center for Remote Sensing of Ice Sheets (CReSIS) Middle School Program. Sponsored by the National Science Foundation CReSIS Middle School Program offers hands on experience for middle school students. CERSER and NSF offer students the opportunity to study and learn about remote sensing and its vital role in today's society as it relate to climate change and real world problems. The CReSIS Middle School Program is an annual two-week effort that offers middle school students experience with remote sensing and its applications. Specifically, participants received training with Global Positioning Systems (GPS) where the students learned the tools, mechanisms, and applications of a Garmin 60 GPS. As a part of the program the students were required to complete a fieldwork assignment where several longitude and latitude points were given throughout campus. The students had to then enter the longitude and latitude points into the Garmin 60 GPS, navigate their way to each location while also accurately reading the GPS to make sure travel was in the right direction. Upon completion of GPS training the students were able to understand the function of a GPS, how to analyze and comprehend longitude and latitude points, and the importance of GPS devices in real world applications. This paper describes in detail the methodology for organizing a learning environment where participants were able to compile, organize and analyze data, collaborate in a team environment, utilize the scientific method to draw conclusions based on the research they obtained. Various resources and training activities were developed to cultivate student participants' skill set. Students were presented with a problem where they had to develop a hypothesis or scientific question. After clearly defining the problem, it was necessary for the middle school participants to determine the data needed to complete an analysis and ascertain where that data can be found or generated. The training and events held for the CReSIS Middle School Program were proven successful for both these inquiring middle school students at Elizabeth City Middle School, River Road Middle School and the STEM representatives from Elizabeth City State University.Too often, students who live in rural communities or face economic disadvantages often miss out on getting access to important technology. Developing an interest in the STEM fields by a few students' would be considered a total success for the city, community, country, and world.

ASF: Facing the Challenges for 15 Years and Counting

NASA Astrophysics Data System (ADS)

La Belle-Hamer, N.; Nicoll, J.; Atwood, D.; Arko, S.

2006-12-01

The Alaska Satellite Facility (ASF) of the Geophysical Institute, University of Alaska Fairbanks, has just celebrated its 15th year of experience in satellite remote sensing. ASF is involved in a wide range of activities - - from downlinking satellite data to developing data analysis tools, value-added products, and training for Synthetic Aperture Radar (SAR) users. Satellite remote sensing data are acquired, processed, analyzed, and archived by ASF from several satellites; ASF has built expertise in handling and manipulating the data, SAR in particular. SAR is the only satellite imagery in the world today that can be acquired at any time of the day or night and during adverse weather conditions. It can be used to develop value-added products to aid in global climate change research. Examples include the SAR-derived coastal winds in the Gulf of Alaska and Bering Sea; monitoring of the major ice shelves in the Arctic and Antarctic; and examining the impact on sea level from the Greenland ice sheets and glaciers. The challenges facing the use of remote-sensing data in climate change research can be divided into three major categories: economical, political, and technical. The expense of designing, building, and launching a satellite is substantial. The costs of the ground segment including data management can be substantial and should not be neglected. The US agency funding climate often has the research community pushing for new missions against declining federal budgets in direct competition with ongoing missions. On the political front, data policy, data ownership, and cost recovery are issues often perceived as insurmountable by the user community. The technical issues, while challenging, are often the easiest to solve. The Japan Aerospace Exploration Agency, with the successful launch of the Advanced Land Observing System (ALOS), has embarked on a new way of handling the ground segment with the introduction of international data nodes. ASF will serve with NOAA as the Americas ALOS Data Node. With the success of the ALOS data node structure, it may well be that international cooperation will become the standard method for overcoming the challenges of global climate change research.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

PubMed

Truong, Tuyet T A; Hardy, Giles E St J; Andrew, Margaret E

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

PubMed Central

Truong, Tuyet T. A.; Hardy, Giles E. St. J.; Andrew, Margaret E.

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species. PMID:28555147

Earth Observation for monitoring phenology for european land use and ecosystems over 1998-2011

NASA Astrophysics Data System (ADS)

Ceccherini, Guido; Gobron, Nadine

2013-04-01

Long-term measurements of plant phenology have been used to track vegetation responses to climate change but are often limited to particular species and locations and may not represent synoptic patterns. Given the limitations of working directly with in-situ data, many researchers have instead used available satellite remote sensing. Remote sensing extends the possible spatial coverage and temporal range of phenological assessments of environmental change due to the greater availability of observations. Variations and trends of vegetation dynamics are important because they alter the surface carbon, water and energy balance. For example, the net ecosystem CO2 exchange of vegetation is strongly linked to length of the growing season: extentions and decreases in length of growing season modify carbon uptake and the amount of CO2 in the atmosphere. Advances and delays in starting of growing season also affect the surface energy balance and consequently transpiration. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key climate variable identified by Global Terrestrial Observing System (GTOS) that can be monitored from space. This dimensionless variable - varying between 0 and 1- is directly linked to the photosynthetic activity of vegetation, and therefore, can monitor changes in phenology. In this study, we identify the spatio/temporal patterns of vegetation dynamics using a long-term remotely sensed FAPAR dataset over Europe. Our aim is to provide a quantitative analysis of vegetation dynamics relevant to climate studies in Europe. As part of this analysis, six vegetation phenological metrics have been defined and made routinely in Europe. Over time, such metrics can track simple, yet critical, impacts of climate change on ecosystems. Validation has been performed through a direct comparison against ground-based data over ecological sites. Subsequently, using the spatio/temporal variability of this suite of metrics, we classify areas with similar vegetation dynamics. This permits assessment of variations and trends of vegetation dynamics over Europe. Statistical tests to assess the significance of temporal changes are used to evaluate trends in the metrics derived from the recorded time series of the FAPAR.

Large-scale experimental technology with remote sensing in land surface hydrology and meteorology

NASA Technical Reports Server (NTRS)

Brutsaert, Wilfried; Schmugge, Thomas J.; Sellers, Piers J.; Hall, Forrest G.

1988-01-01

Two field experiments to study atmospheric and land surface processes and their interactions are summarized. The Hydrologic-Atmospheric Pilot Experiment, which tested techniques for measuring evaporation, soil moisture storage, and runoff at scales of about 100 km, was conducted over a 100 X 100 km area in France from mid-1985 to early 1987. The first International Satellite Land Surface Climatology Program field experiment was conducted in 1987 to develop and use relationships between current satellite measurements and hydrologic, climatic, and biophysical variables at the earth's surface and to validate these relationships with ground truth. This experiment also validated surface parameterization methods for simulation models that describe surface processes from the scale of vegetation leaves up to scales appropriate to satellite remote sensing.

European Space Agency lidar development programs for remote sensing of the atmosphere

NASA Astrophysics Data System (ADS)

Armandillo, Errico

1992-12-01

Active laser remote sensing from space is considered an important step forward in the understanding of the processes which regulate weather and climate changes. The planned launching into polar orbit in the late 1990s of a series of dedicated Earth observation satellites offer new possibilities for flying lidar in space. Among the various lidar candidates, ESA has recognized in the backscattering lidar and Doppler wind lidar the instruments which can most contribute to the Earth observation program. To meet the schedule of the on-coming flight opportunities, ESA has been engaged over the past years in a preparatory program aimed to define the instruments and ensure timely availability of the critical components. This paper reviews the status of the ongoing developments and highlights the critical issues addressed.

Optical Passive Sensor Calibration for Satellite Remote Sensing and the Legacy of NOAA and NIST Cooperation

PubMed Central

Datla, Raju; Weinreb, Michael; Rice, Joseph; Johnson, B. Carol; Shirley, Eric; Cao, Changyong

2014-01-01

This paper traces the cooperative efforts of scientists at the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) to improve the calibration of operational satellite sensors for remote sensing of the Earth’s land, atmosphere and oceans. It gives a chronological perspective of the NOAA satellite program and the interactions between the two agencies’ scientists to address pre-launch calibration and issues of sensor performance on orbit. The drive to improve accuracy of measurements has had a new impetus in recent years because of the need for improved weather prediction and climate monitoring. The highlights of this cooperation and strategies to achieve SI-traceability and improve accuracy for optical satellite sensor data are summarized1. PMID:26601030

Optical Passive Sensor Calibration for Satellite Remote Sensing and the Legacy of NOAA and NIST Cooperation.

PubMed

Datla, Raju; Weinreb, Michael; Rice, Joseph; Johnson, B Carol; Shirley, Eric; Cao, Changyong

2014-01-01

This paper traces the cooperative efforts of scientists at the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) to improve the calibration of operational satellite sensors for remote sensing of the Earth's land, atmosphere and oceans. It gives a chronological perspective of the NOAA satellite program and the interactions between the two agencies' scientists to address pre-launch calibration and issues of sensor performance on orbit. The drive to improve accuracy of measurements has had a new impetus in recent years because of the need for improved weather prediction and climate monitoring. The highlights of this cooperation and strategies to achieve SI-traceability and improve accuracy for optical satellite sensor data are summarized.

Triple-Pulse Integrated Path Differential Absorption Lidar for Carbon Dioxide Measurement - Novel Lidar Technologies and Techniques with Path to Space

NASA Technical Reports Server (NTRS)

Singh, Upendra N.; Refaat, Tamer F.; Petros, Mulugeta

2017-01-01

The societal benefits of understanding climate change through identification of global carbon dioxide sources and sinks led to the desired NASA's active sensing of carbon dioxide emissions over nights, days, and seasons (ASCENDS) space-based missions of global carbon dioxide measurements. For more than 15 years, NASA Langley Research Center (LaRC) have developed several carbon dioxide active remote sensors using the differential absorption lidar (DIAL) technique operating at the two-micron wavelength. Currently, an airborne two-micron triple-pulse integrated path differential absorption (IPDA) lidar is under development. This IPDA lidar measures carbon dioxide as well as water vapor, the dominant interfering molecule on carbon dioxide remote sensing. Advancement of this triple-pulse IPDA lidar development is presented.

Sen2-Agri country level demonstration for Ukraine

NASA Astrophysics Data System (ADS)

Kussul, N.; Kolotii, A.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

Due to launch of Sentinel-2 mission European Space Agency (ESA) started Sentinel-2 for Agriculture (Sen2-Agri) project coordinated by Universite catholique de Louvain (UCL). Ukraine is selected as one of 3 country level demonstration sites for benchmarking Sentinel-2 data due to wide range of main crops (both winter and summer), big fields and high enough climate variability over the territory [1-2]. Within this county level demonstration main objectives are following: i) Sentinel's products quality assessment and their suitability estimation for the territory of Ukraine [2]; ii) demonstration in order to convince decision makers and state authorities; iii) assessment of the personnel and facilities required to run the Sen2-Agri system and creation of Sen-2 Agri products (crop type maps and such essential climatic variable as Leaf Area Index - LAI [3]). During this project ground data were collected for crop land mapping and crop type classification along the roads within main agro-climatic zones of Ukraine. For LAI estimation we used indirect non-destructive method which is based on DHP-images and VALERI protocol. Products created with use of Sen2-Agri system deployed during project execution and results of neural-network approach utilization will be compared. References Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508. Kussul, N., Skakun, S., Shelestov, A., Lavreniuk, M., Yailymov, B., & Kussul, O. (2015). Regional scale crop mapping using multi-temporal satellite imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 45-52. Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736

Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability.

PubMed

Tagesson, Torbern; Fensholt, Rasmus; Guiro, Idrissa; Rasmussen, Mads Olander; Huber, Silvia; Mbow, Cheikh; Garcia, Monica; Horion, Stéphanie; Sandholt, Inge; Holm-Rasmussen, Bo; Göttsche, Frank M; Ridler, Marc-Etienne; Olén, Niklas; Lundegard Olsen, Jørgen; Ehammer, Andrea; Madsen, Mathias; Olesen, Folke S; Ardö, Jonas

2015-01-01

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350-1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land-atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of ~-7.5 g C m(-2)  day(-1) during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350-1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models. © 2014 John Wiley & Sons Ltd.

Food Price Volatility and Decadal Climate Variability

NASA Astrophysics Data System (ADS)

Brown, M. E.

2013-12-01

The agriculture system is under pressure to increase production every year as global population expands and more people move from a diet mostly made up of grains, to one with more meat, dairy and processed foods. Weather shocks and large changes in international commodity prices in the last decade have increased pressure on local food prices. This paper will review several studies that link climate variability as measured with satellite remote sensing to food price dynamics in 36 developing countries where local monthly food price data is available. The focus of the research is to understand how weather and climate, as measured by variations in the growing season using satellite remote sensing, has affected agricultural production, food prices and access to food in agricultural societies. Economies are vulnerable to extreme weather at multiple levels. Subsistence small holders who hold livestock and consume much of the food they produce are vulnerable to food production variability. The broader society, however, is also vulnerable to extreme weather because of the secondary effects on market functioning, resource availability, and large-scale impacts on employment in trading, trucking and wage labor that are caused by weather-related shocks. Food price variability captures many of these broad impacts and can be used to diagnose weather-related vulnerability across multiple sectors. The paper will trace these connections using market-level data and analysis. The context of the analysis is the humanitarian aid community, using the guidance of the USAID Famine Early Warning Systems Network and the United Nation's World Food Program in their response to food security crises. These organizations have worked over the past three decades to provide baseline information on food production through satellite remote sensing data and agricultural yield models, as well as assessments of food access through a food price database. Econometric models and spatial analysis are used to describe the connection between shocks and food prices, and to demonstrate the importance of these metrics in overall outcomes in food-insecure communities.

Remote Sensing of Ocean Color

NASA Astrophysics Data System (ADS)

Dierssen, Heidi M.; Randolph, Kaylan

The oceans cover over 70% of the earth's surface and the life inhabiting the oceans play an important role in shaping the earth's climate. Phytoplankton, the microscopic organisms in the surface ocean, are responsible for half of the photosynthesis on the planet. These organisms at the base of the food web take up light and carbon dioxide and fix carbon into biological structures releasing oxygen. Estimating the amount of microscopic phytoplankton and their associated primary productivity over the vast expanses of the ocean is extremely challenging from ships. However, as phytoplankton take up light for photosynthesis, they change the color of the surface ocean from blue to green. Such shifts in ocean color can be measured from sensors placed high above the sea on satellites or aircraft and is called "ocean color remote sensing." In open ocean waters, the ocean color is predominantly driven by the phytoplankton concentration and ocean color remote sensing has been used to estimate the amount of chlorophyll a, the primary light-absorbing pigment in all phytoplankton. For the last few decades, satellite data have been used to estimate large-scale patterns of chlorophyll and to model primary productivity across the global ocean from daily to interannual timescales. Such global estimates of chlorophyll and primary productivity have been integrated into climate models and illustrate the important feedbacks between ocean life and global climate processes. In coastal and estuarine systems, ocean color is significantly influenced by other light-absorbing and light-scattering components besides phytoplankton. New approaches have been developed to evaluate the ocean color in relationship to colored dissolved organic matter, suspended sediments, and even to characterize the bathymetry and composition of the seafloor in optically shallow waters. Ocean color measurements are increasingly being used for environmental monitoring of harmful algal blooms, critical coastal habitats (e.g., seagrasses, kelps), eutrophication processes, oil spills, and a variety of hazards in the coastal zone.

New Satellite Project Aerosol-UA: Remote Sensing of Aerosols in the Terrestrial Atmosphere

NASA Technical Reports Server (NTRS)

Milinevsky, G.; Yatskiv, Ya.; Degtyaryov, O.; Syniavskyi, I.; Mishchenko, Michael I.; Rosenbush, V.; Ivanov, Yu.; Makarov, A.; Bovchaliuk, A.; Danylevsky, V.;

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

NASA Technical Reports Server (NTRS)

Galin, Natalia; Worby, Anthony; Markus, Thorsten; Leuschen, Carl; Gogineni, Prasad

2012-01-01

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform.

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

Remote Sensing Approach to Drought Monitoring to Inform Range Management at the Hopi Tribe and Navajo Nation

NASA Astrophysics Data System (ADS)

El Vilaly, M. M.; Van Leeuwen, W. J.; Didan, K.; Marsh, S. E.; Crimmins, , M. A.

2012-12-01

The Hopi Tribe and Navajo Nation are situated in the Northeastern corner of Arizona in the Colorado River Plateau. For more than a decade, the area has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources while exacerbating soil erosion. Moreover, these persistent droughts threaten ecosystem services, agriculture, and livestock production activities, and make this region sensitive to inter-annual climate variability and change. The limited hydroclimatic observations, bolstered by numerous anecdotal drought impact reports, indicate that the region has been suffering through an almost 15-year long drought which is threatening its socio-economic development. The objective of this research is to employ remote sensing data to monitor the ongoing drought and inform management and decision-making. The overall goals of this study are to develop a common understanding of the current status of drought across the area in order to understand the existing seasonal and inter-annual relationships between climate variability and vegetation dynamics. To analyze and investigate vegetation responses to climate variability, land use practices, and environmental factors in Hopi and Navajo nation during the last 22 years, a drought assessment framework was developed that integrates climate and topographical data with land surface remote sensing time series data. Multi-sensor Normalized Difference Vegetation Index time series data were acquired from the vegetation index and phenology project (vip.arizona.edu) from 1989 to 2010 at 5.6 km, were analyzed to characterize the intra-annual changes of vegetation, seasonal phenology and inter-annual vegetation response to climate variability and environmental factors. Due to the low number of retrieval obtained from TIMESAT software, we developed a new framework that can maximize the number of retrieval. Four vegetation development stages, annual integrated NDVI (Net Primary Production (NPP)), minimum annual NDVI, maximum annual NDVI, and annual amplitude, were extracted using that new framework. A multi-linear regression has been applied to these vegetation phenology metrics as well as to the relationship between pheno-metrics and environmental variables, to detect potential vegetation changes and to examine the existing relationship between vegetation dynamics and rainfall and elevation gradients. The results suggest that vegetation behavior is foremost governed by rainfall gradients (R-square =0.74). Trend analyses confirmed that around 80 percent of pixels showed a general decline of greenness with confidence level of 95% (p< 0.05), while 4 percent showed a general greening up. Vegetation in the area showed a significant and strong relationship with elevation and precipitation gradients. This correlation was more prominent at mid-elevations, which could be explained by the snowmelt dynamics and hydrological redistribution of water at that elevation. These tools, methods and results can be used to aid in monitoring and understanding climate change and variability impacts on vegetation productivity, ecosystem services, and water resources of the region, and to inform decision-makers and range managers at Hopi Tribe and Navajo nation. Keywords: drought, remote sensing, time series, vegetation dynamics, Hopi Tribe and Navajo Nations

Three Decades of Remote Sensing Based Tropical Forests Phenological Patterns and Trends

NASA Astrophysics Data System (ADS)

Didan, K.

2010-12-01

The faint climatic seasonality of tropical rain forests is believed to be the reason these biomes lack strong and detectable seasonality. Forest seasonality is a critical element of ecosystem functions. It moderates the echo-hydrology, carbon, and nutrient exchange of the area. While deciduous forests exhibit distinct and strong seasonality, tropical forests do not, yet they play a large role in the cycling of energy and mass. Tropical forests represent a large percentage of vegetated land and their importance to the Earth system stems from their biological diversity, their habitat role, their role in regulating global weather, and the role they play in carbon storage. While Tropical forests are well buffered by their sheer size, their vulnerability to climate change is exacerbated by the human pressure. All of this begs the questions of what are the patterns and characteristic of tropical forests phenology and are there any detectable trends over the last three decades of synoptic remote sensing. These three decades comprise different episodes of droughts and an ever increasing level of human encroachment. In so far understanding the function and dynamic of these biomes, field studies continue to play a major role, but synoptic remote sensing is emerging as a viable tool to addressing the spatial and temporal scale associated with this problem. Recent studies of Brazilian rainforest with synoptic remote sensing point to a sizable seasonal signal coincident with the dry season. However, these studies were not extensive in time or space and did not look at other rainforests. Using data from AVHRR and MODIS, we generated a 30 year record of the 2 bands Enhance Vegetation Index (EVI2), and analyzed the patterns and trends of land surface phenology across all tropical forests using the homogeneous phenology cluster approach. We chose EVI because of its superior performance over these dense forests, and we selected the homogeneous phenology cluster approach to abate the noise associated with single pixel approaches. This approach reasonably assumes that land surface seasonality is homogeneous over areas with similar climate, soil, elevation gradient, aspect, and land cover. Our goal was to establish the patterns and characteristic of the land surface phenology of these biomes and to analyze their spatio-temporal trends. Strong variability was observed across the forests of South America, Central Africa and South East Asia. The South American forest exhibited the faintest seasonal signal with the strongest response to droughts. These forests show a more distinct and ever more evident dry season response. The Tropical forest of central Africa shows a more evident seasonal signal and a trend where the season is breaking into a bimodal growth mode, with two distinct and separate growth periods. The Tropical forest of South East Asia shows a more fragmented seasonality that most likely is the result of human pressure and its impact on land cover. Although we have not looked at the underlying climatic factors, we hypothesize that the seasonality of these forests are mostly shaped by the climatic patterns which would suggest that these trends will become more evident as climate continues to change.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

Detecting trends in regional ecosystem functioning: the importance of field data for calibrating and validating NEON airborne remote sensing instruments and science data products

NASA Astrophysics Data System (ADS)

McCorkel, J.; Kuester, M. A.; Johnson, B. R.; Krause, K.; Kampe, T. U.; Moore, D. J.

2011-12-01

The National Ecological Observatory Network (NEON) is a research facility under development by the National Science Foundation to improve our understanding of and ability to forecast the impacts of climate change, land-use change, and invasive species on ecology. The infrastructure, designed to operate over 30 years or more, includes site-based flux tower and field measurements, coordinated with airborne remote sensing observations to observe key ecological processes over a broad range of temporal and spatial scales. NEON airborne data on vegetation biochemical, biophysical, and structural properties and on land use and land cover will be captured at 1 to 2 meter resolution by an imaging spectrometer, a small-footprint waveform-LiDAR and a high-resolution digital camera. Annual coverage of the 60 NEON sites and capacity to support directed research flights or respond to unexpected events will require three airborne observation platforms (AOP). The integration of field and airborne data with satellite observations and other national geospatial data for analysis, monitoring and input to ecosystem models will extend NEON observations to regions across the United States not directly sampled by the observatory. The different spatial scales and measurement methods make quantitative comparisons between remote sensing and field data, typically collected over small sample plots (e.g. < 0.2 ha), difficult. New approaches to developing temporal and spatial scaling relationships between these data are necessary to enable validation of airborne and satellite remote sensing data and for incorporation of these data into continental or global scale ecological models. In addition to consideration of the methods used to collect ground-based measurements, careful calibration of the remote sensing instrumentation and an assessment of the accuracy of algorithms used to derive higher-level science data products are needed. Furthermore, long-term consistency of the data collected by all three airborne instrument packages over the NEON sites requires traceability of the calibration to national standards, field-based verification of instrument calibration and stability in the aircraft environment, and an independent assessment of the quality of derived data products. This work describes the development of the calibration laboratory, early evaluation of field-based vicarious calibration, development of scaling relationships, and test flights. Complementary laboratory- and field-based calibration of the AOP in addition to consistency with on-board calibration methods provide confidence that low-level data such as radiance and surface reflectance measurements are accurate and comparable among different sensors. Algorithms that calculate higher-level data products including essential climate variables will be validated against equivalent ground- and satellite-based results. Such a validated data set across multiple spatial and temporal scales is key to enabling ecosystem models to forecast the effects of climate change, land-use change and invasive species on the continental scale.

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

Interannual variation of the surface temperature of tropical forests from satellite observations

DOE PAGES

Gao, Huilin; Zhang, Shuai; Fu, Rong; ...

2016-01-01

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

Optical properties of volcanic ash: improving remote sensing observations.

NASA Astrophysics Data System (ADS)

Whelley, Patrick; Colarco, Peter; Aquila, Valentina; Krotkov, Nickolay; Bleacher, Jake; Garry, Brent; Young, Kelsey; Rocha Lima, Adriana; Martins, Vanderlei; Carn, Simon

2016-04-01

Many times each year explosive volcanic eruptions loft ash into the atmosphere. Global travel and trade rely on aircraft vulnerable to encounters with airborne ash. Volcanic ash advisory centers (VAACs) rely on dispersion forecasts and satellite data to issue timely warnings. To improve ash forecasts model developers and satellite data providers need realistic information about volcanic ash microphysical and optical properties. In anticipation of future large eruptions we can study smaller events to improve our remote sensing and modeling skills so when the next Pinatubo 1991 or larger eruption occurs, ash can confidently be tracked in a quantitative way. At distances >100km from their sources, drifting ash plumes, often above meteorological clouds, are not easily detected from conventional remote sensing platforms, save deriving their quantitative characteristics, such as mass density. Quantitative interpretation of these observations depends on a priori knowledge of the spectral optical properties of the ash in UV (>0.3μm) and TIR wavelengths (>10μm). Incorrect assumptions about the optical properties result in large errors in inferred column mass loading and size distribution, which misguide operational ash forecasts. Similarly, simulating ash properties in global climate models also requires some knowledge of optical properties to improve aerosol speciation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Huilin; Zhang, Shuai; Fu, Rong

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

Inter-annual variation of the surface temperature of tropical forests from SSM/I observations

NASA Astrophysics Data System (ADS)

Gao, H.; Fu, R.; Li, W.; Zhang, S.; Dickinson, R. E.

2014-12-01

Land surface temperatures (LST) within tropical rain forests contribute to climate variation, but observational data are very limited in these regions. In this study, all weather canopy sky temperatures were retrieved using the passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) over the Amazon and Congo rainforests. The remote sensing data used were collected from 1996 to 2012 using two separate satellites—F13 (1996-2009) and F17 (2007-2012). An inter-sensor calibration between the brightness temperatures collected by the two satellites was conducted in order to ensure consistency amongst the instruments. The interannual changes of LST associated with the dry and wet anomalies were investigated in both regions. The dominant spatial and temporal patterns for inter-seasonal variations of the LST over the tropical rainforest were analyzed, and the impacts of droughts and El Niños (on LST) were also investigated. The remote sensing results suggest that the morning LST is mainly controlled by atmospheric humidity (which controls longwave radiation) whereas the late afternoon LST is controlled by solar radiation.

Remote sensing of soil moisture using airborne hyperspectral data

USGS Publications Warehouse

Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.

2011-01-01

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

Remote sensing of soil moisture using airborne hyperspectral data

USGS Publications Warehouse

Finn, Michael P.; Lewis, Mark (David); Bosch, David D.; Giraldo, Mario; Yamamoto, Kristina H.; Sullivan, Dana G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, Craig; Williams, Michael S.

2011-01-01

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

USGS Publications Warehouse

Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

2013-01-01

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

Remotely Sensed Spatio-Temporal Variability of Snow Cover in Himalayan Region with Perspective of Climate Change

NASA Astrophysics Data System (ADS)

Dhakal, S.; Ojha, S.

2017-12-01

Climate change and its impact of water resource have gained tremendous attention among scientific committee, governments and other stakeholders since last couple of decades, especially in Himalayan region. In this study, we purpose remotely sensed measurements to monitor snow cover, both spatially and temporal, and assess climate change impact on water resource. The snow cover data from MODIS satellite (2000-2010) have been used to analyze some climate change indicators. In particular, the variability in the maximum snow extent with elevations, its temporal variability (8-day, monthly, seasonal and annual), its variation trend and its relation with temperature have been analyzed. The snow products used in this study are the maximum snow extent and fractional snow covers, which come in 8-day temporal and 500m and 0.05 degree spatial resolutions, respectively. The results showed a tremendous potential of the MODIS snow product for studying the spatial and temporal variability of snow as well as the study of climate change impact in large and inaccessible regions like the Himalayas. The snow area extent (SAE) (%) time series exhibits similar patterns during seven hydrological years, even though there are some deviations in the accumulation and melt periods. The analysis showed relatively well inverse relation between the daily mean temperature and SAE during the melting period. Some important trends of snow fall are also observed. In particular, the decreasing trend in January and increasing trend in late winter and early spring may be interpreted as a signal of a possible seasonal shift. However, it requires more years of data to verify this conclusion.

Remote Sensing for Food Security Monitoring in Afghanistan

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2008-01-01

Two decades of war have severely weakened Afghanistan s economy and infrastructure. Along with larger impacts on civil stability, education and health care, the current conflict in Afghanistan has resulted in widespread hunger and destitution. The 2005 National Risk and Vulnerability Assessment conducted by the United Nations found that 6.6 million Afghans do not meet their minimum food requirements and approximately 400,000 people each year are seriously affected by natural disasters, such as droughts, floods and extreme weather conditions. Given the poor security situation in the country, systems that will enable remote observations of variations of climate and their impacts on food production are critical for providing an appropriate and timely response. This chapter describes the remote sensing systems and food security analyses that the US Agency for International Development s Famine Early Warning Systems Network (FEWS NET) conducts in Afghanistan to monitor and provide information to international donors to ensure that adequate assistance is provided during this time of development and recovery.

Timber Volume and Biomass Estimates in Central Siberia from Satellite Data

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Kimes, Daniel S.; Kharuk, Vyetcheslav I.

2007-01-01

Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. The biggest deficiency of the existing ground based forest inventories is the uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse, lacking, and often decades old. Remote sensing methods can help overcome these problems. In this joint US and Russian study, we used the moderate resolution imaging spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) and produced a map of timber volume for a 10degx12deg area in Central Siberia. Using these methods, the mean timber volume for the forested area in the total study area was 203 m3/ ha. The new remote sensing methods used in this study provide a truly independent estimate of forest structure, which is not dependent on traditional ground forest inventory methods.

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, northwest China

NASA Astrophysics Data System (ADS)

Wu, X.; Shen, Y.; Wang, N.; Pan, X.; Zhang, W.; He, J.; Wang, G.

2017-12-01

Snowmelt water is an important freshwater resource in the Altay Mountains in northwest China, and it is also crucial for local ecological system, economic and social sustainable development; however, warming climate and rapid spring snowmelt can cause floods that endanger both eco-environment and public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature-index model based on remote sensing coupled with high-resolution meteorological data obtained from NCEP reanalysis fields that were downscaled using Weather Research Forecasting model, then bias-corrected using a statistical downscaled model. Validation of the forcing data revealed that the high-resolution meteorological fields derived from downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of temperature-index model based on remote sensing were calibrated for spring 2014, and model performance was validated using MODIS snow cover and snow observations from spring 2012. The results show that the temperature-index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash-Sutchliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt runoff was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt runoff accounts for 72% of spring runoff and 21% of annual runoff. Snowmelt is the main source of runoff for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt runoff predictions, so as to prevent snowmelt-induced floods, and also provide a generalizable approach that can be applied to other remote locations where high-density, long-term observational data is lacking.

Climate-mediated spatiotemporal variability in terrestrial productivity across Europe

NASA Astrophysics Data System (ADS)

Wu, X.; Babst, F.; Ciais, P.; Frank, D.; Reichstein, M.; Wattenbach, M.; Zang, C.; Mahecha, M. D.

2014-06-01

Quantifying the interannual variability (IAV) of the terrestrial ecosystem productivity and its sensitivity to climate is crucial for improving carbon budget predictions. In this context it is necessary to disentangle the influence of climate from impacts of other mechanisms underlying the spatiotemporal patterns of IAV of the ecosystem productivity. In this study we investigated the spatiotemporal patterns of IAV of historical observations of European crop yields in tandem with a set of climate variables. We further evaluated if relevant remote-sensing retrievals of NDVI (normalized difference vegetation index) and FAPAR (fraction of absorbed photosynthetically active radiation) depict a similar behaviour. Our results reveal distinct spatial patterns in the IAV of the analysed proxies linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions in both crop yield and remote-sensing observations. Our results further indicate that variations in the water balance during the active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity-related variables in temperate Europe. Overall, we observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe during the 1975-2009 period. In the same regions, a simultaneous increase in the IAV of water availability was detected. These findings suggest intricate responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become potentially more sensitive to changes in water availability in the dry regions in Europe. The changing sensitivity of terrestrial productivity accompanied by the changing IAV of climate is expected to impact carbon stocks and the net carbon balance of European ecosystems.

Utilizing Satellite Precipitation Products to Understand the Link Between Climate Variability and Malaria

NASA Astrophysics Data System (ADS)

Maggioni, V.; Mousam, A.; Delamater, P. L.; Cash, B. A.; Quispe, A.

2015-12-01

Malaria is a public health threat to people globally leading to 198 million cases and 584,000 deaths annually. Outbreaks of vector borne diseases such as malaria can be significantly impacted by climate variables such as precipitation. For example, an increase in rainfall has the potential to create pools of water that can serve as breeding locations for mosquitos. Peru is a country that is currently controlling malaria, but has not been able to completely eliminate the disease. Despite the various initiatives in order to control malaria - including regional efforts to improve surveillance, early detection, prompt treatment, and vector management - malaria cases in Peru have risen between 2011 and 2014. The purpose of this study is to test the hypothesis that climate variability plays a fundamental role in malaria occurrence over a 12-year period (2003-2014) in Peru. When analyzing climate variability, it is important to obtain high-quality, high-resolution data for a time series long enough to draw conclusion about how climate variables have been and are changing. Remote sensing is a powerful tool for measuring and monitoring climate variables continuously in time and space. A widely used satellite-based precipitation product, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), available globally since 1998, was used to obtain 3-hourly data with a spatial resolution of 0.25° x 0.25°. The precipitation data was linked to weekly (2003-2014) malaria cases collected by health centers and available at a district level all over Peru to investigate the relationship between precipitation and the seasonal and annual variations in malaria incidence. Further studies will incorporate additional climate variables such as temperature, humidity, soil moisture, and surface pressure from remote sensing data products and climate models. Ultimately, this research will help us to understand if climate variability impacts malaria incidence rates and to determine which regions of the country are most affected.

Climate Model Diagnostic and Evaluation: With a Focus on Satellite Observations

NASA Technical Reports Server (NTRS)

Waliser, Duane

2011-01-01

Each year, we host a summer school that brings together the next generation of climate scientists - about 30 graduate students and postdocs from around the world - to engage with premier climate scientists from the Jet Propulsion Laboratory and elsewhere. Our yearly summer school focuses on topics on the leading edge of climate science research. Our inaugural summer school, held in 2011, was on the topic of "Using Satellite Observations to Advance Climate Models," and enabled students to explore how satellite observations can be used to evaluate and improve climate models. Speakers included climate experts from both NASA and the National Oceanic and Atmospheric Administration (NOAA), who provided updates on climate model diagnostics and evaluation and remote sensing of the planet. Details of the next summer school will be posted here in due course.

The NASA NEESPI Data Portal: Products, Information, and Services

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory; Loboda, Tatiana; Csiszar, Ivan; Romanov, Peter; Gerasimov, Irina

2008-01-01

Studies have indicated that land cover and use changes in Northern Eurasia influence global climate system. However, the procedures are not fully understood and it is challenging to understand the interactions between the land changes in this region and the global climate. Having integrated data collections form multiple disciplines are important for studies of climate and environmental changes. Remote sensed and model data are particularly important die to sparse in situ measurements in many Eurasia regions especially in Siberia. The NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) NEESPI data portal has generated infrastructure to provide satellite remote sensing and numerical model data for atmospheric, land surface, and cryosphere. Data searching, subsetting, and downloading functions are available. ONe useful tool is the Web-based online data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure), which allows scientists to assess easily the state and dynamics of terrestrial ecosystems in Northern Eurasia and their interactions with global climate system. Recently, we have created a metadata database prototype to expand the NASA NEESPI data portal for providing a venue for NEESPI scientists fo find the desired data easily and leveraging data sharing within NEESPI projects. The database provides product level information. The desired data can be found through navigation and free text search and narrowed down by filtering with a number of constraints. In addition, we have developed a Web Map Service (WMS) prototype to allow access data and images from difference data resources.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

Remote Sensing of Supercooled Cloud Layers in Cold Climate Using Ground Based Integrated Sensors System and Comparison with Pilot Reports and model forecasts

NASA Astrophysics Data System (ADS)

Boudala, Faisal; Wu, Di; Gultepe, Ismail; Anderson, Martha; turcotte, marie-france

2017-04-01

In-flight aircraft icing is one of the major weather hazards to aviation . It occurs when an aircraft passes through a cloud layer containing supercooled drops (SD). The SD in contact with the airframe freezes on the surface which degrades the performance of the aircraft.. Prediction of in-flight icing requires accurate prediction of SD sizes, liquid water content (LWC), and temperature. The current numerical weather predicting (NWP) models are not capable of making accurate prediction of SD sizes and associated LWC. Aircraft icing environment is normally studied by flying research aircraft, which is quite expensive. Thus, developing a ground based remote sensing system for detection of supercooled liquid clouds and characterization of their impact on severity of aircraft icing one of the important tasks for improving the NWPs based predictions and validations. In this respect, Environment and Climate Change Canada (ECCC) in cooperation with the Department of National Defense (DND) installed a number of specialized ground based remote sensing platforms and present weather sensors at Cold Lake, Alberta that includes a multi-channel microwave radiometer (MWR), K-band Micro Rain radar (MRR), Ceilometer, Parsivel distrometer and Vaisala PWD22 present weather sensor. In this study, a number of pilot reports confirming icing events and freezing precipitation that occurred at Cold Lake during the 2014-2016 winter periods and associated observation data for the same period are examined. The icing events are also examined using aircraft icing intensity estimated using ice accumulation model which is based on a cylindrical shape approximation of airfoil and the Canadian High Resolution Regional Deterministic Prediction System (HRDPS) model predicted LWC, median volume diameter and temperature. The results related to vertical atmospheric profiling conditions, surface observations, and the Canadian High Resolution Regional Deterministic Prediction System (HRDPS) model predictions are given. Preliminary results suggest that remote sensing and present weather sensors based observations of cloud SD regions can be used to describe micro and macro physical characteristics of the icing conditions. The model based icing intensity prediction reasonably agreed with the PIREPs and MWR observations.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Assessing the impacts of climate change in Mediterranean catchments under conditions of data scarcity

NASA Astrophysics Data System (ADS)

Meyer, Swen; Ludwig, Ralf

2013-04-01

According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. While there is scientific consensus that climate induced changes on the hydrology of Mediterranean regions are presently occurring and are projected to amplify in the future, very little knowledge is available about the quantification of these changes, which is hampered by a lack of suitable and cost effective hydrological monitoring and modeling systems. The European FP7-project CLIMB is aiming to analyze climate induced changes on the hydrology of the Mediterranean Basins by investigating 7 test sites located in the countries Italy, France, Turkey, Tunisia, Gaza and Egypt. CLIMB employs a combination of novel geophysical field monitoring concepts, remote sensing techniques and integrated hydrologic modeling to improve process descriptions and understanding and to quantify existing uncertainties in climate change impact analysis. The Rio Mannu Basin, located in Sardinia; Italy, is one test site of the CLIMB project. The catchment has a size of 472.5 km2, it ranges from 62 to 946 meters in elevation, at mean annual temperatures of 16°C and precipitation of about 700 mm, the annual runoff volume is about 200 mm. The physically based Water Simulation Model WaSiM Vers. 2 (Schulla & Jasper (1999)) was setup to model current and projected future hydrological conditions. The availability of measured meteorological and hydrological data is poor as common to many Mediterranean catchments. The lack of available measured input data hampers the calibration of the model setup and the validation of model outputs. State of the art remote sensing techniques and field measuring techniques were applied to improve the quality of hydrological input parameters. In a field campaign about 250 soil samples were collected and lab-analyzed. Different geostatistical regionalization methods were tested to improve the model setup. The soil parameterization of the model was tested against publically available soil data. Results show a significant improvement of modeled soil moisture outputs. To validate WaSiMs evapotranspiration (ETact) outputs, Landsat TM images were used to calculate the actual monthly mean ETact rates using the triangle method (Jiang and Islam, 1999). Simulated spatial ETact patterns and those derived from remote sensing show a good fit especially for the growing season. WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. Output results were analyzed for climate induced changes on selected hydrological variables. While the climate projections reveal increased precipitation rates in the spring season, first simulation results show an earlier onset and an increased duration of the dry season, imposing an increased irrigation demand and higher vulnerability of agricultural productivity.

Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?

Treesearch

Yulong Zhang; Conghe Song; Lawrence E. Band; Ge Sun; Junxiang Li

2017-01-01

Accurately monitoring global vegetation dynamics with modern remote sensing is critical for understanding the functions and processes of the biosphere and its interactions with the planetary climate. The MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for this purpose. To date, theMODIS teamhad released...

The role of remote sensing in process‐scaling studies of managed forest ecosystems

Treesearch

Jeffrey G. Masek; Daniel J. Hayes; M. Joseph Hughes; Sean P. Healey; David P. Turner

2015-01-01

Sustaining forest resources requires a better understanding of forest ecosystem processes, and how management decisions and climate change may affect these processes in the future. While plot and inventory data provide our most detailed information on forest carbon, energy, and water cycling, applying this understanding to broader spatial and temporal domains...

Mapping deforestation and forest degradation using Landsat time series: a case of Sumatra—Indonesia

Treesearch

Belinda Arunarwati Margono

2013-01-01

Indonesia experiences the second highest rate of deforestation among tropical countries (FAO 2005, 2010). Consequently, timely and accurate forest data are required to combat deforestation and forest degradation in support of climate change mitigation and biodiversity conservation policy initiatives. Remote sensing is considered as a significant data source for forest...

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product

USDA-ARS?s Scientific Manuscript database

Climate warming over the past half century has led to observable changes in vegetation phenology and growing season length; which can be measured globally using remote sensing derived vegetation indices. Previous studies in mid- and high northern latitude systems show temperature driven earlier spri...

Tunnel-Site Selection by Remote Sensing Techniques

DTIC Science & Technology

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

Biological and remote sensing perspectives of pigmentation in coral reef organisms.

PubMed

Hedley, John D; Mumby, Peter J

2002-01-01

Coral reef communities face unprecedented pressures on local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Optical remote sensing, from satellites or aircraft, is possibly the only means of measuring the effects of such stresses at appropriately large spatial scales (many thousands of square kilometres). To map key variables such as coral community structure, percentages of living coral or percentages of dead coral, a remote sensing instrument must be able to distinguish the reflectance spectra (i.e. "spectral signature", reflected light as a function of wavelength) of each category. For biotic classes, reflectance is a complex function of pigmentation, structure and morphology. Studies of coral "colour" fall into two disparate but potentially complementary types. Firstly, biological studies tend to investigate the structure and significance of pigmentation in reef organisms. These studies often lack details that would be useful from a remote sensing perspective such as intraspecific variation in pigment concentration or the contribution of fluorescence to reflectance. Secondly, remote sensing studies take empirical measurements of spectra and seek wavelengths that discriminate benthic categories. Benthic categories used in remote sensing sometimes consist of species groupings that are biologically or spectrally inappropriate (e.g. merging of algal phyla with distinct pigments). Here, we attempt to bridge the gap between biological and remote sensing perspectives of pigmentation in reef taxa. The aim is to assess the extent to which spectral discrimination can be given a biological foundation, to reduce the ad hoc nature of discriminatory criteria, and to understand the fundamental (biological) limitations in the spectral separability of biotic classes. Sources of pigmentation in reef biota are reviewed together with remote sensing studies where spectral discrimination has been effectively demonstrated between benthic categories. The basis of reflectance is considered as the sum of pigmented components, such as zooxanthellae, host tissues and skeletons of corals. Problems in the empirical in situ measurement of reflectance are identified, such as the differing types of reflectance which can be measured, the interaction of the light field with morphology, and depth-dependent variability of measured reflectance due to fluorescence. The latter is estimated in some cases to introduce an error of up to 20% when depth differs by 8 m. Spectral features useful in discriminating reef benthos are identified and related to pigmentation. The slope in the reflectance spectra between 650 and 690 nm is dependent on chlorophyll-a concentration and can be used to discriminate bare sand with no algal component from chlorophyll-a containing benthos (algae, corals). The slope in reflectance at various locations between 500 and 560 nm can be useful in discriminating bleached and unbleached corals, possibly due to reduced peridinin concentration. Rhodophyta may be discernible by the presence of a dip in reflectance at 570 nm, due to a phycoerythrin absorption peak. However, the utility of some discriminatory criteria in deeper waters is mitigated by the relatively poor transmission of light through water at longer wavelengths (especially > 600 nm). Contrary to suggested categorizations of fluorescent pigments in coral host tissues, it is shown that these pigments form an almost continuous distribution with respect to their excitation and emission peaks. Remote sensing by induced fluorescence is a promising approach, but further details about the variation and distribution of these pigments are required. It is hoped that this review will promote cross-disciplinary collaboration between pigment biologists and the reef remote sensing community. Where possible, the discriminative criteria adopted in remote sensing should be related to biological phenomena, thus lending an intuitive, process-orientated basis for interpreting spectral data. Similarly, remote sensing may provide a novel scaling perspective to biological studies of pigmentation in reef organisms.

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Classification of permafrost active layer depth from remotely sensed and topographic evidence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peddle, D.R.; Franklin, S.E.

1993-04-01

The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to thismore » study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth.« less

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Flood Management Enhancement Using Remotely Sensed Data

NASA Technical Reports Server (NTRS)

Romanowski, Gregory J.

1997-01-01

SENTAR, Inc., entered into a cooperative agreement with NASA Goddard Space Flight Center (GSFC) in December 1994. The intent of the NASA Cooperative Agreement was to stimulate broad public use, via the Internet, of the very large remote sensing databases maintained by NASA and other agencies, thus stimulating U.S. economic growth, improving the quality of life, and contributing to the implementation of a National Information Infrastructure. SENTAR headed a team of collaborating organizations in meeting the goals of this project. SENTAR's teammates were the NASA Marshall Space Flight Center (MSFC) Global Hydrology and Climate Center (GHCC), the U.S. Army Space and Strategic Defense Command (USASSDC), and the Alabama Emergency Management Agency (EMA). For this cooperative agreement, SENTAR and its teammates accessed remotely sensed data in the Distributed Active Archive Centers, and other available sources, for use in enhancing the present capabilities for flood disaster management by the Alabama EMA. The project developed a prototype software system for addressing prediction, warning, and damage assessment for floods, though it currently focuses on assessment. The objectives of the prototype system were to demonstrate the added value of remote sensing data for emergency management operations during floods and the ability of the Internet to provide the primary communications medium for the system. To help achieve these objectives, SENTAR developed an integrated interface for the emergency operations staff to simplify acquiring and manipulating source data and data products for use in generating new data products. The prototype system establishes a systems infrastructure designed to expand to include future flood-related data and models or to include other disasters with their associated remote sensing data requirements and distributed data sources. This report covers the specific work performed during the seventh, and final, milestone period of the project, which began on 1 October 1996 and ended on 31 January 1997. In addition, it provides a summary of the entire project.

Investigation of Arctic mixed-phase clouds by combining airborne remote sensing and in situ observations during VERDI, RACEPAC and ACLOUD

NASA Astrophysics Data System (ADS)

Ehrlich, André; Bierwirth, Eike; Borrmann, Stephan; Crewell, Susanne; Herber, Andreas; Hoor, Peter; Jourdan, Olivier; Krämer, Martina; Lüpkes, Christof; Mertes, Stephan; Neuber, Roland; Petzold, Andreas; Schnaiter, Martin; Schneider, Johannes; Weigel, Ralf; Weinzierl, Bernadett; Wendisch, Manfred

2016-04-01

To improve our understanding of Arctic mixed-phase clouds a series of airborne research campaigns has been initiated by a collaboration of German research institutes. Clouds in areas dominated by a close sea-ice cover were observed during the research campaign Vertical distribution of ice in Arctic mixed-phase clouds (VERDI, April/May 2012) and the Radiation-Aerosol-Cloud Experiment in the Arctic Circle (RACEPAC, April/May 2014) which both were based in Inuvik, Canada. The aircraft (Polar 5 & 6, Basler BT-67) operated by the Alfred Wegener Institute for Polar and Marine Research, Germany did cover a wide area above the Canadian Beaufort with in total 149 flight hours (62h during VERDI, 87h during RACEPAC). For May/June 2017 a third campaign ACLOUD (Arctic Clouds - Characterization of Ice, aerosol Particles and Energy fluxes) with base in Svalbard is planned within the Transregional Collaborative Research Centre TR 172 ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3 to investigate Arctic clouds in the transition zone between open ocean and sea ice. The aim of all campaigns is to combine remote sensing and in-situ cloud, aerosol and trace gas measurements to investigate interactions between radiation, cloud and aerosol particles. While during VERDI remote sensing and in-situ measurements were performed by one aircraft subsequently, for RACEPAC and ACLOUD two identical aircraft are coordinated at different altitudes to horizontally collocate both remote sensing and in-situ measurements. The campaign showed that in this way radiative and microphysical processes in the clouds can by studied more reliably and remote sensing methods can be validated efficiently. Here we will illustrate the scientific strategy of the projects including the progress in instrumentation. Differences in the general synoptic and sea ice situation and related changes in cloud properties at the different locations and seasons will be addressed to illustrate the broad spectrum of the observations. Exemplary results will be highlighted.

Introduction to the physics and techniques of remote sensing

NASA Technical Reports Server (NTRS)

Elachi, Charles

1987-01-01

This book presents a comprehensive overview of the basics behind remote-sensing physics, techniques, and technology. The physics of wave/matter interactions, techniques of remote sensing across the electromagnetic spectrum, and the concepts behind remote sensing techniques now established and future ones under development are discussed. Applications of remote sensing are described for a wide variety of earth and planetary atmosphere and surface sciences. Solid surface sensing across the electromagnetic spectrum, ocean surface sensing, basic principles of atmospheric sensing and radiative transfer, and atmospheric remote sensing in the microwave, millimeter, submillimeter, and infrared regions are examined.

7th IGRSM International Remote Sensing & GIS Conference and Exhibition

NASA Astrophysics Data System (ADS)

Shariff, Abdul Rashid Mohamed

2014-06-01

IGRSM This proceedings consists of the peer-reviewed papers from the 7th IGRSM International Conference and Exhibition on Remote Sensing & GIS (IGRSM 2014), which was held on 21-22 April 2014 at Berjaya Times Square Hotel, Kuala Lumpur, Malaysia. The conference, with the theme Geospatial Innovation for Nation Building was aimed at disseminating knowledge, and sharing expertise and experiences in geospatial sciences in all aspects of applications. It also aimed to build linkages between local and international professionals in this field with industries. Highlights of the conference included: Officiation by Y B Datuk Dr Abu Bakar bin Mohamad Diah, Deputy Minister of Minister of Science, Technology & Innovation Keynote presentations by: Associate Professor Dr Francis Harvey, Chair of the Geographic Information Science Commission at the International Geographical Union (IGU) and Director of U-Spatial, University of Minnesota, US: The Next Age of Discovery and a Future in a Post-GIS World. Professor Dr Naoshi Kondo, Bio-Sensing Engineering, University of Kyoto, Japan: Mobile Fruit Grading Machine for Precision Agriculture. Datuk Ir Hj Ahmad Jamalluddin bin Shaaban, Director-General, National Hydraulic Research Institute of Malaysia (NAHRIM), Malaysia: Remote Sensing & GIS in Climate Change Analyses. Oral and poster presentations from 69 speakers, from both Malaysia (35) and abroad (34), covering areas of water resources management, urban sprawl & social mobility, agriculture, land use/cover mapping, infrastructure planning, disaster management, technology trends, environmental monitoring, atmospheric/temperature monitoring, and space applications for the environment. Post-conference workshops on: Space Applications for Environment (SAFE), which was be organised by the Japan Aerospace Exploration Agency (JAXA) Global Positioning System (GPS) Receiver Evaluation Using GPS Simulation, which was be organised by the Science & Technology Research Institute for Defence (STRIDE), and sponsored by RFI Technologies Sdn. Bhd. and Aeroflex Inc. Two awards were presented by Dr Noordin Ahmad, Director-General of the National Space Agency during the conference's closing ceremony: Best Paper Award: Dr Rizatus Shofiyati, Indonesian Center for Agricultural Land Resources Research and Development (ICALRD), Indonesia: Indonesian Drought Monitoring from Space. A Report of SAFE Activity: Assessment of Drought Impact on Rice Production in Indonesia by Satellite Remote Sensing and Dissemination with Web-GIS Best Student Paper Award: Rosnani Rahman, Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Malaysia: Monitoring the Variability of Precipitable Water Vapor Over the Klang Valley, Malaysia During Flash Flood The success of the IGRSM 2014 was due to commitments of many: authors, keynote speakers, session chairpersons, the organising and technical programme committees, student volunteers from Universiti Putra Malaysia (UPM), and many others of various roles. We acknowledge the sponsors of IGRSM 2014, namely Antaragrafik Systems Sdn. Bhd. and Geospatial Media and Communications Sdn. Bhd. We also thank all exhibitors and contributors: E J Motiwalla, Fajar Saintifik Sdn. Bhd., Bandwork GPS Solutions Sdn. Bhd., Tenaga Nasional Bhd., TSKAY Technology Sdn. Bhd., Geo Spatial Solutions Sdn. Bhd. and Accutac Sdn. Bhd. Associate Professor Sr Dr Abdul Rashid Mohamed Shariff Chairman 7th IGRSM International Remote Sensing & GIS Conference and Exhibition (IGRSM2014) President Institution of Geospatial and Remote Sensing Malaysia (IGRSM), 2012-2014

[Thematic Issue: Remote Sensing.

ERIC Educational Resources Information Center

Howkins, John, Ed.

1978-01-01

Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…

75 FR 65304 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-22

... Commercial Remote Sensing (ACCRES); Request for Nominations AGENCY: National Oceanic and Atmospheric... Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was... Atmosphere, on matters relating to the U.S. commercial remote sensing industry and NOAA's activities to carry...

Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angles Basin

NASA Technical Reports Server (NTRS)

Fu, Dejian; Pongetti, Thomas J.; Sander, Stanley P.; Cheung, Ross; Stutz, Jochen; Park, Chang Hyoun; Li, Qinbin

2011-01-01

The Los Angeles air basin is a significant anthropogenic source of greenhouse gases and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warming Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distributions of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angeles Basin

NASA Technical Reports Server (NTRS)

Fu, Dejian; Sander, Stanley P.; Pongetti, Thomas J.; Cheung, Ross; Stutz, Jochen

2010-01-01

The Los Angeles air basin is a significant anthropogenic source of greenhouse gasses and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warning Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distribution of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

Remote sensing of frozen lakes on the North Slope of Alaska

USGS Publications Warehouse

French, N.; Savage, S.; Shuchman, R.; Edson, R.; Payne, J.; Josberger, E.

2004-01-01

We used synthetic aperture radar (SAR) images from the ERS-2 remote sensing satellite to map the freeze condition of lakes on Alaska's North Slope, the geographic region to the north of the Brooks Range. An mage from March 1997, to coincide with the period of maximum freeze depth, was used for the frozen lake mapping. Emphasis was placed on distinguishing between lakes frozen to the lakebed and lakes with some portion unfrozen to the bed (a binary classification). The result of the analysis is a map identifying lakes as frozen to the lakebed and lakes not frozen to the lakebed. This analysis of one SAR image has shown the feasibility of a simple technique for mapping frozen lake condition for supporting decision making and understanding impacts of climate change on the North Slope.

Assessing fire emissions from tropical savanna and forests of central Brazil

NASA Technical Reports Server (NTRS)

Riggan, Philip J.; Brass, James A.; Lockwood, Robert N.

1993-01-01

Wildfires in tropical forest and savanna are a strong source of trace gas and particulate emissions to the atmosphere, but estimates of the continental-scale impacts are limited by large uncertainties in the rates of fire occurrence and biomass combustion. Satellite-based remote sensing offers promise for characterizing fire physical properties and impacts on the environment, but currently available sensors saturate over high-radiance targets and provide only indications of regions and times at which fires are extensive and their areal rate of growing as recorded in ash layers. Here we describe an approach combining satellite- and aircraft-based remote sensing with in situ measurements of smoke to estimate emissions from central Brazil. These estimates will improve global accounting of radiation-absorbing gases and particulates that may be contributing to climate change and will provide strategic data for fire management.

Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance

NASA Technical Reports Server (NTRS)

Eck, Thomas F.; Dye, Dennis G.

1991-01-01

A new satellite remote sensing method for estimating the amount of photosynthetically active radiation (PAR, 400-700 nm) incident at the earth's surface is described and tested. Potential incident PAR for clear sky conditions is computed from an existing spectral model. A major advantage of the UV approach over existing visible band approaches to estimating insolation is the improved ability to discriminate clouds from high-albedo background surfaces. UV spectral reflectance data from the Total Ozone Mapping Spectrometer (TOMS) were used to test the approach for three climatically distinct, midlatitude locations. Estimates of monthly total incident PAR from the satellite technique differed from values computed from ground-based pyranometer measurements by less than 6 percent. This UV remote sensing method can be applied to estimate PAR insolation over ocean and land surfaces which are free of ice and snow.

Biomass Burning Emissions from Fire Remote Sensing

NASA Technical Reports Server (NTRS)

Ichoku, Charles

2010-01-01

Knowledge of the emission source strengths of different (particulate and gaseous) atmospheric constituents is one of the principal ingredients upon which the modeling and forecasting of their distribution and impacts depend. Biomass burning emissions are complex and difficult to quantify. However, satellite remote sensing is providing us tremendous opportunities to measure the fire radiative energy (FRE) release rate or power (FRP), which has a direct relationship with the rates of biomass consumption and emissions of major smoke constituents. In this presentation, we will show how the satellite measurement of FRP is facilitating the quantitative characterization of biomass burning and smoke emission rates, and the implications of this unique capability for improving our understanding of smoke impacts on air quality, weather, and climate. We will also discuss some of the challenges and uncertainties associated with satellite measurement of FRP and how they are being addressed.

A Remotely Sensed Global Terrestrial Drought Severity Index

NASA Astrophysics Data System (ADS)

Mu, Q.; Zhao, M.; Kimball, J. S.; McDowell, N. G.; Running, S. W.

2012-12-01

Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse eco-social impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. We developed a method to generate a near-real-time remotely sensed Drought Severity Index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly and annual frequencies. The new DSI integrates and exploits information from current operational satellite based terrestrial evapotranspiration (ET) and Vegetation greenness Index (NDVI) products, which are sensitive to vegetation water stress. Specifically, our approach determines the annual DSI departure from its normal (2000-2011) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (r=0.43) with the precipitation based Palmer Drought Severity Index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely-sensed global terrestrial DSI enhances capabilities for near-real-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.

Assessment of Wildfire Risk in Southern California with Live Fuel Moisture Measurement and Remotely Sensed Vegetation Water Content Proxies

NASA Astrophysics Data System (ADS)

Jia, S.; Kim, S. H.; Nghiem, S. V.; Kafatos, M.

2017-12-01

Live fuel moisture (LFM) is the water content of live herbaceous plants expressed as a percentage of the oven-dry weight of plant. It is a critical parameter in fire ignition in Mediterranean climate and routinely measured in sites selected by fire agencies across the U.S. Vegetation growing cycle, meteorological metrics, soil type, and topography all contribute to the seasonal and inter-annual variation of LFM, and therefore, the risk of wildfire. The optical remote sensing-based vegetation indices (VIs) have been used to estimate the LFM. Comparing to the VIs, microwave remote sensing products have advantages like less saturation effect in greenness and representing the water content of the vegetation cover. In this study, we established three models to evaluate the predictability of LFM in Southern California using MODIS NDVI, vegetation temperature condition index (VTCI) from downscaled Soil Moisture Active Passive (SMAP) products, and vegetation optical depth (VOD) derived by Land Parameter Retrieval Model. Other ancillary variables, such as topographic factors (aspects and slope) and meteorological metrics (air temperature, precipitation, and relative humidity), are also considered in the models. The model results revealed an improvement of LFM estimation from SMAP products and VOD, despite the uncertainties introduced in the downscaling and parameter retrieval. The estimation of LFM using remote sensing data can provide an assessment of wildfire danger better than current methods using NDVI-based growing seasonal index. Future study will test the VOD estimation from SMAP data using the multi-temporal dual channel algorithm (MT-DCA) and extend the LFM modeling to a regional scale.

Assimilation of Remotely-Sensed Snow information to improve streamflow predictions in the Southwestern US

NASA Astrophysics Data System (ADS)

López-Burgos, V.; Rajagopal, S.; Martinez Baquero, G. F.; Gupta, H. V.

2009-12-01

Rapidly growing population in the southwestern US is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial information of water fluxes from remote sensing platforms, and hydrological models coupled with ground based data is an important step towards this goal. This project is exploring the use of Snow Water Equivalent (SWE) estimates to update the snow component of the Variable Infiltration Capacity model (VIC). SWE estimates are obtained by combining SNOTEL data with MODIS Snow Cover Area (SCA) information. Because, cloud cover corrupts the estimates of SCA, a rule-based method is used to clean up the remotely sensed images. The rules include a time interpolation method, and the probability of a pixel for been covered with snow based on the relationships between elevation, temperature, lapse rate, aspect and topographic shading. The approach is used to improve streamflow predictions on two rivers managed by the Salt River Project, a water and energy supplier in central Arizona. This solution will help improve the management of reservoirs in the Salt and Verde River in Phoenix, Arizona (tributaries of the lower Colorado River basin), by incorporating physically based distributed models and remote sensing observations into their Decision Support Tools and planning tools. This research seeks to increase the knowledge base used to manage reservoirs and groundwater resources in a region affected by a long-term drought. It will be applicable and relevant for other water utility companies facing the challenges of climate change and decreasing water resources.

Using High Resolution Remote Sensing Images to Investigate Hydrologic Connectivity and Degradation Thresholds along a Precipitation Gradient in Semiarid Australia

NASA Astrophysics Data System (ADS)

Azadi, S.; Saco, P. M.; Moreno-de las Heras, M.; Willgoose, G. R.

2016-12-01

Arid and semiarid landscapes are particularly sensitive to climatic and anthropogenic disturbances. Previous work has identified that these landscapes are prone to undergo critical degradation thresholds above which rehabilitation is difficult to achieve. This threshold behaviour is tightly linked to the overland flow redistribution and an increase in hydrologic connectivity associated with the climatic or anthropogenic disturbances. In fact, disturbances (such as wildfire, overgrazing or harvesting activities) can disrupt the spatial structure of vegetation, increase landscape hydrologic connectivity, trigger erosion and produce a substantial loss of water. All these effects can eventually affect ecosystem functionality (e.g. Rainfall Use Efficiency). In this study, we explore the impact of degradation processes induced by vegetation disturbances (mostly due to grazing pressure) on ecosystem functionality and connectivity along a precipitation gradient (250 mm to 490 mm annual average rainfall) using a combination of remote sensing observations and Digital Elevation Model data. The sites were carefully selected in the Mulga landscapes bioregion (New South Wales, Queensland) and in sites of the Northern Territory in Australia, which display similar vegetation characteristics and good quality rainfall information. Vegetation patterns and the percent of fractional cover were obtained from high resolution remote sensing images (IKONOS, QuickBird and Pleiades). We computed rainfall use efficiency and precipitation marginal response using local precipitation data and MODIS vegetation indices. We estimated mean Flowlength as an indicator of structural hydrologic connectivity using vegetation binary maps and digital elevation models. We compared the trends for several sites along the precipitation gradient, and found that disturbances substantially increase hydrologic connectivity following a threshold behaviour that affects landscape functionality. Though this threshold behaviour is found in all sites, the plots in higher rainfall landscapes show evidence of higher resilience.

Offshore Radiation Observations for Climate Research at the CERES Ocean Validation Experiment

NASA Technical Reports Server (NTRS)

Rutledge, Charles K.; Schuster, Gregory L.; Charlock, Thomas P.; Denn, Frederick M.; Smith, William L., Jr.; Fabbri, Bryan E.; Madigan, James J., Jr.; Knapp, Robert J.

2006-01-01

When radiometers on a satellite are pointed towards the planet with the goal of understanding a phenomenon quantitatively, rather than just creating a pleasing image, the task at hand is often problematic. The signal at the detector can be affected by scattering, absorption, and emission; and these can be due to atmospheric constituents (gases, clouds, and aerosols), the earth's surface, and subsurface features. When targeting surface phenomena, the remote sensing algorithm needs to account for the radiation associated with the atmospheric constituents. Likewise, one needs to correct for the radiation leaving the surface, when atmospheric phenomena are of interest. Rigorous validation of such remote sensing products is a real challenge. In visible and near infrared wavelengths, the jumble of effects on atmospheric radiation are best accomplished over dark surfaces with fairly uniform reflective properties (spatial homogeneity) in the satellite instrument's field of view (FOV). The ocean's surface meets this criteria; land surfaces - which are brighter, more spatially inhomogeneous, and more changeable with time - generally do not. NASA's Clouds and the Earth's Radiant Energy System (CERES) project has used this backdrop to establish a radiation monitoring site in Virginia's coastal Atlantic Ocean. The project, called the CERES Ocean Validation Experiment (COVE), is located on a rigid ocean platform allowing the accurate measurement of radiation parameters that require precise leveling and pointing unavailable from ships or buoys. The COVE site is an optimal location for verifying radiative transfer models and remote sensing algorithms used in climate research; because of the platform's small size, there are no island wake effects; and suites of sensors can be simultaneously trained both on the sky and directly on ocean itself. This paper describes the site, the types of measurements made, multiple years of atmospheric and ocean surface radiation observations, and satellite validation results.

Husbandry Emissions Estimation: Fusion of Mobile Surface and Airborne Remote Sensing and Mobile Surface In Situ Measurements

NASA Astrophysics Data System (ADS)

Leifer, I.; Hall, J. L.; Melton, C.; Tratt, D. M.; Chang, C. S.; Buckland, K. N.; Frash, J.; Leen, J. B.; Van Damme, M.; Clarisse, L.

2017-12-01

Emissions of methane and ammonia from intensive animal husbandry are important drivers of climate and photochemical and aerosol pollution. Husbandry emission estimates are somewhat uncertain because of their dependence on practices, temperature, micro-climate, and other factors, leading to variations in emission factors up to an order-of-magnitude. Mobile in situ measurements are increasingly being applied to derive trace gas emissions by Gaussian plume inversion; however, inversion with incomplete information can lead to erroneous emissions and incorrect source location. Mobile in situ concentration and wind data and mobile remote sensing column data from the Chino Dairy Complex in the Los Angeles Basin were collected near simultaneously (within 1-10 s, depending on speed) while transecting plumes, approximately orthogonal to winds. This analysis included airborne remote sensing trace gas information. MISTIR collected vertical column FTIR data simultaneously with in situ concentration data acquired by the AMOG-Surveyor while both vehicles traveled in convoy. The column measurements are insensitive to the turbulence characterization needed in Gaussian plume inversion of concentration data and thus provide a flux reference for evaluating in situ data inversions. Four different approaches were used on inversions for a single dairy, and also for the aggregate dairy complex plume. Approaches were based on differing levels of "knowledge" used in the inversion from solely the in situ platform and a single gas to a combination of information from all platforms and multiple gases. Derived dairy complex fluxes differed significantly from those estimated by other studies of the Chino complex. Analysis of long term satellite data showed that this most likely results from seasonality effects, highlighting the pitfalls of applying annualized extensions of flux measurements to a single campaign instantiation.

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

NASA Astrophysics Data System (ADS)

Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, Arijit; Singh, Sarnam; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, Ch. Sudhakar; Gupta, Stutee; Pujar, Girish; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, Poonam; Singh, J. S.; Chitale, Vishwas; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, Deepak; Karnatak, Harish; Saran, Sameer; Giriraj, A.; Padalia, Hitendra; Kale, Manish; Nandy, Subrato; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, Chiranjibi; Singh, D. K.; Devagiri, G. M.; Talukdar, Gautam; Panigrahy, Rabindra K.; Singh, Harnam; Sharma, J. R.; Haridasan, K.; Trivedi, Shivam; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, Madhura; Nagabhatla, Nidhi; Prasad, Nupoor; Tripathi, O. P.; Prasad, P. Rama Chandra; Dash, Pushpa; Qureshi, Qamer; Tripathi, S. K.; Ramesh, B. R.; Gowda, Balakrishnan; Tomar, Sanjay; Romshoo, Shakil; Giriraj, Shilpa; Ravan, Shirish A.; Behera, Soumit Kumar; Paul, Subrato; Das, Ashesh Kumar; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, Uma; Menon, A. R. R.; Srivastava, Gaurav; Neeti; Sharma, Subrat; Mohapatra, U. B.; Peddi, Ashok; Rashid, Humayun; Salroo, Irfan; Krishna, P. Hari; Hajra, P. K.; Vergheese, A. O.; Matin, Shafique; Chaudhary, Swapnil A.; Ghosh, Sonali; Lakshmi, Udaya; Rawat, Deepshikha; Ambastha, Kalpana; Malik, Akhtar H.; Devi, B. S. S.; Gowda, Balakrishna; Sharma, K. C.; Mukharjee, Prashant; Sharma, Ajay; Davidar, Priya; Raju, R. R. Venkata; Katewa, S. S.; Kant, Shashi; Raju, Vatsavaya S.; Uniyal, B. P.; Debnath, Bijan; Rout, D. K.; Thapa, Rajesh; Joseph, Shijo; Chhetri, Pradeep; Ramachandran, Reshma M.

2015-07-01

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge's life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).

Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

2017-12-01

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

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

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

1993-01-01

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

Upscaling Our Approach to Peatland Carbon Sequestration: Remote Sensing as a Tool for Carbon Flux Estimation.

NASA Astrophysics Data System (ADS)

Lees, K.; Khomik, M.; Clark, J. M.; Quaife, T. L.; Artz, R.

2017-12-01

Peatlands are an important part of the Earth's carbon cycle, comprising approximately a third of the global terrestrial carbon store. However, peatlands are sensitive to climatic change and human mismanagement, and many are now degraded and acting as carbon sources. Restoration work is being undertaken at many sites around the world, but monitoring the success of these schemes can be difficult and costly using traditional methods. A landscape-scale alternative is to use satellite data in order to assess the condition of peatlands and estimate carbon fluxes. This work focuses on study sites in Northern Scotland, where parts of the largest blanket bog in Europe are being restored from forest plantations. A combination of laboratory and fieldwork has been used to assess the Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP) and respiration of peatland sites in different conditions, and the climatic vulnerability of key peat-forming Sphagnum species. The results from these studies have been compared with spectral data in order to evaluate the extent to which remote sensing can function as a source of information for peatland health and carbon flux models. This work considers particularly the effects of scale in calculating peatland carbon flux. Flux data includes chamber and eddy covariance measurements of carbon dioxide, and radiometric observations include both handheld spectroradiometer results and satellite images. Results suggest that despite the small-scale heterogeneity and unique ecosystem factors in blanket bogs, remote sensing can be a useful tool in monitoring peatland health and carbon sequestration. In particular, this study gives unique insights into the relationships between peatland vegetation, carbon flux and spectral reflectance.

Investigating the Potential Range Expansion of the Vector Mosquito Aedes aegypti in Mexico with NASA Earth Science Remote Sensing Results

NASA Astrophysics Data System (ADS)

Crosson, W. L.; Eisen, L.; Estes, M. G.; Estes, S. M.; Hayden, M.; Lozano-Fuentes, S.; Monaghan, A. J.; Moreno Madriñán, M. J.; Ochoa, C.; Quattrochi, D.; Tapia, B.; Welsh-Rodriguez, C. M.

2012-12-01

In tropical and sub-tropical regions, the mosquito Aedes aegypti is the major vector for the virus causing dengue, a serious public health issue in these areas. Through ongoing NSF- and NASA-funded studies, field surveys of Aedes aegypti and an integrated modeling approach are being used to improve our understanding of the potential range of the mosquito to expand toward heavily populated high elevation areas such as Mexico City under various climate change and socio-economic scenarios. This work serves three primary objectives: (1) Employ NASA remotely-sensed data to supplement the environmental monitoring and modeling component of the project. These data -- for example, surface temperature, precipitation, vegetation indices, soil moisture and elevation -- are critical for understanding the habitat necessary for mosquito survival and abundance; (2) Implement training sessions to instruct scientists and students from Mexico and the U.S. on how to use remote sensing and implement the NASA SERVIR Regional Visualization and Monitoring System; (3) Employ the SERVIR framework to optimize the dissemination of key project results in order to increase their societal relevance and benefits in developing climate adaptation strategies. Field surveys of larval, pupal and adult Aedes aegypti, as well as detailed physical and social household characteristics, were conducted in the summers of 2011and 2012 at geographic scales from the household to the community along a transect from sea level to 2400 m ASL. These data are being used in models to estimate Aedes aegypti habitat suitability. In 2011, Aedes aegypti were identified at an elevation of over 2150 m in Puebla, the highest elevation at which this species has been observed.

Mapping wood density globally using remote sensing and climatological data

NASA Astrophysics Data System (ADS)

Moreno, A.; Camps-Valls, G.; Carvalhais, N.; Kattge, J.; Robinson, N.; Reichstein, M.; Allred, B. W.; Running, S. W.

2017-12-01

Wood density (WD) is defined as the oven-dry mass divided by fresh volume, varies between individuals, and describes the carbon investment per unit volume of stem. WD has been proven to be a key functional trait in carbon cycle research and correlates with numerous morphological, mechanical, physiological, and ecological properties. In spite of the utility and importance of this trait, there is a lack of an operational framework to spatialize plant WD measurements at a global scale. In this work, we present a consistent modular processing chain to derive global maps (500 m) of WD using modern machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data using the Google Earth Engine platform. The developed approach uses a hierarchical Bayesian approach to fill in gaps in the plant measured WD data set to maximize its global representativeness. WD plant species are then aggregated to Plant Functional Types (PFT). The spatial abundance of PFT at 500 m spatial resolution (MODIS) is calculated using a high resolution (30 m) PFT map developed using Landsat data. Based on these PFT abundances, representative WD values are estimated for each MODIS pixel with nearby measured data. Finally, random forests are used to globally estimate WD from these MODIS pixels using remote sensing and climate. The validation and assessment of the applied methods indicate that the model explains more than 72% of the spatial variance of the calculated community aggregated WD estimates with virtually unbiased estimates and low RMSE (<15%). The maps thus offer new opportunities to study and analyze the global patterns of variation of WD at an unprecedented spatial coverage and spatial resolution.

Monitoring and Predicting the Export and Fate of Global Ocean Net Primary Production: The EXPORTS Field Program

NASA Astrophysics Data System (ADS)

Exports Science Definition Team

2016-04-01

Ocean ecosystems play a critical role in the Earth's carbon cycle and its quantification on global scales remains one of the greatest challenges in global ocean biogeochemistry. The goal of the EXport Processes in the Ocean from Remote Sensing (EXPORTS) science plan is to develop a predictive understanding of the export and fate of global ocean primary production and its implications for the Earth's carbon cycle in present and future climates. NASA's satellite ocean-color data record has revolutionized our understanding of global marine systems. EXPORTS is designed to advance the utility of NASA ocean color assets to predict how changes in ocean primary production will impact the global carbon cycle. EXPORTS will create a predictive understanding of both the export of organic carbon from the euphotic zone and its fate in the underlying "twilight zone" (depths of 500 m or more) where variable fractions of exported organic carbon are respired back to CO2. Ultimately, it is the sequestration of deep organic carbon transport that defines the impact of ocean biota on atmospheric CO2 levels and hence climate. EXPORTS will generate a new, detailed understanding of ocean carbon transport processes and pathways linking upper ocean phytoplankton processes to the export and fate of organic matter in the underlying twilight zone using a combination of field campaigns, remote sensing and numerical modeling. The overarching objective for EXPORTS is to ensure the success of future satellite missions by establishing mechanistic relationships between remotely sensed signals and carbon cycle processes. Through a process-oriented approach, EXPORTS will foster new insights on ocean carbon cycling that will maximize its societal relevance and be a key component in the U.S. investment to understand Earth as an integrated system.

Investigating the Potential Range Expansion of the Vector Mosquito Aedes Aegypti in Mexico with NASA Earth Science Remote Sensing Results

NASA Technical Reports Server (NTRS)

Crosson, W. L.; Estes, M. G.; Estes, S. M.; Hayden, M.; Monaghan, A. J.; Eisen, L.; Lozano-Fuentes, S.; Ochoa, C.; Tapia, B.; Welsh-Rodriquez, C. M.;

Annual regression-based estimates of evapotranspiration for the contiguous United States based on climate, remote sensing, and stream gage data

NASA Astrophysics Data System (ADS)

Reitz, M. D.; Sanford, W. E.; Senay, G. B.; Cazenas, J.

2015-12-01

Evapotranspiration (ET) is a key quantity in the hydrologic cycle, accounting for ~70% of precipitation across the contiguous United States (CONUS). However, it is a challenge to estimate, due to difficulty in making direct measurements and gaps in our theoretical understanding. Here we present a new data-driven, ~1km2 resolution map of long-term average actual evapotranspiration rates across the CONUS. The new ET map is a function of the USGS Landsat-derived National Land Cover Database (NLCD), precipitation, temperature, and daily average temperature range (from the PRISM climate dataset), and is calibrated to long-term water balance data from 679 watersheds. It is unique from previously presented ET maps in that (1) it was co-developed with estimates of runoff and recharge; (2) the regression equation was chosen from among many tested, previously published and newly proposed functional forms for its optimal description of long-term water balance ET data; (3) it has values over open-water areas that are derived from separate mass-transfer and humidity equations; and (4) the data include additional precipitation representing amounts converted from 2005 USGS water-use census irrigation data. The regression equation is calibrated using data from 2000-2013, but can also be applied to individual years with their corresponding input datasets. Comparisons among this new map, the more detailed remote-sensing-based estimates of MOD16 and SSEBop, and AmeriFlux ET tower measurements shows encouraging consistency, and indicates that the empirical ET estimate approach presented here produces closer agreement with independent flux tower data for annual average actual ET than other more complex remote sensing approaches.

Validation of Satellite Derived Cloud Properties Over the Southeastern Pacific

NASA Astrophysics Data System (ADS)

Ayers, J.; Minnis, P.; Zuidema, P.; Sun-Mack, S.; Palikonda, R.; Nguyen, L.; Fairall, C.

2005-12-01

Satellite measurements of cloud properties and the radiation budget are essential for understanding meso- and large-scale processes that determine the variability in climate over the southeastern Pacific. Of particular interest in this region is the prevalent stratocumulus cloud deck. The stratocumulus albedos are directly related to cloud microphysical properties that need to be accurately characterized in Global Climate Models (GCMs) to properly estimate the Earth's radiation budget. Meteorological observations in this region are sparse causing large uncertainties in initialized model fields. Remote sensing from satellites can provide a wealth of information about the clouds in this region, but it is vital to validate the remotely sensed parameters and to understand their relationship to other parameters that are not directly observed by the satellites. The variety of measurements from the R/V Roger Revelle during the 2003 STRATUS cruise and from the R/V Ron Brown during EPIC 2001 and the 2004 STRATUS cruises are suitable for validating and improving the interpretation of the satellite derived cloud properties. In this study, satellite-derived cloud properties including coverage, height, optical depth, and liquid water path are compared with in situ measurements taken during the EPIC and STRATUS cruises. The remotely sensed values are derived from Geostationary Operational Environmental Satellite (GOES) imager data, Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites, and from the Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The products from this study will include regional monthly cloud climatologies derived from the GOES data for the 2003 and 2004 cruises as well as micro and macro physical cloud property retrievals centered over the ship tracks from MODIS and VIRS.

Mapping playa evaporite minerals and associated sediments in Death Valley, California, with multispectral thermal infrared images

USGS Publications Warehouse

Crowley, J.K.; Hook, S.J.

1996-01-01

Efflorescent salt crusts and associated sediments in Death Valley, California, were studied with remote-sensing data acquired by the NASA thermal infrared multispectral scanner (TIMS). Nine spectral classes that represent a variety of surface materials were distinguished, including several classes that reflect important aspects of the playa groundwater chemistry and hydrology. Evaporite crusts containing abundant thenardite (sodium sulfate) were mapped along the northern and eastern margins of the Cottonball Basin, areas where the inflow waters are rich in sodium. Gypsum (calcium sulfate) crusts were more common in the Badwater Basin, particularly near springs associated with calcic groundwaters along the western basin margin. Evaporite-rich crusts generally marked areas where groundwater is periodically near the surface and thus able to replenish the crusts though capillary evaporation. Detrital silicate minerals were prevalent in other parts of the salt pan where shallow groundwater does not affect the surface composition. The surface features in Death Valley change in response to climatic variations on several different timescales. For example, salt crusts on low-lying mudflats form and redissolve during seasonal-to-interannual cycles of wetting and desiccation. In contrast, recent flooding and erosion of rough-salt surfaces in Death Valley probably reflect increased regional precipitation spanning several decades. Remote-sensing observations of playas can provide a means for monitoring changes in evaporite facies and for better understanding the associated climatic processes. At present, such studies are limited by the availability of suitable airborne scanner data. However, with the launch of the Earth Observing System (EOS) AM-1 Platform in 1998, multispectral visible/near-infrared and thermal infrared remote-sensing data will become globally available. Copyright 1996 by the American Geophysical Union.

Quantifying Recent Recharge to the Saq Transboundary Aquifer System using an Integrated Geophysical, Geochemical, and Remote Sensing-based Approach

NASA Astrophysics Data System (ADS)

Fallatah, O.; Ahmed, M.; Akanda, A. S.; Boving, T.; Cardace, D.

2017-12-01

Abstract: The Saq aquifer system represents one of the most significant transboundary aquifers in the Arabian Peninsula that extends between northern parts of Saudi Arabia, Iraq and Jordan. Recent studies show that the Saq aquifer system is witnessing rapid groundwater depletions of -6.52 ± 0.29 mm/year (-3.49 ± 0.15 km3/year) that are highly correlated with increasing groundwater extraction for irrigation and observed water level declines in regional supply wells. In addition, the region is receiving record low amounts of precipitation in recent years. Thus, quantifying the groundwater recharge rate of the Saq is essential to sustainable present and future utilization of the groundwater resources in that system. In this study, we develop and apply an integrated Geophysical, Geochemical, and Remote Sensing-based approach to quantify the recharge rates of the Saq aquifer system given the areal distribution of the Saq transboundary aquifer system, the interaction between the Saq aquifer and the overlying aquifers, as well as the very limited rates of recharge through precipitation. Specifically, we set out to accomplish the following: (1) delineate and examine the areal extent of the Saq aquifer recharge domains using geologic, climatic, and remote sensing data; (2) investigate the origin of, and recent contributions to, the groundwater in the Saq aquifer system by examining the isotopic compositions of groundwater samples collected from the Saq aquifer; and (3) estimate, to the first order, the magnitude of modern recharge utilizing the Gravity Recovery and Climate Experiment (GRACE) data and rainfall time-series of the region. Results from this paper will help us to apply the suitable location for drilling and determine the best extraction scenarios.

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Urban Climate Resilience - Connecting climate models with decision support cyberinfrastructure using open standards

NASA Astrophysics Data System (ADS)

Bermudez, L. E.; Percivall, G.; Idol, T. A.

2015-12-01

Experts in climate modeling, remote sensing of the Earth, and cyber infrastructure must work together in order to make climate predictions available to decision makers. Such experts and decision makers worked together in the Open Geospatial Consortium's (OGC) Testbed 11 to address a scenario of population displacement by coastal inundation due to the predicted sea level rise. In a Policy Fact Sheet "Harnessing Climate Data to Boost Ecosystem & Water Resilience", issued by White House Office of Science and Technology (OSTP) in December 2014, OGC committed to increase access to climate change information using open standards. In July 2015, the OGC Testbed 11 Urban Climate Resilience activity delivered on that commitment with open standards based support for climate-change preparedness. Using open standards such as the OGC Web Coverage Service and Web Processing Service and the NetCDF and GMLJP2 encoding standards, Testbed 11 deployed an interoperable high-resolution flood model to bring climate model outputs together with global change assessment models and other remote sensing data for decision support. Methods to confirm model predictions and to allow "what-if-scenarios" included in-situ sensor webs and crowdsourcing. A scenario was in two locations: San Francisco Bay Area and Mozambique. The scenarios demonstrated interoperation and capabilities of open geospatial specifications in supporting data services and processing services. The resultant High Resolution Flood Information System addressed access and control of simulation models and high-resolution data in an open, worldwide, collaborative Web environment. The scenarios examined the feasibility and capability of existing OGC geospatial Web service specifications in supporting the on-demand, dynamic serving of flood information from models with forecasting capacity. Results of this testbed included identification of standards and best practices that help researchers and cities deal with climate-related issues. Results of the testbeds will now be deployed in pilot applications. The testbed also identified areas of additional development needed to help identify scientific investments and cyberinfrastructure approaches needed to improve the application of climate science research results to urban climate resilence.