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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Ecological Forecasting in the Applied Sciences Program and Input to the Decadal Survey

Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecosystems will change in the future in response to environmental factors. Further, Ecological Forecasting employs observations and models to predict the effects of environmental change on ecosystems. In doing so, it applies information from the physical, biological, and social sciences and promotes a scientific synthesis across the domains of physics, geology, chemistry, biology, and psychology. The goal is reliable forecasts that allow decision makers access to science-based tools in order to project changes in living systems. The next decadal survey will direct the development Earth Observation sensors and satellites for the next ten years. It is important that these new sensors and satellites address the requirements for ecosystem models, imagery, and other data for resource management. This presentation will give examples of these model inputs and some resources needed for NASA to continue effective Ecological Forecasting.

Ecology↗
NASA NTRS2023

A Community Convention for Ecological Forecasting: Output Files and Metadata Version 1.0

This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

Michael C. Dietze↗
NASA NTRS2005

An Agent-Based Interface to Terrestrial Ecological Forecasting

The latest generation of NASA Earth Observing System (EOS) satellites has brought a new dimension to continuous monitoring of the living part of the Earth System, the biosphere. EOS data can now provide weekly global measures of vegetation productivity and ocean chlorophyll, and many related biophysical factors such as land cover changes or snowmelt rates. However, the highest economic value would come from forecasting impending conditions of the biosphere, to allow decision makers to mitigate dangers or exploit positive trends. NASA's strategic plan for the Earth Science Enterprise i d e n a s ecological forecasting as a focus for research. Ecological forecasting predicts the effects of changes in the physical, chemical and biological environment on ecosystem activity. Possible applications of such a system include predicting shortfalls or bumper crops of agricultural production, populations of threatened or invasive species or wildfire danger in time to allow improves preparation and logistical efficiency. Petabytes of remote sensing data are now available to help measure, understand and forecast changes in the Earth system, but using these data effectively can be surprisingly hard. The volume and variety of data files and formats are daunting. Simple data management activities, such as locating and transferring files, changing file formats, gridding point data, and scaling and reprojecting gridded data, can consume far more personnel time and resources than the actual data analysis. Some scientists commit to a particular data source or resolution just because using anything different would be more effort that it's worth. Better tools can help, but most of the tools developed to date are little more than shell scripts; they lack the flexibility to meet the diverse needs of users and are difficult to extend to handle changes in available data sources.

Golden, Keith↗
NASA NTRS2017

Ecological Forecasting Project Management with Examples

Once scientists publish results of their projects and studies, all too often they end up on the shelf and are not otherwise used. The NASA Earth Science Division established its Applied Sciences Program (ASP) to apply research findings to help solve and manage real-world problems and needs. ASP-funded projects generally produce decision support systems for operational applications which are expected to last beyond the end of the NASA funding. Because of NASAs unique perspective of looking down on the Earth from space, ASP studies involve the use of remotely sensed information consisting of satellite data and imagery as well as information from sub-orbital platforms. ASP regularly solicits Earth science proposals that address one or more focus areas; disasters mitigation, ecological forecasting, health and air quality, and water resources. Reporting requirements for ASP-funded projects are different from those typical for research grants from NASA and other granting agencies, requiring management approaches different from other programs. This presentation will address the foregoing in some detail and give examples of three ASP-funded ecological forecasting projects that include: 1) the detection and survey of chimpanzee habitat in Africa from space, 2) harmful algal blooms (HABs) in the California Current System affecting aquaculture facilities and marine mammal populations, and 3) a call for the public to identify North America wildlife in Wisconsin using trail camera photos. Contact information to propose to ASP solicitations for those PIs interested is also provided.

Management↗
NASA NTRS2004

An Agent-Based Interface to Terrestrial Ecological Forecasting

This paper describes a flexible agent-based ecological forecasting system that combines multiple distributed data sources and models to provide near-real-time answers to questions about the state of the Earth system We build on novel techniques in automated constraint-based planning and natural language interfaces to automatically generate data products based on descriptions of the desired data products.

Golden, Keith↗
NASA NTRS2020

Costa Rica & Panama Ecological Forecasting II: Identifying Current and Future Areas of Environmental Concern in La Amistad International Park to Inform Resource Management

Seven percent of all scientifically known life forms lie within the 202,230 square miles of Central America, making this area ecologically unique and increasing the need for environmental management. The Mesoamerican Biological Corridor forms a conservation partnership throughout Central America to establish a forested corridor of over 600 protected areas. Although conservation programs exist, deforestation still afflicts the area, putting strain on these diverse ecosystems. La Amistad International Park in southern Costa Rica and northern Panama in particular faces conflicting land use changes. Expanding agricultural development and urbanization, combined with concern over indigenous land rights, have raised questions about the implementation of sustainability goals and communication strategies within the region. To help address these issues, the NASA DEVELOP Costa Rica & Panama Ecological Forecasting II team continued a partnership with the Ministry of Environment and Energy in Costa Rica and the National Environmental Authority in Panama. The team created a Land Use Conflict Identification Strategy (LUCIS) model based on land cover maps for 2019 and 2029 that were created in term I using imagery from Landsat 8 Operational Land Imager (OLI). With partner input, the team applied weights to different objectives and combined suitability maps to identify areas of potential biodiversity conflict. Additionally, the team created a Short-term Forest Change (SFTC) Tool using Terra Moderate Resolution Imaging Spectroradiometer, Landsat 8 OLI, and Sentinel-2 Multispectral Instrument to help the partners identify areas of immediate, major forest changes. The LUCIS model indicated higher agricultural growth when compared to the ecological category.

Ecological Forecasting↗
NASA NTRS2020

Costa Rica & Panama Ecological Forecasting II: Identifying Current and Future Areas of Environmental Concern in La Amistad International Park to Inform Resource Management

Seven percent of all scientifically known life forms lie within the 202,230 square miles of Central America, making this area ecologically unique and increasing the need for environmental management. The Mesoamerican Biological Corridor forms a conservation partnership throughout Central America to establish a forested corridor of over 600 protected areas. Although conservation programs exist, deforestation still afflicts the area, putting strain on these diverse ecosystems. La Amistad International Park in southern Costa Rica and northern Panama in particular faces conflicting land use changes. Expanding agricultural development and urbanization, combined with concern over indigenous land rights, have raised questions about the implementation of sustainability goals and communication strategies within the region. To help address these issues, the NASA DEVELOP Costa Rica & Panama Ecological Forecasting II team continued a partnership with the Ministry of Environment and Energy in Costa Rica and the National Environmental Authority in Panama. The team created a Land Use Conflict Identification Strategy (LUCIS) model based on land cover maps for 2019 and 2029 that were created in term I using imagery from Landsat 8 Operational Land Imager (OLI). With partner input, the team applied weights to different objectives and combined suitability maps to identify areas of potential biodiversity conflict. Additionally, the team created a Short-term Forest Change (SFTC) Tool using Terra Moderate Resolution Imaging Spectroradiometer, Landsat 8 OLI, and Sentinel-2 Multispectral Instrument to help the partners identify areas of immediate, major forest changes. The LUCIS model indicated higher agricultural growth when compared to the ecological category.

Ecological Forecasting↗
NASA NTRS2020

Costa Rica & Panama Ecological Forecasting II: Identifying Current and Future Areas of Environmental Concern in La Amistad International Park to Inform Resource Management

Seven percent of all scientifically known life forms lie within the 202,230 square miles of Central America, making this area ecologically unique and increasing the need for environmental management. The Mesoamerican Biological Corridor forms a conservation partnership throughout Central America to establish a forested corridor of over 600 protected areas. Although conservation programs exist, deforestation still afflicts the area, putting strain on these diverse ecosystems. La Amistad International Park in southern Costa Rica and northern Panama in particular faces conflicting land use changes. Expanding agricultural development and urbanization, combined with concern over indigenous land rights, have raised questions about the implementation of sustainability goals and communication strategies within the region. To help address these issues, the NASA DEVELOP Costa Rica & Panama Ecological Forecasting II team continued a partnership with the Ministry of Environment and Energy in Costa Rica and the National Environmental Authority in Panama. The team created a Land Use Conflict Identification Strategy (LUCIS) model based on land cover maps for 2019 and 2029 that were created in term I using imagery from Landsat 8 Operational Land Imager (OLI). With partner input, the team applied weights to different objectives and combined suitability maps to identify areas of potential biodiversity conflict. Additionally, the team created a Short-term Forest Change (SFTC) Tool using Terra Moderate Resolution Imaging Spectroradiometer, Landsat 8 OLI, and Sentinel-2 Multispectral Instrument to help the partners identify areas of immediate, major forest changes. The LUCIS model indicated higher agricultural growth when compared to the ecological category.

Ecological Forecasting↗
NASA NTRS2021

Southern Bhutan Ecological Forecasting II: Utilizing NASA Earth Observations to Model Land Cover Change and Elephant Wildlife Corridors in Southern Bhutan

The diverse landscapes of Bhutan host a rich biodiversity of animal and plant species. Asian elephants (Elephas maximus) are a flagship keystone wildlife species whose conservation is essential for the functioning of Bhutan’s forest ecosystems. Despite this, increasing habitat loss and human-elephant conflict continue to be detrimental to the survival of Asian elephants. The lack of information on Bhutan’s elephants and land use and land cover (LULC) trends present major challenges for Bhutan in modeling locations with suitable habitat and biological corridors for elephants. The DEVELOP Southern Bhutan Ecological Forecasting II team at NASA Goddard Space Flight Center partnered with the Bhutan Foundation, Bhutan Tiger Center, and Bhutan Ecological Society to help address this problem. The team mapped LULC in Bhutan for 2010 and 2015 by utilizing NASA Earth observations, including Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) to acquire information on historical LULC patterns and view apparent land cover change. The team used the Linkage Pathways Tool of the Linkage Mapper Toolbox in ArcMap to derive biological corridor maps from habitat suitability model outputs of the previous term and known locations of protected areas in Bhutan. The corridor maps were used to view and assess corridor suitability and connectivity between protected parks. Project results are being provided to partners to help make informed decisions on the placement and conservation of elephant movement corridors.

Thinley Yidzin Wangden↗
NASA NTRS2002

Biological Invasions: A Challenge In Ecological Forecasting

The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being considered by NASA's Earth Science Vision for 2025. The invasive species problem is complex and presents many challenges. Developing an invasive species predictive capability could significantly advance the science and technology of ecological forecasting.

Schnase, J. L.↗
NASA NTRS2016

Everglades Ecological Forecasting II: Utilizing NASA Earth Observations to Enhance the Capabilities of Everglades National Park to Monitor & Predict Mangrove Extent to Aid Current Restoration Efforts

Mangroves act as a transition zone between fresh and salt water habitats by filtering and indicating salinity levels along the coast of the Florida Everglades. However, dredging and canals built in the early 1900s depleted the Everglades of much of its freshwater resources. In an attempt to assist in maintaining the health of threatened habitats, efforts have been made within Everglades National Park to rebalance the ecosystem and adhere to sustainably managing mangrove forests. The Everglades Ecological Forecasting II team utilized Google Earth Engine API and satellite imagery from Landsat 5, 7, and 8 to continuously create land-change maps over a 25 year period, and to allow park officials to continue producing maps in the future. In order to make the process replicable for project partners at Everglades National Park, the team was able to conduct a supervised classification approach to display mangrove regions in 1995, 2000, 2005, 2010 and 2015. As freshwater was depleted, mangroves encroached further inland and freshwater marshes declined. The current extent map, along with transition maps helped create forecasting models that show mangrove encroachment further inland in the year 2030 as well. This project highlights the changes to the Everglade habitats in relation to a changing climate and hydrological changes throughout the park.

Kirk, Donnie↗
NASA NTRS2020

Southern Bhutan Ecological Forecasting Tech Paper: Modeling Asian Elephant (Elephas maximus) Habitat Suitability along the Southern Bhutan Border with NASA Earth Observations

Asian elephants (Elephas maximus) are a flagship species essential for the functioning of forest ecosystems, and they also have cultural significance in Bhutan. Elephants receive the highest legal protection as listed under Schedule I of the Bhutan Forests and Nature Conservation Act, 1995. Yet, they face threats of extinction due to poaching for ivory as well as the loss and fragmentation of their habitat. Due to the recent clearing of forests and the growing populations in these areas, there has been an increase in incidents of human-elephant conflict. These conflicts have been detrimental to farmer’s annual harvests and livelihoods and have led to retaliatory killing and injury of elephants in southern Bhutan. The DEVELOP Southern Bhutan Ecological Forecasting team partnered with the Bhutan Foundation and Bhutan Tiger Center to help address this problem. The team integrated NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Shuttle Radar Topography Mission (SRTM) to acquire information on land cover change and elephant habitat suitability along the southern border of Bhutan. The team utilized Esri ArcGIS Pro and Software for Assisted Habitat Modeling (SAHM) for data analysis, modeling, and visualization. The team used elephant occurrence data and environmental variables to model current habitat suitability for migrating elephant populations. This analysis provided partners with maps to inform decisions about the placement and conservation of elephant corridors and helped build their capacity to use satellite data for future studies and project planning.

Palchen Wangchuk↗
NASA NTRS2020

Southern Bhutan Ecological Forecasting Project Summary - Modeling Asian Elephant (Elephas Maximus) Habitat Suitability along the Southern Bhutan Border with NASA Earth Observations

Asian elephants (Elephas maximus) are a flagship species essential for the functioning of forest ecosystems, and they also have cultural significance in Bhutan. Elephants receive the highest legal protection as listed under Schedule I of the Bhutan Forests and Nature Conservation Act, 1995. Yet, they face threats of extinction due to poaching for ivory as well as the loss and fragmentation of their habitat. Due to the recent clearing of forests and the growing populations in these areas, there has been an increase in incidents of human-elephant conflict. These conflicts have been detrimental to farmer’s annual harvests and livelihoods and have led to retaliatory killing and injury of elephants in southern Bhutan. The DEVELOP Southern Bhutan Ecological Forecasting team partnered with the Bhutan Foundation and Bhutan Tiger Center to help address this problem. The team integrated NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Shuttle Radar Topography Mission (SRTM) to acquire information on land cover change and elephant habitat suitability along the southern border of Bhutan. The team utilized Esri ArcGIS Pro and Software for Assisted Habitat Modeling (SAHM) for data analysis, modeling, and visualization. The team used elephant occurrence data and environmental variables to model current habitat suitability for migrating elephant populations. This analysis provided partners with maps to inform decisions about the placement and conservation of elephant corridors and helped build their capacity to use satellite data for future studies and project planning.

DEVELOP Project Summary↗
NASA NTRS2020

Fisher's Peak Ecological Forecasting - Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation

Lauren Lad↗
NASA NTRS2020

Fisher's Peak Ecological Forecasting: Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation.

DEVELOP Tech Paper↗
NASA NTRS2020

Great Basin Ecological Forecasting II: Assessing and Forecasting Live Fuel Moisture Content of Wildfire Fuels for the Eastern Great Basin to Improve Wildfire Timing and Severity Predictions

The eastern Great Basin (EGB) extends throughout the states of Arizona, Colorado,Idaho, Utah, and Wyoming, covering approximately 411,000 km2. In recent years, wildfires in the EGB have increased in frequency and size, representing a growing concern for our partners at the Bureau of Land Management (BLM), the National Weather Service (NWS), and the Great Basin Coordination Center (GBCC). Live fuel moisture (LFM) is an important factor in predicting wildfire risk, as dry vegetation requires less energy to combust than wet vegetation. Land managers currently derive LFM levels from just 165 in situ sites in the EGB. In order to provide partners with a more accurate assessment of LFM, the team used data from the National Elevation Dataset, Aqua and Terra Moderate Resolution Imaging Spectroradiometer, and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite. These datasets include vegetation indices, evapotranspiration, and topographic variables, which were used to create biweekly forecasts of LFM throughout the EGB. An accuracy assessment was conducted using historical in situ data from our partners at the BLM and the GBCC. This model allowed our partners to make informed decisions regarding resource allocation in response to the predicted timing and severity of wildfires in the EGB.

Ecological Forecasting↗