Results for “tree” — Page 6

NASA NTRS2004

Finding Minimum-Power Broadcast Trees for Wireless Networks

Some algorithms have been devised for use in a method of constructing tree graphs that represent connections among the nodes of a wireless communication network. These algorithms provide for determining the viability of any given candidate connection tree and for generating an initial set of viable trees that can be used in any of a variety of search algorithms (e.g., a genetic algorithm) to find a tree that enables the network to broadcast from a source node to all other nodes while consuming the minimum amount of total power. The method yields solutions better than those of a prior algorithm known as the broadcast incremental power algorithm, albeit at a slightly greater computational cost.

Arabshahi, Payman↗

NASA NTRS2011

Effective Tree Scattering and Opacity at L-Band

This paper investigates vegetation effects at L-band by using a first-order radiative transfer (RT) model and truck-based microwave measurements over natural conifer stands to assess the applicability of the tau-omega) model over trees. The tau-omega model is a zero-order RT solution that accounts for vegetation effects with effective vegetation parameters (vegetation opacity and single-scattering albedo), which represent the canopy as a whole. This approach inherently ignores multiple-scattering effects and, therefore, has a limited validity depending on the level of scattering within the canopy. The fact that the scattering from large forest components such as branches and trunks is significant at L-band requires that zero-order vegetation parameters be evaluated (compared) along with their theoretical definitions to provide a better understanding of these parameters in the retrieval algorithms as applied to trees. This paper compares the effective vegetation opacities, computed from multi-angular pine tree brightness temperature data, against the results of two independent approaches that provide theoretical and measured optical depths. These two techniques are based on forward scattering theory and radar corner reflector measurements, respectively. The results indicate that the effective vegetation opacity values are smaller than but of similar magnitude to both radar and theoretical estimates. The effective opacity of the zero-order model is thus set equal to the theoretical opacity and an explicit expression for the effective albedo is then obtained from the zero- and first- order RT model comparison. The resultant albedo is found to have a similar magnitude as the effective albedo value obtained from brightness temperature measurements. However, it is less than half of that estimated using the theoretical calculations (0.5 - 0.6 for tree canopies at L-band). This lower observed albedo balances the scattering darkening effect of the large theoretical albedo with a first-order multiple-scattering contribution. The retrieved effective albedo is different from theoretical definitions and not the albedo of single forest elements anymore, but it becomes a global parameter, which depends on all the processes taking place within the canopy, including multiple-scattering.

Kurum, Mehmet↗

NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗

NASA NTRS2019

Twentieth Century Redistribution in Climatic Drivers of Global Tree Growth

Energy and water limitations of tree growth remain insufficiently understood at large spatiotemporal scales, hindering model representation of interannual or longer-term ecosystem processes. By assessing and statistically scaling the climatic drivers from 2710 tree-ring sites, we identified the boreal and temperate land areas where tree growth during 1930–1960 CE responded positively to temperature (20.8 ± 3.7 Mio km2; 25.9 ± 4.6%), precipitation (77.5 ± 3.3 Mio km2; 96.4 ± 4.1%), and other parameters. The spatial manifestation of this climate response is determined by latitudinal and altitudinal temperature gradients, indicating that warming leads to geographic shifts in growth limitations. We observed a significant (P < 0.001) decrease in temperature response at cold-dry sites between 1930–1960 and 1960–1990 CE, and the total temperature-limited area shrunk by −8.7 ± 0.6 Mio km2. Simultaneously, trees became more limited by atmospheric water demand almost worldwide. These changes occurred under mild warming, and we expect that continued climate change will trigger a major redistribution in growth responses to climate.

Babst, Flurin↗

NASA NTRSDate not supplied

Utilizing NASA Earth Observations to Evaluate Urban Tree Canopy and Land Surface Temperature for Green Infrastructure Development and Urban Heat Mitigation in Huntsville, AL

Huntsville, Alabama’s population has grown by 10.8% since 2010, due in part to the city’s advancing engineering industry. Rapid urban growth negatively impacts the environment by decreasing tree canopy cover and increasing impervious surface cover, which can intensify the urban heat island effect. To examine the impacts of this urban growth on the environment, the team partnered with the City of Huntsville to utilize Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Space Station’s Global Ecosystems Dynamic Investigation (GEDI) and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). The team utilized these Earth observations in combination with ancillary datasets to create a suite of end products to assist in mitigating the effects of extreme heat due to urban expansion and tree canopy loss. Rasters of annual land surface temperature (LST) was calculated in Google Earth Engine from 2010 to 2019. The team derived land cover classes through supervised and threshold classification methods to distinguish trees, other vegetation types, impervious surfaces, and water. From 2010 to 2019, LST increased approximately 4 °F for all census tracts within the city and the total amount of tree cover increases less than 3%. The findings will aid the city in future decision-making processes by indicating areas that would benefit from increased green infrastructure.

Greta Paris↗

NASA NTRS2020

Huntsville Urban Development: Utilizing NASA Earth Observations to Evaluate Urban Tree Canopy and Land Surface Temperature for Green Infrastructure Development and Urban Heat Mitigation in Huntsville, AL

Huntsville, Alabama’s population has grown by approximately 11% since 2010, due in part to the city’s advancing engineering industry. Rapid urban growth negatively impacts the environment by decreasing tree canopy cover and increasing impervious surface cover, which can intensify the urban heat island effect. To examine the impacts of this urban growth on the environment, the team partnered with the City of Huntsville to utilize Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Space Station’s Global Ecosystems Dynamic Investigation (GEDI) and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). The team utilized these Earth observations in combination with ancillary datasets to create a suite of end products to assist in mitigating the effects of extreme heat due to urban expansion and tree canopy loss. Annual land surface temperature (LST) was calculated and land cover classes were derived through supervised and threshold classification methods to distinguish trees, other vegetation types, impervious surfaces, and water. From 2010 to 2019, LST increased approximately 4 °F for all census tracts within the city and the total amount of tree cover increased by approximately 3%. The findings will aid the city in future decision-making processes by indicating areas that would benefit from increased green infrastructure.

Greta Paris↗

NASA NTRS2020

Huntsville Urban Development: Utilizing NASA Earth Observations to Evaluate Urban Tree Canopy and Land Surface Temperature for Green Infrastructure Development and Urban Heat Mitigation in Huntsville, Alabama

Huntsville, Alabama’s population has grown by approximately 11% since 2010, due in part to the city’s advancing engineering industry. Rapid urban growth negatively impacts the environment by decreasing tree canopy cover and increasing impervious surface cover, which can intensify the urban heat island effect. To examine the impacts of this urban growth on the environment, the team partnered with the City of Huntsville to utilize Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Space Station’s Global Ecosystems Dynamic Investigation (GEDI) and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). The team utilized these Earth observations in combination with ancillary datasets to create a suite of end products to assist in mitigating the effects of extreme heat due to urban expansion and tree canopy loss. Annual land surface temperature (LST) was calculated and land cover classes were derived through supervised and threshold classification methods to distinguish trees, other vegetation types, impervious surfaces, and water. From 2010 to 2019, LST increased approximately 4 °F for all census tracts within the city and the total amount of tree cover increased by approximately 3%. The findings will aid the city in future decision-making processes by indicating areas that would benefit from increased green infrastructure.

Greta Paris↗

NASA NTRS2022

Phoenix Climate: Employing NASA Earth Observations to Conduct Site Suitability Analyses on Residential Tree Planting Initiatives in Phoenix, AZ

Phoenix, Arizona is the hottest city in the United States, with daytime summer temperatures consistently reaching upwards of 100°F. As these daytime temperatures continue to climb, heat-related illnesses and morbidity also increase. The City of Phoenix hopes to secure funding to implement the American Rescue Plan Act (ARPA) residential tree equity accelerator program. This funding will be used for targeted investments in underserved neighborhoods to increase tree canopy cover, engaging 5,000 households across selected neighborhoods. By partnering with the City of Phoenix, the Arizona Office of Heat Mitigation, and Arizona State University’s Urban Climate Research Center, our team identified residential neighborhoods, block groups within qualified census tracts, and parcels to be prioritized in the ARPA program. We conducted an analysis using NASA Earth observations, movement and heat exposure data, sociodemographic data, and tree canopy data. For Earth observations, we acquired daytime land surface temperature from the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and land cover classification from the United States Geological Survey (USGS) National Land Cover Database (NLCD). The project will support the prioritization of city resources and tree plantings based on community vulnerability, as well as help initiate public engagement efforts and literacy with an interactive dashboard and GIS layers that contribute to the city’s property information portal.

Alison Bautista↗

NASA NTRSDate not supplied

SysML Success Tree for DAVINCI In-Situ Campaign Requirements Validation and Redundancy Assessment

The DAVINCI mission will scientifically study the Venusian atmosphere to better understand the current state of the planet and its evolutionary history. This will be accomplished by deploying a descent probe to collect in-situ atmospheric dynamic and spectroscopic measurements, characterize ambient temperatures and pressures during descent, and surface imaging below the cloud layer. To better characterize the DAVINCI In-Situ Campaign, a success tree, developed in the SysML tool, MagicDraw, was created to define the events necessary to have a successful in-situ campaign, for each phase of the campaign. This success tree was also used to validate DAVINCI Program, Mission, and Element level requirements. In addition, because DAVINCI is a single-string mission, the success tree was also used to identify areas of redundancy and resiliency throughout the campaign. The initial redundancy assessment recommended further on-orbit evaluation of the probe’s communication system, as well as further evaluation of the ability of the mass spectrometer turbo-molecular pump to handle the expected decent environment. During the integration and test phase of the overall mission, the success tree will be used to guide the mission validation process, helping to ensure that the in-situ mission elements can perform in accordance with the DAVINCI In-Situ Concept of Operations document.

model-based↗

NASA NTRSDate not supplied

SysML Success Tree for DAVINCI In-Situ Campaign Requirements Validation and Redundancy Assessment

The DAVINCI mission will scientifically study the Venusian atmosphere to better understand the current state of the planet and its evolutionary history. This will be accomplished by deploying a descent probe to collect in-situ atmospheric dynamic and spectroscopic measurements, characterize ambient temperatures and pressures during descent, and surface imaging below the cloud layer. To better characterize the DAVINCI In-Situ Campaign, a success tree, developed in the SysML tool, MagicDraw, was created to define the events necessary to have a successful in-situ campaign, for each phase of the campaign. This success tree was also used to validate DAVINCI Program, Mission, and Element level requirements. In addition, because DAVINCI is a single-string mission, the success tree was also used to identify areas of redundancy and resiliency throughout the campaign. The initial redundancy assessment recommended further on-orbit evaluation of the probe’s communication system, as well as further evaluation of the ability of the mass spectrometer turbo-molecular pump to handle the expected decent environment. During the integration and test phase of the overall mission, the success tree will be used to guide the mission validation process, helping to ensure that the in-situ mission elements can perform in accordance with the DAVINCI In-Situ Concept of Operations document.

ConOps↗

NASA NTRSDate not supplied

SysML Success Tree for DAVINCI In-Situ Campaign Requirements Validation and Redundancy Assessment

The DAVINCI mission will scientifically study the Venusian atmosphere to better understand the current state of the planet and its evolutionary history. This will be accomplished by deploying a descent probe to collect in-situ atmospheric dynamic and spectroscopic measurements, characterize ambient temperatures and pressures during descent, and surface imaging below the cloud layer. To better characterize the DAVINCI In-Situ Campaign, a success tree, developed in the SysML tool, MagicDraw, was created to define the events necessary to have a successful in-situ campaign, for each phase of the campaign. This success tree was also used to validate DAVINCI Program, Mission, and Element level requirements. In addition, because DAVINCI is a single-string mission, the success tree was also used to identify areas of redundancy and resiliency throughout the campaign. The initial redundancy assessment recommended further on-orbit evaluation of the probe’s communication system, as well as further evaluation of the ability of the mass spectrometer turbo-molecular pump to handle the expected decent environment. During the integration and test phase of the overall mission, the success tree will be used to guide the mission validation process, helping to ensure that the in-situ mission elements can perform in accordance with the DAVINCI In-Situ Concept of Operations document.

model-based↗

NASA NTRS2006

A dynamic fault tree model of a propulsion system

We present a dynamic fault tree model of the benchmark propulsion system, and solve it using Galileo. Dynamic fault trees (DFT) extend traditional static fault trees with special gates to model spares and other sequence dependencies. Galileo solves DFT models using a judicious combination of automatically generated Markov and Binary Decision Diagram models. Galileo easily handles the complexities exhibited by the benchmark problem. In particular, Galileo is designed to model phased mission systems.

propulsion system↗

NASA NTRS2018

Detecting Early Warning Signals of Tree Mortality in Boreal North America Using Multiscale Satellite Data

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.

heat↗

NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗

NASA NTRS2022

Arizona Water Resources Ii: Utilizing Aerial Imagery and Nasa Earth Observations to Assess Pinyon-Juniper Tree Mortality in Flagstaff, AZ

Pinyon-juniper woodlands (PJW) provide critical and resilient habitat for the local mammal and small bird species of Arizona's northern xeric environment. Drought in Arizona has been persistent for many decades, yet in 2021 PJW experienced a mass tree mortality event at the Wupatki National Monument (WNM) and in other areas across the American Southwest. Previously, researchers at the National Park Service (NPS) and the team from the NASA DEVELOP National Program attempted to quantify the extent of mortality in Northern Arizona between 2015 and 2021 using high resolution National Agricultural Imagery Program (NAIP) aerial photographs. This project aimed to improve the previous term’s methodology and expanded the comparison of the post-mortality event in 2021 to include tree cover assessments for 2017 and 2019. In this iteration, the team utilized NAIP imagery in conjunction with Landscape Fire and Resource Management Planning Tools (LANDFIRE) to calculate the total difference in PJW mortality using an unsupervised classification model trained from multi-date Modified Soil-Adjusted Vegetation Index (MSAVI) and the Visible Atmospherically Resistant Index (VARI) data for the study area. The research also assessed correlations between tree mortality and environmental factors using Western Land Data Assimilation System (WLDAS) modelled climate data. Average PJW mortality from 2015 to 2021 was 21.63% including 19.8% in WNM with the vast majority of dieback occurring between 2019 and 2021. The correlations were weak with the most correlated variables being bare soil evaporation (0.15), rainfall (0.14), groundwater storage (0.13), and wind speed (0.12), perhaps indicating drought as a likely driver of PJW mortality.

pinyon-juniper woodlands↗

NASA NTRS1994

Microclimate, Water Potential, Transpiration, and Bole Dielectric Constant of Coniferous and Deciduous Tree Species in the Continental Boreal Ecotone of Central Alaska

Tree canopy microclimate, xylem water flux and xylem dielectric constant have been monitored in situ since June 1993 in two adjacent natural forest stands in central Alaska. The deciduous stand represents a mature balsam poplar site on the Tanana River floodplain, while the coniferous stand consists of mature white spruce with some black spruce mixed in. During solstice in June and later in summer, diurnal changes of xylem water potential were measured to investigate the occurrence and magnitude of tree transpiration and dielectric constant changes in stems.

bole dielectric constant coniferous trees deciduou↗

NASA NTRS1970

Remote sensing of changes in morphology and physiology of trees under stress

Results of continuing studies of forest trees subjected to varying types of stress are reported. Both greenhouse and field studies are included. Greenhouse work with tree seedlings exposed to varying levels of NaCl and CaCl2 in the soil indicated that, in the initial stages, palisade cells shrink and the amount of air space in the leaf increases. As the severity of damage increases, the cells of the spongy mesophyll shrink and flatten, and the amount of air space in the leaf decreases. Statistical analysis of foliar reflectance and associated moisture content data led to a series of regression equations for predicting foliar moisture content from reflectance data. Equations were calculated for three species, yellow birch (Betula alleghaniensis Britton), sugar maple (Acer saccharum Marsh.) and white ash (Fraxinus americana L.) having multiple correlation coefficients of 0.98, 0.94 and 0.93 respectively. Interpretation of multispectral imagery of the Ann Arbor Forestry Test Site (NASA Site 190) provided evidence that infections of Fomes annosus can be detected in the early stages. Infections of two needle cast diseases were also detected in conifer plantations in the test site. A study of automatic interpretation of multispectral scanner imagery for tree species recognition provided encouraging results.

Olson, C. E., Jr.↗

NASA NTRS1973

Data multiplexer using a tree switch

Self-decoding FET-hybrid or integrated-circuit tree configuration uses minimum number of components and can be sequenced by clock or computer. Redundancy features can readily be incorporated into tree configuration; as tree grows in size and more sensors are included, percentage of parts that will affect given percentage of sensors steadily decreases.

Easton, R. A.↗