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NASA NTRS2022

Yellowstone Ecological Forecasting: Assessing Change in Aspen Extent in Northern Yellowstone National Park

The removal and reintroduction of the gray wolf (Canis lupus) in Yellowstone National Park have shaped the ecological composition of this distinct landscape, representing a textbook example of trophic dynamics. With particular importance to conservation science, researchers have studied the trophic cascades between wolves and species such as elk (Cervus canadensis) and quaking aspen (Populus tremuloides). In conjunction with the National Park Service, Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, this project utilized satellite remote sensing to investigate the long-term trends in aspen extent. Through random forest modeling and phenological approaches, Landsat 5 Thematic Mapper (TM; years 1986–2011) and Sentinel-2 Multispectral Instrument (MSI; years 2017–2019) datasets were used to derive color composites, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Tasseled Cap Indices (Brightness, Greenness, Wetness). The International Space System (ISS) Global Ecosystem Dynamics Investigation (GEDI) provided canopy height data. The team consolidated results into maps and time-series which provide an in-depth depiction of aspen stand extent. The National Park Service will use these end products to assist in its management practices and inform wildlife restoration decisions within and beyond Yellowstone National Park.

Kyle Steen↗

NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗

NASA NTRS2010

NASA's Space Lidar Measurements of Earth and Planetary Surfaces

A lidar instrument on a spacecraft was first used to measure planetary surface height and topography on the Apollo 15 mission to the Moon in 1971, The lidar was based around a flashlamp-pumped ruby laser, and the Apollo 15-17 missions used them to make a few thousand measurements of lunar surface height from orbit. With the advent of diode pumped lasers in the late 1980s, the lifetime, efficiency, resolution and mass of lasers and space lidar all improved dramatically. These advances were utilized in NASA space missions to map the shape and surface topography of Mars with > 600 million measurements, demonstrate initial space measurements of the Earth's topography, and measured the detailed shape of asteroid. NASA's ICESat mission in Earth orbit just completed its polar ice measurement mission with almost 2 billion measurements of the Earth's surface and atmosphere, and demonstrated measurements to Antarctica and Greenland with a height resolution of a few em. Space missions presently in cruise phase and in operation include those to Mercury and a topographic mapping mission of the Moon. Orbital lidar also have been used in experiments to demonstrate laser ranging over planetary distances, including laser pulse transmission from Earth to Mars orbit. Based on the demonstrated value of the measurements, lidar is now the preferred measurement approach for many new scientific space missions. Some missions planned by NASA include a planetary mission to measure the shape and dynamics of Europa, and several Earth orbiting missions to continue monitoring ice sheet heights, measure vegetation heights, assess atmospheric CO2 concentrations, and to map the Earth surface topographic heights with 5 m spatial resolution. This presentation will give an overview of history, ongoing work, and plans for using space lidar for measurements of the surfaces of the Earth and planets.

Abshire, James B.↗

NASA NTRS2011

Investigating the Potential Range Expansion of the Vector Mosquito Aedes Aegypti in Mexico with NASA Earth Science Remote Sensing Results

Dengue (Breakbone) fever is caused by one of four viruses carried by mosquitoes in tropical and subtropical areas. Cases have increased dramatically in the past few decades; there are currently approximately 100 million infections annually around the globe. Our project will integrate environmental observations, including weather, land use, vegetation type, amount and greenness, soil moisture, and mosquito populations with investigations of the human dynamics of the system via household surveys.

Estes, Sue M.↗

NASA NTRS2016

Reevaluating Suitability Estimates Based on Dynamics of Cropland Expansion in the Brazilian Amazon

Agricultural suitability maps are a key input for land use zoning and projections of cropland expansion. Suitability assessments typically consider edaphic conditions, climate, crop characteristics, and sometimes incorporate accessibility to transportation and market infrastructure. However, correct weighting among these disparate factors is challenging, given rapid development of new crop varieties, irrigation, and road networks, as well as changing global demand for agricultural commodities. Here, we compared three independent assessments of cropland suitability to spatial and temporal dynamics of agricultural expansion in the Brazilian state of Mato Grosso during 2001 2012. We found that areas of recent cropland expansion identified using satellite data were generally designated as low to moderate suitability for rainfed crop production. Our analysis highlighted the abrupt nature of suitability boundaries, rather than smooth gradients of agricultural potential, with little additional cropland expansion beyond the extent of the flattest areas (0-2% slope). Satellite-based estimates of the interannual variability in the use of existing crop areas also provided an alternate means to assess suitability. On average, cropland areas in the Cerrado biome had higher utilization (84%) than croplands in the Amazon region of northern Mato Grosso (74%). Areas of more recent expansion had lower utilization than croplands established before 2002, providing empirical evidence for lower suitability or alternative management strategies (e.g., pasture soya rotations) for lands undergoing more recent land use transitions. This unplanted reserve constitutes a large area of potentially available cropland (PAC)without further expansion, within the management limits imposed for pest management and fallow cycles. Using two key constraints on future cropland expansion, slope and restrictions on further deforestation of Amazon or Cerrado vegetation, we found little available flat land for further legal expansion of crop production in Mato Grosso. Dynamics of cropland expansion from more than a decade of satellite observations indicated narrow ranges of suitability criteria, restricting PAC under current policy conditions, and emphasizing the advantages of field-scale information to assess suitability and utilization.

Morton, Douglas C.↗

NASA NTRS1974

Remote sensing in Iowa agriculture: Identification and classification of Iowa's crops, soils and forestry resources using ERTS-1 and complimentary underflight imagery

The author has identified the following significant results. Springtime ERTS-1 imagery covering pre-selected test sites in Iowa showed considerable detail with respect to broad soil and land use patterns. Additional imagery has been incorporated into a state mosaic. The mosaic was used as a base for soil association lines transferred from an existing map. The regions of greatest contrast are between the Clarion-Nicollet-Webster soil association area and adjacent areas. Landscape characteristics in this area result in land use patterns with a high percentage of pasture, hay, and timber. The soil association areas of the state that have patterns interpreted to be associated with intensive row crop production are: Moody, Galva-Primghar-Sac, Clarion-Nicollet-Webter, Tama-Muscatine, Dinsdale-Tama, Cresco-Lourdes, Clyde, Kenyon-Floyd-Clyde, and the Luton-Onawa-Salix area on the Missouri River floodplain. Forestland estimates have been attained for an area in central Iowa using wintertime ERTS-1 imagery. Visual analysis of multispectral, temporal imagery indicates that temporal analysis for cropland identification and acreage analyses procedures may be a very useful tool. Combinations of wintertime, springtime, and summertime ERTS-1 imagery separate most vegetation types. Timing can be critical depending upon crop development and harvesting times because of the dynamic nature of agricultural production.

Mahlstede, J. P.↗

NASA NTRS1998

Airborne lidar observations of smoke haze during SCAR-B 1995

The Smoke, Clouds, Aerosol, and Radiation Brazil(SCAR-B) field campaign was conducted to study the effects that widespread and persistent biomass burning have upon radiative and chemical processes in the atmosphere. The radiative transfer characteristics of the atmosphere are altered by the introduction of particulate and gaseous materials which are the products of the combustion of vegetative material at ground level. These substances are transported and distributed horizontally and vertically by atmospheric dynamical processes which may be perturbed by the heat energy from the fires. As the pollutants disperse, their physical and chemical properties change substantially. A complete description of the effects of smoke requires that the evolution back to the natural situation be fully examined. A most important component of smoke haze investigation is finding its vertical and horizontal distribution in relation to the driving factors of dynamics and the related horizontal transport. In this presentation, we employ data from the Cloud Lidar System(CLS), carried aboard the NASA ER-2 aircraft, to provide a unique view of the particulate or aerosol loading produced by fires, especially with regard to the geometrical distribution of the aerosols in the vertical plane. The lidar has the ability to measure aerosol optical properties in a continuous fashion at quite fine vertical and horizontal resolution. The results from the lidar provide measurements that are largely independent of influences that corrupt passive instruments and thus it can serve to corroborate their results. The extended horizontal and vertical range of lidar results can also augment ground based and airborne in situ measurements which have limited horizontal and vertical scope. We present the results of our analysis of CLS observations taken during the SCAR-B field campaign. Observations of the the aerosol optical thickness from the Aerosol Robotic Network(AERONET) of solar photometers are employed in conjunction with CLS data to derive extinction to backscatter ratio values which are used to convert the lidar backscatter coefficient into extinction coefficient. The extinction coefficient is integrated vertically to find aerosol optical thickness along ER-2 flight tracks. We use images of the CLS derived extinction coefficient to depict its horizontal and vertical distribution. Multispectral photometer optical thickness is used to compute the Angstrom exponent. With these, we examine the hypothesis that the values of extinction to backscatter ratio can be related to the Anstrom coefficient since both of these would be a function of the refractive index and size distribution of the aerosols.

Hart, William D.↗

NASA NTRS2012

Stochastic Convection Parameterizations

computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts

atmospheric models↗

NASA NTRS2020

Advanced Silicon Avalanche Photodiodes on NASA's Global Ecosystem Dynamics Investigation (GEDI) Mission

Silicon Avalanche Photodiodes (APDs) are used in NASA’s Global Ecosystem Dynamics Investigation (GEDI) which was launched in December 2018 and is currently measuring the Earth’s vegetation vertical structure from the International Space Station. The APDs were specially made for space lidar with a much lower hole-to-electron ionization coefficient ratio (k-factor ~0.008) than that of commercially available silicon APDs in order to reduce the APD excess noise from the randomness of the avalanche gain. A silicon heater resistor was used under the APD chip to heat the device up to 70°C and improve its quantum efficiency at 1064 nm laser wavelength while maintaining a low dark current such that the overall signal to noise ratio is improved. Special APD protection circuits were used to raise the overload damage threshold to prevent device damage from strong laser return by specular surfaces, such as still water bodies, and space radiation events. The APD and a hybrid transimpedance amplifier circuit were hermetically sealed in a package with a sufficiently low leak rate to ensure multi-year operation lifetime in space. The detector assemblies underwent a series of pre-launch tests per NASA Goddard Environmental Verification Standard for space qualification. They have performed exactly as expected with GEDI in orbit. A detailed description of the GEDI detector design, signal and noise model, and test results are presented in this paper.

Sun, Xiaoli↗

NASA NTRSDate not supplied

Yellowstone Ecological Forecasting: Assessing Change in Aspen Extent in Northern Yellowstone National Park

The removal and reintroduction of the gray wolf (Canis lupus) in Yellowstone National Park have played an important role in shaping the ecological composition of this distinct landscape, and it is a textbook example of multi-trophic dynamics. With particular importance to conservation science, the inter-trophic cascades between wolves and species such as the elk (Cervus canadensis) and the quaking aspen (Populus tremuloides) have been extensively studied. In conjunction with the National Park Service, Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, this project utilized satellite remote sensing to investigate the long-term trends in aspen extent. Through random forest modeling and phenological approaches, Sentinel-2 Multispectral Instrument (MSI; years 2017–2019) and Landsat 5 Thematic Mapper (TM; years 1987–2011) datasets were used to derive an Enhanced Vegetation Index (EVI), a Normalized Difference Vegetation Index (NDVI), Tasseled Cap Indices (Brightness, Greenness, Wetness), and RGB true color composites. The International Space System Global Ecosystem Dynamics Investigation (ISS GEDI) was used to analyze canopy height. Results were consolidated into maps and time-series that provide an in-depth and intricate depiction of aspen stand extent. The end products will assist the National Park Service in its management practices and inform wildlife restoration and rewilding decisions within and beyond the contexts of Yellowstone National Park.

Kyle Steen↗

NASA NTRS1992

RGB-NDVI colour composites for visualizing forest change dynamics

The study presents a simple and logical technique to display and quantify forest change using three dates of satellite imagery. The normalized difference vegetation index (NDVI) was computed for each date of imagery to define high and low vegetation biomass. Color composites were generated by combining each date of NDVI with either the red, green, or blue (RGB) image planes in an image display monitor. Harvest and regeneration areas were quantified by applying a modified parallelepiped classification creating an RGB-NDVI image with 27 classes that were grouped into nine major forest change categories. Aerial photographs and stand history maps are compared with the forest changes indicated by the RGB-NDVI image. The utility of the RGB-NDVI technique for supporting forest inventories and updating forest resource information systems are presented and discussed.

Sader, S. A.↗

NASA NTRS2018

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Habitat

The combined effects of fire history, climate, and landscape features (e.g., edges) on habitat specialists need greater focus in fire ecology studies, which usually only emphasize characteristics of the most recent fire. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights, which are dynamic because of frequent fires. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells (that represented potential territories) because fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities vary between states as functions of environmental covariates. Covariates included vegetative type, edges (e.g., roads, forests), precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presence/absence of fire covariate, but also fire history covariates: time since the previous fire, the longest fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Edges reduced the effectiveness of fires in setting degraded scrub and flatwoods into earlier successional states making mechanical cutting an important tool to compliment frequent prescribed fires.

Breininger, David R.↗

NASA NTRS2022

Idaho Wildfires II: Assessing the Relationship Between Drought Indicators and Wildfire Risk to Enhance Hazard Modeling and Inform Mitigation Planning

The western United States has experienced twenty years of increased and prolonged drought which have exacerbated wildfire hazards. These jeopardize population centers through increased risks to ecosystem services, local economies, and livelihoods. The Idaho Office of Emergency Management, Water Resources, and Department of Lands are seeking methods to dynamically monitor these conditions and update models that inform hazard mitigation planning and resource allocation. Towards this, these agencies partnered with NASA DEVELOP to produce drought-enhanced wildfire hazard models. Part of a two-term project, the two teams revised the state’s static wildfire hazard model with refined data layers and remotely-sensed data to reflect dynamic ecosystem responses to drought conditions and wildfire potential. Our team distinguished between rangeland and forestland ecosystems, and investigated relationships between drought metrics and vegetation condition using TerrSet Earth Trends Modeler. This analysis determined that total precipitation at a 5-month lag interval (r 2 = 0.72) along with the Evaporative Stress Index (r 2 = 0.69); and precipitation at a 5-month interval (r 2 = 0.42) were important drivers in rangeland and forestland, respectively. These driver variables were incorporated into a temporally dynamic wildfire hazard map. Our team used linear regression to correlate hazard ratings with wildfire frequency. For the year 2020, neither the enhanced hazard model (p < 0.10, r 2 = 0.01) nor the state’s static model (p < 0.05, r 2 = 0.03) were strongly correlated with actual wildfire frequency as they expressed an inverse relationship between wildfire hazard and frequency. This suggests wildfire occurrence is complex and not necessarily driven by the variables used.

Wildfire↗

NASA NTRS1992

A toy terrestrial carbon flow model

A generalized carbon flow model for the major terrestrial ecosystems of the world is reported. The model is a simplification of the Century model and the Forest-Biogeochemical model. Topics covered include plant production, decomposition and nutrient cycling, biomes, the utility of the carbon flow model for predicting carbon dynamics under global change, and possible applications to state-and-transition models and environmentally driven global vegetation models.

Parton, William J.↗

NASA NTRS2023

Leaf Functional Traits in Relation to Species Composition in an Arctic–Alpine Tundra Grassland

The relict arctic–alpine tundra provides a natural laboratory to study the potential impacts of climate change and anthropogenic disturbance on tundra vegetation. The Nardus stricta-dominated relict tundra grasslands in the Krkonoše Mountains have experienced shifting species dynamics over the past few decades. Changes in species cover of the four competing grasses—Nardus stricta, Calamagrostis villosa, Molinia caerulea, and Deschampsia cespitosa—were successfully detected using orthophotos. Leaf functional traits (anatomy/morphology, element accumulation, leaf pigments, and phenolic compound profiles), were examined in combination with in situ chlorophyll fluorescence in order to shed light on their respective spatial expansions and retreats. Our results suggest a diverse phenolic profile in combination with early leaf expansion and pigment accumulation has aided the expansion of C. villosa, while microhabitats may drive the expansion and decline of D. cespitosa in different areas of the grassland. N. stricta—the dominant species—is retreating, while M. caerulea did not demonstrate significant changes in territory between 2012 and 2018. We propose that the seasonal dynamics of pigment accumulation and canopy formation are important factors when assessing potential “spreader” species and recommend that phenology be taken into account when monitoring grass species using remote sensing.

Lena Hunt↗

NASA NTRS2001

Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective

A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.

Dungan, Jennifer L.↗

NASA NTRS2014

Interannual Variations and Trends in Global Land Surface Phenology Derived from Enhanced Vegetation Index During 1982-2010

Land swiace phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstmted to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This srudy detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examIned across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and OSL varied considerably during 1982-2010 across the globe. Generally, the interarmual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative OSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3 decades. OGI mainly showed late trends in the Southern Hemisphere of Africa while GSL was reversed from reduced GSL trends (1982-1999) to prolonged trends (2000-2010). In Australia, GSL exhibited considerable interannual variation, but the consistent trend lacked presence in most regions. Finally, the proportion of pixels with significant trends was less than I% in most of climate regions although it could be as large as 10%.

remote sensing↗

NASA NTRS2021

Recovery: Fast and Slow—Vegetation Response During the 2012–2016 California Drought

The 2012–2016 California Drought severely impacted natural vegetation across a wide range of environmental gradient. Although several studies have reported an increase in plant water stress and mortality, the spatiotemporal variations of ecosystem productivity responses and the associated environmental and biological drivers remain unclear. Here, using Enhanced Vegetation Index from the Moderate resolution imaging spectrometer, we found that 45% of the natural ecosystems showed an abrupt change (breakpoint [BP]) in productivity during 2012–2016. There were three major contrasting temporal patterns of productivity responses: (i) a steady increase under higher temperature followed by a decline due to accumulated moisture depletion (high elevation forest) or temperature decrease (high elevation nonforest), (ii) gradual decline during the drought followed by a rapid recovery within 1 year after drought stress was partially relieved, and (iii) both a gradual decline and an abrupt decline. The magnitude of abrupt changes was negatively correlated (r = −0.80, p < 0.001) with initial gradual changes. Overall, changes during BP offset, on average, 57% of the preceding gradual responses. The spatial variability in ecosystem response patterns is driven by both environmental and biological factors. Particularly, for forests, positive BP was driven by increasing rainfall and decreasing temperature, while negative BP was mainly driven by the precipitation anomaly. By 2019, 33% of the natural vegetation have recovered to the level of EVI in 2010. Ecosystem responses to multiyear droughts can influence ecosystem dynamics in a complex pattern. Multiple ecohydrological factors should be considered to understand and predict the long-term drought impacts on ecosystems.

Xi Yang↗