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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River basin

Wildfires impact vegetation mortality and productivity and are increasing in intensity, frequency, and spatial area in the western United States. The rates of vegetation recovery after fires play a major role in the reestablishment of biomass and ecosystem functioning (e.g., structure, resilience, and productivity), but such recovery rates are poorly understood. Here we use remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify the resistance and resilience of leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET) to 138 wildfires of various burn severity across the Columbia River basin (CRB) of the Pacific Northwest in 2015. Increasing burn severity caused lower resistance and resilience for all three variables. Resistance and resilience are highest in grasslands, intermediate in savanna, and lowest in needleleaf evergreen forests, consistent with the adaptation of these vegetation types to fire. LAI has consistently lower resistance and resilience than GPP and ET, which is consistent with physical and physiological mechanisms that compensate for reduced LAI. Resilience is influenced by precipitation, vapor pressure deficit (VPD), and burn severity across all three vegetation types; however, burn severity plays a more minor role in grasslands. Increasing wildfire severity will reduce the resistance and resilience and lengthen the recovery time of vegetation structure and fluxes with climate change, with significant consequences for the provision of ecosystem functioning and implications for model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0

Abstract. Fire is a fundamental part of the Earth system, with impacts on vegetation structure, biomass, and community composition, the latter mediated in part via key fire-tolerance traits, such as bark thickness. Due to anthropogenic climate change and land use pressure, fire regimes are changing across the world, and fire risk has already increased across much of the tropics. Projecting the impacts of these changes at global scales requires that we capture the selective force of fire on vegetation distribution through vegetation functional traits and size structure. We have adapted the fire behavior and effects module, SPITFIRE (SPread and InTensity of FIRE), for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime, and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf, and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown, and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown, and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees, and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire-burned area and its effects, which could feed back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m−1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m−1. The results support a positive grass–fire feedback across the region and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
DOE OSTI2023

Baltimore Ecosystem Study: Soil atmosphere fluxes of carbon dioxide, nitrous oxide and methane, 1998 - ongoing

The Baltimore Ecosystem Study (BES) established a network of long-term permanent biogeochemical study plots in 1998. These plots provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The network of study plots includes forest plots (upland and riparian), chosen to represent the range of forest conditions in the area and grass plots (to represent home lawns). Plots are instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature, and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998 and 2015. Data from these plots has been published in Groffman et al. (2006, 2009), Groffman and Pouyat (2009), Savva et al. (2010), Costa and Groffman (2013), Duncan et al. (2013), Waters et al. (2014), Ni and Groffman (2018), Templeton et al. (2019). Literature Cited Costa, K.H. and P.M. Groffman. 2013. Factors regulating net methane flux in urban forests and grasslands. Soil Science Society of America Journal 77:850 - 855. Duncan, J. M., L. E. Band, and P. M. Groffman. 2013. Towards closing the watershed nitrogen budget: Spatial and temporal scaling of denitrification. Journal of Geophysical Research Biogeosciences 118:1-5; DOI: 10.1002/jgrg.20090 Groffman PM, Pouyat RV, Cadenasso ML, Zipperer WC, Szlavecz K, Yesilonis IC,. Band LE and Brush GS. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Groffman, P.M., C.O. Williams, R.V. Pouyat, L.E. Band and I.C. Yesilonis. 2009. Nitrate leaching and nitrous oxide flux in urban forests and grasslands. Journal of Environmental Quality 38:1848-1860. Groffman, P.M. and R.V. Pouyat. 2009. Methane uptake in urban forests and lawns. Environmental Science and Technology 43:5229-5235. DOI: 10.1021/es803720h. Ni, X. and P.M. Groffman. 2018. Declines in methane uptake in forest soils. Proceedings of the National Academies of Science of the United States of America 115:8587-8590. Savva, Y., K. Szlavecz, R. V. Pouyat, P. M. Groffman, and G. Heisler. 2010. Effects of land use and vegetation cover on soil temperature in an urban ecosystem. Soil Science Society of America Journal 74:469-480. Templeton, L., M.L. Cadenasso, J. Sullivan, M. Neel and P.M. Groffman. 2019. Changes in vegetation structure and composition of urban and rural forest patches in Baltimore from 1998 to 2015. Forest Ecology and Management. In press. Waters, E.R., J.L. Morse, N.D. Bettez and P.M. Groffman. 2014. Differential carbon and nitrogen controls of denitrification in riparian zones and streams along an urban to exurban gradient. Journal of Environmental Quality 43:955–963.

Groffman, Peter M↗
DOE OSTI2020

DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels"

DayCent data and results for:J. L. Field, T. L. Richard, E. A. Smithwick, H. Cai, M. S. Laser, D. S. LeBauer, S. P. Long, K. Paustian, Z. Qin, J. J. Sheehan, P. Smith, M. Q. Wang, L. R. Lynd, Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels. Proceedings of the National Academy of Sciences (2020). https://doi.org/10.1073/pnas.1920877117 This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script 'autodaycent.py' (written for Python 2.7) opens an interactive command line routine that facilitates:* Calibrating the DayCent pine growth model.* Initializing DayCent for a set of case studies sites.* Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation. * Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model (https://www.nrel.colostate.edu/projects/daycent/) compatible with other operating systems are available upon request.Please send questions/comments to John.L.Field@gmail.com

Agro-ecosystem function and prediction↗
NASA NTRS1972

Pattern recognition of native plant communities: Manitou Colorado test site

Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information about 11 vegetation classes and two nonvegetation classes at the Manitou Experimental Forest. Intensive preprocessing of the scanner signals was required to eliminate a serious scan angle effect. Final processing of the normalized data provided acceptable recognition results of generalized plant community types. Serious errors occurred with attempts to classify specific community types within upland grassland areas. The consideration of the convex mixtures concept (effects of amounts of live plant cover, exposed soil, and plant litter cover on apparent scene radiances) significantly improved the classification of some of the grassland classes.

Driscoll, R. S.↗
NASA NTRS1971

Information content of simulated space photographs as a function of various levels of image resolution.

The results of research are reported on the information content in simulated space photographs derivable as a function of various levels of image resolution. Whenever certain resource features could not be consistently identified on simulated low-resolution imagery, attempts were made to define the required level of image resolution that would allow a skilled interpreter to discriminate one feature from another. The results of this research, conducted within a chaparral-hardwood-grassland environment in California, indicate that simulated ERTS data contain sufficient information to allow an interpreter to discriminate between woody vegetation, grassland, and water bodies. However, if more detailed information is desired, the imagery must have a ground resolution of at least 50 ft, showing shape, size, texture, and shadow characteristics within each vegetation type. Spaceborne data, therefore, often will have to be supplemented by higher resolution aircraft imagery, depending on the types of resource information being sought.

Lauer, D. T.↗
NASA NTRS1972

Inventory and monitoring of natural vegetation and related resources in an arid environment by the use of ERTS-A imagery

The author has identified the following significant results. The southern Arizona test site includes vegetation representing shrub types of the Sonoran and Chihuahuan Deserts and Arizona chaparral. Also represented are grassland types of the desert grassland, juniper, and oak woodland types, and coniferous forest types. Relationships between plant species and selected terrain features are given along with the terrain feature classes used for the analyses. The purpose for determining the degree to which such relationships exist is to develop a body of knowledge to constitute the associated evidence that a photointerpreter may consult when interpreting vegetation subjects on small scale imagery. Imagery of this scale class contains little image detail which can be interpreted directly in terms of vegetation. On the other hand, some terrain features are the most salient features of that same imagery. Exploitation of those features for vegetation identification, inventory, and analysis can be accomplished only after establishing the existence of ecological relationships.

Schrumpf, B. J.↗
NASA NTRS1973

Use of satellite imagery for wildland resource evaluation in the Great Basin

The author has identified the following significant results. Upon receipt of ERTS-1 imagery, scale and resolution determinations were made. It was found that the scale was quite close to the anticipated 1:1 million, and that each ERTS-1 frame had a coverage of approximately 12,500 square miles. A water body 275 feet across was identified on the MSS 7 band as a determination of maximum resolution. Large cities were easily distinguished on all bands. Paved highways and railroads were able to be identified when there was extreme contrast between these structures and surrounding areas. Landforms were identified on all bands, but band 5 proved superior. Vegetation of varying types was identified using band 7. Fields and agricultural areas were identified as was phreatophytic vegetation. Timber, pinyon-juniper, sagebrush, mountain brush, and annual grassland communities were identified on selected frames. Phenology comparisons of selected test sites were made using sequential U-2 imagery. It was found that gray scale and color IR changes relating to the phenology of certain annual grassland, mountain brush, and marsh communities could be identified. Water bodies were identified and delineated using band 7.

Tueller, P. T.↗
NASA NTRS1973

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Eucalyptus tree stands killed by low temperatures in December 1972 were outlined by image enhancement of two separate dates of ERTS-1 images (January 22, 1973-I.D. 1183-18175 and April 22, 1973-I.D. 1273-18183). Three stands larger than 500 meters in size were detected very accurately. In Colorado, range and grassland communities were analyzed by visual interpretation of color composite scene I.D. 1028-17135. It was found that mixtures of plant litter, amount and kind of bare soil, and plant foliage cover made classification of grasslands very difficult. Changes in forest land use were detected on areas as small as 5 acres when ERTS-1 color composite scene 1264-15445 (April 13, 1973) was compared with 1966 ASCS index mosaics (scale 1:60,000). Verification of the changes were made from RB-57 underflight CIR transparencies (scale 1:120,000).

Heller, R. C.↗
NASA NTRS1976

The ten-ecosystem study investigation plan

With the continental United States divided into ten forest and grassland ecosystems, the Ten Ecosystem Study (TES) is designed to investigate the feasibility and applicability of state-of-the-art automatic data processing remote sensing technology to inventory forest, grassland, and water resources by using Land Satellite data. The study will serve as a prelude to a possible future nationwide remote sensing application to inventory forest and rangeland renewable resources. This plan describes project design and phases, the ten ecosystem, data utilization and output, personnel organization, resource requirements, and schedules and milestones.

Kan, E. P.↗
NASA NTRS1979

Ten-Ecosystem Study (TES) site 9, Washington County, Missouri

The author has identified the following significant results. Sufficient spectral separability exists among softwood, hardwood, grassland, and water to develop a level 2 classification and inventory. Using the tested automatic data processing technology, softwood and grassland signatures can be extended across the county with acceptable accuracy; with more dense sampling, the hardwood signature probably could also be extended. Fall was found to be the best season for mapping this ecosystem.

Echert, W. H.↗
NASA NTRS1985

Directional reflectance factor distributions for cover types of Northern Africa

Directional reflectance factors that spanned the entire exitance hemisphere were collected on the ground throughout the morning period for common cover types in Tunisia, Africa. NOAA 7/8 AVHRR bands 1(0.58-0.68 micron) and 2 (0.7301.1 micron) were used in data collection. The cover types reported were a plowed field, annual grassland, steppe grassland, hard wheat, salt plain, and irrigated wheat. Several of these cover types had geometric structures that are extreme as compared to those reported in the literature. Comparisons were made between the dynamics of the observed reflectance distributions and those reported in the literature. It was found that the dynamics of the measured data could be explained by a combination of soil and vegetation scattering components. The data and analysis further validated physical principles that cause the reflectance distribution dynamics as proposed by field and simulation studies in the literature. Finally, the normalized difference transformation (Band 2 - Band 1)/(Band 1 + Band 2), which is useful in monitoring vegetation cover, generally decreased the variation in signal with changing view angle. However, several exceptions were noted.

Kimes, D. S.↗
NASA NTRS1986

Directional reflectance distributions of a hardwood and pine forest canopy

A hand-held radiometer with AVHRR bands 1 and 2 was used to measure the directional reflectance distributions for both a hardwood and a pine forest canopy from a helicopter platform; canopy characteristics were also measured on the ground. The reflectance distributions obtained are compared with the scattering behavior of agricultural and natural grassland canopies. In addition, the Kimes (1983) three-dimensional radiative transfer model is used to document the unique radiant transfers that occur in forest canopies in virtue of their geometric structure. Both the measurements and the model calculations show that dense forest canopy scattering is similar to that for crops and grasslands. Attention is given to the effects of sparse forest canopies.

Kimes, D. S.↗
NASA NTRS1988

Selecting a spatial resolution for estimation of per-field green leaf area index

For any application of multispectral scanner (MSS) data, a user is faced with a number of choices concerning the characteristics of the data; one of these is their spatial resolution. A pilot study was undertaken to determine the spatial resolution that would be optimal for the per-field estimation of green leaf area index (GLAI) in grassland. By reference to empirically-derived data from three areas of grassland, the suitable spatial resolution was hypothesized to lie in the lower portion of a 2-18 m range. To estimate per-field GLAI, airborne MSS data were collected at spatial resolutions of 2 m, 5 m and 10 m. The highest accuracies of per-field GLAI estimation were achieved using MSS data with spatial resolutions of 2 m and 5 m.

Curran, Paul J.↗
NASA NTRS1992

Inferring total canopy APAR from PAR bidirectional reflectances and vegetation indices in tallgrass prairie

The fraction of photosynthetically active radiation (PAR) absorbed by a vegetated canopy (APARc) or landscape (APARs) is a critical parameter in climate processes. A grassland study examined: 1) whether APARs can be estimated from PAR bidirectional exitance fractions; and 2) whether APARs is correlated with spectral vegetation indices (SVIs). Data were acquired with a high resolution continuous spectroradiometer at 4 sun angles on grassland sites. APARs was computed from the scattered surface PAR exitance fractions. The nadir APARs value was the most variable diurnally; it provided a good estimate of the average surface APARs at ~95 percent. APARc was best represented by exitance factors between 30-60* forward.

Middleton, Elizabeth M.↗
NASA NTRS1992

Spectral bidirectional and hemispherical reflectance characteristics of selected sites in the Streletskaya steppe

Measurements of plant canopy bidirectional reflectance made by the PARABOLA (portable apparatus for rapid acquisition of bidirectional observations of the land and atmosphere) instrument in three spectral bands are analyzed for steppe grassland sites of differing productivity levels. The variation of spectral reflectance and the normalized difference vegetation index in the solar principal plane is presented. Comparisons are made with PARABOLA measurements from selected first ISLSCP field experiment (FIFE) grassland sites in the Konza prairie, Kansas. The Streletskaya steppe sites showed no strong hot spot reflectance, while this effect was present in some FIFE sites but absent in others. The hot spot effect seems to be dependent on canopy geometry and background reflectance characteristics of these sites. Spectral hemispherical reflectance was computed from the angular integration of the bidirectional measurements for the steppe sites. Total shortwave albedo was estimated from these hemispherical reflectance measurements and compared to albedo measured by pyranometers. The albedo estimates from PARABOLA were found to be approximately 12-17 percent higher than the pyranometer measurements.

Eck, Thomas F.↗
NASA NTRS1993

AVHRR-LAC estimates of forest area in Madagascar, 1990

Three AVHRR-LAC data sets acquired in September 1990 and January 1991 were used to map the forest resources of Madagascar. The island was partitioned into four strata to include: (1) the western hardwoods, (2) the central grasslands, (3) the eastern rainforest, and (4) spiny forest. Each stratum was classified separately using AVHRR-LAC data in conjunction with 1984-1988 Landsat-MSS photoproducts. The results of AVHRR classification indicate that approximately 11 percent of the island is covered by forest. Estimates of forest area, by stratum, are as follows: western hardwoods, 6697 sq km; central grasslands, 2830 sq km; eastern rainforest 34,167 sq km; and spiny forest, 17,224 sq km. The total forest area on the 587,041 sq km island is estimated to be 60,918 sq km. The AVHRR forest map was compared to a mid-1970s land cover map which was developed using Landsat-MSS photoproducts. The average class agreement between the mid 1970s ground reference map and the 1990 AVHRR-LAC map was 78.2 percent, the overall accuracy was 81.1 percent. Much of the per-pixel disagreement between the ground reference and AVHRR maps involved areas identified as forest in the 1970s and as nonforest in 1990.

Nelson, R.↗