Search NASA⌕ Search

SEARCH · Search NASA

Results for “Vegetation Index”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Delaware Basin Ecological Forecasting: Identifying Vegetation Trends and Atmospheric Stressors in the Guadalupe Mountains and Carlsbad Caverns National Parks

The Guadalupe Mountains and Carlsbad Caverns National Parks, located in the Delaware Basin in the southwestern United States, observed both a decrease in precipitation and an increase in temperature over the last decade. Furthermore, activity from local oil fields generated nitrogen dioxide (NO2) plumes that spread over the parks and augmented the effects of the drought. NO2 is a precursor for tropospheric ozone (O3) which is known to have adverse effects on vegetation and ecosystems at large. These new climate dynamics prompted the National Park Service (NPS) to collaborate with NASA DEVELOP to assess the impact on vegetation within the parks. We used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to assess vegetation health, water stress, and precipitation in the affected parks. After creating a homogenous reference area in the Sierra Diablo Mountains (SDM), the team visualized vegetation health through a Normalized Difference Vegetation Index (NDVI) time series map from 2010-2021. This did not show strong evidence that the NO2 plume is causing vegetation decline. Following this, we created a water stress map with a Normalized Difference Moisture Index (NDMI) time series map from 2010-2021, which revealed a pattern of increasing water stress. We also confirmed that precipitation in the region decreased over the span of 2010-2021. These observations and findings will allow the NPS Intermountain Region to more effectively plan for the preservation and maintenance of vegetation health within the parks.

Jack Mezger↗
NASA NTRS2022

Delaware Basin Ecological Forecasting: Identifying Vegetation Trends and Atmospheric Stressors in the Guadalupe Mountains and Carlsbad Caverns National Parks

The Guadalupe Mountains and Carlsbad Caverns National Parks, located in the Delaware Basin in the southwestern United States, observed both a decrease in precipitation and an increase in temperature over the last decade. Furthermore, activity from local oil fields generated nitrogen dioxide (NO2) plumes that spread over the parks and augmented the effects of the drought. NO2 is a precursor for tropospheric ozone (O3) which is known to have adverse effects on vegetation and ecosystems at large. These new climate dynamics prompted the National Park Service (NPS) to collaborate with NASA DEVELOP to assess the impact on vegetation within the parks. We used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to assess vegetation health, water stress, and precipitation in the affected parks. After creating a homogeneous reference area in the Sierra Diablo Mountains, the team visualized vegetation health through a Normalized Difference Vegetation Index (NDVI) time series map from 2010-2021. This did not show strong evidence that the NO2 plume is causing vegetation decline. Following this, we created a water stress map with a Normalized Difference Moisture Index (NDMI) time series map from 2010-2021, which revealed a pattern of increasing water stress. We also confirmed that precipitation in the region decreased over the span of 2010-2021. These observations and findings will allow the NPS Intermountain Region to more effectively plan for the preservation and maintenance of vegetation health within the parks.

Jack Mezger↗
NASA NTRS2008

A VARI-Based Relative Greenness from MODIS Data for Computing the Fire Potential Index

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate the VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FP outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.

Schneider, P.↗
NASA NTRS1982

Multifrequency remote sensing of soil moisture

Multifrequency sensor data collected at Guymon, Oklahoma and Dalhart, Texas using NASA's C-130 aircraft were used to determine which of the all-weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. In comparison to other active and passive microwave sensors the L-band radiometer (1) was influenced least by ranges in surface roughness; (2) demonstrated the most sensitivity to soil moisture differences in terms of the range of return from the full range of soil moisture; and (3) was less sensitive to errors in measurement in relation to the range of sensor response. L-band emissivity related more strongly to soil moisture when moisture was expressed as percent of field capacity. The perpendicular vegetation index as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture.

Theis, S. W.↗
NASA NTRS1989

Modelling Rift Valley fever (RVF) disease vector habitats using active and passive remote sensing systems

The NASA Ames Ecosystem Science and Technology Branch and the U.S. Army Medical Research Institute of Infectious Diseases are conducting research to detect Rift Valley fever (RVF) vector habitats in eastern Africa using active and passive remote-sensing. The normalized difference vegetation index (NDVI) calculated from Landsat TM and SPOT data is used to characterize the vegetation common to the Aedes mosquito. Relationships have been found between the highest NDVI and the 'dambo' habitat areas near Riuru, Kenya on both wet and dry data. High NDVI values, when combined with the vegetation classifications, are clearly related to the areas of vector habitats. SAR data have been proposed for use during the rainy season when optical systems are of minimal use and the short frequency and duration of the optimum RVF mosquito habitat conditions necessitate rapid evaluation of the vegetation/moisture conditions; only then can disease potential be stemmed and eradication efforts initiated.

Ambrosia, Vincent G.↗
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 NTRS1998

Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus

Leaf Area Index (LAI) is one of the most commonly employed biophysical parameters used to characterize vegetation canopies and scale leaf physiological processes to larger scales. For example, LAI is a critical parameter used in regional scale estimates of evapotranspiration, photosynthesis, primary productivity, and carbon cycling (Running et al., 1989; Dorman and Sellers, 1989; Potter et al., 1993). LAI is typically estimated using ratio-based techniques, such as the Normalized Difference Vegetation Index (NDVI: e.g. Tucker 1979; Asrar et al., 1989; Sellers 1985, 1987). The physical basis behind this relationship depends on the high spectral contrast between scattered near-infrared (NIR) and absorbed red radiation in canopies. As the number of leaves present in a canopy increases over a unit area, NIR reflectance increases, while red reflectance decreases, resulting in an increase in the ratio. Through time series and image compositing, NDVI provides an additional temporal measure of how these parameters change, providing a means to monitor fluxes and productivity (Tucker et al., 1983). NDVI, while highly successful for agriculture and grassland ecosystems has been found to be less successful in evergreen chaparral and forested ecosystems (Badhwar et al., 1986; Gamon et al., 1993; Hall et al., 1995). Typically, the relationship between NDVI and LAI becomes progressively more asymptotic at LAI values above three (Sellers, 1985), although linear relationships have been observed in conifers at LAis as high as 13 (Spanner et al., 1990). In this paper, we explore an alternative approach for estimating LAI for remotely sensed data from AVIRIS based on estimates of canopy liquid water. Our primary objective is to test the hypothesis that the depth of the liquid water bands expressed in canopy reflectance spectra at 960, 1200, 1400 and 1900 nm increases with increasing LAI in canopies. This study builds from work by Roberts et al. (1997), in which liquid water was shown to increase following a gradient of increasing LAI ranging from grasslands to coniferous forests. In that study, it was observed that forests, which showed little variation in NDVI, showed significant variation in liquid water. In order to test this hypothesis, we analyzed field spectra measured over Populus resprouts of known LAI and monitored changes in liquid water in young Populus stands as they aged over a 4-year time span. The study was conducted in south-central Washington, in a clonal Populus fiber farm owned and operated by Boise-Cascade near the town of Wallula.

Roberts, Dar↗
NASA NTRS2018

Investigations of Hail Damage Swaths Using Various Satellite Remote Sensing Platforms

Severe thunderstorms that bring damaging winds and large hail can cause significant damage to agricultural crops. Severe thunderstorms can cause upwards of several hundreds of millions of dollars in damage to agricultural areas. Formal ground surveys are not conducted on these areas of damage, like they are for suspected tornado damaged areas. IF ground surveys were conducted, they would likely be time and resources consuming due to their large spatial extent. Satellite remote sensing has been frequently used in identification and analysis of these hail damage swaths. Previous analysis have looked at the simple change in damaged vegetation to looking at the damage areas in satellite imagery with varying spatial resolutions. One study has even looked at the impacts that these damage swaths can have on the land surface, associated fluxes and how they affect numerical weather prediction. Previous studies have focused on using optical remote (VIS, NIR, SWIR) sensing instruments and derived indices, such as Normalized Difference Vegetation Index (NDVI) for analysis. NDVI is used to monitor the health (greenness) of the vegetation. Optical sensors however are limited by sky conditions over the areas they are imaging and certain bands are further limited by the diurnal cycle. These limitations can lead to sometimes upwards of 7 to 10 day gaps of the surface not being imaged, especially during the height of summer convection. One way to obtain more views of the surface, regardless of the sky conditions or time of day is through the use of synthetic aperture radar (SAR). SAR sensors are active instruments that transmit in the microwave portion of the EM spectrum. The surface and its characteristics will determine the amount of energy scattered back to the sensor. The SAR sensors then measure amplitude and phase of wavelength coming back from surface.

Bell, Jordan R.↗
NASA NTRSDate not supplied

Relating Land Cover Change to Runoff Distribution Using NASA Earth Observations in Riley County, Kansas

Riley County, Kansas, has observed increased levels of flooding, potentially due to changes in land use/land cover (LULC) and seasonal vegetation variation. This study contrasts two methods of generating runoff curve numbers (CN) from 2006-2020. (1) The traditional Soil Conservation Service CN calculation method uses a look-up table and tracked LULC to determine runoff changes. These tables allow for CN values specific to various land and crop cover types and account for various farming best practices but lack flexibility in calculations for various seasons or plant health. (2) A dynamic method employs normalized difference vegetation index (NDVI) compiled over the rainy season each year to calculate CN using seasonal vegetation. This method allows for a more precise analysis of runoff variability within and between rainy seasons because it can be updated with greater temporal detail and it captures higher spatial resolutions by using NDVI as a proxy for LULC. This study further uses inputs from the United States Geologic Survey (USGS) National Land Cover Database (NLCD), the United States Department of Agriculture (USDA) Cropland Data Layer, and Landsat imagery to create more precise LULC raster datasets including both urban cover and crop-specific land use and curve number maps of the area. Results can guide decision makers in the City of Manhattan, Riley County Department of Planning and Development, Riley County Conservation District, the Kansas Forest Service, and the Kansas Department of Health and Environment toward informed decisions on resiliency strategies to address future flooding.

Ella Griffith↗
NASA NTRS2020

Riley County Water Resources Project Summary - Comparing Runoff Curve Calculation Methods to Inform Local Resiliency Initiatives in Riley County, Kansas

Riley County, Kansas, has observed increased levels of flooding, potentially due to changes in land use/land cover (LULC) and seasonal vegetation variation. This study contrasts two methods of generating runoff curve numbers (CN) from 2006-2020. (1) The traditional Soil Conservation Service CN calculation method uses a look-up table and tracked LULC to determine runoff changes. These tables allow for land cover-specific CN and account for various farming techniques but lack flexibility in calculations for various seasons or plant health. (2) A dynamic method employs normalized difference vegetation index (NDVI) compiled over the rainy season each year to calculate CN using seasonal vegetation. This method allows for a more precise analysis of runoff variability within and between rainy seasons because it can be updated with greater temporal detail and captures higher spatial resolutions by using NDVI as a proxy for LULC. This study further uses inputs from the United States Geological Survey (USGS) National Land Cover Database (NLCD), the United States Department of Agriculture (USDA) Cropland Data Layer, and Landsat imagery to create more precise LULC raster datasets including both urban cover and crop-specific land use and curve number maps of the area. Results can guide decision makers in the City of Manhattan, Riley County Department of Planning and Development, Riley County Conservation District, the Kansas Forest Service, and the Kansas Department of Health and Environment toward informed decisions on resiliency strategies to address future flooding.

DEVELOP Project Summary↗
NASA NTRS1993

Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images

Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained using the Fourier analysis.

Menenti, M.↗
NASA NTRS2002

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗
NASA NTRS2016

Benefit of Modeling the Observation Error in a Data Assimilation Framework Using Vegetation Information Obtained From Passive Based Microwave Data

A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.

Vegetation↗
NASA NTRS1987

Monitoring global vegetation using Nimbus-7 37 GHz data - Some empirical relations

The difference of the vertically and horizontally polarized brightness temperatures observed by the 37 GHz channel of the SMMR on board the Nimbus-7 satellite are correlated temporally with three indicators of vegetation density, namely the temporal variation of the atmospheric CO2 concentration at Mauna Loa (Hawaii), rainfall over the Sahel and the normalized difference vegetation index derived from the AVHRR on board the NOAA-7 satellite. SMMR 37 GHz and AVHRR provide complementary data sets for monitoring global vegetation, the 37 GHz data being more suitable for arid and semiarid regions as these data are more sensitive to changes in sparse vegetation. The 37-GHz data might be useful for understanding desertification and indexing Co2 exchange between the biosphere and the atmosphere.

Choudhury, B. J.↗
NASA NTRS1977

Effective use of Landsat for range monitoring and management - An example on a regional scale

Some results of a study undertaken in the Great Plains region to evaluate the use of Landsat for monitoring rangeland ecosystems are briefly summarized. A vegetation parameter, the Transformed Vegetation Index (TVI), was developed as a green biomass indicator. It was determined that for rangelands with good vegetative cover, and which are not heavily infested with brush, green biomass can be estimated in increments of 250-300 kg/ha with a 95% probability. At least five levels of range feed conditions can be mapped with TVI data using the MSS Band 6.

Deering, D. W.↗
NASA NTRS1988

Estimating soil wetness using satellite data

Improved estimates of soil wetness were obtained using observations from both the NIMBUS-7 Scanning Multichannel Microwave Radiometer (SMMR) and the NOAA-7 Advanced Very High Resolution Radiometer (AVHRR). SMMR 66 GHz frequency, horizontal polarization, brightness temperature T(BH) was first correlated with soil wetness, as computed using an Antecedent Precipitation Index (API) model, for a number of SMMR ground resolution areas involving a fairly wide range of vegetation densities. The API generally accounted for more than 70 percent of the observed temporal variability in T(BH), with linear correlations being significant at the 1 percent level. The regression slope of T(BH) versus API correlated well, at the 1 percent level, with a vegetation index derived from AVHRR visible and near-infrared observations. The regression intercept was found to correlate less satisfactorily, but was significant at the 5 percent level. These linear regression results were used to develop a diagnostic model for soil wetness using SMMR and AVHRR data only.

Choudhury, Bhaskar J.↗
NASA NTRS1998

Diurnal Reflectance Changes in Vegetation Observed with AVIRIS

Among the most important short-term dynamic biological processes are diurnal changes in canopy water relations. Plant regulation of water transport through stomatal openings affects other gaseous transport processes, often dramatically decreasing photosynthetic fixation of carbon dioxide during periods of water stress. Water stress reduces stomatal conductance of water vapor through the leaf surface and alters the diurnal timing of stomatal opening. Under non-water stressed conditions, stomates typically open soon after dawn and transpire water vapor throughout the daylight period. During stress periods, stomates may close for part of the day, generally near mid-day. Under prolonged stress conditions, stomatal closure shifts to earlier times during the day; stomates may close by mid-morning and remain closed until the following morning - or remain closed entirely. Under these conditions the relationship between canopy greenness (e.g., measured with a vegetation index or by spectral mixture analysis) and photosynthetic fixation of carbon is lost and the remotely sensed vegetation metric is a poor predictor of gas exchange. Prediction of stomatal regulation and exchange of water and trace gases is critical for ecosystem and climate models to correctly estimate budgets of these gases and understand or predict other processes like gross and net ecosystem primary production. Plant gas exchange has been extensively studied by physiologists at the leaf and whole plant level and by biometeorologists at somewhat larger scales. While these energy driven processes follow a predictable if somewhat asymmetric diurnal cycle dependent on soil water availability and the constraints imposed by the solar energy budget, they are nonetheless difficult to measure at the tree and stand levels using conventional methods. Ecologists have long been interested in the potential of remote sensing for monitoring physiological changes using multi-temporal images. Much of this research has focused on day-to-day changes in water use, especially for agricultural applications. Ustin et al. showed seasonal changes in canopy water content in chaparral shrub could be estimated using optical methods. Vanderbilt et al. followed asymmetric diurnal changes in the reflectance of a walnut orchard, but could not attribute specific reflectance changes to specific changes in canopy architecture or physiology. Forests and shrub lands in California experience prolonged periods of drought, sometimes extending six months without precipitation. The conifer and evergreen chaparral communities common to the foothill region around the central valley of California retain their foliage throughout the summer and have low transpiration rates despite high net radiation and temperature conditions. In contrast, grasslands and drought resistant deciduous species in the same habitat are seasonally dormant in summer. Because of differences in the mechanisms of drought tolerance, rooting depth and physiology between different plant communities in the region, it is likely that they display differences in diurnal water relations. The presence of diverse plant communities provides an opportunity to investigate possible diurnal landscape patterns in water relations that could be observed by an airborne hyperspectral scanner. This investigation of AVIRIS data collected over forest and shrub land represents the continuation of a prior investigation involving spectral mixture analysis of diurnal effects in the same AVIRIS data set.

Vanderbilt, V. C.↗
NASA NTRS2008

Climate Change Implications to Vegetation Production in Alaska

Investigation of long-term meteorological satellite data revealed statistically significant vegetation response to climate drivers of temperature, precipitation and solar radiation with exclusion of fire disturbance in Alaska. Abiotic trends were correlated to satellite remote sensing observations of normalized difference vegetation index to understand biophysical processes that could impact ecosystem carbon storage. Warming resulted in disparate trajectories for vegetation growth due to precipitation and photosynthetically active radiation variation. Interior spruce forest low lands in late summer through winter had precipitation deficit which resulted in extensive fire disturbance and browning of undisturbed vegetation with reduced post-fire recovery while Northern slope moist alpine tundra had increased production due to warmer-wetter conditions during the late 1990s and early 2000s. Coupled investigation of Alaska s vegetation response to warming climate found spatially dynamic abiotic processes with vegetation browning not a result from increased fire disturbance.

Neigh, Christopher S.R.↗