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NASA NTRS1986

Satellite-derived leaf-area-index and vegetation maps as input to global carbon cycle models - A hierarchical approach

A hierarchical procedure for developing a leaf area index (LAI) map of deciduous boreal forests is studied. The collection of spectral reflectance data from the Boundary Waters Canoe area in Minnesota using helicopter-, high-altitude aircraft-, and Landsat-mounted spectral sensors is described. The relationship between LAI and biomass and the reflectance ratio is analyzed. The sensitivity of canopy reflectance in the visible and infrared to the LAI of the canopy for various boreal forest species is evaluated. The data reveal that Landsat data are useful for producing LAI maps of deciduous forest areas and the maps provide data which clarifies the function of vegetation in the global carbon cycle models.

Badhwar, G. D.↗

NASA NTRS1985

North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer

Spectral vegetation index measurements derived from remotely sensed observations show great promise as a means to improve knowledge of land vegetation patterns. The daily, global observations acquired by the advanced very high resolution radiometer, a sensor on the current series of U.S. National Oceanic and Atmospheric Administration meteorological satellites, may be particularly well suited for global studies of vegetation. Preliminary results from analysis of North American observations, extending from April to November 1982, show that the vegetation index patterns observed correspond to the known seasonality of North American natural and cultivated vegetation. Integration of the observations over the growing season produced measurements that are related to net primary productivity patterns of the major North American natural vegetation formations. Regions of intense cultivation were observed as anomalous areas in the integrated growing season measurements. Significant information on seasonality, annual extent and interannual variability of vegetation photosynthetic activity at continental and global scales can be derived from these satellite observations.

Goward, S. N.↗

NASA NTRS2017

Monitoring Land Surface Albedo and Vegetation Dynamics Using High Spatial and Temporal Resolution Synthetic Time Series from Landsat and the MODIS BRDF/NBAR/Albedo Product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warmingcooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500-meter Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF (Bidirectional Reflectance Distribution Function) / NBAR (Nadir BRDF-Adjusted Reflectance) / albedo products and 30-meter Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDFAlbedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30-meter Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30-meter albedos for the intervening daily time steps in this study. These enhanced daily 30-meter spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of 0.006. These synthetic time series provide much greater spatial detail than the 500 meter gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 kilometers by 14 kilometers) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF-Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30-meter resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Vegetation Index↗

NASA NTRS1982

Microwave remote sensing of soil moisture, volume 1

Multifrequency sensor data from 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. The perpendicular vegetation index (PVI) 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. A linear equation was developed to estimate percent field capacity as a function of L-band emissivity and the vegetation index. The prediction algorithm improves the estimation of moisture significantly over predictions from L-band emissivity alone.

Mcfarland, M. J.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-13211-DND

On March 11, 2011 the Great Tohoku Earthquake occurred approximately 70 kilometers off the coast of Japan. This magnitude 9.0 earthquake was closely followed by a massive tsunami that reached 7 meters in height. Using NASA Earth Observation Systems (EOS), we will assess the damage to the area impacted by this disaster. Landsat 5 Thematic Mapper (TM) will be used to create normalized difference vegetation index (NDVI) maps and MODIS on Aqua and Terra will be used to produce enhanced vegetation index (EVI) maps. CALIPSO and HYSPLIT will also be used to track smoke released from the Fukushima Daiichi Nuclear Power Plant to assess where the radioactive steam traveled to. An analysis of the economic impact of this disaster will be conducted to help policy makers in the United States and in Japan. This research will highlight the ability of NASA EOS in providing support for disaster response around the world.

Malcom Jones, Jr.↗

NASA NTRS2012

Filling-in of Far-Red and Near-Infrared Solar Lines by Terrestrial and Atmospheric Effects: Simulations and Space-Based Observations from SCHIAMACHY and GOSAT

Mapping of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global information on the functional status of vegetation including light use efficiency and global primary productivity that can be used for global carbon cycle modeling. Space-based measurement of solar-induced chlorophyll fluorescence is challenging, because its signal is small as compared with the much larger reflectance signal. Ground- and aircraft-based approaches have made use of the dark and spectrally-wide 02-A (approx 760 nm) and O2-B (approx 690 nm) atmospheric features to detect the weak fluorescence signal. More recently, Joiner et a1. and Frankenberg et a1. focused on longer-wavelength solar Fraunhofer lines that can be observed with space-based instruments such as the currently operational GOSAT. They showed that fluorescence can be detected using Fraunhofer lines away from the far-red chlorophyll-a fluorescence peak even when the surface is relatively bright. Here, we build on that work by developing methodology to correct for instrumental artifacts that produce false filling-in signals that can bias fluorescence retrievals. We also examine other potential sources of filling-in at far-red and NIR wavelengths. Another objective is to explore the possibility of making fluorescence measurements from space with lower spectral resolution instrumentation than the GOSAT interferometer. We focus on the 866 nm Ca II solar Fraunhofer line. Very few laboratory and ground-based measurements of vegetation fluorescence have been reported at wavelengths longer than 800 mn. Some results of fluorescence measurements of corn leaves acquired in the laboratory using polychromatic excitation at wavelengths shorter than 665 nm show that at 866 nm, the measured signal is of the order of 0.1-0.2 mw/sq m/nm/sr. In this work we use the following satellite observations: We use SCIAMACHY channel 5 in nadir mode that covers wavelengths between 773 and 1063 nm at a spectral resolution of 0.54 nm. GOSAT has two instrument packages: the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (CAI). We use TANSO-FTS band 1, which extends from approximately 758 to 775 mn and we use cloud fraction derived from the CAL We compare satellite-derived fluorescence with the Enhanced Vegetation Index (EVI), an Aqua/MODIS-derived vegetation reflectance-based index that indicates relative greenness and is used to infer photosynthetic function.

Joiner, J.↗

NASA NTRS2012

Filling-in of Far-Red and Near-Infrared Solar Lines by Terrestrial and Atmospheric Effects: Simulations and Space-Based Observations from SCIAMACHY and GOSAT

Mapping of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global information on the functional status of vegetation including light use efficiency and global primary productivity that can be used for global carbon cycle modeling. Space-based measurement of solar-induced chlorophyll fluorescence is challenging, because its signal is small as compared with the much larger reflectance signal. Ground- and aircraft-based approaches have made use of the dark and spectrally-wide O2-A ( approx 760 nm) and O2-B (approx 690 nm) atmospheric features to detect the weak fluorescence signal. More recently, Joiner et al. and Frankenberg et al. focused on longer-wavelength solar Fraunhofer lines that can be observed with space-based instruments such as the currently operational GOSAT. They showed that fluorescence can be detected using Fraunhofer lines away from the far-red chlorophyll-a fluorescence peak even when the surface is relatively bright. Here, we build on that work by developing methodology to correct for instrumental artifacts that produce false filling-in signals that can bias fluorescence retrievals. We also examine other potential sources of filling-in at far-red and NIR wavelengths. Another objective is to explore the possibility of making fluorescence measurements from space with lower spectral resolution instrumentation than the GOSAT interferometer. We focus on the 866nm Ca II solar Fraunhofer line. Very few laboratory and ground-based measurements of vegetation fluorescence have been reported at wavelengths longer than 800 nm. Some results of fluorescence measurements of corn leaves acquired in the laboratory using polychromatic excitation at wavelengths shorter than 665nm show that at 866 nm, the measured signal is of the order of 0.1-0.2 mW/sq m/nm/sr. In this work, we use the following satellite observations: We use SCIAMACHY channel 5 in nadir mode that covers wavelengths between 773 and 1063nm at a spectral resolution of 0.54 nm. GOSAT has two instrument packages: the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (CAI). We use TANSO-FTS band 1, which extends from approximately 758 to 775nm and we use cloud fraction derived from the CAI. We compare satellite-derived fluorescence with the Enhanced Vegetation Index (EVI), an Aqua/MODIS-derived vegetation reflectance-based index that indicates relative greenness and is used to infer photosynthetic function.

Joiner, J.↗

NASA NTRSDate not supplied

Quantifying Post-fire Vegetation Recovery on the Colorado Front Range

Forest composition and structure in the Colorado Front Range has been altered by changing wildfire regimes, particularly in response to increased fire severity. Subsequent reductions in post-fire tree regeneration can result in chronic impacts to ecological function and water quality. This project partnered with the U.S. Forest Service to estimate tree canopy recovery from 15 to 21 years after four Colorado Front Range fires that burned between 1996 and 2002— the Bobcat, Buffalo Creek, Hayman, and High Meadows fires—using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI). First, we evaluated relationships between recovery metrics derived from spectral vegetation indices (i.e. post-fire vegetation, normalized burn ratio, percent unrecovered vegetation) and field-collected counts of post-fire tree seedling regeneration. Then, we used high-resolution imagery paired with Landsat timeseries spectral and geomorphometric data to map forest canopy cover 15-21 years post-fire. Finally, we assessed ecological drivers of recovery including topography, climate, soils, and fire severity using Random Forest. While we found that basic vegetation index metrics failed to distinguish field-measured tree regeneration, our data showed that both aspect and burn severity were important drivers of vegetation recovery. North-facing aspects tended to have higher pre-fire Normalized Difference Vegetation Index (NDVI) and thus higher burn severities. However, areas that burned at high severity experienced slower vegetation recovery, regardless of aspect suggesting that high severity fire can mute favorable site conditions on north-facing aspects (i.e. moister, more developed soils). While this work propels our understanding of variables that influence vegetative recovery, vegetation type, forest conversion, and watershed function, more work should focus on what vegetation indices identifying areas of recovery indicate across different forest types and burn severities.

NASA DEVELOP↗

NASA NTRS1991

The measurement of mangrove characteristics in southwest Florida using SPOT multispectral data

An intensive in situ sampling program near Marco Island, Florida during 19-23 October 1988 collected information on mangrove type, maximum canopy height, and percent canopy closure. These data were correlated with selected vegetation index information derived from analysis of SPOT multispectral (XS) data obtained on 21 October 1988. The Normalized Difference (ND) vegetation index information was the most highly correlated index with percent canopy closure (r = 0.91). Percent canopy closure information can be used as a surrogate for mangrove density which is of great value when predicting which parts of the mangrove ecosystem are at greatest risk after an oil spill occurs. Such information is very valuable when constructing oil spill Environmental Sensitivity Index (ESI) Maps for tropical regions of the world.

Jensen, John R.↗

NASA NTRS1984

Considerations of and improvements to large-scale vegetation monitoring

The NOAA-7 polar orbiting sun-synchronous operational satellite carries the 5-channel advanced very high-resolution radiometer which acquires data globally at a spatial resolution of 4 km on a daily basis. These data provide a means for frequently monitoring global vegetation at continental scales. Techniques for compositing and cloud screening a green leaf density vegetation index product for Africa are presented for 9 sequential days beginning August 16, 1982 and are compared with a semi-operational vegetation index product produced by NOAA.

Gatlin, J. A.↗

NASA NTRS1983

Monitoring global vegetation using NOAA-7 AVHRR data

The NOAA-7 polar orbiting, sun-synchronous, operational satellite carries the 5 channel Advanced Very High Resolution Radiometer (AVHRR). Data are acquired globally at a resolution of 4 km on a daily basis. This data provides the means of frequently monitoring global vegetation on continental scales. Techniques for compositing and cloud screening a vegetation index for Africa are presented. The sample data set covers 9 days beginning August 16, 1983 and is compared with a semi-operational vegetation index product produced by NOAA.

Gatlin, J. A.↗

NASA NTRS2015

Contribution of Phenological and Physiological Variations on Northern Vegetation Productivity Changes over Last Three Decades

Plant phenology and maximum photosynthetic state determine spatiotemporal variability of gross primary productivity (GPP) of vegetation. Recent warming induced impacts accelerate shifts of phenology and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we investigate 1) how vegetation phenology and physiological status (maximum photosynthesis) are evolved over last three decades and 2) how such components (phenology and physiological status) contribute on inter-annual variation of the GPP during the last three decades. We utilized both long-term remotely sensed (GIMMS (Global Inventory Modeling and Mapping Studies), NDVI3g (Normalized Difference Vegetation Index 3rd generation) and MODIS (Moderate Resolution Imaging Spectroradiometer)) to extract larger scale phenology metrics (growing season start, end and duration); and productivity (i.e., growing season integrated vegetation index, GSIVI) to answer these questions. For evaluation purpose, we also introduced field-measured phenology and productivity datasets (e.g., FLUXNET) and possible remotely-sensed and modeled metrics at continental and regional scales. From this investigation, we found that onset of the growing season has advanced by 1.61 days per decade and the growing season end has delayed by 0.67 days per decade over the circumpolar region. This asymmetric extension of growing season results in a longer growing-season trend (2.96 days per decade) and widespread increasing vegetation-productivity trend (2.96 GSIVI per decade) over Northern land. However, the regionally-diverged phenology shift and maximum photosynthetic state contribute differently characterized productivity, inter-annual variability and trend. We quantified that about 50 percent, 13 percent and 6.5 percent of Northern land's inter-annual variability are dominantly controlled by the onset of the growing season, the end of the growing season and the maximum photosynthetic state, respectively. Productivity characterization over the other approximately 30 percent region has been driven by these co-dominant drivers. Our study clearly shows that regionally different contribution of phenological and physiological components on characterizing vegetation production over the last three decades.

productivity↗

NASA NTRS2007

A Rapidly Prototyped Vegetation Dryness Index Evaluated for Wildfire Risk Assessment at Stennis Space Center

MVDI, which effectively involves the differencing of NDMI and NDVI, appears to display increased noise that is consistent with a differencing technique. This effect masks finer variations in vegetation moisture, preventing MVDI from fulfilling the requirement of giving decision makers insight into spatial variation of fire risk. MVDI shows dependencies on land cover and phenology which also argue against its use as a fire risk proxy in an area of diverse and fragmented land covers. The conclusion of the rapid prototyping effort is that MVDI should not be implemented for SSC decision support.

Ross, Kenton↗

NASA NTRS2009

Terrestrial Carbon Sinks in the Brazilian Amazon and Cerrado Region Predicted from MODIS Satellite Data and Ecosystem Modeling

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2004. Net ecosystem production (NEP) flux for atmospheric CO2 in the region for these years was estimated. Consistently high carbon sink fluxes in terrestrial ecosystems on a yearly basis were found in the western portions of the states of Acre and Rondonia and the northern portions of the state of Par a. These areas were not significantly impacted by the 2002-2003 El Nino event in terms of net annual carbon gains. Areas of the region that show periodically high carbon source fluxes from terrestrial ecosystems to the atmosphere on yearly basis were found throughout the state of Maranhao and the southern portions of the state of Amazonas. As demonstrated though tower site comparisons, NEP modeled with monthly MODIS Enhanced Vegetation Index (EVI) inputs closely resembles the measured seasonal carbon fluxes at the LBA Tapajos tower site. Modeling results suggest that the capacity for use of MODIS Enhanced Vegetation Index (EVI) data to predict seasonal uptake rates of CO2 in Amazon forests and Cerrado woodlands is strong.

Potter, C.↗

NASA NTRS1981

Meteorological satellite data: A tool to describe the health of the world's agriculture

Local area coverage data acquired aboard the TIROS-N satellite family by the advanced very high resolution radiometer systems was examined to determine the agricultural information current. Albedo differences between channel 2 and channel 1 of the advanced very high resolution radiometer LAC (called EVI) are shown to be closely correlated to the Ashburn vegetative index produced from LANDSAT multispectral scanner data which have been shown to vary in response to "greenness", soil moisture, and crop production. The statistical correlation between the EVI and the Ashburn Vegetative Index (+ or - 1 deg) is 0.86.

Gray, T. I., Jr.↗

NASA NTRS1991

Wheat yield estimation at the farm level using TM Landsat and agrometeorological data

A model for estimating wheat yields on the farm level was developed, that integrates the Landsat TM data and agrometeorological information. Results obtained for a test site in southern Brasil for years of 1986 and 1987 show that the vegetation index derived from Landsat TM could account for the 60 to 40 percent wheat-yield variability observed between the two crop years. Compared to results using either the Landsat TM vegetation index or the agrometeorological data alone, the joint use of both types of data in a single model yielded a significant improvement.

Rudorff, B. F. T.↗

NASA NTRS1995

Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models

This study outlines a method for the estimation of regional patterns of surface moisture availability (M(sub 0)) and fractional vegetation (Fr) in the presence of spatially variable vegetation cover. The method requires relating variations in satellite-derived (NOAA, Advanced Very High Resolution Radiometer (AVHRR)) surface radiant temperature to a vegetation index (computed from satellite visible and near-infrared data) while coupling this association to an inverse modeling scheme. More than merely furnishing surface soil moisture values, the method constitues a new conceptual and practical approach for combining thermal infrared and vegetation index measurements for incorporating the derived values of M(sub 0) into hydrologic and atmospheric prediction models. Application of the technique is demonstrated for a region in and around the city of Newcastle upon Tyne situated in the northeast of England. A regional estimate of M(sub 0) is derived and is probabbly good for fractional vegetation cover up to 80% before errors in the estimated soil water content become unacceptably large. Moreover, a normalization scheme is suggested from which a nomogram, `universal triangle,' is constructed and is seen to fit the observed data well. The universal triangle also simplifies the inclusion of remotely derived M(sub 0) in hydrology and meteorological models and is perhaps a practicable step toward integrating derived data from satellite measurements in weather forecasting.

Gillies, Robert R.↗

NASA NTRS1987

Satellite observed seasonal and inter-annual variation of vegetation over the Kalahari, the Great Victoria Desert, and the Great Sandy Desert - 1979-1984

Time-series observations by two spaceborne sensors over three desert regions, the Kalahari (in southern Africa) and the Great Victoria Desert and the Great Sandy Desert (in western Australia), are presented. The observations are by the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite from April 1982 to December 1984, and by the Scanning Multichannel Microwave Radiometer on board the Nimbus-7 satellite from January 1979 to February 1985. The objective was to compare and contrast seasonal and interannual variation of vegetation over these three deserts using the normalized difference vegetation index and the 37 GHz brightness temperature. The seasonal variation from both sensors was found to be most pronounced over the Kalahari, followed by the Great Sandy Desert and the Great Victoria Desert. The normalized difference vegetation index was roughly identical over the two Australian deserts and was significantly higher for the Kalahari. There was no consistent change from both sensors over the two Australian deserts, but a consistent decrease from 1979 to 1984 over the Kalahari was found in the 37 GHz microwave data.

Choudhury, B. J.↗