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At least 145 records · Page 8

Seasonal grassland productivity forecast for the U.S. Great Plains using Grass–Cast

Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge —trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season above ground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in larger scale decision-making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-of growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass-Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.

54 ENVIRONMENTAL SCIENCES↗

Spring Hydrology Determines Summer Net Carbon Uptake in Northern Ecosystems

Increased photosynthetic activity and enhanced seasonal CO2 exchange of northern ecosystems have been observed from a variety of sources including satellite vegetation indices (such as the Normalized Difference Vegetation Index; NDVI) and atmospheric CO2 measurements. Most of these changes have been attributed to strong warming trends in the northern high latitudes (greater than or equal to 50N). Here we analyze the interannual variation of summer net carbon uptake derived from atmospheric CO2 measurements and satellite NDVI in relation to surface meteorology from regional observational records. We find that increases in spring precipitation and snow pack promote summer net carbon uptake of northern ecosystems independent of air temperature effects. However, satellite NDVI measurements still show an overall benefit of summer photosynthetic activity from regional warming and limited impact of spring precipitation. This discrepancy is attributed to a similar response of photosynthesis and respiration to warming and thus reduced sensitivity of net ecosystem carbon uptake to temperature. Further analysis of boreal tower eddy covariance CO2 flux measurements indicates that summer net carbon uptake is positively correlated with early growing-season surface soil moisture, which is also strongly affected by spring precipitation and snow pack based on analysis of satellite soil moisture retrievals. This is attributed to strong regulation of spring hydrology on soil respiration in relatively wet boreal and arctic ecosystems. These results document the important role of spring hydrology in determining summer net carbon uptake and contrast with prevailing assumptions of dominant cold temperature limitations to high-latitude ecosystems. Our results indicate potentially stronger coupling of boreal/arctic water and carbon cycles with continued regional warming trends.

Carbon Uptake↗

Vegetation Cover Change in the Upper Kings River Basin of the Sierra Nevada Detected Using Landsat Satellite Image Analysis

The Sierra Nevada of California is a region where large wildfires have been suppressed for over a century. A detailed geographic record of recent changes in vegetation cover across the Sierra Nevada remains a gap that can be filled with satellite remote sensing data. Results from Landsat image analysis over the past 25 years in the Upper Kings River basin showed that consistent, significant increases in the normalized difference vegetation index (NDVI) have not extended above 2000 m elevation, where cold temperatures presumably limit the growing season. Moreover, mean increases in NDVI since 1986 at elevations below 2000 m (which cover about half of the total basin area) have not exceeded 9%, even in the most extreme precipitation yearly comparisons. NDVI has decreased significantly at elevations above 2000 m throughout the basin in relatively wet year comparisons since the mid-1980s. These findings conflict with any assumptions that ET fluxes and river flows downstream could have been markedly altered by vegetation change over most of the Upper Kings River basin in recent decades.

normalized difference vegetation index (NDVI)↗

Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics

Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIR v ), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIR v and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIR v offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R 2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R 2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. In conclusion, future efforts to fully integrate the complementary strengths of NIR v and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.

54 ENVIRONMENTAL SCIENCES↗

Responses of tundra plant community carbon flux to experimental warming, dominant species removal and elevation

1. Rising temperatures can influence ecosystem processes both directly and indirectly, through effects on plant species and communities. An improved understanding of direct versus indirect effects of warming on ecosystem processes is needed for robust predictions of the impacts of climate change on terrestrial ecosystem carbon (C) dynamics. 2. To explore potential direct and indirect effects of warming on C dynamics in arctic tundra heath, we established a warming (open top chambers) and dominant plant species (Empetrum hermaphroditum Hagerup) removal experiment at a high and low elevation site. We measured the individual and interactive effects of warming, dominant species removal and elevation on plant species cover, the normalized difference vegetation index (NDVI), leaf area index (LAI), temperature, soil moisture and instantaneous net ecosystem CO 2 exchange. 3. We hypothesized that ecosystems would be stronger CO 2 sinks at the low elevation site, and that warming and species removal would weaken the CO 2 sink because warming should increase ecosystem respiration (ER) and species removal should reduce gross primary productivity (GPP). Furthermore, we hypothesized that warming and species removal would have the greatest impact on processes at the high elevation where site temperature should be most limiting and dominant species may buffer the overall community to environmental stress more compared to the low elevation site where plants are more likely to compete with the dominant species. 4. The instantaneous CO 2 flux, which reflected a weak CO 2 sink, was similar at both elevations. Neither experimental warming nor dominant species removal significantly changed GPP or instantaneous net ecosystem CO 2 exchange even though species removal significantly reduced ER, NDVI and LAI. 5. Our results show that even the loss of dominant plant species may not result in significant landscape-scale responses of net ecosystem CO 2 exchange to warming. They also show that NDVI and LAI may be limited in their ability to predict changes in GPP in these tundra heaths systems. Our study highlights the need for more detailed vegetation analyses and ground-truthed measurements in order to accurately predict direct and indirect impacts of climatic change on ecosystem C dynamics.

54 ENVIRONMENTAL SCIENCES↗

Remote Sensing Approach for Monitoring Tree Health Adjacent to Transmission Corridors

This study presents an initial proof-of-concept for a satellite-based remote sensing approach to identify and monitor potential areas of poor tree health across the entire BPA service territory on an annual basis. We tested three variants of “delta peak NDVI” ( ΔPN ) change detection metrics that express interannual variation in primary productivity relative to a baseline by comparing ΔPN values for known insect/disease disturbances and nearby reference locations. All three metrics showed promise for detecting poor tree health in the year during disturbance, but the metric based on the difference from the long-term (2016-2024) median ( Δ Med PN ) was preferred due to its responsiveness to change in the years during and after disturbance, resilience to interannual variation, and ease of interpretation as being above or below normal. Comparison of Δ Med PN grouped by relative severity of disturbance indicated it was not sensitive enough to detect “low” severity disturbances, as mapped by USGS’s LANDFIRE program, but could distinguish “moderate” and “high” severity disturbances from reference locations. These findings informed selection of a threshold for Δ Med PN , which was combined with areas exhibiting negative NDVI to map potential areas of concern. Visual inspection of before/after high-resolution imagery and NDVI time series showed that many areas of concern aligned with visible signs of defoliation and die-off as well as other types of disturbance (e.g., landslides, logging, road grading, flooding). Some areas of concern are thought to be false detections caused by persistent shadow, and some could not be explained with visual inspection due to spatiotemporal limitations of before/after imagery. In summary, our approach shows promise for large-scale monitoring of tree health adjacent to BPA transmission lines, but additional work is recommended to improve model sophistication and remove noise.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Remote Sensing Detection of Growing Season Freeze-Induced Defoliation of Montane Quaking Aspen (Populus tremuloides) in Southern Utah, USA

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

54 ENVIRONMENTAL SCIENCES↗

Multitemporal dimensionality of images of normalized difference vegetation index at continental scales

Two sets of multitemporal data derived from NOAA world data product are analyzed by means of principal components analysis in order to examine their underlying multitemporal dimensionality. Specifically, images of the normalized difference vegetation index (NDVI) were analyzed for eight 3-week periods for Africa and ten 3-week periods for North America sampled from throughout the year extending from April 1982 to March 1983. The two multitemporal sets of images displayed remarkable similarities in terms of their first two components, the first corresponding very closely to the annualized integrated NDVI and the second to the seasonality of the NDVI. In particular, for the African data set the feature space defined by the first two components allows separation of the main cover types.

Townshend, J. R. G.↗

Satellite remote sensing of rangelands in Botswana. II - NOAA AVHRR and herbaceous vegetation

The relation between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in Tamasane, Shakwe, and Masama in eastern Botswana is studied using 1983-1984 AVHRR data. The procedures for Landsat MSS interpolation of ground measurements and the data processing of the AVHRR data are described. The temporal sequence AVHRR global-area coverage (GAC) composite NDVI is examined. The AVHRR GAC composite NDVI and biomass and Landsat MSS interpolations of field measurements are analyzed and compared.

Prince, S. D.↗

Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates

Weekly AVHRR Normalized Difference Vegetation Index (NDVI) values for 1983-1984 for seven sites of diverse climate in North America were correlated with results of an ecosystem simulation model of a hypothetical forest stand for the corresponding period at each site. The tendency of raw NDVI data to overpredict photosynthesis and transpiration on water limited sites was shown to be partially corrected by using an aridity index of annual radiation/annual precipitation. The results suggest that estimates of vegetation productivity using the global vegetation index are only accurate as annual integrations, unless unsubsampled local area coverage NDVI data can be tested against forest photosynthesis, transpiration and aboveground net primary production data measured at shorter time intervals.

Running, Steven W.↗

Seasonal LAI in slash pine estimated with LANDSAT TM

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using LANDSAT Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R(sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of LANDSAT TM data for studying seasonal dynamics in forest canopies.

Curran, Paul J.↗

A comparison of SMMR and AVHRR data for continental land cover characterization

Images using reflected visible and NIR data and images using passive microwave data were compared in terms of their usefulness for characterizing land-cover types in South America and Africa. The former images are of the normalized difference vegetation index (NDVI), and the latter images are of the microwave polarization difference temperature (MPDT). The combined use of MPDT and NDVI data sets show clear synergistic benefits in using the two data sets. However, the evidence suggests that for most cover types, increasing the temporal frequency of the NDVI images is more advantageous than incorporating MPDT data sets.

Townshend, J. R. G.↗

Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale

An analytical framework is provided for examining the physically based behavior of the normalized difference vegetation index (NDVI) in terms of the variability in bulk subpixel landscape components and with respect to variations in pixel scales, within the context of the stochastic-geometric canopy reflectance model. Analysis focuses on regional scale variability in horizontal plant density and soil background reflectance distribution. Modeling is generalized to different plant geometries and solar angles through the use of the nondimensional solar-geometric similarity parameter. Results demonstrate that, for Poisson-distributed plants and for one deterministic distribution, NDVI increases with increasing subpixel fractional canopy amount, decreasing soil background reflectance, and increasing shadows, at least within the limitations of the geometric reflectance model. The NDVI of a pecan orchard and a juniper landscape is presented and discussed.

Jasinski, Michael F.↗

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.↗

Assessment of regional biomass-soil relationships using vegetation indexes

The development of photosynthetic active biomass in different ecological conditions, as indicated by normalized difference vegetation indices (NDVIs) is compared by performing a stratified sampling (based on soil assocations) on data acquired over Indiana. Data from the NOAA-10 AVHRR were collected for the 1987 and 1988 growing seasons. An NDVI transformation was performed using the two optical bands of the sensor (0.58-0.68 microns and 0.72-1.10 microns). The NDVI is related to the amount of active photosynthetic biomass present on the ground. Samples of NDVI values over 45 fields representing eight soil associations throughout Indiana were collected to assess the effect of soil conditions and acquisition date on the spectral response of the vegetation, as shown by the NDVIs. Statistical analysis of results indicate that land-cover types (forest, forest/pasture, and crops), soil texture, and soil water-holding capacity have an important effect on vegetation biomass changes as measured by AVHRR data. Acquisition dates should be selected with condideration of the phenological stages of vegetation. Sampling of AVHRR data over extended areas should be stratified according to physiographic units rather than man-made boundaries. This will provide more homogeneous samples for statistical analysis.

Lozano-Garcia, D. Fabian↗

Seasonal LAI in slash pine estimated with Landsat TM

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using Lnandsat Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R (sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of Landsat TM data for studying seasonal dynamics in forest canopies.

Curran, Paul J.↗

Atmospheric correction algorithm for NOAA-AVHRR products - Theory and application

The present evaluation of the effects of atmospheric constituents on NOAA AVHRR visible and near-IR data first gives attention to the general remote sensing equation and then addresses the magnitude of the atmospheric effects for AVHRR solar bands with respect to their impact on the normalized difference vegetation index (NDVI) and the surface bidirectional reflectance. Possible approaches to the acquisition of atmospheric data are then considered, and examples of atmospheric correction of surface reflectance and NDVI are discussed. The correction of the aerosol effect is essential in the case of NDVI study of densely vegetated areas.

Tanre, Didier↗

Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS

ARVI, which is here proposed as a tool for the remote sensing of vegetation by the EOS MODIS sensor, takes advantage of the presence of the blue channel in MODIS, together with the red and near-IR channels composing the normalized-difference vegetation index (NDVI). Unlike the NDVI, ARVI resists atmospheric effects via a self-correction process for the effects of the atmosphere on the red channel. ARVI is presently shown to have a dynamic range similar to NDVI's, but is on average less sensitive to atmospheric effects by a factor of 4. A single combination of the blue and red channels in the ARVI may be used in all or most remote-sensing applications.

Kaufman, Yoram J.↗