Advanced Visible Infrared Imaging Spectrometer Research Articles

Soil clay minerals abundance mapping using AVIRIS-NG data

Advanced hyperspectral remote sensing technology can identify various clay minerals in the soil. The improved spectral and spatial resolution of the airborne hyperspectral sensor can be used for a better interpretation of clay mineral abundance in the soil. This research utilizes the high-resolution image data acquired from Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) on an airborne platform to map the abundance of clay minerals in the Udaipur region of Rajasthan (24° 34′ 16.5720′' N, 73° 41′ 29.5584′' E). The representative soil samples for various soil clay minerals, e.g., kaolinite, montmorillonite, and illite, were classified from AVIRIS-NG data using a spectral feature fitting algorithm. A total of thirty sites were selected from agricultural and wastelands for soil sample collection. X-ray diffraction (XRD) analysis of representative soil samples was carried out to find the percentage and type of clay minerals present. Regression analysis between absorption peak depth values estimated from the hyperspectral image of the sampling sites in the spectral region from 2205 to 2214 nm and corresponding clay content value showed a significant relationship. The regression line obtained for the known pixels was used to prepare the mineral abundance map over the study area. This study significantly indicates the dominance of montmorillonite clay mineral over kaolinite and illite in the soils of the Udaipur region.

Synergistic use of ASTER, L-band ALOS PALSAR, and hyperspectral AVIRIS-NG data for exploration of lode type gold deposit – A study in Hutti Maski Schist Belt, India

In this study, we have explored the utility of advanced hyperspectral, dual-polarized microwave, and advanced multispectral data to delineate potential areas of mineralization associated with the lode type gold deposit in Hutti-Maski Schist Belt Karnataka, India. We derived different image-enhanced products using visible-near-infrared (VNIR) and short-wave infrared (SWIR) spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate major rock types of gold-bearing greenstone belt such as metabasalt, amphibolite, younger alkali feldspar rich granite, granodiorite, and tonalitic-granodioric gneiss (TTG). The principal component (PC) image composite of ASTER VNIR-SWIR bands was not only used to distinguish different granitoids but also confirmed their sharp instructive nature of contact with the greenstone rocks. We also used an index image composite using ASTER SWIR bands to discriminate granitoid, basalt and amphibolite. We could demarcate few lineaments extending through the mineralized zone (e.g., Hutti, Heera-Buddine mines, etc.) using the fused products of ASTER and two polarization channels of the Phased-array L band Synthetic Aperture Radar (PALSAR) sensor of the Advanced Land Observation Satellite (ALOS). All these image-enhanced products are validated using the reference geological map of the study area and field data collected along selected field -traverses. The mineralogical and petrographical data of the altered rocks from the study site revealed the formation of calcite, scapolite, and other minerals. Represenattive reflectance spectrum of the altered rock was used to spatially delineate the surface distribution of the patchy and small alteration zones using airborne hyperspectral data of the Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) sensor. In this regard, a unified spectral map of altered rocks was derived by integrating two alteration maps prepared independently using two different partial sub-pixel algorithms (constrained energy minimization and matched filtering). Occurrences of altered rocks in the vicinity to favorable geological lineaments and their spatial proximity to metavolcanic-younger felsic intrusive contacts suggest their importance as the potential sites for detailed exploration.

Detection of Salt Marsh Vegetation Stress and Recovery after the Deepwater Horizon Oil Spill in Barataria Bay, Gulf of Mexico Using AVIRIS Data

The British Petroleum Deepwater Horizon Oil Spill in the Gulf of Mexico was the biggest oil spill in US history. To assess the impact of the oil spill on the saltmarsh plant community, we examined Advanced Visible Infrared Imaging Spectrometer (AVIRIS) data flown over Barataria Bay, Louisiana in September 2010 and August 2011. Oil contamination was mapped using oil absorption features in pixel spectra and used to examine impact of oil along the oiled shorelines. Results showed that vegetation stress was restricted to the tidal zone extending 14 m inland from the shoreline in September 2010. Four indexes of plant stress and three indexes of canopy water content all consistently showed that stress was highest in pixels next to the shoreline and decreased with increasing distance from the shoreline. Index values along the oiled shoreline were significantly lower than those along the oil-free shoreline. Regression of index values with respect to distance from oil showed that in 2011, index values were no longer correlated with proximity to oil suggesting that the marsh was on its way to recovery. Change detection between the two dates showed that areas denuded of vegetation after the oil impact experienced varying degrees of re-vegetation in the following year. This recovery was poorest in the first three pixels adjacent to the shoreline. This study illustrates the usefulness of high spatial resolution airborne imaging spectroscopy to map actual locations where oil from the spill reached the shore and then to assess its impacts on the plant community. We demonstrate that post-oiling trends in terms of plant health and mortality could be detected and monitored, including recovery of these saltmarsh meadows one year after the oil spill.

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary, USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible–Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R 2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R 2 = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite‐based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively. **Current address: University of Minnesota, Department of Forest Resources, St Paul, MN 55108, USA.

Mapping Downy Brome (Bromus tectorum) Using Multidate AVIRIS Data

Invasive plants impose threats to both natural and managed ecosystems. Downy brome is among the most aggressive invasive weeds that has infested the shrub-steppe ecoregion of eastern Washington. Hyperspectral remote sensing has potential for early detection and for monitoring the spread of downy brome—information that is essential for developing effective management strategies. Two airborne hyperspectral Advanced Visible Infrared Imaging Spectrometer (AVIRIS) images (electromagnetic spectrum ranging from 400 to 2,500 nm) were acquired at a nominal 4-m ground resolution over a study area in south-central Washington on July 27, 2000 and May 5, 2003. We used a mixture-tuned matched filtering (MTMF) algorithm to classify downy brome and predict its percent cover in each dataset plus a merged multiseasonal dataset using the transformed bands from a minimum noise fraction (MNF) output. The correlation coefficient was 0.79, calculated for the multidate MTMF predicted downy brome abundance, compared to 0.41 and 0.51 derived from the July 2000 and May 2003 data, respectively. Although this study used high spatial resolution (∼3 to 4 m) hyperspectral imagery, this result shows that data acquired in different seasons is more effective for detection of downy brome invasion, compared to single-date datasets. These results support expanded use of multitemporal data for weed mapping to capitalize on spectral differences between seasons for weeds, in this case downy brome, and the surrounding environment.

Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes

Three linked leaf and canopy radiative transfer models were used to assess uncertainties in three vegetation architectures for the relationships between canopy water content and Equivalent Water Thickness (EWT). The leaf radiative transfer model PROSPECT was linked to SAILH, rowMCRM, and FLIM canopy reflectance models to generate synthetic spectra for a range of leaf and canopy parameters under closed, row-structured, and forest canopy architectures, respectively. Leaf water content ( C w) and leaf area index (LAI) were used to calculate canopy water content. Leaf and canopy parameters that affect the retrieval of EWT, estimated by the MODTRAN-based fitting technique, were used to investigate their influence on the water content estimates. Results showed a consistent relationship between retrieved EWT and canopy water content for the different simulated architectural scenarios. It was shown that EWT was negatively affected by leaf dry matter and soil background. Retrievals of EWT from hyperspectral Advanced Visible Infrared Imaging Spectrometer (AVIRIS) imagery at three study sites were then used for cross-validation of the Moderate Resolution Imaging Spectrometer (MODIS) data, assessing the behavior of NDVI, EVI, NDWI, and SIWSI as potential indicators of vegetation water content. All four MODIS indexes showed consistent agreement with retrievals of EWT from AVIRIS imagery at the agricultural site and the savanna–shrub site, with EVI having the highest correlation. However, at the conifer forest study site the two water indexes, NDWI and SIWSI, yielded better agreement with retrievals of EWT than NDVI and EVI. The performance of NDVI was inconsistent across sites. This manuscript demonstrates the importance of canopy architecture when estimating EWT by showing that large errors are obtained when EWT estimates derived from absorption feature curve-fitting are applied to different canopy types. These errors are propagated in simple indexes that produce inconsistent results when applied to divergent vegetation conditions.

Hyperspectral imaging of chlorophyll in the Caballo Reservoir in New Mexico

Chlorophyll is a good water quality indicator for determining other pollutants in inland water bodies. Chlorophyll concentrations can be measured by remote sensing techniques; however, studies show that the broad wavelength spectral data available on current satellites do not permit discrimination between chlorophyll and suspended sediments. Hyperspectral imaging by satellite and aircraft sensors provide significant relationships between chlorophyll and radiance/reflectance measurements. This paper describes the identification of chlorophyll concentrations using hyperspectral imagery in inland water bodies. This hyperspectral study site is located in the Caballo Reservoir along the Rio Grande in the State of New Mexico. Run off from agricultural fields, failing septic tanks, and other sources of pollutants enter this water body. NASA's Jet Propulsion Laboratory flew the flight line on June 30, 1999 and acquired the images in the study with an Advanced Visible infrared Imaging Spectrometer (AVIRIS). No groundtruth measurements were available at the time of the AVIRIS flight mission to compare to the images of the flight line. The images were atmospherically corrected for distortions using the ACORN software. Hyperspectral image analysis techniques using ENVI software were used for identifying potential chlorophyll areas. The spectral matching signature method was used to identify chlorophyll near the shores of the Caballo Reservoir. This method allows mapping of the chlorophyll constituent using the Spectral Angle Mapper (SAM) classification technique. The clean–up tasks afterwards are easier for eliminating any point or non–point pollutant sources because it reduces equipment and field sampling costs by obtaining water quality pollutant concentrations over a large geographical area.

Mapping successional boreal forests in interior central Alaska

It is necessary to develop new satellite methods to monitor boreal forests responses to climate warming. Remotely sensed maps derived from hyperspectral Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) data were developed and compared for the four conifer forest ecosystems at the Bonanza Creek Experimental Forest, a long-term ecological research site on the Tanana River flood plain near Fairbanks, Alaska. The site was first stratified into montane, lowland alluvial plain, and flood plain zones based on topography. A classification of six forest and three non-forest types was created from AVIRIS images and compared on a pixel-by-pixel basis to a published vegetation map, a classified SPOT (Satellite Pour l'Observation de la Terre) image, and a hybrid SPOT image and digital elevation model classification. A comparison of AVIRIS with SPOT results showed that the AVIRIS classification was consistently more accurate (74, 43, and 43% overall accuracy, respectively). Hyperspectral classification methods have promise for mapping forest ecosystems in other boreal regions when little or no ground data are available for validation. The time difference between the creation of these maps show that substantial ecosystem changes have occurred over the past 15 years, demonstrating the need for developing a capability to obtain cost-effective landscape characterization.

Mapping successional boreal forests in interior central Alaska

It is necessary to develop new satellite methods to monitor boreal forests responses to climate warming. Remotely sensed maps derived from hyperspectral Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) data were developed and compared for the four conifer forest ecosystems at the Bonanza Creek Experimental Forest, a long-term ecological research site on the Tanana River flood plain near Fairbanks, Alaska. The site was first stratified into montane, lowland alluvial plain, and flood plain zones based on topography. A classification of six forest and three non-forest types was created from AVIRIS images and compared on a pixel-by-pixel basis to a published vegetation map, a classified SPOT (Satellite Pour l'Observation de la Terre) image, and a hybrid SPOT image and digital elevation model classification. A comparison of AVIRIS with SPOT results showed that the AVIRIS classification was consistently more accurate (74, 43, and 43% overall accuracy, respectively). Hyperspectral classification methods have promise for mapping forest ecosystems in other boreal regions when little or no ground data are available for validation. The time difference between the creation of these maps show that substantial ecosystem changes have occurred over the past 15 years, demonstrating the need for developing a capability to obtain cost-effective landscape characterization.

Deriving Water Content of Chaparral Vegetation from AVIRIS Data

Spectral imaging data acquired with Advanced Visible Infrared Imaging Spectrometer over Point Dume (Los Angeles County, CA, USA) were used to assess the ability of hyperspectral reflectance data to estimate canopy Relative Water Content (RWC) at the landscape level. The study was performed on 23 vegetation stands comprised of three characteristic chaparral plant communities, with contrasting phenological stages and canopy cover. Several estimates of water content based on the near-infrared (NIR; reflectance indices and water thickness derived from reflectance and radiance data) and shortwave infrared (SWIR) water absorption bands were compared to measurements of vegetation structure and water content made on the ground. The Water Index (WI) and Normalized Difference Water Index (NDWI), reflectance indices formulated from the NIR water absorption bands, were the best indicators of canopy RWC estimated from combining leaf relative water content with measures of canopy structure. A stepwise multiple regression revealed that canopy structure explained 36% and 41% of the variation in WI and NDWI, respectively. The explained variance in WI and NDWI increased to 44% and 48% when leaf relative water content was included in the model. By contrast, the inclusion of leaf relative water content did not contribute significantly to the explained variance in indices formulated using SWIR water absorption bands and in those based on water thickness. The relationship between WI and the canopy RWC significantly improved when only data from plots with green vegetation cover >70% were considered ( r 2=0.88, p<0.001). All the indices studied had an important structural component (as indicated by the strong correlation with NDVI), yet only the indices WI and NDWI additionally responded to water content. These results indicate that the WI and NDWI are sensitive to variations in canopy relative water content at the landscape scale.

Remote Sensing of Soil Properties in the Santa Monica Mountains I. Spectral Analysis

AVIRIS (Advanced Visible/Infrared Imaging Spectrometer) bands were simulated from laboratory spectra to test their performance in analyzing soil properties from a semiarid region. Multivariate analysis, specifically principal component analysis and canonical discriminant analysis, as well as band depth analysis were applied to study the effect of organic matter, iron content, and texture in a set of samples from two valleys in the Santa Monica Mountains Recreation Area, California. Results showed that total iron and organic matter content were the main factors affecting spectral shape, although sand content significantly affected the spectral contrast of the absorption features. It was shown as well that the elimination of the atmospheric water bands from the analysis did not strongly affect the retrieval of spectral information related to these properties.

Monitoring Pacific Coast Salt Marshes Using Remote Sensing

The rapid decline in the extent and health of coastal salt marshes has created a need for nondestructive methods for evaluating the condition of salt marsh ecosystems. This paper describes simultaneous uses of field sampling and remote sensing approaches to understand salt marsh ecosystem functions and species distributions and discusses the implications for salt marsh monitoring using remote sensing. Three sites along the Petaluma River near the entrance into San Pablo Bay, California, which represented a range of soil salinity, water content, and nutrients, were studied. Standing biomass was directly assessed by field sampling and indirectly estimated through canopy reflectance. The sites were dominated by almost monotypic stands of Salicornia virginica, Spartina foliosa, and Scirpus robustus. For Salicornia, we found a positive relationship between salinity and biomass up to a threshold of 42 g/kg, after which biomass declined monotonically with increasing salinity. No Scirpus or Spartina were found at soil salinities >20 g/kg. Although significantly different levels of nitrate and ammonium nitrogen were found in the interstitial water and soils at these sites, no strong relationships were found between biomass and nitrate nitrogen. Soil ammonium nitrogen, in contrast, was positively related to biomass. Soil redox and salinity increased with elevation and distance from the shoreline, while soil moisture and H2S decreased. Canopy biomass was estimable using remotely sensed spectral vegetation indices at 58–80% accuracy depending on species. Simple Vegetation Index (VI) and Atmospherically Resistant Vegetation Index (ARVI) measured by handheld field spectrometers were the best estimators of green biomass for high cover of Salicornia. Soil Adjusted Vegetation Index (SAVI) and Soil Adjusted and Atmospherically Resistant Vegetation Index (SARVI) gave the best estimates for Spartina while the Global Environment Monitoring Index (GEMI) was the best estimate for Scirpus. The relationships between vegetation indices and biomass were developed from field spectra. The VI was used to estimate spatial patterns of biomass across the salt marsh from Landsat satellite Thematic Mapper (TM) data. The TM image showed spatial patterns corresponding with species zones and biomass abundance. Narrow band reflectance features measured with a handheld spectrometer can be used to predict canopy plant water content (R2 = 63%). Interpolated estimates of water content from field-measured canopy reflectance were shown to relate to variation in salinity and soil moisture. Canopy water content was estimated from Airborne Advanced Visible Infrared Imaging Spectrometer data, which showed similar spatial patterns at the site. Results indicate that both biomass production and canopy water content can be accurately determined from remotely sensed spectral measures. Species-specific differences in these characteristics may be used for monitoring species distribution and abundance from airborne or satellite images.

MONITORING PACIFIC COAST SALT MARSHES USING REMOTE SENSING

The rapid decline in the extent and health of coastal salt marshes has created a need for nondestructive methods for evaluating the condition of salt marsh ecosystems. This paper describes simultaneous uses of field sampling and remote sensing approaches to understand salt marsh ecosystem functions and species distributions and discusses the implications for salt marsh monitoring using remote sensing. Three sites along the Petaluma River near the entrance into San Pablo Bay, California, which represented a range of soil salinity, water content, and nutrients, were studied. Standing biomass was directly assessed by field sampling and indirectly estimated through canopy reflectance. The sites were dominated by almost monotypic stands of Salicornia virginica, Spartina foliosa, and Scirpus robustus. For Salicornia, we found a positive relationship between salinity and biomass up to a threshold of 42 g/kg, after which biomass declined monotonically with increasing salinity. No Scirpus or Spartina were found at soil salinities >20 g/kg. Although significantly different levels of nitrate and ammonium nitrogen were found in the interstitial water and soils at these sites, no strong relationships were found between biomass and nitrate nitrogen. Soil ammonium nitrogen, in contrast, was positively related to biomass. Soil redox and salinity increased with elevation and distance from the shoreline, while soil moisture and H2S decreased. Canopy biomass was estimable using remotely sensed spectral vegetation indices at 58–80% accuracy depending on species. Simple Vegetation Index (VI) and Atmospherically Resistant Vegetation Index (ARVI) measured by handheld field spectrometers were the best estimators of green biomass for high cover of Salicornia. Soil Adjusted Vegetation Index (SAVI) and Soil Adjusted and Atmospherically Resistant Vegetation Index (SARVI) gave the best estimates for Spartina while the Global Environment Monitoring Index (GEMI) was the best estimate for Scirpus. The relationships between vegetation indices and biomass were developed from field spectra. The VI was used to estimate spatial patterns of biomass across the salt marsh from Landsat satellite Thematic Mapper (TM) data. The TM image showed spatial patterns corresponding with species zones and biomass abundance. Narrow band reflectance features measured with a handheld spectrometer can be used to predict canopy plant water content (R2 = 63%). Interpolated estimates of water content from field-measured canopy reflectance were shown to relate to variation in salinity and soil moisture. Canopy water content was estimated from Airborne Advanced Visible Infrared Imaging Spectrometer data, which showed similar spatial patterns at the site. Results indicate that both biomass production and canopy water content can be accurately determined from remotely sensed spectral measures. Species-specific differences in these characteristics may be used for monitoring species distribution and abundance from airborne or satellite images.

Multivariate statistical classification of soil spectra

The purpose of this work was to evaluate whether AVIRIS (Advanced Visible Infrared Imaging Spectrometer) bands can be used to discriminate between soils having similar properties, as well as to compare AVIRIS spectra with those from laboratory measurements. Multivariate analysis techniques show that two soils belonging to the same series and a third soil belonging to a different, but related series can be discriminated at a high level of accuracy using reflectance data from AVIRIS or from laboratory measurements. It is also shown that wavelengths important in discriminating soils were highly correlated between AVIRIS and laboratory data. The distribution of variance and weighting functions also show consistent patterns between these data sets.