MODIS/ASTER Airborne Simulator Research Articles

Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burn ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index with mid infrared (dNDVIMID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER Airborne Simulator (MASTER) instrument. The field data consist of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR–SWIR combination and three with a NIR–MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. The results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVIMID approaches. The results yielded an R2 of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVIMID indices was lower with an R2 = 0.61 for the best performing dNDVIMID index. The dNBR also outperformed the dNDVIMID in terms of spectral optimality across both fires. The best performing dNBR index yielded median optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVIMID index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVIMID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVIMID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity.

Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards

Remote sensing can inform agricultural knowledge of crop water use through observation of land surface temperature, which can act as an indicator of plant function and health. This study uses remotely sensed data to quantify thermal variability within fruit and nut orchards during an intense drought period in California's Central Valley (2013–2015). First, fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were derived for a variety of crop species using visible-shortwave infrared (VSWIR) spectra imaged by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Fractional estimates were then used to select thermal endmembers for each class using simultaneously collected MODIS/ASTER Airborne Simulator (MASTER) thermal imagery and a crop species map. Expected pixel temperatures of non-stressed crop fields were then modeled, and the per-pixel difference between measured and expected temperatures was calculated as a temperature residual. Crop residuals serve to capture variability in temperature that may be attributable to differences in crop health and/or management practices. We found multiple distinct thermal classes to exist across the study site. Furthermore, crop temperatures correlated to expected crop ET rates, and temperature residuals showed correlations to changes in crop yields during the study period. Further assessment of findings revealed an increase in temperature residuals during the study period that is consistent with increasing stress, likely linked to the progression of drought. The method presented here shows utility for regional agricultural analysis of crop water use and is particularly relevant for ECOSTRESS and the upcoming Surface Biology & Geology mission.

Megacity-scale analysis of urban vegetation temperatures

In cities, vegetation temperature is important for quantifying water use, microclimates, and water and energy fluxes, as demonstrated by urban climate models and in situ studies. Remote sensing is capable of observing land surface temperatures (LST) across a city; however, its ability to quantify vegetation canopy temperatures is limited because of LST variability resulting from urban surface heterogeneity, differences in vegetation fraction and non-vegetated material, and the coarse resolution of available thermal imagery. This study is a large-scale analysis of urban surface composition and temperature variability across the Los Angeles, USA, metropolitan area (4466 km2). Sub-pixel fractions of two plant functional types (tree and turfgrass) and four urban materials (impervious surface, commercial roof, non-photosynthetic vegetation, and soil) were quantified using hyperspectral imagery (Airborne Visible/Infrared Imaging Spectrometer). Fractional cover gradients of plant types and non-vegetated materials were developed using 1.7 million pixels, from which we modeled LST changes using simultaneously collected thermal imagery (MODIS-ASTER Airborne Simulator). Vegetation LST variability was mapped by subtracting modeled LST from observed LST and investigated relative to building density and vegetation management. Overall, LST varied significantly among plant functional types and urban material types. Across heterogeneous mixtures, LST and vegetation fraction exhibited a negative, linear relationship, with the slopes of LST change showing significant differences between trees and turfgrass. The map of vegetation LST variability had a standard deviation of 3.5 °C, indicating significant variability across Los Angeles independent of vegetation type or fractional cover. Building density was observed to affect tree and turfgrass LST differently, while a negative relationship was observed between vegetation LST and irrigation (R2 = 0.55). Our results show that an LST signal of vegetation function, distinct from that of vegetation fractional cover, can be observed and modeled at city-scales in fractional mixture analysis, indicating potential for improved understanding of urban microclimates.

A MODIS/ASTER Airborne Simulator (MASTER) Imagery for Urban Heat Island Research

Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect.

Monitoring the Impacts of Severe Drought on Southern California Chaparral Species using Hyperspectral and Thermal Infrared Imagery

Airborne hyperspectral and thermal infrared imagery acquired in 2013 and 2014, the second and third years of a severe drought in California, were used to assess drought impacts on dominant plant species. A relative green vegetation fraction (RGVF) calculated from 2013–2014 Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data using linear spectral unmixing revealed seasonal and multi-year changes relative to a pre-drought 2011 reference AVIRIS image. Deeply rooted tree species and tree species found in mesic areas showed the least change in RGVF. Coastal sage scrub species demonstrated the highest seasonal variability, as well as a longer-term decline in RGVF. Ceanothus species were apparently least well-adapted to long-term drought among chaparral species, showing persistent declines in RGVF over 2013 and 2014. Declining RGVF was associated with higher land surface temperature retrieved from MODIS-ASTER Airborne Simulator (MASTER) data. Combined collection of hyperspectral and thermal infrared imagery may offer new opportunities for mapping and monitoring drought impacts on ecosystems.

Rooftop Surface Temperature Analysis in an Urban Residential Environment

The urban heat island (UHI) phenomenon is a significant worldwide problem caused by rapid population growth and associated urbanization. The UHI effect exacerbates heat waves during the summer, increases energy and water consumption, and causes the high risk of heat-related morbidity and mortality. UHI mitigation efforts have increasingly relied on wisely designing the urban residential environment such as using high albedo rooftops, green rooftops, and planting trees and shrubs to provide canopy coverage and shading. Thus, strategically designed residential rooftops and their surrounding landscaping have the potential to translate into significant energy, long-term cost savings, and health benefits. Rooftop albedo, material, color, area, slope, height, aspect and nearby landscaping are factors that potentially contribute. To extract, derive, and analyze these rooftop parameters and outdoor landscaping information, high resolution optical satellite imagery, LIDAR (light detection and ranging) point clouds and thermal imagery are necessary. Using data from the City of Tempe AZ (a 2010 population of 160,000 people), we extracted residential rooftop footprints and rooftop configuration parameters from airborne LIDAR point clouds and QuickBird satellite imagery (2.4 m spatial resolution imagery). Those parameters were analyzed against surface temperature data from the MODIS/ASTER airborne simulator (MASTER). MASTER images provided fine resolution (7 m) surface temperature data for residential areas during daytime and night time. Utilizing these data, ordinary least squares (OLS) regression was used to evaluate the relationships between residential building rooftops and their surface temperature in urban environment. The results showed that daytime rooftop temperature was closely related to rooftop spectral attributes, aspect, slope, and surrounding trees. Night time temperature was only influenced by rooftop spectral attributes and slope.

Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign

The 2013–2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land–sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change.

Estimating clear-sky all-wave net radiation from combined visible and shortwave infrared (VSWIR) and thermal infrared (TIR) remote sensing data

The surface radiation budget is characterized by the all-wave net radiation. Existing parameterization methods of estimating surface net radiation require the input of many surface and atmospheric parameters and rely heavily on the availability and accuracy of such data. Other methods involve retrieval of the shortwave (0.3–3.0μm) and longwave (3.0–100.0μm) components separately from visible and shortwave infrared (VSWIR) and thermal infrared (TIR) data, respectively. The proposed HyspIRI mission will carry a VSWIR spectrometer and a TIR multispectral imager with a nadir spatial resolution of 60m. The broad range of spectral information will provide a unique opportunity to directly estimate the surface net radiation from the high-resolution HyspIRI data. In this study, we propose a new algorithm to estimate the clear-sky instantaneous all-wave net radiation over the land surface by combining VSWIR and TIR remote sensing data. The new method is based on extensive modeling of atmospheric radiative transfer over the entire solar spectrum. The algorithm is first tested with MODIS data. Validation with 1-year measurements at seven Surface Radiation Budget Network (SURFRAD) sites in 2013 demonstrates that the new method can accurately estimate clear-sky instantaneous all-wave net radiation with a root mean square error (RMSE) of 70.6W/m2, which is better than the method of separating shortwave and longwave components. MODIS/ASTER Airborne Simulator (MASTER) data from the HyspIRI preparatory airborne campaign are also used as a proxy to demonstrate the suitability of the algorithm for data from the future HyspIRI mission.

Leaf area index retrieval of orchards based on airborne MASTER data

Leaf area index( LAI) is an important parameter for decrypting canopy structure,and the accurate acquisition of orchards LAI plays an important role in monitoring the growing condition and estimating the yield of orchards. In this paper,the orchard blocks in the middle of California in USA were chosen as the study area,and LAI was retrieved using normalized difference water index( NDWI) through comparing regression models with surveyed leaf area indices and three vegetation indices composed of normalized difference vegetation index( NDVI),normalized difference infrared index( NDII) and NDWI based on the MODIS /ASTER airborne simulator( MASTER) image,which acquired flying along the solar plane. The results show that the image acquired by flying perpendicular to the solar plane has the phenomenon of maximum brightness gradients because of the bidirectional reflectance of surface object,whereas the image acquired by flying along the solar plane fails to show such a phenomenon. The comparison between three vegetation index models also shows that NDVI is easy to reach saturation in higher coverage area,and NDWI is more suitable for LAI retrieval in the study area because NDWI model has higher R2 and smaller RMSE. The results of this study can enrich the LAI retrieval theory and provide theoretical and data support for LAI scale problem.

Relationships between dominant plant species, fractional cover and Land Surface Temperature in a Mediterranean ecosystem

The Hyperspectral Infrared Imager (HyspIRI) is a proposed satellite mission that combines a 60m spatial resolution Visible-Shortwave Infrared (VSWIR) imaging spectrometer and a 60m multispectral thermal infrared (TIR) scanner. HyspIRI would combine the established capability of a VSWIR sensor to discriminate plant species and estimate accurate cover fractions with improved Land Surface Temperatures (LST) retrieved from the TIR sensor. We evaluate potential synergies between Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) maps of dominant plant species and mixed species assemblages, fractional cover, and MODIS/ASTER Airborne Simulator (MASTER) LST utilizing multiple flight lines acquired in July 2011 in the Santa Barbara, California area. Species composition and green vegetation (GV), non-photosynthetic vegetation (NPV), impervious, and soil cover fractions were mapped using Multiple Endmember Spectral Mixture Analysis with a spectral library derived from 7.5m imagery. Temperature-Emissivity Separation (TES) was accomplished using the MASTER TES algorithm. Pixel-based accuracy exceeded 50% for 23 species and land cover classes and approached 75% based on pixel majority in reference polygons. An inverse relationship was observed between GV fractions and LST. This relationship varied by dominant plant species/vegetation class, generating unique LST–GV clusters. We hypothesize clustering is a product of environmental controls on species distributions, such as slope, aspect, and elevation as well as species-level differences in canopy structure, rooting depth, water use efficiency, and available soil moisture, suggesting that relationships between LST and plant species will vary seasonally. The potential of HyspIRI as a means of providing these seasonal relationships is discussed.

Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data

Retrievals of vegetation canopy water content (CWC) from remotely sensed imagery can improve our understanding of the water cycle and help manage irrigation of agricultural crops. Optical remote sensing data can be used to detect seasonal CWC variation but whether they are sensitive enough for detecting diurnal CWC variation remains unknown. This paper investigates whether MODIS/ASTER airborne simulator (MASTER) data can be used to detect diurnal variation in CWC over well irrigated almond and pistachio orchards in the southern San Joaquin Valley of California, USA. MASTER images were first corrected for the Bi-directional Reflectance Distribution Function (BRDF) effect to remove cross-track variation in reflectance amplitude. Two spectral indexes, the Normalized Difference Infrared Index (NDII) and the Normalized Difference Vegetation Index (NDVI), were derived from corrected morning and afternoon MASTER imagery and related to the field-measured CWC. At the ground level, a significant decrease (~9%) in CWC occurred from morning to afternoon (p<0.0001). The field-measured CWC was positively correlated with MASTER-derived NDII and NDVI for both morning (NDII: r2=0.67, NDVI: r2=0.56, p<0.0001) and afternoon (NDII: r2=0.42, NDVI: r2=0.39, p<0.001) data. The diurnal change in CWC also led to a statistically significant spectral change that was observed as a 4% decline in NDII (p<0.005) or 2% decline in NDVI (p<0.0005). Our results show that the diurnal variation in CWC can be detected for the irrigated orchards using simple spectral indexes derived from MASTER data, with higher sensitivity for NDII than for NDVI as expected. The results also demonstrate the potential for remote sensing to improve crop management and better understand plant physiological changes at field to regional scales.

Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region.

Integrating visible, near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data

Hyperspectral and thermal infrared (TIR) multispectral remote sensing have great potential for surface geological mapping. This paper investigates the potential impact of combining these data on the comparative accuracy of different classification methods. A series of simulated datasets based on the characteristics of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER) sensors was created from surface reflectance and emissivity data derived from library spectra of 16 common minerals and rocks occurring in Cuprite, Nevada. System noise, illumination effects, the presence of vegetation, and spectral mixing were added to create the simulated data. Five commonly used classification algorithms, minimum distance, maximum likelihood classification, binary encoding, spectral angle mapper (SAM) and spectral feature fitting (SFF), were applied to all datasets. All the classification methods, excluding binary encoding, achieved nominal to significant improvement in overall accuracy when applied to the combined datasets in comparison to using only the AVIRIS dataset. Furthermore, certain classification methods of the combined datasets show a marked increase in individual rock or mineral class accuracies. Limestone, silicified and muscovite, for instance, show an improvement of almost 30% or greater in either producer's or user's accuracy using the combined datasets with SAM. SFF provides a great improvement in accuracy for limestone, quartz and muscovite. In terms of overall comparative accuracy for the individual and the combined datasets, maximum likelihood classification shows the best performance. For the simulated AVIRIS data, SFF was generally superior to SAM, although the accuracy of SAM applied to the combined datasets was slightly better than that of SFF. SAM applied to the combined datasets increases classification accuracy for some minerals and rocks which do not exhibit distinct absorption feature in the TIR region, while for SFF, only the accuracy of minerals and rocks with characteristic absorption features in the TIR region is improved.

Mars remote‐sensing analog studies in the Badwater Basin, Death Valley, California

The search for evaporites on Mars has important implications for the role that liquid water has played in shaping the planet's geologic, climatic, and potential biologic history. Orbital investigations of surface mineralogy are crucial to this exploration effort. With the exception of coarse‐grained gray hematite at a restricted number of sites and trace amounts of carbonate in globally distributed dust deposits, the Thermal Emission Spectrometer (TES) and Thermal Emission Imaging System (THEMIS) instruments have yet to find widespread mineralogical evidence of aqueously formed minerals. This may reflect the coarse spatial resolution of TES (3 × 5 km/pixel) and low spectral resolution of THEMIS (10 bands between 6.5 and 14.5 μm). Spectral mapping in the Badwater Basin, Death Valley, California, was conducted to better understand the capabilities of TES and THEMIS in detecting evaporite minerals. High‐resolution MODIS/ASTER Airborne Simulator (MASTER) data, degraded to TES and THEMIS spatial resolutions, were used to evaluate the detection limits of sulfates and carbonates. To assess the validity of this spectral remote sensing, a quantitative ground truth analysis of surface mineralogy in the Badwater Basin was performed. The analysis was based on thin section petrography, X‐ray diffraction, electron microprobe, and laboratory and field thermal emission spectrometer analyses. Taken together, the results of all five methods provided enough constraints for a robust interpretation that was in general agreement with the spectral remote‐sensing mapping study for ∼90% of the surface samples examined.

Data fusion of hyperspectral and SAR images

A novel technique is proposed for data fusion of earth remote sensing. The method is developed for land cover classification based on fusion of remote sensing images of the same scene collected from multiple sources. It presents a framework for fusion of multisource remote sensing images, which consists of two algorithms, referred to as the greedy modular eigenspace (GME) and the feature scale uniformity transformation (FSUT). The GME method is designed to extract features by a simple and efficient GME feature module, while the FSUT is performed to fuse most correlated features from different data sources. Finally, an optimal positive Boolean function based multiclass classifier is further developed for classification. It utilizes the positive and negative sample learning ability of the minimum classification error criteria to improve classification accuracy. The performance of the proposed method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the airborne synthetic aperture radar (SAR) images for land cover classification during the PacRim II campaign. Experimental results demonstrate that the proposed fusion approach is an effective method for land cover classification in earth remote sensing, and improves the precision of image classification significantly compared to conventional single source classification.

Surface unit characterization of the Mauna Ulu flow field, Kilauea Volcano, Hawai′i, using integrated field and remote sensing analyses

Investigation of surface units in the Mauna Ulu flow field, Kilauea Volcano, Hawai′i, was conducted through field and remote sensing analyses. Recently acquired Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MASTER (MODIS/ASTER airborne simulator) datasets, which cover the visible- through thermal-wavelength region, have been analyzed and correlated with a field-based characterization of Mauna Ulu flow-unit surfaces. Field observations were made of unit morphologies (decimeter- to decameter-scale) as well as small-scale (millimeter- to centimeter-scale) surface textures. Estimates were made of the extent of surface crust spallation, as well as the abundance of vesicles and phenocrysts on the surface crust and spallation surfaces. The field-based characteristics were compared with remote sensing data, including visible and near-infrared reflectance spectra, thermal infrared emissivity spectra, and emissivity-derived small-scale roughness. Visible-wavelength characteristics are controlled primarily by color, luster, and decimeter- to meter-scale surface irregularities, whereas thermal-wavelength characteristics are primarily controlled by the abundance of small-scale roughness elements such as vesicles. Although individual data pixels are not of sufficient resolution to identify typical individual lava flow units using point spectra, mean spectra allow characterization of unit surface crusts. Flow-unit morphologies and surface textures are related to differences in emplacement and post-emplacement modification of ′a′a and pahoehoe lobes. Pahoehoe surface properties reflect the flow regime during emplacement and the history of cooling, degassing, and crystal formation/settling. The nature of pahoehoe surface crusts indicates that within the wide range of emplacement conditions expressed within the flow field, two sets of conditions are dominant, producing a bronzy, smooth crust or a dark, rough crust. The former makes up the majority of the pahoehoe surfaces and reflects minimal cooling and degassing prior to emplacement, whereas the latter reflects cooling and crystallization during storage within the flow field. This study provides a detailed characterization of Mauna Ulu surface units, and presents an integrated field and remote sensing approach to mapping and interpreting the emplacement of lava flow fields.

Land cover classification using MODIS–ASTER airborne simulator (MASTER) data and NDVI: a case study of the Kochang area, Korea

New sensors and new technologies for data processing provide more capabilities to map and extract information about land cover and land use. MODIS–ASTER airborne simulator (MASTER) data acquired during the PACRIM II mission were used for land cover classification in the Kochang area, Korea. Twenty-two bands covering the visible, near-infrared, and shortwave infrared wavelengths were used in the study. A minimum noise fraction (MNF) transform was used for feature extraction to select an optimum subset of data in terms of classification accuracy. The first six MNF images obtained the most accurate classification result among all MNF data combinations. Classification using five normalized difference vegetation index (NDVI) images from the visible red band and five different near-infrared bands achieved a relatively good result (overall accuracy of 78.6%) and showed additional discriminative information for land cover classification. The integration of spectral information and NDVI data using a hierarchical classification method substantially improved the classification accuracy compared to classification with spectral data alone (from 84.35% to 90.62%). The knowledge from visual inspection of classification results and analysis of confusion matrices was used to build rules used in the hierarchical classification.