Airborne Multispectral Scanner Research Articles

A05 航空機搭載型 MSS による三宅島の二酸化硫黄ガス濃度分布の定量 (2001/12/17)

A05 航空機搭載型 MSS による三宅島の二酸化硫黄ガス濃度分布の定量 (2001/12/17)

PA13 航空機搭載型 MSS による伊豆鳥島の山体表面温度観測 (2003/8/10)

PA13 航空機搭載型 MSS による伊豆鳥島の山体表面温度観測 (2003/8/10)

Land surface temperature retrieved from airborne multispectral scanner mid-infrared and thermal-infrared data.

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes at local/global scales. In this paper, a LST retrieval method was proposed from airborne multispectral scanner data comparing one mid-infrared (MIR) channel and one thermal infrared (TIR) channel with the land surface emissivity given as a priori knowledge. To remove the influence of the direct solar radiance efficiently, a relationship between the direct solar radiance and water vapor content and the view zenith angle and solar zenith angle was established. Then, LST could be retrieved with a split-window algorithm from MIR/TIR data. Finally, the proposed algorithm was applied to the actual airborne flight data and validated with in situ measurements of land surface types in the Baotou site in China on 17 October 2014. The results demonstrate that the difference between the retrieved and in situ LST was less than 1.5 K. The bais, RMSE, and standard deviation of the retrieved LST were 0.156 K, 0.883 K, and 0.869 K, respectively, for samples.

Enhancement of bioenergy estimations within forests using airborne laser scanning and multispectral line scanner data

Abstract Forest biomass is a substantial source of renewable energy and is becoming increasingly important due to environmental and economic reasons. In Germany, several studies have assessed the bioenergy potential for large areas, e.g. for an entire Federal state. However, in most cases it was not possible to provide detailed maps showing the biomass and the sustainable energy potential for individual forest stands. Thus, the aim of this study was to develop a new and robust method that provides detailed information regarding the spatial distribution of biomass and forest residues as a potential energy resource using a combination of remotely sensed and in situ data. A case study was carried out in a mixed forest in Southern Germany. First, regression analyses were applied to identify relationships between field measurements with several remote sensing metrics to estimate timber volume, mean stem diameter and age. Cross-validation yielded relative root mean square errors (RMSEs) of 30.20% for volume, 27.92% for diameter and 28.81% for the estimation of the age. The absolute RMSEs were smaller than the standard deviation of the observed variables. Next, the regression equations were used to compute attributes for individual forest stands. Stand attributes were then used to model forest residues. To estimate the sustainable annual potential, the actual harvest volume, as defined by forest management planning, was included in the model. Different model parameters were analyzed and an average potential from 0.993 to 1.181 t ha −1 a −1 was computed. The results were compared to previous studies in Germany.

Radiometric normalization of sensor scan angle effects in optical remote sensing imagery

Radiometric distortions along the scan line, caused mainly by the atmosphere, reflection properties of the imaged surface objects, and topography are observed frequently in images acquired by optical remote‐sensing sensors, especially airborne sensors with a wide field of view. In practical applications, such as classification or mosaicking of image data, a relative correction or normalization of these radiometric distortions is needed. Therefore, the goal of this paper is to derive a relative correction algorithm for multispectral line scanner images, which allows the spectral radiance values for different scan angles to be converted to radiance values of a defined reference scan angle. Under the simplifying assumption of complete diffuse reflectors, these radiometric distortions are modelled using a linear equation between the radiances of a pair of scan angles for each class of surface objects separately. A linear regression, applied to all these equations, results in a linear model for the radiance correction, a new empirical radiometric correction method, which is a generalization of earlier proposed strictly additive or multiplicative models. The proposed method is based solely on the digital image data. So, no additional information or user interaction is required, and the method is fully automatic. Applications for data acquired with the airborne multispectral scanner DAEDALUS AADS 1268 ATM show the effectiveness and potential of the proposed method especially for mosaicking and classification purposes.

Evaluation of Eelgrass Beds Mapping Using a High-Resolution Airborne Multispectral Scanner

Eelgrass (Zostera marina) can provide vital ecological functions in stabilizing sediments, influencing current dynamics, and contributing significant amounts of biomass to numerous food webs in coastal ecosystems. Mapping eelgrass beds is important for coastal water and nearshore estuarine monitoring, management, and planning. This study demonstrated the possible use of high spatial (approximately 5 m) and temporal (maximum low tide) resolution airborne multispectral scanner on mapping eelgrass beds in Northern Puget Sound, Washington. A combination of supervised and unsupervised classification approaches were performed on the multispectral scanner imagery. A normalized difference vegetation index (NDVI) derived from the red and near-infrared bands and ancillary spatial information, were used to extract and mask eelgrass beds and other submerged aquatic vegetation (SAV) in the study area. We evaluated the resulting thematic map (geocoded, classified image) against a conventional aerial photograph interpretation using 260 point locations randomly stratified over five defined classes from the thematic map. We achieved an overall accuracy of 92 percent with 0.92 Kappa Coefficient in the study area. This study demonstrates that the airborne multispectral scanner can be useful for mapping eelgrass beds in a local or regional scale, especially in regions for which optical remote sensing from space is constrained by climatic and tidal conditions.

Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS

The spatial properties of gaps have an important influence upon the regeneration dynamics and species composition of forests. However, such properties can be difficult to quantify over large spatial areas using field measurements. This research considers how we conceptualize and define forest canopy gaps from a remote sensing point of view and highlights the inadequacies of passive optical remotely sensed data for delineating gaps. The study employs the analytical functions of a geographical information system to extract gap spatial characteristics from imagery acquired by an active remote sensing device, an airborne light detection and ranging instrument (LiDAR). These techniques were applied to an area of semi-natural broadleaved deciduous forest, in order to map gap size, shape complexity, vegetation height diversity and gap connectivity. A vegetation cover map derived from imagery from an airborne multispectral scanner was used in combination with the LiDAR data to characterize the dominant vegetation types within gaps. Although the quantification of these gap characteristics alone is insufficient to provide conclusive evidence on specific processes, the paper demonstrates how such information can be indicative of the general status of a forest and can provide new perspectives and possibilities or further ecological research and forest monitoring activities.

Mapping of water constituents in Lake Constance using multispectral airborne scanner data and a physically based processing scheme

A physically based method to determine water constituents in case II waters from imaging spectrometer data is presented. The processing scheme includes modules for sunglitter correction, aerosol retrieval, atmospheric correction, and the retrieval of water constituents from subsurface reflectance spectra in case II waters. For the aerosol retrieval, the sun glitter, and the atmospheric correction modules, an azimuthally resolved radiative transfer model for a multilayer atmosphere-ocean system with a flat water surface is used. The retrieval of water constituents is achieved by an iterative fitting algorithm to adjust modeled and measured underwater reflectances. The processing scheme is applied to airborne multispectral scanner Daedalus AADS1268 in ATM (airborne thematic mapper) modus. Owing to the sensor properties (reduced radiometric sensitivity of the blue channel), it was not possible to work completely without additional data; Gelbstoff absorption could not be retrieved. It had to be inputted and was kept constant over the mission area. The processing system has been tested for multitemporal mapping of water constituents at Lake Constance. The results show the system as an efficient and reliable tool for the inversion of remote sensing data from natural waters.

Hierarchical data fusion for mapping soil units at field scale

We analyzed a highly complex soilscape of fluvial sediments by a hierarchical expert system. Using (i) inquiries, (ii) relief analysis on basis of a DEM 5, and (iii) soils' apparent electrical conductivity (EM38) as a database, we first defined zones of identical pedogenic context. Next, multi-temporal remote sensing data of winter wheat were obtained by an airborne multi-spectral scanner (Daedalus-ATM), which gives radiometric information with a geometric (ground) resolution of 1 m 2 (pixel size). Leaf area index (LAI) was semi-physically modeled using red and near-infrared canopy reflectances and related to above-ground biomass. Further, the resulting spatial patterns of vegetation parameters were processed by image analysis methods, i.e. an opening–closing procedure using a circular element with a radius of 5 m. These coarser patterns of LAI and biomass, respectively, were interpreted as patterns of site quality within each zone of pedogenic context. By our multi-temporal approach we were able to distinguish between stationary and time-variant pattern. Combined with point calibration on basis of a 50-m raster we identified available water capacity (AWC) and O 2 deficiency due to stagnant water as the most important soil properties constituting site quality for plant growth. Our results will be used for precision agriculture practices in future.

뉴로-퍼지 모델을 이용한 항공다중분광주사기 영상의 지표면 분류

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

Remote sensing of snow grain-size and impurities from Airborne Multispectral Scanner data using a snow bidirectional reflectance distribution function model

AbstractAlgorithms to retrieve the snow grain-size and the concentration of snow impurities were developed using a theoretical bidirectional reflectance distribution function (BRDF) model of the snow surface. In this model, snow grains are assumed to be independent spherical ice particles, and the BRDF is calculated with multiple scattering by snow particles. Using these algorithms, the snow grain-size and snow impurities were retrieved from Airborne Multispectral Scanner (AMSS) images at the visible (λ = 0.545 μm) and near-infrared (λ =1.24,1.64 and 2.23 μm) wavelengths observed over the flat snowfield in eastern Hokkaido, Japan, in February 1998. The estimated snow grain-size and impurities were consistent with the results of in situ measurements on the snow surface. For snow grain-size, measured reflectances in the different near-infrared AMSS channels indicated grain-size differences in the vertical profile of the snowpack.

Assessing the relation between the distribution of salt-marsh communities and the sediment budget of the North Norfolk coast

Very high resolution imagery from an airborne multi-spectral scanner has been used to estimate the distribu- tion of different salt-marsh communities along a 30-km stretch of the North Norfolk coast. Field observations have been used to develop a mathematical relationship between the vegeta- tion, physical environment and sediment accumulation. This relationship has been used to produce provisional sediment accretion maps for the North Norfolk coast. Structure of the model A mathematical model based on the analysis of field data has been constructed to estimate the rate at which sediment accumulates on a salt marsh. The model has been designed to fit within the SDSS framework to allow a variety of users to investigate the sensitivity of the assumptions made and to inves- tigate the possible effects of sea level rise. Analysis of field observations was carried out using stepwise linear regression for a variety of vegetation types independently. In each case the depth of sediment accumulated on a single tide was found to be signifi- cantly positively correlated with the length of time an area is submerged and the amount of sediment in the water and negatively correlated with distance from the nearest creek and the density of the sediment. These individual equations were then combined to give the form shown below. Considering accumula- tion per unit time the vegetation coefficient (v) can be shown to have units of area. This implies that, all other things being equal, species with a large or complex cross-sectional area trap sediment more ef- ficiently than smaller or less complex species. The final form of the model is:

Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery

Hyperspectral imagery from the airborne multispectral scanner was evaluated for discrimination and mapping of vegetation components in a semi-arid rangeland environment in Southern Australia. Automated unmixing of two image strips with 5-m resolution revealed several vegetation endmembers in the visible, near infrared (near-IR), and short-wave infrared portions of the imagery. Identity of the endmembers was determined through examination of their short-wave infrared and full-wavelength spectra, and their mapped distributions and correlation with percent cover of vegetation species were measured in sample plots. In addition, to assist interpretation of the image signatures, short-wave infrared reflectance spectra for the dominant vegetation components at the study site were collected with a portable infrared mineral analyzer (PIMA) spectrometer. Endmembers separately mapped included Eucalyptus and other trees such as sugarwood, understorey chenopod shrubs, dry plant litter, and soil surface cryptogamic crust. Several endmembers were significantly positively correlated with field measurements of plant cover. Most of the tree canopy endmembers showed broad cellulose-lignin absorption features in the short-wave infrared (SWIR), and narrower absorptions caused by plant waxes and oils. The field spectra confirmed that the sclerophyll and xerophytic plants show identifiable cellulose, lignin, and plant wax absorption features, even when live and actively photosynthesising. This spectral expression of biochemical constituents in live plants points to the benefit of using the whole spectral range from visible to short-wave infrared in vegetation studies.

Pattern decomposition method and a new vegetation index for hyper-multispectral satellite data analysis

Abstract We have developed the “pattern decomposition method” based on linear spectral mixing of ground objects for n-dimensional satellite data. In this method, spectral response patterns for each pixel in an image are decomposed into three components using three standard spectral shape patterns determined from the image data. Applying this method to AMSS (Airborne Multi-Spectral Scanner) data, eighteen-dimensional data are successfully transformed into three-dimensional data. Using the three components, we have developed a new vegetation index in which all the multispectral data are reflected. We consider that the index should be linear to the amount of vegetation and vegetation vigor. To validate the index, its relations to vegetation types, vegetation cover ratio, and chlorophyll contents of a leaf were studied using spectral reflectance data measured in the field with a spectrometer. The index was sensitive to vegetation types and vegetation vigor. This method and index are very useful for assessment of vegetation vigor, classifying land cover types and monitoring vegetation changes.

Extraction of smooth cordgrass (spartina alterniflora) biomass and leaf area index parameters from high resolution imagery

High resolution (3 x 3 m) Calibrated Airborne Multispectral Scanner (CAMS) data of Murrells Inlet, S. C. were collected on August 2, 1997. Original CAMS data and various vegetation indices were correlated with in situ a) harvested total above‐ground biomass clippings, and b) leaf‐area‐index (LAI) measurements obtained using a ceptometer. The non‐intrusive in situ LAI measurements were found to be significantly correlated with in situ above‐ground biomass measurements, suggesting that future field sampling of biomass and LAI can be obtained using a ceptometer. Near‐infrared and middle‐infrared bands as well as several vegetation indices were highly correlated with in situ biomass and LAI measurements. The first biomass map of a tidal estuary in South Carolina was produced. The biomass map represents an important base line against which future biomass inventories may be compared.

航空機搭載用マルチバンドスキャナー（ＡＭＳＳ）の開発

The Airborne Multi-Spectral Scanner (AMSS) is an airborne scanning spectrometer which provides spectral information similar to that which will be provided by the Global Imager (GLI) scheduled to be launched on the ADEOS-II platform in 2000. The AMSS acquires high spatial resolution imagery in the range of 0.40 to 12.45 microns, and a cross track scan width of +/- 35 degrees. This paper introduce the outline of AMSS and the result of image quality check using Integrating Sphere and Black body. As a result, it was confirmed that the data of visible and near-infrared region has yet meet to the specifications and insufficient for scientific use.

On the information content of multiple view angle (MVA) images

This paper examines the potential to distinguish land cover types in digital images acquired at several different sensor view angles with respect to a fixed area on the Earth's surface. Images recorded by an airborne multispectral scanner over an area of arable farmland are used to generate four such multipleview-angle (MVA) datasets: each consists of data obtained at six sensor view angles in a single spectral waveband: green (0.52-0.605 mu m), red (0.63-0.69 mu m), near-infrared (0.76-0.90 mu m), and middle infrared (1.55-1.75 mu m), respectively.The data are initially presented in the form of single-band MVA false-colour composite images. These are used to illustrate the extent to which different surface materials can be distinguished visually in MVA data. The concept of MVA (cf.,multispectral) feature space is then introduced and the separability of different land cover types within it is explored. It is suggested that single-band MVA data contain two main components of statistical variance directional and spectral . Their relative contributions to the total statistical variance in single-band MVA data is assessed using linear correlation analysis and principal components analysis (PCA). It is shown that while the spectral component tends to dominate in all wavebands, particularly in the near-infrared, the directional component nevertheless provides an important means of distinguishing certain cover types. The implications of these findings are discussed in relation to the parameters used in current bidirectional reflectance distribution function (BDRF) models and the development of 'angular indices' for vegetation monitoring (cf., traditional multispectral vegetation indices).

航空機ＭＳＳリモートセンシングによるヒートアイランド現象の解析 気温と地表面温度の同時観測による検討

航空機ＭＳＳリモートセンシングによるヒートアイランド現象の解析 気温と地表面温度の同時観測による検討

Gross feature detection in remotely sensed CAMS imagery using a multi-paradigm software approach

Rules used to classify regions in images that have been remotely sensed from airborne or satellite platforms are not universal truths even for gross feature detection (i.e., trees, man-made objects, water, etc.). The classification of each pixel in a data set obtained from an aerial or satellite image can be a difficult and time-consuming process for both a computer and a human. Consequently, throughout the National Aeronautics and Space Administration (NASA) information systems researchers are seeking innovative approaches that will assist human experts in the time-consuming endeavor of classifying remotely sensed data. This article reports on a study, commissioned by the director of NASA's John C. Stennis Space Center (SSC) in Mississippi, to investigate the use of Artificial Intelligence (AI) techniques in the classification of remotely sensed data collected using the Calibrated Airborne Multispectral Scanner (CAMS). The study resulted in the development of a proof-of-concept software system that takes a multi-paradigm software approach to gross feature detection in remotely sensed CAMS data.

Development of a method to predict the heat island potential using remote sensing and GIS data

Most studies directed to urban climate simulation have been carried out under the assumption that an urban canopy layer is a sublayer comprised of various roughnesses, and as a consequence, difficulties have arisen in obtaining a suitable heat balance for such a layer. This paper, therefore, focuses on investigating the surface temperature distributions of all urban surfaces via a new index based on sensible heat flux, named the heat island potential (HIP), which can be employed in urban development planning to evaluate the urban thermal environment. Simulation results following the use of HIP are verified by side-looking airborne multi-spectral scanner (MSS) and geographic information system (GIS) data; thereby enabling elucidation of these thermal effects on the atmosphere. In addition, a heat balance algorithm is developed which simulates urban surfaces such that construction of urban thermal images is possible.