Multispectral Scanner Data Research Articles

The relation between reflected radiation and yield on the Broadbalk winter wheat experiment†

Abstract In the classical experiment on Broadbalk field, Rothamsted, winter wheat has been grown continuously under various treatments since 1843. Reflected radiation in red (R) and near-infrared (NIR) wavebands was measured over the field in 1987, twice with an airborne multispectral scanner (MSS) from an altitude of 600m and five times with a muhiband radiometer from 2 m above the crop surface. The normalized difference vegetation index, NDVI= ( NIR−R)/ (NIR + R), was calculated for each date and its relation with harvest yield investigated. The NDVI determined from the airborne MSS data was correlated with yield, and the correlation was found to increase if the variable effect of productivity was introduced into the relationship. The NDVI values calculated from the ground radiometry were more strongly correlated with yield, however. The differences in yield on Broadbalk are caused mainly by the amounts of nitrogen-containing fertilizer applied. The results suggest that the radiation measured by airborn...

Precipitable Water Estimation from High-Resolution Split Window Radiance Measurements

A technique that uses the spatial variance of image brightness temperature to derive total column precipitable water is applied to high-resolution multispectral aircraft scanner data for the June 19, 1986 COHMEX day. The technique has several advantages over other approaches in that it requires only relative calibration accuracy, is less susceptible to instrument error, and does not directly use a priori information. Results indicate significant horizontal variability of precipitable water at the mesoscale. Precipitable water gradients of 6 mm per 10 km are not uncommon. The results verify well against special rawinsonde measurements and the ensuing cloud field development. While only applied to this specialized aircraft data, the applicability of the technique to operational AVHRR and VAS data is discussed.

Landsat TM versus MSS data for forest type identification

Abstract Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) data were digitally analyzed for forest type identification in the Kisatchie Ranger District, Kisatchie National Forest, Louisiana. Ground‐verification maps were produced from field surveys and interpretation of 1.12,000 and 1: 58,000 color‐infrared (CIR) aerial photography of nine compartments. Stand boundary and soils maps were input to a digital Geographic Information System (GIS) with the Landsat and ground‐verification data. ‐ Unsupervised classifications of the Landsat data did not identify the above cover types well. Supervised classifications were tested by stand agreement to the ground verification. The highest four‐class agreement was obtained for the TM classification (76 percent). Three‐class (open, pine, and hardwoods) stand agreements (81 (MSS) and 85 (TM) percent) were not significantly different as tested by analysis of variance (alpha level 0.1).

Landsat image enhancement and analysis approach for geological studies of central part of Goa, India

With the advent of multispectral scanners and the availability of digital data, information extraction through remote sensing has become one of the viable tools for studying natural resources. Normally thick vegetation and soil cover are common obstacles while geologically studying an area remotely. The study area, Goa, is largely covered by settlements, private mines, and dense vegetation. This makes it difficult to decipher lithology, structures and to find their extension by ground surveying. In this paper, an attempt has been made to study a variety of image enhancement and analysis techniques to delineate geological features, lineaments, and several landuse features. The information gathered from land use features and vegetation cover is also utilized in delineating lithology and lineaments. Landsat Multi-Spectral Scanner (MSS) data both in the visual and digital form have been used for the analysis. Various photographic techniques such as Bas-relief, combined printing of positive and negative for different bands, color composites, and digital image processing techniques like ratioing, principal component analysis and ratioing of the first two principal components have been applied for geological information extraction. This paper examines comparative utility of enhancement techniques in studying geological aspects. It is found that the ratio image of PCI and PC2 gives most significant and detailed information with maximum contrast and sharp boundaries. Bas-relief images are excellent for identifying geomorphic features and lineaments.

Cloud and surface textural features in polar regions

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

Classification of Forest Vegetation in North-Central Minnesota Using Landsat Multispectral Scanner and Thematic Mapper Data

Abstract Computer classifications of Landsat-5 Thematic Mapper (TM) and Multispectral Scanner (MSS) data were evaluated to determine how forest and sensor characteristics affect the classification accuracy of Minnesota forest cover types. The test area was Itasca State Park in north central Minnesota. The experiments involved comparisons of sensors differing in spectral and spatial resolution. To evaluate classification performance the Landsat classification maps were compared on a pixel by pixel basis with a digitized reference map of the Park. Classification results were compared for statistically significant differences using discrete multivariate statistics. Classification accuracies ranged from 26 to 86%, depending upon the sensor, number of classes, and performance measure used. The most significant result was that the increased spectral/radiometric resolution of the TM data resulted in 7-15% absolute increase in forest cover classification accuracy over MSS data using conventional methods. The best spectral band combination was one band each from the visible, near infrared, and middle infrared. For. Sci. 36(2):330-342.

The derivation of water volume reflectances from airborne MSS data using in situ water volume reflectances, and a combined optimization technique and radiative transfer model

Abstract Water volume reflectance images of three water cooling reservoirs were derived from airborne Daedalus DS-1260 multispectral scanner (MSS) data using a radiative transfer model to eliminate atmospheric effects and to derive downwell-ing irradiances. Sixty in situ water volume reflectances and the associated sensor and sun geometries were input into the radiative transfer model. Using the radiative transfer model to generate water volume reflectance values at each field site, an optimization procedure minimized the difference between modelled and in situ water volume reflectances resulting in optimized sensor brightness value to radiance conversion gain and bias factors, and optimized surface visual range and mean terrain background reflectance. Subsequently water volume reflectance images were derived at six wavelengths encompassing the blue to near-infrared using the optimized atmospheric parameters and conversion factors as inputs into the radiative transfer model. Modelled reflectance images we...

APPLYING SATELLITE MULTI-SPECTRAL SCANNER DATA FOR LANDSLIDES PREDICTION

APPLYING SATELLITE MULTI-SPECTRAL SCANNER DATA FOR LANDSLIDES PREDICTION

Supervised classification approach for assessment of forest resources in part of u.p. plains, india using landsat-3 data

Forests in the plains of Uttar Pradesh are depleted to great extent. Existing figures on the area under forest, though contradictory, indicate a grim situation of forest cover. In the present study, supervised classification technique with maximum likelihood algorithum has been used to assess the forest in the region extending between Lucknow through Allahabad to Mirzapur city in the plains of Uttar Pradesh. It has been possible to successfully identify and map 5 different categories of forests by computer processing of Landsat-3 Multispectral Scanner data. The area under each category has also been computed. Whatever little forest exists in this area is also greatly influenced by biotic interferences. The vegetation formation in these forests is thus degraded and/or secondary. Spectral behaviour of various categories of forests have also been discussed.

Relative photosynthetic activity of agricultural lands from airborne carbon dioxide and satellite data

Abstract The relative net photosynthetic activity of crop, grass and woodland areas was estimated from airborne eddy correlation carbon dioxide flux density measurements for various transects within a 100 km by 300 km agricultural region in southern Manitoba. Landsat Multispectral Scanner (MSS) data were used to estimate the density and vegetation class boundaries associated with these measurements. The airborne carbon dioxide flux measurements were made during good growing conditions, namely with soil moisture reserves (> 50 per cent field capacity), high radiation (950 W/m2), cloudless skies and warm air temperature (> 25°C). The carbon dioxide flux ranged from (a) -20 to -35 kg CO2 ha−1 h−1 for the heavy crop canopies of the cereal and oilseed growing areas located on the clay and clay loam soils, (b) -12 to -25kg CO2 ha−1 h−1 for the mixed crop grassland and dense woodlands on loam to sandy loam soils and (c) -8 to - 15 kg CO2 ha−1 h−1 for the mixed grassland areas on sandy loam to sandy soils. Indice...

An assessment of SPOT XS and Landsat MSS data for digital classification of near-urban land cover

Abstract The potential of SPOT Multispectral (XS) and Landsat Multispectral Scanner (MSS) for automatically outlining agricultural near-urban interfaces, by means of classifications, was tested for a study area around Yogyakarta, Central Java. Photo-interpretation of SPOT panchromatic (P) images and aerial photographs acquired during the past 20 years was used as a reference to evaluate maps derived from SPOT XS and Landsat MSS and to quantify the evolution of this area. SPOT XS mapping proved to be reliable, whereas only overall information could be derived from Landsat. Due to the usual heterogeneity of the Javanese landscapes and to the present unavailability of locally-adapted lextural classifiers, the visual analysis of SPOT P images provided better results than the digital processing of SPOT XS data.

An investigation of spectral change as influenced by irrigation and evapotranspiration volume estimation in western Nebraska

Retrospective satellite image data were evaluated for their ability to demonstrate the influence of center-pivot irrigation development in western Nebraska on spectral change and climate-related factors for the region. Periodic images of an albedo index and a normalized difference vegetation index (NDVI) were generated from calibrated Landsat multispectral scanner (MSS) data and used to monitor spectral changes associated with irrigation development from 1972 through 1986. The albedo index was not useful for monitoring irrigation development. For the NDVI, it was found that proportions of counties in irrigated agriculture, as discriminated by a threshold, were more highly correlated with reported ground estimates of irrigated agriculture than were county mean greenness values. A similar result was achieved when using coarse resolution Advanced Very High Resolution Radiometer (AVHRR) image data for estimating irrigated agriculture.

Relationship between Landsat MSS data and forest tree parameters

Abstract Band ratios, indices and radiance in the four channels of the Multi-spectral Scanner (MSS) on the Landsat-4 satellite for October 1984 and March 1985 were correlated with mean tree parameters of teak plantations (age, mean tree height, mean tree diameter at breast height, mean canopy diameter, mean canopy volume). The Landsat MSS data for March (when teak trees are leafless) were more suitable than the Landsat MSS data for October for categorizing tree parameters.

Factor analysis and classification of remotely sensed data for monitoring tidal flats

Interest in using remote sensing techniques, principally those involving satellite, in Wadden Sea research has centred on attempting a classification of the various sediment surface types present. Unlike most recent studies which have used mainly Landsat Multispectral Scanner data, we have assessed the feasibility of using Landsat Thematic Mapper data, which in conjunction with time series aerial photography, forms the basis of a strategy for remotely sensing the Wadden Sea. This paper focusses on an approach for extracting potentially “hidden” within-pixel information from multispectral data sets. A hierarchical (unsupervised) classification of a Thematic Mapper image successfully classified five different classes, including land, saltmarsh, water, cloud and tidal flat areas. This procedure thus enabled a “masking-out” of all classes other than those classified as tidal flat, following which a factor analysis was used to determine the minimum number of independent factors necessary to explain the observed variation in the signal received by the satellite. Three factors accounted for a total of 82% of the variation in all seven TM channels. Preliminary studies of the primary factor (score) image shows a good correlation with existing latterday cartographic data. Considering the proximate relationship between topography and other important biotic and abiotic sedimentary characteristics, this approach may prove valuable for future applications of satellite data for monitoring long-term change in physical and thus biological Wadden Sea characteristics. Ongoing research efforts are focussing on a classification and quantification of sub-pixel patchiness using aerial photography and ground surveys. The approaches taken and results obtained to date are discussed.

Spectral texture for improved class discrimination in complex terrain

Abstract A spatial co-occurrence algorithm has been used to derive image texture from Landsat Multispectral Scanner (MSS) data to increase classification accuracy in a moderate relief, boreal environment in eastern Canada. The aim was to investigate ‘data-driven improvements’, including those available through digital elevation modelling. Overall classification accuracy using MSS data alone was 59·1 per cent when compared to a biophysical inventory of the area compiled primarily by aerial photointerpretation. This increased to 66·2 per cent with MSS plus texture and to 89·8 per cent when MSS data were analysed with geomorphometry extracted from a digital elevation model (DEM). The introduction of MSS texture resulted in statistically significant increases in individual class accuracies in classes that were also well defined using the geomorphometric and integrated data sets. This suggested that some of the additional information provided by geomorphometry was also contained in spectral texture. It was als...

Classification of vegetation in the Western Australian wheatbelt using Landsat MSS data

We examined the use of Landsat multispectral scanner (MSS) data to provide preliminary information on broad vegetation types present within nature reserves in the wheatbelt region of Western Australia. We analysed Landsat data for an area of natural vegetation for which ground survey and aerial photographic data are available. We used canonical variate analysis to examine the degree of spectral separation between training sites selected in the main structural vegetation types. The training classes were then grouped into spectral classes and an allocation procedure used to map the pixels in the reserve into these classes. The analysis provided a good correspondence between spectral classes and broad vegetation types recognised from aerial photography, but did not discriminate between differences in dominant species (e.g. between different types of Eucalypt woodland). The classification derived from the study reserve was then applied successfully to two nearby reserves, indicating that the data can be used to provide initial information on the broad vegetation types present in wheatbelt reserves, although it is not suitable for finer resolution studies.

Thematic Mapper laser profile resolution of holocene lake limit, Great Salt Lake Desert, Utah

The limit of Great Salt Lake's maximum expansion onto the floor of the Great Salt Lake Desert during Holocene time is marked by a break in slope which is clearly depicted in terrain models which merge Thematic Mapper (TM) multispectral scanner data and Aerial Profiling of Terrain System (APTS) laser profile data. Mean elevations of APTS-coincident TM pixels are strongly correlated with reflectance in TM Bands 5 and 7 when and where sufficient moisture contrasts among terrain components are high. TM-APTS models and field surveys place the highest Holocene standing water at an elevation of 4221 ft (1286.7 m).

Pixel and sub-pixel accuracy in geometrical correction of AVHRR imagery

Abstract This paper makes two contributions to the problem of geometrical rectification of Advanced Very High Resolution Radiometer (AVHRR) image data. The first is an automatic procedure for the location of ground control points (GCPs) without the involvement of operator intervention and using a library of 'chips’ of AVHRR data for the GCPs. The second involves the adaptation of a method previously used by Torlegard for Landsat Multi-spectral Scanner (MSS) data to the case of AVHRR data to attempt to achieve sub-pixel accuracy in the rectification; we have achieved an accuracy of 26 per cent of the pixel edge and have every hope that this can be reduced to below 20 per cent in the future.

The effects of a complex environment on crop separability with landsat TM

Studies in the midwestern United States have shown that crop variations associated with regionally varying environmental factors can be important to crop inventory design using Landsat multispectral scanner data. This study examined the consequences of a more complex environment and a higher resolution sensor. Unsupervised classification accuracy and divergence of crop spectra were determined for six crop classes from early season Landsat Thematic Mapper (TM) scenes for 31 sites across New York State. These indicators of crop separability were related to 12 environmental variables. Although statistically significant models were found for individual crops, no significant relationship was found to characterize general crop separability. Regional variation is too large to be ignored in an early season TM-based inventory of areas as complex as New York; however, stratification based on readily obtainable environmental data is of only moderate value in describing the variation.

Fractal Statistics of Cloud Fields

Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data, with 80 and 30 m spatial resolution, respectively, have been employed to study the spatial structure of boundary-layer and intertropical convergence zone (ITCZ) clouds. The probability distributions of cloud areas and cloud perimeters are found to approximately follow a power-law, with a different power (i.e., fractal dimension) for each cloud type. They are better approximated by a double power-law behavior, indicating a change in the fractal dimension at a characteristic size which depends upon cloud type. The fractal dimension also changes with threshold. The more intense cloud areas are found to have a higher perimeter fractal dimension, perhaps indicative of the increased turbulence at cloud top. A detailed picture of the inhomogeneous spatial structure of various cloud types will contribute to a better understanding of basic cloud processes, and also has implications for the remote sensing of clouds, for their effects on remote sensing of other parameters, and for the parameterization of clouds in general circulation models, all of which rely upon plane-parallel radiative transfer algorithms.