Landsat Thematic Mapper Bands Research Articles

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

AbstractSpecies distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.

Urban Growth Monitoring using Remote Sensing and Geo-Informatics: Case Study of Gandhinagar, Gujarat State (India)

The Gandhinagar town has undergone unprecedented urban growth after assuming the status of a capital of Gujarat state, India in 1971. The Gandhinagar city lies on the western bank of river Sabarmati and city is also known as the “Tree capital of India” because of greenery with 54% green cover. The urban sprawl in Gandhinagar town was analyzed using multi-temporal Landsat TM data from 1981 to 2015. Spectral indices namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Built-up Index (BUI) were generated from the Landsat TM bands covering visible Red (R), Near Infrared (NIR) and Short Wave Infrared (SWIR) wavelength regions. Spectral variations in built-up, open spaces, urban vegetation and water areas were studied by generating two-dimensional spectral plots of NDBI and BUI. The spectral response of built up areas like Urban-dense and urban-sparse classes are quite distinct from other cover types like open spaces, urban vegetation and water. The results indicate that the normalized difference built-up index (NDBI) and Built-up Index (BUI) were very useful for mapping urban built-up areas in Gandhinagar town using Landsat Thematic Mapper (TM) data. The built-up area in Gandhinagar increased from 1100.1 ha during Jan 1989 to 1907.3 ha during Jan 2015 which indicates about 73 per cent increase in the built-up area during the period of 26 years. The urban vegetation which includes parks and gardens in the city has also shown increasing trend during the period of 1989 to 2015. However, the area under open spaces has decreased as the development of built-up area has increased as per development plan of the Gandhinagar town.

Comparison of remote sensing indices for monitoring of desert cienegas

ABSTRACTThis research considers the applicability of different vegetation indices at 30 m resolution for mapping and monitoring desert wetland (cienega) health and spatial extent through time at Cienega Creek in southeastern Arizona, USA. Multiple stressors including the risk of decadal-scale drought, the effects of current and predicted global warming, and continued anthropogenic pressures threaten aquatic habitats in the southwest and cienegas are recognized as important sites for conservation and restoration efforts. However, cienegas present a challenge to satellite-imagery based analysis due to their small size and mixed surface cover of open water, exposed soils, and vegetation. We created time series of five well-known vegetation indices using annual Landsat Thematic Mapper (TM) images retrieved during the April–June dry season, from 1984 to 2011 to map landscape-level distribution of wetlands and monitor the temporal dynamics of individual sites. Indices included the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII). One topographic index, the Topographic Wetness Index (TWI), was analyzed to examine the utility of topography in mapping distribution of cienegas. Our results indicate that the NDII, calculated using Landsat TM band 5, outperforms the other indices at differentiating cienegas from riparian and upland sites, and was the best means to analyze change. As such, it offers a critical baseline for future studies that seek to extend the analysis of cienegas to other regions and time scales, and has broader applicability to the remote sensing of wetland features in arid landscapes.

REGIONAL GEOLGICAL MAPPING IN TROPICAL ENVIRONMENTS USING LANDSAT TM AND SRTM REMOTE SENSING DATA

Abstract. Landsat Thematic Mapper (TM) and Shuttle Radar Topography Mission (SRTM) data were used to produce geological maps in tropical environments. Lineament, lithology and landform maps were produced for all states in peninsular Malaysia in this study. Kedah, Perak and Terengganu states have been selected as case studies to demonstrate the results of the data and techniques used. Directional filtering technique was applied to Landsat TM bands 4, 5 and 3 for lineament mapping. The lithology map was produced using Landsat TM bands combination consist of bands 4, 3 and 2. Digital elevation model and landform map were produced using SRTM data in 3 Dimension (3D) and 2 Dimension (2D) perspective views, respectively. The produced geological maps and the remote sensing data and methods applied in this study are mostly appropriate for hazard risk mapping applications and mineral exploration projects in the peninsular Malaysia and tropical environments.

Spatio-Temporal Assessment of Tuz Gölü, Turkey as a Potential Radiometric Vicarious Calibration Site

The paper provides an assessment of Tuz Gölü, a site in Turkey proposed for the radiometric vicarious calibration of satellite sensors, in terms of its spatial homogeneity as expressed in visible and near-infrared (VNIR) wavelengths over a 25-year period (1984–2009). By combining the coefficient of variation (CV) and Getis statistic (Gi*), a spatially homogenous and temporally stable area at least 720 m × 330 m in size was identified. Analysis of mid-summer Landsat Thematic Mapper (TM) images acquired over the period 1984–2009 showed that the hemispherical-directional reflectance factor of this area had a spatial variability, as defined by the CV, in the range of 0.99% to 3.99% in Landsat TM bands 2–4. This is comparable with the reported variability of other test sites around the world, but this is the first time an area has been shown to have this degree of homogeneity over such a long period of time.

The Effects of Tidal Inundation on the Reflectance Characteristics of Coastal Marsh Vegetation

Hyperspectral and sophisticated multispectral imagery are increasingly used to obtain detailed information on species composition, condition, biomass, and other characteristics of coastal marshes. However, how differing levels of tidal inundation affect the reflectance characteristics of emergent marsh vegetation remains not well documented. In 1994–1996, general reflectance spectra were collected for three common brackish marsh species with different canopy architectures (Schoenoplectus americanus, Spartina patens, and Spartina cynosuroides) in field experiments in which changing levels of tidal inundation were simulated in specially nonreflected enclosures. Reprocessing the data with software not then available has allowed greater delineation of variations in the data, especially in the red–near-infrared boundary, that previously could not be differentiated. Spectra for all three species showed significant reductions in near-infrared reflectance (900–1100 nm) with progressive substrate inundation, the sharpest decline occurring for S. americanus, for which the narrow, tapering culms allowed substantial submergence of total aboveground biomass (and greatly decreased leaf area index [LAI]) after water depths on substrates reached 15 cm. Increasing inundation produced a particularly significant change in the 700–825-nm band of the spectral curve. Maximum peak percent reflectance shifted as much as 40 nm in these wavelengths, with two maxima over 100 nm apart in wavelength appearing in both the S. americanus and S. patens curves at water depths >30 cm. Simulations of Landsat Thematic Mapper bands show that the Normalized Difference Vegetation Index (NDVI) is highly correlated with LAI and suggest that NDVI-based estimates for marsh biomass can be strongly influenced by the effects of marsh canopy submergence on LAI.

Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico

Predicting broad‐scale patterns of biodiversity is challenging, particularly in ecosystems where traditional methods of quantifying habitat structure fail to capture subtle but potentially important variation within habitat types. With the unprecedented rate at which global biodiversity is declining, there is a strong need for improvement in methods for discerning broad‐scale differences in habitat quality. Here, we test the importance of habitat structure (i.e. fine‐scale spatial variability in plant growth forms) and plant productivity (i.e. amount of green biomass) for predicting avian biodiversity. We used image texture (i.e. a surrogate for habitat structure) and vegetation indices (i.e., surrogates for plant productivity) derived from Landsat Thematic Mapper (TM) data for predicting bird species richness patterns in the northern Chihuahuan Desert of New Mexico. Bird species richness was summarized for forty‐two 108 ha plots in the McGregor Range of Fort Bliss Military Reserve between 1996 and 1998. Six Landsat TM bands and the normalized difference vegetation index (NDVI) were used to calculate first‐order and second‐order image textures measures. The relationship between bird species richness versus image texture and productivity (mean NDVI) was assessed using Bayesian model averaging. The predictive ability of the models was evaluated using leave‐one‐out cross‐validation. Texture of NDVI predicted bird species richness better than texture of individual Landsat TM bands and accounted for up to 82.3% of the variability in species richness. Combining habitat structure and productivity measures accounted for up to 87.4% of the variability in bird species richness. Our results highlight that texture measures from Landsat TM imagery were useful for predicting patterns of bird species richness in semi‐arid ecosystems and that image texture is a promising tool when assessing broad‐scale patterns of biodiversity using remotely sensed data.

Factors affecting the accuracy of forest clear-cut area estimation on medium spatial resolution satellite winter images

Keskmise ruumilise lahutusega talvistelt satelliidipiltidelt tehtavate lageraiealade pindalahinnangute täpsust mõjutavad faktorid The aim of this paper was to investigate the influence of attributes, describing clear-cut patch size, age, shape, nearest neighbours and habitat conditions on classification results of medium spatial resolution Landsat Thematic Mapper (TM) satellite images. The study area was Võru State Forest District in South-East Estonia and the satellite images used were made in late winter when the ground was covered with snow. Clear-cuts as significantly changed areas in forests were discerned from non-changed areas with thresholding of a two-date difference image. The results of the statistical analysis showed that clear-cut attributes had an influence on image classification results. The most influential variables (presented in decreasing order of significance) are the mean of clear-cut patch pixel values on the difference image, the relative boundary length with forest, the relative boundary length with coniferous forest and the clear-cut area to perimeter ratio. The age of clear-cut and habitat conditions had no statistically significant influence on classification results. The set of influential attributes remained the same when the classifications were performed on two more liberal and on two more conservative thresholding levels. Images in the visible and near infrared spectral region (Landsat TM bands 1-4) revealed appropriate for clear-cut mapping. The difference in the area of a single clear-cut patch represented in the forestry database to that classified from a Landsat TM image was about a sixth of the patch size. This implies the utility of medium resolution satellite images in clear-cut activity assessments in particular areas but not so much the applicability of these images for single patch area estimations.

Canopy reflectance in cotton for growth assessment and lint yield prediction

A field experiment was conducted in 2001–2002 to investigate relationships between canopy spectral reflectance and leaf area index (LAI), aboveground biomass (ABM), and lint yield of irrigated cotton across four N fertilizer rates of 0, 56, 112, and 168 kg N ha −1. These N rates were used to generate a wide range of difference in canopy structure and lint yield. Measurements of canopy reflectance were made throughout the growing season using a hand-held spectroradiometer. Samples for LAI and ABM were obtained four (2001) or five (2002) times during squaring and fruiting. Mean reflectance values in red ( R red) and near infrared ( R NIR) regions were obtained from canopy reflectance data based on the Landsat Thematic Mapper bands. The reflectance ratio vegetation index (RVI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wide dynamic range vegetation index (WDRVI) as well as several hyperspectral reflectance indices were calculated. Most reflectance indices had exponential relationships with both LAI and ABM and reached saturation at high LAI and ABM, but were linearly correlated with log(LAI) and log(ABM). Relative lint yield was linearly correlated to the reflectance indices measured any time after the first square stage and the strongest correlation was obtained at the early flower stage with r 2 of 0.56–0.89 ( P < 0.01). Therefore, the canopy reflectance indices measured at early flower stage of cotton growth could serve as input to a crop growth model for predicting potential yield loss. These results indicate that the early flower stage is an appropriate time to collect canopy reflectance data for cotton yield estimation.

Determination of Turbidity in Kourris Dam in Cyprus Utilizing Landsat TM Remotely Sensed Data

Determination of turbidity is a common component of water-quality assessments. In regions where there are a lot of inland waters such as dams, sampling even a small proportion of those dams for monitoring and assessing water quality is cost prohibitive. Satellite remote sensing has the potential to be a powerful tool for assessing water quality over large spatial scales. The overall objective of this study was to examine whether Landsat-5 TM (Thematic Mapper) and Landsat-7 ETM+ (Enhanced Thematic Mapper) could be used to measure turbidity across theKourris Dam, which is the biggest dam in Cyprus. This paper presents the results obtained by applying the linear regression analysis in order to examine the relationship between the turbidity measurements measured in-situ during the satellite overpass against at-satellite atmospheric corrected reflectance values. It has been found that the reflectance, after atmospheric correction, at LandsatTMBands 1 and 3 is strongly related with turbidity levels after linear regression analysis. The most significant correlation was occurred when reflectance in TM band 3 and logarithmic reflectance in TM band 3 were correlated with turbidity measurements. Indeed, the correlation coefficient (R) when atmospheric corrected reflectance (ρ) in the LandsatTMband 3 were correlated against turbidity, before atmospheric correction was R = 0.38 and after atmospheric correction was R = 1; and when atmospheric corrected logarithmic reflectance (Log ρ) in the Landsat TM band 3 were correlated against turbidity, before atmospheric correction was R = 0.46 and after atmospheric correction was R = 1.

Does the azimuth orientation of Norway spruce ( Picea abies/L./Karst.) branches within sunlit crown part influence the heterogeneity of biochemical, structural and spectral characteristics of needles?

The goal of this study was to determine if selected biochemical, structural and spectral properties of Norway spruce needles are influenced by the azimuth orientation of the branch. Three youngest needle age classes from 20 mature (100 years old or older) Norway spruce trees were sampled from upper branches of the sunlit production crown part from each of the 4 cardinal azimuth orientations. Photosynthetic pigments, soluble phenolic compounds and selected spectral and structural characteristics were determined for each needle age class. The content of photosynthetic pigments and soluble phenolic compounds did not differ among needles from different azimuth-oriented branches, nor did the optical reflectance indices Normalized Difference Vegetation Index (NDVI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI)/Optimized Soil-Adjusted Vegetation Index (OSAVI), Red Edge Inflection Point (REIP) and Landsat Thematic Mapper bands 5 and 4 (TM5/TM4). No variation in volume properties, tissue volume proportions and cross-section shape characteristics of 3rd-year needles rejected our hypothesis that there would be variation in needle structural properties according to the azimuth orientation of branches. Consequently, we concluded that a random sampling of similar-aged needles within the sunlit production crown part may be used to study biochemical or structural and spectral needle properties of a mature Norway spruce growing in forest stands without a significant slope. In addition, the results obtained from a branch of one azimuth orientation should be representative for the whole sunlit portion of the crown. Consequences of these findings for Norway spruce health monitoring using remote sensing techniques are discussed.

A land‐cover classification for landslide susceptibility mapping by using feature components

Classifying original bands and/or image components may cause unsatisfactory results in fields that have heterogeneous reflectance. In such cases, the demand for accurate land‐use, land‐cover, vegetation, and forestry information may require more specific components. The components should represent peculiar information collected from several inputs for target land covers. In this study, a new technique of land‐cover classification was explored to prepare an input which increases the success of landslide susceptibility mapping in a subtropical region, Asarsuyu Catchment Area (Duzce). Land‐cover mapping is a difficult issue in this area by only carrying out field studies and aerial‐photo interpretations. Moreover, applying different classifications of Landsat Thematic Mapper bands and/or their secondary products does not produce acceptable results. For this reason, vegetation indices, soil/surface moisture indices, topographic wetness index and drainage density were calculated to produce feature representative components for the land‐cover classification process. Results obtained from the proposed technique show that feature representative components significantly improve the conventional classification accuracy from 77% to 89% and the resultant land‐cover map is such a valuable input for landslide susceptibility mapping that it increases the success of the landslide susceptibility map from 63% to 88%.

Remote sensing of crop residue cover and soil tillage intensity

Management of plant litter or crop residues in agricultural fields is an important consideration for reducing soil erosion and increasing soil organic C. Current methods of quantifying crop residue cover are inadequate for characterizing the spatial variability of residue cover within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Iowa in May and June 2004. Crop residue cover was measured in corn ( Zea mays L.) and soybean ( Glycine max Merr.) fields using line-point transects. Spectral residue indices using Landsat TM bands were weakly related to crop residue cover. With the Hyperion data, crop residue cover was linearly related to the cellulose absorption index (CAI), which measures the relative intensity of cellulose and lignin absorption features near 2100 nm. Coefficients of determination ( r 2) for crop residue cover as a function of CAI were 0.85 for the May and 0.77 for the June Hyperion data. Three tillage intensity classes, corresponding to intensive (<15% residue cover), reduced (15–30% cover) and conservation (>30% cover) tillage, were correctly identified in 66–68% of fields. Classification accuracy increased to 80–82% for two classes, corresponding to conventional (intensive + reduced) and conservation tillage. By combining information on previous season's (2003) crop classification with crop residue cover after planting in 2004, an inventory of soil tillage intensity by previous crop type was generated for the whole Hyperion scene. Regional surveys of soil management practices that affect soil conservation and soil C dynamics are possible using advanced multispectral or hyperspectral imaging systems.

The effects of successional age and forest site type on radiance of forest clear-cut communities

Remote sensing offers reliable information on the rate and pattern of clear-cut logging and provides this information as relatively frequent updates, over large areas, and at relatively low cost. Temporal gaps in yearly images in a multiyear data set ranging from 1 year to multiple years can lead to inaccuracies in the determination of the area of clear-cuts and the estimation of clear-cut age. This study tested the dependence of reflectance in Landsat Thematic Mapper bands 1–5 and 7 and normalized difference vegetation index (NDVI) on forest age, gap size and forest type within the first 10 years following clear-cut logging in different seasons. The results of the study show that late winter images with snow-covered ground are phenologically the best timed images for detecting forest clear-cut area regionally. Spring and summer image are more useful for estimation of the successional age of clear-cut areas in various soil fertility and moisture conditions.

Issues related to delineation of forest boundaries on Landsat Thematic Mapper winter images

This paper describes an approach for estimating the effect of factors influencing the determination of forest boundaries on medium-resolution satellite images. Forest edges were delineated on a Landsat Thematic Mapper (TM) image made in late winter under plain snow cover conditions. The study investigated the best Landsat TM spectral band and threshold value for the detection of forest edges in winter images. It was hypothesized that shadows cast by trees on forest edges on the bright snow of the surrounding open area make north- or north-west-facing forest edges less sharp than edges facing in other directions. If this holds true for medium-resolution Landsat TM satellite images, forest area change studies should carefully consider images taken under different atmospheric and solar elevation conditions in order to distinguish real changes at forest edges from those stemming from different conditions of solar illumination. The results of the study show that there is no significant shadow effect, as the reflectance contrast at forest edges exposed in different azimuthal directions does not differ on Landsat TM winter images under plain snow cover conditions. Landsat TM bands 2-4 are all equally good for the detection of the forest edge location at an average value of reflectance. These results are valid for forest edges that have remained stable for several decades.

An assessment of Landsat TM band 6 thermal data for analysing land cover in tropical dry forest regions

This research investigates the utility of integrating Landsat band 6 thermal data for land cover classification and, specifically, for the differentiation between successional stages of forest growth. Such successful differentiation has become critical for the assessment of land cover and land use change. The study area is in the state of Yucatan, Mexico, which is a region of predominantly tropical dry forest. Thermal band data measures the emission of energy from the Earth's surface and, as this is a function of the surface cover, it can be used as a determinant of land cover type based on the temperatures measured. This research uses a technique of land cover classification utilizing Landsat Thematic Mapper (TM) thermal band data. Land cover classes were found to relate strongly (an eta correlation ratio of 0.804) to band six calculated black body temperatures. Satellite coverages of black body temperatures also provided a unique spatial analysis of the image area using continuous data, not discrete classes. Discriminant analysis revealed that band 6 contains considerable information for the discrimination of land cover classes in the dry tropical forest ecosystem, with a coefficient of 0.354. Band 6 also outperformed many of the individual spectral bands of information, despite their greatly improved (×16) resolution.

Determining depth and ice thickness of shallow sub-Arctic lakes using space-borne optical and SAR data

An approach to determine depth and ice thickness of shallow lakes and ponds using Landsat Thematic Mapper (TM) and European Remote Sensing (ERS)-1 Synthetic Aperture Radar (SAR) data is presented. A summertime Landsat TM image is used to map lake bathymetry and multi-date ERS-1 images acquired during winter are utilized to determine when and which lakes freeze to the bottom during winter. The two remotely sensed derived products are then combined to estimate ice thickness from lakes and ponds on a monthly basis. The approach has been developed and tested successfully in a sub-Arctic tundra-forest landscape in the Hudson Bay Lowland near Churchill, Manitoba, Canada. Lake depth estimates derived from Landsat TM band 2 generally compared well with measurements obtained in the field, especially in the tundra zone [rms error (RMSE) = 15 cm]. Maximum ice thickness estimates were also within the range of those typically measured during winter in this study area (tundra and forest-tundra zones @ 1.6 m; open forest zone ; 1.2 m). Results indicate that the approach is particularly well suited for estimating depth and ice thickness of shallow oligotrophic and ultra-oligotrophic lakes that are widespread in many regions above treeline. However, the results also suggest that the Landsat-based approach will require further testing and improvement if one wishes to map bathymetry for shallow lakes in which large nutrient concentrations or amounts of suspended sediments are found.

Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas

Hyperspectral sensors can make narrow-band measurements for several hundred regions of the electromagnetic spectrum, and with increasing frequency, multi-dates of remotely sensed data are being used for Earth observation purposes. The use of more spectral bands is creating greater demand for larger computer storage capacity and faster data processors. This study evaluates the use of raw Thematic Mapper (TM) band combinations and several derived vegetation indices to determine optimal vegetation indices and band combinations for discriminating among six grassland management practices in eastern Kansas. Results showed that among the transformed dataset, the Greenness Vegetation Index was found to be the best for discriminating among grassland management types. When evaluating the raw TM bands, TM4 (NIR) was always selected in Discriminate Analysis as the best discriminating variable. There is no significant improvement in grassland discrimination by using a combination of the raw TM bands and the vegetation indices. Increasing the number of TM bands by using multiple dates of imagery will improve discrimination accuracy up to a point, but the use of too many bands (greater than 10 or 12) can actually decrease discrimination accuracy.

Wetland classification for Swedish CORINE Land Cover adopting a semi-automatic interactive approach

In a collaboration between the Swedish Space Corporation and the National Land Survey of Sweden, work has been carried out to develop a satellite-data-based method for mapping wetlands for the Swedish CORINE Land Cover database. The aim was two-fold: (1) develop a method for mapping the wetlands in five classes, namely "inland marshes", "wet mires", "other mires", "exploited peatlands", and "salt marshes", with a minimum mapping unit of 1 ha; and (2) investigate if the classes "wet mires" and "other mires" could be obtained more reliably from satellite data than directly from 1 : 50 000 scale topographic maps. Data sources used in the project included geo-referenced Landsat thematic mapper (TM) images, digital topographic map data, digital vegetation data, and colour infrared aerial photographs. The development work included analysis of spectral signature characteristics, classification tests, method definition, method verification, pilot production, and accuracy assessment. The method developed to obtain the five wetland classes is a combination method. The classes inland marshes, salt marshes, and exploited peatlands are mapped using geographical information system (GIS) operations, combining information from the topographic maps, and visual interpretation of Landsat TM data. For the classes wet mires and other mires, a stepwise semi-automatic approach was adopted, where level slicing in ratios or in single Landsat TM bands is used to produce the classes. The evaluation showed high accuracy for all five classes, with overall accuracies ranging from 90% to 100%. It was also concluded that the semi-automatic interactive approach used to produce wet mires and other mires gave the same high accuracy as the topographic map, or a better result than the map for areas in northern Sweden where the representation of wet mires on the map is not as good. The method described is used for production of the wetland classes for the Swedish CORINE Land Cover.

Surface albedo measurements over snow and blue ice in thematic mapper bands 2 and 4 in Dronning Maud Land, Antarctica

Ground‐based observations of broadband, narrowband (in Landsat thematic mapper bands 2, 0.52–0.60 μm, and 4, 0.76–0.90 μm), and bidirectional reflectance are used to study the albedo of blue ice and snow. The measured broadband albedos for blue ice and snow are 0.60 and 0.78, respectively. The narrowband albedos in bands 2 and 4 for snow are 0.97 and 0.86, respectively, and for blue ice are 0.87 and 0.51, respectively. The measurements are point values and used to verify existing weighting functions between the broadband and narrowband albedos. Blue ice data do not comply with the existing functions. Furthermore, a new function for blue ice is derived. A simple two‐stream model is used to illustrate the behavior of the snow and ice albedo as a function of reflection and absorption. The bidirectional reflectance measurements are used to derive bidirectional reflectance distribution functions (BRDFs) for snow and blue ice. The BRDF of blue ice exhibits a maximum brightening in the forward limb around a zenith angle close to the solar zenith angle, indicating specular reflectance of blue ice which is not visible for snow and glacier ice surfaces.