Integration of Landsat and aeromagnetic data as aid to the structural analysis of Crete and S.E. Peloponessus

Abstract

The paper comments on the usefulness of remotely-sensed data (Land-sat MSS images in both digital and photographic format—aeromagnetic data) in the tectonic analysis of areas of Greece. The island of Crete was the main case study area, while a general analysis was also carried out in the South Eastern Peloponessus.

Both image processing (spectral and spatial analysis) of the Landsat CCTs of Crete and computer analysis of the features mapped on the images have been carried out. Aeromagnetic data are also analysed using advanced processing techniques (spectral analysis and deconvolution). Structural interpretations are improved by the study of the enhanced Landsat images and aeromagnetic maps, while the use of the various computer techniques makes the analysis of the mapped patterns easier and more accurate.

The combined interpretation of aeromagnetic and Landsat MSS data added several significant structural features, previously unrecognised from separate interpretations of aeromagnetic data and Landsat images.     

The utility of multi-sensor data for mapping eroded lands

The availability of remote sensing data with improved spatial, spectral and radiometric resolution is now available to fully exploit their potential for a specific application subject to the relative merits and the limitations of each sensor's data. Presented here is a case study where Landsat MSS and TM; and SPOT MLA data for part of the Bijapur district, southern India, which were acquired on the same day, have been evaluated for mapping eroded lands. The approach involves the geometric registration of all three data to a common map grid using tie points and third order polynomial transform; and resampling the MSS and TM data to a 20m by 20 m pixel dimension and radiometric normalization. Thematic maps showing eroded lands were generated on a micro-VAXbased DIPIX system using a maximum likelihood classifier. Accuracy estimates were made for the thematic maps following stratified unaligned random sampling technique, and subsequently, computing overall accuracy and Kappa coefficient. Spectral separability and classification accuracy was maximum from SPOT-MLA data followed by a combination of Landsat MSS band 1, SPOT-MLA band 2 and Landsat TM band 4; Landsat TM, a combination of Landsat MSS, TM and SPOT MLA; and Landsat MSS data.     

Application of thematic mapper data to corn and soybean development stage estimation

A model, utilizing direct relationship between remotely sensed spectral data and the development stage of both corn and soybeans has been proposed and published previously (Badhwar and Henderson, 1981; and Henderson and Badhwar, 1984). This model was developed using data acquired by instruments mounted on trucks over field plots of corn and soybeans as well as satellite data from Landsat. In all cases, the data was analyzed in the spectral bands equivalent to the four bands of Landsat multispectral scanner (MSS). In this study the same model has been applied to corn and soybeans using Landsat-4 Thematic Mapper (TM) data combined with simulated TM data to provide a multitemporal data set in TM band intervals. All data (five total acquisitions) were acquired over a test site in Webster County, Iowa from June to October 1982. The use of TM data for determining development state is as accurate as with Landsat MSS and field plot data in MSS bands. The maximum deviation of 0.6 development stage for corn and 0.8 development stage for soybeans is well within the uncertainty with which a field can be estimated with procedures used by observers on the ground in 1982.     

Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape

An investigation is conducted, for a complex vegetated land area, into the statistical relationship between remotely sensed thermal emissions and reflected spectral radiance. The Kauth—Thomas Tasseled Cap transformation is employed to infer the albedo and amount of green vegetation present from Landsat multispectral scanner (MSS) observations. Reflective data and thermal infrared data were acquired from the Heat Capacity Mapping Mission (HCMM) satellite along with reflective data from the Landsat 3 MSS for a site near Hartford, CT on a single date. Results are presented which indicate that thermal emissions had the greatest association with the amount of vegetation as indicated by a multispectral index, while albedo did not exhibit any substantial relationship with these emissions. These findings are explained in terms of the enhanced latent heat flux to the atmosphere associated with actively transpiring vegetation.     

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 also noted that individual texture orientations resulted in higher class accuracies than average texture measures; this is probably related to structural (slope/aspect) characteristics of specific vegetation communities.     

Determining the rate of forest conversion in Mato Grosso,Brazil, using Landsat MSS and AVHRR data

Abstract

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and non-forest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. Slate-wide estimates of forest conversion indicate that between 1981 and 1984, 353966 ha ±77 000 ha (0·4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis)and fire activity (estimated using AVHRR data)was noisy (R²= 0·41). The results suggest that AVHRR data may be put to better use as a stratification tool rather than as a subsidiary variable in list sampling.     

Effect of Landsat thematic mapper sensor parameters on land cover classification

Selected sensor parameter differences between TM and MSS were assessed through classification performance of a suburban/regional test site. Overall classification accuracy of a seven-band Landsat TM scene in comparison to MSS yielded an improvement in accuracy from 74.8% to 83.2%. To study the possible causes for the difference in classification performance, key sensor parameter differences between MSS and TM, including 1) spatial resolution (30 m for TM versus 80 m for MSS), 2) quantization level (256 levels for TM versus 64 for MSS), and 3) spectral regions (seven bands in four major spectral regions for TM versus four bands in two regions for MSS), were evaluated. Landsat TM data were processed to simulate all possible combinations of these MSS and TM parameters, yielding a three-factor design with two levels per factor. The results indicated that the added spectral regions (TM 1, TM 5, and TM 7) and to a lesser degree the increase in quantization level to eight bits produced the improved TM classification accuracy. However, in this study, the higher 30 m spatial resolution of TM contributed to a reduced classification accuracy from increased within-field variability or class heterogeneity.     

Remote sensing of salinity in the San Francisco Bay Delta

Salinity of the San Francisco Bay Delta has been studied for the past seven decades. There is a significant gradient in salinity within this estuarine system that influences the growth and distribution of phytoplankton as well as the abundance and migration of shrimp and fish population. Several government agencies which have jurisdictions over this area are attempting to gather extensive data for effectively monitoring of this estuary. Repetitive remotely sensed data acquired from Landsat may be considered by these agencies as having the potential to provide a cost-effective method for gathering and processing water quality related data. In this study, Landsat multispectral scanner (MSS) data and color and color infrared photographs acquired from a U-2 aircraft were combined with surface measurements for salinity mapping of the San Francisco Bay Delta. The salinity measurements and U-2 photography were obtained simultaneously and coincident with landsat overpass. A regression model was developed between the surface truth data and Landsat digital data for 29 preselected sample sites and was then extended to the entire study area. The results included a salinity map of the study area and the statistical summaries. The results were in general agreement with the reported distribution of salinity values in the literature for the same time of the year. Based on the results and the associated analyses of natural color and color infrared photographs and Landsat color composite imagery, it was concluded that: (1) it was virtually impossible, at least within this test site, to establish any quantitative judgement regarding the salinity values by visual interpretation of the imagery; and (2) the present study constitutes the first effort to successfully use Landsat digital data for salinity mapping, by means of digital processing, for this geographic area.     

Statistical models of fragmented land cover and the effect of coarse spatial resolution on the estimation of area with satellite sensor imagery

The feasibility of correcting for errors in apparent extent of land cover types on coarse spatial resolution satellite imagery was analysed using a modelling approach. The size distributions for small burn scars mapped with two Landsat Multi-spectral Scanner (MSS) images and ponds mapped with an ERS-1 synthetic aperture radar (SAR) image were measured using geographical information system (GIS) software. Regression analysis showed that these size distributions could be modelled with two types of statistical distributions a power distribution and an exponential distribution. A comparison of the size distributions of small burn scars as observed with the Landsat MSS imagery to the distribution observed with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery indicated that distortions due to the coarse spatial resolution of AVHRR caused overestimation of the burn area. This bias was primarily caused by detection in two or three AVHRR pixels of burns whose actual size was on the order of a single AVHRR pixel. Knowledge of the type of the actual size distribution of small fragments in a scene and the causes of distortion may lead to methods for correcting area estimates involving models of the size distribution observed with coarse imagery and requiring little or no recourse to fine scale data.     

Moorland plant community recognition using landsat MSS data

Landsat MSS data are examined to determine to what extent moorland plant community types can be recognized in the Plynlimon area of Wales. A detailed comparison is made between spectral radiance data and community composition. Attention is drawn to the importance of a phenological understanding of the dominant plant species and in particular the proportion of living and standing dead plant parts as an important aid to distinguishing between species and communities.     

Pre-harvest wheat yield and production estimation for the Punjab,India

Two different approaches to relate wheat yield with spectral indices derived from remotely-sensed data have been explored for the state of Punjab, India. In the study based on site-level approach yield obtained from crop-cutting sites was found to be linearly related to NIR/Red ratio derived from Landsat MSS data of corresponding sites in Ludhiana and Patiala districts of Punjab. Incorporation of agrometeorological data was also tried. Certain inherent limitations of the site-level approach led to the district-level studies which focused on the relation of district yields with corresponding average spectral indices derived from satellite sensors like Landsat MSS and lRS-LISS-i. Significant correlations were observed in all cases and the relation based on Landsat MSS/IRS LISS-I data was used for trial forecast of wheat yields for 1989–90 season. A comparison of remote-sensing based production forecast showed good agreement with the conventional estimate of Bureau of Economics and Statistics at state level although at district level, deviations were larger.     

An approach for assessing boreal forest conditions based on combined use of satellite SAR and multi-spectral data

The degree and spatial distribution of boreal forest ecosystem degradation in Russia are not well known. The objective of this study is to develop an interpretation basis for analysis of satellite remote sensing data using a set of indicators characterizing the ecological situation and the degree of industrial pollution. European Remote Sensing Satellite (ERS) Synthetic Aperture Radar (SAR) and Landsat Multi-Spectral Scanner (MSS) data are used in combination for this purpose, along with an exceptionally extensive in situ data set of ground measurements of spectral radiance of pine biocenose components, and the results of moss chemistry and bio-indicator studies from the ecologically stressed St Petersburg region. It is shown that ERS SAR images provide an assessment of forested area distribution and forest type classification. The main factors of variability in parameters such as Normalized Difference Vegetation Index (NDVI) that are most strongly related to in situ indicators reflecting the state of the forest are identified. A supervised classification of forest degradation was performed on the basis of the NDVI values from the Landsat images. The results obtained make it possible to specify the areas at a local level and perform regional assessments. The potential for multi-temporal ERS SAR and multi-spectral sensor observations to trace the dynamics of changes in forest ecosystems is evaluated.     

Regression and ratio estimators to integrate AVHRR and MSS data

Regression and ratio estimators are used to integrate AVHRR-GAC and Landsat MSS digital data to estimate forest area in the continental United States. Forestlands are enumerated for the 48 contiguous states using five different AVHRR-GAC data sets. The five GAC data sets tested, each with a spatial resolution of 4 km, were composed of different combinations of vegetation index and thermal data acquired over the nine month growing period in 1984. Twenty Landsat MSS scenes were selected countrywide and used to calibrate AVHRR forest estimates. Results indicated that the GAC and MSS forest estimates were not highly correlated; R² values ranged from 0.5 to 0.7. Although the ratio of means and linear regression corrections were, on the average, closer to national U.S. Forest Service forest area estimates, these correction procedures did not consistently improve GAC estimates of forest area. GAC forest area estimates tended to be high in densely forested regions such as the northeast and low in sparsely forested areas. This fact, and the low correlation coefficients, indicate that AVHRR data should be used for primary stratification (with MSS as the second stage) and not as an auxiliary variable in a regression correction procedure.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

Large-area relation of landsat MSS and NOAA-6 AVHRR spectral data to wheat yields

Landsat MSS data transformed into Kauth-Thomas greenness were averaged over 5 n.mi. × 6 n.mi. sample segments from the U.S. Great Plains winter and spring wheat (Triticum aestivum) regions, and related by regression analysis to yields reported by county, crop reporting district (crd), and state levels. Evidence of a linear relation between winter- and spring-wheat yields and Landsat spectral data at a broad scale is shown for 1978 and 1979. A common slope of about 1.6 (Bu/A)/unit greenness is discerned for the relation between yield and spectral greenness. Tests at both a smaller scale on sets of field-level spectral data and yield and at a large scale on 25 mi. × 25 mi. gridded spectral data from the NOAA-6 avhrr sensor support the relation. The implications of these results to yield estimation from satellite spectral data are discussed.     

Analysis of digital LANDSAT MSS and SEASAT SAR data for use in discriminating land cover at the urban fringe of Denver,Colorado

Digital SEASAT synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) data were evaluated to determine their utility to discriminate suburban and regional cover in the eastern fringe area of the Denver, Colorado, metropolitan area. The primary emphasis of the study was land-cover discrimination performance of MSS versus SAR and SAR/MSS combined. In addition, both a median-filtering and a data-smoothing procedure were tested in an attempt to increase the spectral separability between land-use/land-cover classes for SAR data. The results indicated that analysis of LANDSAT MSS data alone provided a significantly (α = 0·05) higher overall classification accuracy or improved spectral class separation than the best SEASAT SAR classification. However, when using LANDSAT MSS and SEASAT SAR data simultaneously, a significant increase in classification accuracy was obtained. Analysis indicated that SEASAT SAR data provided a measure of surface geometry that complemented the reflective characteristics of LANDSAT MSS visible and near-infrared data. Smoothing and median filtering provided significant improvement in classification accuracy over non-filtered SAR data.     

Water quality mapping from Landsat digital data

Landsat multispectral scanner (MSS) data, and U-2 colour and colour infrared photographs were combined with in situ data for the assessment of water quality parameters within the San Francisco Bay-Delta. The water quality parameters of interest included turbidity and suspended solids. The U-2 photography and water quality samples were obtained simultaneously and coincidently with Landsat overpass. Regression models were developed between each of the water quality parameter measurements and Landsat digital data for 29 pre-selected sample sites. These regression models were then extended to the entire study area for mapping the water quality parameters of interest. The results included a series of colour-coded maps, each pertaining to one of the water quality parameters, and the statistical summaries. Areas of relatively high biological activity were clearly discernible on digitally enhanced Landsat MSS data.     

多尺度下汾河流域生态环境质量评价与时序分析

选择1970s的Landsat MSS、1993年和2009年的Landsat TM遥感影像,利用归一化植被指数、归一化差异水体指数、阈值法、谱间关系法、监督分类与非监督分类相结合的方法对汾河流域信息进行提取,选择上、中、下游,行政地区和小流域3个尺度,参考环境保护总局发布的《生态环境状况评价技术规范》,选择生物丰度指数、水网密度指数、植被覆盖度、土壤退化指数与环境质量指数对汾河流域进行生态环境质量评价,并进行时序分析。结果表明：在3个不同尺度下汾河流域生态环境质量均为一般或较差;从1970s到1993年生态环境质量降低,1993年到2009年流域生态环境质量开始改善;不同尺度下的生态环境质量得分不同,但变化趋势相同。     

基于混合光谱分解的兰州城市热岛与下垫面空间关系分析

利用Landsat ETM+数据,采用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面,通过单窗算法反演地表真实温度,对兰州市中心城区的夏季城市热岛强度与城市下垫面的空间分布关系进行相关分析。结果显示,利用中等分辨率ETM+影像对兰州中心城区不透水面和植被盖度分布提取,其成本较低,精度令人满意;兰州城区植被覆盖、不透水面与热岛强度的分布呈空间正自相关,地表温度的空间依赖性极强,与植被盖度和不透水面在空间方向上的相关性差异较大。     

Using Landsat TM data to estimate carbon release from burned biomass in an Alaskan spruce forest complex

Fire disturbance in boreal forests can release carbon to the atmosphere stored in both the aboveground vegetation and the organic soil layer. Estimating pyrogenic emissions of carbon released during biomass burning in these forests is useful for understanding and estimating global carbon budgets. In this work, we have developed a method to estimate carbon efflux for the burned black spruce in an Alaskan forest by combining information derived from Landsat Thematic Mapper (TM) data and field measurements. We have used the spatial and spectral information of TM data to identify and measure two important factors: pre-burn black spruce density and burn severity. Field measurements provided estimates of aboveground and ground layer carbon per unit area for the pre-burn Landsat spectral classes, and percentage of carbon consumed for the post-burn Landsat spectral classes. Carbon release estimates for the burned black spruce were computed using field data and the co-occurrence of the pre-burn and post-burn spectral classes. The estimated carbon released was 39.9tha-1, which is 57% greater than an estimate computed using AVHRR data and estimates of pre-burn biomass and carbon fractions consumed that were not site specific or spatially varying. We conclude that the spectral bands and spatial resolution of Landsat TM data provide the potential for improved estimates of pyrogenic carbon efflux relative to the coarser spectral and spatial resolution of other multispectral sensors.