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Use of the “Tasseled Cap” Transformation for the Interpretation of Satellite Images
Abstract and Figures
In this paper are treated the aspects regarding spectral enhancement “tasseled
cap” applied to the satellite images of medium resolution spatial Landsat 5 Thematic Mapper and
Landsat 7 Enhanced Thematic Mapper Plus. The technique is similar to the principal components
analysis and suppose the reduction of the data dimension requisite for interpretation of satellite
registering. The analysis of the image obtained shows that this can be used on the large area to
differentiation of land use.
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... Remote sensing and GIS techniques have been used to detect changes in land use, forest disturbances, and/or land degradation particularly utilizing Landsat multitemporal images (Amine & Hadria, 2012;Boori, Netzband, Choudhary, & Voženílek, 2015;Devries, Pratihast, Verbesselt, Kooistra, & Herold, 2016;Hislop et al., 2018;Plaiklang et al., 2008;Schultz et al., 2016). Meanwhile, identifying current land use/land cover through classification of Landsat images has been extensively utilized in several researches and contexts (Ali & Salman, 2016;Amine & Hadria, 2012;Boori et al., 2015;Devries et al., 2016;Rokni, Ahmad, Selamat, & Hazini, 2014;Schultz et al., 2016;Vorovencii, 2007;Wilson & Sader, 2002;Young et al., 2017). These techniques can be utilized to satisfy our objectives to wit: (1) identify and measure past and present land cover, (2) estimate vegetation loss, and (3) determine past and present urban density, and, lastly, calculate the urban growth rates of the cities of Cebu, Mandaue, and Lapu-Lapu (excluding Olango Island), as well as the municipalities of Consolacion, Cordova, and Liloan. ...
... We proceed with Tasseled Cap Transformation to utilize the rest of the available bands i.e. G and R (Ali & Salman, 2016;Amine & Hadria, 2012;Schultz et al., 2016;Vorovencii, 2007). The different bands of TCT (Brightness (TCb), Greenness (TCg), and Wetness (TCw)) are calculated from the surface reflectance images from all years using the following equations (Eq. ...
The rapid urbanization in Cebu in recent years changed the land use patterns in the province. Areas allotted to forest or agricultural use are on a decline. In order to gain insight to the extent of vegetation loss in relation to the expanding urban sprawl in Cebu, multi-temporal analysis of Landsat images covering Cebu City, Mandaue City, Consolacion, Liloan, Lapu-Lapu City (excluding Olango island), and Cordova for the period 1994 to 2019 was conducted. The NDVI, NDMI, and Tasseled Cap (TC) indices were computed to aid in the Land Use/Land Cover classification of the different Landsat images. After classification, urban density, growth rate, and vegetation loss were estimated. Urbanization was found to have widened in all the of the study areas but at different paces. Notable for their growth rates are Mandaue City and Lapu-Lapu City. Lapu-Lapu have four of the "fastest" growing grids while Mandaue have the 5 th fastest growing grid. The direction of urbanization since 1994 was also determined. The identification of areas of rapid growth and considerable vegetation loss provide government officials basis for land use regulation. Enactment of updated land use plans by local governments is recommended to ensure the rational allocation and proper use of limited land resources.
... The values from wetness component of tasselled cap transformation (TCT) provide insights on presence of surface water. It indicates the health of vegetation and further assists in differentiating the crops from grassy land (Vorovencii, 2007). All the variables are normalised to fit between the value of -1 and 1, making them comparable to each other and over different years using min-max normalisation technique. ...
The city of Chandigarh has been urbanizing and expanding at an aggressive rate. Despite the urban expansion being mostly planned in nature has shown underlying indications of deteriorating ecological health in the city and its abutting natural resources. Changing migration patterns and decreasing vegetation cover are just few of the indicators raising the need to analyse the ecological quality of the region. Ecological quality can be seen as a measure of the health of an environment to sustain life. Remote sensing can be used to monitor the land surface over varied spatiotemporal extents. This study uses the Remote Sensing-based Ecological Index (RSEI) to study the spatiotemporal changes in the environmental quality of the city of Chandigarh over four decades. Four factors i.e., greenness, wetness, dryness, and heat that affect environmental quality are analysed using principal component analysis to calculate RSEI. The contribution of each of the indicators to RSEI and the spatial correlation of results are studied using correlation analysis and Moran’s Index. Landcover maps are developed using Cart classifier to understand the growth patterns and establish relation to changes in ecological index values. The change in RSEI for individual land cover categories shows the degradation of ecological health in natural resources. The RSEI values of vegetation and surface water show a deteriorating trend from 1991 to 2020. Furthermore, the study area shows intense degradation of RSEI values in the city outskirts where a major shift to built-up landcover has taken place. The association of landcover change and its impact on ecological quality can assist planners in adopting suitable strategies to assure that ecological health is integrated when urban expansion is carried out. This study provides insights into the development strategies and their impact on the ecological resources of the city that may otherwise not be identified by overall RSEI value and landcover assessment.
... Tasseled Cap Analysis is based on a linear transformation of data from the original image into three new axes (brightness, greenness, and wetness) which become features of the transformation and allows the creation of other useful raster by-product files like polylines (coastlines; Scott et al., 2003;Vorovencii, 2007). These three bands and the NDVI band were used for ESRI's unsupervised ISO classification algorithm to obtain a binary classification of land and sea for all seven Landsat images (Daniels, 2012). ...
Time-series analysis of satellite imageries is useful in studying changes in coastlines and the nature of the landcover dynamics in coastal environments. This study aimed to investigate the relationship between coastal erosion-accretion and landcover changes in Kuakata. Landsat TM and Landsat OLI/TIRS satellite imageries at a nearly 5-year interval between the years 1989 and 2020 were used to compare changes within five major landcover classes in the study area – 1) mangrove vegetation, 2) settlements, 3) agricultural land, 4) waterbody and 5) beach. Net land loss over the past 31 years was estimated to be 1.73 km2 within the 93.05 km2 study area. Linear regression rates were calculated to identify the area most prone to erosion. The average erosion rate along the coastline was estimated to be 2.09 m/year. Most erosion occurred along the western part of the coast while the highest accretion was limited to an area in the east. Study findings also suggested that changes in beach and mangrove vegetation classes have a significant spatial and statistical correlation with coastal erosion-accretion processes. These findings can help the policymakers implement coastal zonation, and preventive and rehabilitative measures to save the tourism industry and agriculture in Kuakata.
... 2008, 2010, 2012, 2014, and 2016) • Shoreline change statistics (i.e. SCE, NSM, EPR, and LRR ) computation (Vorovencii, 2007) MATERIALS AND METHODS ...
Shoreline change monitoring has become a crucial part of sustainable resource management and disaster risk management. The conventional way of shoreline monitoring was based on field data collection which was very costly, time taking and labor intensive. The recent advancement in Geo-informatics has transformed the conventional ways of field data collection, mapping and analysis in a digital, cost effective, and efficient one. In this study, we used geo-informatics to analyze the shoreline trend of Clifton beach, Karachi, Pakistan for the duration of 2008-2016. We acquired the remotely sensed Landsat TM and ETM+ data for years 2008, 2010, 2012, 2014 and 2016 and extracted shorelines using Tasseled Cap image enhancement and NDVI techniques. With these extracted shorelines, we analyzed the shoreline trend on 63 transits (100 m spaced) along the 6330 m long Clifton beach shoreline using Digital Shoreline Analysis System (DSAS). Results show that during short time span of 2008-2012, there
is a general accretion trend along the Clifton beach. However, after 2012 the trend is changed at some sites. In toto, Clifton beach has experienced net accretion, or very minute erosion during 2008-2016. The shoreline change parameters calculated in this study i.e. Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LLR) will be helpful in predicting the future shoreline positions of Clifton beach. For conservation of natural shape of the beach, the analyzed shoreline trend can be correlated to the natural phenomenon and human activities at study area in last decade and precautionary measures can be taken. The time span of this study can be increased for more in depth understanding of Clifton beach shoreline trend.
... Bright soils are poor in organic matter (Bachaoui et al., 2014). Accordingly, uncovered soils or soils with sparse vegetation are perceived by high brightness values (Gleriani, 2003;Healey et al., 2005;Vorovencii, 2007). The brightness index is able to discriminate the vegetation from minerals in the soil, such as quartz or carbonates, especially when they are dry (Bannari et al., 2016;Baptista and Teobaldo, 2017;Demattê et al., 2014). ...
The inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the “polluter pays principle”, even more in Brazil where the areas occupied by degraded pastures are enormous.
... In remote sensing science, wetness stands for the moisture contents of soil/vegetation, brightness represents variations in soil background reflectance, and greenness reflects variations in the vigor of green vegetation. To discover more about the co-variations and interactions of greenness, brightness, and wetness, the Tasseled Cap Transformation (TCT) is used, which involves the conversion of original band data into composite band readings based on the weighted sum of select separate channels (Vorovencii, 2007). In fact, the TCT is a global vegetative index that separates the amount of soil brightness, vegetation, and moisture content into individual pixels (Watkins, 2005). ...
... Soil moisture is an important parameter for hydrological and ecological modeling practices as well as for land surface process models (Smith 2010). The remote estimation of soil moisture content is a highly laborious task and is also one of the most challenging practices in remote sensing of soil properties (Vorovencii 2007). A variety of spectral indices have been developed by researchers to estimate soil water content based on the application of different sections of the electromagnetic wave (from visible to microwave) (Liang 2001). ...
A surface urban heat island (SUHI) is a significant meteorological phenomenon of the microclimate and environment in urban territories. Knowledge about the variations of SUHI is critical for urban planning and public welfare. In the current study, the seasonal and spatial changes of the Istanbul SUHI and its interactions with spectral indicators of the urban heat phenomenon including the normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and surface albedo were analyzed. The National Aeronautics and Space Administration (NASA) L2 thermal products (brightness temperature) of Landsat 8 imageries were used to calculate land surface temperature (LST) values. The thermal islands of the study area were detected based on the Urban Thermal Field Variation Index method. The retrieved LST values showed acceptable agreement with in situ observations of mean daily temperature for all the seasons. Monthly precipitation, however, demonstrated good correlation with summer and autumn LSTs. It is found that the central parts of the metropolitan area were subject to the most intense SUHI in the spring and summer seasons. Outskirt areas showed higher thermal values during cooler seasons of autumn and winter. The results of spatio-temporal interactions of SUHI and the spectral indicators revealed a negative correlation for NDVI and TCW and a positive correlation for surface albedo during different seasons from summer 2017 to spring 2018. The highest and lowest correlations were found between SUHI and TCW (spring) and surface albedo (winter), respectively. The regression results overall suggested that TCW and NDVI were the best indicators of SUHI in Istanbul. Surface albedo was not recommended for seasonal monitoring practices of SUHI in the study area due to the high differences in its seasonal interactions.
... Su denominación fue otorgada por la forma en que los datos se plasmaban sobre una distribución gráfica conocida como "gorro de borla" (Chuvieco, 1990(Chuvieco, , 2002. Esta técnica es sumamente beneficiosa en cuanto a las potencialidades que ofrece la interpretación de la información satelital que de su cálculo deriva (Vorovencii, 2007). Las posibles aplicaciones de esta técnica se relacionan con la capacidad de reducción de información redundante presente en las diferentes bandas satelitales, mejorando el desempeño en los procesos de clasificación por diferentes métodos. ...
... Since erosion processes are more intense in locations with unprotected soils or with low vegetation cover, the brightness index could be isolated during the application of TC due to its relation to the spectral variation of soils with little or no vegetation coverage (Gleriani, 2003;Vorovencii, 2007). Four images were acquired on 11/17/2017, 2/14/ 2018 (rainy season), 04/30/2018 and 07/19/2018 (dry season). ...
Water erosion has historically been assessed by various methods, with the purpose to help reducing this phenomenon. However, application of models capable to handle complex relationships between large numbers of variables is still relatively scarce. The method of Partial Least Squares-Path Modeling (PLS-PM), used in this study, was able to expose complex causal paths between soil erosion and potentially related factors, namely “Surface Runoff”, “Environmental Land Use Conflicts”, “Soil Fertility” and “Relief Factors”, within the Environmental Protection Area of Uberaba River Basin (EPA) located in Minas Gerais state, Brazil. In the context of PLS-PM, soil erosion (dependent) and the related factors (independent) are called latent variables and described by measured or estimated parameters. For example, the “Relief Factors” were described by measured drainage density and topographic slope. These were linked to the corresponding latent variables through weights and the later joined to each other through paths. During the PLS-PM runs, weights and paths were quantified and latent variables interpreted in regard to their importance for soil erosion and spatial incidence. The spatial incidence was used to prioritize areas for soil conservation. To test the model, data were obtained from soil samples (texture and fertility parameters) or digitally extracted from cartographic products (e.g., maps of soil loss, land use, brightness index, topographic slope, drainage density), at 37 sites within the EPA. The PLS-PM results revealed that 70.2% of soil erosion is predicted by the independent variables (R² = 0.702), and that “Soil Fertility” and “Environmental Land Use Conflicts” were the most influencing ones (β = −0.758 and β = 0.346, respectively). These variables can be managed by man, through implementation of effective soil conservation measures and respect for suitable land use. It is therefore urgent to act in these regard, considering the socioeconomic and environmental importance of the EPA.
... The images were then fed into ENVI ® software to generate the plots. Since most of the information (95-98%) is contained in the first three transformations (Vorovencii, 2007), i.e. brightness, greenness and wetness (third), only these three transformations were performed and presented herein. The whole process is summarized in a flowchart in Fig. 4. It led to the development of TCT maps based on multi-sensor Landsat images and associated statistical analyses. ...
Tasseled cap transformation (TCT) has been used to observe relationships among soil moisture, vegetation cover, and canopy condition. Time series Landsat satellite images of high resolution may provide continuous and accurate observations of the land surface, which can be further analyzed using the TCT for natural hazard events. This study explores the use of a unique dispersion phenomenon of TCT for observing the dynamics of vegetation cover and landscape changes due to a major hurricane landfall in the United States. This is based on the Landsat images taken during the Hurricane Bob event, during which the hurricane made landfall in the highly developed New England area in late August 1991. A unique comparison of the TCT time series plots illustrating the relative TCT dispersion phenomenon, which addresses the landfall's profound impact on the Mattapoisett River watershed in 1991, reflects the interactions among biosphere, atmosphere, hydrosphere, and lithosphere in hurricane prone regions. This analysis can be done without the use of ground truth data and can be further supported by the multitemporal and multidimensional change detection of box plots in terms of brightness and greenness to gain more biophysical interpretation. Findings unveil an inherent earth system process via the varying levels of dispersion among brightness, greenness, and wetness over the coastal watershed with environmental sustainability implications.
The capability to automatically detect vegetation changes using multitemporal remotely sensed image data is of upmost importance to many global-change research projects. A procedure to automatically map vegetation changes within arid and semi-arid regions of the southwestern United States is presented. Multitemporal Landsat Multispectral Scanner (MSS) images were the primary data source, but some preliminary work was also done using same-date Visible-Infrared Spin-Scan Radiometer (VISSR) data for comparison with the MSS results. The change-detection procedure includes multitemporal image calibration using a hybrid method that we developed for the project; the hybrid calibration allows a radiometric calibration to be applied to historical data by using field-radiance information rather than a modeling procedure. The results indicate that a calibrated visible band is more sensitive than the widely used Normalized Difference Vegetation Index (NDVI) in detecting vegetation changes in the arid and semi-arid environments of the southwestern United States. Changes were detected in the desert environment, where the vegetation density is relatively low, with both Landsat MSS and GOES VISSR images. Some changes detected by the automatic procedure were confirmed in the field during two of the Landsat overpasses. The changes corresponded mostly to the blooming of ephemeral or annual vegetation.
A new tasseled cap transformation based on Landsat 7 at-satellite reflectance was developed. This transformation is most appropriate for regional applications where atmospheric correction is not feasible. The brightness, greenness and wetness of the derived transformation collectively explained over 97% of the spectral variance of the individual scenes used in this study. Keyword: tasseled cap transformation, at-satellite reflectance, and Landsat 7. Running headline: At-satellite reflectance based tasseled cap transformation.
The application of multispectral scanner (MSS) data to vegetation and soils studies can be facilitated by use of data transformations which reduce the number of channels to be considered, provide a more direct association between signal response and physical processes on the ground, and highlight the particular types of information of greatest interest to the user. One such transformation, the TM (Thematic Mapper) Tasseled Cap transformation, is described. Previously reported results are summarized and the results of new analyses pertaining to vegetation, soils, and external effects information contained in the TM Tasseled Cap feature space are presented.
A transformation of TM waveband reflectance factor data is presented which produces features analogous to TM Tasseled Cap brightness, greenness, and wetness. The approach to adjusting the transformation matrix to other types of reflectance factor data (different instrument or band response) is described in general terms.
Zugl.: Hannover, Univ., Diss., 1996.
The fundamental concepts on which the Tasseled Cap transformations of MSS and TM data are based - particularly the identification of inherent data structures - are explained and discussed. Emphasis on the structures present in data from any given sensor, which are themselves the expression of physical characteristics of scene classes, provides a number of advantages, including (a) reduction in data volume with minimal information loss; (b) spectral features which can be applied, without re-definition or adjustment, to any data set for a given sensor; (c) spectral features which can be directly associated with important physical parameters; and (d) easier integration of data from multiple sensors.
The time trajectories of agricultural data points as seen in Landsat signal space form a pattern suggestive of a tasselled woolly cap. Most of the important crop phenomena can be described using this three dimensional construct: the distribution of signals from bare soil, the processes of green development, yellow development, and shadowing and harvesting. A linear preprocessing transformation which isolates green development, yellow development and soil brightness is used to reduce the dimension of the signal space. Specific measurable pattern elements of the tasselled cap are used to estimate and correct atmospheric haze and moisture effects.
In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.
-Analiza componentelor principale în teledetecţia satelitară
- I Vorovencii
- I Pădure
Vorovencii, I., Pădure, I., 2005 -Analiza componentelor principale în teledetecţia satelitară.
Revista de Cadastru, RevCAD nr. 5. Editura Aeternitas Alba Iulia (p.133-140). ISSN 1583-2279.
The Tasseled Cap Transformation in Remote Sensing
- W Thayer
Thayer, W., -The Tasseled Cap Transformation in Remote Sensing. San José State University
Economics Department.