Multitemporal Landsat Thematic Mapper Data Research Articles

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.

Mapping coffee plantations with Landsat imagery: an example from El Salvador

Considering the potential of shaded coffee plantations mixed with natural vegetation for promoting biodiversity conservation, this project assessed the utility of multi-date Landsat Thematic Mapper (TM) satellite imagery for the characterization of natural vegetation versus coffee plantations in western El Salvador. For assembling a multi-temporal Landsat TM data set, we applied a regression analysis model to remove cloud cover and cloud shadows. Then, through a hybrid classification approach, a nine-class land use/land cover (LULC) map was generated. We identified two types of coffee plantations (‘open-canopy’ and ‘close-canopy’) along with natural forest/shrubland, mangrove, water bodies, sandy coastal soils, bare soil, urban areas and agriculture. Notwithstanding the small sample size of the accuracy data, our assessment revealed an overall accuracy of 76.7% (Kappa coefficient = 0.68), considering only the four classes with independent field data. The overall classification accuracy for distinguishing coffee plantations from non-mangrove natural forest was 81.6% and the classification accuracy for distinguishing ‘open-canopy’ from ‘close-canopy’ coffee plantations was 85.7%. We are encouraged by the results of this prototype study. They indicate that remote-sensing techniques can be used to distinguish different classes of coffee production systems and to differentiate coffee from natural forest.

Change Vector Analysis in Posterior Probability Space: A New Method for Land Cover Change Detection

Postclassification comparison (PCC) and change vector analysis (CVA) have been widely used for land use/cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from an individual image classification error, while a strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This letter proposes a new method named CVA in posterior probability space (CVAPS), which analyzes the posterior probability by using CVA. The CVAPS approach was applied and validated by a case study of land cover change detection in Shunyi District, Beijing, China, based on multitemporal Landsat Thematic Mapper data. Accuracies of “change/no-change” detection and “from-to” types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land use/cover change detection.

Classifying and Mapping Wildfire Severity

This study evaluates six different approaches to classifying and mapping fire severity using multi-temporal Landsat Thematic Mapper data. The six approaches tested include: two based on temporal image differencing and ratioing between pre-fire and post-fire images, two based on principal component analysis of pre- and post-fire imagery, and two based on artificial neural networks, one using just postfire imagery and the other both pre- and post-fire imagery. Our results demonstrated the potential value for any of these methods to provide quantitative fire severity maps, but one of the image differencing methods (ND4/7) provided a flexible, robust, and analytically simple approach that could be applied anywhere in the Continental U.S. Based on the results of this test, the ND4/7 was implemented operationally to classify and map fire severity over 1.2 million hectares burned in the Northern Rocky Mountains and Northern Great Plains during the 2000 fire season, as well as the 2001 fire season (Gmelin and Brewer, 2002). Approximately the same procedure was adopted in 2001 by the USDA Forest Service, Remote Sensing Applications Center to produce Burned Area Reflectance Classifications for national-level support of Burned Area Emergency Rehabilitation activities (Orlemann, 2002).

Remote sensing and GIS integration for land cover analysis, a case study: Bozcaada Island

In this study, remote sensing and geographic information system (GIS) techniques were used in order to accomplish land cover change of Bozcaada Island, Turkey, by using multitemporal Landsat Thematic Mapper data. Digital image processing techniques were conducted for the processes of image enhancement, manipulation, registration and classification for land cover change analysis. The land cover changes between two different dates were visualized and analyzed by using Geographic Information System techniques. The results showed that remotely sensed data and GIS are effective and powerful tools for carrying out changes on land cover of the island and monitoring of its impact on the environment.

Multitemporal evaluation of topographic normalization methods on deciduous forest tm data

Five empirical and nonempirical parametric topographic normalization methods (the cosine, SCS, Minnaert, b correction, and c correction methods) were applied to multitemporal Landsat Thematic Mapper data (bands 1-5 and 7) collected in different periods of the growing season (April, June, and July) of a mixed deciduous forest area (340 ha) in the northern Apennines. The effectiveness of the models at removing topographic control, preserving internal data variability, and consistently normalizing radiance for flat pixels from band to band and image to image was evaluated. The entirely empirical b correction outperformed the other considered methods without relying on any photometric function.

Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops

The objective of this research was to evaluate the synergistic effects of multitemporal European remote sensing satellite 1 (ERS-1) synthetic aperture radar (SAR) and Landsat thematic mapper (TM) data for crop classification using a per-field artificial neural network (ANN) approach. Eight crop types and conditions were identified: winter wheat, corn (good growth), corn (poor growth), soybeans (good growth), soybeans (poor growth), barley/oats, alfalfa, and pasture. With the per-field approach using a feed-forward ANN, the overall classification accuracy of three-date early- to mid-season SAR data improved almost 20%, and the best classification of a single-date (5 August) SAR image improved the overall accuracy by about 26%, in comparison to a per-pixel maximum-likelihood classifier (MLC). Both single-date and multitemporal SAR data demonstrated their abilities to discriminate certain crops in the early and mid-season; however, these overall classification accuracies (<60%) were not sufficiently high for operational crop inventory and analysis, as the single-parameter, high-incidence-angle ERS-1 SAR system does not provide sufficient differences for eight crop types and conditions. The synergy of TM3, TM4, and TM5 images acquired on 6 August and SAR data acquired on 5 August yielded the best per-field ANN classification of 96.8% (kappa coefficient = 0.96). It represents an 8.3% improvement over TM3, TM4, and TM5 classification alone and a 5% improvement over the per-pixel classification of TM and 5 August SAR data. These results clearly demonstrated that the synergy of TM and SAR data is superior to that of a single sensor and the ANN is more robust than MLC for per-field classification. The second-best classification accuracy of 95.9% was achieved using the combination of TM3, TM4, TM5, and 24 July SAR data. The combination of TM3, TM4, and TM5 images and three-date SAR data, however, only yielded an overall classification accuracy of 93.89% (kappa = 0.93), and the combination of TM3, TM4, TM5, and 15 June SAR data decreased the classification accuracy slightly (88.08%; kappa = 0.86) from that of TM alone. These results indicate that the synergy of satellite SAR and Landsat TM data can produce much better classification accuracy than that of Landsat TM alone only when careful consideration is given to the temporal compatibility of SAR and visible and infrared data.

Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data

To facilitate the identification of appropriate post-fire watershed treatments and minimize erosion effects after socio-economically important fires, Interagency Burned Area Emergency Rehabilitation (BAER) teams produce initial timely estimates of the fire perimeter and classifications of burn severity, forest mortality, and vegetation mortality. Accurate, cost-effective, and minimal time-consuming methods of mapping fire are desirable to assist rehabilitation efforts immediately after containment of the fire. BAER teams often derive their products by manually interpreting color infrared aerial photos and/or field analysis. Automated classification of multispectral satellite data are examined to determine whether they can provide improved accuracy over manually digitized aerial photographs. In addition, pre-fire vegetation data are incorporated to determine whether further gains in accuracy of mapped canopy consumption can be made. BAER team classifications from the Cerro Grande Fire were compared to estimates of overstory consumption produced using a pre-fire vegetation classification, and a change detection algorithm using bands 4 and 7 from July 1997 pre-fire Landsat Thematic Mapper (TM) and July 2000 post-fire Enhanced Thematic Mapper (ETM) data. BAER team classifications are highly correlated to overstory consumption and should produce high Kappa statistics when verified using the same dataset. Our three-class supervised classification of the change image incorporating a pre-fire vegetation classification yielded the highest Kappa at 0.86. A three-class unsupervised classification of the change image yielded a lower Kappa of 0.72. BAER team classifications yielded Kappas ranging from 0.38 to 0.63 using the same verification dataset.

A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China

The Zhujiang Delta of South China has experienced a rapid urban expansion over the past two decades due to accelerated economic growth. This paper reports an investigation into the application of the integration of remote sensing and geographic information systems (GIS) for detecting urban growth and assessing its impact on surface temperature in the region. Remote sensing techniques were used to carry out land use/cover change detection by using multitemporal Landsat Thematic Mapper data. Urban growth patterns were analysed by using a GIS-based modelling approach. The integration of remote sensing and GIS was further applied to examine the impact of urban growth on surface temperatures. The results revealed a notable and uneven urban growth in the study area. This urban development had raised surface radiant temperature by 13.01 K in the urbanized area. The integration of remote sensing and GIS was found to be effective in monitoring and analysing urban growth patterns, and in evaluating urbanization impact on surface temperature.

Neural Networks, Multitemporal Landsat Thematic Mapper Data and Topographic Data to Classify Forest Damages in the Czech Republic

RÉSUMÉDans cette étude, on utilise des données multidates du capteur “Thematic Mapper” de Landsat et des données topographiques pour classifier à l'aide de réseaux de neurones artificiels les dommages subis par les forêts de conifères en République tchèque. La comparaison entre la classification basée sur les réseaux de neurones et les études précédentes et une régression logistique multinomiale utilisant des données d'expérimentation et de validation identiques démontre que la performance de l'algorithme de rétro-propagation est comparable mais non supérieure aux méthodes conventionnelles. Les réseaux de neurones sont moins utiles pour la classification des dommages forestiers que les algorithmes conventionnels notamment en raison de leur dépendance sur des poids d'intrants fixés de façon aléatoire et dû au fait que l'entraînement par rétro-propagation requiert beaucoup de temps. Cependant, les réseaux de neurones permettent d'intégrer et d'extraire l'information des données multisources ayant des distributions différentes ou inconnues.

Multitemporal Landsat TM data for stand-based management in northern US forest ecosystems

Multitemporal Landsat Thematic Mapper (TM) imagery was processed and forest canopy change information was extracted over a 415 km2 area in north central Minnesota. The study covered two-, four-, and six-year disturbance monitoring cycles. At the pixel level, the detection of canopy depletion and increment dynamics over the six-year interval was 94% accurate. The stand-based accuracy was 714 out of 759 (also 94%). Here change was detected in at least 50% of the total number of pixels that made up the individual stands. This paper specifically discusses the information content of multitemporal multispectral satellite data from the perspective of traditional stand management. Cross-verification using contingency matrices and geographical information system (GIS) modelling, combined with intensive field verification, has shown that the majority of the so-called "change classification errors" were not errors at all, but a powerful source of substand information that can significantly impact sustainable resource management. While the digital disturbance monitoring methodology as developed for this study does not perform its task at the stand level, stand data on change can be readily extracted with the additional benefit of explicit substand information being made available to the resource manager.

Mapping of desert locust habitats in Africa using Landsat Thematic Mapper data

The results of the research at the Sudan Red Sea coast, which is part of the GTZ project “Integrated Biological Control of Grasshoppers and Locusts” will be presented. For detecting desert locust biotopes multitemporal Landsat Thematic Mapper data and ground truth information are analyzed. The Normalized Difference Vegetation Index (NDVI) shows the areas covered with vegetation. These areas are masked and then classified with the maximum likelihood classifier. Rectification and ground truthing are carried out by means of a GPS (Global Positioning System). The results of the multitemporal classification are merged with an enhanced satellite scene. The final map shows the potential desert locust biotopes. The current desert locust breeding area can be demarcated by combining the classification results with up-to-date data from monitoring satellites, such as Meteosat and NOAA. Further research is carried out in Mali and Mauritania.

Monitoring salt playa dynamics using Thematic Mapper data

Various image analysis techniques have been applied to multitemporal Landsat Thematic Mapper data collected over a four-year period. The results have been used to monitor sediment transport processes in semi-arid south central Tunisia. Process-domains on salt playas in the study area have been identified and mapped. Seasonal, interseasonal, and spatial changes on these playas have been related to sediment, salt, and moisture regimes. Surface water and groundwater activity, and eolian processes, are identified as important sediment, transport processes. The observations bring into question the traditional groundwater-based model of salt playa geochemistry; a revised set of integrated geochemical/geomorphological models is invoked.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>