Post-classification Technique Research Articles

Post Classification Using Cellular Automata for Landsat Images

The research presented in this paper aims at improving the accuracy of land-use maps produced from classification of Landsat images of mega cities in developing countries. In other words, the main objective of this paper is to find a suitable post classification technique that gives optimum results for Landsat images of mega cities in developing countries. To reach our goal, the paper presents a classification of two TM-Landsat sub scenes using a traditional statistical classifier (Maximum Likelihood) into four land cover classes (vegetation-water-Desert-Urban); then the accuracy assessment for the produced land-cover map will be calculated. Following to this step, three post processing techniques- Majority Filter, Probability label Relaxation (PLR), and Cellular Automata (CA) - will be applied in order to improve the accuracy of the previously produced land cover map. Finally, the same accuracy assessment measurements will be calculated for the two land-cover maps produced by each of the above post classification techniques. Initial results will show that CA outperformed the other techniques. In this paper we propose a methodology to implement a satellite image post classification Algorithm with cellular Automata.

Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy

This article explores the simultaneous use of pre-classification and post-classification change-detection techniques to map and monitor land-cover and land-use change using multi-temporal Landsat Multi-spectral Scanner and Enhanced Thematic Mapper plus data over one of the most important tourism centres of Italy (e.g. Pisa Province) for 1972, 2000 and 2006. Pre-classification approaches of principal-component analysis and band combination are potentially tailored to reduce data redundancy of the satellite imagery in order to highlight different objects of significance for change-detection analysis across time-series data. In this work, the application of pre-classification techniques could contribute to produce land-cover and land-use maps with higher quality of classification. At this point, the average value of overall classification accuracies for the three classification outputs was an estimated 90%. Then, ‘from–to’ change information, as well as the area and the type of landscape transformations, are provided through the post-classification technique. The findings of this study show that the province of Pisa has significantly experienced a high rate of deforestation and urban development over past decades. It is revealed that artificial structures (e.g. urban and industrial zones) in Pisa Province increased at a change rate of around 265% and forested land decreased from approximately 45% to 32% of the total area of the province between 1972 and 2006. Likewise, the perceptible growth of built-up structures from about 4% to 10.6% in Pisa City during this 34-year period has imposed a heavy pressure on the landscape of Pisa.

Using Landsat data to determine land use/land cover changes in Gmhane, Turkey

Sustainable land use and development require timely, accurate and detailed information of land resources as well as changes in the land resource. 'What is changing in where' question is answered by multiple observations over a period of time. Remote sensing technology provides such a monitoring requirement. The aim of this study was to analyze land use/ land cover (LU/LC) changes between 1975 and 2000 in GumuAŸhane, Turkey, using satellite images. Three Landsat images from 1975, 1987 and 2000 were used to determine changes. A post classification technique was used based on a hybrid classification approach (unsupervised and supervised). Images were classified into five LU/LC types; urban, agriculture, forest, pasture and other lands. It is found that significant changes in land cover occurred between the years 1975 and 2000. The results showed an increase in urban, forest and agriculture and a decrease in pasture and other lands between 1975 and 2000. In this period, urban land increased from 1.25 to 3.78% of the total area. While the area of pasture decreased from 26.74 to 23.44% of the total area, the area of forest and agriculture increased from 37.32 to 39.51% and from 8.83 to 13.27%, respectively. Key words: Image classification, change detection, remote sensing, land use/land cover.

Conservation implications of mapping rare ecosystems using high spatial resolution imagery: recommendations for heterogeneous and fragmented landscapes

Protection of rare ecosystems requires information on their abundance and spatial distribution, yet mapping rare ecosystems, particularly those which are fragmented, is a challenge. Use of high spatial resolution satellite imagery is increasing, in part because it may be well-suited for mapping fine-scale components of landscapes. We classified high spatial resolution QuickBird imagery of coastal British Columbia, Canada into late seral forest associations. With an emphasis on rare forest associations, we compared the classification accuracies resulting from contrasting accuracy assessment techniques. We also evaluated the impact of post-classification image smoothing on the quantity and configuration of rare forest associations mapped. Less common associations were generally classified with lower accuracies than more abundant associations, however, accuracies varied depending on the assessment technique used. In particular, ignoring the presence of fine-scale heterogeneity falsely lowered the estimates of map accuracy by approximately 20%. Smoothing, while generally increasing the accuracies of rare forest associations, had a large effect on their predicted spatial extent and configuration. Simply due to smoothing, areal estimates of rare associations differed by as much as 36%, the number of patches decreased by 73% on average, and mean patch size increased by up to 650%. Our findings indicate that routinely used post-classification and map assessment techniques can greatly impact the portrayal of rare and fragmented ecosystems. Further research is needed on the specific challenges of mapping and assessing the accuracy of rare ecosystems in fragmented and heterogeneous landscapes.

Using landsat data to determine land use/land cover changes in Samsun, Turkey

The rapid industrialization and urbanization of an area require quick preparation of actual land use/land cover (LU/LC) maps in order to detect and avoid overuse and damage of the landscape beyond sustainable development limits. Remote sensing technology fits well for long-term monitoring and assessment of such effects. The aim of this study was to analyze LU/LC changes between 1980 and 1999 in Samsun, Turkey, using satellite images. Three Landsat images from 1980, 1987 and 1999 were used to determine changes. A post classification technique was used based on a hybrid classification approach (unsupervised and supervised). Images were classified into six LU/LC types; urban, agriculture, dense forest, open forest-hazelnut, barren land and water area. It is found that significant changes in land cover occurred over the study period. The results showed an increase in urban, open forest/hazelnut, barren land and water area and a decrease in agriculture and dense forest in between 1980 and 1999. In this period, urban land increased from 0.77% to 2.47% of the total area, primarily due to conversions from agricultural land and forest to a lesser degree. While the area of dense forest decreased from 41.09% to 29.64% of the total area, the area of open forest and hazelnut increased from 6.73% to 11.88%.

Detection of land use/land cover changes for the northern part of the Nile delta (Burullus region), Egypt

The monitoring of land use/land cover changes along the north part of the Nile delta, Egypt is very important for the planner, management, governmental and non-governmental organizations and the scientific community. This information is essential for planning and implementing policies to optimize the use of natural resources and accommodate development whilst minimizing the impact on the environment. To study these changes along the study area, two sets of Landsat Thematic Mapper (TM) data were used. TM data are useful for this type of study due to its high spatial resolution, spectral resolution and low repetitive acquisition (16 days). A post-classification technique is used in this study based on hybrid classification (unsupervised and supervised). Each method used was assessed, and checked in field. Nine land use/land cover classes are produced. The overall accuracy for a 1984 image is 78% and for a 1997 image is 80%. The objective of this study was to detect land use/land cover changes, and to assess the nature of future change following construction of the international coastal road which crosses the study area.