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Comparative analysis of Landsat TM, ETM+, OLI and EO-1 ALI satellite images at the Tisza-tó area, Hungary
53-62Views:296Satellite images are important information sources of land cover analysis or land cover change monitoring. We used the sensors of four different spacecraft: TM, ETM+, OLI and ALI. We classified the study area using the Maximum Likelihood algorithm and used segmentation techniques for training area selection. We validated the results of all sensors to reveal which one produced the most accurate data. According to our study Landsat 8’s OLI performed the best (96.9%) followed by TM on Landsat 5 (96.2%) and ALI on EO-1 (94.8%) while Landsat 7’s ETM+ had the worst accuracy (86.3%).
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Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories
194-202Views:947The remote sensing techniques provide a great possibility to analyze the environmental processes in local or global scale. Landsat images with their 30 m resolution are suitable among others for landcover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) were investigated from the aspect of land cover types: water body (W); plough land (PL); forest (F); vineyard (V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilarities and the correlation of spectral indices were examined. In BU – GL – F categories similar NDVI values were calculated, but the other land cover types differed significantly. The water related indices (NDWI, MNDWI) were more effective (especially the MNDWI) to enhance water features, but the values of other categories ranged from narrower interval. Weak correlation were found among the indices due to the differences caused by the water land cover class. Statistically, most land cover types differed from each other, but in several cases similarities can be found when delineating vegetation with various water content. MNDWI was found as the most effective in highlighting water bodies.
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Vegetation changes of Sundarbans based on Landsat imagery analysis between 1975 and 2006
1-9Views:325The Sundarbans in Bangladesh and India is the largest single block of tidal halophytic mangrove forest in the world. This forest is threatened by effect of climate change and manmade activities. The aim of this paper is to show changes in vegetation cover of Sundarbans since 1975 using Landsat imagery. Normalized Difference Vegetation Index is applied to quantify and qualify density of vegetation on a patch of land. Estimated land area (excluded water body) of this forest is 66% in Bangladesh, and 34% in India, respectively. Net erosion since 1975 to 2006 is ~5.9%. In vicinity of human settlement, areal changes are not observed since 1975. The mangrove forest is decreased by 19.3% due severe tropical cyclone in 1977 and 1988. Moreover, the dense forest is damaged by about 50%. However, more than 25 years is taken by Sundarbans to recover from damage by a severe tropical cyclone. The biodiversity of Sundarbans depends to fresh water flow through it. Therefore, the future of Sundarbans depends to the impact of climate change which has further effect to increasing intensity and frequency of severe tropical cyclone and salinity in water channels in Sundarbans.
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Time series analysis of major land resources using Landsat images in a part of district Jhansi, Uttar Pradesh, India
41-57Views:19Space born technology, with its repetitive nature, uses electromagnetic energy to capture digital data from the Earth's surface by remote sensing systems. The purpose of this research is to track changes in land resources with six time series (2003-09, 2003-15, 2003-21, 2009-15, 2009-21 and 2015-21) over a period of 18 years. Multi-date Landsat images of 2003, 2009, 2015 and 2021 have been used to monitor the changing pattern. Level – I classification scheme composed by NRSC/ ISRO and supervised Maximum Likelihood Classification (MLC) techniques were used to identify and classify land use/ land cover features located in Jhansi Tehsil. The findings show that there have been significant changes in land resources over the years. The area under agriculture land, built-up and waterbodies were increased by 48.83%, 53.53% and 106.73% while forest/ tree outside forest and wastelands were reduced by 59.74% and 38.68% respectively It is concluded that, the expansion of key land resources indicates the growth in population and socio-economic activities whereas the loss in some land resources might be due to human induced progressive activities.
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Landuse/landcover change process in a tropical semi-arid zone: case of two rural communes (Chadakori and Saé-Saboua) in Maradi region, Republic of Niger
1-12Views:196The study aimed to analyze the process of Landuse/Landcover change of two rural communes (Saé Saboua and Chadakori) of Maradi region (Republic of Niger) over the past 28 years (1986 – 2014), through landscape structure analysis by diachronic cartographic approach and landscape indices. Mixed classification of temporal series of Landsat images led to identifying six Landuse/Landcover (LULC) classes, namely ”cultivated land under shrubs and trees”, ”cultivated land under trees”, “continuous cropland”, ”fallow/pasture land”, ”forest reserve”, and ”settlement”. The composition and structure of the studied landscapes have greatly changed from 1986 to 2014. The class ”cultivated land under trees” was the landscape matrix in 1986 with 38.65% of landscape total area but in 2001 and 2014 the class ”continuous cropland” became the landscape matrix. The changes also affected the ”forest reserve” which was transformed to smallholder agricultural land from 1986 to 2014. The area occupied by classes ”cultivated land under trees” changed from 38.65% in 1986 to 8.78% in 2014; and from 1986 to 2014, the area occupied by ”fallow/pasture land” has decreased of about 16%. The decrease in these classes was in favor of ¨continuous crop land¨, ¨settlement¨ and “cultivated land under shrubs and trees” which respectively gained 38%, 0.3% and 8.15% of their areas in 1986. The results of this study reflect the problem of access to land and even land saturation in semi-arid region, a consequence of strong population growth. They also contribute to a better rethinking of agricultural practices in order to initiate adaptation and resilience strategies for the population facing food insecurity and poverty.
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A spatio-temporal urban expansion modeling a case study Teheran metropolis, Iran
10-19Views:104During the past decades, urban growth has been accelerating with the massive immigration of population to cities. Urban population in the world was estimated as 2.9 billion in 2000 and predicted to reach 5.0 billion in 2030. Rapid urbanization and population growth have been a common phenomenon, especially in the developing countries such as Iran. Rapid population growth, environmental changes and improper land use planning practices in the past decades have resulted in environmental deterioration, haphazard landscape development and stress on the ecosystem structure, housing shortages, insufficient infrastructure, and increasing urban climatological and ecological problems. In this study, urban sprawl assessment was implemented using Shannon entropy and then, Artificial Neural Network (ANN) has been adopted for modeling urban growth. Our case study is Tehran Metropolis, capital of Iran. Landsat imageries acquired in 1988, 1999 and 2010 are used. According to the results of sprawl assessment for this city, this city has experienced sprawl between 1988 to 2010. Dataset include distance to roads, distance to green spaces, distance to developed area, slope, number of urban cells in a 3 by 3 neighborhood, distance to fault and elevation. Relative operating characteristic (ROC) method have been used to evaluate the accuracy and performance of the model. The obtained ROC equal to 0.8366.
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Monitoring the changes of a suburban settlement by remote sensing
76-83Views:68Satellite images and aerial photos support settlement surveys and provide valuable information of their physical environment. Aerial photos are excellent tools to overview large areas and simultaneously provide high-resolution images making them efficient tools to monitor built-up areas and their surroundings. Aerial photos can also be used to collect complex spatial data as well as to detect various temporal changes on the land surface, such as construction of illegal edifices and waste dumps. The 10 to 30-meter resolution SPOT and Landsat images are usually insufficient for site specific data collection and analysis. However, the recently available 0.5-meter resolution satellite images have broadened the scope of monitoring and data collection projects. Beyond environmental and urban monitoring, the new available high-resolution satellite images simplify the everyday work of local authorities and will facilitate the development of governmental databases that include spatial information for public utilities and other communal facilities.