-Land use classification is an important part of many remote-sensing applications. A lot of research has gone into the application of classifiers to remote-sensing images. Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover. In this paper, we have proposed an efficient technique for classifying the multispectral s...

The objective of this research was to determine the best model and compare performances in terms of producing landuse maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance ofmean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral anglemapper (SAM), and support vector machine (SVM) were...

Over the past decades, most of the remote sensing studies dealt with natural environment. The advance of remote sensing satellites and the innovative methods of proceeding high resolution images have contributed to integrated urban studies. This project is an attempt to use remote sensing technology so as to monitor the urban sprawl of the city of Thessaloniki and detect the changes occurred th...

This paper analyses an opportunity to integrate remote sensing data in a forecasting scheme of river inflow to the Krasnodar reservoir. MODIS MOD10A2 eight-day composite snow cover data was selected as the basic remote sensing information. Based on these data, a database which consists of maximal snow extent maps covering the Kuban river basin over the period from March 2000 to the present, alo...

A number of indicators and signals that lend themselves to monitoring by remote-sensing satellites provide a warning of land degradation. These include loss of vegetative cover, wind and water erosion, soil salinization, deterioration of the soil structure, drier soil, increased reflectance, higher land surface temperatures, and changes in land cover type. But because soil and vegetation change...

An important approach to unsupervised pixel classification in remote sensing satellite imagery is to use clustering in the spectral domain. The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (...

The degradation rate of Mediterranean steppes, especially in North Africa is 1% per year, and this considered a high  rate of degradation. This study conducted in 2014 in the south slope of the Al-Jabal Al-Akhdar, northeast Libya to quantify the vegetation recovery rate and assess selected Vegetation Indices (VIs) for mapping rangelands degradation status using remote sensing technology. Throug...

Analyzing the process of land use and cover changes during long periods of time and predicting the future changes is highly important and useful for the land use managers. In this study, the land use maps in the Ardabil plain in north-west part of Iran for four periods (1989, 1998, 2009 and 2013) are extracted and analyzed through remote sensing technique, using the land-sat satellite images. T...

The growing population and increasing socio-economic necessities creates a pressure on land use/land cover. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in part of Hableh Rood Watershed of Iran using Landsat 7 and 8 (Sensor ETM+ and OLI) ...