The purpose of multispectral satellite imagery preprocessing for Land Cover Classification is creation of enhanced satellite images before further processing and imagery analysis with final land classification and automated linear object and area border detection for selected classes of objects.

REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat ima...

Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely ...

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. De...

We studied the land-cover data used by the regional climate model REMO and the land surface model JSBACH, for Finland and surrounding areas. To date, the land-cover data determining REMO’s surface parameterisations have originated from the Global Ecosystem classification of the Global Land Cover Characteristics (GLCC-GEC) database. The same database has also been used as basis for prescribed pl...

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 ...

In this paper, the author evaluates the results of applying two evolving classification techniques, decision trees (DT) and artificial neural networks’ back-propagation (ANN-BP), to obtain land cover and land use classes from Quickbird XS data combined with spatial metrics, in a selected urban zone in Bogota, Colombia. In order to improve the quality of the land cover classification, additional...

Temporal trajectories of land-cover change provide important information on landscape dynamics that are critical to our understanding of complex human–environment adaptive systems. The increasing availability of long time series of satellite images, especially the recent free release of multi-decadal Landsat satellite archive, presents a great opportunity to improve our ability to detect land-c...

Hyper spectral and multi spectral image analysis is the commonly used technique for land use and land cover classification. Effective use of the land cover can play a vital role in the development of country. Multi spectral satellites use passive sensor, hence the only source of energy involved in the acquisition of satellite imagery is the reflectance of the sun. In order to investigate the ro...

Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SA...