a r t i c l e i n f o Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral ...

Since the mid 1980s an European Land Cover dataset has been regularly produced for land cover changes, land cover map (CORINE), high resolution forest layer and built-up areas including soil sealing. Within the GMES (Global Monitoring for Environment and Security) Fast Track Land Service 2006-2008 a new dataset of orthorectified satellite images has to be produced covering the EU25 and neighbou...

This paper attempts to integrate satellite imagery such as Cartosat-1 with high spatial resolution and IRS P6 LISS IV with high spectral resolution using digital image fusion algorithms. This integration and mixing of hispectral and higher spatial data with complementary spectral and spatial characteristics, has proved to be promising to obtain images with high spatial and spectral resolution s...

Semivariogram functions are compared to cooccurrence matrices for classification of digital image texture, and accuracy is assessed using test sites. Images acquired over the following six different spectral bands are used: 1) SPOT HRV, near infrared; 2) Landsat thematic mapper (TM), visible red; 3) India Remote Sensing (IRS) LISS-II, visible green; 4) Magellan, Venus, S-band microwave; 5) shut...

A methodology is proposed for extracting information on land cover based on hyperspectral reflectance data derived from satellite image, without supervising with ground truth. The reflectance percentage, being a characteristic feature of the ground object acts as an indirect guidance to the classification and hence the method is named semi-supervised classification. It is tried with IRS LISS IV...

Extraction of vegetation is an important step for agricultural, forest and greenery mapping. The proposed method examines the complex process of land cover vegetation pattern classification using an IRS-1C LISS III image. Pre-processing was done by employing partial differential equation (PDE). Normalized differential vegetation index (NDVI) was applied to separate vegetation features from the ...

Land cover of Finnish Lapland was classified to 16 land cover classes using optical IRS LISS, Spot XS and MODIS satellite images, ancillary GIS data and decision tree classifier. The aim of this study was to test decision tree classifier for land cover classification and study the effects of its parameters to classification result. In the best case, the overall accuracy was about 68% for all 16...

This paper reports results of an experiment LRVE (Leaf Area Index Retrieval and Validation Experiment) that was conducted over agricultural areas of Central India during winter season of 2001-02, aimed at relating field measurements of LAI to space borne IRS LISS-III data, preparation of site-level LAI maps and validation of MODIS-based 1 km LAI global fields,. Measurements of fieldlevel LAI, a...