High-resolution images of forest areas taken by drone or satellite, further integrated with airborne and terrestrial laser scanning data, can provide early warning of damage – even of individual trees afflicted by pests.

Efficient, accurate data collection from imagery is the key to an economical generation of useful geospatial products. Incremental developments of traditional geospatial data collection and the arrival of new image data sources cause new software packages to be created and existing ones to be adjusted to enable such data to be processed. In the past, BAE Systems’ digital photogrammetric workstation, SOCET SET ® , met fi n de siècle expectations in data processing and feature extraction. Its successor, SOCET GXP ® , addresses today’s photogrammetric requirements and new data sources. SOCET GXP is an advanced workstation for mapping and photogrammetric tasks, with automated functionality for triangulation, Digital Elevation Model (DEM) extraction, orthorectification and mosaicking, feature extraction and creation of 3-D models with texturing. BAE Systems continues to add sensor models to accommodate new image sources, in response to customer demand. New capabilities added in the latest version of SOCET GXP facilitate modeling, visualization and analysis of 3-D features.

Land use land cover change (LULC) has become part of the global science agenda and the understanding of LULC change is vital for planning sustainable management of natural resources. The study has employed multi- temporal satellite imagery to examine the LULC change in the Abbottabad District from 1989 to 2019. Images from Landsat-5, Landsat-7, and Landsat-8 Thematic Mapper (TM) for the same season were acquired from the USGS for the years of 1989, 1999, 2009 and 2019. The images were pre-processed by atmospheric correction, extraction of the study area and band composite. The supervised image classification using Maximum Likelihood Classifier and accuracy assessment were applied to prepare LULC maps of the Abbottabad District. In the last three decades, the study area witnessed number of changes in the pattern of LULC due to population growth, rapid urbanization and increased development of infrastructure, which cumulatively led to the emergence of new patterns being employed for land use. Results of the analysis involving the classified maps show that agricultural land and bare land have decreased, respectively 15.73% and 3.81%, whereas water resources have decreased significantly by 0.58%. This study reveals that GIS can be used as an informative tool to detect LULC changes. However, for planning and management, as well as to gain better insight into the human dynamics of environmental variations on the regional scale, it is crucial to have information about temporal LULC transformation patterns in the study area.

Today, the new era with Very High Resolution Satellite (VHRS) imageries as IKON OS, QuickBird, EROS, Orb View etc., provides orthophoto in large scale of 1 :5 OOO, to update existing maps, to compile general-purpose or thematic maps. Orthophotomap in the scale of I :5 OOO with Ground Sampling Distance of 0.5 m is one of three important sources for establishing GIS together with a Digital Elevation Model of ±LO m accuracy in height and a topographic map in the scale of 1: IO OOO. Therefore, the accuracy of products of VHRS imageries affects reliability of GIS. Nevertheless, the accuracy of products of processing VHRS imageries is at first dependent on chosen geometrical sensor models. The understanding of geometrical sensor models of VHRS imageries is very important for improving processing of VHRS imageries. The polynomial models are to provide a simple, generic set of equations to represent the indirect relationship between the ground and its image. The polynomial models or replacement sensor models must not only model the ground-to-image relationship accurately. Physical (or parametrical) model describes dir~ctly strict geometrical relations between the terrain and its image, using satellite's orbital parameters and basing on the co-linearity condition. In such model, the above-mentioned multi-source distorting factors are taken into consideration. In this paper a review of practical accuracy of geometrical models of VHRS imageries taken from different research institutions in the world in last years has been presented.

Since 2000 when first imageries of Space Imaging of one metre resolution satellite products appeared on the World market, many institutions started using them for cartographic production such as orthophotomaps on a large scale. A choice of the mathematic sensor models of imageries for their orthorectification in producing orthophotomaps is one of the main investigation directions. In order to restitute the functional relation between imageries and their ground space, the use of sensor models is required. They can be grouped into two classes, the generalized sensor models (geometric or replacement sensor models) and physical or parametric models. The paper presents a brief overview of the geometric models such as RPC (Rational Polynomial Coefficients). Their properties, and in particular their advantages and disadvantages are discussed. Also the parametric models, developed by the authors are presented in this paper. They are based on time-dependent collinearity equation of the mathematic relation between ground space and its imageries through parameters describing the sensor position in satellite orbit and position of the orbit in the geocentric system.