Introduction

Land use and Land cover (LULC) information is a vital input for various developmental, environmental and resource planning applications at regional as well as global scale process models. LULC dynamics are analysed through changes in the state of an object or phenomenon by observing it at different times (Singh, 1989). Timely and accurate change detection of natural resources constitutes the foundation for greater understanding of the relationships and interactions between human and natural phenomena. It enables monitoring temporal dynamics of spatial aspects involving diverse ecosystems, forest changes, etc. Furthermore, remote sensing data pertaining to LULC provide spatio-temporal information of agricultural crops, wastelands, seasonal dynamics of wetlands/surface water bodies, forest, vegetation etc. which helps in analyzing reliably the landscape dynamics (Kandrika and Roy, 2008).

Land use (LU) change can be obtained from multi satellite sensor data (spatio temporal data) using pre-classification or post-classification and pattern recognition algorithms (Duda et al., 2005). These classification algorithms can be either supervised, unsupervised, hybrid of soft classification techniques. In addition to the normal routine methods of estimating the LULC change in a landscape, landscape metrics or spatial metrics are being used in recent times particularly in landscape ecology (Gustafson, 1998). Spatial metrics are spatially consistent and provide detailed information about structures and patterns (Herold et al., 2005) and are being used to quantify shape and pattern of vegetation in natural landscape based on categorical, patch-based representation at a landscape, class and patch level (McGarigal and Marks, 1995). Computation of spatial metrics using multi-scale or temporal datasets, aids in assessing the changes in the degree of spatial heterogeneity. Thus, the information derived from several change detection techniques along with spatial metrics on temporal scales help in understanding the change phenomenon that benefits the planning and management towards a sustainable use of land resources. In this context, the present paper analyses the spatio-temporal landscape dynamics of Mandhala, a medium altitude, temperate watershed in Himachal Pradesh, India. Main objectives are to understand landscape dynamics through (i) LULC analysis using temporal remote sensing data (1972, 1989, 2000 and 2007) and (ii) computation of spatial metrics including forest fragmentation indices.