India’s forest areas have been decreasing rapidly, and a system of continuous operational monitoring is necessary.
Coloured infrared aerial photography helps assess the annual occurrence of losses, especially in the less accessible areas.
Thermal scanners can detect fire. It is through remote sensing that the actual forest covers of India was known through findings of satellite data analysis as highlighted by the Forest Survey of India. Only 10.88 per cent forms the closed forests of adequate density.
In the last 40 years, as much as 4.32 million hectares of forest land have been lost, 0.7 million hectares encroached upon and the rest subjected to shifting cultivation (Government of India, 2007; Singh et al., 2003). Deforestation has increased because of large-scale consumption of fuel wood.
Each year, Bangalore alone consumes 0.4 million tonnes of fuel wood, which is equivalent to, 5,000 hectares of forests. The cattle carrying capacity of forests for grazing purposes is 90 million head, as against the 400 million cattle population (Mehrotra and Suri, 2002).
The first attempt to categorize forest cover types by computer analysis of LANDSAT digital data was done in 1978 for Nagaland. In this study, a colour-coded categorized map delineated the broad forest cover types. Similar studies in Mizoram and Andhra Pradesh were also conducted. In a study conducted by NRSA, the satellite digital covering of entire Periyar-Thodupuzha drainage basin was analyzed. Multi-stage approach is being adopted by the National Remote Sensing Agency (NRSA), which gives information like timber volume.
Apart from forestland classification, stock mapping and volume estimation, remote sensing is also used for damage assessment and fire detection, which is a common feature of Indian forests. GIS is also used in biodiversity conservation plan (Ravan, 1999; Kushwaha et al., 2000).
Use of aerial photographs or other imageries is to complement and improve or reduce fieldwork rather than absolutely replacing it. The Forest Survey of India (FSI) prepared forest cover type and land use maps on 1: 50,000 and 1: 63,360 scale by interpreting medium to small-scale panchromatic aerial photographs for about 4, 20,000 km in India.
The main application of remote sensing in forest management has been focused on timber harvest planning and monitoring of logging and deforestation. FSI has also suggested establishment of about 30 industries relating to pulp and paper mills, plywood, saw mills, fiber board, hard board and newsprint, based on the results of forest inventory using aerial photo interpretation. This project was applied extensively in Central India, Kerala, Andhra Pradesh and Manipur.
Alaknanda valley in the Chamoli district of Uttrakhand is considered one of the most severely degraded areas in the Central Himalayas. At the behest of Dasholi Gram Swarjya Mandal (DGSM), the Space Application Centre took up study to monitor the impact of Chipko movement using multi-temporal satellite data available since 1972. Satellite data of 1972, 1982, and 1991 were interpreted and the spatial distribution of forest type and other land use/cover have been mapped.
Analysis of these results In terms of forest lost and gain showed that Chipko movement, launched in 1973, has helped forest gain of 8.2 per cent during 1972-91. The present study attempts to classify various vegetation classes using time integrated NDVI (T-NDVI) values derived from IRS-P3 WiFS data.
The study area forms the hilly northern part of East Godavari district (Eastern Ghats), Andhra Pradesh. The major land use/cover classes in the study area include moist mixed dry deciduous vegetation, degraded/scrub forest, fallow lands, water bodies and shifting cultivation areas, accounting for about 9,265 km2. The forest of the study area corresponds to southern dry mixed deciduous forests and southern tropical forests as classified by Champion and Seth. IRS-P3 WIFS data of 15/2/98, 25/2/98, 21/3/98, 19/4/98 and 3/5/98 covering study area has been taken for analysis.
Vegetation indices are built as linear combinations of albedos or bi-directional reflectance, which depend on radiation properties of surface and can be effectively utilized for discriminating different vegetation types. NDVI is sensitive to the presence of green vegetation and has been successfully used in numerous regional and global applications for studying the state of vegetation.
The typical phonology of dry deciduous and moist mixed vegetation types is clearly evident from the IRS-P3 WiFS data. The deciduous vegetation could be easily distinguished from the moist mixed vegetation with its characteristics tonal variation and also from rest of the classes, viz., shifting cultivation areas and degraded scrub forests. The results of the study Indicate that discrimination of the forest types is possible using T-NDVI characteristics and this method can be effectively utilized for forest type classification.
Coarser resolution satellite data is increasingly used for land use/cover monitoring and mapping at regional scale. Several studies indicate the Importance of vegetation indices in the delineation of land use/land cover classed and vegetation types. The present study suggests the utility of time integrated NDVI approach using coarse resolution IRS-P3 WIFS data for forest types and land use/land cover discrimination at broad spatial scales (Chaturvedi and Barthwal, 1999; Nagaraja, 2002; NanotI et al., 2000).
The IRS-IB FCC imagery, FSI topographical maps and ground observations were used to generate the spatial Information on the extent of forest cover in the Chilla Sanctuary of Rajajl National Park in Shivallks. It Includes Gohri and Chilla forest ranges. The study area, falling in Pauri Garhwal district of newly created Uttarakhand state, covers a geographical area of about 258 km2. The visual interpretation of satellite Imagery was carried out for stratification of various forest types and densities. Image elements like tone, texture, shape, size, shadow, location and association were used to identify various forest/vegetation cover categories.
According to the study done by this author, mixed Sal forests happens to be the most dominant land cover occupying 63 per cent of the total sanctuary area. It is second largest cover category followed by pure Sal forests plantations. Remote sensing and GIS technologies provide vital information support in terms of relevant, reliable and timely information needed for conservation planning. The reliable information about forest cover in Chilla Sanctuary has been already discovered (Kushwaha, Munkhtuya and Roy, 2000).
Forest stock assessment has been done using IRS LISS-III and PAN merged data in Timli Forest Range, Dehradun. The study demonstrates the use of high resolution IRS-IC LISS-III and PAN merged data for growing stock assessment in Timli Forest Range in the west of Dehradun.
The merged datasets was generated using principal component-based image fusion. The merged data had advantage of colour and high resolution from LISS-III and PAN respectively. It facilitated in differentiation and mapping of a number of forest categories in terms of type and density (Mehrotra and Suri, 2002). The study was carried out in Timli Forest Range located 45 km west of Dehradun. Timli village and Chidiya valley agricultural land fall within the forest of the study area (Singh, Das and Kushwaha, 2003).
According to the data obtained by remote sensing sources, the majority of area was found to be Sal forest (54.6%) followed by Sal estimated to be 2.08 million m. The present studies demonstrate the utility of IRS-IC LISS-III and PAN merged data in discrimination of forest type and canopy density and estimation of growing stock. Forest cover type map and forest canopy density generated with remote sensing data can be significant inputs for the forests management (Narayanan, 2000).
Singh, Roy and Dash have done another study using IRS-P3 MOS data in the Indo-Gangetic basin and peninsular regions. Monitoring and mapping of the Himalayan region on day-to-day basis is possible using the broadband remote sensing sensors. The spatial resolution of microwave sensors is poor compared to visible optical sensors. The visible sensors suffer from the problem of cloud cover, especially in high altitude regions, such as the Himalayan region. The spectral reflectance of snow, ice, cloud, vegetation and bare ground has distinct behaviour.
However, due to the presence of cloud, the information from the snow, ice, vegetation and bare ground are marked by the high reflectance of the cloud. The effect of the cloud can be distinguished in the Short Wave Infrared (SWIR). The Institute of Space Sensor Technology of the German Aerospace Centre (DLR) has developed a Visible/Near Infrared (VIS/NIR) imaging spectrometer named Modular Optoelectronic Scanner (MOS) launched on 21 March 1996 on-board the Indian Remote Sensing (IRS) satellite IRS-P3 into a sun-synchronous polar orbit at 817 km.
MOS sensors consist of two separate imaging spectro-radiometers MOS-A (4 bands) and MOS-B (13 bands) in the VIS and NIR and a CCD-line camera MOS-C (1 band) in the SWIR. The image has been classified using Maximum Likelihood Supervised Classification method from MOS-B data (Rao et al., 2003).
Roy et al. (2002) has contributed an article on map atlas on biodiversity characterization. This is a multi-institutional programme on bio-prospecting of biological wealth jointly supported by Department of Space and Department of Biotechnology. The map atlas allows identifying gaps in the conservation planning by setting priority in decision-making and at management level for conservation of biodiversity.
Biosphere reserves areas are those habitats where landscape ecological conditions are favourable for natural speciation and evolutionary process. The satellite data provided the key input. Digital Elevation Model (DEM) was used to prepare terrain complexity map.
The resultant map is the biological richness. The goals and scales of inventorying and monitoring programmes may change with time. The present effort to characterize vegetation cover, fragmentation, disturbance and biological richness across the landscape is organized in the form of map atlas.
It allows us to identify gaps in the conservation and management areas, and can help in biodiversity conservation planning by setting priority areas. This information is immensely valued in the biodiversity hotspots of the country. It is expected that maps will be used for planning detailed ground level inventories of flora and fauna by premier institutions (Babu Rao, 2001; Bhagabati, 2001).
A creation of biosphere reserves, which is considered as restored polygon in different bio-geographic regions, is one of the important programmes of Government of India. UNESCO-MAB initiated programme as an Integral part of wide spectrum of complementary and transverse scale observations from man-on-the ground to geosynchronous satellites and polar orbital platforms.
Such a vast task can be largely assisted by recent advances in computer-based GIS. In the wildlife census, the fish counts, migratory bird numbering, their resting spots, etc., could be photographed through thermal infrared light imagery, this would provide a considerable management Input to the protected areas and biodiversity (Sankar et al., 2003; Singh, 1994; Singh and Bortamuly, 2005).