I. J. Singh

Assessment and monitoring of estuarine mangrove forests of Goa using satellite remote sensing

The present study highlights the application of satellite remote sensing in the assessment and monitoring of the mangrove forests along the coastline in Goa state of India. Based on onscreen visual interpretation techniques various land use and land cover classes have been mapped and classified. An attempt has been made to analyse changes in the mangrove forest cover from 1994 to 2001 using IRS-1B LISS-II and IRS-1D LISS-III data. An increase in the mangrove vegetation in the important estuaries has been found during 1994 and 2001. During this period, the mangrove forest increased by 44.90 per cent as a result of increased protection and consequent regeneration. Plantation of mangrove species has been raised in 876 ha (1985 to 1997) by the State Forest Department¨

Growing stock estimation through stratified sampling on satellite remote sensing data

Aerial photographs are being extensively used for forest surveys i.e. forest cover type mapping, assessment of growing stock, estimation of area, vegetation studies, etc.Satellite remote sensing technology offers new possibilities and scope for achieving some of the above applications with higher accuracy and reliability. The assessment of growing stock through subjective stratification has also become possible with increase in spatial and spectral resolution. In the present study, the LANDSAT TM FCC (1966) has been visually interpreted on the basis of tonal characteristics. Stratified random sampling method has then used for determination of number of sample units for collection of ground Inventory data. Using regression equations, the volume per hectare of individual forest cover types were calculated.Satellite remote sensing data has been used for initial stratification, distribution of sample plots and calculation of area under various forest cover types. Estimate has been made for available commecial and non-commercial growing stocks in the study area.

Forest stock assessment using irs LISS III and pan merged data in Timli Forest range, Dehradun

This study demonstrates the use of high resolution IRS1C LISS-III and PAN merged data for growing stock assessment in Timli Forest Range, west of Dehradun. The merged data set 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. The homogeneous forest strata were field inventoried for individual tree height and diameter using sample plots following two-phase sampling design. The plot inventory data was analysed to arrive at image level growing stock estimates. The study revealed that pure sal forest has maximum growing stock followed by sal mixed forest and miscellaneous forest. The study also shows good scope of high resolution data for growing stock assessment.

Vegetation analysis and study of its dynamics in Chandaka Wildlife Sanctuary (Orissa) using aerospace remote sensing

Changes brought in habitat conditions due to increasing human influences on natural areas have posed serious threat to wildlife. Remote Sensing has probably omerged as one of the most viable techniques to assess and monitor habitat conditions. Comparative analysis of maps of two-time period can provide authentic data with respect to changes brought in the habitat conditions. Chandaka Wildlife Sanctuary, covering an area of 213.71 sq. km in Orissa is one of the natural reserves of elephants which has undergone serious changes brought in through anthropogenic activities of urban areas of Cuttack and Bhubaneshwar lying within the proximity of the sanctuary. The natural reserve, an ideal habitat for elephants, was connected to neighbouring extensive forest belts. These connections have been either degraded or deforested over the years. The present study analyses the types of habitat available in the sanctuary using remote sensing data (aerial and satellite). Vegetation-type maps of 1975 have been prepared from B/W aerial photographs of 1:25,000 scale. For assessing the current vegetation types, maps have been prepared from Indian Remote Sensing Satellite (LISS II) false colour composite on 1:50,000 scale. Comparative evaluation of the maps indicates changes in the vegetation pattern, increase in mining and agriculture areas within the sanctuary. Stratified field sampling of vegetation types provide structural characteristics of the vegetation. Bamboo has been found to extend in the valleys and side slopes of the sanctuary area during past 15 years. An analysis on response of vegetation in all major vegetation types mapped have been made in the context of the invasion of Eupatorium odoratum. Finally, bamboo biomass has been assessed through stratified random sampling as it constitutes a major elephant food source.

Evaluation of microwave remote sensing data for forest stratification and canopy characterisation

The study presents digital preprocessing techniques, visual mapping capability of airborne X-band SAR data having diverse vegetation types in tropical wet climate. Spatial textural analysis methods have also been evaluated to enhance discriminability of the forest types and features. Attempt has been made to enhance the information by merging optical remote sensing data with microwave X-band response. Finally, the backscattering digital values have been correlated with qualitative and quantitative vegetation parameters. The Leaf Area Index has shown significant relationship with SAR image digital number value.

Monitoring of forest cover type and landuse classes through remote sensing techniques

Ranikhet tahsil being situated in mountaineous region of the Himalaya has been influenced by fast changes in forest cover and landuse during the recent past. Remote sensing technique has been employed to monitor the changes in forest cover and imporant landuse classes. Landsat MSS (FCC) and Landsat TM (FCC) of 1972 and 1986 respectively has been visually interpreted.The study highlights the potential of remote sensing techniques for monitoring the changes in forest cover and land use classes.

QUANTIFICATION OF FOREST STOCK USING REMOTE SENSING AND GIS

Forest stock is key information required in forest mensuration, forest utilization and forest management. The present study highlights the application of the state-of-the-art technology of satellite remote sensing, Geographical Information System (GIS) and Global Positioning System (GPS) in quantification of forest growing stock. The stratification of forest in to various forest cover types and structural canopy density classes and use of sampling procedures facilitate in obtaining the required information in the shortest time and cost effective manner. In general, the forest areas refer to natural stands of woody vegetation in which trees predominate. Forests are known to be one of the very important renewable natural resources which change with time and space. The direct benefits from forests are mainly timber and non-timber minor forest products. The list of indirect benefits is quite huge, including the amelioration of climate, soil and water conservation, biodiversity conservation, habitat for a variety of fauna, tourism and recreation, etc. Broadly, there are six major groups in h,dian forests, namely tropical moist forests, tropical dry forests, montane sub-tropical forests, montane temperate forests, sub-alpine forests and alpine forests. These have been further divided into 16 types, 46 sub-types and 221 ecologically stable formations (Champion and Seth, 1968). The information about the size of forest areas and the growing stock are the two important parameters of forest inventory. Forest inventory, therefore, makes an attempt to describe the quality and quantity of forests. It is undertaken at the national or state levels for knowing the location, extent, nature, condition and productive capacity of forests. The outputs are used for formulating the policies, plans/projects and monitoring purposes.

Comparison of Sampling Methods for Inventorying the Stand Volume Using Satellite Remote Sensing

Remote sensing is being increasingly used for forest resource inventory as it saves time and the cost. Aerial photographs and satellite images have been effectively utilized for forest inventory all over the world. This study highlights the application of IRS LISS-III imagery for inventorying the stand volume in Lachchhiwala Forest Range of Siwaliks. The satellite image was visually interpreted for forest type and density stratification. Both random as well as stratified random sampling techniques were used to see their impact on the volume estimates. Field sampling was done in the plots of 0.1 ha size. The total growing stock in all types of forests in the study area was estimated to be 1.87 mill.m3, of which Sal Forest accounted for 1.32 mill.m3, Sal Mixed Forest for 0.09 mill.m3, Mixed Sal Forest for 0.08 mill.m3, Miscellaneous Forest for 0.06 mill.m3 and Forest Plantations for 0.02 mill.m3. The results were compared with an independent field-based inventory carried out by forest department. The two sampling methods were compared by ratioing of the mean of variance (gain in precision) and it was found that the timber volume estimates using stratified random sampling technique were 15 per cent more accurate than simple random sampling. The satellite image-based inventory using stratified random sampling was found to have about 90 per cent correspondence with the inventory done by the Forest Department.

Forest management using remote sensing and GIS in Barbatpur range, Betul forest division

Forest management is defined as the practical application of the scientific, technical and economic principles to forestry. Working plan is the key document to accomplish well-defined descriptive, prescriptive activities of forest management. In the present study, the role of remote sensing and GIS in forest management has been investigated. Qualitative and quantitative analysis have been made (forest cover type with overall accuracy 83.17%; average volume per ha 66.2 m3; total growing stock 119217.95 m3and total number of bamboo culms 9228416). Suitable sites for afforestation and joint forest management have been identified. The prescriptions for various forest stands in context with management working circles have also been made.

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation.Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map.Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)