Sarnam Singh

Estimation of Leaf Area Index in dry deciduous forests from IRS‐WiFS in central India

Leaf Area Index (LAI) is an important biophysical parameter necessary to infer vegetation vigour, seasonal vegetation variability and different physiological and biochemical processes of vegetation. Gap fraction analysis has been carried out to estimate plot‐wise LAI. Tropical dry deciduous forests of the study area can be categorized into two prominent phases—growing and senescent phases. Efforts have been made to observe the relationship between ground based LAI values and satellite derived parameters, such as radiance values and also different vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), maximum, minimum, amplitude, sum and radiance NDVI values for both the phases. Multi‐temporal IRS 1C WiFS data have been used. WiFS provides information in two bands: red (0.62–0.68 µm) and near‐infrared (0.77–0.86 µm). All the images from the representative months have been corrected radiometrically and geometrically. NDVI values have been derived for all the representative months. Among various parameters maximum NDVI values showed better relationships with LAI for both the phases. For the growing phase R 2 (coefficient of determination) was 0.79, whereas for the senescent phase it was 0.48. These empirical relationships have been used to estimate LAI at a regional level. The LAI estimate for growing and senescent phases ranged from <1 to 4.25.

Vegetation cover type mapping in mouling national park in Arunachal Pradesh, Eastern Himalayas- an integrated geospatial approach

Improving image classification and its techniques have been of interest while handling satellite data especially in hilly regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify evergreen forests/vegetation in Moulirig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy. Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from 40.81 to 83.67%. Maximum-forested area (252.80 km2) in national park is covered by sub-tropical evergreen forest followed by temperate broad-leaved forest (147.09 km2). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang sub-watershed, in and around western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas.

India's Silent Valley and its threatened rain-forest ecosystems

Most of the features that are commonly attributed to typical tropical rain-forests, such as a preponderance of woody vegetation and species with leaves in the mesophyll size-class, tall slender trees with ‘flying buttress’ and unusually thin bark, multilayering of vegetation with abundance of epiphytes and stranglers, evergreenness, strong tendency to change in species composition in time and space, and high diversity of dominance, are plentifully displayed by the forests of the Silent Valley in southwestern India. A relatively high species-richness, remarkably thin bark of trees, and high total tree-basal area, indicate that the valley embodies a virgin forest and that conditions for growth are very favourable. Because of the terrain, heterogeneity in habitats is well marked. The proposed construction of a dam and large flooding reservoir threatens to bring about several undesirable alterations in the environment of the Silent Valley rain- and riparian forests, and the disturbances that would follow such construction and flooding would be highly detrimental to the diversity of the forests and to the complexity of their structure. Hence a plea is made for the setting aside forthwith of a proposed major ‘Silent Valley Biosphere Reserve’, which could safeguard a unique part of the worlds genetical heritage and one of its most interesting complexes of natural ecosystems.

Performance of seedlings of various life forms on landslide-damaged forest sites in Central Himalaya

1. Growth performance of six plant species of early successional communities was studied on three landslide-damaged sites of varying ages (3-, 6- and 8-year-old), in the Central Himalaya. 2. The six species were equally divided among trees, shrubs and herbs. Of these, two species were nodule forming (Alnus nepalensis, a tree species, and Desmodium tilaefolium, a shrub species). 3. The results indicated that suitable species mixture around A. nepalensis can be developed to hasten the revegetation process on bare sites. 4. A. nepalensis is suggested as the principal species for revegetation, not only because its seedlings have maximum dry mass and maximum litterfall nutrients, but also because it can nurse other species by providing nitrogen from the root nodules, an important contribution both to natural successions and to artificial revegetation. 5. Location of safe and suitable microsites on the damaged sites would hasten the process of revegetation.

Mapping the spatial distribution of air and water pollutants in Kolleru Lake, India using geographical information systems (GIS)

Kolleru Lake, a wetland located in India, is one of the largest natural freshwater lakes and is an important sanctuary for indigenous and migratory birds, particularly in winter seasons. The lake is located between latitudes 16°32′ and 16°47′N and longitudes 81°05′ and 81°27′E. The lake is connected to the sea through the Upputeru River, at a distance of 60 km. The lake water is mainly used for drinking water, agriculture, fishing and aquaculture purposes. The lake ecosystem is deteriorating due to the industrial, agricultural and aquacultural activities. High volume sampler was used for the collection of air pollutants, namely suspended particulate matter, nitrogen oxide and sulfur dioxide from the lake at four locations over a period of one year. Water samples were collected from the lake in three seasons in a year over a period of three years and analyzed for water quality parameter, namely total suspended solids, hardness, chloride, sodium, chemical oxygen demand and biochemical oxygen demand. The aim...

Characterisation of ecological parameters in tropical forest community—A remote sensing approach

Satellite Remote Sensing data has been used for vegetation mapping, initial stratification, distribution of sample plots and for calculating the area under different vegetation types. Primary and secondary analyses of vegetation has been done using phytosociological ground data collected from sample piots to assess the ecological importance of different species. Interrelationships among different communities have been evaluated through various available indices. The spatial distribution and vegetation analysis indicate that commercial extraction of natural forests of Andaman has set in retrogression. The evergreen forests subjected to shorter rotation of commercial exploitation are being invaded with seral deciduous species. The study highlights the status of forests (spatial and community) and stresses the need to conserve germplasm present in the natural evergreen forests.

Assessingjhum-induced forest loss in Dibang valley, Arunachal Himalayas — A remote sensing perspective

There has been a significant advancement in the application of remote sensing from various space altitudes for inventorying and monitoring ofjhum (shifting) cultivation associated forest loss. The dynamic nature ofjhum system, complex physiography, small size of individualjhum plots and their discontinuous nature of distribution, highly heterogeneous vegetation and ever-changing atmospheric condition in the Arunachal Himalaya posses a great challenge to local flora and fauna. Indian Remote Sensing (IRS)-1C/1D LISS-III data were used to classify the current and abandonedjhum areas in Dibang valley district. The amount of area occupied by current and abandonedjhum corresponds to 199.34 km2(1.53%) and 225.40 km2(1.73%) respectively. Field data were collected following stratified random sampling method to gather information on plant community occurring in abandonedjhum cultivated areas. It was observed that only nine species out of 45 contribute to 50% of the important value index (IVI). Of the 45 species, 7 species (15.56%) have been found to be endemic to Eastern Himalayas. Population inducedjhum cultivation has led to deforestation, biodiversity loss, increased surface soil erosion, and sedimentation of water bodies in this area. The potential use of satellite-derived maps can best be used for better management and land use planning.

Assessment of digital image classification algorithms for forest and land-use classification in the eastern Himalayas using the IRS LISS III sensor

The article describes the assessment and discusses the potential of different methods of classifying Linear Imaging Self-Scanning Sensor (LISS) III sensor data for vegetation-cover type and land-use mapping in the hilly terrain of the eastern Arunachal Pradesh, in the northeastern part of India. The forest cover types and their distribution in the study area are governed by climatic conditions and topographical features, which change along the gradient from tropical in the south to alpine in the Greater Himalayas in the north. Arunachal Pradesh is part of the Himalayan Biodiversity Hotspot and is at the tri-junction of Indian, Sino-Japanese, and Indo-Malayan floristic regions. The evergreen forests in the area represent climatic climaxes, which are partly original virgin and partly affected by anthropogenic pressures. Due to phenological and physiognomic similarities in ecotone regions, the differences in the spectral reflectance between adjacent forest types are not well pronounced. The age of the forests, terrain characteristics, and the nature of the vegetation itself could be other reasons for the near-similar reflectance. It is for these reasons that conventional classification algorithms for supervised and unsupervised classification do not perform well. Therefore, there is a need to find a suitable method for vegetation and land-use mapping with high mapping accuracy in this region, which is a biodiversity hotspot. A suitable classification technique is important to characterize vegetation-cover type in this complex terrain. We tested unsupervised, supervised, hybrid, object-oriented, and expert classification techniques for vegetation and land-use mapping on Indian Remote Sensing (IRS)-1C LISS III data. The expert classifier produced the highest accuracy (93.39%) followed by object-oriented, hybrid, and supervised and unsupervised classification techniques.

Leaf Area Index retrieval using Hyperion EO-1 data based vegetation indices in Himalayan forest system

Present Study is being taken to retrieve Leaf Area Indexn(LAI) in Himalayan forest system using vegetation indices developed from Hyperion EO-1 hyperspectral data. Hemispherical photograph were captured in the month of March and April, 2012 at 40 locations, covering moist tropical Sal forest, subtropical Bauhinia and pine forest and temperate Oak forest and analysed using an open source GLA software. LAI in the study region was ranging in between 0.076 m2/m2 to 6.00 m2/m2. These LAI values were used to develop spectral models with the FLAASH corrected Hyperion measurements.Normalized difference vegetation index (NDVI) was used taking spectral reflectance values of all the possible combinations of 170 atmospherically corrected channels. The R2 was ranging from lowest 0.0 to highest 0.837 for the band combinations of spectral region 640 nm and 670 nm. The spectral model obtained was, spectral reflectance (y) = 0.02x LAI(x) - 0.0407.

A comparative analysis of extended water cloud model and backscatter modelling for above-ground biomass assessment in Corbett Tiger Reserve

Forest biomass acts as a backbone in regulating the climate by storing carbon within itself. Thus the assessment of forest biomass is crucial in understanding the dynamics of the environment. Traditionally the destructive methods were adopted for the assessment of biomass which were further advanced to the non-destructive methods. The allometric equations developed by destructive methods were further used in non-destructive methods for the assessment, but they were mostly applied for woody/commercial timber species. However now days Remote Sensing data are primarily used for the biomass geospatial pattern assessment. The Optical Remote Sensing data (Landsat8, LISS III, etc.) are being used very successfully for the estimation of above ground biomass (AGB). However optical data is not suitable for all atmospheric/environmental conditions, because it can’t penetrate through clouds and haze. Thus Radar data is one of the alternate possible ways to acquire data in all-weather conditions irrespective of weather and light. The paper examines the potential of ALOS PALSAR L-band dual polarisation data for the estimation of AGB in the Corbett Tiger Reserve (CTR) covering an area of 889 km2. The main focus of this study is to explore the accuracy of Polarimetric Scattering Model (Extended Water Cloud Model (EWCM) with respect to Backscatter model in the assessment of AGB. The parameters of the EWCM were estimated using the decomposition components (Raney Decomposition) and the plot level information. The above ground biomass in the CTR ranges from 9.6 t/ha to 322.6 t/ha.