FOREST CANOPY DENSITY ASSESSMENT USING HIGH RESOLUTION LISS-4 DATA IN YAMUNANAGAR DISTRICT, HARYANA

<p><strong>Abstract.</strong> Forest plays an important role not only in providing ecological services but also economic goods to human beings. However, with increase in population there is a wide gap between demand and supply of these goods and services. This has lead to reduction in forest cover which needs to be taken care on regular time interval. To manage the existing forest area and also to increase the forest cover Forest Canopy Density (FCD) methodology is the main factor which was given by International Tropical timber Organization (ITTO). High resolution remote sensing LISS-4 data gives us chance to assess the quality of forest in terms of FCD as Rikimaru et al (1999) stated that FCD is one important parameter to assess forest cover quality. High resolution LISS-4 data analysis for FCD was never attempted before. Authors here attempted to assess the FCD utilizing methodology adopted by Rikimaru (1999), Huang (2001), Azizia (2008). The adopted methodology is one of the most efficient and cost effective way to derive the FCD. For this study Resourcesat-2 LISS-4 post monsoon data of year 2017 for Yamunanagar district was used to assess FCD within notified forest boundary. Notified forest boundaries at cadastral level prepared previously by Haryana Space Applications Centre (HARSAC) was used. The degree of forest canopy density is expressed in percentages: i.e. &amp;lt;<span class="thinspace"></span>10% FCD (scrub land), 10–20% (Open Forest-I), 20–40% (Open Forest-II), 40–60% (Moderate Dense), 60–80% (Medium Dense) and &amp;gt;<span class="thinspace"></span>80% (Highly Dense). Forest Canopy Density was based on three indices i.e. Advanced Vegetation Index (AVI), Bare Soil Index (BSI) and Canopy Shadow Index (CSI). Accuracy assessment was done based on ground data and comparison with Coterminous Google Earth imagery and it was found that the devised methodology has achieved overall accuracy of 93% with kappa coefficient of 0.9153. The result shows that maximum forest area in Yamunanagar district is in medium dense FCD category which is approximately 23948.08 acres. This study tells us that 24.2% of the total forest area is under scrub land and open forest which should be focussed for activities in working plan to increase the forest cover. This paper highlights the utility of high resolution satellite data for monitoring and management of forest and improvement in its quality. This attempt provided large scale (1<span class="thinspace"></span>:<span class="thinspace"></span>10,000) maps to the forest managers to better equip them in planning for afforestation, reforestation and rehabilitation of water logged areas, environment management and their future aspect.</p>

Geospatial Analysis of Land Use Land Cover Change Modeling in Phewa Lake Watershed of Nepal by Using GEOMOD Model

Improper practices of land use/ land cover (LULC) are deteriorating watershed conditions. Remote sensing and GIS tools were used to study LULC dynamics using GEOMOD Model and predict the future LULC scenario for years 2015 and 2020, in terms of magnitude and direction, based on past trend in Phewa Lake watershed, Kaski district, Nepal. Due to the proximate and underlying causes, land use and land cover change has become the main challenge of the present world The analysis of LULC pattern during 1995, 2000, 2005 and 2010 using satellite-derived maps has shown that the biophysical and socio-economic drivers including slope, road network and settlements proximity have influenced the spatial pattern of the watershed LULC. These lead to an accretive linear growth of Medium to Fairly Dense Forest, Open Forest, Waste Land and Built-up Land but decrease in other LULC classes. Annual rates of increase from 1995 to 2010 in Medium to Fairly Dense Forest, Open Forest, Waste Land and Built-up land were 75.15, 32.7, 10.14 and 24.2 ha/ year respectively, while the rates decrease in Dense Forest, Terrace Agriculture, Valley Agriculture and Bush/Scrub land were 42.58, 58.17, 27.46 and 2.48 ha/year respectively. The predicted LULC scenario for 2015 and 2020, with reasonably good accuracy would provide useful inputs to the LULC planners for effective management of the watershed. The study is a maiden attempt that revealed the expansion of Waste and Built-up Land, which is the main driving force for loss of Agriculture Land and Grass Land, and an increase in Medium to Fairly Dense Forest and Open Forest leading to decrease in Dense Forest and Bush/Scrub Land in the watershed.The Himalayan Physics Vol. 6 & 7, April 2017 (65-72)

EVALUATE THE CAPABILITY OF LANDSAT8 OPERATIONAL LAND IMAGER FOR SHORELINE CHANGE DETECTION/INLAND WATER STUDIES

This paper explored the capability of Landsat-8 Operational Land Imager (OLI) for post classification change detection analysis and mapping application because of its enhanced features from previous Landsat series. The OLI support vector machine (SVM) classified data was successfully classified with regard to all six test classes (i.e., open land, residential land, forest, scrub land, reservoir water and waterway). The OLI SVM-classified data for the four seasons (i.e. winter, spring, summer and autumn seasons) were used for change detection analysis of six situations; situation1: winter to spring seasonal change detection resulted reduction in reservoir water mapping and increases of scrub land; situation 2: winter to summer seasonal change detection resulted increase in dam water mapping and increase of scrub land. winter to summer which resulted reduction in dam water mapping and increase of vegetation; situation 3: winter to summer seasonal change detection resulted increase in increase in open land mapping; situation 4: spring to summer seasonal change detection resulted reduction of vegetation and shallow water and increase of open land and reservoir water; situation; 5: spring to autumn seasonal change detection resulted increase of reservoir water and open land; and Situation 6: summer to autumn seasonal change detection resulted increase of open land. OLI SVM classified data found suitable for post classification change detection analysis due to its resulted higher overall accuracy and kappa coefficient.

PASTURE IMPROVEMENT IN LOW ALTITUDE TUSSOCK AND SCRUB-LAND

The tussock grasslands and scrub-infested areas of the South Island vary considerably, according to altitude, climatic factors of rainfall, temperature, and wind, and the influence of mankind. To appreciate fully the theme of this paper a brief description of the locality, soil type, and ecology of the particular district will be helpful in evaluating the problems and the methods adopted to bring about an improvement in fertility and production. The area known as Traquair, Lee Stream, Wehenga, and Hindon represents some 300,000 acres and is situated 25 to 40 miles by road in a north to north-westerly direction from Dunedin. Broadly speaking it is an inland plateau bounded by the Maungatua Mountains in the south, the Lammermoor Range in the west, Deep Stream in the-north, and the Taieri River and Silver Peak Mountains in the east. Abrupt medium to deep gullies and gorges with extensive flat tops are characteristic of the topography of the country, which in altitude varies from 600 to 1,500 ft above sea level. Shelter trees are restricted to homestead plantings and small shelter belts.

GRASSING OF MANUKA SCRUB LAND IN THE AUCKLAND PROVINCE

One settler may turn a wilderness of manuka scrub into a green sward of ryegrass and white clover and make a prosperous farm of an apparently barren land, another may fail and leave one of those rather tragic landmarks - an abandoned farm - on the countryside; for the development of poor land is full of pitfalls and the balance between success and failure is very slight