Home Articles Geo-spatial Analysis of Lesser Himalayan Landscape For Characterizing Resource Utilization Pattern (Nainital...

Geo-spatial Analysis of Lesser Himalayan Landscape For Characterizing Resource Utilization Pattern (Nainital Lake Region)

Mr. Nishant Arora
Research Associate,
Central Soil & Water conservation Research & Training Institute (ICAR), Dehradun

Dr. M.C. Porwal
Scientist (SF),
Indian Institute of Remote Sensing (NRSA), Dehradun.

1. Introduction
Nature has nourished life since its existence. From the very beginning life has sustained on the available resources, Human other then any other organism on the planet earth has always overburdened nature with their desire to extract much more than his requirement. It is because of him that today the world is facing the conflict of increasing population, resource degradation and resource depletion. Over utilization of watershed resources by the growing population has resulted in its degradation in most parts of the world (FAO, 1985). Exhaustive extraction of forest products, degradation of land resources and ever increasing population has not only effected the micro climatic conditions but also has lead to the degradation of the global environment to such an extent that it has created a threat to existence, it is thus important to measure, monitor and manage resources for their sustainable utilization.

In a country like India where population spearheaded with low economic status and small land holding poses a very high pressure on the natural resources and the situation is more worst in the hilly region of the country where terrain and inaccessibility are another constrain which force people to utilize and only extract what ever is accessible. Hence the tug war between the availability and the thriving requirement has lead ecological balance away from the state of equilibrium. It is therefore needed to check and introduce a sustainable resource utilization pattern.

Resource utilization pattern refers to the difference of resource utilization in terms of space, time, culture, group of people and environment; it is therefore necessary to understand the present status and predicting the change in the available resources. Sustainability of resource is to assure the availability for future use by utilizing resource only to such an extent that they do not get exhausted.

In recent years a new branch of space applications i.e. “Remote Sensing” integrated with “Geographical Information System” has emerged as a powerful tool in researches oriented in developing geo-spatial criteria for better resource management practices. Satellite data provides accurate and repetitive data about the landscape and monitors the change over time. Geographical Information System aids in storing, updating and analyzing data and models the environmental parameters to make it user and location specific.

Most importantly, the integration of GIS and Remote Sensing (Geo-spatial Analysis) provides much broader perspective of how interactions between natural and human disturbances create complex patterns of change, each of which operates at different spatial and temporal scales.

The present study is thus an attempt to understand the dynamics of resource utilization pattern in the Lake watersheds of Nainital District, situated in the vicinity of Lower Himalayas.

2. Study area

Geographical Location
The study area “Nainital Lake region” comprises of seven lakes situated in the vicinity of the lower Himalayas. Sattal Lake, Khurpatal Lake, Bhimtal Lake, Naukachiatal Lake, Nal Damantital and Pannatal Lake, all are equally beautiful and attractive as the Naini lake. Region is spread over an area of 94 sq Kms, which marks the catchments of all the seven lakes. The average altitude varies from 600m~2600 m above msl. It lies between 29.24° N to 30.35° N and 79.27° E to 79.37° E respectively.

Climate
The study area experiences between subtropical to temperate climate on high elevation (more than 2000m). The mean annual temperature in summers ranges from 10.6O C to 26.7O C and in winters it varies from 2.8O C to 15.6O C. Rainfall begins earlier in the month of June and continues upto the end of September. Nainital records heavy rainfall in these months mainly because of the local rain. During winter, rains create a considerable fall in temperature. Snowfall is the heaviest in January or in early February. Frost is also experienced in winter season. Summer is pleasant and extends from April to June.

Geology
Nainital is situated in the Lower Himalayas and is the result of tectonic activities and the upliftment of sediments between Tibetan plane and the Indo-gangatic plane. The rock type mainly comprises of sedimentary rocks. Quartzite is the dominating rock type in the region.

Physiography
Physiographically the area can be divided into lower Himalayas and terraces.

Lower Himalayas
This type of physiography is found on less than 2000m above msl as well as northern and southern aspects. The slopes are mostly very steep and highly eroded

Terraces
River terraces are found in both the sides of Balia river and sub divide into two types as lower terraces and upper terraces. Lower terraces are formed by alluvium and upper terraces are formed by ‘in-situ’ parent material.
3. Materials and Methods

Data Used
Remote Sensing and G.I.S based approaches have been followed in the present study.

Satellite Data
The following data has been used in the present study
Table 1: Detail of satellite data used in the study

Data Used Path/Row Date of Pass Bands Width(in mm) Spatial Resolution (m) Swath (Km).
IRS-1D LISS III 98/51 25-03-2000 0.52-0.59
0.62-0.68
0.77-0.86
1.55-0.70
23.5 141
IRS-1DPAN 98/51 23-09-1999 0.5-0.75 5.8 70

Ancillary data

  • SOI toposheet number 53O/7 and 53O/11, Magnetic compass, Questionnaire, Field Performa
  • 3.1 Methodology

    Data preparation
    Satellite image were geometrically and radiometrically corrected and LISS III and PAN data sets were merged for better interpretation of resource information.

    Preparation of base maps
    Base maps, including contour, drainage, road, settlement, village and watershed boundary were obtained form available sources. Data inputting was done through heads-up digitizing in Arc/Info 8.1 Windows NT with UTM projection. The village boundary map was generated from ground survey and was rectified with satellite image and SOI toposheet, using the knowledge-based rules for defining the boundaries. The settlement was marked out from the toposheet and was further rectified using PAN data of the year 1999.

    Field investigation
    Field data is most important part in this study. Detailed questionnaire (appendix 1) for household studies was prepared considering the related information of resources, like population, cattle population, quality and quantity of resources, the distance to collecting resources and the future planning etc.

    Census data collection
    Total population, Cattle population, Agriculture land of each village is collected in village record office.

    Household interview
    Each of the village is visited and random household sample is taken for interview and filling questionnaire. There are 40 questionnaires out of 40 villages achieved one from each village.

    Ground truth data collection
    Ground truth is collected during the field visit with the help of Satellite Image FCC hardcopy, toposheet, magnetic compass and field Performa.

    Visual interpretation and Knowledge base classification
    In mountain area, especially in Himalayas due to the terrain complexity, the spectrum signature is influenced by the elevation, the aspect and slope, which might have same objectives show different reflectance or the different objectives could have the same reflectance. In this situation, having the intensive ground truth, Knowledge base visual classification and on-screen visual recoding and rectification is employed to give best accuracy.

    Land Use/Land Cover Mapping
    Merged (LISS III + PAN) data was used as the source for the land use/land cover mapping. The interpretation key formulated during fieldwork has been used for preliminary land use mapping. Later the image was classified into cover types based on the knowledge based classification and the shadowed areas were put to corresponding classes on the basis of ground knowledge by recoding them. In order to achieve the more accuracy aspect and slope was used as important keys to differentiate the oak and pine.

    Resource Mapping
    After the land use/ land cover is finalized, the classes were regrouped into various resources available. Six classes are identified as agriculture, dry exposed rocks, forest, scrub/grassland and riverbed. The area dominates oak (dense and looped/open), deodar, pine (dense and open) and open mixed forest. Scrub and grassland include scrub/lowland grassland/pasture and highland grassland. The Resource map is prepared as input for studying resource distribution and resource utilization pattern.

    Resource Utilization Pattern
    The data collected from the field was analyzed and processed for the information extraction. Field data collected was divided into three types. One is direct information, which can be attributed to villages viz. quantity of agriculture land. Second is indirect information need to process and also can be attributed to villages, viz. Village consumption rates of fuel and fodder. Third is some general information regarding the alternative of resources?
    Table 3: Field data types

    DirectAttribute Population, household number, cattle population, land information, distance for collecting fodder and fuel wood, other energy utilization information… etc.
    DerivedAttribute Tree fodder requirement, grass fodder requirement, fuel wood requirement, cattle/capita, land/capita, agriculture land, forest land, grassland, etc.

    Fodder and fuel wood requirement evaluation
    Fodder and Fuel wood are very important resources in Nainital watershed. The fodder and fuel wood consumption varies from village to village and from household to household. The calculated average consumption of fodder per cattle (Given that all the kinds of animals have the same requirements) and fuel wood per person from the sampled households. The tree fodder consumption is 5.19kg/day/cattle. The grass fodder consumption is 6.38kg/day/cattle. The fuel wood consumption is 4kg/day/person.

    It is need to clarify that the type of fodder utilization is varying with seasons. During monsoon (from July to September), grass is preferred because it is available everywhere around the villages. While tree fodder and grass from highland is collected during the rest time of a year.

    Resource Distribution Pattern
    Natural resource distribution is uneven due to physiographic factors and human interventions. In the entire lake watershed, forest is well distributed in higher altitude and far from human settlements. Grasslands and scrub are dominated in the southern face and half-mountain area. Agriculture lands are located around the settlements, valley area, gentle slope and flat area. Terrain Accessibility Index (TAI) was calculated considering the terrain complexity viz. slopes (smax) and the maximum distance (dmax) to collect the potential resource. The subjective weightage was given to the smax and dmax to calculate the TAI. The TAI was used to calculate the plainmetric distance for the proximity analysis for calculation of the resource distribution of individual village.

    Analyzing critical areas of resource utilization
    The concept of the critical resources is developed based on the importance of the resources, per capita requirement of biomass, Physical accessibility and Landscape level disturbance regimes.

    The resources importance index was given based on the field knowledge. The accessibility map was prepared taking the slope, settlement and transport into the consideration. The criteria for the accessibility map are depicted in table 4. The landscape parameters were used to qualify/quantify the fragmentation in the available resource. Further the landscape indices were used to calculate the disturbance regimes in the study area
    Table 4: Criteria for Physical Accessibility Map

    Parameters Criteria Index Weightage
    Slope 30° 1
    Settlement Buffer 400m 1
    Road Buffer 400m 1

    Physical Accessibility Index = 0.5*(Slope Index) + 0.25*(Settlement Buffer Index) + 0.25*(Road Buffer Index)
    4. Results

    Land Use/Land Cover Mapping
    The total of 12 land use/cover types were identified

    Oak (Ban Quercus leucotrichophora & Moru Quercus floribunda) forest is a kind of Himalayan moist temperate forests distributed at an altitude 1800m-2400m above msl. It is the dominant forest in this area covering 13.5% (12.66 sq. km) of the land in Nainital lake region.

    Oak is used as fodder, fuel wood and plays a very important role as resource for the subsistence of the villagers. Heavily lopped oak forest can be seen in the Khurpatal lake watershed. The oak forest in Nainital Nagar palika range, Lariakhata block, cantonment and Raj bhavan ranges are still in a better condition.

    Chir Pine (Pinus roxburghii, Sargent) is found in this study area. It is occupying only 28.1%, (26.31 sq. km) of the area.

    About 7.8% (7.367175 Sq Km) of the area is under mixed Pine forest, with Oak and Surai as prominent species distributed in the nagar palika range and khupratal block.

    A large area in this region has been brought under plantation under soil and water conservation schemes. Surai (cupressus torulosa), Deodar (Cedrus deodara), Oak (Quercuc leucotricophora), Telangh (Quercuc floribunda), Pine (pinus roxburghi) etc. are some of the species, which are planted other then some exotic species.

    In satellite image, scrub, pasture and lowland grassland are appearing similar spectrum signature and hard to differentiate. And from the resource point of view, they are having the same function, i.e. offering the grass fodder for cattle. Here we put them together as one class. Kurie (Lantana camara),Ghiangru (Randia tetrasperma), Hisalu ( Rubus ellipticus, smith),Kilmora (Berberis asiatica), Kunji (Rosa macrophylla)are dominant species of shrub, (Chrysopogon Gryllus, Linn, Themeda arundinacea, (Roxb) Ridley, Saccharum spontaneum, LinnIt, A;luda mutica, Linn, are the common fodder spp. used here. This class encompassed 12.21%, (11.43 sq. km) of the area.

    Agriculture is mainly confined in depressions, along gentle slopes and valley sides wherever the water sources and soil conditions are favorable. The total agriculture land found here is 4.65 sp. Km, which is 7.8% of the study area.

    There are four prominent streams in the region. They encompassed only 1.39%(1.31 sq. km) of the area. The slopes on the either side of the streams are very steep are prone to erosion therefore at certain places they are guarded by cemented structures.

    Resource distribution pattern

    Resource Composition
    Natural resources identified are agriculture, forest, scrub and grassland, riverbed, waterbodies. Besides the natural resources, animal also is one major resource for the villagers providing the milk products, using as drought and transportation, etc.

    Forest being the dominant resource covers an area of 64.81Sq Kms., Scrub mainly Lantana cover a large area and is the second dominating cover type covering an area of 17.16 Sq Kms. Agriculture, Settlement, and Dry river beds are 4.74, 2.06and 1.41Sq Kms in area respectively.

    Accompanied with agriculture, animal husbandry composes the major livelihood in this area. The cattle are oxen, cow, buffalo, sheep, goat, mule, and horse. It can be classified as two types, namely milk animal (cow, buffalo, sheep goat) and non-milk animal or drought/transportation animal (oxen, mule).

    Village-wise resource distribution pattern
    Village wise resource distribution is calculated, an example of Sakhola Village is given here. The area is the village is 2.008 Sq Kms. Forest is the major resource with an area of 1,39 Sq Kms, followed by Scrub/lantana spread in an are of .55 Sq Kms. Similarly we can find out the resource distributed in each Village.

    Sakhola Village (Resource distribution)


    Resource utilization pattern
    Resource utilization pattern is important as it gives an insight of the resource requirement; it gives a picture of efforts that is put in to collect it and extent of pressure on the available resources. The resource utilization of the area is very high, per day green fodder requirement is 5.190 Kg/cattle/day, dry fodder requirement is 6.38Kg/cattle/day and fuel wood requirement is 4 Kg/cattle/day. The total number of cattle in entire region is 9927; the total available resource is distributed only on 75% of the area i.e. 79.78 Sq Kms of area. Per cattle viability of resource spatially is .008 Sq Kms but to bring to you kind notice that out of 64.81 Sq Kms of forested area, 26.31Sq Kms of the area is under pine forest, which has less importance as fodder. Otherwise pine needle is used for laying bed for the cattle. And also that the most of the area has highly degraded forest. The list of the village wise cattle census and fodder requirement are listed below, resource requirement maps of tree fodder, grass and fuel wood requirement are also shown along with the total resource map of the area.

    Resource Accessibility Map
    Resource located near the settlement, around the road and located on the gentle slope have the higher accessibility, on the contrary, resources far away from the settlement and road, located on the deep slope are difficult to access. The resource accessibility maps generated in this study show that, in Nainital lake region, among the agriculture land (7949750 sq. m) 67% is high accessible, 24% is medium accessible, the other 9% is low accessible. For oak (30387925 sq. m), only 1% of the area is highly accessible, 40% of it is medium accessible and 59% is low accessible. While, 4% of the grassland (14015975 sq. m) is high accessible, 63% of it is medium accessible and 33% is low accessible



    Table 8: Accessibility wise resource
    It is interesting to know that as the accessibility of the area decreases the percentage of area under forest increases. This study thus postulates that accessibility due to nearness from road, settlement and on gentle slopes has less area under forest then the areas which are less accessible i.e. which are away from road, settlement and on steep slopes. It is also interesting to note that all other categories of Resource type i.e. Agriculture, Scrub etc., decreases as we move away from accessible areas.

    Critical areas of resource utilization
    For and Ecologist it is very essential to know the area where there is highly availability of resources. Accessibility depends on nearness to settlement, proper network of road and gentle slope terrain. Availability of the desired resource for which resource map was assigned weights along with the map of requirement of resource existing in that area. Which was calculated as per the questioners and accessibility map was modeled for find the critical area of resources.

    5. Conclusion
    The study concludes that remote sensing and G.I.S. can be effectively utilized for understanding the distribution and utilization pattern of resources.

    Through this study in the Nainital lake region it was ascertain that, which are the villages having sufficient amount of fuel wood and fodder resources and where are the critical area for resource accessibility.


    Literature Cited

    • Lillesand & kiefer, 1994. Remote Sensing and Image Interpretation, John Willey & sons Inc, Canada.
    • FAO (1985), Tropical Forestry Action Plan, Committee on Forest Development in the Tropics, FAO, UN, Rome.
    • ICIMOD, 2001. Livestock Composition in the Himalayan Mountain. WWW.ICICOM.ORG.NP
    • Norbert Jürgens,2000. Human Dimensions of Biodiversity – the German challenge.
    • Subrat Sharma, L.M.S. Palni and P.S. Roy, 2000. Analysis of Fragmentation and Anthropogenic Disturbances in the Himalayan Forests: Use of Remote Sensing and GIS. GISdevelopment.net Forest Resources
    • Lillesand, T. M. and Kirfer. J. W. (1999). Remote Sensing and image interpretation.(4th ed.). John Wiley & Sons, New York.
    • Goodchild, M. F. (1992) Geographical data modeling, Computers & Geosciences 18: 401-408.
    • Goodchild, M. F. (1994) Integrating GIS and remote sensing for vegetation analysis and modeling: methodological issues. Journal of Vegetation Science 5: 615-626