Mapping of Fuel wood Trees using Geoinformatics

T.V. Ramachandra a,b,c,*


Energy is an integral part of a society and the state of economic development of any region can be assessed from the pattern and consumption quality of its energy. Energy demand increases as the economy grows, bringing along a change in the consumption pattern, which in turn varies with the source and availability of its energy, conversion loss and end use efficiency. The burgeoning population coupled with developmental activities based on ad-hoc decisions have led to resource scarcity in many parts of India. Through the different stages of development, humankind has experimented with various sources of energy ranging from wood, coal, oil and petroleum to nuclear power. However, indiscriminate exploitation of resources and unplanned developmental activities has led to serious ecological and environmental problems. A judicious choice of energy utilisation is required to achieve growth in a sustainable manner. With 70% population in rural areas, there is tremendous demand on resources such as fuel wood, agricultural residues, etc. to meet the daily fuel requirements [1]. Analysis of  the distribution of different energy forms in rural India reveals that out of 11.42 x 1014 kcal, the share of non-commercial energy is 65%, human energy 15% and commercial energy 20% [2]. This clearly shows that 80% of rural energy is met from traditional sources firewood, agricultural residues and animal residues). According to the report of the Fuel wood Study Committee in India (1982), the total requirement of fuel wood is calculated to be about 133 million tonnes/annum whereas the annual availability is estimated to be about 49 million tonnes [3, 4]. A recent survey conducted [2] on energy sources and sector wise consumption in Karnataka State, India reveals that traditional fuel such as firewood (7.44 million tonnes of oil equivalent-43.62%), agro residues (1.510 million tonnes of oil equivalent-8.85%), biogas conditioning (0.250 million tonnes of oil equivalent-8.85%), account for 53.2% of total energy consumption. Dependence on bioresources to meet the daily requirement of fuel, fodder, etc. in rural areas is more than 85% while in urban areas the demand is about 35%. India with its not so huge landmass of 320 million hectares, when compared to its one billion human population, biomass energy thus continues to be a major source of energy and fuel. In order to meet the growing demand for energy, it is imperative to focus on efficient production and uses of biomass energy to meet both traditional (as a heat supplier) and modern fuel (electricity and liquid fuel) requirements. Inventorying of bioresources for sustainable management and conservation has emerged as an important scientific challenge in recent years [5].

Biomass refers to solid carbonaceous material derived from plants and animals. Plant biomass provides the primary energy source by absorbing solar energy through photosynthesis and acts as the foundation for all life forms. Information about biomass  is necessary for estimating and forecasting ecosystem productivity, carbon budgets, nutrient allocation, and fuel accumulation. Biomass is also considered a useful indicator of structural and functional attributes of forest ecosystems across a wide range of environmental conditions [6, 7, 8]. The production of biomass in all its forms for fuel, food, and fodder demands environmentally sustainable land use and integrated planning approach [5].

Sustainable energy management requires a detailed planning from national to State to district (a region marked off for administrative or other purposes) to taluk (A taluk is an administrative subdivision or tier of local government / A taluk is typically a part of a district, and contains villages and/or municipalities) and village levels. For national-level studies, which are best suited to promote policy formulation, the analysis should be carried out at the lowest administrative level for which demographic, social and economic parameters are available, i.e. a village. The disaggregated level of analysis helps to avoid the aggregations and generalisations that so negatively affect energy management strategies. Inappropriate management strategies involving the selection of sites and species can have adverse effects and lead to degradation and abandonment of land. However, the correct selection of plant species can allow the economic production of energy crops in areas previously capable of only low plant productivities. Simultaneously, multiple benefits may accrue to the environment. Such selection strategies allow synergistic increases in food crop yield and decreased fertiliser applications while providing the local source of energy and employment.

Under this situation, it is necessary to estimate spatially available bioresources and evolve better management strategies to ensure the renewability of resources. In this regard, spatial tools such as GIS and Remote sensing data help immensely in providing geographically referenced and spatial distribution of bioreosurces availability and demand. GIS offers possibilities for combining, or integrating, statistical and spatial information about the availability (supply side) and consumption (demand side) of bioresources (fuel wood, charcoal and other biofuels). This accessible, user-friendly technology aids as the decision support system through visualisation of the results of spatial analysis in easily understandable ways and querying. Multi-scale analyses make it possible to show local situations throughout an entire country or region. Satellite remote sensing is well adapted to complement existing strategies for mapping this type of information while also providing important advantages, particularly for large areas.

Remote sensing techniques help in acquiring spatial data at various time intervals (temporal data) of earth resources, which aid in inventorying, mapping and sustainable management of resources [9]. It offers a quick and efficient approach to analyse the drivers responsible for land use changes, which has implications on energy availability, especially energy from bioresources. The multi spectral temporal data are being used effectively for quantification and monitoring of natural resources. This helps in demarcating areas of deforestation, changes in crop productivity, location of groundwater, mineral, oil and other metals, which are required for managing the resources. Remote sensing data and GIS have immense value in mapping of resources and assessment of energy demand on spatial scale. Major application includes,

  • Land cover analysis
  • Land use classification and evaluation of land resources
  • Monitoring and management of natural resources

The terms land use and land cover are often used in natural resources management, meaning types or classes of geographical determinable areas. Land cover analysis is done to discern vegetation, hydrological or anthropogenic features on the  land surface. Land cover provides the ground cover information for baseline thematic maps. The land cover features can be classified using the data of different spatial, spectral and temporal resolutions. Broadly speaking, land cover describes the physical state of the Earth's surface and immediate surface in terms of the natural environment (such as vegetation, soils, groundwater, etc.) and the man-made structures (e.g. buildings). In contrast, land use refers to the various applications and the context of its use. This involves both the manner in which the biophysical attributes of the land are manipulated and the intent underlying that manipulation (the purpose for which the land has been used). Land use and  Land-cover information play an important role  at local and regional as well as at macro level planning. The land-cover changes occur naturally in a progressive and gradual way, however sometimes it may be rapid and abrupt due to anthropogenic activities. The planning and management task is often hampered due to insufficient information on the rates of land-cover and land-use change. Identifying, delineating and mapping land cover on temporal scale provides an opportunity to monitor the changes, required for sustainable management of natural resources. Thus, GIS and Remote Sensing allow spatial analysis approach to address the problem of regional energy planning.

The use of remote sensing data to monitor natural resources has advanced rapidly in recent years, allowing the determination of forest structural properties ranging from species-specific distribution,  leaf area index. to tree height or biomass at the landscape level [10,11,12]. Multi-resolution analysis plays an important role in image processing, it provides a technique to decompose an image and extract information from coarse to fine scales [13, 14]. MSS data with the better spectral resolution allows identification of materials in the scene, while better  spatial resolution data is required to locate those materials. It can extract features from source image, and provide more information than one image can. IRS 1C satellite provides both high-resolution panchromatic (5.8 m spatial resolution covering the spectral range of 0.4 -0.90 m) and low-resolution (23.5 m) multispectral (G, R and NIR bands corresponding to 0.5-0.6, 0.6-07, 0.7-0.9 m) images. It is possible to have several images of the same scene providing different information although the scene is the same. Processing and synergistic combinations of information provided by various sensors would provide high-spatial-spectral-resolution remote sensing data required for inventorying and mapping bioresources on regional scale.

The present study was undertaken to identify and delineate various land use categories and types of tree species present in a particular area to meet the bioenergy demand. The resource base in Kolar under each sector such as forests, agriculture, horticulture and animal residues are analysed spatially. Most parts of Kolar experience severe bioresource scarcity and immediate policy interventions are needed to restore the ecological balance of the region.  Hence, this study is aimed to determine the supply and demand situation of biofuels of the region and spatially link this database to determine the energy surplus and deficit areas. The vegetation map for Kolar was prepared based on supervised classification of multi spectral sensor (MSS) data of IRS-1C (Indian Remote Sensing Satellite – 1C series). The base maps of the study site are prepared using the Survey of India (SOI) toposheets of scale 1:50,000. This includes administrative boundaries, road and drainage networks, contours and boundaries of vegetative areas (areas under forest department). MSS and PAN data were classified with ground data (collected using Global Positioning System-GPS). Image fusion technique was used to integrate the geometric detail of a high-resolution panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MSS) image, particularly important for understanding land use dynamics at a larger scale (1:25000 or lower) and in determining the spectral response patterns of the vegetation. Fused images can extract features such as trees, etc. from source images and provide more information than one scene of MSS image. Merging helps in retaining the spectral advantage of multi spectral bands while taking an advantage of PAN’s spatial resolution. The spectral patterns of different vegetation were determined with a detailed mapping of 30 villages in Kolar taluk using GPS. This also helped in arriving at spectral response pattern of predominant tree species in Kolar taluk. This information was extrapolated to other taluks. This paper discusses an attempt in to map Prosopis juliflora (Mesquite), a fuel wood species that grows in basic soil (black soil) and has the ability to lower soil pH.

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