Ramachandra. T.V., Vijaya Prasad. B.K. and Samapika Padhy Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560 012, India

Biomass Resources Assessment :

Non-availability of accurate, reliable and up-to-date data for various biomass resources is the main reason hindering an accurate assessment of the bioresources of a region. Such data need to be obtained and updated periodically.   Surveying, sampling and analytical procedures is used for the collection of this data. 

1.    Wood Biomass

• Average biomass content per unit area in different forests, forest reserves, plantations, woodland transitions in various climatic zones;
• Average biomass being removed and added in above areas on annual basis in various climatic zones;
• Wood supply and demand for rural and urban areas for all States of the Federation for various applications of wood;
• Energy value of various types of wood available and its present mode of utilization as fuel.

2.    Forrage Grasses and Shrubs

• Average biomass content per unit area in wooded shrub grasslands, shrublands, grassland/shrubland transitions and grasslands, annual harvestable and renewable percentages and methods, energy values, and present utilization.

3.    Residues and Wastes

Crop Residues and Wastes

• Total annual crop production of food and cash crops and their hectarage for various climatic zones.
• Average crop residues and wastes per unit area for each of food and cash crop in different climatic zones, their proportion to net grain yields, harvestable percentages and methods, energy values, and present utilization.

Animal Wastes

• Total number of animals and their categories in different climatic zones/States.
• Average amount and type of feed, and waste production per day for each type of animal, waste collectable percentages and methods, energy values, and present utilization.

The resource base in a region under each sector such as forests, agriculture, horticulture and animal residues is  analysed spatially.

4.   GIS in Bioresources Assessment and Monitoring
The acquisition of basic inventory data is fundamental in the regional energy planning endeavour. Data include vegetation type, soil type, species type, class/stand structure, canopy details, density and the boundaries of management units.  Data collection techniques range from selecting sample plots (quadrats or transects) for ground surveys to using topographic maps,  and emerging Global Positioning Systems (GPS), alongwith Remotely  Sensed data and Geographic Information System (GIS) make direct and substantial contributions.  Geographic Information System can contribute to assessment of bioresoorce availability, demand and offers the potential to predict future needs.

Spatial data input, editing, maps creation, overlaying, reclassification and suitability analyses  characterize the inventorying,  monitoring and decision making process.  Resource assessment include

1. inventorying bioresources available for fuel, food and fodder from  various categories of land cover,
2. related data such as topography, soils, roads and hydrology and
3. assessment of bioresource productivity (from forests, agriculture, horticulture, etc.).

In addition to remote sensing, spatial positioning technologies have begun to influence surveying techniques and, thus resource inventories.  Global Positioning System (GPS) technology is based on a set of orbiting satellites (a total of 24), which provides three dimensional positional fixes with an accuracy within tens of meters.  With four or more satellites in view, a GPS receiver can interpret the carefully timed satellite signals to determine geometrically the latitude, longitude and altitude at the operators position. GIS applications of GPS include georeferencing of satellite imagery and navigating to sample sites for ground truth exercises particularly relevant for forest and plantation inventories.

5.    IRS-1C  LISS -III Data for Bioresource Assessment
IRS-1C  with 23.5 m  spatial resolution provide data outputs adequate or comparable to the scale of 1:50,000. Eleven taluks in Kolar district has been selected using March 1998 data and was analysed using soft classifiers (based on Bayesian probability theory). Bioresource is estimated using yield data for each vegetation type in the inventory: Forests, Agriculture, Horticulture, Shrub land, etc. Yields were multiplied by spatial coverage (area) for each land use category. Talukwise residues from livestock were computed from population and dung yield data for each type of animal.

6.    Interpretation of Remotely Sensed Data for Land Use / Land Cover
Sensor records response based on many characteristics of land surface, including natural and artificial cover. Usually the elements of tone, texture, pattern, shape, size, site and association are used to derive information about land use / cover mapping.

7.    Sampling Frame

An important initial objective is to develop the agro-ecological zonation to  provide a valid basis for extrapolating the results of the supply survey to the regional level.  The woody biomass and agricultural residues surveys requires a regional zonation which reflected the range of natural vegetation as well as agricultural land use.  A suitable zonation is also required to provide a valid sampling frame to spatially link the results of the supply and demand surveys.

The use of satellite imagery enables actual land cover classes to be mapped at a regional level, which is a more preferable approach to developing a valid and robust sampling frame.  There are well-established methodologies for developing land cover zonation at national scales by using multi-temporal imagery to distinguish between patterns of vegetation activity with time.    The imageries, with Geographic Information System and combined with ancillary data on rainfall, topography, climate, and the extent of irrigated farmland to produce a zonation of land cover types for the whole Region.

Sampling units was selected within each IRS scene for field measurement of woody biomass and crop residues. Acquisition dates depends on cloud free period  for both woody vegetation and crop sampling.  This required a trade-off between the optimal season for classifying woody vegetation (June-July)_and for classifying crops at their stage of maximum greenness (March-April for the spring or rabi harvest and September for the autumn or kharif harvest).

8.    Selection of Sampling units for Measurement of Woody Biomass

Sampling units for field measurements of biomass fuels would  fit within each scene.  Both imagery, topographic maps, GPS were used the field work.

Digital classifications of vegetation cover from the LISS be used as a second-level sampling frame for drawing field samples for the woody biomass survey.  For a selection of primary sampling units for field work, to ensure robustness with respect to any variation in classification accuracy between images.  The following approaches were used.

1. The agro-ecological zonation provide a sound sampling frame at the regional level.  The zonation provides a basis for introducing consistency between scenes, in that the proportion of vegetation cover classes falling within each zone would be typical of that zone.
2. For the woody biomass survey, ground truthing was carried out to confirm whether vegetation cover classes derived from the imagery contained significant woody biomass resources.  This included cultivated farmland.

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