Introduction
1.1 Preamble
Planning and implementation of agricultural development activities should take
the cognizance of environmental degradation due to large amount of fertilizer
and pesticide usage. It is reported that agriculture in developing countries
will be conformed to three major challenges in the decades to come:
- Increasing food demand from rapidly growing population as well as economic
growth Stagnating or declining productivity in high productivity region, often
describe as “Green Revolution fatigue”
- Increasing vulnerability of agriculture to the potential climate change.
The concern about the environment is obvious since on the one hand farmers
are using more and more fertilizers, pesticides & herbicides for getting
more yield whereas, on the other hand the excess materials applied to agricultural
land is disturbing the water and land environment. The nutrient load generated
in agricultural area gets washed off with the runoff after the rainfall and
meets surface water bodies causing eutrophication. The degradation of land
also occurs due to other sources i.e. livestock feedlots, application of municipal
sewage waste & sludge (as fertilizer). These sources, which produce especially
large quantities of pollutants per unit area of land, are diffused in nature
and termed as non-point sources (NPS) of pollution. Major agricultural NPS
contaminants include sediments, nutrients & synthetic organic chemicals
(pesticides, herbicides). Since it is difficult to identify and quantify the
non-point source pollution, control of NPS is a serious problem. Identification
of potential NPS pollution in a drainage basin requires information on the
existing crop, acreage of crops, fertilizer requirement of crop and fertilizer
use, soil properties that is texture, depth which govern the runoff potential
of land and slope & elevation of drainage basin that dictates the transport
of pollution load from the land surface.
Remote Sensing technique has demonstrated its potentiality in providing information
about the characteristics and spatial distribution of natural resources viz.
water bodies, land use agricultural field giving multispectral, multitemporal
and multispatial resolution data. Since 1980s, remote sensing technology has
been used in different parts of the world. Use of satellite RS data has also
proved to be more cost effective, reliable, timely and faster than conventional
ground based survey of agricultural area. Spectral reflectance data obtained
from remote sensing is manifestation of integrated effect of weather, soil,
cultural practices and crop characteristics can be used for identifying, monitoring
and assessment. (Lillisand and Kiefer, 1999). In India series of controlled
ground experiments were conducted in different agro-climatic region and to
understand the spectral behaviour of variety of crops. (Narayana, 1999). Identification
of crop type using RS data requires understanding of the spectral behaviour
of crop in different level and influence spectral response. Such understanding
of spectral response helps in interpretation of data collected by various sensors.
Therefore, the cropping pattern to be studied, field size, and crop distribution
determine the choice regarding the spatial resolution and time of acquisition
of digital RS data. Coarser resolution data such as IRS-I D LISS III data with
23.5 m spatial resolutions is useful to identify vegetation. This can also
be used in multi cropped regions characterized by a small field size and scattered
crop distribution. Satellite data should be acquired by large heterogeneity.
The optimum acquisition period can be based on crop calendars of the area and
information collected during pre-field surveys. Amongst above-mentioned parameters,
field size is one of the most important influencing variables. Size of the
field in relation to pixel size determines whether individual field can be
resolved or not. Therefore pixels belonging to large proportion of fields are
mixed pixels and reflectance of these depends upon the crop/ land cover proportion
constituting a pixel.
The present study for assessment of NPS pollution load from agricultural field
in different Tehsils of Nagpur District uses IRS P6 Data for Land use classification
and related data on monthly rainfall, fertilizer usage, cropping pattern from
secondary sources. Since, identification and assessment of non-point source
pollution requires extensive knowledge about the land use, soil, slope, agricultural
activities and the socio-economic background of the area, the Remote Sensing
(RS) and Geographical Information system (GIS) with spatial analysis capabilities
have been used in the present study.