Resolution Imagery based Classification – Pervious & Impervious
and policy decisions of local, county and regional agencies require timely,
accurate data on urban land growth. For example, urban land information is critical
for planning of urban infrastructure such as roads, water and sewers. Other
policy decisions relating to public services also rely on land information,
such as, siting new schools, retail development, public parks and landfills. In
addition, urban growth information is needed to identify natural resource areas
in need of protection.
One important aspect of urban land information
is the extent of impervious surfaces such as roads, parking lots and rooftops
which lead to decreased infiltration of rainfall and increased storm water runoff.
To address land use information needs many cities, counties and other
governmental units routinely acquire high-resolution digital imagery. However,
processing this information has historically been labor intensive and costly. A
number of recent efforts have been directed at reducing the effort and cost of classifying
digital imagery by using automated and semi-automated classification methods
for monitoring and mapping urban land cover and imperviousness.
Although features can
always be extracted from high resolution imagery through manual means, the fact
that it is collected in digital format and is multispectral makes it a good
candidate for an automated approach. The standard automated mapping approach to
date has been to use unsupervised or supervised classification techniques.
These traditional methods are so-called “per-pixel” classifications, relying
entirely upon the spectral information in an image, while neglecting the
spatial arrangement of the pixels. If we were trying to detect features on a
high resolution image, such as Quickbird2 and were to use an unsupervised
classification to detect these features, we would get class values that
represent information at a finer scale than the features in which we are interested.
The LU/LC was
tested for below listed areas:
Current case study
required to carry out satellite based study using High Resolution Satellite
Image to find out the different LU/LC classes. Hybrid classification techniques
were used to extract the required information in raster format into:
The inputs that
were used for this case study were:
·Image type- High
Resolution Satellite Images
·No. of bands used- 03
The following software was used:
methodology followed in this project is
·Image preparation (Sub-setting image as
per area of interest)
·Image classification and compilation
·Assessment (Quality Analysis/Error
The imagery was
delivered from the contractor as high-resolution satellite image. Any image processing software
which is a pixel-based classifier. With its help first of all we generated our
area of interest by subsetting the image. Later, we classified that image
subset as per the client’s requirement.
1.Image Classification and
In Digital image
classification the analyst uses the spectral information represented by the
digital numbers in one or more spectral bands and attempts to classify each
individual pixel based on this spectral information. This type of
classification is termed as ‘spectral pattern recognition’. The objective is to
assign all pixels in the imagery to particular classes or themes. The resulting
classified image comprises a mosaic of pixels, each of which belongs to a
particular theme and is essentially a thematic map of the original image.
The commonly used
classification methods are Supervised and Unsupervised classification. Normally,
multispectral data are used to perform the classification. The objective of
image classification is to identify and portray, as a unique gray level (or
color), the features occurring in an image in terms of the object or type of
land cover these features actually represent on the ground.
Luminous ETS has
adopted an automated process of classification followed by manual cleaning.
These automated processes include:
Hybrid classification is the use of both
supervised and unsupervised techniques to classify an image. Both methods, when
taken singularly, have their drawbacks. Supervised classification requires
knowledge of the area and/or detailed field data. Unsupervised classification
requires minimum initial input from the analyst, but the output takes a
significant amount of time to assign the computer-generated clusters to a known
land cover.Hybrid combines the benefits
of both techniques.
2.2 Land Cover Mapping
The term land cover relates to the type of features
present on the surface of the earth whereas the term land use relates to the human
activity or economic function associated with the specific piece of land.
Depending upon the level of mapping details, its land use can be described as
in urban use, residential use or single-family residential use.
total number of classes which were classified are:
Roads, buildings, driveways, sidewalks, gravel and hard-packed dirt roads
Open water or wetland where the land is substantially inundated. Eg. Open
Man-made and natural barren: construction sites, mines, beaches, etc.
Non-woody plants (also called herbaceous plants or herbs) are plant with a
relatively short-lived shoot system (compared to woody plants).Most angiosperms lack a vascular cambium,
i.e., they are non-woody herbs or herbaceous.
a) Miss classification: During the automated classification process it
occurred that pixels are classified wrongly. Some impervious and mostly woody
pixels are randomly scattered in Non woody classes.
b) Shadow: In automated classification the shadow of the trees were
classified in Water due to the Black signature.
non woody class is masked and extracted separately to pay attention on this
class. It is further classified and picked accurately. When automated process
and picking of the pixels completed then it is manually recoded with the help
of AOI creation to fulfill the quality requirement of the client.
2)Woody: A woody plant is a vascular plant that
has a perennial stem that is above ground and covered by a layer of thickened
bark. Woody plants are adapted to survive from one year to the next; the stem
supports continued vegetative growth above ground from one year to next.
a) Miss classification: The main problem in this class was
misclassification by the automatic classification process. Though the maximum
area was covered but in some of the area to be enhanced. The non woody pixels
were found in the woody classes.
The pixels of this class are clumped and then recoded in the defined class.
Further viewing the quality of the images it was thought that the image should
be masked and then recoded.
impervious objects are the features by which water can’t be percolated.
This class is classified nearer to the desired quality but problem was in the
shape of the feature. The impervious features did not pick the exact shape.
The general classification and processing techniques are not feasible to
extract exactly the features in their proper shape. To overcome with these
problems a semi automated classification technique was used which shows good
results in a very less manual efforts and time.
4)Water &Swimming Pool: To
extract these classes manual recoding technique was preferred because it can be
identified easily and are found less. Manually AOI was created and then it is
recoded in the main classified image.
are the land which remains as such which is not in use for anyone.
classification: This class is misclassified between
impervious and bare.
recoding technique was adopted to overcome with this problem.
of Land Cover Map
Geometrically and radiometrically corrected
satellite images were used for classification. Classification was carried out
using the unsupervised classification in which the number of classes, maximum
iterations and convergence threshold were given first. Further, the
classification was carried on by analyzing the spectral values of the pixels as
well as its tone. Finally, the classes will be finalized as mentioned by the
At many times it becomes
difficult to classify certain pixels
especially the ones which fall in water and shadow areas. Therefore, to avoid
such confusion neighborhood function was run in order to remove water and
kernel filter of 3*3 (function: majority) was used. This was saved in a
particular file for further use.Masking
is an automated process which is performed to overall enhance the image. During
the image classification process its obvious that many pixels can be left
without having a proper class. This process enables us to extract a particular
class in which pixels are left unclassified. This was performed for the woody
and non-woody classes.
After which very small
pixels were clumped and eliminated. Clump performs a contiguity analysis on a
single layer RASTER. Each separate raster region, or clump, is recoded to a
separate class. The output is a single layer raster in which the contiguous
areas are numbered sequentially. Then mosaicking of the extracted classes from
woody were done with the earlier saved file on which neighborhood function was
run. Further, the woody class was masked again so that more information could
be extracted about the woody and impervious classes respectively. Once again
the above mentioned steps were carried out.Finally, both the images were mosaicked again for which neighborhood
function was run. This time it was done in order to remove line feature of
woody class. Finally, it was mosaicked with water body, which was separately
(Quality Analysis/Error removing)
the classification of the image is done, the final assessment was carried out
which comprised of the manual editing along with the Quality Check/Quality
Analysis, so as to remove the errors.
following outputs were prepared-
i)Classified LU/LC Map
ii)Thematic Maps in Img/TIFF format
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advanced over decades from a descriptive to a quantitative science using
physical and biological principles. Now, the challenge is to balance the
continuing need for increased productivity with new and growing concerns about
climate change, climate variability and the associated environmental impacts.
The farming community in India especially is becoming more and more aware of
the weather and its impact on the crop at different phenological stages. During
this decade farmers’ awareness also increased substantiallyabout the increasing
and efficient agrometeorological services mainly weather-based agroadvisory
services.Business community is now gearing up its activities based on monthly
weather forecast along with the agroadvisories and it needs to be addressed
immediately by the Agrometeorologists. Decision Support System with major focus
on pest and diseases based on simple thumb rules for various export potential
crops including some cash crops and horticultural crops needs to be developed.
Another demand and future challenge is to help the Agricultural insurance
sector which plays a pivotal role in settling the farmers’ claims.It becomes absolutely necessary
to provide research, monitoring and advisory services to the farming community
to miminize the weather related or influenced losses and maximize the
production through early fore-warning systems regarding possible pests/diseases
incidence, their remedial actions etc. Another
important area viz., weather based commodity trading is gaining fast momentum
which needs specific attention.It is time that the climate and weather
should be looked as a resource but not as a hazard only.Public Private Participatory approach is fast emerging asan efficient
and effective way for some of the out-reach programs in agriculture sector.
Probably, it is the holistic approach integrating soil, weather and
physiological aspects of crops with remote sensing/GIS techniques that may
address these emerging issues in the next two decades which stands a great
challenge to the Agrometeorologists. As an attempt to meet the future
challenges, one step forward could be introduction of some of the weather based
agribusiness topics in the curriculum of Agrometeorology.
is an agrarian based country and hence agriculture plays a significant role in
the overall socio-economic fabric of India. Weather
becomes more significant in crop management and an expert knowledge of past,
present and future weather will help to solve a host of other problems.It is a known fact that the success of
farming is intimately related to the prevailing weather conditions and
quantitative information much in advance would become necessary to predict/assess
the yields to a great accuracy. Agriculture
remains one of the few areas for which accurate short-term and extended-period
forecasts can create a material benefit. There is an urgent need to integrate
the weather forecast with a real time decision support system leading directly
to solutions/services to the farmers which eventually help the farmers in
saving their crop from all possible losses.
is a well-known fact that most of the farmers are adversely affected by climate
risks in their farming, which include increase in temperature, decrease in
rainy days, increase in precipitation intensity and amount, shorter winter
periods, decreased ground water availability, increased occurrence of drought
and floods, increased duration of water logging etc. which in turn result in
often with the support of other machinery
have been trying to adapt to these climate risks in different ways while only few of them are aware of the crop insurance and its
benefits though the awareness is increasing very fast. Most of these
adaptations are technological often pushed or promoted by research and
extension agencies involved in agriculture like changing the date of planting,
cultivating new paddy varieties that can better tolerate water-logging,
introducing new crops/diversification to vegetables and adopting SRI (Systems
of Rice Intensification) in paddy etc. The
present paper gives a brief note about some of the services and agencies that
arelooking for agrometeorologists for holistic approach. The current demands as
well as future challenges in each of them are emphasized.
WEATHER BASED AGROADVISORIES:
influences agriculture in a profound way. Despite the technological advances,
Indian farmers are mostly dependent on seasonal rains which are highly variable
in time and space. If farmers have advance information about the probable
occurrence of events such as depressions, drought, storms, floods and heat
waves in their geographical locations, the impact of these events on farmers’
livelihood can be reduced to a great extent. Thus weather forecasts are of
great importance to agricultural activities Much research has gone into
characterization of various crop environments, quantification of crop-weather interactions
in relation to crop yield, crop weather modeling and crop-pest-weather dynamics.
It is time to consolidate these findings in different locations, make use of
this information and reach the farmers through agroadvisories on a larger scale
in a more realistic way. At present, the India Meteorological Department in
collaboration with the State Agricultural Universities is giving the Integrated
Agromet Advisory Services which include weather forecast coupled with expert
advises on crop planning, disease and pest incidences as well as different farm
operations like fertilizer, pesticide applications etc. in developing timely
weather based agro-advisories. These weather based agro-advisories can be used
to take up prophylactic plant protection measures, fertilizer application,
irrigation scheduling etc. These can also be used to take up appropriate measures
in day to day field operations to minimize the risk involved in agricultural production
(Rathore and ParvinderMaini, 2008).
A lot needs
to be done to make these advisories more and more farmer-friendly. It is time
that the Agrometeorologists respond to the urgent/pressing needs/demands of the
farmers who insist on receiving a more accurate forecast along with actionable
agroadvisories. Block level forecast is the need of the hour and the farmers
are looking for location specific accurate short, medium and long term
forecasts which will eventually help them in proper planning of the crops,
varieties, sowing schedules and other farm operations for minimizing the yield
losses leading to a higher grain/seed yields.
DECISION SUPPORT SYSTEM:
For real-time forewarnings in
agrometeorology, the reliability of regular, specialized information along with
the real-time crop status is critical. Agrometeorological decision-making in
agricultural operations for healthy crops or crops endangered by pests,
diseases and/or other environmental disasters needs weather forecasting and
climate prediction, where that is possible, to the required accuracies(Blench,
1999).Use of advisories by progressiveand medium size farmers related to accurate predictions of sowing
date, timing of irrigation and fertilizer use strategies, is slowly on the
rise.Crop-weather models that are mainly used for operational yield forecasting
and prediction of phenological development have been generated for a large number
of crops in our country (Aggarwal et al, 1997, 2005and2006).They have different
degrees of complexities and many of these models need to be further refined and
tested before widespread practical application may be expected. Current
research is being focused on detailed soil–water–crop relationships,
determining the adjusted crop genetic coefficients, bridging simulation model
outputs with user needs for applications, and developing practical decision
support systems. The results of these findings may be of immense utility and
form an important component of the decision support system which when coupled
with the crop status on a regional scale would go a long way to alleviate the
farmers’ problems to a great extent.
Recent field survey in some parts of Karnataka, Andhra
Pradesh, Orissa and Chhattisgarhrevealed that there is presently still a considerable gap between the
information needed by poor, small-scale farmers and that is presently available
a vigorous plant growth and rich harvest, satellite based remote sensing-aided
weather based agro-advisories may be a promising way which would enable the
farmers to take the most appropriate actions on real time basis, at a regional
scale. Satellite remote sensing technology is increasingly gaining recognition
as an important source of many agricultural applications as it is superior to
the traditional methods in terms of accuracy and saving of time (Fig.1).
In addition, Geographic Information Systems (GIS) technology is becoming an
essential tool for combining various maps and satellite information sources in
models that simulate the interactions of complex natural systems.
will not only help in planning, advising and monitoring the status of the crops
but also will help in responding quickly for taking immediate planning or
remedial actions.Planning for seeds
distribution, fertilizer supply/requirements, supplying/relocating of
sowing/harvesting equipment, procurement of crop from mandies/markets,
etc., can be tackled effectively through information derived using these
from benefitting the farmers, these services would help the Tractor manufacturers
and farm machinery manufacturers to make tactical decisions on movement of
their farming machinery (like tractor-driven combined harvesters etc.). Also
water pump motor companies in deciding about need based supply or deploying the
appropriate Horse Power motors to different locations. It is needless to
emphasis the underlying fact that all these are challenges that the
Agrometeorologists are required to meet right now.
1: Flow diagram for integration of remote sensing aided weather-based
WEATHER BASED CROP
Crop insurance is one of the main non-structural mechanisms
used to reduce risk in farming. A farmer who insures his crop is guaranteed a
certain level of crop yield or income, which is equivalent, for instance, to 60
or 70 per cent of the long-term average. If, for reasons beyond the farmer’s
control and in spite of adequate management decisions, the yield drops below
the guarantee, the farmer is paid by the insurer a sum equivalent to his loss,
at a price agreed before planting.
Crop insurance schemes can be
implemented relatively easily when there is sufficient spatial variability of
an environmental stress (such as with hail), but they remain extremely
difficult to implement for some of the major damaging factors, such as drought,
which typically affect large areas.
One of the basic tools for insurance
companies is risk analysis (Abbaspour, 1994; Decker, 1997). Crop forecasting
models play a central part, when run with historical data, they provide insight
into the variability patterns of yield (WMO, 2010) Technical Bulletin 134.
The first crop insurance program in the country was introduced in 1972-73
by the ‘General Insurance’ Department of Life Insurance Corporation of India on
H-4 cotton in Gujarat. Later, the then newly set up General Insurance
Corporation of India took over the experimental scheme and subsequently
included Groundnut, Wheat and Potato and implemented in the states of Gujarat,
Maharashtra, Tamil Nadu, Andhra Pradesh, Karnataka and West Bengal.Professor V.
M. Dandekar, often referred to as the “Father of Crop Insurance in India”,
suggested an alternate “Homogeneous Area approach” for crop insurance in the
After continuous evolution, the Comprehensive Crop Insurance Scheme (CCIS)
was introduced with effect from 1stApril 1985 by the Government of
India with the active participation of State Governments.The implementation and
administration of crop insurance schemes, which were being done by General
Insurance Corporation of India (GIC), was taken over by Agriculture Insurance
Company of India Ltd. (AIC) since its commencement of business from 1st April
It is imperative that crop yield hence the crop insurance are closely
related and dependent on prevailing weather conditions during the entire crop
growth cycle. The Agrometeorologists not only make use of the weather forecast but also
should be able to estimate the likelihood of unusual weather events and their
potential impact on every single farmer’s fields/crops (quantitative analysis),
which will be a great challenge.This
creates a great hope that insurance policies based on clever weather based
analytics will one day also protect Indian farmers against the vagaries of the
In many rural areas, disaster often strikes poor farmers hard, forcing them
to make choices that drag their families deeper into poverty. To survive, they
might have to sell their tools for cash to buy food, or take their children out
of schools to save on fees. With weather insurance, farmers can protect the
investment they make in their crops, and feel confident in taking out loans for
fertilizer and better seeds to improve their harvests. (Brian Kahn,IRI,2012).
Now the Agri-insurance companies including some private banks are looking
forward for weather based agroadvisories to be disseminated through SMS or
Voice mail to the end users (farmers) apart from assessing the exact yield at
individual Grampanchayat / village level for their judicious payouts.
DRIVEN AGRICULTURAL COMMODITIES SUPPLY AND TRADING
Agricultural commodities are those which are living
things grown by farmers or ranchers, or, in some cases, such things which have
been minimally processed. Agricultural commodities are often referred to as soft
commodities to distinguish them from metals, energy and other
non-agricultural commodities.There are many
factors that can impact the supply of commodities like weather, acreage
covered, production strikes, crop pests/diseases and technology. Given a
particular locality or region, the produce is totally dependent on the weather more
so when they are rainfed crops. There are a handful of grains
and meat which make up the core of agricultural commodity trading while other
agricultural commodities include rape seed, milk, cocoa, coffee, sugar, frozen
orange juice concentrate, and cotton. Livestock include hogs, pork bellies and
cattle. These commodities are traded in a variety of different grades and
types, and there are other exchange-traded agricultural commodities.
A farmer would like a guaranteed minimum return and
would prefer money now over money later. A purchaser would like to plan on a
maximum price for an agricultural product now and so has an incentive to
mitigate the risk of a price rise at harvest time. Commodities markets were established in the ancient world in rice and
other grains. Supply and demand and unpredictable market conditions have always
added price volatility to agricultural commodities markets, and trading and
hedging techniques have been developed over long periods of time to help
everyone in the market manage their risk.While spot trading, with physical
inspection and delivery does take place, most trading in financial markets is
done through futures contracts. A futures contract is an agreement between a
producer and purchaser that a transaction for a certain quantity of a specific
commodity will take place at a future date and at a particular price. This
smooths out the volatility for both parties and provides liquidity to the
market. Weather forecast
thus has an important role to play in the trading. The trading community is
looking forward to get the medium, long range and seasonal forecast for this
speculation of output of the commodities.
knowledge of the likely volume of future harvests is a crucial factor in the
market. Prices fluctuate as a function of the expected production with a large
fact, prices depend more on the production that the traders anticipate than on
actual production. Accurate forecasts are, therefore, a useful planning tool.
They can also often act as a mechanism to reduce speculation and the associated
price fluctuations, an essential factor in the availability of food to many
In India, prior to the introduction of commodity futures market, the commodity
prices were found to have experienced high volatility. With the introduction of
the commodity futures market in India in 2005, it was expected that weather
shocks should have had smooth transmission on the general price levels.
Economic wellbeing of farmers is going to become better if Agro-market
advice facility is provided and thereby the standard of living of the farmers
shall increase. This facility will actually help the farmers to know the prices
for their products in and around them so that they can take their products to
these places for their better market price.
Lot of research has been done in various aspects of agrometeorology and it
is time to consolidate the results and translate them to actionable weather
based agroadvisories. The Agrometeorologists not only make use of the weather
prediction but also should be able to estimate the likelihood of unusual
weather events and their potential impact on every single farmer’s fields/crops
(quantitative analysis), which will be a great challenge. This creates a
great hope that insurance policies based on clever weather based analytics will
one day also protect Indian farmers against the vagaries of the weather. The
farming as well as the trading community is looking forward for the
weather-based crop produce status coupled with market information which is need
of the hour and more and more such value added information to the
agroadvisories will definitely improve the farmers’ financial status leading to
their ultimate prosperity. Satellite based remote sensing-aided weather based
agro-advisories may be a promising way which would enable the farmers to take
the most appropriate actions on real time basis, at a regional scale. The
agrometeorologists need to gear up to meet the future demands of the
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