Crop Production |
Crop Pattern |
Crop Yield |
Soil Management | Precision Farming |
Relevant Products |
Use of GIS for sampling designs for agricultural surveys
Randhir Singh, Anil Rai and Prachi Misra
Indian Agricultural Statistics Research Institute,
New Delhi - 110012
The last decades witnessed revolutionary changes in the approaches related to spatial
problems because of incredible progress in automation and computer technology especially with
the introduction of modern Geographic Information System (GIS). It is a powerful tool for
storing, retrieving analysing and integrating spatial and non-spatial geographical data apart
from drawing any kind of maps. The development of spatial statistical techniques has been
accelerated parallel to this rapid growth of GIS technologies and there is a need to integrate
the GIS, and spatial statistical techniques and remote sensing.
A number of organisations in India are engaged in developing suitable applications of remote
sensing technology. The initial success of these led to the formulation of crop acreage and
production estimation (CAPE) project which was firrst major project launched under Remote
SENSING Application Mission (RSAM) and Department of Space (DOS) in 1986. First indepenent
attempt in the country towards the use of satellite digital data was made in karnal district of
Haryana using Landsat MSS data by (1989). Dadhawal and Sridhar (1986), Panikar et al. (1987),
Dadhawal et al. (1987), Murthy et al. (1996) etc. have made significant contributions towards
acreage and production estimation of rice and wheat in the country. Singh, et al. (1992) and
Goyal, et al. (1994) presented the use of satellite data alongwith survey data for improving
the efficiency of crop yield estimators. The applications of Remote Sensing and GIS for Land
Use Planning under different projects underaken in the country has been considered by
Krishnamurthy and Adiga (1987).
In case of spatial analysis the data can be described in three ways
A point refers to a single plae and is usually considered as having no
dimension or having dimension which is negligible when compared to study area such location of
industries, houses etc. Line data or networks can be found in GIS to describe economic features
like rivers transportation system etc. Data arranged on polygons have a particular relevance
if a GIS is designed to assist agricultural or socio-economic survey with the help of remote
sensing data. The sampling units in these surveys are based on the area frame obtained with the
help of satellite image or geographical areas like villages, cities, regions etc. There are
mainly three important sources of spatial data, census, surveys and satellite image of
- area or polygons.
Sampling Procedure Based on GIS
Sampling design for spatial data have a long tradition starting from
Mahalanobis (1940). Hedayat et al. (1988) proposed a sampling plan in which contiguous units
are excluded thereby resulting in second order inclusion probabilities being zero corresponding
to pairs of contiguous units.
Arabia (1993) described a sampling technique ‘Dependent Area Units Sequential Technique’
i.e. DUST which avoids the inclusion of neighbouring areas in area sample. This technique can
be consider as an extension of the technique proposed by Hedayat et. al. (1988) and is
described here briefly.
Let there be N non-overlapping area units in the study area.. Let X be a auxiliary
character and let Y be the character under study for geographical area units. This sampling
design consists of three important steps :
As a consequence of this sampling design area units are not selected with uniform criteria or probability, units closer to a selected unit receive smaller probabilit6y as compared to units distance apart. Thus,. in zones displaying a positive spatial correlation, we can save sampling units by scattering them. It has been demonstrated that most of the traditional techniques can be derived as particular cases of DUST. For example, if b1= O, it reduces to simple random sampling whereas if area is stratified and within each strata b1= O, then it reduces to stratified random sampling. Further, setting dij=1 if dij=dmax and O otherwise will give rise to systematic sampling.
- Estimation of spatial correlation b with the help of X at various distance lag. For simplicity distance of decay moder bk b1k
can be applied.
- Testing the stationarity at various order correlation's for identifying the zones, and
- Selecting the first unit by assigning weight 1 and assigning weight (1-b1 dik) for selecting the k-th unit in the sample of size n where k = 2, 3....n and dik is the distance between i-th and k-th area units measured in terms of physical distance between centroids or in terms of order of neighbourhood.