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Transcript of Joint Research Centre - GSARSgsars.org/wp-content/uploads/2016/10/Gallego-Rome-20161024-MS… ·...
J. Gallego,
Rome, October 2016
Use of technology to develop sampling frames.
Which technologies are we talking about?
• Geographic Information Systems
• Global Navigation Systems – GNSS - GPS
• Remote sensing (images from satellites,
aircrafts, etc)
Geographic Information Systems
Software and the information managed by it Software: • Commercial• Free Information• Polygons, lines, points
• Geometrically more precise. • Raster (Array of pixels)
• Often more efficient for complex computationsCartographic Projections• Often automatically managed • But be careful with on-the-fly projection.
Example of projection mismanagement
Georeferencing elements in a list frame
• Plots, parcels, fields: • OK if existing register
• Ad-hoc: too expensive.
• Farms: • coordinates of the farmer’s dwelling?
• Stocking place?
• All plots?
• Small administrative units (Enumeration Areas?). • It is worth making an effort to improve a layer of EA
Global Navigation Satellite Systems (GNSS-GPS)
• Limited use do build the sampling frame. • More important to support field work.
• Approaching a sampled point with known coordinates– But for the final decision on the location, the graphic
document has the priority• Measuring the area of a plot. • The availability of cheap and reasonably accurate GPS has
changed the cost-efficiency comparison in favor of point frames.
• Quality control: ensuring that the surveyor has reached the sampled point
• Sampling points for crop cutting experiments
Satellite images
• Mainly optical images. Main characteristics:• Spatial resolution (0.3 m to 5 km)• Spectral resolution (how many different wavelengths?)• Swath (how many passes are necessary to cover a study area?). • Price
• Public access image layers (Google Earth, Bing)• Useful when the date and spectrum are not critical. • Some problems with the geometric correction.
• Google Earth Engine (not to confuse with Google Earth) • Tools for image analysis in the cloud.
Area Sampling Frames (AFS)
• The units of an AFS have a geographic nature and
are geo-referenced: • Points
• Transects (more for environmental surveys)
• Segments: patches of terrain
• Clusters of points (= incomplete observation of a segment)
Area Frames of segments with physical boundaries.
• Building the sampling frame involves a heavy photo-
interpretation work if the field size is not very large.
AFS defined by a regular grid
Graphic support to survey a square segment
Stratification in an AFS
• Typical definition of strata:• Segments with > 60% arable land
• Segments where certain types of crops are dominant (permanent
crops, irrigated, paddy rice, etc.)
• Non-agricultural (or non-cropland) areas. • Reminder: an imperfect stratification reduces the efficiency
(variance), but does not introduce bias, unless: • The “non-agricultural” stratum is excluded, but it still contains agriculture.
• You redefine a-posteriori the strata (e.g. segment i was in stratum h1, but after the
field survey, it should have been in stratum h2, therefore I relabel it as stratum h2)
• Etc….
Stratification in an AFS
• Ad-hoc photo-interpretation if nothing else is available• Heavy operation unless you do a very coarse photo-interpretation
• Exploiting existing products: • Polygon land cover maps (Corine Land Cover, Africover)…
• Detailed administrative layers (cadaster, register)
• Image classification products for large regions (or global)
– Often too coarse resolution.
Stratification of square segments from a register
• Compute the cropland area in each square or the area of
main types of crops (GIS tool)• Apply your stratification rule.
Global cropland maps
• A possible source for stratification if nothing else is available.
• Coarse resolution (~ 800 m in this example)
Regional/national land cover maps
• Not perfect, but
usually a good
compromise
• Cost efficient? • Doubtful if it is produced
only for stratification.
• Better if multipurpose
Classified image from a previous year
• Not often
available with a
good quality.
Crowdsourcing with public images
Crowdsourcing with public images
• Tools available,
• Cross-comparison to assess the reliability/consistency of
volunteers
• Heterogeneous image dates
• Stimulating participants: rewards (e.g. a smartphone) to
the best scores (number of images/reliability)
• Still heavy for a full stratification.
• Possible alternative: two-phase sampling• Stratification of a large sample
• Subsample for field survey
Area frames of unclustered points
• Two-phase sampling• Large sample photo-
interpreted as strata
• Sub-sample for field
survey
• Example: the
Eurostat LUCAS
survey
• Crowdsourcing can
be explored
Sampling regular clusters of points
Two-stage area frame sampling
It can be seen as an incomplete observation of the segment
Sampling transects in stripe-shaped landscapes
Sampling points Using a transect to estimate % of each crop in a “super-plot”
Sampling stripes
• Particular case of segments with a long and thin shape
• Adapted to low altitude flights
• Some times used to estimate nomadic livestock
Sampling farms in an area frame
• Traditional approaches: • Open Segment
• Closed segment
• Weighted segment
• Less traditional (variants of the weighted segment)• Sampling through unclustered points
• Sampling farms through clustered points
Open segment
• A farm is selected if its headquarters are inside the
segment.
Closed segment
• Only the parts of plots inside the segment (tracts) are
considered. • Often for direct estimation (without interview to the farmer)
Weighted segment
• The additive data coming from the interview with the
farmer are attributed to the tract applying a coefficient
(tract area/farm area)
• The tract area can be computed by a GIS if it does not
include full plots.
Sampling farms through points
• Estimators similar to the weighted segment, but the tract is not needed. • If the points are sampled inside a segment, the tract area disappears from the formulas with
the Horwitz-Thomson estimator.
Sampling enumeration areas (EA) with a probability proportional to the geographical area.
• Random sample of
points and
selection of the
EAs
One-dimensional systematic sampling enumeration areas (EA) ordering in zig-zag
Computing the partial area sums from 1 to k.
Selecting one EA every M km2
Very limited improvement compared to random sampling.
Two-dimensional systematic sampling of enumeration areas (EA)
More substantial improvement if the spatial correlation decreases with the distance.
Two-dimensional systematic sampling of enumeration areas (EA) with probability proportional to arable areaSample of points Overlay ono Land cover map o Images (point photo-interpretation): usually more accurateKeeping only Eas corresponding to points on arable land
Using Enumeration areas as PSU
Sampling satellite images• In the 70’s the USDA started by cutting Landsat MSS
images into pieces of 6x8 miles • Reason: classifying a full image needed a “super-computer”. • In the 80’s cutting images into pieces became meaningless: a
mini-computer or a workstation could classify a Landsat TM image in a reasonable time with the same accuracy of classifying a piece.
• Late 80’s: sampling Spot images made sense because a full coverage of a large region was too expensive (EU-MARS “rapid estimates”).
• Sampling errors were ok, but non-sampling errors and subjectivity were too high because the system was based on direct pixel counting on classified images.
Sampling satellite images• In the 90’s several projects sampled full or quarter
images for global forest (and change) area estimates.• A lot of visual photo-interpretation. The cost of a sampling unit depended
very much on the size of the unit
Sampling satellite images• 2000’s: Very high
resolution images. A full coverage is very expensive.
• Sampling makes sense but the cost of images and processing should go down to become efficient: • Max 600-800 $ per image for
crop area estimation in the EU.
• Assuming the identification accuracy is comparable to a field survey
Some sources of bias
• Area frames are usually better protected against non-
sampling errors (bias) than list frames, but bias can be
introduced in a number of cases.
• Example 1: “extended square segment” • If a plot is partly in the segment, the whole plot is considered.
Stratification of square segments by conversion of a register to raster
• We have a detailed polygon layer
from a previous year
• We want a stratification of a
square grid of 300 m.
• (wrong) solution: we ask our GIS
to convert the polygon layer to a
300 m raster
• And we exclude the “non-
agricultural areas.
• The bias can be around 20-30%