n topo- 
| Aster 
ey and 
e map 
  
  
  
> trees, 
; many 
lor on 
found 
or). ] 
  
7. CLASSIFY IMAGES USING CROP CALENDAR 
SURVEY DATA 
The “operation sequence” (Figure 3) is an essential component 
of any crop calendar. A crop calendar is defined here as: “A 
sequential summary of the dates/periods of essential operations, 
including land preparation, planting, and harvesting, for a 
specific land use; it may apply to a specific plot, but is 
frequently generalized to characterize a specified area.” Plot 
specific crop calendars form the key to map land use with the 
support of (multi-temporal) RS-imagery (Figures 6 and 8). 
Note that the spatial characteristics of a land use system define 
its boundary. For agricultural purposes, a land use system can 
be limited to a plot. A plot is defined here as "A piece of land, 
considered homogeneous in terms of land resources and 
assigned to one specific land use." 
A cropping pattern is traditionally defined as (ASA 1976; 
FAO 1996): “The yearly sequence and spatial arrangement of 
crops or of crops and fallow on a given area”. In view of the 
crop calendar definition, the cropping pattern definition can be 
sharpened to: “The spatial and temporal arrangement of crops 
(trees) on a specific plot.” Generally, a cropping pattern refers 
to a period of one year, but may also contain information on 
crop rotation. The definition contains spatial information 
(within a plot) that is not present in a crop calendar, but lacks 
actual date/period references as provided by a crop calendar. 
Cropping pattern terminology is area a-specific and therefore 
often used to classify land use. Legends of land use maps will 
considerably improve when cropping pattern syntax is used (see 
list of classifiers in the PhD thesis of the author (De Bie 2000). 
Note that the development of a universal land use classification 
system is not considered desirable. Instead, classifiers that 
define and differentiate between existing (commonly used) land 
use classes can develop into a standardized turn-key system that 
is able to merge and generalize data belonging to different 
classification systems. Use of “standardized” classifiers 
supports standardization, but should never cause users to change 
class boundaries unwillingly (practicality must come first!). 
3 weeks agricultural fieldwork - Garmsar, iran 
Surveyed fiolds 
Cover on Jul'01 
N » 
Aster image, Jul'01 
Figure 6. Generalization of Plot Specific Crop Calendar Data 
with the Resulting Land Use Map. 
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring", Hyderabad, India,2002 
  
A practical example regarding *mapping through surveying crop 
calendars’ is provided in Figure 6. In a spreadsheet by plot 
collected data are made visual and sorted on the basis of 
differences in cover that is visual on the Aster image of July 
2001. The problem of time lag between image date and survey 
date has thus become non-relevant. 
After developing 3 groups of crop calendars, using the pixels of 
surveyed sites, the image was classified. The map accuracy was 
determined on the basis of additional plots surveyed. Figure 7 
provides the accuracy matrix on the basis of pixel counts. 
Accuracy and class-differentiation can be further improved by 
using multi-temporal images (Figure 8). 
classification results 
test set 
  
  
Average Accuracy. 70.096 
Average Reliability 91.696 
Overall acc.: 77.2% 
  
  
  
Figure 7. Accuracy Matrix of Map Presented in Figure 6. 
  
& 2150000 
    
     
Paddies with low cropping 
Intensity (1 crop/yr) 
2140000 
| 
Paddies with high cropping 
Intensity (2-3 crops/yr) 
5000 
  
Uplands (rainfed arable 
cropping and orchards) 
Le 
Footslopes (rainfed arable nid 
cropping and orchards) 5000 
Figure 8. Example of Using Multi-Temporal Images to Map 
Paddies with Different Crop Intensities. 
2 2500 
  
  
  
“7500 520000 2500 5000