Application of remote sensing and geographic information systems to forecast dry season paddy yield in the central plain of Thailand (Record no. 6784)

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000 -LEADER
fixed length control field 05371nas|a2200433 i 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260817162936.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 031200s2000 th uu m rtt 00| a1eng d
035 ## - SYSTEM CONTROL NUMBER
System control number .b11816892
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number AIT Thesis no.RD-00-1
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Sudchai Naikaset
245 10 - TITLE STATEMENT
Title Application of remote sensing and geographic information systems to forecast dry season paddy yield in the central plain of Thailand
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Bangkok :
Name of publisher, distributor, etc. Asian Institute of Technology,
Date of publication, distribution, etc. 2000
300 ## - PHYSICAL DESCRIPTION
Extent 90 leaves
490 1# - SERIES STATEMENT
Series statement Thesis ;
Volume/sequential designation no. RD-00-1
500 ## - GENERAL NOTE
General note A thesis submitted in partial ful{uFB01}llment of the requirements for the degree of Master of Science
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M.Sc.) - Asian Institute of Technology, 2000
520 ## - SUMMARY, ETC.
Summary, etc. Remote sensing satellite data have been used to generate the MO}: yield models in various part of the world. As the crop yields are affected by several other factors, such as climatic and soil related variables, generation of such models need to be carried out at local level. A study was carried out to examine the relation between satellite derived Spectral information and soil-related properties with the yield of dry season rice in the central plain of Thailand. Landsat TM data acquired in April 1997 and March 1998, and soil information available at the series level were used to regress with the yield statistics available at sub-district level of the study area. Several yield models containing both Spectral and soil-related variables as predictor variables were generated. The models were applied to predict the yield of dry season rice for the year 1999 by using the spectral information derived from Landsat TM image acquired in February 1999. Digital image classi{uFB01}cation of three sets of images gave quite satisfactory overall classi{uFB01}cation accuracy of 93, 95 and 95 percent for the year 1997, 1998 and 1999, respectively. The class accuracy for dry season rice was 95, 97 and 99 percent, respectively for these images. Since the area is under irrigation, cultivation of second crop of rice is the major land use activity during the dry season, thus easily discriminable with high class accuracies. The areas classi{uFB01}ed as standing rice crop were 40.1, 56.4 and 40.2 percent of the total study area for 1997, 1998 and 1999 images, respectively. Of the 12 independent variables including spectral and soil-related, near-infrared and red bands either alone or in ratio showed higher correlation with rice yield among rest of the Spectral bands in general. Of the soil related variables, organic matter content of the soil showed higher correlation with the yield where as soil depth and soil texture were not found significantly correlated with rice yield. However, in case of 1997 data, a weak correlation was observed between soil texture and yield. The various yield models containing different number of predictor variables were generated. In single year basis, two and three models were generated for the year 1997 and 1998, respectively. For 1997, the final {uFB01}tted model contained two predictor variables and for 1998 three variables. To accommodate the inter-annual variability, both predictor variables and yield of two years were integrated and regression analysis was carried out. The final {uFB01}tted model contained three predicted variables, namely NDVl, OM and ratio 4/3, explaining 61 percent of yield variation. Out of 8 models generated, there were no significant differences between the models so far the prediction power is concerned. The yield of 1999 was forecasted using Model 6, 7 and 8. However, considering that the yield model should be simple, Model 7, which contained NDVI and OM as predictor variables, was relatively superior. This study concluded that that there is correlation between spectra] and soil related variables with yield of dry season rice and which can be explained by their linear relationship to make future forecast. However, some topical recommendations are provided which would help re{uFB01}ning and improving the yield models.
650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Rice
General subdivision Thailand, Central
650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Geographic information systems
General subdivision Thailand, Central
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Apisit Eiumnoh,
Relator term Chairperson
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Lal Samarakoon,
Relator term Examination committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Surat Lertlurn,
Relator term Examination committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Wiboon Boonyatharokul,
Relator term Examination committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element AIT Fellowship,
Relator term Scholarship donor
810 2# - SERIES ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Asian Institute of Technology.
Title of a work Thesis ;
Volume/sequential designation no. RD-00-1
856 ## - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/detail.php?q=B00134">http://203.159.5.9/ait-thesis/detail.php?q=B00134</a>
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a .b11816892
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a 240401
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Operator's initials, OID (RLIN) 0
Cataloger's initials, CIN (RLIN) 001203
First date, FD (RLIN) m
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945 ## - LOCAL PROCESSING INFORMATION (OCLC)
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945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnait
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnarc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 20-AIT Publication
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 40-Archives
909 ## - LOCAL ITEMS USED
Barcode Barcode : 30050120650220
CREATED CREATED : 2000-03-12
RECORD Id RECORD # : i12229003
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Barcode Barcode : 30050120668396
CREATED CREATED : 2000-03-12
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Barcode Barcode : 30050160025192
CREATED CREATED : 2016-03-18
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      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library AIT Publications 17/08/2026   AIT Thesis no.RD-00-1 30050120650220 17/08/2026 1 17/08/2026 20-AIT Publication
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library AIT Publications 17/08/2026   AIT Thesis no.RD-00-1 30050120668396 17/08/2026 2 17/08/2026 20-AIT Publication
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 17/08/2026   AIT Thesis no.RD-00-1 30050160025192 17/08/2026   17/08/2026 40-Archives
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