REMOTE SENSING

STOCHASTIC OPTIMIZATION MODEL USING REMOTE SENSING TECHNOLOGY FOR AGRICULTURAL MANAGEMENT IN AFRICA: 

STOCHASTIC OPTIMIZATION MODEL USING REMOTE SENSING TECHNOLOGY FOR AGRICULTURAL MANAGEMENT IN AFRICA Wesonga Ronald Institute of Statistics and Applied Economics, Department of Planning and Applied Statistics, Makerere University PO. Box 7062 Kampala UGANDA. Email: wesonga@wesonga.com Website: http://www.wesonga.com

OUTLINE: 

OUTLINE 19 January 2008 2 ICAS-IV Beijing China - ICT Session

ABSTRACT: 

ABSTRACT 19 January 2008 3 ICAS-IV Beijing China - ICT Session

INTRODUCTION: 

INTRODUCTION 19 January 2008 4 ICAS-IV Beijing China - ICT Session

AGRICULTURAL MANAGEMENT IN AFRICA: 

AGRICULTURAL MANAGEMENT IN AFRICA 19 January 2008 5 ICAS-IV Beijing China - ICT Session

CLIMATE CHANGE AND AGRICULTURAL MANAGEMENT IN AFRICA: 

CLIMATE CHANGE AND AGRICULTURAL MANAGEMENT IN AFRICA 19 January 2008 6 ICAS-IV Beijing China - ICT Session

USING REMOTE SENSING TECHNOLOGY IN AGRICULTURAL MANAGEMENT IN AFRICA: 

USING REMOTE SENSING TECHNOLOGY IN AGRICULTURAL MANAGEMENT IN AFRICA 19 January 2008 7 ICAS-IV Beijing China - ICT Session

EMINENT AGRIC MNGT PROBLEMS IN AFRICA SOLVABLE BY REMOTE SENSING TECHNIQUES: 

EMINENT AGRIC MNGT PROBLEMS IN AFRICA SOLVABLE BY REMOTE SENSING TECHNIQUES Reliability of data Cost and benefits Timeless Incomplete sample frame and sample size Methods of selection Measurement of area Non sampling errors Gap in geographical coverage Non availability of statistics at disaggregated level.

REMOTE SENSING TECHNIQUES FOR AGRICULTURAL MANAGEMENT: 

REMOTE SENSING TECHNIQUES FOR AGRICULTURAL MANAGEMENT The following factors influence the use of remote sensing in agricultural surveys Characteristics of the agricultural landscape Detection, identification, measurement and monitoring of agricultural phenomena are predicated on the assumption that agricultural landscape features e.g. crops, livestock, crop infestations and soil anomalies have consistently identifiable signatures on the type of remote sensing data. Some of the parameters which may cause these identifiable signatures include crop type, state of maturity, crop density, crop geometry, crop moisture, crop temperature, soil moisture, soil temperature.

REMOTE SENSING TECHNIQUES FOR AGRICULTURAL MANAGEMENT CONT’D: 

REMOTE SENSING TECHNIQUES FOR AGRICULTURAL MANAGEMENT CONT’D 19 January 2008 10 ICAS-IV Beijing China - ICT Session

USE OF WAVELENGTH REGION CORRELATION: 

USE OF WAVELENGTH REGION CORRELATION 19 January 2008 11 ICAS-IV Beijing China - ICT Session

EUMETSAT SATELLITE APPLICATIONS FACILITIES: 

EUMETSAT SATELLITE APPLICATIONS FACILITIES 19 January 2008 12 ICAS-IV Beijing China - ICT Session

DROUGHT MONITORING USING MSG SATELLITE DATA: 

DROUGHT MONITORING USING MSG SATELLITE DATA 19 January 2008 13 ICAS-IV Beijing China - ICT Session

STOCHASTIC OPTIMIZATION MODEL FOR AGRICULTURAL PRODUCTION IN AFRICA: 

STOCHASTIC OPTIMIZATION MODEL FOR AGRICULTURAL PRODUCTION IN AFRICA 19 January 2008 14 ICAS-IV Beijing China - ICT Session

THE MODEL: 

THE MODEL 19 January 2008 15 ICAS-IV Beijing China - ICT Session

NOTATION AND DEFINITIONS: 

NOTATION AND DEFINITIONS 19 January 2008 16 ICAS-IV Beijing China - ICT Session

DECISION VARIABLES: 

DECISION VARIABLES Assumptions System – field/farm is empty(no crops) at the beginning of the planning period All crops grow and are harvested by the end of period T+1 19 January 2008 17 ICAS-IV Beijing China - ICT Session

SOME SAMPLE DATA – MODEL VALIDATION: 

SOME SAMPLE DATA – MODEL VALIDATION 19 January 2008 18 ICAS-IV Beijing China - ICT Session

SOME SAMPLE DATA – MODEL VALIDATION: 

SOME SAMPLE DATA – MODEL VALIDATION 19 January 2008 19 ICAS-IV Beijing China - ICT Session

DISCUSSION: 

DISCUSSION 19 January 2008 20 ICAS-IV Beijing China - ICT Session

CONCLUSION AND RECOMMENDATIONS: 

CONCLUSION AND RECOMMENDATIONS 19 January 2008 21 ICAS-IV Beijing China - ICT Session

SOME SATELLITE IMAGERY: 

SOME SATELLITE IMAGERY 19 January 2008 22 ICAS-IV Beijing China - ICT Session

Slide23: 

1.6 0.8 0.6

Slide24: 

Red Cloud depth as well as snow/ice and droplet differentiation, provided by the visible reflectance at 1.6mm. Green Cloud depth and “greenness” of vegetation, provided by visible reflectance at 0.8mm. Blue Cloud depth, some haze and non green-sensitive land surface information, provided by reflectance at 0.6mm. NATURAL COLOURS physical interpretation

REFERENCES: 

REFERENCES 19 January 2008 25 ICAS-IV Beijing China - ICT Session

REFERENCES CONT’D: 

REFERENCES CONT’D 19 January 2008 26 ICAS-IV Beijing China - ICT Session

ACRONYMS: 

ACRONYMS 19 January 2008 27 ICAS-IV Beijing China - ICT Session