Estimating maize grain yield from crop growth stages using remote sensing and GIS in the Free State Province, South Africa
- Authors: Mditshwa, Sithembele
- Date: 2017
- Subjects: Crop yields Crops -- Physiology
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/6016 , vital:29481
- Description: Early yield prediction of a maize crop is important for planning and policy decisions. Many countries, including South Africa use the conventional techniques of data collection for maize crop monitoring and yield estimation which are based on ground-based visits and reports. These methods are subjective, very costly and time consuming. Empirical models have been developed using weather data. These are also associated with a number of problems due to the limited spatial distribution of weather stations. Efforts are being made to improve the accuracy and timeliness of yield prediction methods. With the launching of satellites, satellite data are being used for maize crop monitoring and yield prediction. Many studies have revealed that there is a correlation between remotely sensed data (vegetation indices) and crop yields. The satellite based approaches are less expensive, save time, data acquisition covers large areas and can be used to estimate maize grain yields before harvest. This study applied Landsat 8 satellite based vegetation indices, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Moisture Stress Index (MSI) to predict maize crop yield. These vegetation indices were derived at different growth stages. The investigation was carried out in the Kopanong Local Municipality of the Free State Province, South Africa. Ground-based data (actual harvested maize yields) was collected from Department of Agriculture, Forestry and Fisheries (DAFF). Satellite images were acquired from Geoterra Image (Pty) Ltd and weather data was from the South African Weather Service (SAWS). Multilinear regression approaches were used to relate yields to the remotely sensed indices and meteorological data was used during the development of yield estimation models. The results showed that there are significant correlations between remotely sensed vegetation indices and maize grain yield; up to 63 percent maize yield was predicted from vegetation indices. The study also revealed that NDVI and SAVI are better yield predictors at reproductive growth stages of maize and MSI is a better index to estimate maize yield at both vegetative and reproductive growth stages. The results obtained in this study indicated that maize grain yields can be estimated using satellite indices at different maize growth stages.
- Full Text:
- Authors: Mditshwa, Sithembele
- Date: 2017
- Subjects: Crop yields Crops -- Physiology
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/6016 , vital:29481
- Description: Early yield prediction of a maize crop is important for planning and policy decisions. Many countries, including South Africa use the conventional techniques of data collection for maize crop monitoring and yield estimation which are based on ground-based visits and reports. These methods are subjective, very costly and time consuming. Empirical models have been developed using weather data. These are also associated with a number of problems due to the limited spatial distribution of weather stations. Efforts are being made to improve the accuracy and timeliness of yield prediction methods. With the launching of satellites, satellite data are being used for maize crop monitoring and yield prediction. Many studies have revealed that there is a correlation between remotely sensed data (vegetation indices) and crop yields. The satellite based approaches are less expensive, save time, data acquisition covers large areas and can be used to estimate maize grain yields before harvest. This study applied Landsat 8 satellite based vegetation indices, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Moisture Stress Index (MSI) to predict maize crop yield. These vegetation indices were derived at different growth stages. The investigation was carried out in the Kopanong Local Municipality of the Free State Province, South Africa. Ground-based data (actual harvested maize yields) was collected from Department of Agriculture, Forestry and Fisheries (DAFF). Satellite images were acquired from Geoterra Image (Pty) Ltd and weather data was from the South African Weather Service (SAWS). Multilinear regression approaches were used to relate yields to the remotely sensed indices and meteorological data was used during the development of yield estimation models. The results showed that there are significant correlations between remotely sensed vegetation indices and maize grain yield; up to 63 percent maize yield was predicted from vegetation indices. The study also revealed that NDVI and SAVI are better yield predictors at reproductive growth stages of maize and MSI is a better index to estimate maize yield at both vegetative and reproductive growth stages. The results obtained in this study indicated that maize grain yields can be estimated using satellite indices at different maize growth stages.
- Full Text:
Multi-temporal analysis of urban land-use and land-cover patterns in Alice, Eastern Cape Province, South Africa
- Authors: Manyanye, Owen
- Date: 2017
- Subjects: Geographic information systems Land use -- Remote sensing Land cover
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/8066 , vital:31506
- Description: South Africa is undergoing rapid urbanization coupled with fast demographic change in the major cities and towns. This economic shift leaves behind underdevelopment, particularly in the rural areas such as the Eastern Cape Province. Underdevelopment of rural Eastern Cape can be understood by revisiting the “native reserve policy” of the Union of South Africa (1910 –1948) and the separate development policies of the apartheid government (1948 –1994). These policies have induced landlessness in the rural Eastern Cape and the destruction of rural livelihoods, poverty and under-development of roads, housing, health, education and sanitation facilities, and constrained development of a sustainable local economy. This study was aimed at determining the temporal and spatial land-use / land-cover changes in and around Alice town in the Eastern Cape Province of South Africa by using multi-date remotely Landsat TM images covering 5 time slices for the years 1984, 1989, 1994, 1999 and 2009. This was done by using supervised classification to objectively reconstruct changes in land-use and land-cover by compiling time-series maps with four information classes and using the Kappa Coefficient to assess the accuracy of all map outputs. Results of this investigation point to significant changes in land-use and land-cover over the 25-year study period between 1984 and 2009 with built-up areas expanding by 3720 hectares from 3227 hectares in 1984 to 6947 hectares in 2009. This observation is important because it enhances our understanding of the dynamics of urban growth and provides useful insights that aid urban development planning and policy formulation. The research concludes by recommending the use time series remotely sensed imagery as a decision-support tool for urban and environment management.
- Full Text:
- Authors: Manyanye, Owen
- Date: 2017
- Subjects: Geographic information systems Land use -- Remote sensing Land cover
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/8066 , vital:31506
- Description: South Africa is undergoing rapid urbanization coupled with fast demographic change in the major cities and towns. This economic shift leaves behind underdevelopment, particularly in the rural areas such as the Eastern Cape Province. Underdevelopment of rural Eastern Cape can be understood by revisiting the “native reserve policy” of the Union of South Africa (1910 –1948) and the separate development policies of the apartheid government (1948 –1994). These policies have induced landlessness in the rural Eastern Cape and the destruction of rural livelihoods, poverty and under-development of roads, housing, health, education and sanitation facilities, and constrained development of a sustainable local economy. This study was aimed at determining the temporal and spatial land-use / land-cover changes in and around Alice town in the Eastern Cape Province of South Africa by using multi-date remotely Landsat TM images covering 5 time slices for the years 1984, 1989, 1994, 1999 and 2009. This was done by using supervised classification to objectively reconstruct changes in land-use and land-cover by compiling time-series maps with four information classes and using the Kappa Coefficient to assess the accuracy of all map outputs. Results of this investigation point to significant changes in land-use and land-cover over the 25-year study period between 1984 and 2009 with built-up areas expanding by 3720 hectares from 3227 hectares in 1984 to 6947 hectares in 2009. This observation is important because it enhances our understanding of the dynamics of urban growth and provides useful insights that aid urban development planning and policy formulation. The research concludes by recommending the use time series remotely sensed imagery as a decision-support tool for urban and environment management.
- Full Text:
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