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Trend Analysis of Small Scale Commercial Sugarcane Production in Post Resettlement Areas of Mkwasine Zimbabwe, Using Hyper-Temporal Satellite Imagery

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Advances in Remote Sensing 2013年第1期2卷 29-34页

作者： shingirirai mutanga Abel Ramoelo Tichatonga Gonah Africa Institute of South Africa Science and Technology Programme Pretoria South Africa Council for Scientific and Industrial Research (CSIR) Earth Observation Research Group Natural Resource and Environment Unit Pretoria South Africa Department of Water Affairs Hydrogeology Section Pretoria South Africa

This study used normalized difference vegetation index (NDVI) derived from Spot Vegetation images as a proxy for sugarcane growth and production model. Time series analysis was undertaken using the moving average computed in R Programming language to monitor sugarcane production after the inception of land reform in the Mkwasine Estate of Zimbabwe. Overall the findings showed a declining trend with a few years of improved production over the 11 year period under investigation. To attest the possible explanations for the observed trend the study related NDVI data with climate variables and considered non climate factors such as land tenure. The predicted yield estimates over the years correlated very well with rainfall. The study therefore envisaged that hyper-temporal satellite imagery can be used to monitor sugarcane production and enhance decision making for future policy direction.

关键词： Trend Analysis Spot Vegetation NDVI and Sugarcane

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Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery

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Advances in Remote Sensing 2013年第3期2卷 269-275页

作者： shingirirai mutanga Chris van Schoor Phindile Lukhele Olorunju Tichatonga Gonah Abel Ramoelo Africa Institute of South Africa Science and Technology Programme Pretoria South Africa

Hyper-temporal satellite imagery provides timely up to date and relatively accurate information for the management of crops. Nonetheless models which use high time series satellite data for sugarcane yield estimation remain scant. This study determined the best optimum time for predicting sugarcane yield using the normalized difference vegetation index (NDVI) derived from SPOT-VEGETATION images. The study used actual yield data obtained from the mill and related it to NDVI of several two-month periods of integration spread along the sugarcane growing cycle. Findings were in agreement with results of previous studies which indicated that the best acquisition period of satellite images for the assessment of sugarcane yield is about 2 months preceding the beginning of harvest. Overall, of the five years tested to determine the relationship between actual yield and integrated NDVI, three years showed a significant positive relationship with a highest r2 value of 85%. The study however warrants further investigation to improve and develop accurate operational sugarcane yield estimation models at the local level given that other years had weak results. Such hybrid models may combine different vegetation indexes with agro-meteorological models which take into account broader crop’s physiological, growth demands, and soil management which are equally important when predicting yield.

关键词： Sugarcane NDVI Yields Spot Vegetation

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