Blueberry Crop Growth Analysis Using Climatologic Factors and Multi-temporal Remotely Sensed Imageries
Panda, Sudhanshu S.
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Blueberries are a type of vegetation with spectral signatures similar to that of forest and grass. Due to their size, they are difficult to distinguish when intermingled with tall grass. Blueberries are grown in the vicinity of evergreen forests to shield them from the wind and save them from freezing. It is also difficult to distinguish blueberries from forest by normal classification methods. Advanced image processing procedures have already been developed by the authors to distinguish blueberry plants from similar land-uses. Tracking the growth of blueberries throughout a season is another difficult task. Blueberry plants start coming to life by March from their dormant stage in winter. In April, the plants reach full vigor with small fruit developing. In May, the plants have matured and the fruit is ready to harvest. By September, the plants start coming into their dormant stage also known as the post harvest stage. One of the objectives of this study is to use multi-temporal SPOT imagery to distinguish the growth stages of blueberry plants in one orchard in Southeast Georgia in a single year. Another objective was to use high resolution National Agriculture Imagery Program (NAIP) imagery from several years to track the growth of post harvest stage blueberry plants in the orchard. Spot panchromatic images dated March, April, May, and September 2004 were used for blueberry growth analysis within a growing season. NAIP imageries of 2005, 2006, 2007, and 2009 were used to analyze the multi-year blueberry plant growth. Two-meter resolution NAIP imagery of 2005 and 2006 were resampled (pan-sharpened) to 1-meter resolution to compare directly with the 1-meter resolution imagery of 2007 and 2009. Weather parameters like air temperature, solar radiation, and precipitation are other pertinent features that contribute towards the blueberry growth. Spectral signatures (Digital Number) of the blueberry orchard (study area) along with the corresponding climatologic data for all four dates were used to develop relational models for predicting blueberry plant growth. The study results established that the combination of remote sensing information and climatologic parameters can track blueberry growth within a growing season and in multi-year comparisons. This study procedure can also be used for yield estimation or determining areas with disease affected blueberry plants.