Blueberry Crop Growth Analysis Using Climatologic Factors and Multi-temporal Remotely Sensed Imageries
Blueberry Crop Growth Analysis Using Climatologic Factors and Multi-temporal Remotely Sensed Imageries
复制标题
利用气候因素和多时相遥感图像进行蓝莓作物生长分析
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
G. Hoogenboom
中科院分区：
文献类型：
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作者：
S. Panda;J. Martín;G. Hoogenboom
蓝莓是一种具有类似于森林和草地的光谱特征的植被类型。由于它们的大小，当它们与高大的草混在一起时很难区分。蓝莓种植在常绿森林附近，以保护它们免受风吹，防止它们结冰。用正常的分类方法也很难区分蓝莓和森林。作者已经开发了先进的图像处理程序，以区分蓝莓植物和类似的土地用途。跟踪整个季节蓝莓的生长是另一项艰巨的任务。蓝莓植物在3月份开始从冬季的休眠阶段恢复活力。到了4月，植物生长旺盛，小果实开始发育。到了5月份，植物已经成熟，果实准备采摘。到9月份，这些植物开始进入休眠阶段，也就是所谓的收获后阶段。这项研究的目标之一是使用多时相SPOT图像来区分佐治亚州东南部一个果园中蓝莓植株在一年中的生长阶段。另一个目标是使用几年来的高分辨率国家农业图像计划(NAIP)图像来跟踪果园中收获后阶段蓝莓植株的生长情况。2004年3月、4月、5月和9月的SPOT全色图像被用于分析一个生长季节内的蓝莓生长。利用2005、2006、2007、2009年的NAIP图像对多年的蓝莓植株生长进行了分析。2005年和2006年的2米分辨率NAIP图像被重新采样(全色锐化)到1米分辨率，以直接与2007年和2009年的1米分辨率图像进行比较。气温、太阳辐射和降水等天气参数也是影响蓝莓生长的其他相关因素。利用蓝莓果园(研究区)的光谱特征(数字数字)以及所有四个日期的相应气候数据，建立了预测蓝莓植株生长的关系模型。研究结果表明，遥感信息和气候参数的结合可以跟踪蓝莓在一个生长季节内的生长情况，并可以在多年比较中进行跟踪。这一研究程序也可用于估测产量或确定蓝莓植株受病害影响的区域。
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.