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首页> 外文期刊>International journal of digital Earth >Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000-2015) data
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Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000-2015) data

机译：使用MODIS 250-M次序列（2000-2015）数据来映射澳大利亚农田的谱匹配技术（SMTS）和自动化裁剪分类算法（ACCAS）

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Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000-2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa=0.814) with six classes: (a) producer's accuracies varying between 72% and 90% and (b) user's accuracies varying between 79% and 90%. ACPs for the individual years 2000-2013 and 2015 (ACP2000-ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000-2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html
机译：在包括休耕地区，包括休耕地区的映射耕地是确定生产的食物数量的重要措施，以及生产的食物，以及生产时（例如季节性）。此外，农田被发现在全球所有人类用水的70％和90％之间的任何地方都被消耗为水势。鉴于这些事实和全球人口的增加到2050年的近100亿，需要常规，快速和自动化的农作物映射逐年和/或季节季节的映射是非常重要的。本研究的总体目标是通过使用两种新方法产生标准和常规农作物，落后，在非常大的地区，（a）在大陆水平应用的定量光谱匹配技术（qsmts）和（b ）基于规则的自动裁剪分类算法（ACCA），其能够后施，现在铸造和未来演员。鉴于其广泛的农作物，农业历史丰富的农业历史，尚未使用多时间遥感，甚至不存在的常规生成农田产品，为该研究选择了澳大利亚。本研究制作了三种不同的农田产品，采用适度分辨率成像分光镜（MODIS）250-M标准化差异植被指数16日复合时间序列数据16年：2000到2015年。该产品包括：（1）农业范围/地区与农田休耕地区，（2）灌溉与雨水种植区，（3）种植强度：单一，双，连续裁剪。使用QSMT生产的2014年（RCP2014）的准确参考裁剪产品（RCP）被用作培训的知识库，然后开发ACCA算法，然后应用于2000 - 2015年的Modis时间序列数据。 2014年的ACCA衍生的农田产品（ACP）与RCP2014之间的相当于89.4％（KAPPA = 0.814）的总体协议：（a）生产者的准确性差不同72％和90％ （b）用户的准确性差异在79％和90％之间。 2000-2013和2015年个人年份（ACP2000-ACP2013，ACP2015）的ACPS与其他几项研究表现出非常强烈的相似之处。 ACCA算法2000 - 2015年ACCA算法准确地捕获了澳大利亚农作物与农田休耕的程度，从而突出了粮食安全分析中的研究价值。 ACCA算法和裁剪产品通过http://croplands.org/app/map和http://weography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html发布

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