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基于MODIS数据的决策树分类方法研究与应用 被引量:89
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作者 刘勇洪 牛铮 王长耀 《遥感学报》 EI CSCD 北大核心 2005年第4期405-412,共8页
介绍了目前国际上流行的两种决策树算法———CART算法与C4·5算法,并引入了两种机器学习领域里的分类新技术———boosting和bagging技术,为探究这些决策树分类算法与新技术在遥感影像分类方面的潜力,以中国华北地区MODIS250m分辨... 介绍了目前国际上流行的两种决策树算法———CART算法与C4·5算法,并引入了两种机器学习领域里的分类新技术———boosting和bagging技术,为探究这些决策树分类算法与新技术在遥感影像分类方面的潜力,以中国华北地区MODIS250m分辨率影像进行了土地覆盖决策树分类试验与分析。研究结果表明决策树在满足充分训练样本的条件下,相对于传统方法如最大似然法(MLC)能明显提高分类精度,而在样本量不足下决策树分类表现差于MLC;并发现在单一决策树生成中,分类回归树CART算法表现较C4·5算法具有分类精度和树结构优势,分类精度的提高取决于树结构的合理构建与剪枝处理;另外在决策树CART中引入boosting技术,能明显提高那些较难识别类别的分类准确率18·5%到25·6%。 展开更多
关键词 决策树 CART算法 C4.5算法 boosting和bagging技术 土地覆盖modis250m
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Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data 被引量:2
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作者 Murali Krishna Gumma Prasad S.Thenkabail +3 位作者 Pardharsadhi Teluguntla Mahesh N.Rao Irshad A.Mohammed Anthony M.Whitbread 《International Journal of Digital Earth》 SCIE EI CSCD 2016年第10期981-1003,共23页
The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia,using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a sh... The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia,using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period.Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season(June–October),followed by a fallow during the rabi season(November–February).These cropland areas are not suitable for growing rabi-season rice due to their high water needs,but are suitable for a short-season(≤3 months),low water-consuming grain legumes such as chickpea(Cicer arietinum L.),black gram,green gram,and lentils.Intensification(double-cropping)in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands.Several grain legumes,primarily chickpea,are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region.The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers:(a)rice crop is grown during the primary(kharif)crop growing season or during the north-west monsoon season(June–October);(b)same croplands are left fallow during the second(rabi)season or during the south-east monsoon season(November–February);and(c)ability to support low water-consuming,short-growing season(≤3 months)grain legumes(chickpea,black gram,green gram,and lentils)during rabi season.Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season,because the moisture/water demand of these crops is too high.The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index(NDVI)time series for one year(June 2010–May 2011)of Moderate Resolution Imaging Spectroradiometer(MODIS)data,using spectral matching techniques(SMTs),and extensive field knowledge.Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics.The producers’and users’accuracies of the cropland fallow classes were between 75%and 82%.The overall accuracy and the kappa coefficient estimated for rice classes were 82%and 0.79,respectively.The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia,with 88.3%in India,0.5%in Pakistan,1.1%in Sri Lanka,8.7%in Bangladesh,1.4%in Nepal,and 0.02%in Bhutan.Decision-makers can target these areas for sustainable intensification of short-duration grain legumes. 展开更多
关键词 Croplands cropland fallow seasonal rice mapping rice-fallow INTENSIFICATION kharif rabi remote sensing double-cropping modis 250 m NDVI spectral matching techniques ground survey data grain legumes potential cropland areas South Asia
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