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基于RGB-NIR图像匹配的作物光谱指数特征可视化分析 被引量:3
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作者 孙红 邢子正 +4 位作者 张智勇 马旭颖 龙耀威 刘宁 李民赞 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2019年第11期3493-3500,共8页
归一化植被指数(NDVI)基于可见光的红色波段(630~680nm)和近红外区(780~1 100nm)的反射光谱进行计算,被认为是作物营养与长势诊断的重要指标。为了低成本、快速、无损的检测作物叶绿素含量,计算植株的NDVI并呈现作物的NDVI分布情况,并... 归一化植被指数(NDVI)基于可见光的红色波段(630~680nm)和近红外区(780~1 100nm)的反射光谱进行计算,被认为是作物营养与长势诊断的重要指标。为了低成本、快速、无损的检测作物叶绿素含量,计算植株的NDVI并呈现作物的NDVI分布情况,并通过不同角度图像的分析,监测作物营养分布与动态。利用可见光和近红外波段双目成像技术获取图像,在讨论可见光(RGB)和近红外(NIR)图像的匹配算法的基础上,经图像分割与光照影响校正后,针对不同测试角度建立了作物植被指数空间分布图,并对其空间分布特征与影响因素进行了可视化分析。试验利用可见光和近红外双目相机对51株玉米植株,分别在90°,54°和35°视角下同步采集RGB和NIR图像。对RGB和NIR图像分别进行高斯滤波和拉普拉斯算子增强预处理后,选取了SURF,SIFT和ORB共3种图像匹配算法,并首先利用其进行RGB-NIR图像匹配对齐,以匹配时间(Time),峰值信噪比(PSNR),信息熵(MI)和结构相似性(SSIM)4个参数作为匹配性能评价指标,分别从时间、准确性、稳定性三个方面综合确定最优匹配方法。其次,研究玉米植株的分割方法包括超绿算法(ExG)和最大类间方差算法(OTSU),分别实现图像中作物和背景的分离,提取分割后的RGB图像R(Red),G(Green),B(Blue)分量和NIR图像分量。基于HSI颜色模型,提取I分量讨论了光照对图像的影响,并利用多灰度级标准板建立了植株光谱反射率校正线性公式。然后,利用R(Red)和NIR图像分量计算图像中每个像素的NDVI值,绘制作物植被指数的空间分布图,从而对比分析了不同拍摄角度下光谱植被指数的分布特征。通过不同角度图像的NDVI分布情况,展示监测作物植株不同位置的叶绿素分布情况。结果显示,RGB-NIR图像匹配时间SIFT(1.865s)>SURF(1.412s)>ORB(1.121s),匹配准确性上SURF≈SIFT>ORB,匹配稳定性上SURF>SIFT>ORB,综合比较选取SURF为最优匹配算法。采用4灰度级标准板对R,G,B,NIR分量校正模型的R2分别为0.78,0.76,0.74,0.77。90°和35°视角分别展现了作物叶和茎的NDVI植被指数分布情况,可为分析和监测作物的营养分布提供技术支持。 展开更多
关键词 RGB和NIR图像 图像处理 图像匹配对齐 植被指数空间分布
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Variation of Thornthwaite Moisture Index in Hengduan Mountains, China 被引量:5
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作者 ZHU Guofeng QIN Dahe +5 位作者 TONG Huali LIU Yuanfeng LI Jiafang CHEN Dongdong WANG Kai HU Pengfei 《Chinese Geographical Science》 SCIE CSCD 2016年第5期687-702,共16页
The Thornthwaite moisture index, an index of the supply of water (precipitation) in an area relative to the climatic demand for water (potential evapotranspiration), was used to examine the spatial and temporal va... The Thornthwaite moisture index, an index of the supply of water (precipitation) in an area relative to the climatic demand for water (potential evapotranspiration), was used to examine the spatial and temporal variation of drought and to verify the influence of environmental factors on the drought in the Hengduan Mountains, China. Results indicate that the Thornthwaite moisture index in the Hengduan Mountains had been increasing since 1960 with a rate of 0.1938/yr. Annual Thomthwaite moisture index in Hengduan Mountains was between -97.47 and 67.43 and the spatial heterogeneity was obvious in different seasons. Thomthwaite moisture index was high in the north and low in the south, and the monsoon rainfall had a significant impact on its spatial distribution. The tendency rate of Thomthwaite moisture index variation varied in different seasons, and the increasing trends in spring were greater than that in summer and autumn. However, the Thomthwaite moisture index decreased in winter. Thomthwaite moisture index increased greatly in the north and there was a small growth in the south of Hengduan Mountains. The increase of precipitation and decrease of evaporation lead to the increase of Thomthwaite moisture index. Thornthwaite moisture index has strong correlation with vegetation coverage. It can be seen that the correlation between Normalized Difference Vegetation Index (NDVI) and Thomthwaite moisture index was positive in spring and summer, but negative in autumn and winter. Correlation between Thornthwaite moisture index and relative soil relative moisture content was positive in spring, summer and autumn, but negative in winter. The typical mountainous terrain affect the distribu- tion of temperature, precipitation, wind speed and other meteorological factors in this region, and then affect the spatial distribution of Thomthwaite moisture index. The unique ridge-gorge terrain caused the continuity of water-heat distribution from the north to south, and the water-heat was stronger than that from the east to west part, and thus determined the spatial distribution of Thornthwaite mois- ture index. The drought in the Hengduan Mountains area is mainly due to the unstable South Asian monsoon rainfall time. 展开更多
关键词 Thomthwaite moisture index Normalized Difference Vegetation Index (NDVI) Kriging interpolation Hengduan Mountains
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Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data 被引量:7
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作者 ZHANG Feng ZHANG Li-Wen +1 位作者 SHI Jing-Jing HUANG Jing-Feng 《Pedosphere》 SCIE CAS CSCD 2014年第4期450-460,共11页
Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index (LST-VI) space has comprehe... Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index (LST-VI) space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer (MODIS) products (MOD09A1 and MODllA2), covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index (TVDI). Three LST-VI spaces were constructed by normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil-adjusted vegetation index (MSAVI), respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space (TVDIE) revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition. 展开更多
关键词 enhanced vegetation index modified soil-adjusted vegetation index normalized difference vegetation index temperature vegetation dryness indices
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