【摘要】：不同空間分辨率農(nóng)作物面積識(shí)別精度是農(nóng)情遙感監(jiān)測(cè)數(shù)據(jù)源選擇的依據(jù)。該文采用WFV(wide field view)、MODIS(moderate-resolution imaging spectroradiometer)、OLI(operational land imager)、Google Earth影像,在天津市武清區(qū)選擇了12 km×14 km的冬小麥種植區(qū)作為研究區(qū)域,采用目視識(shí)別的方法,分析了2、5、10、15、30、100、250 m共7個(gè)空間分辨率尺度下冬小麥面積識(shí)別精度與遙感數(shù)據(jù)分辨率、農(nóng)田景觀破碎度之間的關(guān)系。結(jié)果表明,隨著空間分辨率由2 m變化到250 m,冬小麥面積識(shí)別的總體精度逐步由98.6%降低到70.1%,精度降低28.5%;面積數(shù)量比例由5.5%擴(kuò)大到110.6%,誤差增加105.1個(gè)百分點(diǎn);面積精度呈明顯下降趨勢(shì),數(shù)量誤差呈明顯增加趨勢(shì),數(shù)量誤差的增加速度高于精度下降的趨勢(shì)。高、中、低3個(gè)景觀破碎度條件下,隨著分辨率由2 m降低到250 m,作物識(shí)別精度分別降低了72.8、63.2和47.0個(gè)百分點(diǎn),破碎度的增加導(dǎo)致面積識(shí)別精度下降速度更快;同等分辨率下,破碎度越高的地區(qū)面積識(shí)別精度越低。像元內(nèi)冬小麥占比與可識(shí)別能力密切相關(guān),像元占比達(dá)到45.0%以上時(shí)才能夠被正確識(shí)別為冬小麥類(lèi)型,像元尺度降低導(dǎo)致細(xì)小斑塊丟失是造成面積識(shí)別與數(shù)量精度降低的主要原因。像元空間分辨率越高,冬小麥像元的光譜一致性越強(qiáng),越有利于冬小麥分類(lèi)精度的提高。針對(duì)農(nóng)情遙感監(jiān)測(cè)業(yè)務(wù)運(yùn)行的需要,上述研究結(jié)果可以作為區(qū)域范圍不同用戶(hù)精度要求前提下遙感數(shù)據(jù)源選擇的依據(jù)。
[Abstract]:The precision of crop area recognition with different spatial resolution is the basis for the selection of data sources for remote sensing monitoring of agricultural conditions. In this paper, WFV (wide field view) MODIS (moderate-resolution imaging spectroradiometer) Oli (operational land imager) Earth) image was used to select winter wheat growing area of 12 km 脳 14 km in Wuqing District of Tianjin as research area, and visual recognition method was used. The relationship between the recognition accuracy of winter wheat area and the resolution of remote sensing data and the degree of farmland landscape fragmentation in 7 spatial resolution scales were analyzed. The results showed that with the change of spatial resolution from 2 m to 250 m, the overall precision of winter wheat area recognition was gradually reduced from 98.6% to 70.1%, and the precision was reduced by 28.55.The area ratio was increased from 5.5% to 110.6%, and the error increased by 105.1%. The area accuracy is obviously decreasing, the quantity error is obviously increasing, and the increasing speed of the quantitative error is higher than that of the precision decreasing. Under the condition of high, medium and low landscape fragmentation, with the resolution decreasing from 2 m to 250 m, the precision of crop identification decreased by 72.8% 63.2% and 47.0%, respectively. The higher the degree of fragmentation, the lower the accuracy of area recognition. The proportion of winter wheat in the pixel is closely related to the recognizable ability. When the proportion of the pixel is more than 45.0%, it can be correctly recognized as the winter wheat type. The loss of small patches caused by the reduction of pixel size is the main reason for the reduction of area recognition and quantitative accuracy. The higher the spatial resolution of the pixel, the stronger the spectral consistency of the pixel of winter wheat, which is beneficial to the improvement of the classification accuracy of winter wheat. In order to meet the needs of the operation of remote sensing monitoring, the above research results can be used as the basis for the selection of remote sensing data sources under the premise of different user precision requirements in the region.
【作者單位】： 中國(guó)農(nóng)業(yè)科學(xué)院農(nóng)業(yè)資源與農(nóng)業(yè)區(qū)劃研究所;
【基金】：農(nóng)業(yè)部引進(jìn)國(guó)際先進(jìn)農(nóng)業(yè)科學(xué)技術(shù)項(xiàng)目:農(nóng)業(yè)遙感監(jiān)測(cè)系統(tǒng)關(guān)鍵技術(shù)引進(jìn)(2016-X38)
【分類(lèi)號(hào)】：S512.11;S127

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本文編號(hào)：2214110