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多时相影像的冬小麦种植面积提取及估产 总被引:2,自引:0,他引:2
《遥感信息》2017,(5)
针对多时相影像的农作物种植面积难以实现统一精确提取、不能高效地进行遥感估产研究的问题,以河南省濮阳市为研究区,基于Landsat TM影像,采用基于伪不变特征的相对辐射校正方法,在深入分析濮阳市内6类典型地物光谱的基础上,构建决策树提取冬小麦种植面积。然后,基于MODIS植被指数产品,结合相应年份统计数据进行植被指数校正,分别利用校正后关键生育期的归一化植被指数累计值和增强型植被指数累计值与冬小麦产量进行回归分析,建立冬小麦产量预测模型,利用2011年的产量进行验证。结果表明:各年份冬小麦的提取面积精度均在96.3%以上,利用归一化植被指数和增强型植被指数构建的估产模型,R2分别为0.834和0.926,估产精度分别为95.36%和96.44%。该研究可为市域冬小麦种植区的统一高效提取以及冬小麦产量预测提供参考。 相似文献
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根据冬小麦发育期的同步性、土壤类型、灌溉条件等多项因子, 参考河北省土地利用现状资料和生态功能区划成果, 将河北省冬小麦种植区域划分为五大区。利用多时相MODIS 遥感数据和冬小麦地面GPS 定位调查信息, 充分考虑冬小麦的生长发育特征和下垫面的植被生长特征来剔除小麦生长季节的非麦区信息。在测算冬小麦种植面积、解决混合像元问题时, 改进了“麦土比”的概念,提出了“像元麦土比”的新方法, 将遥感知识、农学知识和地学知识进行有机结合, 形成一套计算机自动提取冬小麦种植信息系统。利用该系统提取了河北省2004~2005 年度冬小麦播种信息, 遥感测算的全省总面积误差为3. 2% , 综合考虑各个地市的偏差情况, 9 个地市面积平均绝对误差为9. 6%。 相似文献
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利用多时相NDVI 监测京郊冬小麦种植信息 总被引:2,自引:2,他引:2
物候和时相信息在农作物种植信息提取方面有十分重要的应用价值, 利用多时相L andsat TM 数据, 结合冬小麦的波谱和时相信息, 成功提取了北京地区的冬小麦种植信息。首先, 选用了2003 年4 月7 日、5 月1 日、5 月25 日、6 月18 日不同时相的4 景TM 卫星影像, 计算了不同时相的NDV I 时间谱图像数据; 其次, 结合北京地区农作物种植的实际情况, 提取并分析了北京春夏季主要植被地物(冬小麦、苜蓿、苗圃、春玉米、树林等) 的NDV I 时间谱特征; 第三, 利用不同时相的NDV I 图像数据, 通过NDV I 图像通道间的逻辑运算算法, 成功提取了2003 年北京地区的冬小麦种植信息, 提取精度达到96. 92%; 最后, 与2002 年收割小麦的统计数据相对比, 监测了北京各郊区县的冬小麦种植结构调整情况。结果表明, 多时相、多光谱遥感数据在作物种植信息的监测中有十分明显的技术优势和重要的应用潜力。 相似文献
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利用MODIS数据监测河北省冬小麦种植信息 总被引:1,自引:0,他引:1
利用多时相MODIS遥感数据和冬小麦地面GPS定位调查信息,参考河北省土地利用现状资料和生态功能区划成果,对河北省冬小麦种植环境进行了分区,充分考虑冬小麦的生长发育特征和下垫面的植被生长特征来选择遥感监测时段,改进了“麦土比”的概念,提出了“象元麦土比”的新方法,将遥感知识、农学知识和地学知识有机结合,形成一套计算机自动提取冬小麦种植信息系统,利用该系统提取了河北省2004-2005年度冬小麦播种信息,遥感测算的全省总面积误差为3.2%,综合考虑各个地市的偏差情况,9个地市面积平均绝对误差为9.6%。本文就此介绍其主要方法及其工作步骤。 相似文献
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基于多时相HJ卫星的冬小麦面积提取 总被引:6,自引:0,他引:6
我国环境与灾害监测预报小卫星HJ-1A/B具有较高的时间和空间分辨率,在作物种植面积提取和长势监测等方面具有较大优势。本文以江苏省姜堰市为研究区,根据冬小麦的物候规律和季相节律的差异性,选取返青期和拔节期两个生育期的HJ卫星影像,借鉴分层信息提取法原理,综合利用监督分类和非监督分类法,结合人机交互目视解译和实地定位调查等资料提取了姜堰市的冬小麦种植面积,总体面积提取精度达到90.22%,样点空间匹配精度为81.25%,实验基地空间匹配精度为80.34%。结果表明:HJ卫星能够用于提取南方地区冬小麦种植面积和长势监测,满足农情监测的需要,且利用多时相遥感影像能有效地增加信息量,实现信息互补,有助于提高监测精度。 相似文献
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利用两期遥感影像,以ArcGIS9.0和ERDAS8.5为平台分析了西安市2000~2007年间土地利用时空变化过程;并对研究区2000~2007 年间土地利用变化引起的生态效应变化进行了测评;结果表明:7 a间西安城区土地利用/覆被变化显著,综合土地动态度为1.9%,建设用地面积逐年增加,年变化率为3.1%;耕地、未利用地、园地、水体面积有所减少,西安城区的生态系统服务价值从2000年的259.8 ×106元减少到2007年的233.6×106元,共减少了26.2×106元;经证实用遥感与地理信息系统技术研究因土地利用/覆被变化引起的生态响应是一种有效的方法。 相似文献
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基于多时相ENVISat ASAR数据的冬小麦识别方法——以北京通州试验区为例 总被引:1,自引:0,他引:1
ENVISat ASAR数据在水稻识别和制图中应用研究比较深入,但对该数据用于小麦识别和制图的技术方法的研究还比较缺乏。以北京市通州区为试验区,研究了利用多时相ENVISat ASAR数据进行冬小麦识别的技术方法。应用GPS实测定位和基于高分辨率World View-1影像解译等手段获取分类精度检验数据,结果表明多时相ASAR数据可用于我国北方冬小麦的识别,对小麦—非小麦两类型的识别总精度为82.64%。 相似文献
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基于遥感影像时间序列的冬小麦种植监测最佳时相选择研究 总被引:6,自引:1,他引:5
遥感影像植被分类的最佳时相对作物种植面积遥感监测非常重要。根据2005~2006年北京冬小麦不同物候期的Landsat TM影像和2006年Spot\|2影像,计算了各时期影像中主要植被类型的光谱可分性距离,分析了北京郊区主要植被物候差异和光谱可分性;对各生育期的遥感影像及其主要组合进行了监督分类,采用总体精度和分类效率指标两个参数,结合地面GPS调查数据,对分类结果进行了精度评价。结果表明:北京地区小麦监测最佳时相是4月上旬,影像分类的总体精度为92.9%,明显优于其它单时相影像的分类结果;发现北京郊区冬小麦光谱分类的最佳时相组合为4月上旬(起身期)和5月下旬(灌浆期),分类总体精度为94%。 相似文献
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应用冬小麦抽穗期的环境小卫星影像对关中地区依据行政区划划分,采用多种遥感监测方法进行冬小麦种植面积提取,提取精度达95.64%。结果表明:抽穗期是关中地区提取冬小麦种植面积的最佳时相,同时也表明环境小卫星可以用来监测大范围的冬小麦种植面积,并得到很高的精度。在未来的研究中,对于地形差异较大的地区,可依据地形和冬小麦的种植结构对研究区进行划分,针对每个子研究区采用不同的遥感监测方法以提高提取精度。 相似文献
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基于多时相MODIS监测冬小麦的种植面积 总被引:8,自引:0,他引:8
论文基于时相和波谱信息,利用MODIS数据监测了北京冬小麦的种植面积。首先,基于地形高度对地物光谱反射值的影响,借助DEM数据对研究区域进行划分。其次,在分析并提取北京地区主要农作物时间谱曲线特征的基础上,设计决策函数,成功提取了北京地区冬小麦的种植面积。最后,比较并分析了非遥感数据对监测精度的影响。研究结果表明,(1)时相信息可以极大的提高农作物种植面积的监测精度。(2)辅助数据的利用,使决策函数的设计更具有针对性,监测结果更可靠。(3)多源多时相遥感数据在农作物种植面积的提取中具有明显的技术优势和重要的应用潜力。 相似文献
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由于雷达遥感的全天候、全天时的优势,使之成为南方大范围农业信息动态监测的最佳遥感手段。应用星载合成孔径雷达(SAR)-RadarSat-2,选择安徽省涡阳县冬小麦产区进行测产研究。利用SAR的多极化特点(HH/HV/VH/VV),针对此卫星影像比较选择VV极化,并在试验田取样,获得试验田产量,从而建立产量和后向散射系数关系的线性测产模型,在提取冬小麦种植区基础上,应用此模型对冬小麦进行大面积产量测产。通过计算,得到了超过80%精度的测产模型和冬小麦较准确的测产结果。研究结果表明:在冬小麦收割前一周左右应用SAR进行测产,从而为农保提供服务,这种思路和方法是行之有效的。 相似文献
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Taking the Hohhot-Baotou-Ordos city group in semiarid area as an example,constructs a system of surface hierarchical structure in the semi-arid area for urbanization.It reveals the urbanization process of different scales since twenty-first Century.The results show that:(1) the process of urbanization in the study area is intense in 2000~2015,especially during the period of 2005~2010.Among them,the expansion of Hohhot city presents extension type,Baotou for filling type,and ordos for enclaving type.(2) The impermeable space continued to increase (low density -medium density -high density increasing),and its proportion in urban areas increased from 62.46% in 2000 to 75.40% in 2015.The green space shows an increasing trend of fluctuation,which reflects that the urbanization process in semi-arid area focuses on the coordinated construction of urbanization and green space.Although the development of the city includes the bare soil space,the process of urbanization obviously reduces the spatial scope of the bare soil,which effectively improves the ecosystem service and human well-being.(3) the change order of impermeable components density is low,medium and high.During this period,the composition of the green space was reduced first and then increased,and the overall micro amplitude increased,indicating that the green space in the urban area was promoted.At the same time,the density of bare soil components showed a trend of high,middle and low,indicating that urbanization was expanded into a large number of bare soil.Then bare soil was further transformed into impervious surface and vegetation,and the ecological environment in the urban area was significantly improved.
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This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn. 相似文献
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In Northwest China,there are many mixed pixels in the winter wheat area,so the combination of decision tree and mixed pixel decomposition is of great significance to improve the interpretation accuracy.The data source of this result is GF-1 satellite data which excellent in the high temporal resolution and high spatial resolution.Based on the difference about variation characteristics and NDVI value for winter wheat and the other crops in different phase data,we build decision tree to extract winter wheat pixels preliminary.Then selected linear spectral mixture model,further analysis the previous data by mixed pixel decomposition,get the final planting area data more exactly.Compared with the winter wheat samples measurement data,calculate the extraction accuracy eventually.The result shows that the extraction accuracy of winter wheat planting area in the study area was more than 90%,Kappa coefficient is close to 0.8,can reflect the distribution of winter wheat in the region accurately.This study found that the method which combined with decision tree classification and pixel unmixing based on high resolution remote sensing image can extract the winter wheat planting area precisely,This is helpful for the development of crop area remote sensing monitoring. 相似文献