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植被生物量高光谱遥感监测研究进展 总被引:2,自引:0,他引:2
植被生物量的评估对于研究全球碳循环具有重大意义,而高光谱遥感技术为精确反演地表属性提供了重要的数据支持。针对如何更好地应用高光谱遥感技术进行植被生物量精确反演的问题,该文详细阐述了国内外应用高光谱技术估测植被生物量的研究进展。对反演植被生物量所涉及的数据源、反演模型的构建方法及其模型特点、反演模型应用对象等内容进行了综合评述,并通过分析认为,高光谱遥感技术较传统的多光谱遥感技术在生物量反演精度上有了显著的提高。同时,对建模方法、多源遥感数据融合以及模型通用性等方面的研究进行了展望,以达到在大尺度范围内对植被生物量进行准确反演的目的。 相似文献
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基于GPS新型L5信号的地表雪深反演研究 总被引:1,自引:0,他引:1
利用GPS多路径反射信号测量地表雪深具有全天候和高时空分辨率的特点,因此其可作为一种代替气象站监测雪深的新手段。然而,先前大多数研究仅使用了GPS L1和L2C波段信噪比数据探测积雪深度。为验证新型的L5信号在雪深反演方面的优越性,本文阐述了GPS-R技术反演雪深的原理,利用Lomb-Scargle周期图法所处理的受积雪表层影响的信噪比数据计算了频谱振幅强度,通过获取频谱特征值与天线高度的关系求解雪深值,最后分别与L1反演结果和实测雪深数据进行了对比。试验结果表明:与现有的GPS-R测量雪深结果相比,利用新型的L5反射信号反演地表雪深的精度更佳;采用GPS-R技术探测雪深对把握测站区域内的雪深变化情况和淡水资源储量具有重要价值。 相似文献
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叶绿素是植被光合作用的主要物质,准确估算叶绿素含量对植被生长健康状况和生态环境研究具有重要意义。本文利用辐射传输机制的PRO4SAIL模型模拟植被冠层光谱,以TM影像为数据源,分析物理模型模拟反射率和遥感影像反射率与叶绿素含量之间的相关性,研究利用多光谱信息定量反演路域植被叶绿素含量的可行性。研究结果表明,植被光谱反射率与叶绿素含量之间有较强的相关性;利用PRO4SAIL模型模拟的冠层反射率反演叶绿素含量具有一定可行性。该研究成果为大面积路域植被冠层叶绿素含量遥感监测提供理论依据与参考。 相似文献
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基于PROSPECT+SAIL模型的遥感叶面积指数反演 总被引:4,自引:1,他引:4
以PROSPECT+SAIL模型为基础,从物理机理角度反演植被叶面积指数(LAI)。首先,通过FLAASH模型进行大气校正,使得图像像元值表达植被冠层反射率; 然后,根据LOPEX 93数据库和JHU光谱数据库选择植物生化参数和光谱数据,以PROSPECT模型模拟出的植物叶片反射率和透射率作为SAIL模型的输入参数,得到植被冠层反射率,将结果与遥感影像的植被冠层反射率对应,回归出植被LAI; 最后,以地面实测数据对遥感反演数据进行验证,并分析了误差的可能来源。 相似文献
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基于经验模型的Hyperion数据植被叶绿素含量反演 总被引:1,自引:0,他引:1
对于反演植被叶绿素含量而言,基于Hyperion等高光谱传感器数据、利用经验方法建模是一种快速准确的方法。利用多种植被的实测数据以及Hyperion模拟数据,分析植被反射率及其变化形式与叶绿素含量的相关性,并进一步针对红边参数、植被指数等分析植被反射率与叶绿素含量的关系,选取最准确的经验建模方法。经过对比,改进的简单比值指数(modified simple ratio,MSR)与叶绿素含量相关性最高,其回归模型能比较准确地反演出叶绿素含量。通过Hyperion图像、利用MSR指数与实测叶绿素含量得到回归模型,建立区域叶绿素含量分布图;并对张掖地区植被叶绿素含量进行了反演,反演结果具有较高精度,相对误差低于5%。 相似文献
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岸基GPS-R反演水面高度的精度、可用性及可靠性,与测站可见卫星落入指定水域方位、入射角度、有效弧段长度与数量密切相关。为提高反演点位的观测效率,本文综合考虑第一菲涅尔区、测站与水系方位关系及不同水系类型入射角区间范围选择等多种因素,提出反演事件的概念,并以此建立岸基GPS-R时空布局优化策略。通过设计仿真试验与实测数据进行验证,结果显示两者具有良好的一致性。但由于现有GPS星座结构导致北半球用户在观测时反演事件分布呈现“北部最多,东西次之”的特点,因此,基于反演事件的优化策略可有效开展测区备选站点评估与选定;同时,根据反演事件可进一步筛选测站最佳时段及其周期变化规律,从而验证了开展测站时空布局优化研究可有效保障岸基GPS-R反演水面高的效率与质量。这对促进GPS-R技术由理论向应用推广,以及充分利用现阶段我国大中型水库大坝、库岸边坡及海岸等周边均布设的GNSS永久观测站开展GPS-R反演水面高度应用,具有重要的参考和使用价值。 相似文献
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机载GPS反射信号土壤湿度测量技术 总被引:5,自引:1,他引:4
随着全球导航定位系统反射信号(GNSS-R)技术的发展, 近年来提出了利用GPS地表反射信号遥感土壤湿度的新方法, 该方法利用地表反射率与土壤介电常数以及介电常数与土壤湿度之间的关系来建立反演模型。为了可以快速方便的利用DMR实测数据反演得到土壤湿度, 本文根据Wang和Schmugge模型建立了土壤介电常数与湿度之间的分段模型, 实现了从原始反射数据到土壤湿度结果的整个反演流程。为了验证反演的可行性, 利用NASA等机构联合进行的SMEX02试验机载数据反演得到的结果表明, GPS反射信号能够有效地反演 相似文献
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Manish P. Kale Shirish A. Ravan P. S. Roy Sarnam Singh 《Journal of the Indian Society of Remote Sensing》2009,37(3):457-471
Satellite remote sensing is a proven tool for mapping landuse patterns and estimating vegetation biomass/carbon. Present study
aims at estimating the potential of forests of Radhanagari WLS (Western Ghats, India) to sequester the atmospheric carbon-di-oxide,
using ground based observations coupled with satellite remote sensing. The study area was stratified for dominant forest types
based on the structure and composition of vegetation and elevation variations. Permanent sample plots were laid down in these
homogeneous vegetation strata (HVS) to make different observations during time 1 and time 2. Carbon sequestration by plantations
was also studied and compared with natural forests. Species and area-specific biomass equations were used for estimating carbon
pool and sequestration. Among natural forests ‘mixed moist deciduous’ forests exhibited highest sequestration rate (8%), whereas,
plantation as obvious had a comparatively higher sequestration rate than natural forests (20.27%). Total carbon sequestration
by forests of the Radhanagari WLS between 2004 and 2006 is 78742.09 tons. Eligible land for reforestation activity under clean
development mechanism (CDM) of Kyoto Protocol was identified using satellite remote sensing using 1989 and 2005 datasets and
it was observed that the potential land that can be used for reforestation activity is 10080 ha. 相似文献
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A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems 总被引:7,自引:0,他引:7
Dengsheng Lu Qi Chen Guangxing Wang Lijuan Liu Guiying Li Emilio Moran 《International Journal of Digital Earth》2016,9(1):63-105
Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling. 相似文献
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Monitoring winter wheat growth in North China by combining a crop model and remote sensing data 总被引:6,自引:0,他引:6
Ma Yuping Wang Shili Zhang Li Hou Yingyu Zhuang Liwei He Yanbo Wang Futang 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):426
Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation. 相似文献
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估算森林地上生物量(AGB)对于全球实现碳中和目标至关重要。本文以美国缅因州Howland森林为研究区域,借助地面实测样地数据,对比分析协同不同数据源(高光谱和LiDAR)和机器学习算法(随机森林、支持向量机、梯度提升决策树和K最邻近回归)的研究,以改善Howland森林的生物量估计精度。结果表明,采用LiDAR和高光谱植被指数变量模型的最佳精度分别为0.874和0.868,协同高光谱和LiDAR变量并采用梯度提升决策树回归模型的精度为0.927,即多源遥感数据要优于单一数据源。高光谱和LiDAR数据的协同使用对于提高类似于Howland地区或更广泛区域的生物量估计的准确性,具有普遍的适用性与一定的应用前景。 相似文献
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植被光能利用率高光谱遥感反演研究进展 总被引:1,自引:0,他引:1
光能利用率是表征植被通过光合作用将所截获/吸收的能量转化为有机干物质效率的指标。光能利用率是植被光合作用的重要概念,也是区域尺度以遥感参数模型监测植被生产力的关键参数。不同植被类型的光能利用率具有明显的时空差异,水分、温度、养分供给等环境胁迫因素会影响植被的光能利用率。随着高分辨率光谱测量传感器的使用,位于可见光和近红外区域的窄波段可以捕捉到植被冠层反射率的细微变化,也促进了光能利用率遥感反演技术的发展。本文结合国际植被光能利用率遥感反演最新研究成果,从基于环境胁迫因子的光能利用率反演,基于植被光谱指数的光能利用率反演、基于叶绿素荧光的光能利用率反演,以及基于涡度相关测量数据和遥感数据相结合的光能利用率反演四个方面,详细介绍了植被光能利用率遥感反演的主要技术方法,并对植被光能利用率遥感研究存在的主要问题和发展趋势进行了讨论。 相似文献
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从高光谱遥感影像提取植被信息 总被引:2,自引:0,他引:2
遥感可以快速有效地监测大面积植被的种类、特性、长势等各类信息。高光谱遥感数据因其特有的高光谱分辨率特性使其在植被生态环境领域具有极大的应用潜力。植被信息作为生态环境评价的重要参数对区域生态环境的监测和建设具有重要的意义。本文基于云南省鹤庆县北衙的高光谱遥感数据用SAM方法对植被信息进行了提取,参考光谱使用ASD光谱辐射仪采集的植被光谱曲线。文中对高光谱遥感影像的辐射定标和大气校正进行了研究,针对影响光谱辐射仪采集的主要因素采取了相应的措施,并对光谱曲线分类及参考光谱曲线的选取进行了研究。将选取出的参考光谱曲线与大气校正后的遥感影像进行SAM匹配提取出植被信息,经过与实地调查资料比较并计算总体精度和kappa系数,计算结果达到预期精度。最后将分类结果转换为矢量图,经过投影转换为大地坐标后制作出北衙植被分布图。 相似文献