遥感反演连续植被叶面积指数的空间尺度效应

叶面积指数（LAI）是描述植被冠层几何结构的最基本的参数,是气候模型、地-气相互作用等过程的重要输入参数．近年来遥感反演LAI的尺度效应问题得到广泛关注．在作物冠层反射率模型基础上,讨论了LAI尺度效应的产生机制,并获得不同尺度之间LAI的转换公式,数值模拟试验和实际遥感图像试验都证明了该尺度转换公式的可靠性．     

遥感反演离散植被有效叶面积指数的空间尺度效应

叶面积指数(LAI)是表征植被几何结构和生长状态的关键生物物理参数,也是气候、能量和碳循环等模型的重要输入参数.遥感反演LAI的尺度效应问题一直备受关注.考虑到尼尔逊参数对离散植被的BRDF模型的影响,我们定义有效叶面积指数LAIeff,LAIeff与传统概念下的叶面积指数具有相同的表征植被冠层内叶子浓密程度的主要功能,因此,本文以讨论有效叶面积指数为主.离散植被的空间尺度效应产生机理与连续植被有所不同,本文在方向性二阶微分方法(DSD)反演有效叶面积指数的基础上,讨论了离散植被空间尺度效应产生的原因,并提出了有效叶面积指数的尺度转换公式.理论分析结果表明高分辨率像元有效叶面积指数反演值的平均值总是大于等于同一目标低分辨率像元的有效叶面积指数反演值.另外,本文基于实际航空高光谱遥感影像,反演了中国河北省张家口市怀来县某植被覆盖区的有效叶面积指数,其反演结果验证了该结论的正确性和尺度转换公式的可靠性.     

FFIM用于机载大气激光雷达 ABCT反演的可行性研究

Fernald前向积分法能否用于机载大气探测激光雷达气溶胶后向散射系数的反演一直是一个有争议的课题.本文利用青岛机载大气探测激光雷达实测数据、国外机载大气探测激光雷达实测数据和机载大气探测激光雷达模拟数据,对Fernald前向积分法应用于不同高度的机载大气探测激光雷达气溶胶后向散射系数反演的误差进行了定量分析,分析结果表明:飞机的飞行高度在3.5 km左右,标定值存在20%的误差时,离地面2 km的高度范围内反演得到的气溶胶后向散射系数的相对误差在12%以内,但在标定点附近相对误差可达20%;飞机飞行高度在7 km左右,当标定值存在100%的误差时,反演得到的气溶胶后向散射系数的相对误差大都在10%~15%之间,标定值存在400%的误差时,反演得到的气溶胶后向散射系数的相对误差大部分在15%~50%之间.本文从理论上对Fernald前向积分法应用于机载大气探测激光雷达气溶胶后向散射系数反演出现负值的原因进行了探讨.研究表明:Fernald前向积分法能够较准确地反演出中高空探测(4.5 km以上)机载大气探测激光雷达气溶胶后向散射系数,但应用于低空探测(4.5 km以下)机载大气探测激光雷达气溶胶后向散射系数反演时,反演误差较大甚至反演结果会出现负值.     

钠多普勒激光雷达回波光子数仿真及大气参数反演

钠多普勒激光雷达利用中层顶区域的钠原子作为示踪物,探测中层顶区域大气风场和温度剖面.本文主要分析钠多普勒激光雷达的探测原理和大气参数反演算法.利用MSISE和HWM93等大气模型给出背景大气温度、密度及风场,并给定钠原子数密度剖面,从激光雷达方程出发,模拟计算了激光雷达的瑞利散射和钠共振荧光散射回波光子数.利用模拟的回波光子数剖面数据,反演得到大气温度、视线风速和钠原子数密度剖面,反演结果与模拟计算用的背景参数符合很好,验证了这一反演方法的正确性.分析了激光频率偏移和激光线宽变化对反演精度的影响.     

全球植被叶面积指数对温度和降水的响应研究

利用一套新的遥感信息反演的叶面积指数(LAI)数据和生物气候数据,建立了全球尺度的LAI与降水及温度的总体相关和距平相关,用以揭示全球尺度的植被季节和年际的变化对气候变化的响应特征.结果发现,全球尺度植被与气候因子的季节和年际变化随不同的生态系统差异明显.植被LAI与温度的总体正相关的最大值出现在北半球的中高纬度地区;LAI与降水的总体正相关高值(＞0.8)出现在亚洲东部、北美洲北部腹地和热带非洲北部的Sahel地区;最大的LAI与温度的正距平相关(0.4-0.6)出现在东南亚南部、非洲Sahel地区的南部和巴西东部等热带地区;而从LAI与降水的距平相关来看,最明显的特征是出现在西伯利亚、亚洲北部和北美西北部的强的负距平相关.本文进一步阐明了出现相关特征差异的陆-气过程物理机制.     

作物生长模型同化MODIS反射率方法提取作物叶面积指数

叶面积指数(LAI)是监测农作物生长的重要参数. 遥感提取LAI的方法之一是通过植被冠层辐射传输模型反演, 但多数模型反演方法都没有考虑作物LAI在不同生长阶段的变化关系. 本文在模型反演的基础上引入用作物生长模型描述的LAI随生长变化的关系, 将作物生长过程中LAI地面先验信息作为约束信息, 提取最优目标参数LAI. 要点是在耦合作物生长模型和辐射传输模型中通过变分算法在作物生长不同时刻序列同化多时相遥感观测和LAI先验知识; 在同化过程中用伴随方法求解代价函数. 采用北京昌平和顺义地区MODIS数据的同化结果显示时序遥感观测能够极大改善被提取LAI的不确定性, 对比MODIS LAI产品发现提取LAI廓线符合实际的作物生长规律, 因而比MODIS LAI产品更加可靠.     

全球植被叶面积指数对温度和降水的响应研究

利用一套新的遥感信息反演的叶面积指数(LAI)数据和生物气候数据,建立了全球尺度的LAI与降水及温度的总体相关和距平相关,用以揭示全球尺度的植被季节和年际的变化对气候变化的响应特征.结果发现,全球尺度植被与气候因子的季节和年际变化随不同的生态系统差异明显.植被LAI与温度的总体正相关的最大值出现在北半球的中高纬度地区;LAI与降水的总体正相关高值(＞0.8)出现在亚洲东部、北美洲北部腹地和热带非洲北部的Sahel地区;最大的LAI与温度的正距平相关(0.4-0.6)出现在东南亚南部、非洲Sahel地区的南部和巴西东部等热带地区;而从LAI与降水的距平相关来看,最明显的特征是出现在西伯利亚、亚洲北部和北美西北部的强的负距平相关.本文进一步阐明了出现相关特征差异的陆-气过程物理机制.     

机载激光雷达对青岛及周边海域的气溶胶探测

工作波长为532 nm的机载大气环境探测激光雷达AEDAL（Atmospheric Environment Detecting Airborne Lidar）装载在CMS_3807飞机上,于2005年11月7～11日期间在青岛地区及周边海域上空进行了飞行探测.此次实验的目的有两个：验证我国用于大气环境探测的激光雷达技术已经具备从地基向空基乃至天基发展的条件;获得青岛地区及周边海域边界层结构及大气气溶胶时空分布变化的特点.激光雷达的高时空分辨率为获取飞行路径上的边界层结构及气溶胶时空分布提供了可能.为了研究下垫面对边界层及气溶胶时空分布的影响,预定的飞行路径上包含了丰富的地形变化,有城市、丘陵、海区等.通过给出11月8日及11日的探测结果,不仅得到了不同地区边界层结构及气溶胶的时空分布特点,还可以看到冷锋、地形、地面气象场等因素对它们的影响.     

森林冠层探测激光雷达的波形特征分析

机载激光雷达通过发射激光脉冲和接收返回波形的方式来探测森林冠层的三维结构,返回波形中包含了森林冠层不同高度处枝叶的后向散射特征.本研究在分析激光脉冲与森林冠层之间的相互作用关系时发现返回波形存在时长展宽现象,为此建立了立体散射体模型,并得到时长展宽量与散射体深度之间的对应关系.根据返回波形的不同特征将散射体划分为简单散射体、立体散射体和复杂散射体三种类型,提出单峰导数法和多峰导数法两种波形分析方法,用于提取相关的波形特征量,并判别散射体的类型.本研究得到了一些初步结果,通过模拟立体散射体的返回波形发现,随着散射体深度的增加,返回波形逐渐被展宽;随着子散射体之间距离的增加,返回波形开始出现两个波峰并成为两个完全独立的子波形.通过对模拟波形和实测波形的波形特征量进行比较发现,模拟波形与实测波形的时长略有差别.总之,激光雷达波形数据很好地描述了森林冠层的后向散射特征,在提高森林参数估测精度方面具有应用潜力.     

三波段梯度差植被指数的理论基础及其应用

针对现有植被指数的不足, 从分析植被的光谱特点入手, 提出一种新的植被指数TGDVI, 并建立了用TGDVI反演植被覆盖度和叶面积指数的方法. 理论分析和模型模拟方法研究表明, 该植被指数具有高饱和点和一定的背景影响消除能力, 并可以和植被覆盖度、叶面积指数建立比较明确的函数关系. 此外, 该植被指数对于薄云的影响也有一定的去除能力. 北京顺义试验数据研究表明, 利用该植被指数反演叶面积指数可以获得比较理想的效果. 从理论上分析了NDVI饱和点低的原因, 指出NDVI的低饱和点是由其定义本身和植被的光谱响应特点决定的, 从一定意义上说是无法克服的. 同时还通过模型模拟证明比值植被指数与叶面积指数之间为近似分段线性关系, 而非普遍采用的线性关系, 这主要是由背景影响以及红光、近红光反射率随叶面积指数增加变化速率不一致造成的.     

High‐resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data

The availability of airborne LiDAR data provides a new opportunity to overcome some of the problems associated with traditional, field‐based, geomorphological mapping such as restrictions on access and constraints of time or cost. The combination of airborne LiDAR data and GIS technology facilitates the rapid production of geomorphological maps of floodplain environments; however, unfiltered LiDAR data, which include vegetation and buildings, are currently more suitable for geomorphological mapping than data that have been filtered to remove these features. Classification of LiDAR data according to elevation in a GIS enables the user to identify and delineate geomorphological features in a manner similar to field mapping, but it is necessary to use a range of classification intervals in order to map the various types of feature that occur within a single reach. Comparison of a LiDAR‐derived geomorphological map with an independently produced field geomorphological map showed a high degree of similarity between the results of the two methods, although ground‐truthing is essential in cases where a high degree of accuracy is required. Ground‐truthing of a LiDAR‐derived geomorphological map showed that around 80% of features mapped using both methods were identified from the LiDAR data, suggesting that the method is suitable for applications such as production of base maps for use in field mapping and selection of sites for detailed investigation. Copyright © 2007 John Wiley & Sons, Ltd.     

Water surface mapping from airborne laser scanning using signal intensity and elevation data

In recent years airborne laser scanning (ALS) evolved into a state‐of‐the‐art technology for topographic data acquisition. We present a novel, automatic method for water surface classification and delineation by combining the geometrical and signal intensity information provided by ALS. The reflection characteristics of water surfaces in the near‐infrared wavelength (1064 nm) of the ALS system along with the surface roughness information provide the basis for the differentiation between water and land areas. Water areas are characterized by a high number of laser shot dropouts and predominant low backscatter energy. In a preprocessing step, the recorded intensities are corrected for spherical loss and atmospheric attenuation, and the locations of laser shot dropouts are modeled. A seeded region growing segmentation, applied to the point cloud and the modeled dropouts, is used to detect potential water regions. Object‐based classification of the resulting segments determines the final separation of water and non‐water points. The water‐land‐boundary is defined by the central contour line of the transition zone between water and land points. We demonstrate that the proposed workflow succeeds for a regulated river (Inn, Austria) with smooth water surface as well as for a pro‐glacial braided river (Hintereisfernerbach, Austria). A multi‐temporal analysis over five years of the pro‐glacial river channel emphasizes the applicability of the developed method for different ALS systems and acquisition settings (e.g. point density). The validation, based on real time kinematic (RTK) global positioning system (GPS) field survey and a terrestrial orthophoto, indicate point cloud classification accuracy above 97% with 0·45 m planimetric accuracy (root mean square error) of the water–land boundary. This article shows the capability of ALS data for water surface mapping with a high degree of automation and accuracy. This provides valuable datasets for a number of applications in geomorphology, hydrology and hydraulics, such as monitoring of braided rivers, flood modeling and mapping. Copyright © 2009 John Wiley & Sons, Ltd.     

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

War landform mapping and classification on the Verdun battlefield (France) using airborne LiDAR and multivariate analysis

Acting as efficient earth-movers, soldiers can be viewed as significant geomorphological drivers of landscape change when replaced in the recent debates on Anthropocene Geomorphology. ‘Polemoforms’, generated by military activities, correspond with a set of human-made landforms of various sizes and geometries. They are particularly common on the World War One battlefield of Verdun (France) which ranks among the largest battles of attrition along the Western Front. The artillery bombardments and building of defensive positions in that battle significantly altered the landscape, resulting in thousands of shell craters, dugouts, and gun positions that have altered both the meso and microtopography. This paper proposes an innovative methodology to make an exhaustive inventory of these small-scale conflict-induced landforms (excluding linear features such as trenches) using a digital terrain model (DTM) acquired by airborne LiDAR on the whole battlefield. Morphometric analysis was conducted using Kohonen's self-organizing maps (SOMs) and hierarchical agglomerative clustering (HAC) in order to quantify and classify the high number of war landforms. This combined approach allowed for mapping more than one million landforms which can be classified into eight different shapes including shell craters and various soldier-made landforms (i.e. shelters, gun positions, etc.). Detection quality evaluation using field observations revealed the algorithm successfully classified 93% of shell craters and 74% of anthropologically constructed landforms. Finally, the iconographic database and map series produced will help archaeologists and foresters to better manage the historic site of Verdun, today covered by a large forest of ~10 000 ha. © 2019 John Wiley & Sons, Ltd.     

Estimating leaf area index by inversion of reflectance model for semiarid natural grasslands

The study developed an integrated reflectance model combining radiative transfer and geometric optical properties in order to inverse leaf area index (LAI) of semiarid natural grasslands. In order to better link remote sensing information with land plants, and facilitate regional and global climate change studies, the model introduced a simple but important geometrical similarity parameter related to plant crown shapes. The model revealed the influences of different plant crown shapes (such as spherical, cylindrical/cuboidal and conic crowns) on leaf/branch angle distribution frequencies, shadow ground coverage, shadowed or sunlit background fractions, canopy reflectance, and scene reflectance. The modeled reflectance data agreed with the measured ones in the three Leymus chinensis steppes with different degradation degrees, which validated the reflectance model. The lower the degradation degree was, the better the modeled data agreed with the measured data. After this reflectance model was coupled with the optimization inversion method, LAI over the entire study region was estimated once every eight days using the eight-day products of surface reflectance obtained by multi-spectral Moderate-Resolution Imaging Spectroradiometer (MODIS) during the growing seasons in 2002. The temporal and spatial patterns of inversed LAI for the steppes with different cover degrees, swamps, flood plains, and croplands agreed with the general laws and measurements very well. But for unused land cover types (sands, saline, and barren lands) and forestlands, totally accounting for about 10% of the study region, the reasonable LAI values were not derived by inversing, requiring further revising of the model or the development of a new model for them. Supported by the National Natural Science Foundation of China (Grant No. 30500076), the National Basic Research Program of China (Grant No. 2007CB714407), and the President Foundation of Graduate University of Chinese Academy of Sciences (Grant No. YZJJ200205)     

Morphology and distribution of dolines on ultramafic rocks from airborne LiDAR data: the case of southern Grande Terre in New Caledonia (SW Pacific)

Dolines are closed geomorphological depressions which are surface manifestations of karstic systems. Usually developed on limestones, they also typify the morphology of the New Caledonian landscape, particularly on the southern massif of the main island (known as Massif du Sud). The specificity of dolines here lies in their development on ultramafic rocks. They are evidences of subsidence, suffosion and collapse phenomena resulting from dissolution weathering of peridotites. However, extensive underground drainage systems are still not yet recognized. Semi‐automatic mapping of dolines is carried out on a 148 km² area of the Massif du Sud from a high accuracy LiDAR digital elevation model. In this area 8601 dolines ranging from 1 m² to 2 km² are identified and morphologically characterized with precision. Most are small, shallow and round‐shaped, yet more complex shapes are locally observed. Size distribution analysis allows the setting of a threshold of 20 000 m² above which surface processes rather than chemical weathering control doline evolution. Doline density analysis reveals high concentrations on flat areas where ferricrete overlies the complete weathering profile, especially in the case of elevated rainy watersheds. Dolines are aligned and elongated along a north 135° ± 5° major fracture direction, which is inherited from the obduction of the Pacific Plate upper mantle in the Late Eocene. Finally, we propose a pioneering morphometric typology of dolines that provides important clues as to pseudokarstic activity. We define collapse, bowl‐shaped and flat bottom dolines. Collapse and bowl‐shaped dolines are assumed to denote active pseudokarst. They may widen and deepen, or eventually be filled by sediments. They are distinguished from flat bottom dolines that are partially to completely filled, which suggests that they are associated with paleo‐pseudokarsts. However the groundwater flow paths associated with the genesis and evolution of dolines must be clarified, thus collapse and bowl‐shaped dolines should be hydrologically monitored. Copyright © 2016 John Wiley & Sons, Ltd.     

Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this re...     

Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index

Vegetation processes are seldom considered in lumped conceptual rainfall–runoff (RR) models although they have significant impacts on runoff via the control of evapotranspiration. This paper incorporates the remotely-sensed the moderate resolution imaging spectrometer mounted on the polar-orbiting terra satellite-leaf area index (MODIS-LAI) data into Xinanjiang rainfall–runoff model and assesses the model performance on 210 catchments in south-east Australia. The results show that the inclusion of LAI data improves both the model calibration results as well as the daily runoff prediction in ungauged catchments. It is likely that more significant improvements to the model structure to integrate the remotely-sensed vegetation and other data can further reduce the uncertainty in runoff prediction in ungauged catchments.     

应用MODIS数据监测千岛湖流域植被覆盖动态(2001-2013年)

流域植被覆盖状况对于水源地生态环境保护具有重要的指示作用.当前的水质目标管理不仅要着眼于湖库水质参数控制,更应该从整个流域的角度维系生态平衡.在此背景下,依托长时间序列MODIS遥感数据对千岛湖流域2001-2013年植被覆盖状况进行监测,采用最小二乘法趋势分析和Mann-Kendall显著性检验方法分析了千岛湖流域植被的空间分布特征、时间变化特征与长期变化趋势.研究表明该方法能够有效地监测流域植被覆盖的时空动态变化:1)从空间分布上来看,千岛湖流域植被覆盖状况整体较好,但同时也发现受人为干扰较大的地域如河、湖附近的城镇建设用地、农业用地以及园地,其NDVI值明显低于自然林地;2)从时间变化特征上看,2001-2013年千岛湖流域植被年际NDVI在0.69~0.73之间波动,且近年来有增长趋势,年内季节性NDVI动态分析表明高时间分辨率的MODIS数据能够用来区分常绿植被与落叶植被的物候特征,以分析不同植被类型对流域氮、磷流失的风险差异;3)从变化趋势上看,2001-2013年植被覆盖状况改善的区域远大于退化的区域,其中改善区域约占流域面积的55.90%,呈现出一定退化状态的区域约占29.60%(严重退化区域仅占3.97%),而相对稳定不变区域约占14.51%.经与气温与降水等气候因子进行相关性分析表明,植被NDVI与气温呈显著正相关,而降水则不敏感,说明气温是研究区植被生长的主导气候因子.同时发现,人类活动对局部植被变化影响较大.研究结果可为流域水资源与生态环境保护提供空间数据支撑.     

A simplified storm index method to extrapolate intensity–duration–frequency (IDF) curves for ungauged stations in central Chile

Most of meteorological stations in Chile register rainfall amounts once every 24 h. The creation of intensity–duration–frequency (IDF) curves requires continuous recorded data, and this insufficiency of proper instrumentation has resulted in a lack of IDF curves nationwide. The objective of this study is to further develop and evaluate the feasibility of a new method to estimate IDF curves in ungauged stations under Mediterranean climates of central Chile. A technique used to address this problem is the use of a storm index (S_I), also known as the ‘K’ method, which allows the construction of IDF curves from stations with discontinuous data, by extrapolating data from stations with continuous records, as long as daily rainfall intensities for both stations differ by less than 2 mm h^?1. To test the applicability of this method, S_I values were calculated for 40 meteorological stations located throughout Central Chile (latitudes 30°S to 40°S). The extrapolated IDF curves were then compared with observed data, and the goodness of fit was determined. The results indicate that the storm index method can adequately estimate hourly IDF curve values for stations lacking of continuous rainfall data. Copyright © 2014 John Wiley & Sons, Ltd.