植被FAPAR的遥感模型与反演研究

FAPAR是遥感估算陆地生态系统植被净第一性生产力(NPP)的重要参数。FAPAR模型是否能真实反映植被冠层吸收光合有效辐射状况,将直接影响遥感估算植被NPP和碳循环的准确性。从FAPAR机理出发,考虑土壤反射率、冠层结构、太阳入射角等多种因素,构建了全新的定量FAPAR反演模型,并分析了太阳天顶角、LAI、土壤背景等因素与FAPAR的关系。与蒙特卡罗模拟结果的对比和用地面实测数据的验证表明该模型拥有较高的精度。选择甘肃张掖盈科灌区为研究区,利用PROBA-CHRIS高光谱多角度数据反演得到了LAI和FAPAR,并用同步观测数据验证了反演结果。     

黑河流域山前绿洲灌溉农田蒸散发模拟研究

基于Penman-Monteith蒸散公式, 应用土壤-植被-大气系统水分和能量传输理论对Shuttleworth-Wallace蒸散模型的参数进行改进, 得出解析计算农田作物蒸腾和土壤蒸发的双源模型. 对黑河流域山前绿洲农田春小麦生长期土壤蒸发、作物蒸腾以及总蒸散过程进行了模拟研究. 对模型的计算结果以田间观测和水量平衡方法进行验证, 误差目标NSE=0.98, 说明该模型用于农田蒸发和蒸腾的计算是合理的. 对影响蒸发和蒸腾的主导因子净辐射、叶面积指数、土壤含水量进行了相关性分析, 得出三者的变化对土壤蒸发、作物蒸腾的影响. 通过不同时期日蒸散发量变化特征的分析, 表明土壤、冠层两个界面对能量和水汽传输的交互影响效应显著.     

西北荒漠草甸植被光谱反射特征研究

选取位于西北干旱内陆地区的安西荒漠草场为观测试验区,分别对盐生低地草甸、极旱荒漠草甸和荒漠灌丛三类草地进行了地面反射光谱测定,并分析了主要荒漠草甸植被光谱反射的一般特征和红边参数特征,进而探讨了形成草地光谱特征差异的主要内在原因和影响因素。结果表明:在近红外波段,由于荒漠植被类别间叶片内部结构变化大,因此冠层光谱反射率差异较大;同一植被冠层光谱反射率的大小主要受植被长势的影响;受沙地背景的影响,在近红外波段,植被冠层的光谱反射率要明显小于叶片的光谱反射率;荒漠植被冠层光谱的红边也具有“双峰”现象,红边特征的参数表现为:沙地植被>绿洲植被>沙漠植被。对安西荒漠植被光谱特征的分析研究,对于研究干旱区荒漠植被的理化性能、遥感反演、植被分类、植被调查等都具有重要的意义。     

冬小麦遥感冠层温度监测土壤含水量的试验研究

在冬小麦主要生育期(2002年4月初到5月底),对不灌溉的冬小麦测定了冠层温度、地温、气温以及土壤含水量,计算了冠气温差且分析了冠层温度和冠气温差与不同土层厚度的土壤含水量相关关系。结果表明:14:00的冠层温度能较好地反映20cm土层的土壤含水量变化,但与其它各土层相关性有较大的波动性;14:00的冠气温差能较好地反映40cm以上土层的土壤含水量变化,二者的相关性很高,在20cm、40cm土层,两者相关系数R2分别为0.98866、0.99389,这为用区域遥感数据反演主要生育期冬小麦的冠气温差进而监测区域40cm土壤含水量提供了实验性的依据;拔节期和灌浆期,用14:00冠气温差来拟合各土壤层的土壤含水量有较高的精度,从而为用区域遥感数据监测区域土壤含水量提供了经验性的模型。     

基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演

为了监测复垦矿区土壤的有机质含量, 综合利用光谱分析、统计学习理论与方法以及智能优化理论与方法, 研究了矿区复垦土壤有机质含量与土壤光谱之间的关系, 在此基础上建立了土壤有机质含量高光谱反演模型, 实现土壤有机质含量定量检测.首先对原始土壤光谱数据进行预处理, 然后进行相关性分析, 提取450 nm、500 nm、650 nm、770 nm、1 460 nm和2 140 nm作为特征波段, 最后利用多元线性回归(multiple linear regression, MLR)、偏最小乘回归(partial least squares regression, PLSR)和粒子群优化支持向量机回归(particle swarm optimization support vector machine regression, PSO-SVM)方法建立了土壤有机质含量的高光谱定量反演模型, 并对模型进行验证.3种模型的验证结果如下: MLR、PLSR和PSO-SVM模型的R2分别为0.79、0.83和0.85, RMSE分别为5.26、4.93和4.76.实验结果表明, 无论从模型的稳定性还是预测能力上, PSO-SVM都要优于其他两个模型.      

基于航空高光谱遥感数据的黑土地有机质含量反演:以黑龙江省建三江地区为例

掌握黑土地有机质含量对黑土资源利用与保护具有重要意义,而高光谱卫星影像的缺乏制约了区域尺度土壤有机质反演研究的开展.以黑龙江省建三江黑土区为例,采用CASI/SASI航空高光谱数据、ASD（analytical spectral devices）地面光谱数据和土壤样品有机质含量数据,基于有机质含量与光谱反射率的相关性和定量关系,构建最优的回归模型并开展研究区土壤有机质含量遥感反演.结果表明:偏最小二乘法回归模型比多元逐步回归模型更稳定（判定系数分别为0.885和0.653）,且精度更高（均方根误差分别为0.424和0.744）;采用偏最小二乘模型反演的结果与地面化探结果基本一致.      

基于Hyperion数据的矿区废弃土土壤恢复潜力

人类大规模地进行矿山开采造成生态环境问题, 导致土壤发生变化, 并引起矿山环境发生变化, 矿区废弃土土壤恢复有利于该地区矿山环境的改进.在野外实测光谱的基础上, 对江西德兴矿区废弃土土壤恢复潜力进行分析, 并结合Hyperion高光谱数据, 通过光谱吸收指数法反演铁离子含量、粘土矿物含量和有机质含量.结果显示, 在德兴矿区一号坝、二号坝、四号坝和铜矿的土壤有益成分相对较低.铁、粘土矿物和有机质含量高值区主要分布为有林地, 说明该区域土壤质量较好; 一号、二号、四号尾矿库、废石场及选矿厂等地区, 由于矿山的开采活动破坏了原有的土壤, 土地耕种存在一定的难度, 应采取相应的工程、生物和区域生态环境的改良等措施对土壤进行恢复治理.      

土壤有机碳含量高光谱建模研究——以青藏高原三江源区为例

土壤有机碳(SOC)是陆地生态系统碳循环的重要组成部分,也是评价区域土壤质量、土地退化程度和作物产量的重要指标。高寒生态系统土壤有机碳含量估算,对于高寒地区土壤碳库核算和土壤质量评价等都具有重要意义。本研究以青藏高原三江源区作为研究区,基于野外采集的272个土壤样本的SOC和土壤光谱室内测试数据,首先对原始光谱数据进行一阶微分(FD)、二阶微分(SD)、倒数对数(RL)、去包络线(CR)和多元散射校正(MSC)等多种数学变换;然后基于8种光谱变换数据与SOC含量的相关性分析提取特征波段,利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、支持向量机(SVM)和随机森林(RF)4种方法,分别构建SOC含量的高光谱反演模型;对各种模型的模拟精度和稳定性进行评价,明确SOC含量反演的最佳光谱变换和模型组合模式。结果表明：三江源区SOC含量较高,且不同植被类型和不同土壤类型的SOC含量差异较大;总体上,单一数学变换形式中基于一阶微分(FD)变换构建的反演模型的模拟精度最高(尤其是FD-RF组合模型,其验证集R²=0.86,RMSE=8.40,RPD=2.64);多种数...     

基于CASI & SASI航空高光谱的雄安新区西南部农田土壤 重金属镍含量反演研究

农田土壤中重金属元素富集会严重制约农作物的生长,且对人类健康造成潜在威胁。高光谱遥感数据具有极高的光谱分辨率,因而可在土壤重金属污染元素信息的定量研究中发挥重要作用。本文以雄安新区西南部及其周边农田土壤作为研究对象,在实验室测定土壤重金属元素Ni的含量,并与土壤可见-近红外高光谱数据建立土壤重金属Ni含量的定量估测模型,进一步基于CASI&SASI航空高光谱数据快速反演研究区农田土壤重金属Ni的含量,获取其分布特征。本文研究并建立了研究区土壤重金属元素基于不同光谱变换形式的多元逐步回归、偏最小二乘回归和BP神经网络统计估算模型,通过模型验证与对比,探索研究区土壤重金属Ni元素含量的最优反演模型。研究结果表明: （1）基于各光谱变换的BP神经网络模型的建模和预测精度整体上大于偏最小二乘法和多元逐步回归法模型,模型拟合精度高,预测能力较好;（2）综合来看,一阶微分处理能普遍改善模型预测效果,其中BP神经网络模型的一阶微分变换结果最佳,对于Ni元素建模精度R2高达97.1%,验证集精度R2高达98%以上;（3）选用精度最好的BP神经网络模型,通过CASI&SASI高光谱数据对研究区重金属Ni含量进行反演,反演结果与实测Ni含量数据一致性很好。     

遥感提取植物生理参数LAI/FPAR的研究进展与应用

植物生理参数LAI/FPAR是2个重要的陆地特征参量。利用遥感光谱模型并结合地面验证是提取区域尺度的LAI/FPAR最有效的途径。提取LAI/FPAR的模型主要有光谱指数模型和辐射传输模型两类,经过精确的辐射标定和大气纠正的遥感数据可以得到较高精度的LAI/FPAR数据。影响LAI/FPAR精度的因素很多,其中主要因素是像元的异质性、植被类型和物候期等。LAI/FPAR与作物产量有更直接的关系,也是大量作物生长模型的基础,利用这些参数可以实现真实的作物产量预测,特别是开展全球尺度的单产预测。     

A comparison of the predictive potential of various vegetation indices for leaf chlorophyll content

Leaf chlorophyll is an important indicator of nutritional stress, photosynthetic capacity, and growth status of plants. Therefore, it is important to accurately estimate leaf chlorophyll content over a range of spatial and temporal scales. However, traditional methods for chlorophyll measurement mainly rely on chemical analysis, which is not universally applicable to dynamic monitoring of plants in large areas because it is time consuming and requires destructive measurements. Hyperspectral remote sensing has enormous potential for accurate retrieval of plant biochemical parameters. Therefore, it has become the most popular means to retrieve chlorophyll content, by establishing empirical relationships between different vegetation indices and chlorophyll content. In recent years, many vegetation indices have been developed for the inversion of chlorophyll content. Only the vegetation indices that are less affected by the external environment are used, in order to establish a strong and robust model. Therefore, it is very important to assess the anti-disturbance ability of different vegetation indices. In this paper, a new method is proposed to quantitatively assess the anti-disturbance ability of vegetation indices. The anti-disturbance ability of several vegetation indices that are commonly used for chlorophyll estimation was evaluated using this method. We concluded that the anti-disturbance ability of VogD, TCARI/OSAVI, and Datt99 is stronger than the other vegetation indices tested. However, if a vegetation index is not sensitive to chlorophyll content, predicting chlorophyll content holds no value even though it has a strong anti-disturbance ability. Therefore, we used the slope of the best fitting function between the vegetation index and chlorophyll content (defined as d(VI)/d(Chlorophyll content)) to measure the sensitivities of different indices to chlorophyll content. Finally, we found that TCARI/OSAVI was one of the best vegetation indices to estimate chlorophyll content at the leaf level. However, we have only considered three factors to evaluate the anti-disturbance ability of different vegetation indices, which is still far from enough because the canopy reflectance is affected by many factors. Therefore, we should account for more factors in the future.     

Estimating leaf chlorophyll contents by combining multiple spectral indices with an artificial neural network

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R₅₂₅). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R² = 0.96 and RMSE = 5.80ug.cm^?2 for Cab if validated by SIM30, while R² = 0.95 and RMSE = 6.39ug.cm^?2 for SLR. NN also gives satisfactory result with R² = 0.80 and RMSE = 5.93ug.cm^?2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R² and 12.20ug.cm^?2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.     

Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest

Biophysical and biochemical plant foliage parameters play a key role in assessing vegetation health. Those plant parameters determine the spectral reflectance and transmittance properties of vegetation; therefore, hyperspectral remote sensing, particularly imaging spectroscopy, can provide estimates of leaf and canopy chemical properties. Based on the relationship between spectral response and biochemical/biophysical properties of the leaves and canopies, the PROSPECT radiative transfer model simulates the interaction of light with leaves. In this study, more than 1100 leaf samples from the Amazon forest of Ecuador were collected at several study sites, some of which are affected by petroleum pollution, and across the vertical profile of the forest. For every sample, field spectroscopy at leaf level was conducted with a spectroradiometer. The goal of this study was to assess leaf optical properties of polluted and unpolluted rainforest canopies across the vertical profile and identify vegetation stress expressed in changes of biophysical and biochemical properties of vegetation. An ANOVA followed by Holme’s multiple comparisons of means and a principal component analysis showed that photosynthetic pigments, chlorophyll and carotenoids have significantly lower levels across the vertical profile of the forest, particularly in sites affected by petroleum pollution. On the other hand, foliar water content showed significantly higher levels in the polluted site. Those findings are symptoms of vegetation stress caused by reduced photosynthetic activity and consequently decreased transpiration and water-use efficiency of the plants. Cross-comparison between SPAD-502 chlorophyll content meter index and chlorophyll content showed strong positive correlation coefficients (r = 0.71 and r ² = 0.51) which suggests that using the SPAD-502 chlorophyll index itself is sensitive enough to detect vegetation stress in a multispecies tropical forest. Therefore, the SPAD-502 can be used to assess chlorophyll content of vegetation across polluted and non-polluted sites at different canopy layers. The results presented in this paper contribute to the very limited literature on field spectroscopy and radiative transfer models applied to the vertical profile of the Amazon forest.     

干旱和复水对喀斯特石生穗枝赤齿藓水分及光合生理的影响

研究了石生穗枝赤齿藓对喀斯特环境变迁的水分及光合生理适应,为喀斯特石漠化生态环境的恢复与治理提供依据。选择贵州普定石漠化区域交织型石生穗枝赤齿藓（Erythrodontium julaceum (Schwaegr.) Par.）为材料,测定水分和光合生理等指标。结果表明:干旱胁迫下石生穗枝赤齿藓水势（Ψs）、自由水含量（Va）、组织总含水量和相对含水量（RWC）降低,束缚水（Vs）、水分饱和亏（WSD）和Vs/Va比值增大,复水后各水分生理指标均有不同程度的恢复。RWC与qN负相关,与Fv/Fm、Yield、ETR、qP、Pn呈正相关关系;叶绿素含量总体呈出先升后降再升高的趋势。轻度干旱胁迫Pn逐渐下降,重度急剧下降,光合作用受到了严重的影响;随干旱胁迫进程蒸腾速率（Tr）的变化未见显著差异。复水后各荧光参数在轻中度胁迫下能恢复到正常水平,而重度胁迫较难恢复到对照水平。喀斯特石生穗枝赤齿藓具有适应岩溶干湿交替的水分代谢和光合生理机制,是石漠化地区植被恢复与重建过程中的先锋物种。      

甘肃生态经济系统的能值分析及其可持续性评估

比较生态足迹指数、环境可持续性指数（ESI）、自然资本指数（NCI）等作为衡量生态经济系统可持续性指标的不足后，选择能值分析及其指标作为定量评价区域可持续发展状态的方法和指标。通过对甘肃1994—2004年的净能值产出率（NEYR）、能值投入率（RIR）、环境负载率（ELR）和可持续发展能值指数（ESI）等7项指标的计算及趋势分析，并与其他国家和地区比较得出：甘肃经济还处于较低水平，开放程度依然有限，能值消费和利用结构也是低层次的，近年工业化步伐虽然加快，但要实现甘肃的可持续发展，仍需重视经济发展的作用，通过进口低能值、高能量的产品而输出高能值的产品和服务，达到自组织最优化状态（能源利用高效率）和系统对外最大功（强势竞争力）；甘肃可持续发展指数（ESI）的波动下降与甘肃经济的快速增长不无关系。重视经济增长和控制其对环境系统的压力，保持可持续发展指数的平缓降低尤为重要。     

Investigating vegetation biophysical and spectral parameters for detecting light to moderate grazing effects: a case study in mixed grass prairie

Identifying effective vegetation biophysical and spectral parameters for investigating light to moderate grazing effects on grasslands improves management practices on grasslands. Using mixed grasslands as a case study, this paper compares responses of vegetation biophysical properties and spectral parameters derived from satellite images to grazing intensity, and identifies the suitable biophysical and spectral parameters to detect grazing effects in these areas. Biophysical properties including cover, canopy height and Leaf area index (LAI) were measured in three sites with different grazing managements and one benchmark site in 2008 and 2009 in Grasslands PlaceTypeNational Park and surrounding provincial pastures, Canada. Thirteen vegetation spectral indices, calculated by statistically combining different spectral information, were evaluated. The results indicate that canopy height and the ratio of photosynthetically active vegetation cover to non-photosynthetically active vegetation cover (PV/NPV) showed significant differences between ungrazed and grazed sites. All spectral vegetation indices except the canopy index (CI) show significant differences between grazing treatments. Red-Near infrared (Red-NIR) based vegetation indices, such as Modified Triangular Vegetation Index 1 (MTVI1), Soil-adjusted Vegetation Index (SAVI), are significantly correlated to the PV/NPV. Green/Mid-infrared (Green/MIR) related vegetation indices, i.e. Plant Senescence Reflectance Index (PRSI) and Normalized Canopy Index (NCI), show significant correlation with canopy height. Models based on a linear combination of MTVI1 and SAVI were developed for PV/NPV and PRSI and NCI for canopy height. Models that simulated PV/NPV and canopy height show significant correlations with grazing intensity, suggesting the feasibility of remote sensing to quantify light to moderate grazing effects in mixed grasslands.     

The relationship of vegetation and soil differentiation during the formation of black-soil-type degraded meadows in the headwater of the Qinghai-Tibetan Plateau, China

In alpine meadow ecosystems, considerable spatial heterogeneity in forb-dominant vegetation exists as a result of severe grassland degeneration; however, there is limited quantitative information on the vegetative differences between degenerated and pristine grasslands. Therefore, a field study, which seeks to identify the edaphic factors driving the variation in plant composition and distribution, was conducted in a severely degraded alpine meadow located in the Qinghai-Tibetan Plateau, NW China. Five meadows, an original meadow and four degraded meadows, were used to determine the differentiation and relationships between the vegetation and soil of degraded alpine meadows. The dominated species of these degraded meadows are Ligularia virgaurea–Artemisia gmelinii (LA), Oxytropis ochrocephala–Leontopodium nanum (OL), Aconitum pendulum–Potentilla anserina (AP) and Stellera chamaejasme–Artemisia nanschanica (SA), respectively. The results indicate that vegetation cover, grass biomass, species number and diversity indices clearly decrease from the original to the degraded meadow. Soil water, clay and nutrient content are also reduced with grassland degradation in surface and subsoil layers. The joint study of floristic and edaphic variables confirms that the soil features, especially the bulk density, sand content, pH, salinity, N and K, mainly determine the establishment of vegetation in the severely degraded fields of this study. These results may be useful for alpine grassland ecosystem restoration and management.     

Spectral data source effect on crop state estimation by vegetation indices

Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. Even though differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from spaceborne multispectral imagery and point-based field spectroscopy in application to crop state estimation. For this purpose, irrigated chickpea field was monitored by RapidEye satellite mission and additional measurements by field spectrometer were obtained. Estimated VIs average and coefficient of variation from each observation were compared with physical crop measurements: leaf water content, LAI and chlorophyll level. The results indicate that indices calculated from spaceborne spectral images regardless of the claimed response commonly react on phenology of the irrigated chickpea. This feature makes spaceborne spectral imagery an appropriate data source for monitoring crop development, crop water needs and yield prediction. VIs calculated from field spectrometer were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability (up to 69%) of the calculated values. Consequently, the high spatial variability of field spectral measurements depreciates the estimation agricultural field state by average mean only. Nevertheless, the spatial variability might have certain behavior trend, e.g., a significant increase in the active growth or stress and can be an independent feature for field state assessment.     

植物日光诱导叶绿素荧光的遥感原理及研究进展

日光诱导叶绿素荧光（Solar-Induced Fluorescence,SIF）与植被光合作用关系密切,可能成为研究植物光合作用及相关参数的新型遥感手段。总结了SIF的提取算法、遥感模型、传感器以及在植被早期胁迫探测和光能利用率估算等领域应用的最新进展,并提出了SIF遥感有待解决的关键问题。     

A comparison of forest fire indices for predicting fire risk in contrasting climates in China

The relationships between fire danger indices and fire risk have been extensively studied in many regions of the world. This work uses partial effect analysis in semiparametric logistic regression models to assess the nonlinear relationships among location, day, altitude, fire danger indices, normalized difference vegetation index (NDVI), and fire ignition from 1996 to 2008 in four different climatic regions in China. The four regions are North China (NR), Northeast China (NE), Southeast China (SE), and Southwest China (SW). The three main results are as follows: First, different fire danger indices are selected as significant variables dependent on the region. The inter-regional difference could be partially explained by difference in local weather and vegetation conditions. Second, spatial location exerts highly significant effects in all four regions. NDVI values are selected as explained variable for NR, NE, and SE on fire ignitions. On a daily scale, altitude influences fire ignition for NR, SE, and SW. Third, the robustness of the probability models used in NE, SE, and SW is better than that in NR on a daily scale. The semiparametric logistic regression model used in this study is useful for assessing the ability of fire danger indices to estimate probabilities of fire ignition on a daily scale. This study encourages further research on assessing the predictive ability of fire danger indices developed at other temporal and spatial scales in China.