基于遥感信息的作物模型重新初始化/参数化方法研究初探

作物模型与遥感信息的结合有助于利用遥感监测的大范围植被信息解决作物模型区域应用时模型初始状态和参数值难以确定的问题。该文借助叶面积指数(LAI)将经过华北冬小麦(Triticum aestivium)适应性调整的WOFOST模型与经参数调整检验的SAIL-PROSPECT模型相嵌套,利用嵌套模型模拟作物冠层的土壤调整植被指数(SAVI),在代表点上借助FSEOPT优化程序使模拟SAVI_s与MODIS遥感数据合成SAVI_m的差异达到最小,从而对WOFOST模型重新初始化。结果表明,借助于遥感信息,出苗期的重新初始化使模拟成熟期与按实际出苗期模拟的结果相差在2天以内,模拟的LAI和总干重的误差比按实际出苗期模拟结果的误差降低3~8个百分点;返青期生物量的重新初始化使模拟LAI和地上总干重在关键发育时刻的误差降至16%以内,模拟LAI和贮存器官重在整个生育期内都更加接近实测值;对返青期生物量的动态调整显示返青到抽穗期间较少次数的遥感数据即能有效地提高作物模型的模拟效果。与国外同类研究相比,该文在作物模型本地化、重新初始化变量和优化比较对象的选择上都有所不同,而利用遥感数据动态调整作物模型初始状态或参数值更具有新意。该文对区域尺度上利用遥感信息优化作物模型的研究具有基础性、探讨性意义。     

Hyperspectral remote sensing of plant pigments

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.     

Modelling Plant Responses to Elevated CO2: How Important is Leaf Area Index?

BACKGROUND AND AIMS: The problem of increasing CO(2) concentration [CO(2)] and associated climate change has generated much interest in modelling effects of [CO(2)] on plants. While variation in growth and productivity is closely related to the amount of intercepted radiation, largely determined by leaf area index (LAI), effects of elevated [CO(2)] on growth are primarily via stimulation of leaf photosynthesis. Variability in LAI depends on climatic and growing conditions including [CO(2)] concentration and can be high, as is known for agricultural crops which are specifically emphasized in this report. However, modelling photosynthesis has received much attention and photosynthesis is often represented inadequately detailed in plant productivity models. Less emphasis has been placed on the modelling of leaf area dynamics, and relationships between plant growth, elevated [CO(2)] and LAI are not well understood. This Botanical Briefing aims at clarifying the relative importance of LAI for canopy assimilation and growth in biomass under conditions of rising [CO(2)] and discusses related implications for process-based modelling. MODEL: A simulation exercise performed for a wheat crop demonstrates recent experimental findings about canopy assimilation as affected by LAI and elevation of [CO(2)]. While canopy assimilation largely increases with LAI below canopy light saturation, effects on canopy assimilation of [CO(2)] elevation are less pronounced and tend to decline as LAI increases. Results from selected model-testing studies indicate that simulation of LAI is often critical and forms an important source of uncertainty in plant productivity models, particularly under conditions of limited resource supply. CONCLUSIONS: Progress in estimating plant growth and productivity under rising [CO(2)] is unlikely to be achieved without improving the modelling of LAI. This will depend on better understanding of the processes of substrate allocation, leaf area development and senescence, and the role of LAI in controlling plant adaptation to environmental changes.     

基于多源遥感数据的大豆叶面积指数估测精度对比

近年来遥感技术的革新促使遥感源越来越丰富.为分析多源遥感数据的叶面积指数(LAI)估测精度,本文以大豆为研究对象,利用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合地面实测LAI构建经验回归模型,比较3类遥感数据(地面高光谱数据、无人机多光谱影像以及高分一号WFV影像)对大豆LAI的估测能力,并从传感器几何位置和光谱响应特性以及像元空间分辨率三方面分析讨论了3类遥感数据的LAI反演差异.结果表明： 地面高光谱数据模型和无人机多光谱数据模型都可以准确预测大豆LAI(在α=0.01显著水平下,R²均>0.69,RMSE均<0.40);地面高光谱RVI对数模型的LAI预测能力优于无人机多光谱NDVI线性模型,但两者差异不大(E_A相差0.3%,R²相差0.04,RMSE相差0.006);高分一号WFV数据模型对研究区内大豆LAI的预测效果不理想(R²<0.30,RMSE>0.70).针对星、机、地三类遥感信息源,地面高光谱数据在反演LAI方面较传统多光谱数据有优势但不突出;16 m空间分辨率的高分一号WFV影像无法满足田块尺度作物长势监测的需求;在保证获得高精度大豆LAI预测值和高工作效率的前提条件下,基于无人机遥感的农情信息获取技术不失为一种最佳试验方案.在当今可用遥感信息源越来越多的情况下,农业无人机遥感信息可成为指导田块精细尺度作物管理的重要依据,为精准农业研究提供更科学准确的信息.     

不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系

研究了不同土壤水氮条件下水稻 (Oryzasativa) 冠层光谱反射特征和植株水分状况的量化关系。结果表明, 水稻冠层近红外光谱反射率随土壤含水量的降低而降低, 短波红外光谱反射率随土壤含水量的降低而升高。相同土壤水分条件下, 高氮水稻的冠层含水率高于低氮水稻的冠层含水率 ;同一水分条件下, 高氮处理的可见光区和短波红外波段光谱反射率低于低氮处理, 近红外波段光谱反射率高于低氮处理。发现拔节后比值植被指数 (R810 /R460 ) 与水稻叶片含水率和植株含水率呈极显著的线性相关, 模型的检验误差 (RootmeansquareError, RMSE) 分别为 0.93和 1.5 0。表明比值植被指数R810 /R460 可以较好地监测不同生育期水稻叶片和植株含水率。     

暖温带地区3种森林群落叶面积指数和林冠开阔度的季节动态

以辽东栎(Quercus liaotungensis)为主的落叶阔叶林、华北落叶松(Larix principis-rupprechtii)林和油松(Pinus tabulaeformis)林是暖温带林区具有代表性的森林群落类型。该研究应用国内外流行的半球图方法,通过对这3种森林群落叶面积指数和林冠开阔度的测定和综合比较,分析了叶面积指数和林冠开阔度的季节动态,揭示了暖温带地区不同类型森林群落叶面积指数和林冠开阔度的特征。研究结果表明,落叶阔叶林(优势种为辽东栎、棘皮桦(Betula dahurica)和五角枫(Acer mono))和华北落叶松林两种落叶森林群落的叶面积指数值均随生长季的到来而呈现增长的趋势,最大值出现在8月;林冠开阔度值随着生长季的到来而下降,最大值出现在11月。落叶阔叶林的叶面积指数和林冠开阔度的季节动态较之华北落叶松林明显。油松是常绿树种,其群落叶面积指数和林冠开阔度的变化程度均不明显,但林冠开阔度的变化趋势也是与叶面积指数的变化趋势相反。通过计算得出叶面积指数和林冠开阔度相关显著,并且呈现指数回归的关系。此研究结果为以遥感途径获取暖温带地区叶面积指数提供了地面校正依据,为研究该地区植被林冠的异质性及其造成的影响,以及进一步对该地区林分、景观和区域尺度上碳、水分和通量等方面的模拟提供了基础数据。     

基于遥感与模型耦合的冬小麦生长预测

遥感的空间性、实时性与作物生长模型的过程性、机理性优势互补,将两者有效耦合已成为提高作物生长监测预测能力的重要手段之一。提出了一种基于地空遥感信息与生长模型耦合的冬小麦预测方法,该方法基于初始化/参数化策略,以不同生育时期的小麦叶面积指数(LAI)和叶片氮积累量(LNA)为信息融合点将地面光谱数据(ASD)及HJ-1 A/B CCD、Landsat-5 TM数据与冬小麦生长模型(WheatGrow)耦合,反演得到区域尺度生长模型运行时难以准确获取的部分管理措施参数(播种期、播种量和施氮量),在此基础上实现了对冬小麦生长的有效预测。实例分析结果表明,LNA较LAI对模型更敏感,以之作为耦合点的反演效果较好。另外,抽穗期是遥感信息与生长模型耦合的最佳时机,对播种期、播种量和施氮量反演的RMSE值分别达到5.32 d、14.81 kg/hm²、14.11 kg/hm²。生长模型与遥感耦合后的模拟结果很好地描述了冬小麦长势和生产力指标的时空分布状况,长势指标的模拟相对误差小于0.25,籽粒产量模拟的相对误差小于0.1。因此研究结果可为区域尺度冬小麦生长的监测预测提供重要理论依据。     

Remote sensing of heterogeneity in photosynthetic efficiency, electron transport and dissipation of excess light in Populus deltoides stands under ambient and elevated CO2 concentrations, and in a tropical forest canopy, using a new laser-induced fluorescence transient device

Determining the spatial and temporal diversity of photosynthetic processes in forest canopies presents a challenge to the evaluation of biological feedbacks needed for improvement of carbon and climate models. Limited access with portable instrumentation, especially in the outer canopy, makes remote sensing of these processes a priority in experimental ecosystem and climate change research. Here, we describe the application of a new, active, chlorophyll fluorescence measurement system for remote sensing of light use efficiency, based on analysis of laser‐induced fluorescence transients (LIFT). We used mature stands of Populus grown at ambient (380 ppm) and elevated CO₂ (1220 ppm) in the enclosed agriforests of the Biosphere 2 Laboratory (B2L) to compare parameters of photosynthetic efficiency, photosynthetic electron transport, and dissipation of excess light measured by LIFT and by standard on‐the‐leaf saturating flash methods using a commercially available pulse‐modulated chlorophyll fluorescence instrument (Mini‐PAM). We also used LIFT to observe the diel courses of these parameters in leaves of two tropical forest dominants, Inga and Pterocarpus, growing in the enclosed model tropical forest of B2L. Midcanopy leaves of both trees showed the expected relationships among chlorophyll fluorescence‐derived photosynthetic parameters in response to sun exposure, but, unusually, both displayed an afternoon increase in nonphotochemical quenching in the shade, which was ascribed to reversible inhibition of photosynthesis at high leaf temperatures in the enclosed canopy. Inga generally showed higher rates of photosynthetic electron transport, but greater afternoon reduction in photosynthetic efficiency. The potential for estimation of the contribution of outer canopy photosynthesis to forest CO₂ assimilation, and assessment of its response to environmental stress using remote sensing devices such as LIFT, is briefly discussed.     

一种估测小麦冠层氮含量的新高光谱指数

提出了一种估测小麦冠层氮含量的新高光谱指数--微分归一化氮指数(FD-NDNI)。以FieldSpec Pro FR地物光谱仪采集拔节后至孕穗前小麦的冠层光谱190份,随机抽取142份作为训练集,其余48份作为预测集。将光谱以小波阈值去噪法去噪后,利用其525、570 与730 nm处的一阶导数值,采用差值、比值以及归一化的方法构建了12种光谱指数以实现小麦冠层氮含量的估测,并与mNDVI705、mSR以及NDVI705等22种常用指数进行了比较分析。发现指数FD-NDNI对小麦冠层氮含量的估测结果最佳,其估测模型(指数形式)校正集决定系数(C-R²)与预测集决定系数(P-R²)分别达0.818与0.811,优于mNDVI705等常用指数。进一步分析表明,在各指数中,FD-NDNI对叶面积系数最不敏感,可最有效地避免冠层郁闭度等因素对氮含量估测的影响。为优化结果,采用最小二乘支持向量回归算法(LS-SVR)对模型进行了改进,当模型惩罚系数C与RBF核函数参数g取得最优解6.4与1.6时,其C-R²与P-R²分别提高至0.846与0.838,具有比指数模型更高的精度。结果表明:FD-NDNI是小麦冠层氮含量估测的优选指数,LS-SVR为建模的优选算法。     

Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review

As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (T(s)) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between T(s) and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. T(s) increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on T(s); at the vegetation level, T(s) is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.     

于星载通道光谱指数与小麦冠层叶片氮素营养指标的定量关系

利用空间遥感信息大面积监测小麦冠层氮素营养状况和生产力指标具有重要意义和应用前景.本研究基于不同施氮水平下小麦冠层反射光谱信息,利用响应函数模拟基于不同卫星通道构建的光谱指数(包括单波段、比值光谱指数和归一化光谱指数),分析基于星载通道的光谱指数与小麦冠层叶片氮素营养指标的定量关系,确定监测小麦冠层叶片氮素营养的较好卫星传感器和光谱波段,建立小麦冠层氮素营养指标监测方程.结果表明：利用NDVI(MSS₇, MSS₅)、NDVI(RBV₃, RBV₂)、TM₄、CH₂、MODIS₁和MODIS₂遥感数据可以预估小麦叶片氮含量(LNC),其决定系数（R₂）在0.60以上;应用NDVI(PB₄, PB₂)、NDVI(CH₂, CH₁)、NDVI(MSS₇, MSS₅)、RVI(MSS₇, MSS₅)、MODIS₁和MODIS₂可以预测小麦叶片氮积累量(LNA),其R₂大于0.86.比较而言,NDVI(MSS₇, MSS₅)和NDVI(PB₄, PB₂)分别为预测小麦LNC和LNA的适宜星载通道光谱参数.     

Ground and remote sensing-based measurements of leaf area index in a transitional forest and seasonal flooded forest in Brazil

Leaf area index (LAI) is a key driver of forest productivity and evapotranspiration; however, it is a difficult and labor-intensive variable to measure, making its measurement impractical for large-scale and long-term studies of tropical forest structure and function. In contrast, satellite estimates of LAI have shown promise for large-scale and long-term studies, but their performance has been equivocal and the biases are not well known. We measured total, overstory, and understory LAI of an Amazon-savanna transitional forest (ASTF) over 3 years and a seasonal flooded forest (SFF) during 4 years using a light extinction method and two remote sensing methods (LAI MODIS product and the Landsat-METRIC method), with the objectives of (1) evaluating the performance of the remote sensing methods, and (2) understanding how total, overstory and understory LAI interact with micrometeorological variables. Total, overstory and understory LAI differed between both sites, with ASTF having higher LAI values than SFF, but neither site exhibited year-to-year variation in LAI despite large differences in meteorological variables. LAI values at the two sites have different patterns of correlation with micrometeorological variables. ASTF exhibited smaller seasonal variations in LAI than SFF. In contrast, SFF exhibited small changes in total LAI; however, dry season declines in overstory LAI were counteracted by understory increases in LAI. MODIS LAI correlated weakly to total LAI for SFF but not for ASTF, while METRIC LAI had no correlation to total LAI. However, MODIS LAI correlated strongly with overstory LAI for both sites, but had no correlation with understory LAI. Furthermore, LAI estimates based on canopy light extinction were correlated positively with seasonal variations in rainfall and soil water content and negatively with vapor pressure deficit and solar radiation; however, in some cases satellite-derived estimates of LAI exhibited no correlation with climate variables (METRIC LAI or MODIS LAI for ASTF). These data indicate that the satellite-derived estimates of LAI are insensitive to the understory variations in LAI that occur in many seasonal tropical forests and the micrometeorological variables that control seasonal variations in leaf phenology. While more ground-based measurements are needed to adequately quantify the performance of these satellite-based LAI products, our data indicate that their output must be interpreted with caution in seasonal tropical forests.     

祁连山区青海云杉林冠层叶面积指数的反演方法

叶面积指数(Leaf area index, LAI)是陆地生态系统的一个十分重要的结构参数。随着空间精细化模型的发展和基于过程的分布式模拟技术的应用, 对LAI的区域估算显得越来越重要, 但目前尚缺乏有效的估算手段。该项研究以青海云杉(Picea crassifolia)林为研究对象, 利用LAI-2000冠层分析仪、鱼眼镜头法和经验公式法对林冠层LAI进行了测定, 观测值分别为1.03~3.70、0.48~2.26和2.27~8.20, 显然, 仪器测定值偏低。针对针叶的集聚效应导致仪器测定值偏低的现象, 利用跟踪辐射与冠层结构测量仪(TRAC)测定的青海云杉林聚集系数计算调整系数, 对鱼眼镜头法获取的LAI值进行订正。根据高分辨率的遥感数据反演青海云杉林的植被指数与LAI的关系, 最后获得了较合理的该地区林冠层LAI的空间分布图。     

Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity

Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m²/m² and biases of 0.42–0.55 m²/m² for all three simulated datasets and an RMSE of 1.22 m²/m² with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties.     

Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest,India

Leaf area index (LAI) and light extinction coefficient (k) are the key structural parameters controlling many canopy functions like radiation and water interception, radiation extinction, water and gas exchange. The present study aims at developing predictive models for generating spatial distribution of LAI and k by integrating remote sensing imagery and field data. The study was carried out in a tropical moist deciduous forest of Uttarakhand, India. Various spectral variables were derived from Landsat 8 Operational Land Imager (OLI) data of 8 May 2017 to predict LAI and k. In-situ measurements of LAI, incident Photosynthetically Active Radiation (PAR) above canopy (I_o) and below canopy (I) were taken using CI-110 Plant Canopy Imager. Canopy gap fraction and k (using Beer-Lambert's equation) were calculated. Random Forest (RF) algorithm was used to predict the spatial distribution of LAI and k using the best predictor variables. The best predictor variables for LAI included band 6 (Short wave infra-red (SWIR) -1) and band 7 (SWIR-2), tasseled cap wetness, Moisture Stress Index (MSI), and Normalized Difference Moisture Index (NDMI). For prediction of k, the best predictor variables were band 6 (SWIR-1) and band 7 (SWIR-2), NDMI, tasseled cap wetness, MSI and Normalized Difference Vegetation Index (NDVI). These variables were selected to generate RF-based models to predict LAI and k. On validation, the models were able to predict LAI with R² = 0.79 and % RMSE = 14.25% and k with R² = 0.77 and % RMSE = 11.98%. The predicted LAI and k followed an inverse relation in accordance with the Beer Lambert's Law. The results showed that RF can be effectively applied to predict the spatial distribution of LAI and k.     

植被叶面积指数遥感监测模型

叶面积指数是植被定量遥感的重要参数,区域的时序列叶面积指数揭示了区域生态的演化过程,反演方法上主要是通过植被指数建立相关模型实现的,对于不同地区或不同气候带而言,模型的通用性以及各种植被指数在模型中的灵敏度都需做进一步的探讨。以江苏省宜兴市作为研究区,采用2002年8月22日获得的Landsat-5TM图像数据和2003年8月23～26日采用LAI-2000进行的野外实测植被叶面积指数(LAI)数据,分别探讨了植被指数(VI)与LAI的一元、多元线性回归模型和非线性回归模型,其中的非线性回归模型包括对数、指数、乘幂和多项式回归模型。结果表明,VI与LAI之间的最佳回归模型为多元线性回归模型,R2达0.864;采用逐步选择剔除法,遴选出了用于回归模型的植被指数为RVI、PVI、SAVIL=0.35、MSAVI、ARVIγ=1、ARVIγ=0.5和SARVI。经模型LAI=-ln((VI-VI∞)/(VIg-VI∞))/KVI检验,预测值(y)与实测值(x)的拟合度较好y=0.5345x 1.3304,R2为0.7379。RVI与LAI的三次多项式回归模型也较好,R2为0.7806。再次为RVI与LAI的一元线性回归模型,R2为0.7726,比值植被指数RVI在反演叶面积指数模型中具有较高的灵敏度。     

A Generic Equation for Nitrogen-limited Leaf Area Index and its Application in Crop Growth Models for Predicting Leaf Senescence

Appropriate quantification of leaf area index (LAI) is importantfor accurate prediction of photosynthetic productivity by cropgrowth models. Estimation of LAI requires accurate modellingof leaf senescence. Many models use empirical turnover coefficients,the relative leaf-death rate determined from frequent fieldsamplings, to describe senescence during growth. In this paper,we first derive a generic equation for nitrogen-determined photosyntheticallyactive LAI (LAI_N), and then describe a method of using thisequation in crop growth models to predict leaf senescence. Basedon the theory that leaf-nitrogen at different horizons of acanopy declines exponentially, LAI_N, which is counted from thetop of the canopy to the depth at which leaf-nitrogen equalsthe minimum value for leaf photosynthesis, is calculated analyticallyas a function of canopy leaf-nitrogen content. At each time-stepof crop growth modelling, LAI_Nis compared to an independentcalculation of the non-nitrogen-limited LAI assuming no leafdeath during that time-step (LAI_NLD). In early stages, LAI_Nishigher than LAI_NLD; but with the advancement of crop growth,LAI_Nwill become smaller than LAI_NLD. The difference betweenLAI_NLDand LAI_N, whenever LAI_Nis smaller than LAI_NLD, gives theestimate of leaf area senesced at the time-step; the senescedleaf area divided by specific leaf area (SLA) gives the estimateof senesced leaf mass. The method was incorporated into twocrop models and the models adequately accounted for the LAIobserved in field experiments for rice and barley. The novelfeatures of the approach are that: (1) it suggests a coherent,biologically reasonable picture of leaf senescence based onthe link with photosynthesis and leaf nitrogen content; (2)it avoids the use of empirical leaf-turnover coefficients; (3)it avoids over-sensitivity of LAI prediction to SLA; and (4)it is presumably of sufficient generality as to be applicableto plant types other than crops. The method can be applied tomodels where leaf-nitrogen is used as an input variable or issimulated explicitly. Copyright 2000 Annals of Botany Company Leaf area index, leaf senescence, canopy nitrogen, modelling     

Detecting successional changes in tropical forest structure using GatorEye drone-borne lidar

Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables’ relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven second-growth and two old-growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly correlated with the canopy Shannon index. Canopy height, LAI, and AGB did not show a clear pattern as a function of forest age. However, gap fraction and spatial heterogeneity increased with forest age, whereas understory LAI decreased with forest age. Canopy height was strongly correlated with AGB. The heterogeneous mosaic created by successional forest patches across human-managed tropical landscapes can now be better characterized. Drone-lidar systems offer the opportunity to improve assessment of forest recovery and develop general mechanistic carbon sequestration models that can be rapidly deployed to specific sites, an essential step for monitoring progress within the UN Decade on Ecosystem Restoration.     

利用遥感光谱法进行农田土壤水分遥感动态监测

自 1 997年 4月至 1 998年 1 0月 ,在甘肃省定西县进行了大面积 0～ 5 0 cm土层农田土壤水分按每 1 5 d本底资料实际观测 ,对此间收到的 5幅 TM与 7幅 NOAA卫片数据资料进行了加工处理 ,并对地面光谱资料也进行了观测。在光谱反演与光谱和土壤水分相关性分析基础上 ,利用遥感技术和地理信息系统 ,初步建立了典型试验区 ( 3× 3km2 )遥感信息与土壤含水量之间的遥感光谱相关监测模型 ,做出了观测区土壤水分含量分布图和得到了大面积农田土壤水分宏观动态监测结果 ,并同地面实测土壤水分进行了精度校正。研究结果表明 ,文中提出的“光学植被盖度”概念 ,对土壤水分遥感监测研究是有益的 ,利用遥感光谱法和数学统计方法求出了有关物理参数 ,初步建立了 TM与 NOAA光谱水分监测模型 ,其模型监测 0～2 0 cm土层含水量的精度达到 90 %以上 ,实际监测土壤水分精度达到 72 .3% ;在遥感监测 2 0～ 5 0 cm土层土壤含水量中 ,利用遥感模型监测土壤水分精度达到 80 %以上 ,实际遥感监测精度达到 60 %左右 ,其结果可有效指导干旱半干旱雨养农业区春耕时间和动态监测大面积土壤墒情 ,可为农业生产提供科学依据。另外 ,经地面大量观测表明 ,一般来说 ,当土壤含水量为田间最大持水量的 5 5 %～ 85 %时 ,从生长状况和经济     

帽儿山地区森林冠层叶面积指数的地面观测与遥感反演

叶面积指数(leaf area index,LAI)是陆地生态系统最重要的结构参数之一,遥感和基于冠层孔隙率模型的光学仪器观测是快速获取LAI的有效方法,但由于植被叶片的聚集效应,这些方法通常只能获取有效叶面积指数(effective LAI,LAIe).本文以东北林业大学帽儿山实验林场为研究区,利用LAI2000观测森林冠层LAIe,并结合TRAC观测的叶片聚集度系数估算了森林冠层LAI,并通过分析基于Landsat5-TM数据计算的不同植被指数与LAIe之间的关系,建立了该区森林LAI遥感估算模型.结果表明:研究区阔叶林的LAI和LAIe基本相当,而针叶林的LAI比LAIe大27%;减化比值植被指数(reduced simple ratio,RSR)与该区LAIe的相关性最好(R2=0.763,n=23),最适合该区LAI的遥感提取.当海拔<400 m时,LAI随海拔高度的上升而快速增大;当海拔在400～750 m时,LAI随海拔高度的上升缓慢增大;当海拔>750 m时,LAI呈下降趋势.研究区森林冠层LAI与森林地上生物量存在显著的正相关关系.