Estimation of big sagebrush leaf area index with terrestrial laser scanning

Accurate monitoring and quantification of the structure and function of semiarid ecosystems is necessary to improve carbon and water flux models that help describe how these systems will respond in the future. The leaf area index (LAI, m² m⁻²) is an important indicator of energy, water, and carbon exchange between vegetation and the atmosphere. Remote sensing techniques are frequently used to estimate LAI, and can provide users with scalable measurements of vegetation structure and function. We tested terrestrial laser scanning (TLS) techniques to estimate LAI using structural variables such as height, canopy cover, and volume for 42 Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis Beetle & Young) shrubs across three study sites in the Snake River Plain, Idaho, USA. The TLS-derived variables were regressed against sagebrush LAI estimates calculated using specific leaf area measurements, and compared with point-intercept sampling, a field method of estimating LAI. Canopy cover estimated with the TLS data proved to be a good predictor of LAI (r² = 0.73). Similarly, a convex hull approach to estimate volume of the shrubs from the TLS data also strongly predicted LAI (r² = 0.76), and compared favorably to point-intercept sampling (r² = 0.78), a field-based method used in rangelands. These results, coupled with the relative ease-of-use of TLS, suggest that TLS is a promising tool for measuring LAI at the shrub-level. Further work should examine the structural measures in other similar shrublands that are relevant for upscaling LAI to the plot-level (i.e., hectare) using data from TLS and/or airborne laser scanning and to regional levels using satellite-based remote sensing.     

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.     

Utilizing in situ directional hyperspectral measurements to validate bio-indicator simulations for a corn crop canopy

Two radiative transfer canopy models, SAIL and the two-layer Markov-Chain Canopy Reflectance Model (MCRM), were coupled with in situ leaf optical properties to simulate canopy-level spectral band ratio vegetation indices with the focus on the photochemical reflectance index in a cornfield. In situ hyperspectral measurements were made at both leaf and canopy levels. Leaf optical properties were obtained from both sunlit and shaded leaves. Canopy reflectance was acquired for eight different relative azimuth angles (ψ) at three different view zenith angles (θ_v), and later used to validate model outputs. Field observations of PRI for sunlit leaves exhibited lower values than shaded leaves, indicating higher light stress. Canopy PRI expressed obvious sensitivity to viewing geometry, as a function of both θ_v and ψ. Overall, simulations from MCRM exhibited better agreements with in situ values than SAIL. When using only sunlit leaves as input, the MCRM-simulated PRI values showed satisfactory correlation and RMSE, as compared to in situ values. However, the performance of the MCRM model was significantly improved after defining a lower canopy layer comprised of shaded leaves beneath the upper sunlit leaf layer. Four other widely used band ratio vegetation indices were also studied and compared with the PRI results. MCRM simulations were able to generate satisfactory simulations for these other four indices when using only sunlit leaves as input; but unlike PRI, adding shaded leaves did not improve the performance of MCRM. These results support the hypothesis that the PRI is sensitive to physiological dynamics while the others detect static factors related to canopy structure. Sensitivity analysis was performed on MCRM in order to better understand the effects of structure related parameters on the PRI simulations. LAI showed the most significant impact on MCRM-simulated PRI among the parameters studied. This research shows the importance of hyperspectral and narrow band sensor studies, and especially the necessity of including the green wavelengths (e.g., 531 nm) on satellites proposing to monitor carbon dynamics of terrestrial ecosystems.     

Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance

A collection of 368 advanced lines and cultivars of spring wheat（Triticum aestivum L.） from Chile, Uruguay, and CIMMYT（Centro Internacional de Mejoramiento de Maíz y Trigo）, with good agronomic characteristics were evaluated under the Mediterranean conditions of central Chile. Three different water regimes were assayed： severe water stress（SWS, rain fed）, mild water stress（MWS; one irrigation around booting）, and full irrigation（FI; four irrigations： at tillering,flag leaf appearance, heading, and middle grain filling）. Traits evaluated were grain yield（GY）, agronomical yield components,days from sowing to heading, carbon isotope discrimination（△^13C） in kernels, and canopy spectral reflectance. Correlation analyses were performed for 70 spectral reflectance indices（SRI） and the other traits evaluated in the three trials. GY and △^13C were the traits best correlated with SRI, particularly when these indices were measured during grain filling. However,only GY could be predicted using a single regression, with ResearchNormalized Difference Moisture Index（NDMI2： 2,200; 1,100）having the best fit to the data for the three trials. For △^13C, only individual regressions could be forecast under FI（r^2： 0.25–0.37）and MWS（r^2： 0.45–0.59） but not under SWS（r^2： 0.03–0.09）.NIR‐based SRI proved to be better predictors than those that combine visible and NIR wavelengths.     

利用数字图像估测棉花叶面积指数

叶面积指数是指示棉花长势、产量形成和高产群体调控等信息的重要结构特征参数。本研究的目的在于利用基于冠层图像光照叶片和光照土壤分量的图像透光率估测棉花叶面积指数。通过3年不同种植密度、品种、施氮量和灌水量的田间试验,在棉花不同的生育期用数码相机、LAI-2000冠层仪和线性光量子传感器采集数据并进行破坏性取样测定,分析图像透光率的有效性和建立LAI估测模型,进而对图像方法、LAI-2000和破坏性取样方法进行比较和分析。结果表明：(1)在太阳高度角最大且变化最小的正午时段,数码相机测量的图像透光率与线性光量子传感器测量的冠层透光率较一致且相对稳定。(2)图像透光率能反映除吐絮期以外各时期的冠层透光状况,但是当LAI大于5时图像透光率出现饱和。(3)综合分析2009和2010年数据,建立了图像透光率估测LAI的模型(R2=0.8438, SE=0.5605);利用2007年独立试验资料检验估测模型的性能,模型检验的拟合度较高(R2=0.8767)且预测误差较小(RMSE=0.4305),当LAI＞5时模型的预测能力降低。(4)数字图像、LAI-2000和破坏性取样三种方法测量的LAI值之间均呈现显著的线性相关(R2＞0.85),但是图像透光率的饱和性致使当LAI＞5时明显低估叶面积指数。     

Estimation of leaf area index and canopy openness in broad-leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography

We estimated leaf area index (LAI) and canopy openness of broad-leaved forest using discrete return and small-footprint airborne laser scanner (ALS) data. We tested four ALS variables, including two newly proposed ones, using three echo types (first, last, and only) and three classes (ground, vegetation, and upper vegetation), and compared the accuracy by means of correlation and regression analysis with seven conventional vegetation indices derived from simultaneously acquired high-resolution near-infrared digital photographs. Among the ALS variables, the ratio of the “only-and-ground” pulse to “only” pulse (OGF) was the best estimator of both LAI (adjusted R ² = 0.797) and canopy openness (adjusted R ² = 0.832), followed by the ratio of the pulses that reached the ground to projected lasers (GF). Among the vegetation indices, the normalized differential vegetation index (NDVI) was the best estimator of both LAI (adjusted R ² = 0.791) and canopy openness (adjusted R ² = 0.764). Resampling analysis on ALS data to examine whether the estimation of LAI and canopy openness was possible with lower point densities revealed that GF maintained a high adjusted R ² until a fairly low density of about 0.226 points/m², while OGF performed marginally when the point density was reduced to about 1 point/m², the standard density of high-density products on the market as of February 2008. Consequently, the ALS variables proposed in the present study, GF and OGF, seemed to have great potential to estimate LAI and canopy openness of broad-leaved forest, with accuracy comparable to NDVI, from high-resolution near-infrared imagery.     

Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress

Maize is one of the most widespread grain crops in the world; however, more than 70% of corn in China suffers some degree of drought disaster every year. Leaf area index (LAI) is an important biophysical parameter of the vegetation canopy and has important significance for crop yield estimation. Using the data of canopy spectral reflectance and leaf area index (LAI) for maize plants experiencing different levels of soil moisture from 2011 to 2012, the characteristics of the canopy reflective spectrum and its first derivative, and their relationships to leaf area index, were analyzed. Soil moisture of the control group was about 75% while that of the drought stress treatment was about 45%. In addition, LAI retrieval models for maize were established using vegetation indices (VIs) and principal component analysis (PCA) and the models were tested using independent datasets representing different soil water contents and different developmental stages of maize. The results showed that canopy spectral reflectances were in accordance with the characteristics of green plants, under both drought stress and at different developmental stages. In the visible band, canopy reflectance for both healthy and damaged vegetation had a green-wavelength peak and a red-wavelength valley; reflectance under drought stress, especially in the green peak (about 550 nm) and the red valley (about 676 nm) was higher than in the control group. In the near-infrared band, the canopy spectral reflectance decreased substantially between 780 and 1350 nm under drought stress. Moreover, the red edge of the spectrum was shifted toward blue wavelengths. The first derivative spectrum showed a double peak phenomenon at the edge of the red band at different developmental stages: the main peak appeared between 728 and 732 nm and the minor peak at about 718 nm. The double peaks become more obvious through the growth and development of the maize, with the most notable effect during the silking and milk stages, after which it gradually decreased. During maize growth, the LAI of all plants, regardless of soil moisture conditions, increased, and the largest LAI also occurred during the silking and milk stages. During those stages, the LAI of plants under different drought stress levels was significantly lower (by 20% or more) than in normal plants with sufficient water supplies. The LAI was highly significantly correlated with canopy spectral reflectance in the bands from 350 nm to 510 nm, from 571 nm to 716 nm, and from 1450 nm to 1575 nm. Also, the LAI was significantly correlated with red edge parameters and several VIs. The Perpendicular Vegetation Index (PVI) had the best correlation with LAI, with a coefficient of determination (R²) of 0.726 for the exponential correlation. Using dependent data, a LAI monitoring model for the maize canopy was constructed using PCA and VI methods. The test results showed that both the VI and PCA methods of monitoring maize LAI could provide robust estimates, with the predicted values of LAI being significantly correlated with the measured values. The model based on PVI showed higher precision under the drought stresses, with a correlation coefficient of 0.893 (n = 27), while the model based on PCA was more precise under conditions of adequate soil moisture, with a correlation coefficient of 0.877 (n = 32). Therefore, a synthesis of the models based on both VI and PCA could be more reliable for precisely predicting LAI under different levels of drought stresses in maize.     

Negative relationship between photosynthesis and late-stage canopy development and senescence over Tibetan Plateau

Canopy greening, which is associated with significant canopy structure changes, is the most notable signal of ecosystem changes in response to anthropogenic climate change. However, our knowledge of the changing pattern of canopy development and senescence, and its endogenous and climatic drivers is still limited. Here, we used the Normalized Difference Vegetation Index (NDVI) to quantify the changes in the speed of canopy development and senescence over the Tibetan Plateau (TP) during 2000–2018, and used a solar-induced chlorophyll fluorescence dataset as a proxy for photosynthesis, in combination with climate datasets to decipher the endogenous and climatic drivers of the interannual variation in canopy changes. We found that the canopy development during the early green-up stage (April–May) is accelerating at a rate of 0.45–0.8 × 10⁻³ month⁻¹ year⁻¹. However, this accelerating canopy development was largely offset by a decelerating canopy development during June and July (−0.61 to −0.51 × 10⁻³ month⁻¹ year⁻¹), leading to the peak NDVI over the TP increasing at a rate of only one fifth of that in northern temperate regions, and less than one tenth of that in the Arctic and boreal regions. During the green-down period, we observed a significant accelerating canopy senescence during October. Photosynthesis was found to be the dominant driver for canopy changes over the TP. Increasing photosynthesis stimulates canopy development during the early green-up stage. However, slower canopy development and accelerated senescence was found with larger photosynthesis in late growth stages. This negative relationship between photosynthesis and canopy development is probably linked to the source–sink balance of plants and shifts in the allocation regime. These results suggest a sink limitation for plant growth over the TP. The impact of canopy greening on the carbon cycle may be more complicated than the source-oriented paradigm used in current ecosystem models.     

三种回归分析方法在Hyperion影像LAI反演中的比较

借助GPS进行地面精确定位,利用LAI-2000冠层分析仅在攸县黄丰桥林场开展130个样地(60m×60m)的叶面积指数(Leaf Area Index,LAI)测量.采用FLAASH模块对Hyperion数据进行大气校正并与地面同步冠层观测数据进行拟合,通过研究地面实测LAI与Hyperion影像波段及其衍生的系列植被指数(NDVI、RVI等)的相关性,筛选出估算叶面积指数的植被指数因子.应用曲线估计、逐步回归及偏最小二乘三种回归分析技术分别建立叶面积指数的最优估算模型.结果表明:参与建模的因子中,比值植被指数(RVI)与LAI的相关性最大,敏感性最高,其次是SARVI0.1,NDVI705,NDVI,SARVI0.1,SARVI0.25;曲线估计、逐步回归分析和偏最小二乘回归三种分析方法所建的6个回归模型中,偏最小二乘回归的拟合效果最好,预测值与实测值的决定系数R2为0.84、曲线估计的拟合效果最低,预测值与实测值的决定系数R2为0.64;建模精度分析表明,选用5-6个自变量因子进行LAI建模是可靠的,以6个植被因子建立的偏最小二乘回归模型预测精度最高.     

Differential ecophysiological response of deciduous shrubs and a graminoid to long-term experimental snow reductions and additions in moist acidic tundra, Northern Alaska

Changes in winter precipitation that include both decreases and increases in winter snow are underway across the Arctic. In this study, we used a 14-year experiment that has increased and decreased winter snow in the moist acidic tussock tundra of northern Alaska to understand impacts of variation in winter snow depth on summer leaf-level ecophysiology of two deciduous shrubs and a graminoid species, including: instantaneous rates of leaf gas exchange, and δ¹³C, δ¹⁵N, and nitrogen (N) concentrations of Betula nana, Salix pulchra, and Eriophorum vaginatum. Leaf-level measurements were complemented by measurements of canopy leaf area index (LAI) and depth of thaw. Reductions in snow lowered summer leaf photosynthesis, conductance, and transpiration rates by up to 40 % compared to ambient and deep snow conditions for Eriophorum vaginatum, and reduced Salix pulchra conductance and transpiration by up to 49 %. In contrast, Betula nana exhibited no changes in leaf gas exchange in response to lower or deeper snow. Canopy LAI increased with added snow, while reduced winter snow resulted in lower growing season soil temperatures and reduced thaw depths. Our findings indicate that the spatial and temporal variability of future snow depth will have individualistic consequences for leaf-level C fixation and water flux by tundra species, and that these responses will be manifested over the longer term by changes in canopy traits, depth of thaw, soil C and N processes, and trace gas (CO₂ and H₂O) exchanges between the tundra and the atmosphere.     

华北落叶松人工林蒸散及产流对叶面积指数变化的响应

定量评价林地蒸散和产流等水文过程对冠层叶面积指数(LAI)的响应,对于深入认识森林植被的生态水文过程及其发生机制,实现半干旱区林水综合管理和区域可持续发展是非常必要的。应用集总式生态水文模型BROOK90,模拟分析了不同降水年型(丰水年、平水年、枯水年)下,位于半干旱区的宁夏六盘山叠叠沟小流域内华北落叶松(Larix principis-rupprechtii)人工林的水文过程对冠层LAI变化的响应关系。结果发现:林分总蒸散量、冠层截留量、蒸腾量与LAI都呈显著的正相关关系(R~20.99,P0.01),而土壤蒸发量、产流量则与LAI均呈显著的负相关关系(R~20.99,P0.01);在不同的降水年型下,各水文过程变量与LAI的关系都可以很好地用指数函数来表达,且都存在着一个LAI阈值。当LAI低于阈值时,各水文过程变量随LAI的变化幅度较大;但高于阈值时,各变量的变化十分缓慢并趋于稳定。在不同降水年型下,各变量LAI阈值之间存在着一定的差异。一般地,丰水年各变量的LAI阈值要大于枯水年,尤其是冠层截留和土壤蒸发。在丰水年,各水文过程变量随LAI增加而变化的速率要比在平水年、枯水年更快,说明在水分充足年份中各变量的波动更多取决于LAI变化,而在水分亏缺的年份中则可能更多地受到水分条件的限制。模拟结果表明,通过减少冠层LAI(如间伐)导致的林分的降低蒸散耗水和增加产流的作用是有限的,这是由于林分蒸散降低的幅度要比LAI降低的幅度小。例如,在平水年,当LAI从4.2变为2.0(减少幅度52.4%)时,林分年蒸散仅从357.2 mm减少至333.9 mm(减少幅度6.5%)。     

Estimating seasonal aboveground biomass of a riparian pioneer plant community: An exploratory analysis by canopy structural data

The aboveground biomass (AGB) of vegetation is of central importance for ecosystem services by providing a measure of productivity. Models have been developed for estimating AGB via canopy structural variables in both fundamental and applied ecological studies. However, the potential of canopy structural variables for describing AGB dynamics throughout a growing season are still unclear. This study focuses on the AGB seasonal dynamics of a pioneer community, Cynodon dactylon (L.) Pers. (Bermuda grass), in a newly-formed riparian habitat at China’s Three Gorges Reservoir. The objectives are (1) to determine the most important structural variable for estimating AGB at different growing stages during the season, and (2) to develop a model that can estimate AGB at the different growing stages and using multiple structural variables. We sampled the C. dactylon community six times during the growing season from May to September 2016. Six variables were engaged in the analysis, including five canopy structural variables, i.e., canopy height (H), canopy cover (CC), leaf area index (LAI), the volume related variables V_LAI (H × LAI) and V_CC (H × CC), and one seasonal growth effect variable (SV). We conducted univariate linear regression analysis to determine the most important estimator of AGB and the best subset regression analysis were used to develop the AGB estimation model. The detected most important AGB estimator changed with different growing stages throughout a season. Canopy structural characteristics of the community are key factors for determining such changes. Cover was the most important variable for AGB estimation during the early growing season and V_LAI was the most important variable in the mid and end of the growing season. The developed best multivariate models explained an additional 11% in AGB variance on average for the different growing stages compared with the univariate models using the most important estimators. SV was found to be useful in developing an acceptance general AGB estimation model appropriate for the entire growing season. The findings of this study are expected to provide knowledge for guiding sampling work and to assist with modeling AGB and understanding the AGB seasonal dynamics in the future.     

林下植被对遥感估算马尾松LAI的影响

叶面积指数是一项极其重要的描述植被冠层结构的植被特征参量。根据植被物候规律,利用中国环境卫星CCD多光谱影像和野外马尾松样区调查数据,通过建立不同季节和不同郁闭度样区马尾松LAI和影像NDVI经验回归模型,并利用一个新的LAI观测方式定量比较乔木层LAI和生态系统总LAI(包括草本层、灌木层和乔木层)的差异,研究林下植被对马尾松反演的影响程度。结果表明:(1)由于林下植被的物候变化,冬季林下植被对马尾松LAI反演影响最小,马尾松NDVI和LAI线性关系R2维持在0.65;夏季林下植被影响最大,线性关系R2只有0.25;春季和秋季影响居中,NDVI和LAI线性关系R2在0.47附近。但是,受林下植被影响较小的A类样区4个季节内NDVI和LAI线性关系基本都在0.60以上(夏季略低于0.60);(2)乔木层LAI和总LAI差距非常大,最大差距达到2.93,相差的比例最大达到了2.45倍;(3)总LAI和NDVI相关关系显著,其中线性关系R2达到0.66,对数关系R2可达到0.68,而乔木层LAI和NDVI相关关系较差,线性关系R2只有0.30。分别建立冬季和其它季节实测总LAI和NDVI的关系,可以估算出林下植被对马尾松LAI反演的影响程度。     

Development of equations to predict the influence of floor space on average daily gain,average daily feed intake and gain : feed ratio of finishing pigs

Floor space allowance for pigs has substantial effects on pig growth and welfare. Data from 30 papers examining the influence of floor space allowance on the growth of finishing pigs was used in a meta-analysis to develop alternative prediction equations for average daily gain (ADG), average daily feed intake (ADFI) and gain : feed ratio (G : F). Treatment means were compiled in a database that contained 30 papers for ADG and 28 papers for ADFI and G : F. The predictor variables evaluated were floor space (m²/pig), k (floor space/final BW^0.67), Initial BW, Final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type and study length (d). Multivariable general linear mixed model regression equations were used. Floor space treatments within each experiment were the observational and experimental unit. The optimum equations to predict ADG, ADFI and G : F were: ADG, g=337.57+(16 468×k)−(237 350×k²)−(3.1209×initial BW (kg))+(2.569×final BW (kg))+(71.6918×k×initial BW (kg)); ADFI, g=833.41+(24 785×k)−(388 998×k²)−(3.0027×initial BW (kg))+(11.246×final BW (kg))+(187.61×k×initial BW (kg)); G : F=predicted ADG/predicted ADFI. Overall, the meta-analysis indicates that BW is an important predictor of ADG and ADFI even after computing the constant coefficient k, which utilizes final BW in its calculation. This suggests including initial and final BW improves the prediction over using k as a predictor alone. In addition, the analysis also indicated that G : F of finishing pigs is influenced by floor space allowance, whereas individual studies have concluded variable results.     

Environmental Limits of Tall Shrubs in Alaska’s Arctic National Parks

We sampled shrub canopy volume (height times area) and environmental factors (soil wetness, soil depth of thaw, soil pH, mean July air temperature, and typical date of spring snow loss) on 471 plots across five National Park Service units in northern Alaska. Our goal was to determine the environments where tall shrubs thrive and use this information to predict the location of future shrub expansion. The study area covers over 80,000 km² and has mostly tundra vegetation. Large canopy volumes were uncommon, with volumes over 0.5 m³/m² present on just 8% of plots. Shrub canopy volumes were highest where mean July temperatures were above 10.5°C and on weakly acid to neutral soils (pH of 6 to 7) with deep summer thaw (>80 cm) and good drainage. On many sites, flooding helped maintain favorable soil conditions for shrub growth. Canopy volumes were highest where the typical snow loss date was near 20 May; these represent sites that are neither strongly wind-scoured in the winter nor late to melt from deep snowdrifts. Individual species varied widely in the canopy volumes they attained and their response to the environmental factors. Betula sp. shrubs were the most common and quite tolerant of soil acidity, cold July temperatures, and shallow thaw depths, but they did not form high-volume canopies under these conditions. Alnus viridis formed the largest canopies and was tolerant of soil acidity down to about pH 5, but required more summer warmth (over 12°C) than the other species. The Salix species varied widely from S. pulchra, tolerant of wet and moderately acid soils, to S. alaxensis, requiring well-drained soils with near neutral pH. Nearly half of the land area in ARCN has mean July temperatures of 10.5 to 12.5°C, where 2°C of warming would bring temperatures into the range needed for all of the potential tall shrub species to form large canopies. However, limitations in the other environmental factors would probably prevent the formation of large shrub canopies on at least half of the land area with newly favorable temperatures after 2°C of warming.     

An indirect method of estimating leaf area index in a tropical deciduous forest of India

Rapid, reliable and meaningful estimates of leaf area index (LAI) are essential to functional characterization of forest ecosystems including biomass and primary productivity studies. Accurate LAI estimates of tropical deciduous forest are required in studies of regional and global change modeling. Tropical deciduous forest due to higher species richness, multiple species association, varied phenophases, irregular stem densities and basal cover, multistoried canopy architecture and different micro-climatic conditions offers dynamism to the understanding of the LAI dynamics of different PFTs in an ecosystem. This investigation reports a new indirect method for measurement of leaf area index (LAI) in a topical moist deciduous forest in Himalayan foothills using LAI-2000 Plant Canopy Analyzer. We measured the LAI in two seasons (summer; leaf senescence stage and post-monsoon; full green stage) in three (dry miscellaneous, sal mixed and teak plantations) plant functional types (PFT) in Katerniaghat Wildlife Sanctuary, India. Ground LAI values ranged between 2.41 and 6.89, 1.17 and 7.71, and 1.92 and 5.19 during post-monsoon season and 1.36–4.49, 0.67–3.1 and 0.37–1.83 during summer season in dry miscellaneous, sal mixed and teak plantation, respectively. We observed strong correlation between LAI and community structural parameters (tree density, basal cover and species richness), with maximum with annual litter fall (R² > 0.8) and aboveground biomass (AGB) (R² > 0.75). We provided equations relating LAI with AGB, which can be utilized in future studies for this region and can be reasonably extrapolated to other regions with suitable statistical extrapolations. However, the relations between LAI and other parameters can be further improved with incorporation of data from optimized and seasonal sampling. Our indirect method of LAI estimation using litter fall as a proxy, offers repetitive potential for LAI estimate in other PFTs with relatively time and cost-effective way, thereby generating quicker and reliable data for model run for regional and global change studies.     

Test of Accuracy of LAI Estimation by LAI-2000 under Artificially Changed Leaf to Wood Area Proportions

The accuracy of LAI-2000 Plant Canopy Analyzer for leaf (LAI) and plant (PAI) area indexes measurements was tested in 20-year-old Norway spruce stand using the reduction of canopy biomass. Needle and branch areas were reduced progressively upward every one meter. Values of effective leaf area index (LAI_e), as an uncorrected product of LAI-2000, were compared with directly estimated LAI and PAI values after each reduction step. LAI-2000 underestimates PAI and LAI values according to LAI-2000 rings readings, and varied proportions between leaf and wood areas. The values of LAI_c have been increased with decreasing of the view angle of the relevant LAI-2000 rings. Therefore, the underestimation of LAI becomes smaller when the readings near the horizon are masked. More accurate results, for projected LAI (LAI_p) calculation, are produced by LAI-2000 when some dense grids of measurement points and the most vertical ring readings (0 –13 °) are used. Correction factor 1.6 is possible to use for unreduced canopy hemi-surface LAI estimation, when the last rings (i.e. 5^th and 4^th rings, 47 –74 °) are excluded. Correction factor of 1.25 can be used to compute LAI_p if the angle readings under 43 °are also masked.     

岷江上游植被冠层降水截留的空间模拟

 通过对岷江上游实地踏查和定位观测研究,结合MODIS遥感数据,利用“3S”技术对岷江上游植被冠层降水截留进行了空间模拟。研究结果表明：岷江上游植被叶面积指数（LAI）与增强性植被指数（EVI）以二项式关系拟合效果较好。由于归一化植被指数（NDVI）存在的饱和问题,研究采用EVI反演LAI,统计结果表明：岷江上游LAI值在0～2之间的占28.57%,在2～4.5之间的占63.06%,大于4.5的占8.37%,其中LAI最大值为7.394;从冠层最大降水截留模拟结果来看: 植被较好的地区,如卧龙、米亚罗的植被冠层最大降水截留量较大,而干旱河谷、上游高山草甸等地的植被冠层最大降水截留量相对较低;附加冠层降水截留与降雨量呈线性相关,模型验证时以此为基础,模型模拟的结果较为理想。     

Catalogo delle Alghe Raccolte Lungo IL Litorale di Trani

Abstract

Canopy light interception (CPFD_Int), spectral irradiance, leaf water potential, gas- exchange and optical properties were measured in an irrigated vineyard (Vitis vinifera L. cv Montepulciano) trained to the so-called tendone system in which leaf area index (LAI) was varied by means of 50% (T50) or 75% (T75) cluster removal. The 20.5 t ha^?1 yield in the unthinned treatment (UT) decreased by only 36% in T50 and by 52% in T75. LAI and CPFD_Int similarly increased until summer pruning when LAI was 1.75 m² m^?2 in UT, and 25.6% or 62.2% higher in T50 and T75, respectively. The two thinned treatments had only 12.4% higher CPFD_Int than in UT (1167.1 μmol m^?2 s^?1) due to the increased leaf self-shading. The red-to-far red ratio (R: FR) was as low as 0.10 below the canopy. Light-saturated CO₂ assimilation (A _max) in June averaged 14.4 μmol m^?2 s^?1 in sun-exposed leaves, and 7.6 μmol m^?2 s^?1 in shade leaves. By contrast, the apparent quantum yield of CO₂ assimilation (φ_e) was not significantly affected by leaf position, averaging 0.029 and 0.070 mol mol^?1 in June and October, respectively. Middle and low canopy leaves had only 27 or 6%, respectively, of the top canopy leaves actual CO₂ assimilation rate.     

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.