Plant Area Index Research Articles

Dissipation Intermittency Increases Long-Distance Dispersal of Heavy Particles in the Canopy Sublayer

The dispersion of heavy particles such as seeds within canopies is evaluated using Lagrangian stochastic trajectory models, laboratory, and field experiments. Inclusion of turbulent kinetic energy dissipation rate intermittency is shown to increase long-distance dispersal (LDD) by contributing to the intermittent ejection of particles to regions of high mean velocity outside the canopy volume. Model evaluation against controlled flume experiments, featuring a dense rod canopy, detailed flow measurements, and imaged trajectories of spherical particles, demonstrates that superimposing a terminal velocity on the fluid velocity is insufficient to determine the particle dispersal kernel. Modifying the trajectory model by adding dissipation intermittency is found to be significant for dispersal predictions along with the addition of inertial and crossing trajectories’ effects. Comparison with manual seed-release experiments in a forest using wind-dispersed seeds shows that the model captures most of the measured kernels when accepted uncertainties in plant area index and friction velocity are considered. Unlike the flume experiments, the model modifications for several wind-dispersed seeds have minor effects on short-distance dispersal. A large increase was predicted in LDD when including dissipation intermittency for the forest experiment. The main results suggest that fitting or calibrating models to the ‘main body’ of measured kernels may not offer extrapolating foresight to LDD predictions. As inertial effects were found mostly negligible in the field conditions here, the extended trajectory model requires specifying only the seed’s terminal velocity and a constant variance of the normalized dissipation rate. Therefore, the proposed modifications can be readily applied to classical trajectory models so as to improve LDD predictions.

Effects of urban green infrastructure (UGI) on local outdoor microclimate during the growing season.

This study analyzed how the variations of plant area index (PAI) and weather conditions alter the influence of urban green infrastructure (UGI) on microclimate. To observe how diverse UGIs affect the ambient microclimate through the seasons, microclimatic data were measured during the growing season at five sites in a local urban area in The Netherlands. Site A was located in an open space; sites B, C, and D were covered by different types and configurations of green infrastructure (grove, a single deciduous tree, and street trees, respectively); and site E was adjacent to buildings to study the effects of their façades on microclimate. Hemispherical photography and globe thermometers were used to quantify PAI and thermal comfort at both shaded and unshaded locations. The results showed that groves with high tree density (site B) have the strongest effect on microclimate conditions. Monthly variations in the differences of mean radiant temperature (∆Tmrt) between shaded and unshaded areas followed the same pattern as the PAI. Linear regression showed a significant positive correlation between PAI and ∆Tmrt. The difference of daily average air temperature (∆Ta) between shaded and unshaded areas was also positively correlated to PAI, but with a slope coefficient below the measurement accuracy (±0.5 °C). This study showed that weather conditions can significantly impact the effectiveness of UGI in regulating microclimate. The results of this study can support the development of appropriate UGI measures to enhance thermal comfort in urban areas.Electronic supplementary materialThe online version of this article (doi:10.1007/s10661-015-4943-2) contains supplementary material, which is available to authorized users.

The impacts of mountain pine beetle disturbance on the energy balance of snow during the melt period

AbstractMountain snowpacks provide most of the annual discharge of western US rivers, but the future of water resources in the western USA is tenuous, as climatic changes have resulted in earlier spring melts that have exacerbated summer droughts. Compounding changes to the physical environment are biotic disturbances including that of the mountain pine beetle (MPB), which has decimated millions of acres of western North American forests. At the watershed scale, MPB disturbance increases the peak hydrograph, and at the stand scale, the ‘grey’ phase of MPB canopy disturbance decreases canopy snow interception, increases snow albedo, increases net shortwave radiation, and decreases net longwave radiation versus the ‘red’ phase. Fewer studies have been conducted on the red phase of MPB disturbance and in the mixed coniferous stands that may follow MPB‐damaged forests. We measured the energy balance of four snowpacks representing different stages of MPB damage, management, and recovery: a lodgepole pine stand, an MPB‐infested stand in the red phase, a mixed coniferous stand (representing one successional trajectory), and a clear‐cut (representing reactive management) in the Tenderfoot Creek Experimental Forest in Montana, USA. Net longwave radiation was lower in the MPB‐infested stand despite higher basal area and plant area index of the other forests, suggesting that the desiccated needles serve as a less effective thermal buffer against longwave radiative losses. Eddy covariance observations of sensible and latent heat flux indicate that they are of similar but opposite magnitude, on the order of 20 MJ m−2 during the melt period. Further analyses reveal that net turbulent energy fluxes were near zero because of the temperature and atmospheric vapour pressure encountered during the melt period. Future research should place snow science in the context of forest succession and management and address important uncertainties regarding the timing and magnitude of needlefall events. Copyright © 2015 John Wiley & Sons, Ltd.

Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada

Information on spatial and temporal variability of crop growth status is important for understanding the interaction between plants and environmental conditions. In particular, information at the sub-field level is critical for detecting and combating within-field yield-limiting factors for implementation of precision agriculture. High-resolution optical satellite sensors are useful tools to acquire the necessary information in this regard. This study used multi-temporal RapidEye data to estimate the green effective plant area index (PAI) and leaf chlorophyll content of two field crops, spring wheat (Triticum aestivum L.) and canola (Brassica napus L.). The objective was to evaluate the capability of RapidEye imagery for detecting within-field variability of crop growth conditions. Field experiments were carried out in northern Ontario over two consecutive years, 2012 and 2013. Several vegetation indices were selected and derived from the RapidEye data to correlate with crop PAI measured using digital hemispherical photography and leaf chlorophyll content index (CCI) measured using a CCM-200 chlorophyll meter. Results showed that the effective PAI and CCI of the two crops were best correlated with different indices. For effective PAI, the modified triangular vegetation index 2 (MTVI2) was the best for spring wheat (R2=0.74, RMSE=0.72 and n=200) whereas the red-edge normalized difference vegetation index (NDVIRE) was the best for canola (R2=0.83, RMSE=0.49 and n=147). For CCI, the chlorophyll vegetation index (CVI) was the best for spring wheat (R2=0.78, RMSE=4.1 and n=128) whereas the red-edge chlorophyll index (CIRE) was the best for canola (R2=0.33, RMSE=6.4 and n=88). The study also confirmed that the combined transformed chlorophyll absorption reflectance index/optimized soil adjusted vegetation index (TCARI/OSAVI) was satisfactory for spring wheat leaf chlorophyll content estimation when the crop canopy is not sparse. Using selected vegetation indices, RapidEye data can be used to map the within-field variability of crop growth conditions, which is useful for applications in precision agriculture. The reflectance in the red-edge or the green bands is useful in mapping crop leaf chlorophyll variability.

Reliability and limitations of a novel terrestrial laser scanner for daily monitoring of forest canopy dynamics

Leaf area index (LAI) or plant area index (PAI) are commonly used to represent canopy structure and dynamics, but daily estimation of these variables using traditional ground-based methods is impractical and prone to multiple errors during data acquisition and processing. Existing terrestrial laser scanners can provide accurate representation of forest canopy structure, but the sensors are expensive, data processing is complex, and measurements are typically confined to a single event, which severely limits their utility in the interpretation of canopy trends indicated by remotely sensed data. We tested a novel, low-cost terrestrial laser scanner for its capacity to provide reliable and successive assessments of canopy PAI in an evergreen eucalypt forest. Daily scans comprised of 920 range measurements were made by three scanners at one forest site over a two-year period, providing mostly consecutive estimates of PAI, and of vertical structure profiles (as Plant Area Volume Density, PAVD). Data filtering, involving objective statistical methods to identify outliers, indicated that scan quality was adversely affected by moist weather and moderate wind speeds (>4ms−1), suggesting limited utility in some forest environments. Data cleaning (associated with sensor malfunctions) plus filtering removed 32 to 49% of scans, leaving on average 57% of data over the two-year period. Nonetheless, we found strong agreement between lidar-derived PAI estimates, and those from monthly hemispherical images (±0.1 PAI); with both methods indicating mostly stable PAI over multiple seasons. The PAVD profiles from the laser scanner indicated that leaf flush in the upper canopy concomitantly balanced leaf loss from the middle canopy in summer, which was consistent with measured summer peaks in litter fall. This clearly illustrated the advantages of three-dimensional lidar data over traditional two-dimensional PAI estimates in monitoring tree phenology, and in interpreting changes in canopy reflectance as detected by air- and space-borne remotely sensed data.

Roles of dominant understory Sasa bamboo in carbon and nitrogen dynamics following canopy tree removal in a cool‐temperate forest in northern Japan

AbstractTo clarify the role of dense understory vegetation in the stand structure, and in carbon (C) and nitrogen (N) dynamics of forest ecosystems with various conditions of overstory trees, we: (i) quantified the above‐ and below‐ground biomasses of understory dwarf bamboo (Sasa senanensis) at the old canopy‐gap area and the closed‐canopy area and compared the stand‐level biomasses of S. senanensis with that of overstory trees; (ii) determined the N leaching, soil respiration rates, fine‐root dynamics, plant area index (PAI) of S. senanensis, and soil temperature and moisture at the tree‐cut patches (cut) and the intact closed‐canopy patches (control). The biomass of S. senanensis in the canopy‐gap area was twice that at the closed‐canopy area. It equated to 12% of total biomass above ground but 41% below ground in the stand. The concentrations of NO3− and NH4+ in the soil solution and soil respiration rates did not significantly change between cut and control plots, indicating that gap creation did not affect the C or N dynamics in the soil. Root‐length density and PAI of S. senanensis were significantly greater at the cut plots, suggesting the promotion of S. senanensis growth following tree cutting. The levels of soil temperature and soil moisture were not changed following tree cutting. These results show that S. senanensis is a key component species in this cool‐temperate forest ecosystem and plays significant roles in mitigating the loss of N and C from the soil following tree cutting by increasing its leaf and root biomass and stabilizing the soil environment.

Estimation of Canopy and Woody Components Clumping Indices at Three Mature Picea crassifolia Forest Stands

Many previous studies have shown that the underestimation of the plant area index (PAI) by traditional optical instruments is mainly due to the clumping effects of forest canopies. However, the evaluation of the clumping effects of canopy (leaves, stems, branches, fruits, and flowers) and woody components (stems and branches) within forest canopies is still a challenging task for current indirect instruments and algorithms. In this study, two key issues of the gap size distribution algorithm (CC) were discussed first, such as the stop conditions of the gap-removal procedure and gap-removing methods. Four types of gap-removing schemes were compared and their impacts to the estimation of canopy and woody components clumping indices (Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> ) were analyzed. Obvious discrepancies were found between the estimates of Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> from the four gap-removing schemes at each zenith angle. A new gap-removal scheme was proposed subsequently. After that, a solution was presented to estimate Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> at multiple zenithal and azimuthal directions in three mature Picea crassifolia forest stands. These estimates were retrieved based on multispectral canopy imager (MCI) equipment and CC algorithm. The estimates from the MCI equipment were later compared with those from the TRAC instrument. Small differences were found between the Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> estimates from the MCI and TRAC instruments, indicating that the MCI instrument was effective for evaluating the clumping effects of forest canopies at multiple zenithal and azimuthal directions. Results showed that both of Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> change with the zenithal and azimuthal directions.

CHANGE DETECTION IN BRAZILIAN SAVANNAS USING SEMIVARIOGRAMS DERIVED FROM NDVI IMAGES

The Normalized Difference Vegetation Index (NDVI) is often used to extract information from vegetated areas since it is directly related to vegetation parameters such as percent of ground cover, photosynthetic activity of the plant and leaf area index. The aim of this paper was to analyze the potencial of semivariograms generated from NDVI values to detect changes in vegetated areas, analyzing their behavior (shape) and derived metrics (range, sill and nugget). Semivariograms were generated from NDVI values derived from Landsat TM images of May 2010, June 2010 and July 2011. The study area is located in the northern state of Minas Gerais, Brazil, and is covered by Brazilian savannas vegetation, totalizing 1,596 ha. Semivariograms were generated after the exploratory data analysis. Models were fitted, validated and their metrics analyzed. The results showed a very clear trend where the shape of semivariograms, sill and range were different when deforestation occurred and were similar when the area had not been changed. The model that generated best fit was the Gaussian, however, the three models tested showed behavior that makes it possible to detect changes in vegetation. It suggests that further researches should explore the degree to which the semivariogram can be used to quantify this spatial variability as well as to analyze the influence of sazonality for changing detection in vegetated areas.

The interrelationships of chickpea (Cicer arietinum L.) kernel yield and its components under rainfed conditions

Chickpea (Cicer arietinum L.) is a native crop of Asia which is grown worldwide including Iran. In this study, different selection criteria including correlation, regression and path analysis were used to improve yield. The experimental design was split plot with RCBD replicated four times in which three sowing dates (5 January, 4 February, and 5 March) were used in main plots and four genotypes; two semi bush types and early mature cultivars(ILC482 and Flip84-42) and two stand types and late mature ones (Hashem and Arman), were allocated to subplots. Results showed that Flip84-42 variety and 5 January sowing date had the highest amount of kernel yield, pod number per plant, kernel number per pod, 100 kernel weight, biological yield, and days to flowering. There was a positive correlation between the kernel yield and pod number per plant (r=0.57 ** , p≤0.05), kernel number per pod (r=0.51 ** , p≤0.05), biological yield (r=0.39 * , p≤0.05), plant height (r=0.31 * , p≤0.05), branch number (r=0.22 * , p≤0.05), leaf area index (r=0.59 ** , p≤0.05), and first pod height (r=0.58 ** , p≤0.05). Regression analysis also showed that yield was determined by biological yield, leaf area index and days to flowering. Results of path analysis revealed that biological yield had the greatest direct effect on kernel yield (p=0.61 ** , p≤0.05). This character was followed by number of pods per plant (p=0.31 * , p≤0.05) and leaf area index (p=0.35 * , p≤0.05). Results of this study indicated Flip84-42 and5 January are the best variety and sowing date to cultivate chickpea in Badjgah region under dryland condition. In addition, it can be concluded that rainfed chickpea breeders should pay attention to the traits such as biological yield, leaf area index and days to flowering when selecting high-yielding genotypes.

Vegetation, precipitation and demographic response of a woodland predator: Tawny Owl Strix aluco as an indicator of soil aridity in Castelporziano forest

The demographic performance of avian predators is influenced by climate variations. The Plant Area Index (PAI) is considered a key descriptor of the (evolving) vegetation sensitive to climate and soil aridity and drought stress. To assess if the increased aridity regime in the mediterranean region affects the reproduction of a forest predator, the breeding success of the Tawny Owl Strix aluco was studied over 7 years (1997–2003) in Castelporziano, central Italy. The inter-annual fluctuations in owl demographic output observed in neighbor forest and agricultural sites were compared with climatic variables (rainfall, minimum and maximum air temperature, aridity index) and changes in the PAI estimated by satellite imagery. Out of 326 breeding attempts, the percentage of successful pairs per year ranged between 4 and 28 %, the number of fledglings per successful pair ranged between 1.0 and 1.8 and breeding rate (i.e. the number of fledglings per breeding pair) ranged between 0.1 and 0.4. Demographic, climatic and remotely sensed indexes showed coherent variations among years. Rainfall and lower aridity index are associated with a higher breeding success. The relationship between PAI and breeding rate was found significant only in forest sites. Results suggest that Tawny Owls may positively respond to drier weather conditions by reducing the breeding rate.

Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., &gt; 1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the creation of the LAI-RM: (1) the uncertainty associated with the indirect in situ optical measurements of southeastern United States needle-leaf LAI and (2) the uncertainty in the process of classifying land cover (LC). Uncertainty within the loblolly pine (Pinus taeda) in situ data collection was highest for the assessment of the plant area index (PAI), Le (27.2%), and the woody-to-total ratio, α, (30.6%). The needle-to-shoot ratio, λE, and the element clumping index, ΩE, contributed 14.9% and 9.3%, respectively, to the uncertainty in the calculation of LAI. Combining LC differences (3.4%) with the uncertainty within the loblolly pine component resulted in doubling the LAI-RM variability (σ = 0.50 to σ = 0.97) at the 1 km2 validation site located in Appomattox, Virginia, USA.

Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements

Vegetation phenology studies the timing of recurring seasonal dynamics and can be monitored through estimates of plant area index (PAI). Shifts in spring phenology are a key indicator for the effect of climate change, in particular the start of the growing season of forests. Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, is an active remote sensing technique and measures the forest structure with high spatial detail and accuracy. TLS provides information about the 3D distribution of canopy constituents and vertical plant profiles can be derived from these data. Vertical plant profiles describe the plant area per unit volume as a function of height, and can be used to used to monitor seasonal dynamics through PAI. Here, we present a TLS time series based on 48 measurement days of four sampling locations in a deciduous forest in the Netherlands. Vertical plant profiles are derived for each measurement and allow us to quantify not only total canopy integrated PAI, but also monitor PAI at specific horizontal layers. Sigmoidal models show a good fit to the derived total canopy integrated PAI time series (CV(RMSE) <2.4% and CCC >0.99). The start of season (SOS) based on these models occurs between March 29 and April 3, 2014, depending on the species composition. The SOS derived from the TLS data corresponds well with field observations and occurs 7–12 days earlier compared to the SOS estimate from the MODIS NDVI time series. This is mainly caused by the lower relative standard deviation for TLS measurements in leaf-off conditions (0.72% compared to 2.87% for the MODIS NDVI data), which allows us to significantly detect small changes in phenology earlier. TLS allows us to monitor PAI at specific horizontal layers and we defined an understorey, intermediate and upper canopy layer. Even though our study area had only a sparse understorey, small differences are observed in the SOS between the different layers. We expect that these phenological differences will be more pronounced in multi-layered forests and TLS shows the potential to study seasonal dynamics not only as a function of time, but also as a function of canopy height.

Effect of agronomic practices on growth, dry matter and yield of Rajmash (Phaseolus Vulgaris L.)

Rajmash (Phaseolus vulgaris L.) is an important cash crop widely grown under temperate and subtropical regions. Being a pulse crop it is good substitute of vegetables. To sustain the productivity of such a wonder crop and fulfill the nutritional demand of the ever growing population under changing climate it is necessary to apply integrated agronomic approaches. Integrated management of agronomic practices plays a significant role in the proper growth and development of crops. To test this hypothesis, a field experiment was conducted using Rajmash as a test crop during two consecutive years that is, 2008-2009 and 2009-2010. The experiment was designed using split plot technique. Method of sowing (flat and raised bed) and moisture regime (0.6, 0.8 and 1.0 IW/CPE) was the main plot factor and four nutrient supply systems that is, 100% recommended dose of Nitrogen (NPK) fertilizer –RDF [120:60:40 kg /ha supplied through standard grade Urea (46%N), DAP (46 and 18% P & N) and ], 75% RDF +25% through FYM, 75% RDF + 25% by Biocompost and 75% through NPK + 25% N by Azotobactor was taken as sub plot. A total of 24 treatment combinations were replicated three times. Various growth parameters e.g. plant height (cm), number of branches per plant and leaf area index (%), dry matter accumulation (g/plant) at 30, 60, and 90 and at harvest stage as well as grain and straw yield were recorded. Raised bed technique of sowing with moisture regime of 1.0 IW/CPE along with 75% RDNF+25 % N through bio-compost was found most suitable in term of highest total dry matter production. This increase was positively attributed by significant increase in plant height, number of branches per plant and leaf area index of crop. Application of 100% RDNF increased the seed and straw yield significantly in first year while during second year it was maximum, 23.5 q/ha with the application of 75% RDF + 25% N through bio compost and followed by 100% RDF NPK. Minimum seed and straw yields were obtained under 75% RDF + Azotobactor during both the years while highest values were recorded at F3 and 1.0 IW/CPE ratio. Highest disparity in plant height and leaf area index, under various treatment combinations was recorded at 60 days after sowing of crop. Key words: Sowing methods, nutrient supply system, moisture regime, food security.

Photographic assessment of overstory and understory leaf area index in beech forests under different management regimes in Central Italy

Abstract. Forest understory may be strongly affected by silvicultural practices such as thinning, which simultaneously modulates the overstory canopy cover and influences the availability of light. However, the understory layer is rarely considered in management decisions, partly because methods to estimate understory leaf area index are poorly developed. In this study we used two different restricted view angle photographic methods to estimate overstory plant area index LO (zenith cover photography), understory leaf area index LU (nadir cover photography) and their related canopy attributes (foliage clumping, foliage cover, crown cover, crown porosity). These measurements were performed in beech stands under different management regime. Results from photography indicated that not only overstory but also understory canopy attributes were significantly influenced by forest management. In addition, a significant negative correlation was found between LO and LU. We conclude that the photographic methods are effective for monitoring (overstory and understory) canopy status in managed stands, on account of their rapid and not destructive procedures, which allows large scale implementation of the methods.

A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests

The difficulty of obtaining accurate information about the canopy structure is a current limitation towards higher accuracy in numerical predictions of the wind field in forested terrain. The canopy structure in computational fluid dynamics is specified through the frontal area density and this information is required for each grid point in the three-dimensional computational domain. By using raw data from aerial LiDAR scans together with the Beer–Lambert law, we propose and test a method to calculate and grid highly variable and realistic frontal area density input. An extensive comparison with ground-based measurements of the vertically summed frontal area density (or plant area index) and tree height was used to optimize the method, both in terms of plant area index magnitude and spatial variability. The resolution of the scans was in general low (<2.5reflectionsm−2). A decrease of the resolution produced an increasing systematic underestimation of the spatially averaged tree height, whereas the mean plant area index remained insensitive. The gridded frontal area density and terrain elevation were used at the lower boundary of wind simulations in a 5km×5km area of a forested site. The results of the flow simulations were compared to wind measurements using a vertical array of sonic anemometers. A good correlation was found for the mean wind speed of two contrasting wind directions with different influences from the upstream forest. The results also predicted a high variability on the horizontal and vertical mean wind speed, in close correlation with the canopy structure. The method is a promising tool for several computational fluid dynamics applications requiring accurate predictions of the near-surface wind field.

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R 2 )o f 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.8.085196)

Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods

Continuous field leaf area index (LAI) measurement has become increasingly important for the validation of remote sensing LAI products. A seasonal field campaign was carried out to take continuous LAI measurements over paddy rice fields in NE China in 2012. Three indirect optical methods, LAI-2200, digital hemispherical photography (DHP), and AccuPAR, were compared with a destructive sampling method conducted concurrently. Corrections for the clumping effect were applied to the effective plant area indices (PAIeff) estimated from the indirect optical measurements.Both LAI-2200 and DHP produce consistent PAIeff estimates over the season (R2=0.76, RMSE=0.97). The clumping index (CI) values obtained from DHP generally decrease with plant growth and range between 0.63 and 0.74 during the peak growing period from day of year (DOY) 191−230. The CI values retrieved from DHP photos generally decrease with increasing view angles. The optical PAI and LAI values estimated from LAI-2200 and DHP correspond very well with the destructive values before DOY 230 (R2=0.75, RMSE=1.15 for PAI and R2=0.78, RMSE=0.74 for LAI), and the relative errors are less than 10% and 5%, respectively, for the two instruments. Omitting ring 5 for LAI-2200 generates very accurate PAI and LAI estimations during the peak season. Nevertheless, AccuPAR underestimates the PAIeff, PAI, and LAI values obtained from other methods (up to 30%). After DOY 231, the capability to detect PAI decreases significantly for both destructive and optical methods due to the leaf senescence and the DHP classification difficulty. In general, rice PAI could be accurately estimated with LAI-2200 and DHP before senescence if the clumping effect could be properly taken into account. The seasonal continuous LAI measurements obtained from this study are valuable for the validation of remote sensing LAI products.

도시공원에 식재된 왕벚나무 수종을 중심으로 한 수목활력도의 정량평가지표 개발 및 이론적 고찰에 관한 연구

The vigor condition of trees is an important indicator for the management of urban forested area. But difficulties in how to assess the tree vigor condition still remain. Previous efforts were limited in the 1) measurement of single indicator rather than using multiple indices, 2) purpose-oriented measurement such as for air-pollution effect or specific pathological symptom, and 3) ordinal-scale evaluations by field crews 4) despite human errors based on his/her experiences or prior knowledge. Therefore, this study attempted to develop a quantitative and objective methodology for assessing tree vigor condition, by measuring multiple modules and building the profile inventory. Furthermore, the possibility and limitations were discussed in terms of schematic frames describing tree vigor condition. The vigor condition of 56 flowering cherry plants in urban park were assessed by in-situ measurements of following eight items; growth of crown(Gc), growth of shoots, individual tree volume(Vol), plant area index, woody area index, leaf area index, leaf chlorophyll content(Lc) and leaf water content(Lw). For validation, these measurements were compared with the ranks of holistic tree vigor condition, which were visually assessed using a 4-point grading scale based on the expert's knowledge. As a result, the measures of each evaluation item successfully highlighted a variety of aspects in tree vigor condition, including the states of both photosynthetic and non-photosynthetic parts. The variation in the results depending on evaluated parts was shown within an individual tree, even though the broad agreement among the results was found. The result of correlation analysis between the tested measurements and 4-point visual assessment, demonstrated that the state of water-stressed foliage of the season (Lw) or the development of plant materials since sapling phase (Vol) could be better viewed from the outer appearance of trees than other symptoms. But only based on the visual assessment, it may be difficult to detect the quality of photosynthesis (Lc) or the recent trend in growth of trees (Gc). To make this methodology simplified for the broad-scale application, the tested eight measurements could be integrated into two components by principal component analysis, which was labelled with 'the amount of plant materials' and 'vigor trend', respectively. In addition, the use of these quantitative and multi-scale indicators underlies the importance of assessing various aspects of tree vigor condition, taking into account the response(s) on different time and spatial scale of pressure(s) shown in each evaluated module. Future study should be advanced for various species at diverse developing stages and environment, and the application to wide areas at a periodic manner.

Effects of light intensity modification by reflective aluminized screenhouse on sweet pepper growth and yield

Sweet pepper is one of the ten most consumed vegetables in world. Although it develops better under protected environment, the cultivation in tropical countries is practiced in open field due greenhouse structure higher costs. Unfortunately, such practice has compromised the crop to reach either best yield or fruit quality. Since production and cost are the most important criteria for agricultural production, we aimed to evaluate reflective aluminized polypropylene shading net influence on sweet pepper (Capsicum annuum L.) growth and production as intermediary alternative for low/middle income producers from Brazilian tropical regions. Sweet pepper Magali R hybrid was cultivated in two environments: FC - field conditions (control) and RS - reflective shading net with 40% shading rate. RS caused reductions in incident solar radiation (SR) and photosynthetically active radiation (PAR) on the amount of 46.3% and 48.3%, respectively. There were no significant changes in temperature and relative humidity recorded for the two environments. In addition, RS allowed best use efficiency of photosynthetically active radiation since it promoted higher values of plant height, leaf number and area index than those reached on FC on the amount of 29%, 22% and 80 %, respectively. Similarly, plants grown under RS showed higher yield and marketable fruits and promoted less loses by sunscald.

Local Illumination Influence on Vegetation Indices and Plant Area Index (PAI) Relationships

Relationships between biophysical parameters and radiometric data have been tested and evaluated by several professionals using empirical and/or physical approaches. Remote sensing data collected from airborne or orbital platforms are, of course, influenced by different factors, such as illumination/observation geometry (data collection geometry), atmospheric effects, etc., rather than by target spectral properties. Besides that, the target topographic positioning actually defines the amount of incident energy, as well as the amount of energy that is reflected toward the sensor. The sum of both data collection geometry and topographic positioning defines the so-called “local illumination”. The objective of this paper was to evaluate the influence of local illumination on empirical relationships between a biophysical variable (plant area index, PAI) and two vegetation indices calculated from Resourcesat/Linear Imaging Self-Scanner sensor (LISS-3) orbital data. Local illumination was expressed by the cosine factor (Fcos) and calculated from topographic and solar position data at three different dates. The study area was based on a typical Brazilian southeastern forest fragment located in the Augusto Ruschi municipal preservation park dispersed on roughhouse topography. PAI was estimated by hemispherical photographs taken under the forest canopy from sample points arbitrarily dispersed on the forest fragment. Results confirmed a stronger relationship between vegetation indices and local illumination conditions.