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.     

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.     

The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures

Retrieving leaf chlorophyll content at a range of spatio-temporal scales is central to monitoring vegetation productivity, identifying physiological stress and managing biological resources. However, estimating leaf chlorophyll over broad spatial extents using ground-based traditional methods is time and resource heavy. Satellite-derived spectral vegetation indices (VIs) are commonly used to estimate leaf chlorophyll content, however they are often developed and tested on broadleaf species. Relatively little research has assessed VIs for different leaf structures, particularly needle leaves which represent a large component of boreal forest and significant global ecosystems. This study tested the performance of 47 published VIs for estimating foliar chlorophyll content from different leaf and canopy structures (broadleaf and needle). Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species (Picea mariana and Acer saccharum) and a variety of canopy structures. Leaf reflectance data was collected using an ASD Fieldspec Pro spectroradiometer (400–2500 nm) for over 300 leaf samples. Canopy reflectance data was acquired from the medium resolution imaging spectrometer (MERIS). At the canopy level, with both leaf types combined, the DD-index showed the strongest relationship with leaf chlorophyll (R² = 0.78; RMSE = 3.56 μg/cm²), despite differences in leaf structure. For needleleaf trees alone the relationship with the top VI was weaker (D_[red], R² = 0.71; RMSE = 2.32 μg/cm²). A sensitivity study using simulated VIs from physically-modelled leaf (PROSPECT) and canopy (4-Scale) reflectance was performed in order to further investigate these results and assess the impacts of different background types and leaf area index on the VIs’ performance. At the leaf level, the MNDVI8 index showed a strong linearity to changing chlorophyll and negligible difference to leaf structure/type. At canopy level, the best performing VIs were relatively consistent where LAI ≥ 4, but responded strongly to differences in background at low canopy coverage (LAI = 2). This research provides comprehensive assessments for the use of spectral indices in retrieval of spatially-continuous leaf chlorophyll content at the leaf (MTCI: R² = 0.72; p < 0.001) and canopy (DD: R² = 0.78; p < 0.001) level for resource management over different spatial and temporal scales.     

Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City,Northwest China

Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson's correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m² (AGB), and 75.81 g/m² (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.     

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments

Leaf water status information is highly needed for monitoring plant physiological processes and assessing drought stress. Retrieval of leaf water status based on hyperspectral indices has been shown to be easy and rapid. However, a universal index that is applicable to various plants remains a considerable challenge, primarily due to the limited range of field-measured datasets. In this study, a leaf dehydration experiment was designed to obtain a relatively comprehensive dataset with ranges that are difficult to obtain in field measurements. The relative water content (RWC) and equivalent water thickness (EWT) were chosen as the surrogates of leaf water status. Moreover, five common types of hyperspectral indices including: single reflectance (R), wavelength difference (D), simple ratio (SR), normalized ratio (ND) and double difference (DDn) were applied to determine the best indices. The results indicate that values of original reflectance, reflectance difference and reflectance sensitivity increased significantly, particularly within the 350–700 nm and 1300–2500 nm domains, with a decrease in leaf water. The identified best indices for RWC and EWT, when all the species were considered together, were the first derivative reflectance based ND type index of dND (1415, 1530) and SR type index of dSR (1530, 1895), with R² values of 0.95 (p < 0.001) and 0.97 (p < 0.001), respectively, better than previously published indices. Even so, different best indices for different species were identified, most probably due to the differences in leaf anatomy and physiological processes during leaf dehydration. Although more plant species and field-measured datasets are still needed in future studies, the recommend indices based on derivative spectra provide a means to monitor drought-induced plant mortality in temperate climate regions.     

Ground level environmental protein concentrations in various ecuadorian environments: Potential uses of aerosolized protein for ecological research

Large quantities of free protein in the environment and other bioaerosols are ubiquitous throughout terrestrial ground level environments and may be integrative indicators of ecosystem status. Samples of ground level bioaerosols were collected from various ecosystems throughout Ecuador, including pristine humid tropical forest (pristine), highly altered secondary humid tropical forest (highly altered), secondary transitional very humid forest (regrowth transitional), and suburban dry montane deforested (suburban deforested). The results explored the sensitivity of localized aerosol protein concentrations to spatial and temporal variations within ecosystems, and their value for assessing environmental change. Ecosystem specific variations in environmental protein concentrations were observed: pristine 0.32 ± 0.09 μg/m³, highly altered 0.07 ± 0.05 μg/m³, regrowth transitional 0.17 ± 0.06 μg/m³, and suburban deforested 0.09 ± 0.04 μg/m³. Additionally, comparisons of intra-environmental differences in seasonal/daily weather (dry season 0.08 ± 0.03 μg/m³ and wet season 0.10 ± 0.04 μg/m³), environmental fragmentation (buffered 0.19 ± 0.06 μg/m³ and edge 0.15 ± 0.06 μg/m³), and sampling height (ground level 0.32 ± 0.09 μg/m³ and 10 m 0.24 ± 0.04 μg/m³) demonstrated the sensitivity of protein concentrations to environmental conditions. Local protein concentrations in altered environments correlated well with satellite-based spectral indices describing vegetation productivity: normalized difference vegetation index (NDVI) (r² = 0.801), net primary production (NPP) (r² = 0.827), leaf area index (LAI) (r² = 0.410). Moreover, protein concentrations distinguished the pristine site, which was not differentiated in spectral indices, potentially due to spectral saturation typical of highly vegetated environments. Bioaerosol concentrations represent an inexpensive method to increase understanding of environmental changes, especially in densely vegetated ecosystems with high canopies or in areas needing high spatial and temporal resolution. Further research to expand understanding of the applicability of bioaerosol concentrations for environmental monitoring is supported by this pilot study.     

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.     

Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m⁻¹, when salinity is measured as electrical conductivity of the saturation extract, EC_e). State-of-the-art approaches for creating accurate EC_e maps beyond field scale (i.e., 1 km²) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC_a) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 EC_a-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC_e, ranging from 0 to 38.6 dS m⁻¹. The ANOCOVA EC_a-EC_e model returned a coefficient of determination (R²) of 0.87 and root mean square prediction error (RMSPE) of 3.05 dS m⁻¹. For the remote sensing approach seven years (2007–2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R² = 0.73 and RMSPE = 3.63 dS m⁻¹. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSPE. The worst, median, and best fit scenarios provided global cross-validation R² of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R² of 0.65. The ANOCOVA approach performs particularly well at EC_e values <10 dS m⁻¹, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low EC_e values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1–10 km²) and the remote sensing approach best for landscape to regional scales (>10 km²).     

Leaf litterfall,leaf area index,and radiation transmittance in cypress wetlands and slash pine plantations in north-central Florida

The seasonal dynamics of leaf litterfall and leaf area index (LAI, all-sided basis), light penetration and the vertical distribution of surface area index, and the feasibility of estimating LAI from radiation transmittance were studied from April 1993 to March 1994 in the canopies of three cypress (Taxodium ascendens) wetlands and their surrounding slash pine (Pinus elliottii) uplands in Florida flatwoods. Annual leaf litterfall ranged from 324 to 359 g m^–2 in the wetlands, which was very close to the average for 11 sites throughout Florida of 340±26 g m^–2. The seasonal pattern of the normalized LAI obtained for the dominant tree species in the ecosystems could be used to construct the seasonal dynamics of LAI at the ecosystem scale. The vertical distribution of surface area index in the wetlands was significantly different from that in the surrounding pine uplands. The maximum LAI of cypress wetlands in this area was about 8 m² m^–2, which was higher than the maximum of slash pine plantations of 6 m² m^–2. Cypress leaves were strongly erectophile in space. Results showed that the LAI-2000 canopy analyzer could generally be used to estimate forest LAI, whether the forest canopy was closed or not, if an overall clumping index of 2.00 was applied. However, as LAI decreased, the relative error contained in the radiation-based LAI estimates increased. This indicated that foliage clumping at the stand scale was more important than that at the tree or branch scale.     

Response of ecosystem CO2 exchange to biomass productivity in a high yield grassland

We measured the biomass production and ecosystem carbon CO₂ exchange in a high yield grassland dominated by Miscanthus sinensis. The experimental grassland is managed by mowing once a year in winter every year and the harvested biomass on the ground is left to become the humus. The maximum aboveground and belowground biomasses were 1117 and 2803 g d.w. m^?2 in our grassland. Although the high potential of our grassland for biomass production led to higher carbon uptake than with other types of grassland, the large biomass contributed to a higher respired carbon loss. Biomass increase led to a linear increase in ecosystem respiration. Over the 3 years, RE₁₀ increased with increasing aboveground biomass. The potential gross primary production at a photosynthetic photon flux density of 2000 μmol m² s^?1 logarithmic increased with LAI. These responses of CO₂ exchange to biomass production suggest this grassland behaved as weak CO₂ sink or near carbon neutral (?78 and 17 g C m^?2 year^?1) in current management.     

Canopy structure in savannas along a moisture gradient on Kalahari sands

Measurements of tree canopy architecture were made at six savanna sites on deep, sandy soils, along a gradient of increasing aridity. There was substantial variation in the leaf area estimated within each site, using the same sample frame, but different measurement techniques. The trends in canopy properties in relation to the aridity gradient were consistent, regardless of the technique used for estimating the properties. The effective plant area index for the tree canopy (the sum of the stem area index and the leaf area index (LAI)) declined from around 2 to around 0.8 m² m^?2 over a gradient of mean annual rainfall from 1000 to 350 mm. Stems contributed 2–5% of the tree canopy plant area index. Since the tree canopy cover decreased from 50% to 20% over this aridity range, the leaf area index within the area covered by tree canopies remained fairly constant at 3–4 m² m^?2. Tree leaves tended from a horizontal orientation to a more random orientation as the aridity increased. On the same gradient, the leaf minor axis dimension decreased from around 30 mm to around 3 mm, and the mean specific leaf area decreased from 14 to 5 m² kgha^?1. There was good agreement between LAI observed in the field using a line ceptometer and the LAI inferred by the MODIS sensor on the Terra satellite platform, 2 months later in the same season.     

Foliage type specific susceptibility to ozone in Picea abies,Pinus cembra and Larix decidua at treeline: A synthesis

Cumulative ozone uptake (COU, mmol m⁻²) and O₃ flux (FO₃, nmol m⁻² s⁻¹) were related to physiological, morphological and biochemical characteristics of field-grown mature evergreen Norway spruce [Picea abies (L.) Karst.], Cembran pine [Pinus cembra L.], and deciduous European larch [Larix decidua Mill.] trees at treeline. The threshold COU causing a statistically significant decline in photosynthetic capacity (A_max) ranged between 19.6 mmol m⁻² in current-year needles of evergreen conifers and 22.0 6 mmol m⁻² in short-shoot needles of deciduous L. decidua subjected to exposure periods of ≥84 and ≥43 days, respectively. The higher O₃ sensitivity of deciduous L. decidua than of evergreen P abies and P. cembra was associated with differences in FO₃ and specific leaf area (SLA), both being significantly higher in L. decidua. FO₃ was 5.9 nmol m⁻² s⁻¹ in L. decidua and 2.7 nmol m⁻² s⁻¹ in evergreen conifers. Species-dependent differences were also related to detoxification capacity expressed through total surface area based concentrations of reduced ascorbate and α-tocopherol that both increased with SLA. Findings suggest that differences in O₃ sensitivity between evergreen and deciduous conifers can be attributed to foliage type specific differences in SLA, the latter determining physiological and biochemical characteristics of the treeline conifers.     

Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data

A study was conducted to understand the potential of Landsat-8 in the estimation of gross primary production (GPP) and to quantify the productivity of maize crop cultivated under hyper-arid conditions of Saudi Arabia. The GPP of maize crop was estimated by using the Vegetation Photosynthesis Model (VPM) utilizing remote sensing data from Landsat-8 reflectance (GPP_VPM) as well as the meteorological data provided by Eddy Covariance (EC) system (GPP_EC), for the period from August to November 2015. Results revealed that the cumulative GPP_EC for the entire growth period of maize crop was 1871 g C m⁻². However, the cumulative GPP determined as a function of the enhanced vegetation index – EVI (GPP_EVI) was 1979 g C m⁻², and that determined as a function of the normalized difference vegetation index – NDVI (GPP_NDVI) was 1754 g C m⁻². These results indicated that the GPP_EVI was significantly higher than the GPP_EC (R² = 0.96, P = 0.0241 and RMSE = 12.6%). While, the GPP_NDVI was significantly lower than the GPP_EC (R² = 0.93, P = 0.0384 and RMSE = 19.7%). However, the recorded relative error between the GPP_EC and both the GPP_EVI and the GPP_NDVI was −6.22% and 5.76%, respectively. These results demonstrated the potential of the landsat-8 driven VPM model for the estimation of GPP, which is relevant to the productivity and carbon fluxes.     

Coupling heat flux dynamics with meteorological conditions in the green roof ecosystem

Green roofs can notably modify the thermal properties of the building envelope and adjacent air to bring environmental benefits. This study investigates the heat flux dynamics of the tropical green roof ecosystem to provide a scientific basis for design and management. Green roof experimental plots were established to monitor the total solar radiation, net radiation, and micrometeorological parameters. The data permit calculation of sensible and latent heat fluxes using the Bowen ratio energy balance (BREB) method. The results demonstrated the life cycle characteristics of heat flux components. The dynamic changes of sensible (H), latent (λE) and soil (G) heat fluxes were denoted by single-peak quadratic curves. Net radiation (R_n) was largely determined by quantity and variation trends of λE, reaching at 1300 h a maximum λE of 655 W m^?2 and maximum H of 369 W m^?2. Temporal heat-flux fluctuations were strongly correlated with meteorological variables. Extreme values of H and λE correlated well with precipitation and temperature (R² = 0.78). Dynamics of heat-flux magnitude and partitioning demonstrated notable differences by daily and season periods. They displayed considerable variations in flux partitioning, with Bowen ratios strongly correlated with weather conditions and vegetation types. The energy budget of the green roof ecosystem is unbalanced with a heat loss of about 15.5% caused by soil and canopy heat reserve. The passive indoor cooling effect under the green roof is attributed to the unbalanced energy closure.     

Seasonal changes of the introduced Caulerpa racemosa var. cylindracea (Caulerpales,Chlorophyta) at the northwest limit of its Mediterranean range

A study of the meadows of the invasive Caulerpa racemosa var. cylindracea (Sonder) Verlaque, Huisman et Boudouresque was carried out over one year at Marseilles (Provence, France) where the alga is thriving, probably since 1994, in the cold waters of the north western Mediterranean Sea. At an early phase of colonisation, the C. racemosa meadow is characterized by a patchy distribution pattern. Several years are necessary to obtain a dense and continuous meadow. In one area colonized for more than 4 years, C. racemosa has developed a continuous meadow with wide seasonal variations. Maximum development was reached in autumn (biomass: 82 ± 3 g DW m⁻²; length of stolons: 1162 ± 86 m m⁻²; number of apices: 8360 ± 405 m⁻²; number of erect axes: 20955 ± 1499 m⁻²) and the minimum from winter to early spring (respectively, 0.3 ± 0.1 g DW m⁻²; 3 ± 1 m m⁻²; 220 ± 55 apices m⁻²; 35 ± 15 erect axes m⁻²). Seasonal variations in the growth rate were highly significant. The season of high growth lasted from June to October. The apical growth rate of a stolon reached a maximum of 7.5 ± 0.3 mm day⁻¹ in early October, then began to decrease significantly from the end of October to December, before becoming nearly nil from January to early May. Annual net production rate expressed in terms of stolon length and biomass was estimated as 5801 m m⁻² a⁻¹ and 612 g DW m⁻² a⁻¹, respectively. During the growth period, the turnover rate of the C. racemosa stolons was estimated at from 25 to 46 days. The growth rate was closely correlated to the seawater temperature (R² = 0.83), whereas no significant correlation was found between growth and irradiance. During the growth period, a decrease in temperature rapidly affects the growth rate, which soon recovers its earlier level when the temperature rises again. In winter, the growth rate decreased rapidly with the seasonal drop in the seawater temperature. Grazing by fish (Sarpa salpa and Boops boops) can also affect the growth rate from September to December by consumption of the erect axes and stolon apices, enhancing the ramification of stolons. Seasonal changes at Marseilles are much sharper than those reported for warmer Mediterranean localities (French Riviera, Italy, Croatia): in winter and early spring C. racemosa meadows decreased and locally disappeared, leaving a barren substrate. C. racemosa survives the lower winter seawater temperatures of the north-western Mediterranean Sea probably in the form of zygotes and/or small fragments (rhizoids, stolons, propagules).     

The effects of light intensity on the growth of Japanese Gambierdiscus spp. (Dinophyceae)

Marine toxic dinoflagellates of the genus Gambierdiscus are the causative agents of ciguatera fish poisoning (CFP), a form of seafood poisoning that is widespread in tropical, subtropical and temperate regions worldwide. The distributions of Gambierdiscus australes, Gambierdiscus scabrosus and two phylotypes of Gambierdiscus spp. type 2 and type 3 have been reported for the waters surrounding the main island of Japan. To explore the bloom dynamics and the vertical distribution of these Japanese species and phylotypes of Gambierdiscus, the effects of light intensity on their growth were tested, using a photoirradiation-culture system. The relationship between the observed growth rates and light intensity conditions for the four species/phylotypes were formulated at R > 0.92 (p < 0.01) using regression analysis and photosynthesis-light intensity (P-L) model. Based on this equation, the optimum light intensity (L_max) and the semi-optimum light intensity range (L_s-opt) that resulted in the maximum growth rate (μ_max) and ≥80% μ _max values of the four species/phylotypes, respectively, were as follows: (1) the L_max and L_s-opt of G. australes were 208 μmol photons m⁻² s⁻¹ and 91–422 μmol photons m⁻² s⁻¹, respectively; (2) those of G. scabrosus were 252 and 120–421 μmol photons m⁻² s⁻¹, respectively; (3) those of Gambierdiscus sp. type 2 were 192 and 75–430 μmol photons m⁻² s⁻¹, respectively; and (4) those of Gambierdiscus sp. type 3 were ≥427 and 73–427 μmol photons m⁻² s⁻¹, respectively. All four Gambierdiscus species/phylotypes required approximately 10 μmol photons m⁻² s⁻¹ to maintain growth. The light intensities in coastal waters at a site in Tosa Bay were measured vertically at 1 m intervals once per season. The relationships between the observed light intensity and depth were formulated using Beer’s Law. Based on these equations, the range of the attenuation coefficients at Tosa Bay site was determined to be 0.058–0.119 m⁻¹. The values 1700 μmol photons m⁻² s⁻¹, 500 μmol photons m⁻² s⁻¹, and 200 μmol photons m⁻² s⁻¹ were substituted into the equations to estimate the vertical profiles of light intensity at sunny midday, cloudy midday and rainy midday, respectively. Based on the regression equations coupled with the empirically determined attenuation coefficients for each of the four seasons, the ranges of the projected depths of L_max and L_s-opt for the four Gambierdiscus species/phylotypes under sunny midday conditions, cloudy midday conditions, and rainy midday conditions were 12–38 m and 12–54 m, 1–16 m and 1–33 m, and 0 m and 0–16 m, respectively. These results suggest that light intensity plays an important role in the bloom dynamics and vertical distribution of Gambierdiscus species/phylotypes in Japanese coastal waters.     

Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido,Japan

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225 km²) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40 m³ ha^–1 and from 30.78 to 180.54 MgC ha^–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r² = 0.80, RMSE = 55.04 m³ ha^–1; aboveground tree carbon content: r² = 0.78, RMSE = 19.10 MgC ha^–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r² = 0.84, RMSE = 62.66 m³ ha^–1; aboveground tree carbon content: r² = 0.76, RMSE = 27.05 MgC ha^–1) or coniferous forests (volume: r² = 0.75, RMSE = 51.07 m³ ha^–1; aboveground tree carbon content: r² = 0.58, RMSE = 19.00 MgC ha^–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.     

Partitioning of latent heat flux at a northern peatland

The partitioning of latent heat flux (Q_E) to vascular plant and moss surface components was assessed for a Sphagnum-dominated bog with a hummock–hollow surface having a sparse canopy of low shrubs. Results from porometry and eddy covariance measurements of Q_E showed evaporation from the moss surface ranged from greater than 50% of total Q_E early in the growing season to less than 20% after a dry period toward the end of the growing season. Both soil moisture and vapour pressure deficit (D_a) affected this partitioning with drier moss and peat, lower water table, and smaller D_a all reducing moss Q_E. Daily maximum moss Q_E ranged from greater than 200 W m⁻² early in the growing season to less than 100 W m⁻² during a dry period. In contrast, vascular contribution to total Q_E increased over the season from a daily maximum of about 150 W m⁻² to 250 W m⁻² due to increase in leaf area by leaf replacement and emergence and to drying of the moss surface. Porometry results showed average daily maximum conductance from bog shrubs was near 8 mm s⁻¹. These conductance values were smaller than those reported for vascular plants from more nutrient-rich wetlands. The effect of increases in D_a on vascular Q_E were moderated by decreases in stomatal conductance. At constant available energy, vascular leaf conductance was reduced by as much as 2 mm s⁻¹ and moss surface conductance was enhanced by up to 3 mm s⁻¹ by large D_a. Considering vascular and non-vascular water transport characteristics and frequency of water table position and given the observed variations of Q_E partitioning with water table location and moss and peat water content, it is suggested that modelling efforts focus on how dry hummocks and wet hollows each contribute to Q_E, especially as related to D_a and soil moisture dynamics.     

Differences in the photoacclimation and photoprotection exhibited by two species of the ciguatera causing dinoflagellate genus,Gambierdiscus

In culture, Gambierdiscus spp. have been shown to prefer irradiances that are relatively low (≤250 μmol photons m⁻² s⁻¹) versus those to which they are frequently exposed to in their natural environment (>500 μmol photons m⁻² s⁻¹). Although several behavioral strategies for coping with such irradiances have been suggested, it is unclear as to how these dinoflagellates do so on a physiological level. More specifically, how do long term exposures (30 days) affect cell size and cellular chlorophyll content, and what is the photosynthetic response to short term, high irradiance exposures (up to 1464 μmol photons m⁻² s⁻¹)? The results of this study reveal that cell size and chlorophyll content exhibited by G. carolinianus increased with acclimation to increasing photon flux density. Additionally, both G. carolinianus and G. silvae exhibited reduced photosynthetic efficiency when acclimated to increased photon flux density. Photosynthetic yield exhibited by G. silvae was greater than that for G. carolinianus across all acclimation irradiances. Although such differences were evident, both G. carolinianus and G. silvae appear to have adequate biochemical mechanisms to withstand exposure to irradiances exceeding 250 μmol photons m⁻² s⁻¹ for at least short periods of time following acclimation to irradiances of up to 150 μmol photons m⁻² s⁻¹.