Forest Leaf Area Index Research Articles

High leaf area index inhibits net primary production in global temperate forest ecosystems.

Within limited growth age in some regions, forest production, including gross primary production (GPP) and net primary production (NPP), was linearly correlated with leaf area index (LAI). However, over wide range of growth age in the global scale, LAI patterns of forest production are unclear. Here, we compiled a subset from the Global Soil Respiration Database (SRDB) for global temperate forest ecosystems. The subset database mainly included forest production, soil respiration, and LAI data in 493 study sites over wide range of forest growth age (0-500years). The results showed that LAI initially increased rapidly, reached a peak at juvenility, decreased slowly until maturity, and again increased possibly with further forest aging (R2 = 0.21, P < 0.001). We found that the dynamics of both GPP and NPP across global temperate forest ecosystems were driven by LAI. GPP initially increased and subsequently stabilized with increasing LAI. NPP peaked at LAI of about 5.6m2m-2, and subsequently decreased. The decrease in NPP resulted from the asymptotic increase in GPP and the continuing decrease in the NPP/GPP ratio with increasing LAI. The decline in the NPP/GPP ratio resulted from the significant increase in autotrophic respiration (Ra), and especially after canopy closure, Ra increased more quickly with increasing LAI than GPP. These results will improve our understanding of the control of LAI on ecosystem production.

Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low &gt; moderate &gt; high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques: A review

Forest vertical stratification directly affects the transfer of solar radiation and water evapotranspiration in the canopy. Consequently, leaf photosynthesis varies in different parts of the canopy, affecting the growth of trees and the succession process of the plant community. The vertical distribution of forest structural parameters, such as the leaf area index (LAI) and clumping index (CI), is critical not only for understanding of the forest carbon, nitrogen, and water cycles and remote sensing radiative transfer processes, but also for forest monitoring and management practices. Forest vertical LAI and CI can be obtained through field measurement and remote sensing inversion methods. For field measurements, LAI-2200, digital hemispherical photography, and photosynthetically active radiation sensors are frequently used instruments. Vertical structural measurements are usually carried out through destructive sampling, tower-base measurement, and mobile elevators. In general, field measurements are more accurate; however, they are time-consuming and laborious, making them unsuitable for large-scale applications. Remote sensing technology uses the passive optical method, the light detection and ranging (LiDAR) technology, and synthetic aperture radar (SAR) methods to estimate forest vertical LAI and CI. The classical passive optical method is based on either the empirical vegetation index estimation method or physical model inversion method. The vegetation index method establishes an empirical relationship between vegetation structural parameters and vegetation indices. Subsequently, the relationship is used to estimate the structural parameters for different layers. The physical model inversion method is based on a physical radiative transfer model. In general, the application of both passive optical and SAR methods in forest vertical parameter retrieval is limited. As an active remote sensing technology, the LiDAR technology has shown its unique advantages in the inversion of vertical structural parameters. Based on the platform used, LiDAR can be classified into three categories: Terrestrial laser scanning (TLS), airborne laser scanning (ALS), and spaceborne laser scanning. The basic rationale of the LiDAR technology is based on the Beer-Lambert Law, which estimates canopy LAI and CI from the canopy gap fraction; both empirical statistical and physical inversion methods are used during the process. The use of LiDAR to estimate LAI and CI is gaining popularity worldwide, but most of the research is based on TLS and ALS. The development of new space-based LiDAR technologies, such as the Global Ecosystem Dynamics Investigation on the International Space Station, will greatly advance the acquisition of forest vertical structural information on a global scale. However, the relatively complex operation and data processing requirements have limited its widespread application. The vertical distribution of forest LAI can be represented by the normal, Weibull, beta, chi-square, and Johnson’s S-B distributions. The CI values usually decrease with the increase in canopy height because the upper part of the canopy usually has larger gaps. However, mature global vertical LAI and CI products that can meet the application requirements are currently unavailable. Due to the limits of ground measurements, forest vertical LAI and CI retrieved from remote sensing lack sufficient ground validation. In field measurements, the LAI values of trees, shrubs, and herbs should be acquired simultaneously in order to validate the remote sensing LAI. The development of wireless sensor networks and unmanned aerial vehicles provides convenient and effective methods to obtain a large amount of vertical structural information useful for validation purposes. Forest vertical LAI and CI have been applied in land surface and radiative transfer models and forest management studies. In future research, new field measurement and remote sensing inversion methods should be explored. Various LiDAR data sources can be used to generate high-quality vertical structural products, and these vertical products should be fully validated to better meet the requirements proposed by the research and management communities.

Applications of dual-wavelength hemispherical photography in urban climatology and urban forestry

Digital hemispherical photography (DHP) is widely used to measure the radiative environment and estimate sky view factors (SVF) in urban areas and leaf area index (LAI) in forests. However, a limitation is the difficulty to distinguish trees from buildings, or leaves from stems and branches. In this study, we collected and processed dual-wavelength photographs recording visible and near-infrared (NIR) light in order to classify pixels into sky, green and woody plant elements, and buildings. Three applications of the method are presented: calculation of partial SVFs accounting for the obstruction of sky by buildings and vegetation separately, the modelling of mean radiant temperature (Tmrt), and the correction of LAI estimates for light intercepted by woody elements and buildings. The obtained partial SVFs were in good agreement with values modelled based on digital surface models. Distinguishing between buildings and vegetation in the modelling of long-wave radiation fluxes in the SOLWEIG model resulted in differences in modelled Tmrt by up to 3 °C. The bias of LAI estimates in urban parks caused by the light interception by woody elements and buildings was found to be relatively small (3–4 %). However, the presented method shows a high potential for estimates of LAI of urban vegetation in densely built-up areas.

Sampling protocols of specific leaf area for improving accuracy of the estimation of forest leaf area index

Litterfall collection is a non-destructive direct method to estimate forest leaf area index (LAI) and validate indirect LAI products. However, the potential errors associated with the variation in specific leaf area (SLA) are rarely explored. Here, we measured the SLA of leaf litter for each tree species in a temperate deciduous forest using the litterfall collection method from 2012 to 2018, and assessed the spatial and temporal variation in SLA and its consequence on LAI estimates. The results showed that the spatial and temporal variation in SLA for the 10 major species across the seven years ranged from 0.8% to 24.3%, with the variation across the nine permanent plots (20 m × 30 m) being higher than that within plot (five 1-m2 traps), and interannual higher than seasonal. Across the 63 plot-years, the 10 simplified SLA sampling protocols introduced the errors in LAI by −12.4% to 22.2% relative to the reference protocol (sampling leaves from each trap at each collection date for the major species). Applying the SLA obtained from one trap in each plot, one plot, at the leaf fall peak for each year, or for a single year induced the errors in LAI by –3.7% to 2.9%, −6.4% to 14.7%, −9.2% to 4.7%, and −8.1% to 8.6%, respectively. Considering the trade-off between inter-plot and interannual variation, we recommend sampling from one-trap for each plot at the leaf-fall peak for measuring SLA and quantifying the spatial pattern of LAI, and sampling each species in the typical plot for measuring SLA and monitoring the temporal fluctuation in LAI. The tight relationship between the MODIS and ground reference LAI across the seven years (R2 = 0.77) validated the use of MODIS LAI to study the long-term change in forest LAI. These findings help to establish a standardized protocol for long-term accurately measuring forest LAI with the litterfall collection.

Interannual Variation of Transpiration and Its Modeling of a Larch Plantation in Semiarid Northwest China

Quantifying the variation of forest transpiration (T) is important not only for understanding the water and energy budget of forest ecosystems but also for the prediction, evaluation, and management of hydrological effects as well as many other ecosystem services of forests under the changes of climate, vegetation, and anthropological impacts. The accurate prediction of T, a key component of water used by forests, requires mechanism-based models describing the T response to environmental and canopy conditions. The daily T of a larch (Larix principis-rupprechtti) plantation was measured through monitoring the sap flow in the growing season (from May to September) of a dry year (2010), a normal year (2012), and a wet year (2014) at a shady slope in the semi-arid area of Liupan Mountains in northwest China. Meanwhile, the meteorological conditions, soil moisture, and forest canopy leaf area index (LAI) were monitored. To get a simple and easily applicable T model, the numerous influencing parameters were grouped into three factors: the atmospheric evapotranspiration demand indicated by the potential evapotranspiration (PET), the soil water supply ability indicated by the relative extractable soil water content (REW), and the vegetation transpiration capacity indicated by the forest canopy LAI. The T model was established as a continuous multiplication of the T response equations to individual factors, which were determined using the upper boundary lines of measured data. The effect of each factor on the T in a dry year (2010) or normal year (2012) was assessed by comparing the measured T in the baseline of the wet year (2014) and the model predicted T, which was calculated through inputting the actual data of the factor (i.e., PET) to be assessed in the dry or normal year and the measured data of other two factors (i.e., REW, LAI) in the baseline of the wet year. The results showed that the mean daily T was 0.92, 1.05, and 1.02 mm; and the maximum daily T was 1.78, 1.92, and 1.89 mm in 2010, 2012, and 2014, respectively. The T response follows a parabolic equation to PET, but a saturated exponential equation to REW and LAI. The T model parameters were calibrated using measured data in 2010 and 2012 (R2 = 0.89, Nash coefficient = 0.88) and validated using measured data in 2014 satisfactorily (R2 = 0.89, Nash coefficient = 0.79). It showed a T limitation in the dry year 2010 for all factors (18.5 mm by PET, 11.5 mm by REW, and 17.8 mm by LAI); while a promotion for PET (1.4 mm) and a limitation for REW (4.2 mm) and LAI (14.3 mm) in the normal year 2012. The daily T model established in this study can be helpful to assess the individual factor impact on T and improve the daily T prediction under changing environmental and canopy conditions.

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely sensed data. However, the performance of GPR in predicting forest biophysical parameters has hardly been examined using full-spectrum airborne hyperspectral data. The main objective of this study was to evaluate the potential of GPR to estimate forest leaf area index (LAI) using airborne hyperspectral data. To achieve this, field measurements of LAI were collected in the Bavarian Forest National Park (BFNP), Germany, concurrent with the acquisition of the Fenix airborne hyperspectral images (400−2500 nm) in July 2017. The performance of GPR was further compared with three commonly used empirical methods (i.e., narrowband vegetation indices (VIs), partial least square regression (PLSR), and artificial neural network (ANN)). The cross-validated coefficient of determination (Rcv2) and root mean square error (RMSEcv) between the retrieved and field-measured LAI were used to examine the accuracy of the respective methods. Our results showed that using the entire spectral data (400−2500 nm), GPR yielded the most accurate LAI estimation (Rcv2 = 0.67, RMSEcv = 0.53 m2 m−2) compared to the best performing narrowband VIs SAVI2 (Rcv2 = 0.54, RMSEcv = 0.63 m2 m−2), PLSR (Rcv2 = 0.74, RMSEcv = 0.73 m2 m−2) and ANN (Rcv2 = 0.68, RMSEcv = 0.54 m2 m−2). Consequently, when a spectral subset obtained from the analysis of VIs was used as model input, the predictive accuracies were generally improved (GPR RMSEcv = 0.52 m2 m−2; ANN RMSEcv = 0.55 m2 m−2; PLSR RMSEcv = 0.69 m2 m−2), indicating that extracting the most useful information from vast hyperspectral bands is crucial for improving model performance. In general, there was an agreement between measured and estimated LAI using different approaches (p > 0.05). The generated LAI map for BFNP using GPR and the spectral subset endorsed the LAI spatial distribution across the dominant forest classes (e.g., deciduous stands were generally associated with higher LAI values). The accompanying LAI uncertainty map generated by GPR shows that higher uncertainties were observed mainly in the regions with low LAI values (low vegetation cover) and forest areas which were not well represented in the collected sample plots. This study demonstrated the potential of GPR for estimating LAI in forest stands using airborne hyperspectral data. Owing to its capability to generate accurate predictions and associated uncertainty estimates, GPR is evaluated as a promising candidate for operational retrieval applications of vegetation traits.

A Spatial Relationship between Canopy and Understory Leaf Area Index in an Old-Growth Cool-Temperate Deciduous Forest

Quantification of leaf area index (LAI) is essential for understanding forest productivity and the atmosphere–vegetation interface, where the majority of gas and energy exchange occurs. LAI is one of the most difficult plant variables to adequately quantify, owing to large spatial and temporal variability, and few studies have examined the horizontal and vertical distribution of LAI in forest ecosystems. In this study, we demonstrated the LAI distribution in each layer from the understory to canopy using multiple-point measurements (121 points) and examined the relationships among layers in a cool-temperate deciduous forest. LAI at each point, and the spatial distribution of LAI in each layer, varied within the forest. The spatial distribution of LAI in the upper layer was more heterogeneous than that of LAI at the scale of the entire forest. Significant negative correlations were observed between the upper- and lower-layer LAI. Our results indicate that the understory compensates for gaps in LAI in the upper layer; thus, the LAI of the entire forest tends to remain spatially homogeneous even in a mature forest ecosystem.

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

Scale-dependent changes in the contributions of biotic and abiotic factors to leaf area index in a natural forest in northeast China

The leaf area index (LAI) is one of the most commonly used parameters for quantifying canopy structure and predicting vegetation growth. However, it remains unclear whether the relative contributions of biotic factors and abiotic factors to stand LAI are modulated by the study scale (or plot size). Additionally, whether tree size affects the contributions of biotic factors to the LAI is unknown. In this study, we measured LAI with a LAI-2200 plant canopy analyzer in subplots of three scales (i.e., 10 m × 10 m, 20 m × 20 m and 30 m × 30 m) in a 9 ha old-growth forest plot in northeast China. We also measured biotic factors for large trees (diameter at breast height, DBH > 9 cm) and small trees (DBH ≤ 9 cm) as well as abiotic factors (e.g., soil properties and light availability quantified by hillshade) in each subplot for three scales. With structural equation modeling (SEM), we found that an increase in the basal area of large trees directly reduced the stand LAI at all three scales and indirectly reduced the LAI by limiting the growth of small trees, but increased stand density (or basal area) for small trees increased the stand LAI. The increased species richness of small trees enhanced the LAI at 10 m × 10 m and 20 m × 20 m; in contrast, the increased species richness of large trees only significantly reduced the LAI at 20 m × 20 m. Increases in a soil property (total phosphorus) directly reduced the LAI at all three scales, but increased light increased the LAI. We also found that the contribution of large trees to LAI was higher at smaller scales but that of small trees increased with increasing scale; the contribution of soil properties to the LAI increased with increasing scale, but light made higher contributions at smaller scales. These results clearly suggest that the relative contributions of biotic factors and abiotic factors to stand LAI are scale-dependent and that tree size plays a key role in revealing the relationships between biotic factors and stand LAI in natural forests.

Drivers of leaf area index variation in Brazilian Subtropical Atlantic Forests

The Atlantic Forest, a global biodiversity hotspot, has changed dramatically due to land use pressures causing deforestation, degradation, and forest fragmentation. A major challenge is to understand and potentially mitigate the consequences of these changes, for the capacity of forests to deliver essential environmental services to rural areas. Here, we focus on unraveling the mechanisms underpinning spatial variation in forest leaf area index. Forest leaf area index can be used as an environmental indicator that controls key forest functions underlying environmental services and is also expected to respond to land use change. Specifically, we use Structural Equation Modelling to determine the direct and indirect pathways that link environmental drivers to canopy leaf area index (LAI) variation across forest types in the Atlantic Forest in Southern Brazil. We sampled 240 sample units (each 4000 m2), from a systematic and permanent forest inventory set which covers the State of Santa Catarina in a 10 km × 10 km grid, using hemispherical photographs. Environmental variables were extracted for each sample unit, including climatic and topographic data as well as indicators of anthropogenic pressure. Our results showed that forest types differed in their leaf area index (but not all of them) and that forest canopies show complex responses to environmental drivers, encompassing direct and indirect pathways. A major pathway was the positive effect of ‘Distance to city’ on the ‘Percentage of cropland in the matrix’. This led to a decline in the distance of the sample unit to the forest edge, indirectly reducing LAI, presumably because of elevated tree mortality at the forest edge. ‘Terrain steepness’ and ‘Rainfall in the driest month’ independently affected the ‘Percentage of cropland in the matrix’ and the ‘Distance to forest edge’. Halting forest fragmentation and increasing fragment size by landscape planning will mitigate these anthropogenic LAI declines. This can be achieved with a combination of legal and market mechanisms, like enforcement of the Brazilian Forest Act regulation on buffer zones around water bodies and steep slopes, landscape planning, and payment for environmental services to compensate the farmers for maintaining forest cover on otherwise productive land.

Retrieval of Vertical Foliage Profile and Leaf Area Index Using Transmitted Energy Information Derived from ICESat GLAS Data

The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites similar in their biophysical parameters. Our method utilized the information from not only the interaction between the laser and the forest but also the sensor configuration, which brought the benefit that our method was free from an empirical input parameter. Specifically, we first derived the transmitted energy profile (TEP) through the lidar 1-D radiative transfer model. Then, the obtained TEP was utilized to calculate the vertical gap distribution. Finally, the vertical gap distribution was taken as input to derive the VFP based on the Beer–Lambert law, and the LAI was calculated by integrating the VFP. Extensive validations of our method were carried out based on the discrete anisotropic radiative transfer (DART) simulation data, ground-based measurements, and the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The validation based on the DART simulation data showed that our method could effectively characterize the VFP and LAI under various canopy architecture scenarios, including homogeneous turbid and discrete individual-tree scenes. The ground-based validation also proved the feasibility of our method: the VFP retrieved from the GLAS data showed a similar trend with the foliage distribution density in the GLAS footprints; the GLAS LAI had a high correlation with the field measurements, with a determination coefficient (R2) of 0.79, root mean square error (RMSE) of 0.49, and bias of 0.17. Once the outliers caused by low data quality and large slope were identified and removed, the accuracy was further improved, with R2 = 0.85, RMSE = 0.35, and bias = 0.10. However, the MODIS LAI product did not present a good relationship with the GLAS LAI. Relative to the GLAS LAI, the MODIS LAI showed an overestimation in the low and middle ranges of the LAI and a saturation at high values of approximately LAI = 5.5. Overall, this method has the potential to produce continental- and global-scale VFP and LAI datasets from the spaceborne lidar system.

Direct measurement of leaf area index in a deciduous needle-leaf forest, eastern Siberia

Accurate calculation of leaf area index (LAI) in boreal forests, which are vulnerable to climate change and have insufficient ground-truth data, is an important but challenging task when evaluating the spatio-temporal variability of ecosystem structures and functions (photosynthesis and evapotranspiration). By applying a direct method, we calculated the LAI of multiple forest layers (i.e., overstory of larch; understory of birch, willow, and alder; and floor vegetation) in a deciduous needle-leaf forest with a somewhat open canopy in eastern Siberia. We performed field studies that included logging one overstory larch tree, harvesting leaves of larch and floor vegetation (e.g., Vaccinium vitis-idaea), sampling leaves of understory trees, and measuring the architecture and morphology of overstory and understory trees and leaves. The plot-scale LAIs of larch overstory, understory trees, and floor vegetation were 1.30, 0.50, and 0.51, respectively, for a total of 2.31. Sum of our plot-scale LAI of larch overstory and understory trees was similar to previously reported results based on indirect methods. Our results indicate that LAI of understory trees and forest floor vegetation is important to improve the accuracy of total LAI of a deciduous needle-leaf forest with a somewhat open canopy in eastern Siberia.

Periodic Relations between Terrestrial Vegetation and Climate Factors across the Globe

In this paper, cross-spectrum analysis was used to verify the agreement of periodicity between the global LAI (leaf area index) and climate factors. The results demonstrated that the LAI of deciduous forests and permanent wetlands have high agreement with temperature, rainfall and radiation over annual cycles. A low agreement between the LAI and seasonal climate variables was observed for some of the temperate and tropical vegetation types including shrublands and evergreen broadleaf forests, possibly due to the diversity of vegetation and human activities. Across all vegetation types, the LAI demonstrated a large time lag following variation in radiation (&gt;1 month), whereas relatively short lag periods were observed between the LAI and annual temperature (around 2 weeks)/rainfall patterns (less than 10 days), suggesting that the impact of radiation on global vegetation growth is relatively slow, which is in accord with the results of previous studies. This work can provide a benchmark of the phenological drivers in global vegetation, from the perspective of periodicity, as well as helping to parameterize and refine the DGVMs (Dynamic Global Vegetation Models) for different vegetation types.

The effect of using different vegetation indices for mangrove leaf area index modelling

Spectral vegetation index (SVI) is a mathematical combination of image spectral bands, especially ranges from visible to near infrared portion of the light spectrum. The purpose of SVI is to emphasize the vegetation content information from an image and doesn’t directly correlate with any physical or bio-physical characteristics of vegetation. One of the important biophysical parameters of vegetation that can be derived from SVI is Leaf Area Index (LAI). LAI can be defined as one half the total green leaf area per unit horizontal ground surface area and considered as an indicator to determine the level of mangrove health. Various SVIs have been developed and different SVI affects the accuracy of the LAI model. This study aimed to (1) compare and contrast the performance of several SVIs applied to WorldView-2 (WV-2) image to estimate the LAI, and (2) find the most accurate model for estimating LAI of mangrove forest in Perancak Estuary, Bali. The SVI used are Simple Ratio (SR), Normalized Differenced Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Atmospherically Resistant Vegetation Index (GARI), dan Wide Dynamic Range Vegetation Index (WDRVI). The LAI models developed were based on the semi-empirical relationships between SVIs and field LAI measured from hemispherical photograph. The corresponding values of both parameters were correlated to find the regression function for the modelling. The results show that the best accuracy was obtained from NDVI which has an R2 value of 0.83 and an estimation accuracy of 89.10 %.

Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0

Abstract. The nitrogen cycle has been continuously disrupted by human activity over the past century, resulting in almost a tripling of the total reactive nitrogen fixation in Europe. Consequently, excessive amounts of reactive nitrogen (Nr) have manifested in the environment, leading to a cascade of adverse effects, such as acidification and eutrophication of terrestrial and aquatic ecosystems, and particulate matter formation. Chemistry transport models (CTMs) are frequently used as tools to simulate the complex chain of processes that determine atmospheric Nr flows. In these models, the parameterization of the atmosphere–biosphere exchange of Nr is largely based on few surface exchange measurement and is therefore known to be highly uncertain. In addition to this, the input parameters that are used here are often fixed values, only linked to specific land use classes. In an attempt to improve this, a combination of multiple satellite products is used to derive updated, time-variant leaf area index (LAI) and roughness length (z0) input maps. As LAI, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD15A2H product. The monthly z0 input maps presented in this paper are a function of satellite-derived normalized difference vegetation index (NDVI) values (MYD13A3 product) for short vegetation types (such as grass and arable land) and a combination of satellite-derived forest canopy height and LAI for forests. The use of these growth-dependent satellite products allows us to represent the growing season more realistically. For urban areas, the z0 values are updated, too, and linked to a population density map. The approach to derive these dynamic z0 estimates can be linked to any land use map and is as such transferable to other models. We evaluated the sensitivity of the modelled Nr deposition fields in LOng Term Ozone Simulation – EURopean Operational Smog (LOTOS-EUROS) v2.0 to the abovementioned changes in LAI and z0 inputs, focusing on Germany, the Netherlands and Belgium. We computed z0 values from FLUXNET sites and compared these to the default and updated z0 values in LOTOS-EUROS. The root mean square difference (RMSD) for both short vegetation and forest sites improved. Comparing all sites, the RMSD decreased from 0.76 (default z0) to 0.60 (updated z0). The implementation of these updated LAI and z0 input maps led to local changes in the total Nr deposition of up to ∼30 % and a general shift from wet to dry deposition. The most distinct changes are observed in land-use-specific deposition fluxes. These fluxes may show relatively large deviations, locally affecting estimated critical load exceedances for specific natural ecosystems.

Canopy structure and understory light characteristics of a natural Quercus aliena var. acuteserrata forest in China northwest: Influence of different forest management methods

Close-to-nature forest management (CNFM), structure-based forest management (SBFM), and secondary forest comprehensive silviculture (SFCS) are three forest management methods that have been promoted in China. Unfortunately, changes to the canopy structure and understory light characteristics after forest management remain unclear. We compared the canopy structure and light characteristics of a natural Quercus aliena var. acuteserrata forest under the four management methods of CNFM, SBFM, SFCS and unmanaged control (CK) after four years of using the WinScanopy instrument. The results suggest that (1) the leaf area index (LAI) values in the CNFM and SBFM were higher than that in the CK, the mean leaf angle (MLA) value in the SBFM was significantly lower than that in the CK. (2) The values of total under radiant energy (TU) and directed radiant energy (Dir) in the SFCS were significantly higher than that in the CK, while the values of diffused radiant energy (Dif) in the CNFM and SBFM were significantly lower than that in the CK. The extinction coefficient (EC) value in three forest management measure was significantly lower than that in the CK. (3) The canopy structure was sensitive to changes in tree crowding, and the light characteristics were sensitive to changes in forest layer numbers, LAI and CO, which explained 81.19% and 94.88% of the total variations in the canopy structure and light characteristics, respectively. Thus, CNFM and SBFM altered the canopy structure, which may be attributed to the LAI that was influenced by tree crowding, and the three forest treatments altered the understory light environment, which may be influenced mainly by the canopy structure. CNFM and SBFM increased the leaf area index of the stand and improved the understory light environment in a natural Quercus aliena var. acuteserrata forest.

Resilience of a subtropical rainforest to annual typhoon disturbance: Lessons from 25-year data of leaf area index

Quantifying ecosystem resilience is critical for predicting how shifts in disturbance regime affect ecosystem structure and function as a result of recent climate change. Studies of disturbance-ecosystem interactions often focus on one or a few disturbance events but the interactions may vary considerably among disturbance events. We used a 25-year LAI (leaf area index) record of the Fushan Experimental Forest (FEF) of northeastern Taiwan to explore forest LAI resilience to annual typhoon disturbance. The four typhoons of 1994 caused the most dramatic change of LAI in both the magnitude and the rate, as well as the longest time for a full recovery. However, typhoons varied in their effects, and not all intense typhoons caused major impact suggesting that result derived from one or a few disturbance events is unlikely to capture the full spectrum of disturbance effect. Using LAI of 4.26 measured following a non-typical year of no typhoon occurrence as a reference, it took 11─16 years before the predicted LAI to return to the reference level, which represents the resilience (time to return) of the forest LAI under minimal typhoon effects. Using the event-based analyses, the average degree of return was 0.86, with values as low as 0.45, suggesting that the LAI of the FEF is not always able to fully recover within the inter-typhoon time period (<1 year). Once returned to the reference state, the LAI of the FEF stayed at the reference state for several months to several years. With annual typhoon disturbance and a return time longer than one year and may be up to more than one decade, the LAI of the FEF is constantly undergoing the disturbance-recovery process and rarely reaches an equilibrium state. Therefore, a system experiencing frequent disturbance such as FEF might be better characterized by non-equilibrium temporal dynamics of its key functions.

Local and landscape-level diversity effects on forest functioning.

Research of the past decades has shown that biodiversity is a fundamental driver of ecosystem functioning. However, most of this biodiversity-ecosystem functioning (BEF) research focused on experimental communities on small areas where environmental context was held constant. Whether the established BEF relationships also apply to natural or managed ecosystems that are embedded in variable landscape contexts remains unclear. In this study, we therefore investigated biodiversity effects on ecosystem functions in 36 forest stands that were located across a vast range of environmental conditions in managed landscapes of Central Europe (Switzerland). Specifically, we approximated forest productivity by leaf area index and forest phenology by growing-season length and tested effects of tree species richness and land-cover richness on these variables. We then examined the correlation and the confounding of these local and landscape-level diversity effects with environmental context variables related to forest stand structure (number of trees), landscape structure (land-cover edge density), climate (annual precipitation) and topography (mean altitude). We found that of all tested variables tree species richness was among the most important determinants of forest leaf area index and growing-season length. The positive effects of tree species richness on these two ecosystem variables were remarkably consistent across the different environmental conditions we investigated and we found little evidence of a context-dependent change in these biodiversity effects. Land-cover richness was not directly related to local forest functions but could nevertheless play a role via a positive effect on tree species richness.

Variation in leaf area index in complex mixed-conifer forests in California’s Sierra Nevada: implications for stocking control

AbstractThe mixed-conifer forests in California’s Sierra Nevada include species from several genera (Pinus, Abies, Pseudotsuga, Calocedrus and Sequoiadendron). These forests have complex disturbance regimes dominated by low to moderate severity fire that often resulted in patchy spatial patterns and multiaged stands. Leaf area index (LAI) describes the total leaf surface area per unit area in a forest community and is related to wood and biomass production and ecosystem values such as water usage, water yields and carbon sequestration. LAI can also serve as a representation of growing space occupancy and the basis for stocking control, including in multiaged stands. Nine study sites were sampled with 22–37 0.05 ha plots per study site to estimate LAI and other metrics. LAI was highest in study sites with greater proportions of shade tolerant Abies and Calocedrus species and on higher productivity sites. Recent drought-related mortality has reduced stocking and LAI. The combination of fire suppression and timber harvest over the past century has resulted in stands with higher densities, and greater proportions of shade tolerant species. Managing these structures to restore their presettlement character will involve reducing overall stocking, increasing proportions of intolerant species and increasing fine-scale heterogeneity. LAI allocation—allocating leaf area to age classes, species or canopy strata—can be used to design new structures that resemble presettlement structures and are resilient to disturbances.