Canopy Leaf Area Index Research Articles

Using heterogeneity indices to adjust basal area – Leaf area index relationship in managed coniferous stands

The structure of contemporary managed forests is complex and deviates from experimental forests which are usually even-aged monocultures and single-storied. To apply theoretical growth and yield functions on managed forests, adjustments are required, especially for leaf area index (LAI) which is a key biophysical variable in process-based growth models. To asses this, the performance of canopy LAI in modelling the basal area (BA) of managed boreal forests dominated by Norway spruce (Picea abies (L). Karst) and Scots pine (Pinus sylvestris L.) was investigated by heterogeneity analysis. The study was based on the assumption that canopy LAI and BA are strongly related and are vital for estimating stand productivity and growth. Managed forests were represented by field data from the 2016 and 2017 Swedish National Forest Inventory (NFI) campaigns. Species-specific LAI conversion parameters were applied on the general plant area index (PAI) values from hemispheric fish-eye photos taken from the permanent and temporary NFI sample plots. The heterogeneity analysis was studied in two parts by: (a) ground-based stand structural heterogeneity (SSH) described by species composition, coefficient of tree diameter variation, tree social status and height-diameter ratio, and (b) spectral heterogeneity (SPH) by vegetation and textural indices developed from Sentinel-2. Species-specific final (with heterogeneity metrics) and base (without heterogeneity metrics) models were fitted for BA-LAI and BA-PAI relationships by nonlinear least squares and generalised additive regression functions, respectively. The performance of models was assessed by the root-mean-squared error (RMSE, m2 ha−1) and the relative root-mean-squared error (RelRMSE, %) metrics. For both species, BA-LAI final models (FMs) accounting for heterogeneity resulted in larger explained variance than the base models (BMs). Compared with the BMs, FMs with SSH reduced the variance by 55% in Norway spruce (RMSE = 3.33, RelRMSE = 15.39) and 43% in Scots pine (RMSE = 3.70, RelRMSE = 17.38). The fit between BA-LAI with SPH also showed an improvement for Norway spruce (RMSE = 5.56) and Scots pine (RMSE = 5.66) over the BMs, suggesting the potential use of Sentinel-2 in future growth models. The results of the study suggest that in growth models when extrapolating theoretical growth functions to managed forests, there is a need to calibrate the models with the forest structural heterogeneity. This is important for drawing realistic conclusions from growth and yield modelling of managed stands of Norway spruce and Scots pine.

Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1–0.2 and 0.0–0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions.

Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits

Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV < 0.19) compared to the top-of-canopy traits expression across all seasons. In contrast, canopy chlorophyll estimated from the top-of-canopy expression demonstrated strong fidelity with Sentinel-2 bands and vegetation indices (RMSE < 0.48 µg/cm2) compared to weighted canopy chlorophyll (RMSE > 0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services.

Super-resolution enhancement of Sentinel-2 image for retrieving LAI and chlorophyll content of summer corn

Sentinel-2 satellite is a new generation of multi-spectral remote sensing technique with high spatial, temporal and spectral resolution. Especially, Sentinel-2 incorporates three red-edge bands with central wavelength at 705, 740 and 783 nm, which are very sensitive to vegetation changing, heath and variations. Unfortunately, their spatial resolution is only 20 m, which is lower comparably. This spatial resolution brings difficulties for mining the potential of Sentinel-2 image in vegetation monitoring. Therefore, we focus on enhancing the spatial resolution of Sentinel-2 red edge band images to 10m using the SupReME algorithm. Furthermore, the summer corn canopy leaf area index (LAI), leaves chlorophyll content (LCC) and canopy chlorophyll content (CCC) were retrieved by the linear and physical models for the corn growth monitoring purpose. The results showed that the spatial resolution of Sentinel-2 images had been enhanced to 10m from original 20m, and the estimation accuracy (EA) was over 97% for pixels planted by summer corn. Moreover, the accuracy of summer corn canopy LAI, LCC and CCC was improved respectively using enhanced Sentinel-2 images by SupReME method. During these three parameters retrieval, the red-edge bands or SWIR bands were introduced into optimal cost function and vegetation index which the accuracy of these models was high. The SupReME algorithm provides a valuable way for Sentinel-2 images enhancement, which is of great potential to mining Sentinel-2 images and multitude its application.

Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p &lt; 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application.

Consequences of environmental heterogeneity for the photosynthetic light environment of a tropical forest

We studied the interplay of atmospheric and canopy structure factors driving the canopy light environment (Photosynthetic Photon Flux Density, PPFD) in primary moist tropical forest in the Tapajós National Forest, Brazil. We quantified the temporal and spatial length scales that characterize intact rain forest inhomogeneities, asking: Are seasonal changes in the canopy radiation balance evident at these scales? We sought to describe the components of intensity, duration and spatial variation of within-canopy PPFD in light of these inhomogeneities. Do fluctuating atmospheric conditions, especially the presence of clouds and precipitation, affect the radiative inputs at both the canopy top and the forest floor?We examined the characteristic scales of heterogeneity in the vertical and the horizontal using a two-part approach. For radiation we combined long-term continuous high-frequency measurements of down- and up-welling short-wave and visible wavebands above the canopy with similarly frequent observations from a dense sensor network at forest floor. Vertical variations in canopy structure, obtained with a ground-based LIDAR, similarly combined intensive observations at the sensor network with occasional large-scale transects along the forest floor. Close similarities in both radiation and canopy structure at both scales support the representativeness of local observations of the wider area.A composite broadband measure similar to the Normalized Difference Vegetation Index (denoted cbNDVI) was constructed from above-canopy observations to compare with reports of remotely sensed canopy reflectivity at this site. We estimated the canopy Leaf Area Index (LAI) by combining observations of the variation in understory transmittance by solar elevation with a commonly-used algorithm. We obtained the conventional whole-canopy extinction coefficient, by applying the Beer-Lambert law.Over the course of a year this forest receives 11,795 mol m−2, only 62% of potential clear-day PPFD – atmospheric transmissivity is reduced by clouds, precipitation, smoke and haze. Very little PAR (≈ 2%) is reflected from the canopy and only 5.7% penetrates to 1 m above the forest floor - overall 92% of PAR is absorbed by the canopy. All radiation balance components closely tracked the dynamics of incoming light, showing little seasonal variation.Understory light observations across the 7.5–28.5 m spanning the understory array sensors showed essentially constant correlation between sensor pairs over time. There was a high degree of local persistence in the understory spatial pattern that varied slowly and directionally with the changing geometry of the sun and canopy structure. Over larger distances (to 1000 m), the patterns of spatial autocorrelation of understory PPFD and outer canopy structure were remarkably similar in shape, both declining rapidly to a more-or-less constant level around 15–20 m, a scale consistent with the dimensions of outer canopy crowns.The vertical pattern of transmission and absorption was estimated by combining understory transmittance with the distribution of canopy surface area obtained from the ground-based LIDAR system. It showed the maximum absorption relatively low in the canopy (8–19 m above ground). Although rather tall (canopy height is at 41.5 m), the extremely elaborate outer surface of the forest suggests the layers highest above ground are of little consequence to the PAR absorption budget.The cbNDVI measure exhibited seasonal variation consistent with other reports (somewhat higher in the wet season) but in contrast to a recent argument that the forest ‘greens up’ during the early dry season. However, except for the variations caused by the angle and intensity of incoming light modulated by atmospheric effects, there was little seasonality in the forest light environment at km67, including: canopy reflectances in several wavebands, all PPFD radiation budget components, the estimated LAI and mid-day PAR extinction coefficient and the length scales of understory PPFD. Canopy transmission was somewhat greater (6.4%) under diffuse skies compared to the least diffuse conditions (6.0%), and slightly more PPFD was absorbed under diffuse (92.7%) versus the least diffuse conditions (92.3%), but was not a significant contribution to the budget.The large diurnal variations in the extinction coefficient seriously affects its utility as a descriptor of canopy radiative properties. We propose an alternative approach for transmitted light: the canopy behaves as: 1.) a constant fraction filter under diffuse conditions, combined with 2.) a variable filter depending on solar elevation for sunfleck conditions. These regimes may be described with simple parameters each having mechanistic relations to canopy structure. In summary, we demonstrate that obtaining the radiation signal at forest floor at high data rate for long periods exploits seasonal sun angle changes to probe canopy structure. Combined with occasional long transect information, temporal and vertical sampling spatially allow improved definition of characteristic scales to describe both the understory light environment and canopy structure.

Basal internode elongation of rice as affected by light intensity and leaf area

Short basal internodes are important for lodging resistance of rice (Oryza sativa L.). Several canopy indices affect the elongation of basal internodes, but uncertainty as to the key factors determining elongation of basal internodes persists. The objectives of this study were (1) to identify key factors affecting the elongation of basal internodes and (2) to establish a quantitative relationship between basal internode length and canopy indices. An inbred rice cultivar, Yinjingruanzhan, was grown in two split-plot field experiments with three N rates (0, 75, and 150 kg N ha−1 in early season and 0, 90, and 180 kg N ha−1 in late season) as main plots, three seedling densities (16.7, 75.0, and 187.5 seedlings m−2) as subplots, and three replications in the 2015 early and late seasons in Guangzhou, China. Light intensity at base of canopy (Lb), light quality as determined from red/far-red light ratio (R/FR), light transmission ratio (LTR), leaf area index (LAI), leaf N concentration (NLV) and final length of second internode (counted from soil surface upward) (FIL) were recorded. Higher N rate and seedling density resulted in significantly longer FIL. FIL was negatively correlated with Lb, LTR, and R/FR (P < 0.01) and positively correlated with LAI (P < 0.01), but not correlated with NLV (P > 0.05). Stepwise linear regression analysis showed that FIL was strongly associated with Lb and LAI (R2 = 0.82). Heavy N application to pot-grown rice at the beginning of first internode elongation did not change FIL. We conclude that FIL is determined mainly by Lb and LAI at jointing stage. NLV has no direct effect on the elongation of basal internodes. N application indirectly affects FIL by changing LAI and light conditions in the rice canopy. Reducing LAI and improving canopy light transmission at jointing stage can shorten the basal internodes and increase the lodging resistance of rice.

Seasonal Variations in Canopy Size and Yield of Rayong 9 Cassava Genotype under Rainfed and Irrigated Conditions

The objectives of this study were to investigate the effect of seasonal variation on canopy size, and the effect of canopy size on light penetration of ‘Rayong 9′ cassava under irrigated and rainfed conditions. Rayong 9 was planted under two water regimes in a randomized complete block design with four replications in May and November for two years. At final harvest, years were significantly different (p ≤ 0.05) for biomass, shoot dry weight, and harvest index and contributed to large portions of total variations in shoot dry weight (56.8%) and HI (44.5%). Planting date was a significant source of variations in all measured characters, and it contributed to the largest portions of variations in biomass, storage root dry weight and storage root fresh weight (46.1–60.9%). Water regimes were not significantly different for most characters except for harvest index (p ≤ 0.01). The canopy of the crop planted in May grew rapidly in early growth stages in the rainy season and then slowly after the rainy season. The canopy of the crop planted in November grew rapidly in the middle to the late growth stages. Irrigation did not significantly increase root yield although it slightly increased canopy development, leaf area index (LAI), light penetration and photosynthesis. Irrigation at the late growth stages of the crop planted in May significantly increased storage root yield. Irrigation at these growth stages helped maintain canopy development, LAI and light penetration.

Maize radiation use-efficiency response to optimally distributed foliar-nitrogen-content depends on canopy leaf-area index

Canopy foliar nitrogen content is an important determinant of crop radiation use efficiency (RUE). Previous studies suggested that nitrogen-deficient maize (Zea mays L.) crops may face a trade-off between light interception and RUE. Quantifying the relevance of this trade-off, however, demands for analytical approaches on the response of canopy photosynthesis to foliar nitrogen content. In this study we set up a theoretical framework based on canopy photosynthesis modelling to determine for maize (i) whether RUE response to total canopy foliar nitrogen (TCFN) is affected by canopy leaf area index (LAI), within a range of conventional TCFN and LAI values, and (ii) the relative advantage, in terms of crop RUE, of optimal vs. uniform foliar nitrogen distribution within the canopy. Vertical profiles of optimally-distributed foliar nitrogen became steeper with increases in LAI and reductions in TCFN. In addition, the higher the LAI, the more pronounced the reductions in RUE in response to reductions in TCFN. Accordingly, the relative advantage of optimal vs. uniform foliar nitrogen distribution increased with canopy LAI and increased with the deficiency of foliar nitrogen. With a small effect of LAI, the optimal profiles of critical-TCFN (i.e. the minimal TCFN to achieve the 95% of the maximal RUE) appeared to be appropriately described by a single curve as a function of the light distribution profile within the canopy. Results of this study highlight the importance of coupling the canopy LAI to the nitrogen availability (or vice-versa) among the crop management decisions oriented to maximize crop production while reducing the negative environmental impacts of nitrogen inputs. We also discuss the potential utility of these results as reference values in diagnosis or monitoring the nitrogen status of maize.

Evaluating seasonal effect on forest leaf area index mapping using multi-seasonal high resolution satellite pléiades imagery

The forest canopy leaf area index (LAI) is an important structural variable directly affecting functions and structures of terrestrial plant ecosystems. Optical remote sensing techniques may provide an alternative in estimating and mapping plant LAI. However, existing studies on using very high resolution (VHR) multitemporal satellite imagery to map and investigate the seasonal effect on plant LAI at a landscape scale are rare. In this study, we proposed to map and analyze forest LAI using four seasonal Pléiades images and corresponding in situ seasonal LAI measurements collected over a natural forest area in the City of Tampa, Florida, USA. A subset of selected spectral/textural features was used to develop pixel-based seasonal LAI regression models through a two-step feature selection procedure and a canonical correlation analysis. Finally, seasonal changes of the mapped LAIs were analyzed and assessed. Several interesting experimental results were created through this study, including: (i) a set of optimal texture parameters for extracting the 1st- and 2nd-order grey level statistical textures from the Pléiades imagery was determined as a window size 5 × 5, a direction 90° and pixel displacement 4 pixels; (ii) textural features were more important than spectral features in estimating and mapping forest LAI, and red band has a higher power in mapping forest LAI than other three multispectral bands; (iii) the late spring Pleiades image resulted in the highest accuracy for estimating and mapping forest LAI; and (iv) there exists a significant seasonal change of forest LAI in the study area and the seasonal effect on forest LAI mapping can be assessed by using the multi-seasonal VHR satellite imagery at a landscape scale. A novel significance for this study is that it is the first time using both spectral and textural information extracted from the multi-seasonal VHR satellite images to assess the seasonal effect on forest LAI mapping at a landscape scale. Since the experimental results and findings were derived from a relatively small study area, further testing and validation work is needed over different forest ecosystems at a landscape scale.

Variability and Uncertainty Challenges in Scaling Imaging Spectroscopy Retrievals and Validations from Leaves Up to Vegetation Canopies.

Imaging spectroscopy of vegetation requires methods for scaling and generalizing optical signals that are reflected, transmitted and emitted in the solar wavelength domain from single leaves and observed at the level of canopies by proximal sensing, airborne and satellite spectroradiometers. The upscaling embedded in imaging spectroscopy retrievals and validations of plant biochemical and structural traits is challenged by natural variability and measurement uncertainties. Sources of the leaf-to-canopy upscaling variability and uncertainties are reviewed with respect to: (1) implementation of retrieval algorithms and (2) their parameterization and validation of quantitative products through in situ field measurements. The challenges are outlined and discussed for empirical and physical leaf and canopy radiative transfer modelling components, considering both forward and inverse modes. Discussion on optical remote sensing validation schemes includes also description of a multiscale validation concept and its advantages. Impacts of intraspecific and interspecific variability on collected field and laboratory measurements of leaf biochemical traits and optical properties are demonstrated for selected plant species, and field measurement uncertainty sources are listed and discussed specifically for foliar pigments and canopy leaf area index. The review concludes with the main findings and suggestions as how to reduce uncertainties and include variability in scaling vegetation imaging spectroscopy signals and functional traits of single leaves up to observations of whole canopies.

Understory interception contributed to the convergence of surface runoff between a Chinese fir plantation and a secondary broadleaf forest

Litter and vegetation under forest canopy (i.e., understory vegetation) are known to reduce soil erosion and contribute substantially to evapotranspiration. However, previous studies rarely examined understory water interception. We conducted a comprehensive study of rainfall partitioning using a water balance equation that included both overstory and understory components in a Chinese fir (Cunninghamia lanceolata) plantation and a nearby naturally regenerated Castanopsis (C. carlesii) forest of similar age in southeastern China. Understory interception was measured by submerging understory components (vegetation and litter) in water for 24 h, which gave an estimate of the amount of water retained (i.e., water holding capacity). The total interception loss of rainfall events (>9.8 mm) that generated surface runoff converged between the two forests, a result of a smaller overstory interception loss (20%) coupled with a greater understory interception loss (14%) in the Chinese fir plantation relative to the Castanopsis forest (overstory 25% and understory 11%). The convergence of the total interception contributed to the convergence of the surface runoff, 1% of the rainfall in both forests. Furthermore, the throughfall partition (78%) was greater whereas the stemflow partition (2%) was smaller in the Chinese fir plantation relative to the Castanopsis forest (throufhfall 70% and stemflow 5%). These patterns in rainfall partitioning could be explained by the greater development of the understory vegetation (hence greater understory interception loss) in the Chinese fir plantation, lower canopy leaf area index (hence smaller overstory interception loss and greater throughfall), and rougher barks (hence smaller stemflow) of Chinese fir trees compared to that of Castanopsis trees. Our study illustrated that a comprehensive evaluation of interception loss must take into account understory components.

Canopy leaf area index at its higher end: dissection of structural controls from leaf to canopy scales in bryophytes.

There is evidence that mosses with miniature foliage elements have extremely large leaf area index (LAI) values, but it is unclear what canopy traits are responsible for these high LAI values in architecturally divergent mosses, and how the inherent trade-offs limiting maximum LAI in vascular plants can be overcome in mosses. To determine the quantitative significance of different traits in determining LAI, we developed a method to dissect LAI into underlying functionally dependent constituent traits at leaf, shoot and canopy scales. The suites of structural traits were studied altogether for 43 moss canopies from 11 species with contrasting light and water requirements along gap-understory gradients to obtain as large a range of variation in moss architecture as possible and evaluate the differentiation in moss LAI in relation to species ecology. Extensive variation in moss structural traits, 11- (shoot length) to 77-fold (shoot number per area, ), was observed at all structural scales from leaf to canopy. However, LAI only varied nine-fold, as the result of two key trade-offs: leaf size vs number trade-off and shoot leaf area vs shoot density trade-off. Owing to these negative relationships, and greater variability in , LAI primarily scaled with . and LAI increased with site light availability, and LAI was greater in open and dry habitat species. This study highlights a huge structural diversity among moss canopies, but indicates that canopies converge to a much narrower range of LAI due to trait trade-offs such that, counterintuitively, minute leaf size and densely leafed stems are not necessarily responsible for high LAI in mosses.

Drought cuts back regeneration in logged tropical forests

Logged tropical forests represent a major opportunity for preserving biodiversity and sequestering carbon, playing a large role in meeting global forest restoration targets. Left alone, these ecosystems have been expected to undergo natural regeneration and succession towards old growth forests, but extreme drought events may challenge this process. While old growth forests possess a certain level of resilience, we lack understanding as to how logging may affect forest responses to drought. This study examines the drought–logging interaction in seedling dynamics within a landscape of logged and unlogged forests in Sabah Malaysia, based on 73 plots monitored before and after the 2015–16 El Niño drought. Drought increased seedling mortality in all forests, but the magnitude of this impact was modulated by logging intensity, with forests with lower canopy leaf area index and above-ground biomass experiencing greater drought induced mortality. Moreover, community traits in more heavily logged forests shifted towards being more ruderal after drought, suggesting that the trajectory of forest succession had been reversed. These results indicate that with reoccurring strong droughts under a changing climate, logged forests that have had over half of their biomass removed may suffer permanently arrested succession. Targeted management interventions may therefore be necessary to lift the vulnerable forests above the biomass threshold.

Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands

Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors.

Response of the daily transpiration of a larch plantation to variation in potential evaporation, leaf area index and soil moisture

Tree transpiration (T) is a major water budget component and varies widely due to the integrated effects of many environmental and vegetation factors. This study aimed to separate, quantify, and then integrate the effects of the main individual factors, to improve water use estimation and manage the hydrological impacts of forests. A field study was conducted at 3 plots of larch (Larix principis-rupprechtii) plantation in the semi-humid area of the Liupan Mountains, northwest China. The main influencing factors were the atmospheric evaporative demand expressed by potential evapotranspiration (PET), the soil water availability expressed by volumetric soil moisture (VSM) within the 0–100 cm layer, and the canopy transpiration capacity expressed by forest canopy leaf area index (LAI). The daily stand T was estimated through the up-scaling of sap-flow data from sampled trees. It displayed a high degree of scattering in response to PET, VSM and LAI, with an average of 0.76 mm·day−1 and range of 0.01–1.71 mm·day−1 in the growing season of 2014. Using upper boundary lines of measured data, the response tendency of T to each factor and corresponding function type were determined. The T increases firstly rapidly with rising PET, VSM and LAI, then gradually and tends to be stable when the threshold of PET (3.80 mm·day−1), VSM (0.28 m3·m−3) and LAI (3.7) is reached. The T response follows a quadratic equation for PET and saturated exponential function for VSM and LAI. These individual factor functions were coupled to form a general daily T model which was then fitted using measured data as: T = (0.793PET − 0.078PET2)·(1 − exp(−0.272LAI))·(1 − exp(−9.965VSM)). It can well explain the daily T variation of all 3 plots (R2 = 0.86–0.91), and thus can be used to predict the response of daily T of larch stands to changes in both environmental and canopy conditions.

An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Using hyperspectral remote sensing technology to monitor leaf area index (LAI) in a timely, fast and non-destructive manner is essential for accurate quantitative crop management. The relationships between existing vegetation indices (VIs) and LAI usually tend to saturate under dense canopies in crop production. The purpose of this study was to propose a new VI in which the estimating saturation is greatly weakened, and prediction accuracy is improved under conditions of high LAI in winter wheat (Triticum aestivum L.). The quantitative relationship between ground-based canopy spectral reflectance and LAI in wheat was investigated. The results showed that the optimized band combination, namely, the form of non-linear vegetation index (NLI) was more sensitive to changes in LAI. When λ(x1) = 798 nm and λ(y2) = 728 nm, the band combination NLI (798,728) had the highest R2 of 0.757. Among the common VIs, the modified triangular vegetation index 2 (MTVI2), the ratio spectral index [RSI (760,730)] and the 2-band enhanced vegetation index (EVI2) gave superior performance (R2 > 0.710) in terms of LAI estimation, but were worse than NLI (798,728). Inspired by the modified non-linear vegetation index (MNLI), NLI (798,728) was further optimized to become a novel optimized non-linear vegetation index (ONLI), which can be calculated by the formula {{left( { 1 { + 0} . 0 5} right) , times , left( { 0. 6, times ,R_{ 7 9 8}^{2} , - ,R_{ 7 2 8} } right)} mathord{left/ {vphantom {{left( { 1 { + 0} . 0 5} right) , times , left( { 0. 6, times ,R_{ 7 9 8}^{2} , - ,R_{ 7 2 8} } right)} { left( { 0. 6, times ,R_{ 7 9 8}^{2} , + ,R_{ 7 2 8} { + 0} . 0 5} right)}}} right. kern-0pt} { left( { 0. 6, times ,R_{ 7 9 8}^{2} , + ,R_{ 7 2 8} { + 0} . 0 5} right)}}. The unified ONLI model gave an R2 of 0.779 and root mean square error (RMSE) of 1.013 across all datasets. These results indicate that the novel ONLI has strong adaptability to various cultivation conditions and can provide a good estimate of LAI in winter wheat.

Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest.

Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015-2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.

Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data

Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p &lt; 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p &lt; 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data.

Reduced Prediction Saturation and View Effects for Estimating the Leaf Area Index of Winter Wheat

Relationships between vegetation indices (VIs) and leaf area index (LAI) tend to saturate in the nadir direction, and vary with crop canopy structure and view zenith angles (VZAs). The objective of this paper was to improve the monitoring accuracy and angular stability of VIs for estimating LAI using multiangular remote sensing data. The relationship between LAI and ground-based hyperspectral spectral reflectance was quantified in winter wheat (Triticum aestivum L.) exhibiting erectophile and planophile growth habits. To further reduce the saturation, species specificity, and angular sensitivity, we developed a saturation factor (SF), based on near-infrared and green bands. Multiplying all VIs by SF greatly improved the association with LAI across all VZAs ( $R^{2} = 0.73{-}0.82$ ). Most VI $\times$ SF values, particularly optimized soil-adjusted VI $\times$ SF, were able to construct universal algorithms across VZAs for accurate estimation of LAI due to the sensitivity of SF to LAI in a dense canopy, and the insensitivity of SF to view effects with larger VZAs. This approach is also promising for the exploitation of multiangular satellite data for the design and calibration of nonview-angle-corrected spectral reflectance, for which the sensor is only deployed at fixed observation direction.