Assessment Of Leaf Area Index Research Articles

Accuracy assessment of LAI, PAI and FCOVER from Sentinel-2 and GEDI for monitoring forests and their disturbance in Central Germany

ABSTRACT Forest monitoring benefits from biophysical parameters such as Leaf Area Index (LAI), Plant Area Index (PAI) and fractional vegetation cover (FCOVER) and can be obtained from optical and LiDAR remote sensing, such as Sentinel-2 (S2) and the Global Ecosystem Dynamics Investigation (GEDI). While GEDI-derived products consider all phyto-elements, those from S2 refer to green elements only. Apart from individual accuracies, systemic deviations among products are thus expectable. However, products from S2 and GEDI lack inter-comparison. We evaluated S2 and GEDI-derived LAI, PAI and FCOVER with digital hemispherical photography observations (DHP) in a forest disturbance hotspot in Germany across various forest conditions, including vital stands, standing deadwood and clearings. We found moderate to high agreement with in-situ data, with highest accuracy for S2-derived LAI (R2 = 0.54) and GEDI-derived FCOVER (R2 = 0.73). Agreements between S2 and GEDI products were low, which we attribute to systematic influences of woody components, GEDI’s limitations in sloped terrain, and saturation of optical signals in dense canopy. In conclusion, findings suggest that while GEDI is effective in dense canopies, S2 products are beneficial for monitoring forest recovery. We also see potential for synergistic use in monitoring standing deadwood for habitat mapping and fire risk assessment.

Assessment of Leaf Area Index and its Relationship with Growth and Yield of Fodder Cowpea as Influenced by Sources of Irrigation and Nutrients

Background: Fodder cowpea is an important leguminous, short duration, warm season crop widely cultivated. Though India ranks first in livestock population, the productivity is found lesser than the world average due to deficiency in availability of green fodders. As fresh water application usage is increasing for the cultivation of field crops and high value crops, fodder crops are found negligible crops but responds well to water and nutrients. Leaf area index is one of the determinative parameters for the increased yield, an attempt was made to determine the relationship between the growth and yield of fodder cowpea with the application of domestic sewage water and nutrients. Methods: studies were conducted during 2019 and 2020 to study the relationship between different sources of irrigation water and nutrients on the growth and yield of fodder cowpea. Treatments in main plot consists of irrigation with water using well water (I1), domestic sewage water (I2) and well water and domestic sewage water alternatively (I3). Treatments in sub plot consists of no nutrient (N1), Farm Yard manure alone (N2), Farm Yard manure + NPK (N3) and NPK alone (N4), in split plot design and replicated thrice. The leaf constant was determined graphically plotted in regression and used for calculation of leaf area. The relationship between leaf area index and the growth and yield parameters of fodder cowpea were determined. Result: Our investigation concluded that the application of domestic sewage water along with farmyard manure + NPK significantly increased plant height, number of leaves per plant, leaf length, leaf width, leaf area and leaf area index of fodder cowpea. The data also reveals that leaf area index correlated positively with plant height, number of leaves per plant, biomass per plant and yield of fodder cowpea.

Avaliação visual do índice de área foliar em campos de café (Coffea arabica L.)

ABSTRACT The application of leaf area index (LAI) in coffee crop management depends on the availability of methodologies for proper estimation. The objective of this study was to develop a methodology for the visual assessment of LAI in coffee fields and to establish a protocol for training, evaluation, and feedback for evaluators. Four rounds of LAI measurements were conducted using visual estimates, two instruments (LAI 2200-C and AccuPAR LP-80), and defoliation of coffee hedgerows in Poás, Costa Rica. In each round, five workers visually estimated the LAI values on two occasions separated by 15 days, and feedback reinforcement was provided to each worker at the end of each round. Visual assessments showed high repeatability and reproducibility and the estimates were adjusted to the linear regression model in most cases. Evaluators improved their capacity to visually assess the LAI throughout the rounds, as the value of R2increased consistently for most workers, with values as high as 0.87. Instrumentation evaluation of LAI produced R2values of 0.5-0.6, with significant underestimation bias. The performance of the different methods is discussed in the context of widely spaced hedgerows. The proposed visual methodology constitutes a statistically sound, rapid, simple, and reliable method for determining the LAI of coffee fields to aid in decision-making for crop management.

Comparative analysis of multi-source data for machine learning-based LAI estimation in Argania spinosa

In this study, we conducted a comprehensive assessment of Leaf Area Index (LAI) estimation using three distinct sources of satellite data: Sentinel-2 imagery, drone imagery (UAVs), and Mohammed VI satellite data. The main objective was to identify the most reliable and precise dataset for predicting LAI, with a focus on evaluating the performance of Random Forest models. For Sentinel-2 imagery, our Random Forest model achieves a robust R-squared (R2) value of 0.89, signifying a strong alignment between predicted and measured LAI values. The associated root-mean-square error (RMSE) is 0.4, indicating high predictive accuracy. In the context of UAVs, our Random Forest model excels, exhibiting an impressive R2 value of 0.93, highlighting a substantial correlation between predicted and measured LAI. The RMSE for drone imagery stands at 0.37, showcasing exceptional predictive accuracy. Finally, the Random Forest model trained on Mohammed VI satellite data yields an R2 value of 0.92, underlining its strong fit with measured LAI values. The RMSE for Mohammed VI imagery is 0.39, further underscoring the model's exceptional predictive accuracy. This comparative analysis underscores the importance of selecting the most suitable satellite data source for LAI estimation in Argania spinosa. UAV imagery emerges as the most accurate choice, closely followed by Mohammed VI Satellite and Sentinel-2 imagery. These findings offer valuable insights for effective monitoring of Argania spinosa and advancing sustainable land management practices in rural ecosystems.

Comparison of Machine Learning Methods for Estimating Leaf Area Index and Aboveground Biomass of Cinnamomum camphora Based on UAV Multispectral Remote Sensing Data

UAV multispectral technology is used to obtain leaf area index (LAI) and aboveground biomass (AGB) information on Cinnamomum camphora (C. camphora) and to diagnose the growth condition of Cinnamomum camphora dwarf forests in a timely and rapid manner, which helps improve the precision management of Cinnamomum camphora dwarf forests. Multispectral remote sensing images provide large-area plant spectral information, which can provide a detailed quantitative assessment of LAI, AGB and other plant physicochemical parameters. They are very effective tools for assessing and analyzing plant health. In this study, the Cinnamomum camphora dwarf forest in the red soil area of south China is taken as the research object. Remote sensing images of Cinnamomum camphora dwarf forest canopy are obtained by the multispectral camera of an unmanned aerial vehicle (UAV). Extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), random forest (RF), radial basis function neural network (RBFNN) and support vector regression (SVR) algorithms are used to study the correlation and estimation accuracy between the original band reflectance, spectral indices and LAI and AGB of Cinnamomum camphora. The results of this study showed the following: (1) The accuracy of model estimation based on RF is significantly different for different model inputs, while the other four models have small differences. (2) The accuracy of the XGBoost-based LAI model was the highest; with original band reflectance as the model input, the R2 of the model test set was 0.862, and the RMSE was 0.390. (3) The accuracy of the XGBoost-based AGB model was the highest; with spectral indices as the model input, the R2 of the model test set was 0.929, and the RMSE was 587.746 kg·hm−2. (4) The XGBoost model was the best model for the LAI and AGB estimation of Cinnamomum camphora, which was followed by GBDT, RF, RFNN, and SVR. This research result can provide a theoretical basis for monitoring a Cinnamomum camphora dwarf forest based on UAV multispectral technology and a reference for rapidly estimating Cinnamomum camphora growth parameters.

Multisensor Assessment of Leaf Area Index across Ecoregions of Ardabil Province, Northwestern Iran

Leaf area index (LAI), one of the most crucial vegetation biophysical variables, is required to evaluate the structural characteristic of plant communities. This study, therefore, aimed to evaluate the LAI of ecoregions in Iran obtained using Sentinel-2B, Landsat 8 (OLI), MODIS, and AVHRR data in June and July 2020. A field survey was performed in different ecoregions throughout Ardabil Province during June and July 2020 under the satellite image dates. A Laipen LP 100 (LP 100) field-portable device was used to measure the LAI in 822 samples with different plant functional types (PFTs) of shrubs, bushes, and trees. The LAI was estimated using the SNAPv7.0.4 (Sentinel Application Platform) software for Sentinel-2B data and Google Earth Engine (GEE) system–based EVI for Landsat 8. At the same time, for MODIS and AVHRR, the LAI products of GEE were considered. The results of all satellite-based methods verified the LAI variations in space and time for every PFT. Based on Sentinel-2B, Landsat 8, MODIS, and AVHRR application, the minimum and maximum LAIs were respectively obtained at 0.14–1.78, 0.09–3.74, 0.82–4.69, and 0.35–2.73 for shrubs; 0.17–5.17, 0.3–2.3, 0.59–3.84, and 0.63–3.47 for bushes; and 0.3–4.4, 0.3–4.5, 0.7–4.3, and 0.5–3.3 for trees. These estimated values were lower than the LAI values of LP 100 (i.e., 0.4–4.10 for shrubs, 1.6–7.7 for bushes, and 3.1–6.8 for trees). A significant correlation (p < 0.05) for almost all studied PFTs between LP 100-LAI and estimated LAI from sensors was also observed in Sentinel-2B (|r| > 0.63 and R2 > 0.89), Landsat 8 (|r| > 0.50 and R2 > 0.72), MODIS (|r| > 0.65 and R2 > 0.88), and AVHRR (|r| > 0.59 and R2 > 0.68). Due to its high spatial resolution and relatively significant correlation with terrestrial data, Sentinel-2B was more suitable for calculating the LAI. The results obtained from this study can be used in future studies on sustainable rangeland management and conservation.

Moderate resolution LAI prediction using Sentinel-2 satellite data and indirect field measurements in Sikkim Himalaya.

The leaf area index (LAI) has been traditionally used as a photosynthetic variable. LAI plays an essential role in forest cover monitoring and has been identified as one of the important climate variables. However, due to challenges in field sampling, complex topography, and availability of cloud-free optical satellite data, LAI assessment on larger scale is still unexplored in the Sikkim Himalayan area. We used two optical instruments, digital hemispherical photography (DHP) and LAI-2200C, to assess the LAI across four different forests following 20 × 20m2 elementary sampling units (ESUs) in the Himalayan state of Sikkim, India. The use of Sentinel-2 derived vegetation indices (VIs) demonstrated a better correlation with the DHP based LAI estimates than using LAI-2200C. Further, the combination of both reflectance bands and VIs were integrated to predict the LAI maps using random forest model. The temperate evergreen forests demonstrated the highest LAI value, while the predicted maps exhibited LAI maxima of 3.4. The estimated vs predicted LAI for DHP and LAI-2200C based estimation demonstrated reasonably good (R2 = 0.63 and R2 = 0.68, respectively) agreement. Further, improvements on the LAI prediction can be attempted by minimizing errors from the inherent field protocols, optimizing the density of field measurements, and representing heterogeneity. The recent rise of frequent forest fires in Sikkim Himalaya prompts for better understanding of fuel load in terms of surface fuel or canopy fuel that can be linked to LAI. The high-resolution LAI map could serve as input to forest fuel bed characterization, especially in seasonal forests with significant variations in green leaves and litter, thereby offering inputs for forest management in changing climate.

UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds

Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90. The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions.

Stem and pseudostem growth play a key role in biomass accumulation of guineagrass in long regrowth cycles

Context Tall bunch-type tropical forage grasses are known for their rapid (true) stem elongation late in the regrowth cycle, even during the vegetative phase. Aims This study aimed to evaluate the stem and pseudostem growth pattern of ‘Mombaça’ guineagrass [Megathyrsus maximus (Jacq.) BK Simon & SWL Jacobs] grown in field conditions and how this relates to its high biomass accumulation during long regrowth cycles. Methods A guineagrass field was managed from December 2017 to January 2019, in three regrowth cycles of 10, 14 and 12 weeks duration. Individual tillers were assessed weekly to measure stem, pseudostem and leaf elongation, leaf appearance; and angle of insertion of the leaves. Aboveground biomass samples were taken weekly for biomass accumulation and leaf area index assessment. Key results At the beginning of each regrowth cycle, the pseudostem elongated while the stem length remained constant. Subsequently, the pseudostem length reached a plateau, while the stem length increased at a constant rate. Because of a positive relationship between pseudostem and leaf laminae lengths, the long pseudostem ensured the formation of long leaves in tall tillers and positive net leaf elongation in long regrowth cycles. Conclusions The high biomass accumulation reflected the continuous positive net leaf elongation by the tillers and was underpinned by the younger leaves being more erect than the older ones, allowing for lower self-shading of the older leaves and greater leaf tissue retention. Implications The high production of tropical forage grasses in late regrowth should be utilised with caution, as it is underpinned by stem elongation and meristem elevation.

Evaluation of Leaf Area Index (LAI) of Broadacre Crops Using UAS-Based LiDAR Point Clouds and Multispectral Imagery

Leaf area index (LAI) is an established structural variable that reflects the 3D leaf layering of vegetation in response to environmental inputs. In this context, unmanned aerial system (UAS)-based methods present a new approach to such plant- to field-scale LAI assessment for precision agriculture applications. This study used UAS-based light detection and ranging (LiDAR) data and multispectral imagery (MSI) as two modalities to evaluate the LAI of a snap bean field, toward eventual yield modeling and disease risk assessment. LiDAR-derived and MSI-derived metrics were fed to multiple biophysical-based and regression models. The correlation between the derived LAI and field-measured LAI was significant. Six LiDAR-derived metrics were fit in eight models to predict LAI, among which the square root of the laser penetration index (LPI) achieved the most accurate prediction result (R²=0.61, nRMSE=19%). The MSI-derived models, which contained both structural features and spectral signatures, provided similar predicting effectiveness, with predicted R²0.5 and nRMSE22%. We furthermore observed variation in model effectiveness for different cultivars, different cultivar groups, and different UAS flight altitudes, for both the LiDAR and MSI approaches. For data collected at a consistent flight altitude, MSI-derived models could even exceed LiDAR-derived models, in terms of accuracy. This finding could support the possibility of replacing LiDAR with more cost-effective MSI-based approaches. However, LiDAR remains a viable modality, since a LiDAR-derived 3D model only required a single predictor variable, while an MSI-derived model relied on multiple independent variables in our case.

Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms

With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data.

MULTI-SENSOR APPROACH TO LEAF AREA INDEX ESTIMATION USING STATISTICAL MACHINE LEARNING MODELS: A CASE ON MANGROVE FORESTS

Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.

A methodological approach for the assessment of basic crown parameters in Scots pine stands

Among other measurement techniques applied for the assessment of leaf area index, direct methods are still valued as the most accurate measures and often implemented as calibration tools. Even though more attention has been given to indirect measurements of tree crown properties in forest ecosystems over the last decades, the present study was designed to discuss the direct (destructive) and indirect (non-destructive) methods used for the assessment of crown measures in the stands defoliated from 20 to 90%. The stands with similar stand characteristics and representing relatively wide range of defoliation served as an appropriate target for the assessment of foliage mass variations.
 Overall, this study showed that the foliage mass or its surface area and defoliation at the stand level can be determined by the conventional methods used for the assessment of defoliation in forest monitoring programme as well as the PAR transmission methods. The findings showed that needle surface area decreased with the increase of tree defoliation; however, the changes of branch and stem surface areas were insignificant. Otherwise, the branch and shoot area contribute significantly to the total vegetation surface area at least in Scots pine stands. This study also strengthened the idea that the indirect measurement of vegetation area index underestimated vegetation area index at least in Scots pine stands defoliated less than 60%. The multivariate regression models were developed using tree diameter at breast height and tree crown defoliation ranges to estimate needle surface area. Keywords: Pinus sylvestris, crown defoliation, needle area index, regression model

Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance

Optical remote sensing (RS) with robust algorithms is needed for accurate assessment of crop canopy features. Despite intensive studies on algorithms, their performance using RS needs to be improved. We evaluated five different algorithms (partial-least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), locally-weighted-PLSR (PLSRLW) and PLSR with feature selection (PLSRFS)) for rapid assessment of leaf area index (LAI) and canopy water content (CWC) for rice canopies using canopy reflectance spectra over visible to short-wave infrared region. Two pooled datasets of LAI (600) and CWC (480) were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season. The performance of each algorithm was evaluated using coefficient of determination (R2). Results showed that PLSRLW performed more accurately than other algorithms with R2 values 0.77 and 0.66 for LAI and CWC, respectively. We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model. The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples, which has similar canopy structure as the test sample. Subsetting of the canopy spectral data results large error values in test dataset, therefore the use of entire season canopy spectral data should be used for model calibration.

Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery

The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopy-atmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a cost-effective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.

Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.

Processing and filtering of leaf area index time series assessed by in-situ wireless sensor networks

A precise and up-to-date situational awareness of crop conditions is important for precision farming. The temporally continuous monitoring of relevant crop parameters has recently been shown to assist in a large number of applications. In this context, the leaf area index (LAI) is a key parameter. However, continuous LAI monitoring using traditional assessment methods is hardly possible and very expensive. For this reason, low-cost sensors based on Wireless Sensor Network (WSN) technology have been developed and interconnected to agricultural in situ sensor networks that seem promising for LAI assessment. In this paper, an approach for the processing and filtering of distributed in situ sensor data for a credible LAI estimation is proposed. This approach is developed based on a long-term WSN deployment in experimental plots with different wheat cultivars (Triticum aestivum L.) and water regimes. Non-negligible environmental impacts on radiation-based LAI assessment are also taken into account. A comparative analysis with a conventional LAI instrument shows that WSNs with adequately processed data gathered by low-cost sensors have the potential to produce credible LAI trajectories with high temporal resolution, that fit the dynamic crop growth process. Moreover, they are also shown to be able to detect yield-limiting trends and even to differentiate between individual wheat cultivars. Hence, those WSNs enable new applications and can greatly support modern crop management, cultivation, and plant breeding.

A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.

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.

Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.