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Assessment of red-edge vegetation indices for crop leaf area index estimation
摘要： This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.
关键词： Sensitivity analysis,Crops,RapidEye,Leaf area index,red-edge,Vegetation index
更新于2025-09-23 15:23:52
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Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data
摘要： The Bamboo species accounts for almost 1% of the Earth’s forested area with an exceptionally fast growth peaking up to 7.5–100 cm per day during the growing period, making it an unique species with respect to measuring and monitoring using conventional forest inventory tools. In addition their widespread coverage and quick growth make them a critical component of the terrestrial carbon cycle and for mitigating the impacts of climate change. In this study, the capability of using airborne Light Detection and Ranging (LiDAR) data for estimating canopy structure and biomass of Moso bamboo (Phyllostachys pubescens) was assessed, which is one of the most valuable and widely distributed bamboo species in the subtropical forests of south China. To do so, we first evaluated the accuracy of using LiDAR data to interpolate the underlying ground terrain under bamboo forests and developed uncertainty surfaces using both LiDAR-derived vegetation and topographic metrics and a Random Forest (RF) classifier. Second, we utilized Principal Component Analysis (PCA) to quantify the variation of the vertical distribution of LiDAR-derived effective Leaf Area Index (LAI) of bamboo stands, and fitted regression models between selected LiDAR metrics and the field-measured attributes such mean height, DBH and biomass components (i.e., culm, branch, foliage and aboveground biomass (AGB)) across a range of management strategies. Once models were developed, the results were spatially extrapolated and compared across the bamboo stands. Results indicated that the LiDAR interpolated DTMs were accurate even under the dense intensively managed bamboo stands (RMSE = 0.117–0.126 m) as well as under secondary stands (RMSE = 0.102 m) with rugged terrain and near-ground dense vegetation. The development of uncertainty maps of terrain was valuable when examining the magnitude and spatial distribution of potential errors in the DTMs. The middle height intervals (i.e., HI4 and HI5) within the bamboo cumulative effective LAI profiles explained more variances by PCA analysis in the bamboo stands. Moso bamboo AGB was well predicted by the LiDAR metrics (R2 = 0.59–0.87, rRMSE = 11.92–21.11%) with percentile heights (h25-h95) and the coefficient of variation of height (hcv) having the highest relative importances for estimating AGB and culm biomass. The hcv explained the most variance in branch and foliage biomass. According to the spatial extrapolation results, areas of relatively low biomass were found on secondary stands (AGB = 49.42 ± 14.16 Mg ha?1), whereas the intensively managed stands (AGB = 173.47 ± 34.16 Mg ha?1) have much higher AGB and biomass components, followed by the extensively managed bamboo stands (AGB = 67.61 ± 13.10 Mg ha?1). This study demonstrated the potential benefits of using airborne LiDAR to accurately derive high resolution DTMs, characterize vertical structure of canopy and estimate the magnitude and distribution of biomass within Moso bamboo forests, providing key data for regional ecological, environmental and global carbon cycle models.
关键词： Biomass,Bamboo,Leaf area index,LiDAR,Canopy structure
更新于2025-09-23 15:23:52
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Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images
摘要： In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ecosystem structures and functions. Studies have shown that the dynamics of canopy structure are related to available water, climate, and human activities in the form of porosity, leaf area index (LAI), and clumping index (CI). Therefore, it is important to identify the biophysical parameters of savanna ecosystems, and undertake practical actions for savanna conservation and management. The canopy openness presents a challenge for evaluating canopy LAI and other biophysical parameters, as most remotely sensed methods were developed for homogeneous and closed canopies. Clumping index is a key variable that can represent the clumping effect from spatial distribution patterns of components within a canopy. However, it is a difficult task to measure the clumping index of the moderate resolution savanna pixels directly using optical instruments, such as the Tracing Radiation and Architecture of Canopies, LAI-2000 Canopy Analyzer, or digital hemispherical photography. This paper proposed a new method using hemispherical photographs combined with high resolution remote sensing images to estimate the clumping index of savanna canopies. The effects of single tree LAI, crown density, and herbaceous layer on the clumping index of savanna pixels were also evaluated. The proposed method effectively calculated the clumping index of moderate resolution pixels. The clumping indices of two study regions located in Ejina Banner and Weichang were compared with the clumping index product over China's landmass.
关键词： hemispherical photograph,clumping index,moderate resolution pixel,high resolution images,leaf area index
更新于2025-09-23 15:22:29
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Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR
摘要： The fraction of absorbed photosynthetically active radiation (fAPAR) is an important plant physiological index that is used to assess the ability of vegetation to absorb PAR, which is utilized to sequester carbon in the atmosphere. This index is also important for monitoring plant health and productivity, which has been widely used to monitor low stature crops and is a crucial metric for food security assessment. The fAPAR has been commonly correlated with a greenness index derived from spaceborne optical imagery, but the relatively coarse spatial or temporal resolution may prohibit its application on complex land surfaces. In addition, the relationships between fAPAR and remotely sensed greenness data may be influenced by the heterogeneity of canopies. Multispectral and hyperspectral unmanned aerial vehicle (UAV) imaging systems, conversely, can provide several spectral bands at sub-meter resolutions, permitting precise estimation of fAPAR using chemometrics. However, the data pre-processing procedures are cumbersome, which makes large-scale mapping challenging. In this study, we applied a set of well-verified image processing protocols and a chemometric model to a lightweight, frame-based and narrow-band (10 nm) UAV imaging system to estimate the fAPAR over a relatively large cultivated land area with a variety of low stature vegetation of tropical crops along with native and non-native grasses. A principal component regression was applied to 12 bands of spectral reflectance data to minimize the collinearity issue and compress the data variation. Stepwise regression was employed to reduce the data dimensionality, and the first, third and fifth components were selected to estimate the fAPAR. Our results indicate that 77% of the fAPAR variation was explained by the model. All bands that are sensitive to foliar pigment concentrations, canopy structure and/or leaf water content may contribute to the estimation, especially those located close to (720 nm) or within (750 nm and 780 nm) the near-infrared spectral region. This study demonstrates that this narrow-band frame-based UAV system would be useful for vegetation monitoring. With proper pre-flight planning and hardware improvement, the mapping of a narrow-band multispectral UAV system could be comparable to that of a manned aircraft system.
关键词： MiniMCA,precision agriculture,leaf area index,productivity,Chemometrics
更新于2025-09-23 15:21:01
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MONITORAMENTO E PROJE??O FUTURA DA VEGETA??O NO PARQUE NACIONAL DO ITATIAIA ATRAVéS DE SENSORIAMENTO REMOTO
摘要： Satellite images of earth observation and meteorological sensors have been used for monitoring land use. Recently products obtained from satellite images have been disseminated, among them, several vegetation indices. EUMETSAT, through the Land –SAF, offers, among other products, the Leaf Area Index (LAI). Daily LAI products have been acquired in raster format corresponding from 01/01/2010 to 30/12/2010. From a pixel located in the central portion of the Itatiaia National Park, a time series was generated, which was analyzed aiming at assessing the dynamics of leaf area index. The tendency observed in this period indicates that LAI decreased during 2010. It was possible to observe that changes in vegetation have close relationship with changes in rainfall and fires that affect the region. The ARIMA (7 1 0) model was able to describe the behavior of the LAI series, producing white noise and indicating correlations among 1, 6 and 7 days among the past observations. The prediction for future values resulted in an average error of 2.74%, indicating the potential of the model to identify changes in vegetation. Models of ARIMA class, in conjunction with orbital products, stand out as promises for use in the analysis of the vegetation of protected areas.
关键词： Leaf area index,Time Series,ARIMA Model,Conservation units
更新于2025-09-19 17:15:36
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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data
摘要： Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.
关键词： hyperspectral,multispectral,vegetation indices,Sentinel-2,machine learning regression algorithms,PROSAIL,field-spectroradiometer,LUT inversion,leaf area index
更新于2025-09-19 17:15:36
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Ultra-Stable Optical Oscillator Transfer to the UV for Primary Thermometry
摘要： To improve the performance of crop models for regional crop yield estimates, a particle filter (PF) was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)—Wheat model. Two experiments involving winter wheat yield estimations were conducted at a field plot and on a regional scale to test the feasibility of the PF-based data assimilation strategy and to analyze the effects of the PF parameters and spatio-temporal scales of assimilating observations on the performance of the crop model data assimilation. The significant improvements in the yield estimation suggest that PF-based crop model data assimilation is feasible. Winter wheat yields from the field plots were forecasted with a determination coefficient (R2) of 0.87, a root-mean-square error (RMSE) of 251 kg/ha, and a relative error (RE) of 2.95%. An acceptable yield at the county scale was estimated with a R2 of 0.998, a RMSE of 9734 t, and a RE of 4.29%. The optimal yield estimates may be highly dependent on the reasonable spatiotemporal resolution of assimilating observations. A configuration using a particle size of 50, LAI maps with a moderate spatial resolution (e.g., 1 km), and an assimilation interval of 20 d results in a reasonable tradeoff between accuracy and effectiveness in regional applications.
关键词： particle filter (PF),yield estimation,data assimilation,Crop model,leaf area index,remote sensing
更新于2025-09-19 17:13:59
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A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring
摘要： A growing need for sampling environmental spaces in high detail is driving the rapid development of non-destructive three-dimensional (3D) sensing technologies. LiDAR sensors, capable of precise 3D measurement at various scales from indoor to landscape, still lack affordable and portable products for broad-scale and multi-temporal monitoring. This study aims to configure a compact and low-cost 3D fusion scanning system (FSS) with a multi-segment Leddar (light emitting diode detection and ranging, LeddarTech), a monocular camera, and rotational robotics to recover hemispherical, colored point clouds. This includes an entire framework of calibration and fusion algorithms utilizing Leddar depth measurements and image parallax information. The FSS was applied to scan a cottonwood (Populus spp.) stand repeatedly during autumnal leaf drop. Results show that the calibration error based on bundle adjustment is between 1 and 3 pixels. The FSS scans exhibit a similar canopy volume profile to the benchmarking terrestrial laser scans, with an r2 between 0.5 and 0.7 in varying stages of leaf cover. The 3D point distribution information from FSS also provides a valuable correction factor for the leaf area index (LAI) estimation. The consistency of corrected LAI measurement demonstrates the practical value of deploying FSS for canopy foliage monitoring.
关键词： terrestrial laser scanning,canopy monitoring,monocular camera,sensor fusion,LiDAR,Leddar,structure from motion,leaf area index
更新于2025-09-12 10:27:22
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Leaf area density from airborne LiDAR: Comparing sensors and resolutions in a temperate broadleaf forest ecosystem
摘要： Forest processes that play an essential role in carbon sequestration, such as light use efficiency, photosynthetic capacity, and trace gas exchange, are closely tied to the three-dimensional structure of forest canopies. However, the vertical distribution of leaf traits is not uniform; leaves at varying vertical positions within the canopy are physiologically unique due to differing light and environmental conditions, which leads to higher carbon storage than if light conditions were constant throughout the canopy. Due to this within-canopy variation, three-dimensional structural traits are critical to improving our estimates of global carbon cycling and storage by Earth system models and to better understanding the effects of disturbances on carbon sequestration in forested ecosystems. In this study, we describe a reproducible and open-source methodology using the R programming language for estimating leaf area density (LAD; the total leaf area per unit of volume) from airborne LiDAR. Using this approach, we compare LAD estimates at the Smithsonian Environmental Research Center in Maryland, USA, from two airborne LiDAR systems, NEON AOP and NASA G-LiHT, which differ in survey and instrument specifications, collections goals, and laser pulse densities. Furthermore, we address the impacts of the spatial scale of analysis as well as differences in canopy penetration and pulse density on LAD and leaf area index (LAI) estimates, while offering potential solutions to enhance the accuracy of these estimates. LAD estimates from airborne LiDAR can be used to describe the three-dimensional structure of forests across entire landscapes. This information can help inform forest management and conservation decisions related to the estimation of aboveground biomass and productivity, the response of forests to large-scale disturbances, the impacts of drought on forest health, the conservation of bird habitat, as well as a host of other important forest processes and responses.
关键词： Leaf area density,Airborne LiDAR,Leaf area index,Forest structure
更新于2025-09-11 14:15:04
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Research on Empirical Model and Gap Rate Model for Estimating Rice Leaf Area Index Based on UAV HD Digital Images
摘要： The Leaf Area Index (LAI), as an important plant characteristic parameter, is of great significance for the monitoring of vegetation growth and the estimation of surface vegetation productivity. Rice is one of the world's major food crops, timely and accurate measurement of rice LAI can provide scientific information on agriculture. The remote sensing system of UAV is characterized by its cost-effective and real-time data acquisition. The method of estimating LAI by remote sensing technology has great advantages over traditional methods and has gradually become a frontier method for agricultural research. At present, the commonly used LAI inversion methods are empirical model method and physical model method. The former is not accurate because not all of the spectral information is used. The latter cannot directly calculate the analytical solution because of the complicated model and many input parameters. The main purpose of this paper is to obtain high-definition digital images of different varieties of rice using low-altitude drones, and to analyze the feasibility of estimating the LAI of rice canopy by empirical model method and porosity model method, and analyze the difference and estimation process between them. There are problems.
关键词： Leaf Area Index(LAI),Remote sensing,Empirical model,Unmanned Aerial Vehicle(UAV),Rice,Gap rate model
更新于2025-09-11 14:15:04