Height Estimation Research Articles

Evaluating geometric measurement accuracy based on 3D model reconstruction of nursery tomato plants by Agisoft photoscan software

Measuring the vegetation structure of a plant with the help of 3D images is a non-destructive method that can be used to monitor the plant’s status during the growing season. Structure from motion (SfM) is a popular optical distance measurement method that uses 2D images to create 3D models. Identifying factors that influence the precision of SfM is important due to its increasing use in phenotyping. The primary objectives of this study were, i) to scrutinize the impact of imaging parameters—including, color checkerboard location, overlap size of images, and background filtering—on the quality indices of 3D models, and ii) to attain precise measurements of leaf area, stem diameter, and height through the combination of images captured from different camera shooting angles. The three nursery tomato plants were photographed at every five-degree rotation of the turntable, five different locations of color checkerboards, and five camera shooting angles. Results indicated the color checkerboard’s presence and position affect the quality indices of the 3D models. Visually and by the lowest mean keypoint size value, the least noisy 3D model was obtained when the wall of the plant pot was covered with a checkerboard. When the base-to-distance (b/y) ratio as the image overlap index increased from 0.1 to 0.2, the generation time of the 3D models decreased by more than two times. Optimal Mean Absolute Percentage Error (MAPE) values of 1.08 %, 1.01 %, and 1.38 % were achieved for the estimation of leaf area, stem diameter, and height, respectively. This was recorded by using images taken at 0°, 30°, and 105° vertical azimuth angles. Constructing a 3D model by integrating images captured from other angles or more azimuth angles represents numerous visual challenges and leads to problems of point mismatch, which affects the accuracy of measurements.

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

An adaptive fusion algorithm for coastal sea altimetry based on dual-frequency Beidou-R carrier phase.

This article proposes an integer ambiguity determination method based on Beidou system-reflectometry (Beidou-R) observations of the carrier phase at the B1I and B3I frequencies. To enhance the accuracy of sea surface height (SSH) estimation, this study introduces a parallel filtering algorithm and an adaptive iterative fusion algorithm, enabling data fusion based on the variance at B1I and B3I frequencies. To validate and evaluate the proposed method, a coastal experiment was conducted at the Shenxian River. In this experiment, reflected signals from GEO and IGSO satellites were collected. Data analysis reveals that the method is effective, demonstrating that the root mean square error (RMSE) of SSH achieves 2.85 cm and 2.89 cm for PRN 04 and PRN 33, respectively. Furthermore, the impact of the elevation angle on measurement accuracy is analyzed. This study aims to propose a method to enhance coastal sea surface height estimation, offering potential advancements in sea surface altimetry.

Building Height Extraction Based on Satellite GF-7 High-Resolution Stereo Image

Abstract. High-resolution remote sensing images can distinguish objects of smaller size, so as to more clearly express the texture features and structural information of objects, and provide a data source for the development of large-scale mapping, high-precision stereo measurement and other fields. The purpose of this paper is to complete the height estimation of the buildings by analyzing the stereoscopic observation formed by the front and rear view images of the Gaofen-7 line array CCD. After determining the roof profile of the building on the rear-view image, assuming a series of object elevations of the building, that is, searching for elevations with a certain step distance within a certain elevation search range, adopt the object-based image matching VLL algorithm, Through the RFM imaging model of the Gaofen-7 sensor, the rear-view contour is projected to the front-view image, and then the PSNR is selected as the similarity measure of the window, and the similarity of the image block area corresponding to the front- and rear-view contour is calculated. Corresponds to the hypothetical object elevation as the estimated height of the building. Under the technical route of this paper, the height of buildings on high-resolution images can be estimated to a level within 3 meters of accuracy.

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

Abstract. Synthetic aperture radar (SAR) has emerged as a promising technology for monitoring crop plant height due to its ability to capture the geometric properties of crops. Radar vegetation index (RVI) has been extensively utilized for qualitative and quantitative remote sensing monitoring of vegetation growth dynamics. However, the combination of crop, growing environment, and temporal dynamics makes crop monitoring data a complex task. Despite the relatively simple underlying mechanisms of this phenomenon, there is still a need for more research to identify specific vegetation structures that correspond to changes in the response of vegetation indices. Building upon this premise, this study utilized a dynamic monitoring model to conduct dynamic monitoring of plant height for three common crops: rice, wheat, and maize. The findings revealed that (1) models developed for specific spatial and temporal scales of particular crop varieties may not accurately predict crop growth in different regions or with different varieties in a timely manner, due to growth variations; (2) these models maintain accuracy over a range of plant heights, such as rice at around 70cm, wheat at around 50cm, and maize at around 150cm; (3) among the three crops, planting density was identified as the main factor influencing the differences in RVI response. This research contributes to our comprehension of the dynamic response of RVI to different growth conditions in crops, and offers valuable insights and references for agricultural monitoring.

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.

A major midlatitude hurricane in the Little Ice Age

Abstract. An unusually severe hurricane (Louisbourg Storm) struck Nova Scotia, Canada, in 1757. Historic records describing storm conditions as well as damage to ships and coastal fortifications indicate an intensity beyond any modern (post-1851) Atlantic cyclones striking the same region, yet this storm struck during a cold climate period known as the Little Ice Age (LIA). Its track and timing coincided with a British naval blockade of a French fleet at Fortress Louisbourg during the Seven Years' War (1756–1763). This provides a unique opportunity to explore growing scientific evidence of heightened storminess in the North Atlantic despite a colder climate expected to suppress hurricane intensification but which research is increasingly showing to have supported North Atlantic storms of exceptional strength. Weather attributes extracted from the logs of naval vessels scattered by the Louisbourg Storm provided multiple hourly observations recorded at different locations. Wave height and wind force estimates at ship locations were compared to extreme storm surge heights calculated for Louisbourg Harbour and a shipwreck site south of Fortress Louisbourg. Comparing these metrics to those of modern analogues that crossed the same bathymetry reflects landfall intensity consistent with a powerful major hurricane. Historical records show this storm originated as a tropical cyclone at the height of hurricane season and intensified into the northern midlatitudes along the Gulf Stream. Its intensity at landfall is consistent with established seasonal climatological models where highly baroclinic westerlies driven by autumn continental cooling encounter intensifying north-tracking tropical cyclones fuelled by sea surface temperatures that peak in autumn. Stronger seasonal contrasts from earlier and colder continental westerlies in the Little Ice Age (LIA) may have triggered explosive extratropical transition from a large hurricane resulting in a more severe strike. It suggests that tropical cyclones lasting days to weeks and the conditions that generate them are likely masked by cooler historic mean annual to multi-decadal LIA climate reconstructions.

Distributed acoustic sensing microseismic reflection imaging for hydraulic fracture and fault lineament characterization

This study presents a novel workflow designed for migrating reflected S waves generated by microseismic events, as recorded by downhole distributed acoustic sensing (DAS), to characterize hydraulic fractures in three dimensions. In contrast to existing fracture imaging techniques, which have encountered challenges in accurately representing fracture networks and often rely on simplified models, our imaging technique does not assume that fractures are planar or in a prespecified orientation. DAS seismic measurements benefit from the large aperture and dense spatial sampling enabled by the kilometers-long fiber and, therefore, are able to capture a large number of strong reflections compared with traditional borehole geophones or accelerometers. We treat microseismic events as high-frequency sources and apply prestack Kirchhoff migration to each individual source after wavefield separation. Fracture imaging results for multiple selected events are then stacked to generate a 3D reflectivity volume, revealing the subsurface fracture and fault networks in intricate detail. The high-resolution fracture images generated by the developed reflection migrating process illuminate the heart of the stimulated volume of the reservoir, a zone that is often challenging to access using conventional surface arrays or active sources. To validate the effectiveness of our workflow, our study uses a data set acquired during a multiwell project in the Eagle Ford Shale and Austin Chalk in South Texas. To assess the accuracy and reliability of the results, the reflection imaging output is integrated with the microseismicity distribution and strain measurements from low-frequency DAS for interpretation. The results of reflection imaging improve our understanding of fracture geometry, including distal fractures that are away from the monitoring well, allow the direct estimation of fracture height and length, and potentially signify the presence of preexisting fluid-filled fault lineaments.

Impact of Urbanization on Cloud Characteristics over Sofia, Bulgaria

Urban artificial surfaces and structures induce modifications in land–atmosphere interactions, affecting the exchange of energy, momentum, and substances. These modifications stimulate urban climate formation by altering the values and dynamics of atmospheric parameters, including cloud-related features. This study evaluates the presence and quantifies the extent of such changes over Sofia, Bulgaria. The findings reveal that estimations of low-level cloud base height (CBH) derived from lifting condensation level (LCL) calculations may produce unexpected outcomes due to microclimate influence. Ceilometer data indicate that the CBH of low-level clouds over urban areas exceeds that of surrounding regions by approximately 200 m during warm months and afternoon hours. Moreover, urban clouds exhibit reduced persistence relative to rural counterparts, particularly pronounced in May, June, and July afternoons. Reanalysis-derived low-level cloud cover (LCC) shows no significant disparities between urban and rural areas, although increased LCC is observed above the western and northern city boundaries. Satellite-derived cloud products reveal that the optically thinnest low-level clouds over urban areas exhibit slightly higher cloud tops, but the optically thickest clouds are more prevalent during warm months. These findings suggest an influence of urbanization on cloudiness, albeit nuanced and potentially influenced by the city size and surrounding physical and geographical features.

Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals

AbstractThe vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar‐1988 Doppler (WSR‐88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2–10 August 2019, using WSR‐88D reflectivity (Z ≥ 10 dBZ) and dual polarization correlation coefficients (0.2 < C.C < 0.9) to study the Williams Flats fire. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire's intensity and convective mixing are maximized. WSR‐88D and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). WSR‐88D heights between the 75th and 90th percentile seem to accurately represent the maximum heights, with the exception of heights estimated during the occurrence of a pyro‐cumulonimbus. Location specific mapping of WSR‐88D and lidar injection heights reveal that they diverge further away from the fire as expected due to BBD settling. Most importantly, WSR‐88D‐derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections.

Estimating Corn Growth Parameters by Integrating Optical and Synthetic Aperture Radar Features into the Water Cloud Model

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VIopt) and the corresponding VIDRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VIDRI indexes were more beneficial than the VIopt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VIDRI indexes (NDVIDRI, NDVIre1DRI, NDVIre2DRI, and S2REPDRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2DRI and S2REPDRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters.

A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.

Height and sex estimation using percutaneous humeral length among Ghanaians

IntroductionHumerus is a long bone that contributes significantly to the total height of an individual. It is sexually dimorphic and is a well-preserved bone postmortem. Height and sex are essential in establishing one's identity and can be estimated from long bones when standing height cannot be easily obtained. Previous studies report that sex and height are population-specific. Hence, the present study sought to establish the relationship between percutaneous humeral lengths and height and formulate models for height and sex determination for the Ghanaian population. Materials and methodsMeasurement of standing height and percutaneous humeral lengths were taken from 286 Ghanaian consented participants with no noticeable lower and upper extremities traumas or pathologies. The data was analysed using Microsoft Excel 2013 and SPSS version 23.0 edition. Correlation analysis was done to determine the correlation of the percutaneous humeral length with height, linear regression and binary logistic analyses were used to derive equations for predicting height and sex from percutaneous humeral length. An independent samples t-test was used to determine the means between males and females, left and right percutaneous humeral lengths. ResultsThere was a strong positive correlation between standing height and left (r = 0.774) and right (r=0.759) humeral lengths. The left humeral length regression equation was a good height estimator in males (75.4%) and females (71.%). The left and right humeral lengths best predicted the sex for the population, with an accuracy of 78.0% and 77.8% for males and 53.8% and 54.6% for females, respectively. ConclusionThis study has added to the limited data on the use of percutaneous humeral length as a substitute for the determination of standing height and sex in the Ghanaian population, and it is also a significant identification tool which can be employed in the field of forensic anthropometry.

Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform

Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R2 value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R2 < 0. For the VIs, height estimation with MTCI yielded the best results, with an R2 of 0.704. The first stage of the TLR model (maximum R2 = 0.664) was significantly better than the second stage (maximum R2 = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R2 values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R2 = 0.984; minimum R2 = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice.

An uncertainty methodology for solar occultation flux measurements: ammonia emissions from livestock production

Abstract. Ammonia (NH3) emissions can negatively affect ecosystems and human health, so they should be monitored and mitigated. This study presents methodology for the estimation of uncertainties in NH3 emissions measurements using the solar occultation flux (SOF) method. The reactive nature of NH3 makes its measurement challenging, but SOF offers a reliable open-path passive method which utilizes solar spectrum data, thereby avoiding gas adsorption within the instrument. To compute NH3 gas fluxes, horizontal and vertical wind speed profiles, as well as plume height estimates and spatially resolved column measurements, are integrated. A unique aspect of this work is the first-time description of plume height estimations derived from ground and column NH3 concentration measurements aimed at uncertainty reduction. Initial validation tests indicated measurement errors between −31 % and +14 % on average, which was slightly larger than the estimated expanded uncertainty ranging from ± 12 % to ± 17 %. Application of the methodology to assess emission rates from farms of various sizes showed uncertainties between ± 21 % and ± 37 %, generally influenced by systematic wind uncertainties and random errors. The method demonstrates the capacity to measure NH3 emissions from both small (∼ 0.5–1 kg h−1) and large (∼ 100 kg h−1) sources in high-density farming areas. Generally, the SOF method provided an expanded uncertainty below 30 % in measuring NH3 emissions from livestock production, which could be further improved by adhering to best application practices. This paper's findings offer the potential for broader applications, such as measuring NH3 fluxes from fertilized fields and in the oil and gas sector. However, these applications would require further research to adapt and refine the methodologies for these specific contexts.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.

Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR

The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R 2 < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

Ship-borne wave gauge using GNSS interferometric reflectometry

ABSTRACT To obtain wide coverage and frequent wave observations, low-cost methods to observe significant wave heights and wave periods from moving vessels were developed by using Global Navigation Satellite System (GNSS) interferometric reflectometry. Because GNSS signals reflected from the sea surface are always delayed respect to those received directly from the satellite, and this delay depends on the distance between the antenna and the sea surface, the amplitude of the received GNSS signals, in which the direct and reflected signals are mixed, shows interferometric variations in the presence of ocean waves. High sampling rate observations of the GNSS signal amplitude can be used to determine the significant wave period and wave height by referring to a look-up table estimated from simulated received GNSS signals based on a realistic wave spectrum. The methods were applied to actual 20-Hz GNSS observations on a ferryboat and qualitatively good estimations of both significant wave heights and wave periods were obtained. Because the look-up tables can be further modified using in situ wave observations, these estimations can be quantitatively improved in the future.

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform.

Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean (Vicia faba L.) and in an on-the-go setting on chia (Salvia hispanica L.), alfalfa (Medicago sativa L.), and wheat (Triticum durum Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R2 = 0.98), faba bean (R2 = 0.96), and wheat (R2 = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R2 in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R2 from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems.

Extension of a Monolayer Energy-Budget Degree-Day Model to a Multilayer One

This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.