scholarly journals Terrestrial laser scanning for plant height measurement and biomass estimation of maize

2014 ◽  
Vol XL-7 ◽  
pp. 181-187 ◽  
Author(s):  
N. Tilly ◽  
D. Hoffmeister ◽  
H. Schiedung ◽  
C. Hütt ◽  
J. Brands ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Laser Scanning ◽  
Point Clouds ◽  
Time Frame ◽  
Coefficient Of Determination ◽  
Biomass Estimation ◽  
Height Measurement ◽  
Growing Period ◽  
Non Destructive

Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estimators. In this contribution, first results of multitemporal surveys on a maize field with a terrestrial laser scanner are shown. The achieved point clouds are interpolated to generate Crop Surface Models (CSM) that represent the top canopy. These CSMs are used for visualizing the spatial distribution of plant height differences within the field and calculating plant height above ground with a high resolution of 1 cm. In addition, manual measurements of plant height were carried out corresponding to each TLS campaign to verify the results. The high coefficient of determination (R² = 0.93) between both measurement methods shows the applicability of the presented approach. The established regression model between CSM-derived plant height and destructively measured biomass shows a varying performance depending on the considered time frame during the growing period. This study shows that TLS is a suitable and promising method for measuring plant height of maize. Moreover, it shows the potential of plant height as a non-destructive estimator for biomass in the early growing period. However, challenges are the non-linear development of plant height and biomass over the whole growing period.

scholarly journals Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring

2014 ◽  
Vol XL-7 ◽  
pp. 43-49 ◽  
Author(s):  
S. Brocks ◽  
G. Bareth
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Low Cost ◽  
Image Acquisition ◽  
Coefficient Of Determination ◽  
Average Height ◽  
Smart Cameras ◽  
Growth Variability ◽  
Growing Period ◽  
Surface Models

Crop-Surface-Models (CSMs) are a useful tool for monitoring in-field crop growth variability, thus enabling precision agriculture which is necessary for achieving higher agricultural yields. This contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. An application that automates image acquisition and transmission was developed. Automated image acquisition took place throughout the growing period of barley in 2013. For three dates where both automated image acquisition and manual measurements of plant height were available, CSMs were generated using a combination of AgiSoft PhotoScan and Esri ArcGIS. The coefficient of determination <i>R</i><sup>2</sup> between the average of the manually measured plant heights per plots and the average height of the developed crop surface models was 0.61 (<i>n</i> = 24). The overall correlation between the manually measured heights and the CSM-derived heights is 0.78. The average per plot of the manually measured plant heights in the timeframe covered by the generated CSMs range from 19 to 95 cm, while the average plant height per plot of the generated CSMs range from 2.1 to 69 cm. These first results show that the presented approach is feasible.

scholarly journals Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

2014 ◽  
Vol 8 (1) ◽  
pp. 083671 ◽  
Author(s):  
Nora Tilly ◽  
Dirk Hoffmeister ◽  
Qiang Cao ◽  
Shanyu Huang ◽  
Victoria Lenz-Wiedemann ◽  
...  
Keyword(s):  
Plant Height ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Paddy Rice ◽  
Biomass Estimation ◽  
Height Measurement ◽  
Surface Models

scholarly journals Weighing trees with lasers: advances, challenges and opportunities

2018 ◽  
Vol 8 (2) ◽  
pp. 20170048 ◽  
Author(s):  
M. I. Disney ◽  
M. Boni Vicari ◽  
A. Burt ◽  
K. Calders ◽  
S. L. Lewis ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Tree Form ◽  
Form And Function ◽  
Challenges And Opportunities ◽  
And Function ◽  
Non Destructive

Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.

Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds

2013 ◽  
Vol 4 (4) ◽  
pp. 344-353 ◽  
Author(s):  
Xiaowei Yu ◽  
Xinlian Liang ◽  
Juha Hyyppä ◽  
Ville Kankare ◽  
Mikko Vastaranta ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Biomass Estimation ◽  
Stem Biomass

scholarly journals High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

2021 ◽  
Vol 12 ◽  
Author(s):  
Leonardo Volpato ◽  
Francisco Pinto ◽  
Lorena González-Pérez ◽  
Iyotirindranath Gilberto Thompson ◽  
Aluízio Borém ◽  
...  
Keyword(s):  
Plant Height ◽  
Ground Truth ◽  
Point Clouds ◽  
Wheat Breeding ◽  
Aerial Imagery ◽  
Coefficient Of Determination ◽  
Growth Stages ◽  
Breeding Lines ◽  
Aerial Imaging ◽  
Field Phenotyping

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

scholarly journals VALIDATING A WORKFLOW FOR TREE INVENTORY UPDATING WITH 3D POINT CLOUDS OBTAINED BY MOBILE LASER SCANNING

2018 ◽  
Vol XLII-2 ◽  
pp. 1163-1168
Author(s):  
J. Wang ◽  
R. Lindenbergh
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Spatial Information ◽  
Open Data ◽  
Point Clouds ◽  
Urban Trees ◽  
Biomass Estimation ◽  
Species Classification ◽  
3D Point Clouds ◽  
Tree Inventory

Urban trees are an important component of our environment and ecosystem. Trees are able to combat climate change, clean the air and cool the streets and city. Tree inventory and monitoring are of great interest for biomass estimation and change monitoring. Conventionally, parameters of trees are manually measured and documented in situ, which is not efficient regarding labour and costs. Light Detection And Ranging (LiDAR) has become a well-established surveying technique for the acquisition of geo-spatial information. Combined with automatic point cloud processing techniques, this in principle enables the efficient extraction of geometric tree parameters. In recent years, studies have investigated to what extend it is possible to perform tree inventories using laser scanning point clouds. Give the availability of a city of Delft Open data tree repository, we are now able to present, validate and extend a workflow to automatically obtain tree data from tree location until tree species. The results of a test over 47 trees show that the proposed methods in the workflow are able to individual urban trees. The tree species classification results based on the extracted tree parameters show that only one tree was wrongly classified using k-means clustering.

scholarly journals Fusion of high resolution remote sensing images and terrestrial laser scanning for improved biomass estimation of maize

2014 ◽  
Vol XL-7 ◽  
pp. 101-108 ◽  
Author(s):  
C. Hütt ◽  
H. Schiedung ◽  
N. Tilly ◽  
G. Bareth
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Satellite System ◽  
Biomass Estimation ◽  
Digital Elevation ◽  
Elevation Model ◽  
In The Beginning

In this study, images from the satellite system WorldView-2 in combination with terrestrial laser scanning (TLS) over a maize field in Germany are investigated. Simultaneously to the measurements a biomass field campaigns was carried out. From the point clouds of the terrestrial laser scanning campaigns crop surface models (CSM) from each scanning date were calculate to model plant growth over time. These results were resampled to match the spatial resolution of the WorldView-2 images, which had to orthorectified using a high resolution digital elevation model and atmosphere corrected using the ATCOR Software package. A high direct correlation of the NDVI calculated from the WorldView-2 sensor and the dry biomass was found in the beginning of June. At the same date, the heights from laser scanning can also explain a certain amount of the biomass variation (<i>r</i><sup>2</sup> = 0.6). By combining the NDVI from WorldView-2 and the height from the laser scanner with a linear model, the R2 reaches higher values of 0.86. To further understand the relationship between CSM derived crop heights and reflection indices, a comparison on a pixel basis was performed. Interestingly, the correlation of the NDVI and the crop height is rather low at the beginning of June (<i>r</i><sup>2</sup> = 0,4, <i>n</i> = 1857) and increases significantly (<i>R</i><sup>2</sup> = 0,79, <i>N</i> = 1857) at a later stage.

scholarly journals Down-Sampling of Point Clouds for the Technical Diagnostics of Buildings and Structures

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Czesław Suchocki ◽  
Wioleta Błaszczak-Bąk
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Morphological Characteristics ◽  
Point Clouds ◽  
Technical Diagnostics ◽  
Destructive Testing ◽  
Existing Structures ◽  
Technical Assessment ◽  
Areas Of Interest ◽  
Non Destructive

Terrestrial laser scanning (TLS) is a non-destructive testing method for the technical assessment of existing structures. TLS has been successfully harnessed for monitoring technical surface conditions and morphological characteristics of historical buildings (e.g., the detection of cracks and cavities). TLS measurements with very high resolution should be taken to detect minor defects on the walls of buildings. High-resolution measurements are mostly needed in certain areas of interest, e.g., cracks and cavities. Therefore, reducing redundant information on flat areas without cracks and cavities is very important. In this case, automatic down-sampling of datasets according to the aforementioned criterion is required. This paper presents the use of the Optimum Dataset (OptD) method to optimize TLS dataset. A Leica ScanStation C10 time-of-flight scanner and a Z+F IMAGER 5016 phase-shift scanner were used during the research. The research was conducted on a specially prepared concrete sample and real object, i.e., a brick citadel located on the Kościuszko Mound in Cracow. The reduction of dataset by the OptD method and random method from TLS measurements were compared and discussed. The results prove that the large datasets from TLS diagnostic measurements of buildings and structures can be successfully optimized using the OptD method.

scholarly journals Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 618 ◽  
Author(s):  
Samuel C. Hassler ◽  
Fulya Baysal-Gurel
Keyword(s):  
Precision Agriculture ◽  
Weed Management ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Unmanned Aircraft ◽  
Ease Of Use ◽  
Biomass Estimation ◽  
Trade Offs ◽  
Legal Constraints

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

scholarly journals Crown Area as a Parameter for Biomass Estimation of Croton sonderianus Müll. Arg.

2019 ◽  
Vol 26 (4) ◽  
Author(s):  
Jeferson Luiz Dallabona Dombroski ◽  
José Rivanildo de Souza Pinto
Keyword(s):  
Plant Height ◽  
Plant Biomass ◽  
Remote Sensing Data ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Basal Diameter ◽  
Crown Area ◽  
Breast Height ◽  
Multiple Stems ◽  
Non Destructive

ABSTRACT Current tree biomass estimation techniques generally use remote sensing data and allometric models for validation, which relate non-destructive parameters to plant biomass, usually employing diameter at the plant base or breast height and plant height. In the Caatinga Biome, many plants present multiple stems, thus making it difficult to measure the plant diameter, and lost branches, which are difficult to correct for. Hence, there is a need for suitable models for Caatinga plants, as well as studies on the possibility of using other parameters. For this study, plant and branch basal diameter, plant height, and crown area of Croton sonderianus plants were measured, and plants were also collected and weighed. Several classic models and their variations were tested. The best models were variations of Naslund (R2 = 0.92; rmse = 1,221) and Schumacher & Hall (R2 = 0.92; rmse = 1,217). Plant height and crown area enables a better biomass estimation than using plant or branch basal diameter.

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