International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

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Remote sensing of the environment

Chlorophyll fluorescence is a non-invasive measurement of photosystem II (PSII) activity and is a commonly used technique in plant physiology. The sensitivity of PSII activity to abiotic and biotic factors has made this a key technique not only for understanding the photosynthetic mechanisms but also as a broader indicator of how plants respond to environmental change. This, along with low cost and ease of collecting data, has resulted in the appearance of a large array of instrument types for measurement and calculated parameters which can be bewildering for the new user. Moreover, its accessibility can lead to misuse and misinterpretation when the underlying photosynthetic processes are not fully appreciated. This review is timely because it sits at a point of renewed interest in chlorophyll fluorescence where fast measurements of photosynthetic performance are now required for crop improvement purposes. Here we help the researcher make choices in terms of protocols using the equipment and expertise available, especially for field measurements. We start with a basic overview of the principles of fluorescence analysis and provide advice on best practice for taking pulse amplitude-modulated measurements. We also discuss a number of emerging techniques for contemporary crop and ecology research, where we see continual development and application of analytical techniques to meet the new challenges that have arisen in recent years. We end the review by briefly discussing the emerging area of monitoring fluorescence, chlorophyll fluorescence imaging, field phenotyping, and remote sensing of crops for yield and biomass enhancement.

Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications

https://www.cell.com/trends/plant-science/pdf/S1360-1385(13)00199-4.pdf

Field high-throughput phenotyping: the new crop breeding frontier

With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experiment...

https://horticulture.wisc.edu/wp-content/uploads/sites/20/2013/09/Fiorani-and-Schurr-2013-Future-Scenarios-of-Plant-Phenotyping2.pdf

Future scenarios for plant phenotyping.

A method for accurate estimation of leaf area density (LAD) and the cumulative leaf area index (LAI) profiles of small trees (Camellia sasanqua and Deutzia crenata) under different conditions was demonstrated, which used precise voxel-based tree models produced by high-resolution portable scanning lidar. In this voxel-based canopy profiling (VCP) method, data for each horizontal layer of the canopy of each tree were collected from symmetrical azimuthal measurement points around the tree using optimally inclined laser beams. The data were then converted into a voxel-based three-dimensional model that reproduced the tree precisely, including within the canopy. This precise voxel model allowed the LAD and LAI of these trees, which have extremely dense and nonrandomly distributed foliage, to be computed by direct counting of the beam-contact frequency in each layer using a point-quadrat method. Corrections for leaf inclination and nonphotosynthetic tissues reduced the estimation error. A beam incident zenith angle near 57.5deg offered a good correction for leaf inclination without knowledge of the actual leaf inclination. Non-photosynthetic tissues were removed by image-processing techniques. The best LAD estimations showed errors of 17% at the minimum horizontal layer thickness and of 0.7% at the maximum thickness. The error of the best LAI estimations was also 0.7%

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Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar

Understanding and diagnosing plant responses to stress will benefit greatly from three-dimensional (3D) measurement and analysis of plant properties because plant responses are strongly related to their 3D structures. Light detection and ranging (lidar) has recently emerged as a powerful tool for direct 3D measurement of plant structure. Here the use of 3D lidar imaging to estimate plant properties such as canopy height, canopy structure, carbon stock, and species is demonstrated, and plant growth and shape responses are assessed by reviewing the development of lidar systems and their applications from the leaf level to canopy remote sensing. In addition, the recent creation of accurate 3D lidar images combined with natural colour, chlorophyll fluorescence, photochemical reflectance index, and leaf temperature images is demonstrated, thereby providing information on responses of pigments, photosynthesis, transpiration, stomatal opening, and shape to environmental stresses; these data can be integrated with 3D images of the plants using computer graphics techniques. Future lidar applications that provide more accurate dynamic estimation of various plant properties should improve our understanding of plant responses to stress and of interactions between plants and their environment. Moreover, combining 3D lidar with other passive and active imaging techniques will potentially improve the accuracy of airborne and satellite remote sensing, and make it possible to analyse 3D information on ecophysiological responses and levels of various substances in agricultural and ecological applications and in observations of the global biosphere.

/pdf/3d-lidar-imaging-for-detecting-and-understanding-plant-3pal76g6up.pdf

3D lidar imaging for detecting and understanding plant responses and canopy structure

Factors that contribute to the accuracy of estimating woody canopy’s leaf area density (LAD) using 3D portable lidar imaging were investigated. The 3D point cloud data for a Japanese zelkova canopy [Zelkova serrata (Thunberg) Makino] were collected using a portable scanning lidar from several points established on the ground and at 10 m above the ground. The LAD profiles were computed using voxel-based canopy profiling (VCP). The best LAD results [a root-meansquare error (RMSE) of 0.21 m 2 m 23 ] for the measurement plot (corresponding to an absolute LAI error of 9.5%) were obtained by compositing the ground-level and 10 m measurements. The factors that most strongly affected estimation accuracy included the presence of non-photosynthetic tissues, distribution of leaf inclination angles, number (N ) of incident laser beams in each region within the canopy, and G(um) (the mean projection of a unit leaf area on a plane perpendicular to the direction of the laser beam at the measurement zenith angle of um). The influences of non-photosynthetic tissues and leaf inclination angle on the estimates amounted to 4.2–32.7% and 7.2– 94.2%, respectively. The RMSE of the LAD estimations was expressed using a function of N and G(um).

/pdf/factors-contributing-to-accuracy-in-the-estimation-of-the-2y40sfw11g.pdf

Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging

We developed a method to produce a 3-D voxel-based solid model of a tree based on portable scanning lidar data for accurate estimation of the volume of the woody material. First, we obtained lidar measurements with a high laser pulse density from several measurement positions around the target, a Japanese zelkova tree. Next, we converted lidar-derived point-cloud data for the target into voxels. The voxel size was 0.5 cm × 0.5 cm × 0.5 cm. Then, we used differences in the spatial distribution of voxels to separate the stem and large branches (diameter > 1 cm) from small branches (diameter ⩽ 1 cm). We classified the voxels into sets corresponding to the stem and to each large branch and then interpolated voxels to fill out their surfaces and their interiors. We then merged the stem and large branches with the small branches. The resultant solid model of the entire tree was composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches, and a cloud of voxels equivalent to small branches that were discretely scattered in mainly the upper part of the target. Using this model, we estimated the woody material volume by counting the number of voxels in each part and multiplying the number of voxels by the unit voxel volume (0.13 cm 3 ). The percentage error of the volume of the stem and part of a large branch was 0.5%. The estimation error of a certain part of the small branches was 34.0%.

http://park.itc.u-tokyo.ac.jp/joho/Omasa/520.pdf

3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar

In the present study, an attempt was made to produce a precise 3D image of a tomato canopy using a portable high-resolution scanning lidar. The tomato canopy was scanned by the lidar from three positions surrounding it. Through the scanning, the point cloud data of the canopy were obtained and they were co-registered. Then, points corresponding to leaves were extracted and converted into polygon images. From the polygon images, leaf areas were accurately estimated with a mean absolute percent error of 4.6%. Vertical profile of leaf area density (LAD) and leaf area index (LAI) could be also estimated by summing up each leaf area derived from the polygon images. Leaf inclination angle could be also estimated from the 3-D polygon image. It was shown that leaf inclination angles had different values at each part of a leaf.

https://www.mdpi.com/1424-8220/11/2/2166/pdf

Fumiki Hosoi

Papers

Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar

3D lidar imaging for detecting and understanding plant responses and canopy structure

Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging

3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar

3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information.