Special Issue "LiDAR for Precision Agriculture"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Dirk Hoffmeister
Website
Guest Editor
Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50932 Köln, Germany
Interests: SRTM; geomorphometry; loess; Digital terrain models; landform classification; stereo satellite imagery; ASTER GDEM

Special Issue Information

Dear Colleagues,

LiDAR as an active and accurate remote sensing technique is used in many applications in precision agriculture (PA) to measure the plant structure, e.g. by crop height, density or heterogeneity, ranging from single plant phenotyping to field scale. Likewise, it is used as single ranging measurements on machinery to scanning devices applied as terrestrial (TLS), mobile (MLS) or airborne laser scanning (ALS), where ALS approaches include newer applications from unmanned aerial vehicles (UAVs). Single point measurements, as well as point clouds from scanning devices, can be used by interpolation in 2.5D rasterized approaches or by direct 3D point cloud analysis. Multi-temporal measurements also allow us to derive plant growth or decrease over time (4D). In addition to the structural measurements, single intensity measurements and full-waveform analysis, as well as a combination with further multi- to hyperspectral records (5D to nD) might show an increased accuracy for biomass modeling, plant health detection or as ground-truth data for satellite-based approaches.

This special issue focuses on original and innovative papers that show the use of LiDAR in agriculture from all platforms, including new sensors, algorithms, and combinations with other sensors, as well as in comparison to photogrammetric or other approaches.

Dr. Dirk Hoffmeister
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LiDAR
  • Laser scanning
  • Precision agriculture
  • 3d point clouds
  • Change detection

Published Papers (1 paper)

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Research

Open AccessArticle
Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management
Remote Sens. 2020, 12(10), 1647; https://doi.org/10.3390/rs12101647 - 21 May 2020
Abstract
Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance [...] Read more.
Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops. Full article
(This article belongs to the Special Issue LiDAR for Precision Agriculture)
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