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LiDAR for Precision Agriculture
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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: closed (31 January 2022) | Viewed by 22944

Special Issue Editor

Dr. Dirk Hoffmeister

E-Mail 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 submissions that pass pre-check are 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 2700 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

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Published Papers (3 papers)

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Research

21 pages, 10895 KiB  
Article
Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR
by Jordan Steven Bates, Carsten Montzka, Marius Schmidt and François Jonard
Remote Sens. 2021, 13(4), 710; https://doi.org/10.3390/rs13040710 - 15 Feb 2021
Cited by 30 | Viewed by 6348
Abstract
Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors [...] Read more.
Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season. Full article
(This article belongs to the Special Issue LiDAR for Precision Agriculture)
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18 pages, 4424 KiB  
Article
Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland
by Xiang Zhang, Yuhai Bao, Dongliang Wang, Xiaoping Xin, Lei Ding, Dawei Xu, Lulu Hou and Jie Shen
Remote Sens. 2021, 13(4), 656; https://doi.org/10.3390/rs13040656 - 11 Feb 2021
Cited by 26 | Viewed by 5090
Abstract
The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation [...] Read more.
The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas. Full article
(This article belongs to the Special Issue LiDAR for Precision Agriculture)
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23 pages, 9387 KiB  
Article
Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management
by Dan Wu, Kasper Johansen, Stuart Phinn and Andrew Robson
Remote Sens. 2020, 12(10), 1647; https://doi.org/10.3390/rs12101647 - 21 May 2020
Cited by 19 | Viewed by 4976
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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