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Remote Sensing
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UAS Applications in Agroforestry
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UAS Applications in Agroforestry

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 4442

Special Issue Editors

Dr. Xiaoyu Song

E-Mail Website
Guest Editor
1. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing, China
Interests: precision agriculture; crop monitoring; crop nitrogen nutrition monitoring; unmanned aircraft systems; image processing
Dr. Chenghai Yang

E-Mail Website
Guest Editor
U.S. Department of Agriculture, 3103 F&B Road, College Station, TX 77845, USA
Interests: precision agriculture; pest management; airborne; image processing; multispectral, hyperspectral and thermal imaging systems; unmanned aircraft systems; electronic and spectral sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenjiang Huang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; image processing; image classification; parameter mapping; system development; agricultural application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of compact and light sensors and the increased carrying capacity of UAVs, the use of UAV platforms with single or multiple sensors can quickly and non-destructively obtain large amounts of data of objects on the ground. This technology has obvious advantages and broad application prospects in many fields such as agriculture, forestry and ecological environments. It has become an important remote sensing tool for crop growth monitoring, yield estimation, nutrition diagnosis, pest and disease monitoring, field management, and forest and grassland resource assessment.

This special issue aims to present the latest UAV remote sensing data processing technology and showcase its latest research trends and application prospects in related agricultural and forestry fields, with an overall goal to promote the research and sustainable development of UAV remote sensing technology in agriculture and forestry.

We particularly welcome contributions that include, but are not limited to, the following topics:

  • Data processing and analysis methods about UAS technology;
  • Application of UAS technology in precision agriculture;
  • Application of UAS technology in forestry monitoring;
  • Application of UAS technology in grassland monitoring.

Dr. Xiaoyu Song
Dr. Chenghai Yang
Prof. Dr. Wenjiang Huang
Guest Editors

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

  • UAS data processing
  • UAS data analysis methods
  • crop growth monitoring
  • yield estimation
  • crop nutrition diagnosis
  • forest monitoring
  • grassland monitoring
  • pest monitoring
  • disease monitoring

Published Papers (2 papers)

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Research

16 pages, 1575 KiB  
Article
Comparison of Winter Wheat Yield Estimation Based on Near-Surface Hyperspectral and UAV Hyperspectral Remote Sensing Data
by Haikuan Feng, Huilin Tao, Yiguang Fan, Yang Liu, Zhenhai Li, Guijun Yang and Chunjiang Zhao
Remote Sens. 2022, 14(17), 4158; https://doi.org/10.3390/rs14174158 - 24 Aug 2022
Cited by 15 | Viewed by 2083
Abstract
Crop yields are important for food security and people’s living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface [...] Read more.
Crop yields are important for food security and people’s living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface hyperspectral data and UAV hyperspectral data and used the partial least squares regression (PLSR) and artificial neural network (ANN) methods to estimate the yield of winter wheat at different growth stages. Verification was performed based on these two types of hyperspectral remote sensing data and the yield was estimated using vegetation indices and a combination of vegetation indices and red-edge parameters as the modeling independent variables, respectively, using PLSR and ANN regression, respectively. The results showed that, for the same data source, the optimal vegetation index for estimating the yield was the same in all of the studied growth stages; however, the optimal red-edge parameters were different for different growth stages. Compared with using only the vegetation indices as the modeling factor to estimate yield, the combination of the vegetation indices and red-edge parameters obtained superior estimation results. Additionally, the accuracy of yield estimation was shown to be improved by using the PLSR and ANN methods, with the yield estimation model constructed using the PLSR method having a better prediction effect. Moreover, the yield prediction model obtained using the near-surface hyperspectral sensors had a higher fitting and accuracy than the model obtained using the UAV hyperspectral remote sensing data (the results were based on the specific growth stressors, N and water supply). This study shows that the use of a combination of vegetation indices and red-edge parameters achieved an improved yield estimation compared to the use of vegetation indices alone. In the future, the selection of suitable sensors and methods needs to be considered when constructing models to estimate crop yield. Full article
(This article belongs to the Special Issue UAS Applications in Agroforestry)
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27 pages, 8695 KiB  
Article
Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method
by Jie Zhang, Xiaoyu Song, Xia Jing, Guijun Yang, Chenghai Yang, Haikuan Feng, Jiaojiao Wang and Shikang Ming
Remote Sens. 2022, 14(16), 3989; https://doi.org/10.3390/rs14163989 - 16 Aug 2022
Cited by 2 | Viewed by 1442
Abstract
Grain protein content (GPC) is an important indicator of nutritional quality of rice. In this study, nitrogen fertilization experiments were conducted to monitor GPC for high-quality Indica rice varieties Meixiangzhan 2 (V1) and Wufengyou 615 (V2) in 2019 and 2020. Three types of [...] Read more.
Grain protein content (GPC) is an important indicator of nutritional quality of rice. In this study, nitrogen fertilization experiments were conducted to monitor GPC for high-quality Indica rice varieties Meixiangzhan 2 (V1) and Wufengyou 615 (V2) in 2019 and 2020. Three types of parameters, including photosynthetic sensitive vegetation indices (VIs), canopy leaf area index (LAI), and crop plant nitrogen accumulation (PNA), obtained from UAV hyperspectral images were used to estimate rice GPC. Two-dimensional and three-dimensional GPC indices were constructed by combining any two of the three types of parameters and all three, respectively, based on the Euclidean distance method. The R2 and RMSE of the two-dimensional GPC index model for variety V1 at the tillering stage were 0.81 and 0.40% for modeling and 0.95 and 0.38% for validation, and 0.91 and 0.27% for modeling and 0.83 and 0.36% for validation for variety V2. The three-dimensional GPC index model for variety V1 had R2 and RMSE of 0.86 and 0.34% for modeling and 0.78 and 0.45% for validation, and 0.97 and 0.17% for modeling and 0.96 and 0.17% for validation for variety V2 at the panicle initiation stage. At the heading stage, the R2 and RMSE of the three-dimensional model for variety V1 were 0.92 and 0.26% for modeling and 0.91 and 0.37% for validation, and 0.96 and 0.20% for modeling and 0.99 and 0.15% for validation for variety V2. These results demonstrate that the GPC monitoring models incorporating multiple crop growth parameters based on Euclidean distance can improve GPC estimation accuracy and have the potential for field-scale GPC monitoring. Full article
(This article belongs to the Special Issue UAS Applications in Agroforestry)
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