Special Issue "Remote and Proximal Sensing Applied to Agriculture and Forest Sciences"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 June 2022.

Special Issue Editors

Dr. Simone Priori
E-Mail Website
Guest Editor
University of Tuscia, Department of Agriculture and Forest Sciences (DAFNE), Viterbo, Italy
Interests: pedology; digital soil mapping; proximal sensing; soil hydrology
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Dr. Antonello Bonfante
E-Mail Website1 Website2
Guest Editor
Consiglio Nazionale delle Ricerche (CNR), Istituto per i sistemi Agricoli e Forestali del Mediterraneo, Via Patacca, 85 - 80056 Ercolano, Napoli, Italy
Interests: hydropedology; precision agriculture; crop adaptation to climate change
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Dr. Anna Brook
E-Mail Website
Guest Editor
Spectroscopy and Remote Sensing Laboratory, Department of Geography and Environmental Studie, Faculty of Social Science, University of Haifa, Haifa 3498838, Israel
Interests: data fusion; image and signal processing; automation target recognition; sub-pixel detection; spectral models across NIR-MIR regions
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Special Issue Information

Dear Colleagues,

Remote and proximal sensing technologies enable the acquisition of diverse spatial data both in agriculture and in forestry, and they represent one of the pillars of the digital agriculture and forestry. New-generation satellites hosting hyperspectral cameras (e.g., Hyperion, EnMap, Shalom, Prisma) along with price decreases in multi- and hyperspectral cameras as well as thermal cameras for airborne and UAV platforms have laid the foundation for important steps ahead in land monitoring. In terms of proximal sensing, innovative platforms ranging from handheld, robotics, and tractor-embedded sensors have been developed in recent years. This Special Issue calls for original and innovative manuscripts related to recent research and activities that demonstrate the proficient use of remote and/or proximal sensing techniques in agriculture and forestry. The topics of the submitted manuscripts include:

  • The applications of innovative sensors or technologies for soil, crops, and forest monitoring
  • Uncertainty and accuracy of remote/proximal sensing techniques
  • Multisource data integration
  • Predictive models based on remote and/or proximal sensing data
  • Comparisons of different techniques
  • Remotely and proximally sensed-assisted agricultural practices
  • Remote sensing of forest disturbances (wildfire, droughts, biotic stresses, etc.)

This Special Issue welcomes diverse types of articles including original research, reviews, and perspective papers (upon consultation with the Editors).

Sincerely,

Dr. Simone Priori
Dr. Antonello Bonfante
Dr. Anna Brook
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 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. Applied Sciences 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 2300 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

  • satellite images
  • hyperspectral
  • thermal images
  • drones
  • digital soil mapping
  • crop monitoring
  • precision agriculture
  • sensors for agriculture
  • forest monitoring

Published Papers (4 papers)

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Research

Article
Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
Appl. Sci. 2021, 11(24), 12164; https://doi.org/10.3390/app112412164 - 20 Dec 2021
Viewed by 344
Abstract
Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to [...] Read more.
Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops. Full article
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Article
Real-Time Remote Sensing of the Lobesia botrana Moth Using a Wireless Acoustic Detection Sensor
Appl. Sci. 2021, 11(24), 11889; https://doi.org/10.3390/app112411889 - 14 Dec 2021
Viewed by 347
Abstract
This article presents a wireless sensor for pest detection, specifically the Lobesia botrana moth or vineyard moth. The wireless sensor consists of an acoustic-based detection of the sound generated by a flying Lobesia botrana moth. Once a Lobesia botrana moth is detected, the [...] Read more.
This article presents a wireless sensor for pest detection, specifically the Lobesia botrana moth or vineyard moth. The wireless sensor consists of an acoustic-based detection of the sound generated by a flying Lobesia botrana moth. Once a Lobesia botrana moth is detected, the information about the time, geographical location of the sensor and the number of detection events is sent to a server that gathers the detection statistics in real-time. To detect the Lobesia botrana, its acoustic signal was previously characterized in a controlled environment, obtaining its power spectral density for the acoustic filter design. The sensor is tested in a controlled laboratory environment where the detection of the flying moths is successfully achieved in the presence of all types of environmental noises. Finally, the sensor is installed on a vineyard in a region where the moth has already been detected. The device is able to detect flying Lobesia botrana moths during its flying period, giving results that agree with traditional field traps. Full article
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Article
Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
Appl. Sci. 2021, 11(23), 11348; https://doi.org/10.3390/app112311348 - 30 Nov 2021
Viewed by 332
Abstract
Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their [...] Read more.
Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries. Full article
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Article
Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3
Appl. Sci. 2021, 11(5), 2238; https://doi.org/10.3390/app11052238 - 03 Mar 2021
Viewed by 1137
Abstract
Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This [...] Read more.
Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This letter frames the crop circles detection problem within a deep learning framework. In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) models for crop circle detection in the desert. In order to quantify the performance, we build a crop circles dataset from images extracted via Google Earth over a desert area in the East Oweinat in the South-Western Desert of Egypt. The dataset totals 2511 crop circle samples. With a small training set and a relatively large test set, plausible detection rates were obtained, scoring a precision of 1 and a recall of about 0.82 for Mask R-CNN and a precision of 0.88 and a recall of 0.94 regarding YOLOv3. Full article
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