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AgriEngineering
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Application of Geographic Information System and Remote Sensing Technology in...
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Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 5084

Special Issue Editors

Dr. Leonardo Conti

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: geographic Information systems; remote sensing; GNSS; environmental monitoring; rural landscape analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gabriel Araújo e Silva Ferraz

E-Mail Website
Guest Editor
Department of Agricultural Engineering, School of Engineering, Federal University of Lavras—UFLA, P.O. Box 3037, Lavras 37200-900, Brazil
Interests: remote sensing; UAV in agriculture and livestock; digital and precision farming and livestock
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Rossi

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: topography; remote sensing; environmental monitoring and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous increase in the world’s population, the rush to accumulate food resources, the global market challenges, free trade, and adaptation to climate change create the reasons for developing modern monitoring technologies in the agricultural and forestry sector. This whole sector has become a strategic asset of the world economy. Over the centuries, it has followed the evolution of traditional techniques and practices to become a complex system of people, structures, and technologies capable of guiding operators' decisions regarding fundamental principles of sustainable agriculture. Technological progress makes it possible to face these challenges with new tools capable of contributing to solving the problems mentioned above. Multiple response expectations can find a solution in the new frontiers of knowledge and analysis offered by GIS and satellite data in support of the environment, territory, and cultural heritage, in line with the reported green transition and resilience needs of this specific historical moment. Geomatics responds to the need to treat large amounts of data of different natures and characteristics in an interdisciplinary and interoperable way, detected with a growing variety of procedures, which must be organized, processed, and managed quickly for the correct mapping of the rural territory.

This Special Issue is aimed at bringing together research reports describing new methodologies and applications related to evaluation and monitoring studies in agriculture and forestry. Contributions could include, but are not limited to:

  • Remote sensing applications (big data, UAVs/drones, machine learning, BIM and SfM);
  • Environmental monitoring and analysis;
  • GIS for cultural heritage and landscape analysis;
  • Sensors performance and data processing;
  • Precision agriculture, forestry and livestock farming;
  • Geomatics and land management;
  • Geomatics and natural hazards;
  • Sustainable development and climate change.

Dr. Leonardo Conti
Prof. Dr. Gabriel Araújo e Silva Ferraz
Dr. Giuseppe Rossi
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • geographic information systems
  • remote sensing
  • rural territory
  • GNSS
  • image analysis
  • unmanned aerial vehicle
  • precision agriculture and forestry

Published Papers (5 papers)

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Research

15 pages, 4657 KiB  
Article
Estimating Cotton Yield in the Brazilian Cerrado Using Linear Regression Models from MODIS Vegetation Index Time Series
by Daniel A. B. de Siqueira, Carlos M. P. Vaz, Flávio S. da Silva, Ednaldo J. Ferreira, Eduardo A. Speranza, Júlio C. Franchini, Rafael Galbieri, Jean L. Belot, Márcio de Souza, Fabiano J. Perina and Sérgio das Chagas
AgriEngineering 2024, 6(2), 947-961; https://doi.org/10.3390/agriengineering6020054 - 09 Apr 2024
Viewed by 417
Abstract
Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers’ decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, [...] Read more.
Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers’ decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, and the inherent complexity and inexplicability of these models hinder their widespread adoption. This paper presents a simpler approach, employing linear regression models fitted from vegetation indices (VIs) extracted from MODIS sensor data on the Terra and Aqua satellites. The aim is to forecast cotton yields in key areas of the Brazilian Cerrado. Using data from 281 commercial production plots, models were trained (167 plots) and tested (114 plots), relating seed cotton yield to nine commonly used VIs averaged over 15-day intervals. Among the evaluated VIs, Enhanced Vegetation Index (EVI) and Triangular Vegetation Index (TVI) exhibited the lowest root mean square errors (RMSE) and the highest determination coefficients (R2). Optimal periods for in-season yield prediction fell between 90 and 105 to 135 and 150 days after sowing (DAS), corresponding to key phenological phases such as boll development, open boll, and fiber maturation, with the lowest RMSE of about 750 kg ha−1 and R2 of 0.70. The best forecasts for early crop stages were provided by models at the peaks (maximum value of the VI time series) for EVI and TVI, which occurred around 80–90 DAS. The proposed approach makes the yield predictability more inferable along the crop time series just by providing sowing dates, contour maps, and their respective VIs. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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26 pages, 12310 KiB  
Article
Some Geospatial Insights on Orange Grove Site Selection in a Portion of the Northern Citrus Belt of Mexico
by Juan Carlos Díaz-Rivera, Carlos Arturo Aguirre-Salado, Liliana Miranda-Aragón and Alejandro Ivan Aguirre-Salado
AgriEngineering 2024, 6(1), 259-284; https://doi.org/10.3390/agriengineering6010016 - 23 Jan 2024
Viewed by 840
Abstract
This study aimed to delineate the most suitable areas for sustainable citrus production by integrating multi-criteria decision analysis, time-series remote sensing, and principal component analysis in a portion of the northern citrus belt of Mexico, particularly in the Rioverde Valley. Fourteen specific factors [...] Read more.
This study aimed to delineate the most suitable areas for sustainable citrus production by integrating multi-criteria decision analysis, time-series remote sensing, and principal component analysis in a portion of the northern citrus belt of Mexico, particularly in the Rioverde Valley. Fourteen specific factors were grouped into four main factors, i.e., topography, soil, climate, and proximity to water sources, to carry out a multi-criteria decision analysis for classifying production areas according to suitability levels. To explore the effect of precipitation on land suitability for citrus production, we analyzed the historical record of annual precipitation estimated by processing 20-year NDVI daily data. The multi-criteria model was run for every precipitation year. The final map of land suitability was obtained by using the first component after principal component analysis on annual land suitability maps. The results indicate that approximately 30% of the study area is suitable for growing orange groves, with specific areas designated as suitable based on both mean annual precipitation (MAP) and principal component analysis (PCA) criteria, resulting in 84,415.7 ha and 95,485.5 ha of suitable land, respectively. The study highlighted the importance of remotely sensed data-based time-series precipitation in predicting potential land suitability for growing orange groves in semiarid lands. Our results may support decision-making processes for the effective land management of orange groves in the Mexico’s Rioverde region. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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23 pages, 4884 KiB  
Article
Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture
by Hugo Rodrigues, Marcos B. Ceddia, Gustavo M. Vasques, Vera L. Mulder, Gerard B. M. Heuvelink, Ronaldo P. Oliveira, Ziany N. Brandão, João P. S. Morais, Matheus L. Neves and Sílvio R. L. Tavares
AgriEngineering 2023, 5(4), 2326-2348; https://doi.org/10.3390/agriengineering5040143 - 06 Dec 2023
Cited by 2 | Viewed by 1054
Abstract
The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based [...] Read more.
The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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13 pages, 7529 KiB  
Article
Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging
by Pavel A. Dmitriev, Boris L. Kozlovsky, Anastasiya A. Dmitrieva, Vladimir S. Lysenko, Vasily A. Chokheli and Tatyana V. Varduni
AgriEngineering 2023, 5(4), 2253-2265; https://doi.org/10.3390/agriengineering5040138 - 01 Dec 2023
Viewed by 906
Abstract
Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress [...] Read more.
Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m−2 s−1 (light stress), and moderate natural light, 50 μmol photons m−2 s−1 (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R666:682)/mean(R552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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16 pages, 4017 KiB  
Article
Spatiotemporal Dynamics of Land Use and Land Cover through Physical–Hydraulic Indices: Insights in the São Francisco River Transboundary Region, Brazilian Semiarid Area
by Lizandra de Barros de Sousa, Abelardo Antônio de Assunção Montenegro, Marcos Vinícius da Silva, Pabrício Marcos Oliveira Lopes, José Raliuson Inácio Silva, Thieres George Freire da Silva, Frederico Abraão Costa Lins and Patrícia Costa Silva
AgriEngineering 2023, 5(3), 1147-1162; https://doi.org/10.3390/agriengineering5030073 - 03 Jul 2023
Cited by 1 | Viewed by 1144
Abstract
This article presents a study on the spatiotemporal dynamics of land cover and use, vegetation indices, and water content in the semiarid region of Pernambuco, Brazil. This study is based on an analysis of satellite images from the years 2016, 2018, and 2019 [...] Read more.
This article presents a study on the spatiotemporal dynamics of land cover and use, vegetation indices, and water content in the semiarid region of Pernambuco, Brazil. This study is based on an analysis of satellite images from the years 2016, 2018, and 2019 using the MapBiomas platform. The results show changes in the predominant land cover classes over time, with an increase in the caatinga area and a decrease in the pasture area. An analysis of the vegetation indices (NDVI and LAI) indicated low vegetation cover and biomass in the study area, with a slight increase in the NDVI in 2018. An analysis of the Modified Normalized Difference Water Index (MNDWI) showed that the water content in the study area was generally low, with no significant variations over time. An increase in the water bodies, mainly due to the construction of a reservoir, was noted. The results of this study have provided important information for natural resource management in the region, including the development of strategies for the sustainable use and management of natural resources, particularly water resources, vegetation cover, and soil conservation. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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