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Geoinformation Technologies in Agriculture and Environment Protection for a Sustainable Future

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5211

Special Issue Editors

Prof. Dr. Mladen Jurišić

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Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: crop production; GIS; multicriteria decision making; inventarization of natural resources; agroecosystems and the environment; farming and cropping systems
Special Issues, Collections and Topics in MDPI journals
Dr. Dorijan Radočaj

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: cropland suitability; land suitability; remote sensing; GIS; predictive mapping; digital soil mapping; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing demands for efficient and environmentally friendly land management, the development of advanced spatial methods and models has become a growing necessity. Agriculture and environment protection, as frequently inseparable disciplines in terms of a sustainable future, are of primary concern in ensuring optimal food production and biodiversity. The integration of geographic information systems (GIS) with modern geoinformation technologies, such as global navigation satellite systems (GNSS) and remote sensing, increases the possibilities and scope of such approaches. This combination allows high efficiency and flexibility for suitability analyses in land management, precision agriculture, urban planning, and many other integral segments of land use. These suitability results serve as a basis for effective decision making in land management by selecting the best possible natural land-use options and minimizing the application of expensive and potentially toxic inputs in the environment.

I am pleased to invite you to this Special Issue, “Geoinformation Technologies in Agriculture and Environment Protection for a Sustainable Future”, which is designed to connect various interdisciplinary fields with modern, efficient, and flexible geoinformation technologies in the GIS environment for sustainable land management.

This Special Issue aims to enrich the present knowledge of the application of geoinformation technologies in agriculture and environment protection for efficient and sustainable land management. Submissions are encouraged to cover a broad range of topics that are conceptualized as the basis for decision making in sustainable land management. Possible topics include, but are not restricted to: agricultural land management, environment protection, precision agriculture, urban planning, and other interdisciplinary sciences that are closely related to a sustainable future in the scope of agriculture and environment protection. Due to the flexibility of GIS and other geoinformation technologies various case studies are also encouraged for submission, which are valuable for experts from any location in the world to evaluate their own methodology in their chosen fields.

We look forward to receiving your contributions!

Prof. Dr. Mladen Jurišić
Dr. Dorijan Radočaj
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. Sustainability 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 2400 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

  • agricultural land management
  • precision agriculture
  • environmental modeling
  • geographic information system (GIS)
  • remote sensing
  • satellite missions
  • global navigation satellite system (GNSS)

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Published Papers (2 papers)

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Research

17 pages, 6703 KiB  
Article
GIS-Based Visitor Count Prediction and Environmental Susceptibility Zoning in Protected Areas: A Case Study in Plitvice Lakes National Park, Croatia
by Mladen Jurišić, Ivan Plaščak, Željko Rendulić and Dorijan Radočaj
Sustainability 2023, 15(2), 1625; https://doi.org/10.3390/su15021625 - 13 Jan 2023
Cited by 4 | Viewed by 2326
Abstract
The most valuable protected natural areas, including national parks, are subjected to the increased visitors count and density, threatening the environmental sustainability and biodiversity conservation. To establish a basis for land management to mitigate these influences, the novel geographic information (GIS)-based environmental susceptibility [...] Read more.
The most valuable protected natural areas, including national parks, are subjected to the increased visitors count and density, threatening the environmental sustainability and biodiversity conservation. To establish a basis for land management to mitigate these influences, the novel geographic information (GIS)-based environmental susceptibility zoning method was proposed. The study area covered the Plitvice Lakes National Park, as the oldest and largest national park in Croatia, using the historical 20-year visitor data with 19 tourist and hiking routes. Two geospatial analysis methods were evaluated as follows: (1) short-term prediction of visitors count data based on a 10-year historical intervals, and (2) the environmental susceptibility zones delineation method integrated two fundamental factors in the assessment of environmental impacts from route density and historical visitors count on a monthly basis. Four accuracy assessment metrics indicated a moderate accuracy of short-term visitors count prediction, with the coefficient of determination ranging from 0.700 to 0.951. The routes which continue from both entrances indicated the largest visitors load is in the central part of the park, mostly located in the moderately restricted zone. These observations indicated moderate present environmental susceptibility with stable outlook, providing an insight for the nature park management adjustment. Full article
(This article belongs to the Special Issue Geoinformation Technologies in Agriculture and Environment Protection for a Sustainable Future)
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18 pages, 6013 KiB  
Article
A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management
by Dorijan Radočaj, Mladen Jurišić, Oleg Antonić, Ante Šiljeg, Neven Cukrov, Irena Rapčan, Ivan Plaščak and Mateo Gašparović
Sustainability 2022, 14(19), 12170; https://doi.org/10.3390/su141912170 - 26 Sep 2022
Cited by 8 | Viewed by 2135
Abstract
With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based [...] Read more.
With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical methods ranked last in the cost–benefit analysis. Despite that, OK could retain its place as the most frequent method for soil mapping in recent studies due to its widespread, user-friendly implementation in GIS software and its univariate character. Further improvement of machine learning methods with regards to computational efficiency could additionally improve their cost–benefit advantage and establish them as the universal standard for soil mapping. Full article
(This article belongs to the Special Issue Geoinformation Technologies in Agriculture and Environment Protection for a Sustainable Future)
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