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Geomatics
Special Issues
Geographical Information Systems and Spatial Analysis in Agriculture and Environment
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Geographical Information Systems and Spatial Analysis in Agriculture and Environment

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 24150

Special Issue Editors

Prof. Dr. Dionissios Kalivas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
Interests: land evaluation; site specific crop management; digital farming; GIS; remote sensing; spatial analysis; soil information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos X. Soulis

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: hydrology; environmental hydrology; hydrological modeling; hydrometry; WRM; irrigation; irrigation water management; soil hydrology; geographical information systems (GIS)
Special Issues, Collections and Topics in MDPI journals
Dr. Emmanouil Psomiadis

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: earth observation; GIS; agriculture; geomorphology; natural resources; disasters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic information systems are digital systems for assessing spatial variation, which have been combined with the evolution of new technologies for data collection—UAV drones, digital cameras, satellite data, sensors, etc.—and transmitted through IoT (Internet of Things) technologies, and their increasing use through the internet. Their capabilities provide significant impetus for solving and confronting contemporary issues faced by the agricultural sector, as well as in ensuring environmental sustainability. New innovative sensors carried on earth observation instruments, tractors and field measuring devices are constantly collecting high-resolution, multitemporal and multispectral data, which supplement and integrate the data collected with more traditional approaches. GIS and other technologies shape these data into information which is accessible and interpretable by farmers and land managers to make efficient and informed decisions. At the same time, geospatial analyses of the human impact on the environment are crucial for a better understanding of the underlying relationships and processes. GIS and advanced earth observation methodologies are paving the way towards a better understanding of ecological and environmental interactions, identifying early indicators of environmental degradation and improving our capacity for risk assessment, timely forecasts and responses. Every year brings considerable progress in GIS, spatial analysis and earth observation technologies and methodologies in agriculture and the environment.

This Special Issue will summarize the contemporary progress and achievements in geomatics, highlight the recent advancements, and present applications in a wide spectrum of topics related to GIS and geospatial analysis in agriculture and environment. It has been coordinated with the 4th Congress of Geographical Information Systems and Spatial Analysis in Agriculture and Environment, and it includes selected papers from this conference, but also welcomes other papers that align with its topics:

Specifically, the Special Issue focuses on topics including, but not limited to, GIS, earth observation and spatial analysis applications such as geoinformatics and geospatial technologies, web-GIS, satellite data, GNSS and GPS, IoT, land information systems, spatial exploratory data analysis, spatial statistical models, spatial interpolation, geostatistics, neural networks and AI, and the use of cloud services for the management of large volumes of spatial data, in the following topics:

  • Land suitability classification;
  • Soil resources protection, land assessment, and land use planning;
  • Water resources analysis, planning, and management;
  • Ecosystem protection, restoration and management;
  • Forests evaluation and management;
  • Natural hazards, geohazards—natural disasters (floods, droughts, fires, landslides, etc.);
  • Spatial digital management of farms and agricultural holdings;
  • Precision agriculture, smart farming, and data collection via spatial digital technologies;
  • Agricultural production and of agricultural ecosystems monitoring;
  • Crop protection, pest and diseases management;
  • Weeds—invasive species;
  • Soil nutrients and fertility management;
  • Sustainable fisheries through the application of contemporary geospatial technologies;
  • Livestock and pasture management.

Prof. Dr. Dionissios Kalivas
Dr. Konstantinos X. Soulis
Dr. Emmanouil Psomiadis
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. Geomatics 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 1000 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

  • GIS
  • geoinformatics
  • spatial analysis
  • remote sensing
  • earth observation
  • agriculture
  • environment
  • precision agriculture
  • natural resources

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Published Papers (5 papers)

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Research

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18 pages, 2488 KiB  
Article
A Google Earth Engine Algorithm to Map Phenological Metrics in Mountain Areas Worldwide with Landsat Collection and Sentinel-2
by Tommaso Orusa, Annalisa Viani, Duke Cammareri and Enrico Borgogno Mondino
Geomatics 2023, 3(1), 221-238; https://doi.org/10.3390/geomatics3010012 - 21 Feb 2023
Cited by 29 | Viewed by 7601
Abstract
Google Earth Engine has deeply changed the way in which Earth observation data are processed, allowing the analysis of wide areas in a faster and more efficient way than ever before. Since its inception, many functions have been implemented by a rapidly expanding [...] Read more.
Google Earth Engine has deeply changed the way in which Earth observation data are processed, allowing the analysis of wide areas in a faster and more efficient way than ever before. Since its inception, many functions have been implemented by a rapidly expanding community, but none so far has focused on the computation of phenological metrics in mountain areas with high-resolution data. This work aimed to fill this gap by developing an open-source Google Earth Engine algorithm to map phenological metrics (PMs) such as the Start of Season, End of Season, and Length of Season and detect the Peak of Season in mountain areas worldwide using high-resolution free satellite data from the Landsat collection and Sentinel-2. The script was tested considering the entire Alpine chain. The validation was performed by the cross-computation of PMs using the R package greenbrown, which permits land surface phenology and trend analysis, and the Moderate-Resolution Imaging Spectroradiometer (MODIS) in homogeneous quote and land cover alpine landscapes. MAE and RMSE were computed. Therefore, this algorithm permits one to compute with a certain robustness PMs retrieved from higher-resolution free EO data from GEE in mountain areas worldwide. Full article
(This article belongs to the Special Issue Geographical Information Systems and Spatial Analysis in Agriculture and Environment)
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23 pages, 6520 KiB  
Article
Exploring the Effect of Balanced and Imbalanced Multi-Class Distribution Data and Sampling Techniques on Fruit-Tree Crop Classification Using Different Machine Learning Classifiers
by Yingisani Chabalala, Elhadi Adam and Khalid Adem Ali
Geomatics 2023, 3(1), 70-92; https://doi.org/10.3390/geomatics3010004 - 18 Jan 2023
Cited by 7 | Viewed by 3346
Abstract
Fruit-tree crops generate food and income for local households and contribute to South Africa’s gross domestic product. Timely and accurate phenotyping of fruit-tree crops is essential for innovating and achieving precision agriculture in the horticulture industry. Traditional methods for fruit-tree crop classification are [...] Read more.
Fruit-tree crops generate food and income for local households and contribute to South Africa’s gross domestic product. Timely and accurate phenotyping of fruit-tree crops is essential for innovating and achieving precision agriculture in the horticulture industry. Traditional methods for fruit-tree crop classification are time-consuming, costly, and often impossible to use for mapping heterogeneous horticulture systems. The application of remote sensing in smallholder agricultural landscapes is more promising. However, intercropping systems coupled with the presence of dispersed small agricultural fields that are characterized by common and uncommon crop types result in imbalanced samples, which may limit conventionally applied classification methods for phenotyping. This study assessed the influence of balanced and imbalanced multi-class distribution and data-sampling techniques on fruit-tree crop detection accuracy. Seven data samples were used as input to adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. A pixel-based approach was applied using Sentinel-2 (S2). The SVM algorithm produced the highest classification accuracy of 71%, compared with AdaBoost (67%), RF (65%), XGBoost (63%), and GB (62%), respectively. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. In addition, the study assessed the effect of size and ratio of class imbalance in the training datasets on algorithms’ sensitiveness and stability. The results show that the highest classification accuracy of 71% could be achieved from an imbalanced training dataset containing only 60% of the original dataset. The results also showed that S2 data could be successfully used to map fruit-tree crops and provide valuable information for subtropical crop management and precision agriculture in heterogeneous horticultural landscapes. Full article
(This article belongs to the Special Issue Geographical Information Systems and Spatial Analysis in Agriculture and Environment)
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21 pages, 5877 KiB  
Article
Multicriteria Decision Method for Siting Wind and Solar Power Plants in Central North Namibia
by Klaudia Kamati, Julian Smit and Simon Hull
Geomatics 2023, 3(1), 47-67; https://doi.org/10.3390/geomatics3010002 - 29 Dec 2022
Cited by 3 | Viewed by 4036
Abstract
We demonstrate the application of geomatics tools (remote sensing and geographic information systems) for spatial data analysis to determine potential locations for wind and solar photovoltaic (PV) energy plants in the Central North region of Namibia. In accordance with sustainable development goal 7 [...] Read more.
We demonstrate the application of geomatics tools (remote sensing and geographic information systems) for spatial data analysis to determine potential locations for wind and solar photovoltaic (PV) energy plants in the Central North region of Namibia. In accordance with sustainable development goal 7 (affordable and clean energy) and goal 13 (climate action), the Namibian government has committed to reducing reliance on fossil fuels. In support of this, suitable locations for renewable energy plants need to be identified. Using multi-criteria decision-making and the analytical hierarchy process, sites were selected considering topographical, economic, climatic, and environmental factors. It was found that the highest potential for solar PV energy plants is in the northwest, southwest, and southern regions of the study area, whereas only the northwest region is highly suitable for wind power plants. These results were substantiated by comparison with global suitability maps, with some differences due to the datasets used. The findings can be used as a guide by governments, commercial investors, and other stakeholders to determine prospective sites for the development of renewable energy in Central North Namibia. Full article
(This article belongs to the Special Issue Geographical Information Systems and Spatial Analysis in Agriculture and Environment)
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Review

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22 pages, 1496 KiB  
Review
Global Research Trends for Unmanned Aerial Vehicle Remote Sensing Application in Wheat Crop Monitoring
by Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Ahmed Mukalazi Kalumba, George Johannes Chirima, Wonga Masiza and Colette De Villiers
Geomatics 2023, 3(1), 115-136; https://doi.org/10.3390/geomatics3010006 - 25 Jan 2023
Cited by 18 | Viewed by 4216
Abstract
Wheat is an important staple crop in the global food chain. The production of wheat in many regions is constrained by the lack of use of advanced technologies for wheat monitoring. Unmanned Aerial Vehicles (UAVs) is an important platform in remote sensing for [...] Read more.
Wheat is an important staple crop in the global food chain. The production of wheat in many regions is constrained by the lack of use of advanced technologies for wheat monitoring. Unmanned Aerial Vehicles (UAVs) is an important platform in remote sensing for providing near real-time farm-scale information. This information aids in making recommendations for monitoring and improving crop management to ensure food security. This study appraised global scientific research trends on wheat and UAV studies between 2005 and 2021, using a bibliometric method. The 398 published documents were mined from Web of Science, Scopus, and Dimensions. Results showed that an annual growth rate of 23.94% indicates an increase of global research based on wheat and UAVs for the surveyed period. The results revealed that China and USA were ranked as the top most productive countries, and thus their dominance in UAVs extensive usage and research developments for wheat monitoring during the study period. Additionally, results showed a low countries research collaboration prevalent trend, with only China and Australia managing multiple country publications. Thus, most of the wheat- and UAV-related studies were based on intra-country publications. Moreover, the results showed top publishing journals, top cited documents, Zipf’s law authors keywords co-occurrence network, thematic evolution, and spatial distribution map with the lack of research outputs from Southern Hemisphere. The findings also show that “UAV” is fundamental in all keywords with the largest significant appearance in the field. This connotes that UAV efficiency was important for most studies that were monitoring wheat and provided vital information on spatiotemporal changes and variability for crop management. Findings from this study may be useful in policy-making decisions related to the adoption and subsidizing of UAV operations for different crop management strategies designed to enhance crop yield and the direction of future studies. Full article
(This article belongs to the Special Issue Geographical Information Systems and Spatial Analysis in Agriculture and Environment)
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22 pages, 3547 KiB  
Review
A Scoping Review of Landform Classification Using Geospatial Methods
by Zama Eric Mashimbye and Kyle Loggenberg
Geomatics 2023, 3(1), 93-114; https://doi.org/10.3390/geomatics3010005 - 24 Jan 2023
Cited by 4 | Viewed by 3389
Abstract
Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. [...] Read more.
Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods. Full article
(This article belongs to the Special Issue Geographical Information Systems and Spatial Analysis in Agriculture and Environment)
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