Special Issue "Geomatics in Forestry and Agriculture: New Advances and Perspectives"

Special Issue Editors

Prof. Dr. Giuseppe Modica
E-Mail Website
Guest Editor
Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Località Feo di Vito, I-89122 Reggio Calabria, Italy
Interests: land cover and land use change dynamics; satellite and UAV remote sensing; landscape analysis and interpretation; remote sensing of vegetation; geographic object-based image analysis; machine learning.
Special Issues and Collections in MDPI journals
Dr. Maurizio Pollino
E-Mail Website
Guest Editor
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development - Laboratory for the Analysis and Protection of Critical Infrastructures (APIC), 00123 Rome, Italy
Interests: GIS and remote sensing applications to environmental studies; risk analysis; critical infrastructures protection; design and development of GIS-based decision support systems (DSS)
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, geomatics science has experienced explosive growth thanks to the great diffusion of UAVs (unmanned aerial vehicles) and the increasing accessibility to free and low-cost satellite remote sensed multispectral data (i.e., Landsat, Sentinel, RapidEye, Planetscope). Geomatics methodologies and techniques (i.e., GIS combined with remote sensing) are essential to explore and characterize agriculture and forestry in the frame of various applications and analyses: agroforestry land survey and mapping, land use/land cover dynamics, urban/rural interactions (e.g., growth/sprawl phenomenon and loss of rural/natural lands), landscape planning and management, land suitability assessment, spatial decision support systems, and precision agriculture and forestry.

In particular, in recent years, the geoscientific community has been focusing on using geomatics-based technologies and approaches to support decision-making in many of the application fields mentioned above. In this sense, spatial methodologies, models, and tools (e.g., multicriteria spatial decision support systems) can support environmental managers and planners in analyzing the interactions between location, development actions, and environmental elements in order to identify a set of effective solutions able to address multiple societal needs and demands. To manage agroforestry resources according to the economic, environmental, and social dimensions of sustainability, such approaches and procedures should examine trade-offs between often competing/conflicting objectives/alternatives.

Moreover, the need for related standard and effective spatial interfaces, geovisual analytic tools, and integrated geographic platforms (e.g., SDIs, spatial data infrastructures) is universally recognized to exploit the capacity of maps to offer an overview of and insight into spatial patterns and relations. To this end, WebGIS-based applications can be implemented and exploited to publish and share geospatial information with experts, stakeholders, local communities, and citizens (e.g., to favor e-participation in the planning tools).

The present Special Issue would like to show and compare different approaches, existing operative proposals, and cases studies concerning Geomatics (GIS, WebGIS, RS (remote sensing)) and UAV applications to agriculture and forestry. The topics of interest include but are not limited to the following keywords:

geomatics; agroforestry; sustainable planning; spatial data processing and fusion; multispectral, hyperspectral, and thermal RS in agroforestry; multicriteria spatial decision support systems for environmental decision making; agroforestry land survey and mapping using UAVs; precision agriculture and forestry

Prof. Dr. Giuseppe Modica
Dr. Maurizio Pollino
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1400 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.


  • geomatics
  • agroforestry
  • sustainable planning
  • spatial data processing and fusion
  • multispectral, hyperspectral, and thermal RS in agroforestry
  • multicriteria spatial decision support systems for environmental decision making
  • agroforestry land survey and mapping using UAVs
  • precision agriculture and forestry

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types
ISPRS Int. J. Geo-Inf. 2021, 10(3), 192; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030192 - 23 Mar 2021
Cited by 3 | Viewed by 470
Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses [...] Read more.
Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy. Full article
(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)
Show Figures

Figure 1

Back to TopTop