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Land
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Ecological Indicators for Landscapes Analysis and Interpretation: The Role of...
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Ecological Indicators for Landscapes Analysis and Interpretation: The Role of Multi-Temporal Geospatial Data

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (14 October 2022) | Viewed by 9002

Special Issue Editors

Dr. Salvatore Praticò

E-Mail Website
Guest Editor
Agriculture Department, Mediterranea University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: remote sensing; change detection; spatial analysis; multispectral data analysis; LiDAR data analysis; UAV data analysis; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Solano

E-Mail Website
Guest Editor
Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, 01100 Viterbo, IT, Italy
Interests: smart forestry; digital forest resources monitoring; landscape ecology; remote sensing; spatial analysis; geoprocessing techniques; ecological indicators
Special Issues, Collections and Topics in MDPI journals
Prof. Salvatore Di Fazio

E-Mail Website1 Website2
Guest Editor
Department of Agriculture, Mediterranean University of Reggio Calabria, Reggio Calabria, Italy
Interests: landscape; vernacular architecture; historic rural heritage; farm buildings; rural infrastructures; ecomuseums; sustainable development; landscape planning; agricultural engineering; urbanism; geography; forestry; sustainable architecture; spatial analysis; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landscape, by its very definition, needs to be understood as a constantly changing entity. Because of this intrinsic characteristic, its study needs to be carried out using proper indicators and multi-temporal approaches in order to detect the changes occurring over time and/or to enable the better characterization of its different components. Nowadays, the ever increasing availability of data collected by various vectors (satellites, airplanes, drones, field sensors, etc.) with different sensors (RGB, multispectral, hyperspectral, SAR, etc.) allows scientists to perform comprehensive and enhanced geospatial analysis, defining different ecological indicators for many fields of application (urban and rural planning, agriculture and forestry, green infrastructures, etc.) at various scales, thus opening the way for innovative forms and themes of landscape analysis and interpretation.

In this Special Issue, we invite papers focusing on, but not limited to, practical applications of ecological indicators derived by geospatial multi-temporal data tools, techniques, and strategies for landscape study and characterization at different spatial scales (local, regional, national) as well as papers that aim to explore the advances that have been made in the assessment and implementation of data processing in landscape dynamics studies. Contributions based on multidisciplinary approaches, resulting from collaboration between researchers and practitioners, and highlighting the effects of technological innovations on landscape services are also welcome.

 

Dr. Salvatore Praticò
Dr. Francesco Solano
Prof. Salvatore Di Fazio
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. Land 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 2600 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.

Dr. Salvatore Praticò
Prof. Salvatore Di Fazio
Dr. Francesco Solano
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. Land 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 2600 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

  • Multi-temporal geospatial time-series for landscape analysis and interpretation
  • Landscape analysis and interpretation using cloud-based processing of ecological indicators and remote sensing datasets
  • Multi-temporal green infrastructure monitoring as landscape ecosystem services quality indicators
  • Ecological indicators and multi-temporal geospatial data in historic cultural landscape conservation
  • Landscape indicators and GIS-based approaches for landscape services analysis

Published Papers (3 papers)

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Research

17 pages, 2667 KiB  
Article
Land Use Change and Prediction for Valuating Carbon Sequestration in Viti Levu Island, Fiji
by Ram Avtar, Apisai Vakacegu Rinamalo, Deha Agus Umarhadi, Ankita Gupta, Khaled Mohamed Khedher, Ali P. Yunus, Bhupendra P. Singh, Pankaj Kumar, Netrananda Sahu and Anjar Dimara Sakti
Land 2022, 11(8), 1274; https://doi.org/10.3390/land11081274 - 8 Aug 2022
Cited by 11 | Viewed by 3829
Abstract
This study examines land use changes and evaluates the past and projected forest carbon sequestration and its valuation in Viti Levu Island, Fiji, through a combination of remote sensing with a geospatial-based modeling approach. Land use classification was performed using Landsat 7 and [...] Read more.
This study examines land use changes and evaluates the past and projected forest carbon sequestration and its valuation in Viti Levu Island, Fiji, through a combination of remote sensing with a geospatial-based modeling approach. Land use classification was performed using Landsat 7 and Landsat 8 imageries of the years 2000 and 2020; then, cellular automata and artificial neural network (CA-ANN) modeling was conducted to predict the land use map of 2040. Carbon sequestration and the economic valuation were estimated using the land use maps of the past, present, and future (2000, 2020, and 2040) within the Integrated Valuation of Ecosystems Trade-off (InVEST) model. The results showed that deforestation occurred during the past two decades, and the forest area was predicted to keep decreasing in 2040, with the major contribution from the conversion to the agricultural area. Local communities’ perceptions confirmed that the forest conversion to croplands would persist due to the demand for fertile lands. This study estimated a loss of −7.337 megatonnes of forest carbon (Mt C) with an economic loss of USD −1369.38 million during 2000–2020 due to deforestation. If the business-as-usual scenario does not change in the near future, a potential carbon loss of −7.959 Mt C is predicted in the upcoming 20 years. The predicted results can be used to assist as a reference in establishing a national baseline and reference level for implementing the REDD+ mechanism in Fiji and sustainably managing the limited pristine forest by implementing forest-related programs. Full article
(This article belongs to the Special Issue Ecological Indicators for Landscapes Analysis and Interpretation: The Role of Multi-Temporal Geospatial Data)
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38 pages, 15026 KiB  
Article
Long-Term Assessment of Spatio-Temporal Landuse/Landcover Changes (LUCCs) of Ošljak Island (Croatia) Using Multi-Temporal Data—Invasion of Aleppo Pine
by Ivan Marić, Lovre Panđa, Josip Faričić, Ante Šiljeg, Fran Domazetović and Tome Marelić
Land 2022, 11(5), 620; https://doi.org/10.3390/land11050620 - 22 Apr 2022
Cited by 3 | Viewed by 2142
Abstract
The karst landscapes of the Mediterranean are regarded as some of the most vulnerable, fragile, and complex systems in the world. They hold a particularly interesting group of small islands with a distinctive, recognizable landscape. The Republic of Croatia (HR), which has one [...] Read more.
The karst landscapes of the Mediterranean are regarded as some of the most vulnerable, fragile, and complex systems in the world. They hold a particularly interesting group of small islands with a distinctive, recognizable landscape. The Republic of Croatia (HR), which has one of the most indented coasts in the world, is particularly known for them. In this paper, we analyzed the spatio-temporal changes (STCs) in the landscape of Ošljak Island, the smallest inhabited island in HR. Landuse/landcover change (LUCC) analysis has been conducted from 1944 to 2021. The methodology included the acquisition of multi-temporal data, data harmonization, production of landuse/landcover (LU/LC) maps, selection of optimal environmental indicators (EIs), and simulation modeling. In total, eleven comparable LU/LC models have been produced, with moderate accuracy. STCs have been quantified using the nine EIs. The dominant processes that influenced the changes in the Ošljak landscape have been identified. The results have shown that, in recent decades, Ošljak has undergone a landscape transformation which was manifested through (a) pronounced expansion of Aleppo pine; (b) deagrarianization, which led to secondary succession; and (c) urban sprawl, which led to the transformation of the functional landscape. The most significant of the detected changes is the afforestation of the Aleppo pine. Namely, in a 77-year span, the Aleppo pine has expanded intensively to an area of 11.736 ha, created a simulation model for 2025, and pointed to the possibility of the continued expansion of Aleppo pine. Specific guidelines for the management of this new transformed landscape have been proposed. This research provides a user-friendly methodological framework that can efficiently monitor LUCCs of a smaller area in the case when geospatial data are scarce and satellite imagery of coarser resolution cannot be used. Moreover, it gives an insight into the availability and quality of multi-temporal data for the HR. Full article
(This article belongs to the Special Issue Ecological Indicators for Landscapes Analysis and Interpretation: The Role of Multi-Temporal Geospatial Data)
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19 pages, 5630 KiB  
Article
A Multitemporal Fragmentation-Based Approach for a Dynamics Analysis of Agricultural Terraced Systems: The Case Study of Costa Viola Landscape (Southern Italy)
by Salvatore Praticò, Francesco Solano, Salvatore Di Fazio and Giuseppe Modica
Land 2022, 11(4), 482; https://doi.org/10.3390/land11040482 - 26 Mar 2022
Cited by 5 | Viewed by 2130
Abstract
Among landscapes, rural landscapes are important because they simultaneously present functional and cultural aspects. Rural landscapes are often created to modify the Earth’s surface according to different human needs and, among these modifying activities, terracing has significant anthropogenic effect. A multitemporal approach that [...] Read more.
Among landscapes, rural landscapes are important because they simultaneously present functional and cultural aspects. Rural landscapes are often created to modify the Earth’s surface according to different human needs and, among these modifying activities, terracing has significant anthropogenic effect. A multitemporal approach that covers a significant time interval is crucial for monitoring the integrity and cultural value of historical rural landscapes. The present research aims to study the fragmentation dynamics detectable in an active agricultural terraced system of historical and cultural relevance over a considerable time interval, by conducting a morphological spatial pattern analysis (MSPA). We analysed a period of about 60 years, from 1955 to 2014, considering five intermediate years (1976, 1989, 1998, 2008, and 2012) and investigated the dynamics that occurred. We detected a trend of abandonment of agricultural terraces, with a reduction in area from 813.25 ha (in 1955) to 118.79 ha (in 2014). The MSPA results showed a decrease in core areas, the most stable pattern, and an increase in the relative importance of other less stable classes. Moreover, we highlighted two different fragmentation dynamics, i.e., one between 1955 and 1976 and the other between 1998 and 2008. Full article
(This article belongs to the Special Issue Ecological Indicators for Landscapes Analysis and Interpretation: The Role of Multi-Temporal Geospatial Data)
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