**Contents**


## **About the Editors**

**Dario Gioia** was born in Lagonegro (Potenza, Italy) the 20th July, 1979 and earned his first degree in Geological Sciences at the University of Basilicata, with a thesis in morphotectonics. He received a PhD in 2009 at the University of Basilicata, with a dissertation dealing with the morphotectonic evolution of the Auletta basin (southern Apennines, Italy). At present, he is a researcher at the Istituto di Scienze del Patrimonio Culturale of the Italian National Council of Research (ISPC-CNR), and his research activity is mainly focused on studies of geomorphological hazard due to slope, fluvial and costal geomorphological processes, and its relationship with cultural and monumental heritage. He is the coordinator of several national projects of the ISPC-CNR, dealing with the analysis of geomorphological processes in areas of high cultural and archaeological value. From 2005, he has been involved as a member in many research projects dealing with issues of geomorphology and Quaternary geology. Moreover, his research activity includes participation as a member of several project programs, such as the National Relevant Research Projects (PRIN) program of the Italian Ministry for the University and Research (MIUR), LIFE and European Fund for Regional Development (EFRD) of the EU. In the March 2017, he qualifies as an Associate Professor of Physical Geography and Geomorphology. Dario Gioia works in the fields of tectonic geomorphology, geomorphological hazard geoarchaeology, landslide mapping and monitoring, the morphometry of drainage networks, and the stratigraphical and structural evolution of tectonic basins. His activity is also focused on the topics of quantitative and theoretical geomorphology such as the application of the Landscape Evolution Model (LEM) to investigate the recent evolution of fluvial and coastal environments and the development of geomorphic indexes to solve issues of landscape/landform classification. His research methodological approaches include geomorphological and geological surveys and photo-aerial interpretation, DEM-supported morphometric studies of landscape and river network evolution, and satellite- and field-based monitoring of landslide activity. He is the co-author of more than 90 papers, 50 of which are published in ISI journals with an impact factor. He is usually the referee of relevant international journals such as *Earth Surface Processes* and *Landforms, Geomorphology* and *Journal of the Geological Society* and has recently been a guest editor for high-impact journals' Special Issues on quantitative geomorphology.

**Maria Danese** was born in Potenza (Italy) and received her first degree in Engineering, Planning and Territorial Management at the University of Basilicata, with a thesis on spatial analysis and urban planning. She achieved a PhD at the University of Pisa, with a dissertation dealing with spatial autocorrelation and its application in urban planning, seismic risk, archaeology and material pattern decay. At present, she is a researcher at the Institute of Heritage Science of the Italian National Council of Research (ISPC-CNR), and her research activity is mainly focused on Geographic Information Science applied to cultural heritage, from archaeological predictive models, to integrated risk analysis, to remote sensing; from the management of heritage data to support for diagnostics and analysis of the architectural and artistic heritage. She is the co-author of more than 100 papers, many of which are published in ISI journals with an impact factor. She is usually the referee of relevant international journals.

### *Editorial* **Spatial Analysis for Landscape Changes**

**Dario Gioia \* and Maria Danese \***

ISPC-CNR, C.da S. Loja, Tito Scalo, I-85050 Potenza, Italy **\*** Correspondence: dario.gioia@cnr.it (D.G.); maria.danese@cnr.it (M.D.)

Landscape is the backcloth over which environmental and anthropic events occur, and recent increasing trends of natural and anthropic processes, such as urbanization, land-use changes, and extreme climate events, have a strong impact on landscape modification. Indeed, day by day, landscape changes are becoming more drastic and faster and there is a growing need for the implementation of effective instruments, tools, and approaches to understand and manage them. A great improvement in the availability of high-resolution DEMs, GIS tools, and algorithms of automatic extraction of landform features and change detections has favored an increase in the analysis of landscape changes, which became an essential instrument for the quantitative evaluation of landscape changes in many research fields. This special issue collects six papers that highlight the usefulness of the quantitative analyses of satellite images and DEMs to solve multidisciplinary issues of landscape changes.

Rui et al. (2020) [1] introduce an analysis of the erosion factors controlling the evolution of a badland area of the National Geological Park of Qian, China. The influence of geological features, climate, groundwater, and soil on the geomorphological evolution of the study area has been discussed, in order to reconstruct a synoptic scheme of the main stages of morpho-evolution.

Yan et al. (2020) [2] describe an interesting approach of the semi-automatic extraction of subaqueous landforms using multibeam bathymetric data. The comparison of three different methods of landform classification (i.e., Wood's criteria, SOM, and geomorphons) highlights that the geomorphon method has the highest degree of accuracy for the automatic extraction of the bedforms of a delta system in China.

Gioia and Schiattarella (2020) [3] investigated the scenarios of sediment flux variation and topographic changes due to dam removal in a small catchment of the southern Italian Apennines. The application of a landscape evolution model (i.e., the Caesar Lisflood LEM) provides a detailed reconstruction of the abrupt geomorphological change induced by baselevel fall due to dam removal, which can be roughly summarized in the significant increase in the erosion ability of the main channels and a strong incision of the reservoir infill.

Martinez et al. (2021) [4] integrate satellite images and drone surveys to investigate the post-fire vegetation regeneration in a forest in Central-Eastern Spain. The spatial analysis of the topographic and image attributes and the application of vegetation indexes indicate that a similar analysis can be useful to evaluate the effect of post-fire vegetation restoration strategies.

Subudhi et al. (2021) [5] propose a new method of image segmentation of hyperspectral satellite images (i.e., the Superpixel-based SSA, SP-SSA), which can provide an improvement to the capturing of object-specific spatio-spectral information. The performance of the method is evaluated using an SVM classifier, suggesting that the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods, in terms of classification accuracy.

Finally, Danese and Gioia (2021) present a review paper aimed at the bibliometric analysis of the research trends in the topic of the special issue, "Spatial Analysis for Landscape Changes". Such an analysis covers the last twenty years and investigates topics, trends, and methods that are connected to the research line through the statistical analysis

**Citation:** Gioia, D.; Danese, M. Spatial Analysis for Landscape Changes. *Appl. Sci.* **2021**, *11*, 11924. https://doi.org/10.3390/ app112411924

Received: 6 December 2021 Accepted: 13 December 2021 Published: 15 December 2021

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of different metrics, such as the number of citations, co-authorship networks, and keyword occurrences. The results of the bibliometric analysis highlight that the topic has received increasing attention in the last years, and research methods are moving toward computerbased automation or the unsupervised detection of landscape patterns and changes.

**Author Contributions:** All authors contributed equally to the preparation of this manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** This publication was only possible with the invaluable contributions from the authors, reviewers, and the editorial team of Applied Sciences.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

