# Spatial Analysis for Landscape Changes: A Bibliometric Review
## 4 Discussion and Concluding Remarks  
In this paper, a detailed bibliometric investigation of the research trend in spatial analysis for landscape changes using the records published in the Web of Science database in the last twenty years was carried out. Such an approach has been widely used to drive scientists that need to understand the hot topic of a research field or territory (see, for example, [2; 3; 4; 52; 53; 54; 55; 2]). Our analysis was conducted with the help of three different software packages: the analysis utility offered by WoS, a GIS software (QGIS), and a specific software for bibliometric mapping, VOSviewer. The results highlight that the topic has received increasing attention in the last two decades. As a matter of fact, we observed a constant and exponential increase in the number of papers and citations since 2000. Such an increase can be partly ascribed to: (i) the growing availability of high-resolution DEMs and remote sensing images; (ii) automatic tools or algorithms of landscape classification and the analysis of land-use changes. Our results also suggest that the research topic is multidisciplinary, ranging from different disciplines such as Environmental Sciences, Earth Sciences, and Ecology, and it is mainly conducted by Chinese, European, and North American research groups. Moreover, on the basis of the statistical analysis of keyword occurrences, it is possible to reconstruct the following main research patterns:  
* The literature concerning climate change and the different aspects connected to it, such as the changes in vegetation and soil, grows in particular in the second decade. In spite of this, in the twenty years considered here, we observe the largest pattern in keywords and the highest number of citations;
* The more representative disciplinary areas are urban and territorial planning and ecology. There are two bigger keyword clusters, respectively, headed by land-use changes and biodiversity, conservation and patterns. Additionally, this area shows an increase in occurrences in all the analyzed periods; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Keyword** & **Occ.1** & **Keyword** & **Occ.** \\ \hline
01 classification & 359 & 26 agent-based modeling & 34 \\
02 simulation & 205 & 27 statistical analysis & 34 \\
03 indicators & 170 & 28 autocorrelation & 33 \\
04 metrics & 156 & 29 aerial photography & 32 \\
05 cellular automata & 113 & 30 pattern analysis & 32 \\
06 change detection & 104 & 31 tool & 32 \\
07 spatial autocorrelation & 103 & 32 hot spot analysis & 31 \\
08 gradient analysis & 84 & 33 leaf index & 31 \\
09 species distribution model & 82 & 34 geographically weighted & 30 \\  & & & regression \\
10 regression & 80 & 35 machine learning & 29 \\
11 DEM & 72 & 36 segmentation & 29 \\
12 map & 71 & 37 network analysis & 28 \\
13 density & 70 & 38 inference & 27 \\
14 logistic regression & 67 & 39 cluster analysis & 26 \\
15 random forest & 65 & 40 distribution model & 26 \\
16 sensitivity analysis & 62 & 41 intensity & 25 \\
17 vegetation index & 60 & 42 fractal & 24 \\
18 geostatistics & 51 & 43 neural network & 23 \\
19 image analysis & 45 & 44 r-package & 23 \\
20 spectral mixture analysis & 44 & 45 spatial statistical analysis & 23 \\
21 land cover classification & 40 & 46 fragstats & 22 \\
22 graph theory & 37 & 47 photogrammetry & 22 \\
23 Markov chain & 36 & 48 spatial prediction & 22 \\
24 object-based classification & 36 & 49 time series analysis & 22 \\
25 pca & 35 & 50 swat & 21 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 1: First 100 keywords with the highest number of occurrences. * A third interesting research pattern shows that the two previously cited fields (i.e., urban and territorial planning and ecology) are not considered only as two separated sectors, but there is a correct trial to integrate them with management, impact estimation, and, most of all, with the diffusion of ecosystem services.