QGIS is the most popular free geospatial software in the world. QGIS belongs to the Open-Source Geospatial Foundation (OSGeo). Among the main strengths of this Geographic Information Systems are: the incorporation of tools via plugins, and a community of users and developers in constant growth. Despite the importance on the use of QGIS on the scientific community, to date there are no systematic studies indicating how the acceptance of this software has evolved through time. Therefore, the objective of this research was to characterize the scientific production and extent where QGIS has been used as their main geospatial tool. We conducted a bibliometric analysis of documents published in Scopus from 2005 to 2020 (931 manuscripts). The annual rate of publications increase was 40.3%. We found strong and positive correlations regarding the number of contributing code programmers (r=0.66, p0.005); and the total income of the QGIS project (r=0.88, p0.001). Seventy-two percent of the publications were included in six fields of study, being Earth and Planetary Sciences the most representative. Italy was the country with larger scientific production, while the USA was the most influential country (being the first, regarding the number of citations). In terms of the countries, the larger number of papers found were from Portugal, Italy, Brazil, and France. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences - ISPRS Archives stands among journals with the largest number of publications (47). In terms of collaborative networks among countries, we found strong links between authors from Germany, Switzerland, Greece, and Spain. Author network analysis showed three solid networks in different fields of study. We observed a favorable trend in the acceptance of QGIS across the world and a widespread development of collaborative networks. The present paper allowed increase the knowledge of geographic information systems, especially the development of scientific production using QGIS.

Mountain ecosystems provide environmental goods, which can be threatened by climate change. Near-Surface Temperature Lapse Rate (NSTLR) is an essential factor used for thermal and hydrological analysis in mountain ecosystems. The aims of the present study were to estimate NSTLR and to identify its relationship with aspect, Local solar zenith angle (LSZA) and Evaporative Stress Index (ESI) for two seasons of the year in a mountain ecosystem at the North of Mexico. Normalized Land Surface Temperature (NLST) was estimated using environmental and topographical variables. LSZA was calculated from slope to consider the effect of solar position. NSTLR was estimated through simple linear models. Observed NSTLR was 9.4 °C km−1 for the winter and 14.3 °C km−1 for the summer. Our results showed variation in NSTLR by season. In addition, aspect, LSZA and ESI also influenced NSTLR regulation. In addition, Northwest and West aspects exhibited the highest NSTLR. LSZA angles closest to 90° were related with a decrease in NSTLR for both seasons. Finally, ESI values associated with less evaporative stress were related to lower NSTLR. These results suggest potential of Landsat-8 LST and ECOSTRESS ESI to capture interactions of temperature, topography, and water stress in complex ecosystems.

The study of above-ground biomass (AGB) is important for monitoring the dynamics of the carbon cycle in forest ecosystems. The emergence of remote sensing has made it possible to analyze vegetation using land surface temperature (LST), Vegetation Temperature Condition Index (VTCI) and evapotranspiration (ET) information. However, relatively few studies have evaluated the ability of these variables to estimate AGB in temperate forests. The aim of the present study was to evaluate the relationship of LST, VTCI and ET with AGB in temperate forests of Durango, Mexico, regarding each season of the year and to develop a AGB estimation model using as predictors LST, VCTI and ET, together with topographic, reflectance and Gray-Level Co-Occurrence Matrix (GLCM) texture variables. A semi-parametric model was generated to analyze the linear and non-linear responses of the predictive variables of AGB using a generalized linear model (GAM). The results show that the best predictors of AGB were longitude, latitude, spring LST, ET, elevation VTCI, NDVI (Normalized Difference Vegetation Index), slope and GLCM mean (R2 = 0.61; RMSE = 28.33 Mgha−1). The developed GAM model was evaluated with an independent dataset (R2 = 0.58; RMSE = 31.21 Mgha−1), suggesting the potential of this modeling approach to predict AGB for the analyzed temperate forest ecosystems.

Aim of study: Land surface temperature (LST) is an essential variable to monitor and characterize forest ecosystems. This variable has been consistently captured for almost four decades by the Landsat program. The current study aimed at identifying trends, knowledge gaps and opportunity areas in the use of Landsat derived LST for the monitoring and analysis of forest ecosystems. Materials and methods: A bibliometric analysis of scientific articles indexed in Scopus in the period 1995-2020 was conducted. Main results: Annual increase rate in the number of publications on the topic analyzed was 22.58%. The journal with more publications on the topic was Proceedings of SPIE, followed by Remote Sensing. The authors with the highest productivity on this topic were C. Quintano, I. Vorovencii, O. E. Yakubailik and M. A. Zoran. Regarding productivity by country, 38 countries with publications on this topic were identified, with the highest productivity located in China, USA and India. This group of countries also represented the most solid network of cooperation between countries. Forest ecosystems more frequently analyzed were temperate forests, followed by tropical forests. The analysis of keywords highlighted topics such as remote sensing, NDVI, MODIS and evapotranspiration. The analysis of thematic evolution indicated that areas of research and interpretation of LST data has evolved in parallel with remote sensing areas. Research highlights: Landsat LST analysis is an evolving topic with potential to contribute to improve ecosystem knowledge and to support diverse challenges in forest resources decision-making.

No abstract