LANDISVIEW 2.0: An upgraded visualization and analytical tool for landscape modeling. (December 2020)

• Record Type: Journal Article
• Authors: Xi, Weimin; Zhou, Xiaohuan; Zhang, Jianwei
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Landscape modeling -- LANDISVIEW -- Spatially explicit models -- Open source tool; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104849
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A major challenge in landscape modeling is to effectively visualize and analyze a massive amount of simulation outputs, which usually are in various raster and vector geospatial data formats. Because the task is often time-consuming and costly, available handy data viewers are limited. Yet, it is a useful tool for land managers to prepare and deploy their management plan in a landscape scale. Here, we upgraded our previous LANDISVIEW (version 1.0) to significantly enhance both visualizing and analyzing four major raster and vector geospatial data formats: ERDAS GIS/ASCII Raster (gis/.asc), ERDAS IMAGINE (.img) and Tagged Image (.tiff), which are the images formats widely used in remote sensing and landscape models (e.g., LANDIS). In addition, the version 2.0 software can serve as a converter for linking spatial data to other widely used landscape spatial analysis tools, including FRAGSTATS. Using this updated version 2.0, we were able to effectively assess a wildfire effect on forest structure and predict forest growth dynamics in responses to future wildfire and drought in northeastern California. Highlights: LANDISVIEW 2.0 significantly enhances visualizing and analyzing four geospatial data formats: gis, .asc, .img and .tiff. The software serves as a converter for linking spatial data to widely used spatial analysis tools, including FRAGSTATS. The multiple windows allow view images simultaneously, including ecoregions, initial communities, drought and wildfire maps. Using this viewer, we effectively assess wildfire and drought effect on forest structure and growth dynamics in California.