Every day millions of new data records with spatial component are produced in the world, which provide valuable information to make decisions and solve business-related issues. However, a large part of this data is hardly analysed because of their different structures and schemas. The aim of the paper is to improve the integration, processing and analysis of unstructured spatial data. During the research, the author analysed geospatial data types and sources, explored NoSQL solutions for geospatial data processing and chose the open-source tools which are the most appropriate for the stated goals, as well as analysed the coverage of forest areas with protected zones using MongoDB database capabilities and visualized results in a map, using QGIS software. MongoDB is a useful tool for geospatial data analysis and has a large number of embedded topology analysis functions and has drivers for widespread programming languages like JavaScript, Python, PHP, Java, Scala, CNo., C, C + +, etc. QGIS has extensions that allow to make connections to databases, including a connection with MongoDB. Using these features, the developers can develop geographic information systems to analyse geospatial data – structured, semi-structured and unstructured. Generally MongoDB is used for real-time data analysis; however, complicated analysis of large data sets can take up to hours and even days, so it is still necessary to find the best solution to get results in an acceptable time for users. Using MongoDB together with Apache Hadoop – the framework to support big data applications – could be a possible solution for this problem.