Accuracy of determining specific parameters of the urban forest using remote sensing

Abstract

This paper reviews the current state of knowledge in the field of urban forest inventory and specific tree parameters derived by remote sensing. The paper discusses the possibilities and limitations of using remote sensing to determine the following characteristics of individual trees acquired during the inventory: position (coordinates), tree height, breast height diameter, tree crown parameters (crown span, height of tree crown basis, crown projection surface), health condition, and tree species. A total of 543 papers published in scientific databases (Scopus® and ScienceDirect®) from the year 2000 to December 2017 have been analyzed; 86 of them were used for the review. The most important outcomes are: (a) the integration of many datasets, in particular spectral data (aerial images and satellite imageries) and structural data (LIDAR), allows the most complex use of remote sensing data and helps to improve the accuracy of parameter estimations as well as the correct identification of tree species; (b) the highest precision of measurement is characteristic of TLS, while ALS data has the largest operating system; (c) remote sensing data applications are associated with a large number of sophisticated processing on very large datasets using often proprietary elaborations; (d) the use of remote sensing data makes it possible to determine the characteristics of urban vegetation at various levels of detail and at different scales.

Keywords

• urban forestry
• remote sensing
• green inventory
• laser scanning
• hyperspectral imaging
• satellite imaging