Abstract
The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such as the semi-automatic and automatic segmentation methods, and natural numerical networks, together with validated examples comparing field surveys and software outputs. The software is robust enough for field work researchers and stakeholders to accurately extract target units’ borders, even on the habitat level. The deep learning algorithm, developed for habitat classification within the NaturaSat software, can also be used in various research tasks or in nature conservation practices, such as identifying ecosystem services and conservation value. The exact maps of the habitats obtained within the project can improve many further vegetation and landscape ecology studies.
Highlights
Remote sensing has become one of the essential methods used to effectively and directly acquire information on the Earth’s surface [1,2]
Remote sensing data and ecological models can play a crucial role in supporting this need and safeguarding natural assets [2]
The NaturaSat software is implemented in C++
Summary
Remote sensing has become one of the essential methods used to effectively and directly acquire information on the Earth’s surface [1,2]. Together with standardized plots and regular in situ measurements, remote sensing is a powerful monitoring engine [3,4], playing an irreplaceable role in acquiring data and fulfilling its potential as an essential tool for evaluating and implementing environmental policy [1]. With increasing pressure on natural resources, land-cover maps and monitoring have reached substantial importance for area planning and resource management [5]. Remote sensing data and ecological models can play a crucial role in supporting this need and safeguarding natural assets [2]. The robustness and complexity of the obtained data in remote sensing indicate their multi-source, multi-scale, high-dimensional, and dynamic-state characteristics. The existing techniques and methods are limited to solve many problems that are a big data challenge, such as difficulty processing and analyzing data in a reasonable time, identifying the correct data to achieve the given task, finding a meaningful structure, etc. [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
