Abstract
Abstract. Classifying and monitoring different vegetation types is important for forest management, food resources, and assessing the potential impacts of climate change. In this regard, several methods have been developed to study them using remote sensing data, and with the advent of neural networks, new methods are being proposed, especially in the field of automatic land use classification. In this research, multispectral Sentinel-2 satellite image has been used due to having spectral information and different spatial resolution for classifying plant species. Deep learning models have the ability to learn and recognize different features of images, but require a large number of training samples, so we used pre-trained ResNet networks with depths of 50, 101 and 152 layers, that trained with BigEarthNet dataset. The main purpose of this study is to evaluate the sensitivity of ResNet networks to spatial resolution. Results show that ResNet 101 was more stable than other networks, and the Resent 50 with an overall accuracy of 76.2 has the highest accuracy at a resolution of 20 meters.
Highlights
Vegetation is one of the most important elements of an ecosystem
By comparing the results obtained in the first and the second experiments, we find that fine-tuning the parameters are necessary and the performance of the networks does not follow the same pattern in both cases
The industrialization of cities has led to the extinction of various vegetation species, so high-precision classification maps are needed for the sustainability and management of natural resources
Summary
Vegetation is one of the most important elements of an ecosystem. Vegetation affects living organisms, global climate and carbon cycle, so vegetation classification is important for natural resource management information on the distribution of vegetation types is a main resource for food chain planning, wildlife habitat, sustainable natural resource management, crop forests, and biodiversity conservation (Lu, Li, Moran, & Kuang, 2014). Remote sensing data are known as the important sources for vegetation classification due to characteristics such as radiometric, spectral, spatial and temporal resolution. With the advance of digital technology and the appearance of different and new needs, it is absolutely necessary to provide modern and intelligent methods to provide an effective and compelling processing of remote sensing images. In this context, data analysis methods are essential to retrieve information from RS images, where classification is one of the main information extraction tasks providing the categorization of the observed surface at pixel level (Alipour-Fard & Arefi, 2020). Convolutional neural network (CNN) is one of the most popular established deep learning architectures
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