Land cover classification in a mixed forest-grassland ecosystem using LResU-net and UAV imagery

Abstract

Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally excels in medical and building classification, but its usefulness in mixed forest-grassland ecosystems in semi-arid to semi-humid climates is unknown. This study proposes a new semantic segmentation network of LResU-net in which residual convolution unit (RCU) and loop convolution unit (LCU) are added to the U-net framework to classify images of different land covers generated by UAV high resolution. The selected model enhanced classification accuracy by increasing gradient mapping via RCU and modifying the size of convolution layers via LCU as well as reducing convolution kernels. To achieve this objective, a group of orthophotos were taken at an altitude of 260 m for testing in a natural forest-grassland ecosystem of Keyouqianqi, Inner Mongolia, China, and compared the results with those of three other network models (U-net, ResU-net and LU-net). The results show that both the highest kappa coefficient (0.86) and the highest overall accuracy (93.7%) resulted from LResU-net, and the value of most land covers provided by the producer’s and user’s accuracy generated in LResU-net exceeded 0.85. The pixel-area ratio approach was used to calculate the real areas of 10 different land covers where grasslands were 67.3%. The analysis of the effect of RCU and LCU on the model training performance indicates that the time of each epoch was shortened from U-net (358 s) to LResU-net (282 s). In addition, in order to classify areas that are not distinguishable, unclassified areas were defined and their impact on classification. LResU-net generated significantly more accurate results than the other three models and was regarded as the most appropriate approach to classify land cover in mixed forest-grassland ecosystems.

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

“…This holds particularly true in scenarios like the segmentation and classification of vegetation land covers [55]. An illustrative example of this complexity is encountered when attempting to segment the boundaries among ecosystems characterized by a combination of both forest and grassland in regions with semi-arid to semi-humid climates [142].…”

Crop weed image recognition of UAV based on improved HRNet-OCRNet

“…Deep learning methods have the advantages of strong generalization ability, good robustness and higher representation ability in target recognition. Previous studies have shown [2,3] that the use of deep learning methods combined with UAV technology can effectively identify ground objects, especially the UAV image segmentation tasks are extremely widely used. Semantic segmentation can classify images pixel by pixel, which has certain advantages in weed recognition [4,5] .…”

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

“…One of the applications of Remote Sensing is the use of imagery to classify and delineate different objects and land cover types on the Earth's surface, a process that involves collecting field data from a series of samples as an input for training a classification model (Zou and Greenberg, 2019;Prentice et al, 2021). Classification in Remote Sensing involves the categorization of response functions recorded in imagery as representations of real-world objects, according to their spectral similarity to the initial values overlapping the samples, which can provide detailed information about land-cover, specifically in a mixed forest-grassland (Corbane et al, 2015;Cullum et al, 2016;Hamylton et al, 2020;Zhang et al, 2021).…”

“…According to a study performed by Christian and Christiane (2014), data collected from UAS can capture more information about environment composition and structure. Thus, using UAS high-resolution cameras is one of the most preferred methods to classify land cover in a mixed savannasgrassland ecosystem (Zhang et al, 2021).…”