Methods for Extracting Fractional Vegetation Cover from Differentiated Scenarios Based on Unmanned Aerial Vehicle Imagery

Changning Sun, Yonggang Ma (), Heng Pan, Qingxue Wang, Jiali Guo, Na Li and Hong Ran
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Changning Sun: College of Ecology and Environment, Xinjiang University, Urumqi 830046, China
Yonggang Ma: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Heng Pan: College of Ecology and Environment, Xinjiang University, Urumqi 830046, China
Qingxue Wang: College of Ecology and Environment, Xinjiang University, Urumqi 830046, China
Jiali Guo: College of Ecology and Environment, Xinjiang University, Urumqi 830046, China
Na Li: College of Ecology and Environment, Xinjiang University, Urumqi 830046, China
Hong Ran: Forestry and Grassland Work Station of Xinjiang Production and Construction Corps, Urumqi 830046, China

Land, 2024, vol. 13, issue 11, 1-34

Abstract: Fractional vegetation cover (FVC) plays a key role in ecological and environmental status assessment because it directly reflects the extent of vegetation cover and its status, yet vegetation is an important component of ecosystems. FVC estimation methods have evolved from traditional manual interpretation to advanced remote sensing technologies, such as satellite data analysis and unmanned aerial vehicle (UAV) image processing. Extraction methods based on high-resolution UAV data are being increasingly studied in the fields of ecology and remote sensing. However, research on UAV-based FVC extraction against the backdrop of the high soil reflectance in arid regions remains scarce. In this paper, based on 12 UAV visible light images in differentiated scenarios in the Ebinur Lake basin, Xinjiang, China, various methods are used for high-precision FVC estimation: Otsu’s thresholding method combined with 12 Visible Vegetation Indices (abbreviated as Otsu-VVIs) (excess green index, excess red index, excess red minus green index, normalized green–red difference index, normalized green–blue difference index, red–green ratio index, color index of vegetation extraction, visible-band-modified soil-adjusted vegetation index, excess green minus red index, modified green–red vegetation index, red–green–blue vegetation index, visible-band difference vegetation index), color space method (red, green, blue, hue, saturation, value, lightness, ‘a’ (Green–Red component), and ‘b’ (Blue–Yellow component)), linear mixing model (LMM), and two machine learning algorithms (a support vector machine and a neural network). The results show that the following methods exhibit high accuracy in FVC extraction across differentiated scenarios: Otsu–CIVE, color space method (‘a’: Green–Red component), LMM, and SVM (Accuracy > 0.75, Precision > 0.8, kappa coefficient > 0.6). Nonetheless, higher scene complexity and image entropy reduce the applicability of precise FVC extraction methods. This study facilitates accurate, efficient extraction of vegetation information in differentiated scenarios within arid and semiarid regions, providing key technical references for FVC estimation in similar arid areas.

Keywords: arid zone; entropy; FVC; UAV; linear mixing model (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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