Vegetation coverage precisely extracting and driving factors analysis in drylands

Wang, Haolin; Gui, Dongwei; Liu, Qi; Feng, Xinlong; Qu, Jia; Zhao, Jianping; Wang, Guangyan; Wei, Guanghui

doi:10.1016/j.ecoinf.2023.102409

Cited by 5 publications

(3 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are five commonly used visible-light vegetation indices: the red-to-green ratio index (RGRI) [44], excess green index (EXG) [45,46], visible light band difference vegetation index (VDVI) [47][48][49], normalized green-red difference index (NGRDI) [50], and normalized green-blue difference index (NGBDI) [51]. Among them, the VDVI has the highest overall accuracy rate of 97.47% [40], which indicates that the visible-wave vegetation index can be used to calculate vegetation coverage and has the highest applicability. The specific formulas are as follows:…”

Section: Discussionmentioning

confidence: 99%

“…Vegetation coverage is the percentage of the vertical projection area of vegetation on the ground relative to the total area of the study area [39]. It represents the plant community that covers the ground surface and is one of the most important indicators of the condition of the surface vegetation [40], as well as an important indicator of ecological environment indicators, soil and water conservation, land use and other aspects [41].…”

Section: Vegetation Coveragementioning

confidence: 99%

“…Healthy green vegetation is usually characterized in nature by strong absorption of red and blue wavelengths and strong reflection of the green wavelengths in the visible waveband of the reflectance spectrum [40]. In the field of plant remote sensing, the commonly used vegetation index is the NDVI [55], but this requires information from near-infrared wavelengths [41].…”

Section: Uav-visible Rgb Band Data Acquisitionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

Zhu,

Li,

Luo

et al. 2024

Sustainability

View full text Add to dashboard Cite

To achieve the transition of rural areas from traditional to modern, the visualization of rural landscape data and feature evaluations are essential. Landscape character assessment (LCA) is a well-established tool that was developed to assess and understand rural landscape features. In recent years, drones have become increasingly attractive for various applications and services due to their low costs and relative ease of operation. Unlike most previous studies that relied solely on drone-based remote sensing or visual esthetic evaluations, this study proposes an innovative assessment method based on landscape characteristic assessment (LCA) and oblique drone photography technology, supported by specific data and survey results. These include various landscape metrics, such as the Shannon diversity index (SHDI), Shannon evenness index (SHEI), vegetation coverage, landscape character zoning, and delineations of various ecologically sensitive areas. This method was applied to study Zhanqi Village in Chengdu, Sichuan Province, China and revealed some unique characteristics of this village. By categorizing and describing the landscape features, the study makes judgments and decisions about them. This is a beneficial attempt to apply the scientific methods of landscape assessments to the production management of aerial drone surveys. This method provides a comprehensive framework for evaluating rural landscape features and demonstrates that the combination of LCA and oblique drone photography technology is feasible for rural landscape research. Additionally, this study emphasizes the need for further research to explore the potential application of this method in continuously evolving urban and rural environments in the future.

show abstract