Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV

Xue, Xiangxin; Liu, Luyao; Han, Peng; Gong, Xiaoqian; Zhang, Qing

doi:10.3390/ijerph192416793

Cited by 7 publications

(3 citation statements)

References 67 publications

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“…First, the single-band image data of remote sensing images in the study area are extracted according to the waveband composition of remote sensing images; second, the waveband operations are performed on the basis of the single-band image data to obtain different vegetation indices. The vegetation indices constructed based on visible bands mainly include the excess green index 15 (EXG), normalized green-red difference index 16 , 17 (NGRDI), normalized green-blue difference index 18 (NGBDI), red-green ratio index 19 (RGRI), green-blue ratio index 20 (GBRI), excess red index, 21 (EXR), and red-green-blue vegetation index 22 (RGBVI), and modified green-red vegetation index 23 (MGRVI). The construction methods of each vegetation index are shown in Table 1.…”

Section: “Fuzzy” Calculation Methods Of Vegetation Covermentioning

confidence: 99%

Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

Tang,

Li,

et al. 2023

J. Appl. Rem. Sens.

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.In order to solve the problems of redundant data acquisition and sparse ground points in dense vegetation areas by conventional unmanned aerial vehicle (UAV) path planning methods, an UAV-airborne LiDAR route optimization method for dense vegetation areas is proposed. First, based on the high-resolution true color remote sensing images of the study area, the “fuzzy” calculation of vegetation coverage for route planning is completed. Then, an optimized ant colony algorithm is proposed for route planning, which introduces vegetation coverage as a reference for route planning and optimizes the pheromone initialization, state transfer rules, pheromone calculation, and update strategies in the classical ant colony algorithm to obtain more ground points. The experimental results show that this method can take into account the vegetation coverage of the flight area and find the area with low vegetation coverage to complete the route planning and efficiently use the sweeping principle of three-dimensional laser scanning to improve the probability of ground point acquisition, with faster iteration speed than the classical ant colony algorithm, and improve the efficiency of ground point acquisition in dense vegetation areas.

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Section: “Fuzzy” Calculation Methods Of Vegetation Covermentioning

confidence: 99%

Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

Tang,

Li,

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…(4) Calculate the variance between the two clusters. ( 5) Determine the threshold of the maximum variance (Xu et al, 2022). where TP, which stands for "true positive," is the object that is correctly classified as vegetation among all the extracted objects; TN, which stands for "true negative," is the object that is correctly classified as nonvegetation among all the extracted objects; FP, which stands for "false positive," is the object that is misclassified as vegetation among all extracted objects; and FN, which stands for "false negative," is the object that is misclassified as non-vegetation among all the extracted objects.…”

Section: Otsu's Threshold Methodsmentioning

confidence: 99%

Extraction and monitoring of vegetation coverage based on uncrewed aerial vehicle visible image in a post gold mining area

et al. 2023

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Vegetation coverage reflects the degree of environmental degradation. Timely and effective monitoring of vegetation conditions is the basis for promoting vegetation protection and improving the ecological environment of mining areas. Exploring vegetation coverage extraction methods and selecting the optimal vegetation index in mining areas can provide scientific reference for estimating vegetation coverage based on vegetation index in mining areas. Uncrewed aerial vehicles (UAVs) are widely used because of their fast real-time performance, high spatial resolution, and easy accessibility. In this study, the performances of nine visible vegetation indices and two threshold segmentation methods for extracting vegetation coverage in a post-gold mining area in the Qinling Mountains were comprehensively compared using visible spectrum UAV images. Of the nine indices, the excess green index (EXG) and visible-band difference vegetation index (VDVI) were the most effective in discriminating between vegetation and non-vegetation by visual interpretation. In addition, the accuracy of the bimodal histogram threshold method in extracting vegetation coverage was higher than that of Otsu’s threshold method. The bimodal histogram threshold method combined with EXG yielded optimal extraction results. Based on optimal methods, the total percentages of fractional vegetation coverage in 2019, 2020, and 2021 were 31.47%, 34.08%, and 42.77%, respectively, indicating that the vegetation in the mining area improved. These results provide valuable guidance for extracting vegetation information and evaluating vegetation restoration in mining areas.

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“…Similar statistical assessments and research were conducted for various types of vegetation cover. To assess the significance of differences in the vegetation index values for different levels of grassland desertification, similar statistical analyses were performed (Xu et al, 2022): the physiological condition of forests during dry periods (Volcani et al, 2005) and the changes in vegetation indices under different forest harvesting methods (Abdollahnejad et al, 2019).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Vegetation indices monitoring by using copernicus data in the old-growth forests of the Republic of Srpska/Bosnia and Herzegovina

Govedar,

Anikić

2024

Front. For. Glob. Change

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IntroductionOld-growth forests (OGFs) are vital for global biodiversity, acting as irreplaceable carbon sinks, fostering ecosystem stability, and preserving unique ecological and cultural heritage. There are three old-growth forests in the Republic of Srpska/Bosnia and Herzegovina (Lom, Janj, and Perućica). They are dominated by mixed forests of beech, fir, and spruce (Piceo-Abieti-Fagetim illyricum). The satellite imagery results confirm that the vegetational indices are crucial for monitoring old-growth forests. Vegetation indices provide significant information for monitoring forest ecosystems. Based on Copernicus Sentinel-2 images, remote sensing is enabled, providing data in various spatial and spectral resolutions. The analysis of images related to old-growth forests allows for monitoring the spatial dynamics of changes in vitality and the health status of forests. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests.MethodsThe research methodology includes the application of Sentinel 2A satellite imagery, which is taken from the European Space Agency (ESA) database. The Sentinel 2 mission consists of two identical satellites (Sentinel 2A and Sentinel 2B) that have multispectral sensors with 13 spectral channels (bands) at spatial resolutions of 10, 20, and 60 m. The analysis included the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and visible atmospheric resistance index (VARI). For determining the significance of differences in vegetation index magnitudes between old-growth forests, a one-way analysis of variance with equal repetitions was employed.Results and discussionThe parts of old-growth forests are located in different stages of development, and the trees in them are characterized by different degrees of vitality and have different values of vegetation indices. It is assumed that parts of old-growth forests in the terminal phase of development with a large number of diseased and rotting trees have the smallest sizes of vegetation indices. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests. Using satellite imagery and their analysis of the obtained sizes of vegetation indices, it is observed that there is a significant difference in their sizes between old-growth forests. The Janj old-growth forest stands out as the area with the highest NDVI values, followed by the Lom old-growth forest, while the Perućica old-growth forest has the lowest NDVI values. However, the Perućica old-growth forest has significantly higher values of EVI and SAVI vegetation indices, while the other two old-growth forests have approximately similar values. The VARI index values are highest in the Lom old-growth forest, slightly lower in the Perućica old-growth forest, and lowest in the Janj old-growth forest. The results of these studies indicate statistically significant differences in vegetation index magnitudes between old-growth forests. The application of Copernicus satellite data enables the monitoring of changes in vegetation index values, which is of great assistance in spatially defining the developmental stages in old-growth forests (OGFs). Considering the relatively small number of sample areas covered by these studies, similar research needs to be conducted on a larger number of sample areas (replications), especially during the vegetation period.

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Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV

Cited by 7 publications

References 67 publications

Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

Extraction and monitoring of vegetation coverage based on uncrewed aerial vehicle visible image in a post gold mining area

Vegetation indices monitoring by using copernicus data in the old-growth forests of the Republic of Srpska/Bosnia and Herzegovina

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