UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?

Prošek, Jiří; Šímová, Petra

doi:10.1016/j.jag.2018.10.009

Cited by 56 publications

(41 citation statements)

References 41 publications

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“…The segmentation results also clearly differentiate between the bare soil and the vegetation, even in the presence of small patches. As reported in recent studies that performed forest tree classification [18,59], the additional information provided by the DSM and NDVI significantly improves the delimitation and discrimination of segments, which was true in our case as well. This is more relevant in conditions in which the three vegetation layers are spatially close, because discrimination based only on spectral information has proven very difficult to achieve in such environments.…”

Section: Segmentationsupporting

confidence: 87%

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Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

et al. 2019

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This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands.

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Section: Segmentationsupporting

confidence: 87%

“…Actually, in the LSMS-segmentation algorithm, the scale factor can be only managed through the adopted values of the range radius and minimum region size parameters. As reported by scholars [56][57][58][59], even today, the visual interpretation of segmentation remains the recommended method to assess the quality of the obtained results.…”

Section: Segmentationmentioning

confidence: 99%

Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

et al. 2019

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“…Vegetation surveys using UAVs mainly obtain high resolution images and analyze the unique spectral information of plants to classify plant species or identify the distribution of habitats [7,13,16,[21][22][23][24]. For such an analysis, a vegetation index (VI) is usually applied, which is derived from the characteristics of various spectral wavelengths [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

Song

Park

2020

Remote Sensing

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In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.

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“…Photogrammetry is a technology that analyzes overlap between RGB photos by examining features common in multiple images from different look angles and generates a three-dimensional point cloud which can be used to create Digital Terrain Models (DTMs), Digital Surface Models (DSMs), and calculate plant height and structure by subtracting the DTM from the DSM (also known as DEM of Difference) [3][4][5][6][7][8]. Results are similar to data acquired by LiDAR [9][10][11][12][13], but come with advantages and disadvantages: data can be acquired with a standard RGB camera instead of a usually more expensive sensor [10,14], while the inability of the RGB camera to penetrate dense canopies can inhibit the construction of an accurate DTM [1,12], thus potentially introducing error into the dataset. Improvements in photogrammetric algorithms have subsequently improved the accuracy of DSMs created from aerial imagery in biometric applications, including forestry and range management, and improvements in computer processing power has made it possible to construct 3D models of progressively larger datasets [12].…”

Section: Introductionmentioning

confidence: 99%

Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

Howell

Jensen

Petersen

et al. 2020

Drones

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In situ measurements of sagebrush have traditionally been expensive and time consuming. Currently, improvements in small Unmanned Aerial Systems (sUAS) technology can be used to quantify sagebrush morphology and community structure with high resolution imagery on western rangelands, especially in sensitive habitat of the Greater sage-grouse (Centrocercus urophasianus). The emergence of photogrammetry algorithms to generate 3D point clouds from true color imagery can potentially increase the efficiency and accuracy of measuring shrub height in sage-grouse habitat. Our objective was to determine optimal parameters for measuring sagebrush height including flight altitude, single- vs. double- pass, and continuous vs. pause features. We acquired imagery using a DJI Mavic Pro 2 multi-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB camera, flown at 30.5, 45, 75, and 120 m and implementing single-pass and double-pass methods, using continuous flight and paused flight for each photo method. We generated a Digital Surface Model (DSM) from which we derived plant height, and then performed an accuracy assessment using on the ground measurements taken at the time of flight. We found high correlation between field measured heights and estimated heights, with a mean difference of approximately 10 cm (SE = 0.4 cm) and little variability in accuracy between flights with different heights and other parameters after statistical correction using linear regression. We conclude that higher altitude flights using a single-pass method are optimal to measure sagebrush height due to lower requirements in data storage and processing time.

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UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?

Cited by 56 publications

References 41 publications

Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

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