Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images

Akar, Alper; Gökalp, Ertan; Akar, Özlem; Yılmaz, Volkan

doi:10.1080/10106049.2016.1178816

Cited by 19 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Mini MCA 6 has six spectroscopic filters for each camera, and the spectroscopic filters can allow a narrow band of wavelength to arrive at the spot on the camera, which means that the bands of this camera are all narrow bands (http://www.tetracam.com/Products-Micro_MCA.htm). The filters have a central wavelength of 490 (10), 550 (10), 680 (10), 720 (10), 800 (10), and 900 (20) nm. The detail of relationship between wavelength and relative monochromatic response filter transmission (%) and peak transmission wavelength of each filter is described in Figure 3.…”

Section: Uav Sensors and Image Acquisitionmentioning

confidence: 99%

“…Even though the parrot sequoia camera can acquire the reflectance of objects directly, reflectance of this level contains too many uncertainties from atmospheric effects and camera noise [19]. Akar et al 2016 improved the classification accuracy of the rangeland using a combination of WorldView-2 and UAV images [20]. However, no ground control point (GCP) and calibration targets were mentioned to generate the orthophoto.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

et al. 2019

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud.

show abstract

Section: Uav Sensors and Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Other researchers have employed supervised and unsupervised classification techniques to identify forest cover in imagery with varying degrees of success, with forest cover often confused with agriculture or rangeland (Yuan et al 2005;Ye et al 2014;Akar et al 2017). For a comprehensive literature review on prior research in mapping forest cover in multispectral imagery, please see Becker et al (2018).…”

Section: Classification-based Approachesmentioning

confidence: 99%

Forest Cover Index for tree cover detection using Landsat-7 multispectral imagery

Feliciano-Cruz¹,

Becker²,

Lasko³

et al. 2019

View full text Add to dashboard Cite

Forest cover monitoring using satellite imagery is important to U.S. military terrain analysis. Mobility models, cover and concealment, and precise surface feature delineation all rely on an accurate forest/nonforest cover layer. However, the challenge remains in distinguishing trees from other vegetative land covers when relying on single date imagery. A Forest Cover Index (FCI) algorithm was previously developed on Worldview-2 imagery and was designed to separate forest cover from nonforest covered areas. In this research, the FCI algorithm was applied to Landsat-7 imagery using the analogous red (636-673 nanometers (nm)) and near-infrared bands (851-878 nm) from a peak summer image on 16 August 2012. The results obtained with Landsat-7 imagery proved satisfactory with an overall accuracy (tree cover versus non-tree cover) of > 83 percent, according to testing with two different accuracy assessment tests. The application of the FCI to Landsat-7 imagery broadens the applicability of the FCI to freely available imagery.

show abstract

“…Vegetation indices developed from remote sensing data have been used to determine gross primary production in pinyon-juniper woodlands [28].There is a range of vegetation indices used to assess vegetation characteristics. Reflectance characteristics from multispectral imagery, particularly the near-infrared and red-edge spectral regions, have been successfully used to assess vegetation growth [29] and to identify different vegetation species [24,30]. A commonly used vegetation index, the normalized difference vegetation index (NDVI) [31], is calculated from the reflectance characteristics of the near-infrared and red bands, and indicates photosynthetic activity.…”

mentioning

confidence: 99%

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Durfee

Ochoa

Mata-González

2019

Forests

View full text Add to dashboard Cite

Monitoring vegetation characteristics and ground cover is crucial to determine appropriate management techniques in western juniper (Juniperus occidentalis Hook.) ecosystems. Remote-sensing techniques have been used to study vegetation cover; yet, few studies have applied these techniques using unmanned aerial vehicles (UAV), specifically in areas of juniper woodlands. We used ground-based data in conjunction with low-altitude UAV imagery to assess vegetation and ground cover characteristics in a paired watershed study located in central Oregon, USA. The study was comprised of a treated watershed (most juniper removed) and an untreated watershed. Research objectives were to: (1) evaluate the density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed; and, (2) assess the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover. Groundbased measurements were used to assess vegetation features in each watershed and as a means to verify analysis from aerial imagery. Visual imagery (red, green, and blue wavelengths) and multispectral imagery (red, green, blue, near-infrared, and red-edge wavelengths) were captured using a quadcopter-style UAV. Canopy cover in the untreated watershed was estimated using two different methods: vegetation indices and support vector machine classification. Supervised classification was used to assess juniper sapling density and vegetation cover in the treated watershed. Results showed that vegetation indices that incorporated near-infrared reflectance values estimated canopy cover within 0.7% to 4.1% of ground-based calculations. Canopy cover estimates at the untreated watershed using supervised classification were within 0.9% to 2.3% of ground-based results. Supervised classification applied to fall imagery using multispectral bands provided the best estimates of juniper sapling density compared to imagery taken in the summer or to using visual imagery. Study results suggest that low-altitude multispectral imagery obtained using small UAV can be effectively used to assess western juniper density and canopy cover. western juniper in particular has arisen in two primary forms: through the encroachment of juniper into areas previously dominated by sagebrush (Artemisia tridentata Nutt.), and through increases in the density of juniper in areas where it was relatively sparse [3]. Historically, juniper was largely found in areas with lower fire risk [4]. Intensive grazing, reduced fire occurrence, and favorable wetter climate conditions have all been cited as reasons for the vast juniper expansion observed in the late 19th and early 20th century [3,5].Juniper expansion is a concern in many rangeland areas as it may lead to reduced water availability for other types of vegetation. Increased juniper canopy cover has been associated with increased bare ground and decreased shrub, forb, and grass cover [6] and reductions in vegetation production and diversity [7]. Several studies [6,[8][9][10][11...

show abstract

Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images

Cited by 19 publications

References 19 publications

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Forest Cover Index for tree cover detection using Landsat-7 multispectral imagery

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Contact Info

Product

Resources

About