Unmanned aerial vehicle (UAV) hyperspectral remote sensing for dryland vegetation monitoring

Mitchell, Jessica J.; Glenn, Nancy F.; Anderson, Matthew; Hruska, Ryan; Halford, Anne; Baun, Charlie; Nydegger, Nick

doi:10.1109/whispers.2012.6874315

Cited by 37 publications

(16 citation statements)

References 9 publications

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“…In total, we downloaded 131 articles that were then re-checked to assess their relevance. Ultimately, we only found 18 original-research articles that assessed both understorey vegetation and used high resolution remote sensing (Table 1; [1,18,19,22,[36][37][38][39][40][41][42][43][44][45][46][47][48][49]). We reviewed all of them.…”

Section: Resultsmentioning

confidence: 99%

“…Although hyperspectral data provides more opportunities to find spectral signatures with less overlap among species, the only two papers reviewed that used this type of data reported only partial successes. Lopatin et al [41] attributed their shortcomings to a relatively coarse spatial resolution, while Mitchell et al [44] attributed theirs to 'complications' on data acquisition and suggested better results could be attained by using a wider spectral range and monitoring at different phenological times.…”

Section: Spectral Sensitivitymentioning

confidence: 99%

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Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Hernández-Santín

Rudge

Bartolo³

et al. 2019

Drones

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Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species.

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Section: Resultsmentioning

confidence: 99%

Section: Spectral Sensitivitymentioning

confidence: 99%

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Hernández-Santín

Rudge

Bartolo³

et al. 2019

Drones

View full text Add to dashboard Cite

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“…Furthermore, hyperspectral sensors have been shrinking in size and weight, and their use onboard of UAVs has become feasible [26][27][28]. Recently, several studies have addressed the use of UAV hyperspectral sensors in vegetation [29,30], crop [31][32][33][34][35][36][37][38][39][40][41][42], forest monitoring [43], and wetland species mapping [44], etc. However, few specific results have been published on mapping mangrove species using UAV hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi'ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

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“…UAV technology has been developing rapidly. A variety of sensors onboard UAV platforms have been implemented and there have been many research projects employing UAVs to collect hyperspectral [12][13][14] and thermal data [15,16]. Some researchers also used LiDAR sensors mounted on UAV platforms to collect elevation data and develop DEMs [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

Yang

Hawthorne

Torres

et al. 2019

Drones

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.

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Unmanned aerial vehicle (UAV) hyperspectral remote sensing for dryland vegetation monitoring

Cited by 37 publications

References 9 publications

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

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