Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges

Fernández‐Guisuraga, José Manuel; Sanz‐Ablanedo, Enoc; Suárez‐Seoane, Susana

doi:10.3390/s18020586

Cited by 102 publications

(87 citation statements)

References 63 publications

(94 reference statements)

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“…The results demonstrated The UAV-based multispectral imagery acquired in the 116 ha of the study showed similar results when compared to the Sentinel-2 data. These findings have already been verified for WordView-2 1-m spatial resolution data [14], but never for Sentinel-2. In fact, for this type of application, the Sentinel-2 proved to be a more cost-effective approach that was able to cover wider areas, providing a short revisit time (five days) and delivering a wider spectral range.…”

Section: Discussionmentioning

confidence: 83%

“…The overall procedure was time-consuming and computationally demanding, with data acquisition taking two months to conduct (resulting in a total of 100 h), and further data processing taking approximately 320 h. Some of these data then had to be discarded due to sensor malfunctions during the flights, in addition to radiometric anomalies found in the acquired images and further data storage problems. The experiment carried out by Fernández-Guisuraga et al [14] allowed the suitability of UAV-based multispectral imagery to be determined when more information in terms of spatial variability in heterogeneous burned areas is needed. Other authors have explored fire severity measuring using UAV-based RGB imagery [17], but some limitations that directly impact its accuracy have been found such as the influence of canopy shadows, photogrammetric errors in canopy modelling and inconsistent illumination across the imagery.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, multiple batteries are needed to cover a considerable area. Fernández-Guisuraga et al [14] used the Parrot SEQUOIA for UAV-based data acquisition during the post-fire monitoring of a 3000-ha area and faced several issues in the process. The overall procedure was time-consuming and computationally demanding, with data acquisition taking two months to conduct (resulting in a total of 100 h), and further data processing taking approximately 320 h. Some of these data then had to be discarded due to sensor malfunctions during the flights, in addition to radiometric anomalies found in the acquired images and further data storage problems.…”

Section: Discussionmentioning

confidence: 99%

“…In the past decade, the use of unmanned aerial vehicles (UAVs) has increased for agroforestry applications [8] and are now being used for forest fire prevention [9], canopy fuel estimation [10], fire monitoring [11,12] and to support firefighting operations [13]. Likewise, studies using UAV-based imagery in post-fire monitoring have been concerned with surveying [14], calibrating satellite-based burn severity indices [15], assessing post-fire vegetation recovery [16], mapping fire severity [17,18], studying forest recovery dynamics [19] and sapling identification [20]. Despite being a cost-effective and a very versatile platform for remote sensed data acquisition that is capable of carrying a wide set of sensors, its usage in surveying big areas can be constrained due to legal [21] and technological limitations such as its autonomy and payload capacity [8].…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

Pádua

Guimarães

Adão

et al. 2020

IJGI

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Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

Pádua

Guimarães

Adão

et al. 2020

IJGI

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“…A variety of multispectral camera and sensor options are available and have been deployed with drones. These range from modified off-the-shelf digital cameras (Lebourgeois et al 2008, for examples see Berra et al 2017, Müllerová et al 2017), to compact purpose-build multi-band drone sensors such as the Parrot Sequoia (Ahmed et al 2017, Fernández-Guisuraga et al 2018) and the MicaSense Red-Edge (Samiappan et al 2017, Dash et al 2017). The Parrot Sequoia and MicaSense Red-Edge sensors are compact bundles (rigs) of 4-5 cameras with Complementary Metal-Oxide-Semiconductor (CMOS) (Weste 2011) sensors, a type of imaging sensor commonly found in the consumer cameras of phones and DSLRs.…”

Section: Introductionmentioning

confidence: 99%

Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes

Assmann

Kerby

Cunliffe

et al. 2018

Preprint

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17Emerging drone technologies have the potential to revolutionise ecological monitoring. The 18 rapid technological advances in recent years have dramatically increased affordability and 19 ease of use of Unmanned Aerial Vehicles (UAVs) and associated sensors. Compact 20 multispectral sensors, such as the Parrot Sequoia (Paris, France) and MicaSense RedEdge 21 (Seattle WA, USA) capture spectrally accurate high-resolution (fine grain) imagery in visible 22 and near-infrared parts of the electromagnetic spectrum, providing supplement to satellite 23 and aircraft-based imagery. Observations of surface reflectance can be used to calculate 24 vegetation indices such as the Normalised Difference Vegetation Index (NDVI) for 25 productivity estimates and vegetation classification. Despite the advances in technology, 26 challenges remain in capturing consistently high-quality data, particularly when operating in 27 extreme environments such as the high latitudes. Here, we summarize three years of 28 ecological monitoring with drone-based multispectral sensors in the remote Canadian Arctic. 2We discuss challenges, technical aspects and practical considerations, and highlight best 30 practices that emerged from our experience, including: flight planning, factoring in weather 31 conditions, and geolocation and radiometric calibration. We propose a standardised 32 methodology based on established principles from remote sensing and our collective field 33 experiences, using the Parrot Sequoia sensor as an example. With these good practises, 34 multispectral sensors can provide meaningful spatial data that is reproducible and 35 comparable across space and time. 36 37

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Monitoring of honey bee floral resources with pollen DNA metabarcoding as a complementary tool to vegetation surveys

Milla

Schmidt‐Lebuhn

Bovill

et al. 2022

Ecol Sol and Evidence

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Monitoring biodiversity is a growing and pressing challenge, particularly as climate change threatens species with extinction and leads to widespread shifts in plant distribution and phenology. Tracking changes via ground vegetation surveys is costly and time‐consuming, hence monitoring of complex and heterogenous communities remains an ongoing challenge. Molecular DNA methods are rapidly being developed to provide fast and reproducible results for environmental monitoring, including diet and ecosystem assessments. Here, we used DNA metabarcoding of pollen foraged by European honey bees (Apis mellifera) to investigate their floral resource use in an urban reserve. We collected three different pollen samples from hives: individual bees, raw honey and pollen traps, and identified plants using two metabarcoding markers (ITS2 and trnL). We then compared the results to a ground vegetation survey of surrounding flowering taxa. Pollen DNA metabarcoding detected 74 taxa (48.6% identified to species) across all pollen sources, compared to 44 taxa recorded by the survey (93% identified to species). Within the metabarcoding results, we identified 25% of the genera and 9% of the species found during the survey, with three of the top 10 flowering genera represented. While honey was the most taxon‐rich pollen source (mean = 8.5, SD = 3.5), followed by honey bees (mean = 5.8, SD = 6.1) and pollen traps (mean = 4.2, SD = 1.7), combining the results of six individual bees could detect similar taxa numbers to honey, while 20 bees were required to detect as many taxa as the survey. We demonstrate how DNA metabarcoding of the pollen foraged by honey bees can detect more flowering taxa than traditional survey methods, and how different pollen sources and genetic markers affect the level of detection of plant taxa. The foraging choices of honey bees matched few species detected by the vegetation survey, therefore pollen metabarcoding is recommended as a complementary approach to ground surveys. Rigorous validation and stringent filtering of metabarcoding results were also required to exclude potential false positives. Altogether, this molecular approach can be used to augment vegetation surveys, while tracking the floral resources used by bees.

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Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges

Cited by 102 publications

References 63 publications

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes

Monitoring of honey bee floral resources with pollen DNA metabarcoding as a complementary tool to vegetation surveys

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