Post-Fire Forestry Recovery Monitoring Using High-Resolution Multispectral Imagery From Unmanned Aerial Vehicles

Pádua, Luís; Adão, Telmo; Guimarães, Nathalie; Sousa, António; Peres, Emanuel; Sousa, Joaquim J.

doi:10.5194/isprs-archives-xlii-3-w8-301-2019

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…However, all remaining applications in terms of fire monitoring can be accomplished using satellite imagery, including those provided by Sentinel-2 MSI. Despite the great effectiveness of the satellite data for post-fire monitoring at a local/regional scale, some applications may require a significantly higher spatial resolution, making UAVs necessary, as is the case in individual tree monitoring [58], which cannot be conducted with satellite data with a decameter resolution or in real-time fire monitoring applications [12]. Thus, the complementarity of the two types of data are proven.…”

Section: Discussionmentioning

confidence: 99%

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: 99%

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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“…Some researchers (18 papers) used fixed-wing UAVs. In two studies, both multirotor and fixed-wing systems were applied, with the latter to achieve a more extensive area coverage [86,87]. A detailed comparison of the different platform characteristics was not considered.…”

Section: Resultsmentioning

confidence: 99%

UAV-Based Forest Health Monitoring: A Systematic Review

et al. 2022

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In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

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“…White et al [19] leveraged OBC for inventorying jack pine succession following a wildfire in northern Michigan, with OAs of 98%. Both Pádua et al [28] and Anderson [29] performed manual OBC for inventorying the phenological condition of forests following fire events. Pádua [28] digitized three classes of post-fire damage severity, in addition to target tree species identification using image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Both Pádua et al [28] and Anderson [29] performed manual OBC for inventorying the phenological condition of forests following fire events. Pádua [28] digitized three classes of post-fire damage severity, in addition to target tree species identification using image segmentation. Anderson [29] constructed a three-class vegetation height map and pine regeneration stand map through the visual interpretation and digitization of image objects.…”

Section: Introductionmentioning

confidence: 99%

Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery

Miller

Hupy

Hubbard

et al. 2022

Drones

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This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in natural disturbance-based management (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed t-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining.

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Post-Fire Forestry Recovery Monitoring Using High-Resolution Multispectral Imagery From Unmanned Aerial Vehicles

Cited by 10 publications

References 20 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

UAV-Based Forest Health Monitoring: A Systematic Review

Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery

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