Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands

Brovkina, Olga; Cienciala, Emil; Surový, Peter; Janata, Přemysl

doi:10.1080/10095020.2017.1416994

Cited by 86 publications

(57 citation statements)

References 20 publications

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“…Multi-and hyper-spectral remote sensing, dense point clouds [217,218] Forest fire monitoring -Before fires: forest prevention, e.g. create fire risk maps, (3D) vegetation maps;…”

Section: Forest Health Monitoringmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

136

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The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

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“…Multi-and hyper-spectral remote sensing, dense point clouds [217,218] Forest fire monitoring -Before fires: forest prevention, e.g. create fire risk maps, (3D) vegetation maps;…”

Section: Forest Health Monitoringmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

136

View full text Add to dashboard Cite

show abstract

“…Most of the studies concerning this topic focused on individual tree detection using different outcomes, then OBIA algorithms are applied [67,[116][117][118]121,123,124], creating a set of clusters. Properties of these clusters were then used to create datasets with different extracted parameters.…”

Section: Tree Species Mapping and Classificationmentioning

confidence: 99%

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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“…where v T k q is transpose of v k q and v k q is a vector whose ith element is defined based on Equation (8). on FDM or SDM can be defined according to Equations (9) and (10).…”

Section: Definitionmentioning

confidence: 99%

“…Inspired by emergence of successful ensembles in the supervised learning [4,8,25], clustering/cluster ensemble has emerged. Cluster ensemble merges multiple data clusters (or data partitions) to construct a consensus partition with a better quality [1].…”

Section: Introductionmentioning

confidence: 99%

Dependability‐based cluster weighting in clustering ensemble

Najafi

Parvïn

Mirzaie

et al. 2020

Statistical Analysis

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After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods.

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Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands

Abstract: Global change research institute (czechGlobe), cas, Brno, czech republic; b Department of ecosystem analyses, institute of forest ecosystem research (ifer), Jilove u prahy, czech republic;

Cited by 86 publications

References 20 publications

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Dependability‐based cluster weighting in clustering ensemble

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