Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

Pilaš, Ivan; Gašparović, Mateo; Novkinić, Alan; Klobučar, Damir

doi:10.20944/preprints202011.0030.v1

Cited by 8 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the fundamental problem in determining the extent of damage (area, degree of damage, volume) is the inability to access such areas. Thus, physical access to areas of interest and field damage assessment is high risk, while on the other hand, remote sensing techniques are a fast and reliable way to assess forest damage (Mitchell et al 2017, Pilaš et al 2020. In this regard, drones, aerial and satellite imagery are available to users (Lechner et al 2020).…”

Section: Study Area and Datamentioning

confidence: 99%

Automatic Forest Degradation Monitoring by Remote Sensing Methods and Copernicus Data

Gašparović

Klobučar²,

Gašparović³

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Nowadays, forests are the most widely spread land cover and therefore play a significant role in ecology and create processes’ dynamics. Forests are threatened by various harmful effects due to biotic (insects, fungi, viruses, weeds, animals) and abiotic (floods, fires, storms, droughts, polluted atmospheres etc.) damages. Also, human damage (anthropogenic impact) is numerous and varied. They are caused by direct human action on the forest and indirect activities and processes (damage due to grazing, consequent devastation and erosion of habitats etc.). Forest devastation and illegal logging are one of the immediate negative human activities that have a detrimental impact on the forest. The research refers to the state forests of two management units, Javornik and Ćorkovača-Karlice in the border area of the Republic of Croatia. Part of the forests (about 3,000 hectares) of these two management units are located in a mine suspected area along the state border with Bosnia and Herzegovina. This research aims to develop an automatic algorithm for forest degradation monitoring by remote sensing methods and Copernicus data. The developed algorithm was based on the Sentinel-2 (S2) optical satellite imagery and Google Earth Engine. The proposed automatic forest degradation monitoring algorithm was based on the ΔNDVI change detection approach. Accuracy assessment was done by independent data in higher, 3-m resolution based PlanetScope imagery. Preliminary results show very similar forest degradation values per all tested forest compartment/subcompartment for automatically generated S2 10-m imagery forest degradation map and 3-m forest maps obtained manually from PlanetScope imagery.

show abstract

Section: Study Area and Datamentioning

confidence: 99%

Automatic Forest Degradation Monitoring by Remote Sensing Methods and Copernicus Data

Gašparović

Klobučar²,

Gašparović³

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…For example, classification methods can help differentiate fully vegetated areas from ‘bald’ areas created by voles 20 and also identify intermediate stages of damage. In pest damage evaluations, different classification approaches have been used, such as vegetation index thresholds, 21 automatic classification methods 26 and supervised classifications based on machine learning, and either pixel‐oriented (each pixel is classified independently) 27 or object‐oriented methods (all pixels within defined objects are included to define spectral behavior through an iterative classification process) 28 . Imagery segmentations of damaged areas from UASs also have been used in the diagnoses of precision applications of control measures by drones 29 .…”

Section: Introductionmentioning

confidence: 99%

Classification of airborne multispectral imagery to quantify common vole impacts on an agricultural field

Plaza

Sánchez

García‐Ariza

et al. 2022

Pest Management Science

View full text Add to dashboard Cite

BACKGROUND: The common vole (Microtus arvalis) is a very destructive agricultural pest. Particularly in Europe, its monitoring is essential not only for adequate management and outbreak forecasting, but also for accurately determining the vole's impact on affected fields. In this study, several alternatives for estimating the damage to alfalfa fields by voles through unmanned vehicle systems (UASs) and multispectral cameras are presented. Currently, both the farmers and agencies involved in the integrated pest management (IPM) programs of voles do not have sufficiently precise methods for accurate assessments of the real impact to crops. RESULTS: Overall, the four multispectral classification methods presented showed similar performances. However, the normalized difference vegetation index (NDVI)-based segmentation exhibited the most accurate and reliable appraisal of the affected areas. Nevertheless, it must be noted that the simplest method, which was based on an automatic classification, provided results similar to those obtained by more complex methods. In addition, a significant direct relationship was found between the number of active burrows and damage to the alfalfa canopy.CONCLUSION: Unmanned vehicle systems, combined with multispectral imagery classification, are an effective and easily transferable methodology for the assessment and monitoring of common vole damage to agricultural plots. This combination of methods facilitates decision-making processes for IPM control strategies against this pest.

show abstract

Detecting Canopy Gaps in Uneven-Aged Mixed Forests through the Combined Use of Unmanned Aerial Vehicle Imagery and Deep Learning

Htun,

Owari,

Tsuyuki

et al. 2024

Drones

View full text Add to dashboard Cite

Canopy gaps and their associated processes play an important role in shaping forest structure and dynamics. Understanding the information about canopy gaps allows forest managers to assess the potential for regeneration and plan interventions to enhance regeneration success. Traditional field surveys for canopy gaps are time consuming and often inaccurate. In this study, canopy gaps were detected using unmanned aerial vehicle (UAV) imagery of two sub-compartments of an uneven-aged mixed forest in northern Japan. We compared the performance of U-Net and ResU-Net (U-Net combined with ResNet101) deep learning models using RGB, canopy height model (CHM), and fused RGB-CHM data from UAV imagery. Our results showed that the ResU-Net model, particularly when pre-trained on ImageNet (ResU-Net_2), achieved the highest F1-scores—0.77 in Sub-compartment 42B and 0.79 in Sub-compartment 16AB—outperforming the U-Net model (0.52 and 0.63) and the non-pre-trained ResU-Net model (ResU-Net_1) (0.70 and 0.72). ResU-Net_2 also achieved superior overall accuracy values of 0.96 and 0.97, outperforming previous methods that used UAV datasets with varying methodologies for canopy gap detection. These findings underscore the effectiveness of the ResU-Net_2 model in detecting canopy gaps in uneven-aged mixed forests. Furthermore, when these trained models were applied as transfer models to detect gaps specifically caused by selection harvesting using pre- and post-UAV imagery, they showed considerable potential, achieving moderate F1-scores of 0.54 and 0.56, even with a limited training dataset. Overall, our study demonstrates that combining UAV imagery with deep learning techniques, particularly pre-trained models, significantly improves canopy gap detection accuracy and provides valuable insights for forest management and future research.

show abstract

Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

Cited by 8 publications

References 46 publications

Automatic Forest Degradation Monitoring by Remote Sensing Methods and Copernicus Data

Automatic Forest Degradation Monitoring by Remote Sensing Methods and Copernicus Data

Classification of airborne multispectral imagery to quantify common vole impacts on an agricultural field

Detecting Canopy Gaps in Uneven-Aged Mixed Forests through the Combined Use of Unmanned Aerial Vehicle Imagery and Deep Learning

Contact Info

Product

Resources

About