Identification of high nature value grassland with remote sensing and minimal field data

Stenzel, Stefanie; Fassnacht, Fabian Ewald; Mack, Benjamin; Schmidtlein, Sebastian

doi:10.1016/j.ecolind.2016.11.005

Cited by 46 publications

(25 citation statements)

References 46 publications

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“…Additionally, assuming a distribution model of the data can sometimes be problematic [29]. Our experiment results indicated that MaxEnt outperformed BSVM, which is different from the findings reported by Mack and Waske (2017) [57] and Stenzel et al (2017) [58]. This phenomenon might have resulted from the different thresholds that were used to transform the model outputs into binary classes.…”

Section: Discussioncontrasting

confidence: 56%

One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

et al. 2017

Remote Sensing

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Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a reliable classifier. However, complete training samples are normally hard and costly to collect, and a common circumstance is that only training samples for a class of interest are available, in which traditional supervised classification methods may be inappropriate. In this study, we investigated the possibility of using a novel one-class classification algorithm, i.e., the presence and background learning (PBL) algorithm, to classify LiDAR data in an urban scenario. The results demonstrated that the PBL algorithm implemented by back propagation (BP) neural network (PBL-BP) could effectively classify a single class (e.g., building, tree, terrain, power line, and others) from airborne LiDAR point cloud with very high accuracy. The mean F-score for all of the classes from the PBL-BP classification results was 0.94, which was higher than those from one-class support vector machine (SVM), biased SVM, and maximum entropy methods (0.68, 0.82 and 0.93, respectively). Moreover, the PBL-BP algorithm yielded a comparable overall accuracy to the multi-class SVM method. Therefore, this method is very promising in the classification of the LiDAR point cloud.

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Section: Discussioncontrasting

confidence: 56%

One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

et al. 2017

Remote Sensing

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“…) that yield high performances compared to other OCC algorithms in remote sensing applications (Stenzel et al. ). Because MaxEnt is very CPU demanding (Mack et al.…”

Section: Discussionmentioning

confidence: 99%

“…; Stenzel et al. ). From an operational point of view, the application of MaxEnt is very promising, as the delineation of training data of unwanted classes (e.g.…”

Section: Methodsmentioning

confidence: 98%

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Lopatin

Dološ

Kattenborn

2019

Remote Sens Ecol Conserv

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Plant invasions can result in serious threats for biodiversity and ecosystem functioning. Reliable maps at very‐high spatial resolution are needed to assess invasions dynamics. Field sampling approaches could be replaced by unmanned aerial vehicles (UAVs) to derive such maps. However, pixel‐based species classification at high spatial resolution is highly affected by within‐canopy variation caused by shadows. Here, we studied the effect of shadows on mapping the occurrence of invasive species using UAV‐based data. MaxEnt one‐class classifications were applied to map Acacia dealbata, Ulex europaeus and Pinus radiata in central‐south Chile using combinations of UAV‐based spectral (RGB and hyperspectral), 2D textural and 3D structural variables including and excluding shaded canopy pixels during model calibration. The model accuracies in terms of area under the curve (AUC), Cohen's Kappa, sensitivity (true positive rate) and specificity (true negative rate) were examined in sunlit and shaded canopies separately. Bootstrapping was used for validation and to assess statistical differences between models. Our results show that shadows significantly affect the accuracies obtained with all types of variables. The predictions in shaded areas were generally inaccurate, leading to misclassification rates between 65% and 100% even when shadows were included during model calibration. The exclusion of shaded areas from model calibrations increased the predictive accuracies (especially in terms of sensitivity), decreasing false positives. Spectral and 2D textural information showed generally higher performances and improvements when excluding shadows from the analysis. Shadows significantly affected the model results obtained with any of the variables used, hence the exclusion of shadows is recommended prior to model calibration. This relatively easy pre‐processing step enhances models for classifying species occurrences using high‐resolution spectral imagery and derived products. Finally, a shadow simulation showed differences in the ideal acquisition window for each species, which is important to plan revisit campaigns.

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“…Recently, many applications demonstrated the large potential of remote sensing information for monitoring vegetation status (e.g., Bock et al, 2005;Förster et al, 2008;Stenzel et al, 2017;Vanden Borre et al, 2011). Luft et al (2014) and Corbane et al (2015) provide a good overview about studies that developed remote sensing approaches to fulfill monitoring demands.…”

Section: Introductionmentioning

confidence: 99%

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

Schmidt

Fassnacht

Neff

et al. 2017

International Journal of Applied Earth Observation and Geoinfor

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Identification of high nature value grassland with remote sensing and minimal field data

Cited by 46 publications

References 46 publications

One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

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