Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Sabat-Tomala, Anita; Raczko, Edwin; Zagajewski, Bogdan

doi:10.3390/rs14010064

Cited by 16 publications

(16 citation statements)

References 49 publications

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“…The results of the classification also depend on the vegetation period in which the airborne data were acquired. It was confirmed that the optimal image acquisition period for plant communities mapping is from the second half of August to the first half of September in Central European vegetation periods, due to the complex physiognomy [13][14][15]78]. This was also confirmed by Calvão and Palmeirim [9], who observed a summer stress (an intense radiation and a water deficit), which changed the registered spectra of different types of vegetation included in the shrubby formations.…”

Section: Discussionsupporting

confidence: 54%

“…In our case, signature pixel values representing different classes were selected from field-verified training polygons and randomized sampled (50:50) as training/validation patterns, limiting the phenomenon of overfitting during classification [77]. Each classification iteration used 500 trees (ntree parameter), which comes from the optimization process to obtain repetitive results [78,79]. The SVM, as a non-parametric classifier, allowed for a flexibility of training data, thus limiting errors and leading to high accuracy [28,80].…”

Section: The Classification Algorithm and Accuracy Assessmentmentioning

confidence: 99%

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Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

et al. 2022

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The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively harbor 10% of the Earth's species, thus containing some of the most unique shrubby formations protecting against environmental natural degradation. Due to shrub species diversity, initial phases of forestland, heterogenous grasses, bare ground and stones, the monitoring of such areas is difficult. For this reason, the aim of this paper is to assess semi-automatic classifications of the shrubby formations based on multispectral Sentinel-2 and visible and near infrared (VINR) AISA-EAGLE II hyperspectral airborne images with a support of Canopy High Model (CHM) as a three-dimensional information and field-verified patterns, based on Match-T/DSM and aerial photos. Support Vector Machine (SVM) and Random Forest (RF) classifiers have been tested on a few scenarios featuring different combinations of spectral and Minimum Noise Fraction (MNF) transformed bands and vegetation indices. Referring to the results, the average overall accuracy for the SVM and AISA images (all tested data sets) was 78.23%, and for the RF: 79.85%. In the case of Sentinel-2, the SVM classifier obtained an average value of 83.63%, while RF: 85.32%; however, in the case of the shrubland, we would like to recommend the RF classifier, because the highest mean value of F1-score achieved was 91.86% (SVM offered few-percent-point worse results), and the required training time was quicker than SVM. Commonly available Sentinel-2 data offered higher accuracies for shrubland monitoring than did the airborne VNIR data.

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Section: Discussionsupporting

confidence: 54%

Section: The Classification Algorithm and Accuracy Assessmentmentioning

confidence: 99%

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

et al. 2022

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“…During splitting, it was ensured that pixels from a single polygon were included in the training or test set to ensure their independence and non-spatial correlation [71]. An iterative accuracy assessment [72] procedure was applied, during which the classification procedure was repeated 100 times, assessing the overall accuracy (OA), the kappa coefficient [73], producer and user accuracy (PA and UA) [74], and the F1-score (F1) for all classes each time based on randomized selected verification pixels from the validation set [75,76]. It helped visualize all results using box graphs presenting the median with a 95% confidence interval and first and third quartiles (Q1, Q3), between Then, using the R language 4.0.3 [66], pixel values were extracted from the data using the raster and rgdal [67] packages.…”

Section: Sensormentioning

confidence: 99%

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

2022

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Climate change and anthropopression significantly impact plant communities by leading to the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to significant elevation gradients, high-mountain plant communities in a small area allow for the monitoring of the most important environmental changes. Additionally, being a tourist attraction, they are exposed to direct human influence (e.g., trampling). Airborne hyperspectral remote sensing is one of the best data sources for vegetation mapping, but flight campaign costs limit the repeatability of surveys. A possible alternative approach is to use satellite data from the Copernicus Earth observation program. In our study, we compared multitemporal Sentinel-2 data with HySpex airborne hyperspectral images to map the plant communities on Tatra Mountains based on open-source R programing implementation of Random Forest and Support Vector Machine classifiers. As high-mountain ecosystems are adapted to topographic conditions, the input of Digital Elevation Model (DEM) derivatives on the classification accuracy was analyzed and the effect of the number of training pixels was tested to procure practical information for field campaign planning. For 13 classes (from rock scree communities and alpine grasslands to montane conifer and deciduous forests), we achieved results in the range of 76–90% F1-score depending on the data set. Topographic features: digital terrain model (DTM), normalized digital surface model (nDSM), and aspect and slope maps improved the accuracy of HySpex spectral images, transforming their minimum noise fraction (MNF) bands and Sentinel-2 data sets by 5–15% of the F1-score. Maps obtained on the basis of HySpex imagery (2 m; 430 bands) had a high similarity to maps obtained on the basis of multitemporal Sentinel-2 data (10 m; 132 bands; 11 acquisition dates), which was less than one percentage point for classifications based on 500–1000 pixels; for sets consisting of 50–100 pixels, Random Forest (RF) offered better accuracy.

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“…This is important for tracking the dynamics of invasion and an assessment of threats to biodiversity. Most works have used aerial data or high-resolution satellite imagery; Sabat-Tomala et al [8], based on airborne hyperspectral HySpex images, Random Forest (RF), and Support Vector Machine (SVM) algorithms, classified three species of expansive and invasive plants (including Solidago spp.) with accuracy of around 0.90 (F1-scores), emphasizing that the results obtained for spring, summer, and autumn images were similar, and the fusion of all the data allowed goldenrod to be mapped with an F1-score of 0.99.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping

Zagajewski,

Kluczek,

Zdunek

et al. 2024

Remote Sensing

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A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm.

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Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Cited by 16 publications

References 49 publications

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping

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