Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Marcinkowska-Ochtyra, Adriana; Jarocińska, Anna; Bzdęga, Katarzyna; Tokarska-Guzik, B.

doi:10.3390/rs10122019

Cited by 45 publications

(45 citation statements)

References 83 publications

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“…Additionally, the Random Forest algorithm has proved its worth in identifying two expansive grassland species, Molinia caerulea and Calamagrostis epigejos, in the Silesia Upland in Poland. HySpex and LiDAR (light detection and ranging) products from the Riegl LMS-Q680i scanner were used in the study, obtaining the highest median Kappa of 0.85 (F1 = 0.89, which is a mathematical product of the user (UA) and producer accuracies (PA)) for M. caerulea identification and 0.65 (F1 = 0.73) for C. epigejos [26].…”

Section: Introductionmentioning

confidence: 99%

“…The significance of statistical differences between the accuracy of the models was checked using the Mann-Whitney-Wilcoxon test [49] (significance level = 0.05). The Mann-Whitney-Wilcoxon test is well suited for testing differences between non-normally distributed populations [26,50]. Distributions of achieved accuracy measures for all classification scenarios were visualized using box plots.…”

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confidence: 99%

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Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

2020

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Invasive and expansive plant species are considered a threat to natural biodiversity because of their high adaptability and low habitat requirements. Species investigated in this research, including Solidago spp., Calamagrostis epigejos, and Rubus spp., are successfully displacing native vegetation and claiming new areas, which in turn severely decreases natural ecosystem richness, as they rapidly encroach on protected areas (e.g., Natura 2000 habitats). Because of the damage caused, the European Union (EU) has committed all its member countries to monitor biodiversity. In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identify Solidago spp., Calamagrostis epigejos, and Rubus spp. on HySpex hyperspectral aerial images. SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature. Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers. The classifications were performed on 430-spectral bands and on the most informative 30 bands extracted using the Minimum Noise Fraction (MNF) transformation. As a result, maps of the spatial distribution of analyzed species were achieved; high accuracies were observed for all data sets and classifiers (an average F1 score above 0.78). The highest accuracies were obtained using 30 MNF bands and 300 sample pixels per class in the training data set (average F1 score > 0.9). Lower training data set sample sizes resulted in decreased average F1 scores, up to 13 percentage points in the case of 30-pixel samples per class.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

2020

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“…• Hyperspectral scanners (Hyspex: VNIR 0.4-0.9 µm & SWIR 0.9-2.5 µm): 470 spectral bands with 1 m spatial resolution; • Airborne Laser Scanner (Riegl Lite Mapper LMS-Q680i): point cloud data acquired with 7 points/m 2 ; • Medium format RGB camera (50Mpix) with 0.1 m spatial resolution. For more technical details about the RS platform, the reader is referred to [86]. Field spectroradiometric measurements were also taken in the field with ASD FieldSpec 4, at selected bright and dark locations to be used in the later stage for atmospheric correction (Section 3.2).…”

Section: Airborne Data Acquisition and Botanical Field Measurementsmentioning

confidence: 99%

“…An extensive campaign of botanical surveys was conducted by teams of specialized botanists, according to a uniform procedure worked out within the HabitARS project (Habitats Airborne Remote Sensing) [86][87][88]. The surveys were synchronized as much as possible with the acquisition of aerial photographs and therefore also carried out three times during the growing seasons.…”

Section: Airborne Data Acquisition and Botanical Field Measurementsmentioning

confidence: 99%

“…In general, the first few bands contain already more than 85% of the inherent spectral information with very limited noise, however the exact number of components to be selected is variable and unknown. In this work, based on eigenvalues and after testing with different number of bands, we decided to retain the first 30 MNF components, also as suggested in other studies [86]. One of the major drawbacks of MNF is the lack of ''explainability'' of the resulting transformed data from a physical point of view, since the original spectral information is not pre-served [59] and therefore it could be difficult to handle and interpret.…”

Section: Feature Selection and The Recursive Feature Elimination-randmentioning

confidence: 99%

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Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

Demarchi¹,

Kania²,

Ciężkowski³

et al. 2020

Preprint

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The use of Hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of Recursive Feature Elimination (RFE) in combination with Random Forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510 and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different Spectral Indexes (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7-0.77 and habitats F1 accuracy around 0.8-0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation

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Biogeochemistry, Biogeography, and Geomatics

Campbell

2023

Biogeochemistry and the Environment

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Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Cited by 45 publications

References 83 publications

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

Biogeochemistry, Biogeography, and Geomatics

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