Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Zagajewski, Bogdan; Kycko, Marlena; Tømmervik, Hans; Bochenek, Zbigniew; Bjerke, Jarle W.; Kłos, A.

doi:10.5586/asbp.3604

Cited by 19 publications

(8 citation statements)

References 58 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analyses confirmed that the most useful indices were Ratio Vegetation Index = Simple Ratio (RVI) and NDVI, and Renormalized Difference Vegetation Index (RDVI), Modified Simple Ratio (MSR), Leaf Area Index (LAI) and Landsat bands scored worse, non-significant correlation values (<0.58). Similar results were achieved for High-Arctic shrubs [11,12] and for alpine meadow plants [13][14][15]. Sentinel-2 images were also successfully used to identify multi-temporal shrubland areas in Inner Mongolia, China [16] and in deforested equatorial areas of Gabon [17].…”

Section: Introductionsupporting

confidence: 61%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionsupporting

confidence: 61%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Increase in area covered is most probably caused by vegetation growth, such as increase in height of individual plants, number of leaves and size of them. A set of remote sensing indices for plant species can be implemented in order to capture the characteristics of individual species that become visible in different parts of the growth [54]. Fast development of invasive species in new habitats and resulting change in spectral properties (during flowering) might be the cause for relative ease when identifying them on the image.…”

Section: Discussionmentioning

confidence: 99%

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

2021

Self Cite

View full text Add to dashboard Cite

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.

show abstract

“…Although costs of flight campaigns have decreased in recent years and their use has become more popular, the cost may be still too high for permanent monitoring. For this reason, attention is often paid to the use of free-of-charge satellite data, which has become a key element of environmental monitoring of large areas and for the protection of biodiversity [2,3]. One of the most popular types of data comes from the Landsat and Sentinel series satellites, which are commonly used to monitor different forest types and allow the identification of up to a dozen tree species [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

et al. 2021

Self Cite

View full text Add to dashboard Cite

Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species.

show abstract

Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Cited by 19 publications

References 58 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

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

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

Contact Info

Product

Resources

About