Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region

Kollert, Andreas; Bremer, Magnus; Löw, Markus; Rutzinger, Martin

doi:10.1016/j.jag.2020.102208

Cited by 47 publications

(53 citation statements)

References 53 publications

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“…Although previous studies have approached the statistical operator issue in the image reduction process, highlighting median as the most-used [48,111], after testing different solutions with a trial-and-error approach, we obtained the best results using mean pixel values.…”

Section: Discussionmentioning

confidence: 85%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

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The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

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Section: Discussionmentioning

confidence: 85%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

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“…Forest plant communities, together with their own typical floristic composition, show exclusive phenological dynamics recognizable by vegetation indices time series [42]. Both vegetation indices time series [42,61] and land surface phenological metrics [61,92] has been successfully used to classify plant communities and natural habitats, discriminating vegetation patterns dominated by species with similar phenology features. Multivariate statistical analysis conducted in this research study allowed to understand how the three generated datasets (the smoothed vegetation curve, the temporal statistics, and the phenological metrics) have the capacity to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients.…”

Section: Discussionmentioning

confidence: 99%

Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

2022

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The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.

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“…Here the dynamic SGF window width proves to be an appropriate mean to deduce reasonable metrics across various forest types and forest growing regions in Austria. Phenology metrics and the reliable MPCs can be used for various downstream analyses, such as for forest type classification [63] or habitat modelling.…”

Section: Take-home Messagesmentioning

confidence: 99%

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

Löw

Koukal

2020

Remote Sensing

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Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky–Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. In addition to spatially detailed maps, zonal statistics on different spatial scales provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances to facilitate sustainable forest management.

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Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region

Cited by 47 publications

References 53 publications

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

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