Capturing Coastal Dune Natural Vegetation Types Using a Phenology-Based Mapping Approach: The Potential of Sentinel-2

Marzialetti, Flavio; Giulio, Silvia; Malavasi, Marco; Sperandii, Marta Gaia; Acosta, Alicia Teresa Rosario; Carranza, María Laura

doi:10.3390/rs11121506

Cited by 39 publications

(50 citation statements)

References 86 publications

(122 reference statements)

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“…Indeed, they act as a sensible and pragmatic tool to show, monitor and preview environmental changes [61]. The obtained results demonstrated that this kind of approach: (i) is cost saving, effective, accurate and could be run periodically to output maps that reflect the "current" status of vegetation and habitats also reducing costs and risks of delaying the reporting requested by the Habitats Directive (92/43/EEC) [19]; (ii) the FPCA is a suitable and elegant solution to simultaneously express the relationships between remotely sensed and ecological field data. The approach could be applied to other vegetation typologies also supporting policy makers with updated maps to monitor and preserve biodiversity with a reduced effort in terms of required resources.…”

Section: Discussionmentioning

confidence: 88%

“…The results suggest that the Mediterranean forest plant association has distinct remotely sensed phenological behaviors and that the main seasonal phenological variations, useful to discriminate the contiguous Mediterranean habitats [17,19,20], extracted from the NDVI Landsat 8 time series using FPCA, are efficacy predictors for mapping the forest plant associations. Thus, the supervised random forest classification, similarly to Zhu and Liu [55], revealed that the main remotely sensed phenological seasonal variations (expressed by the first four pixel-based FPCA scores) contributed to the high OA (87.5%) of the map, much more than the lithological and topographic features.…”

Section: Mapping Performance and Methodological Considerationsmentioning

confidence: 97%

“…The increasing availability of remote sensing data free of charge of high spatial and temporal resolution (e.g., Sentinel-2 and Landsat-8) offers dense multi-temporal measures of greenness, such as the Normalized Difference Vegetation Index (NDVI) [14] times series, that are a useful proxy for the seasonal and inter-annual vegetation phenological changes [15]. These changes have proved useful in the discrimination of contiguous Mediterranean habitats [16][17][18] and to produce phenology-based mapping of the vegetation [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

et al. 2020

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The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations.

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Section: Discussionmentioning

confidence: 88%

Section: Mapping Performance and Methodological Considerationsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

et al. 2020

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“…The experimental results of Frison et al [32] show that radar data is more accurate for phenological estimation than optical data. Flavio et al [33] have tested the phenology-based vegetation mapping method and proved it effective. Some studies have calculated the phenological characteristics of mangroves to derive environmental driving factors that affect their growth [34,35].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

et al. 2020

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Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

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“…The availability of open access remotely sensed data with increasing spatial and spectral resolution is promising in this context. Thus, in a sector of the Mediterranean coast (Lazio region, Italy), Marzialetti et al [4] tested the strength of a phenology-based vegetation mapping approach and statistically compared results with previous studies, making use of open source products across all the processing chain. We identified five accurate land cover classes in three hierarchical levels, with good values of agreement with previous studies for the first and the second hierarchical levels.…”

mentioning

confidence: 99%

Editorial on Special Issue “Applications of Remote Sensing in Coastal Areas”

Topouzelis

Παπακωνσταντίνου

Singha³

et al. 2020

Remote Sensing

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Capturing Coastal Dune Natural Vegetation Types Using a Phenology-Based Mapping Approach: The Potential of Sentinel-2

Cited by 39 publications

References 86 publications

Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

Editorial on Special Issue “Applications of Remote Sensing in Coastal Areas”

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