Remotely-sensed phenology of Italian forests: Going beyond the species

Bajocco, Sofia; Ferrara, Carlotta; Alivernini, Alessandro; Bascietto, Marco; Ricotta, Carlo

doi:10.1016/j.jag.2018.10.003

Cited by 34 publications

(25 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our results suggest the analysis of remote sensed data (Sentinel-2 images) by a phenology-based classification as an effective approach for monitoring natural landscapes. Sentinel-2 images confirmed their high potential for vegetation mapping [12], while the multitemporal analysis of NDVI provided complementary and useful information, proving its convenience even in complex vegetation mosaics, that is to say, beyond their traditional field of application [18,26,28].…”

Section: Discussionmentioning

confidence: 81%

“…Among these, Normalized Difference Vegetation Index (NDVI) [19,20] is a good proxy of canopy biomass [21], and its application for environmental monitoring is highly appreciated [22,23]. Furthermore, the monthly variation of NDVI values across an entire year proved to be a sound surrogate of ecosystem phenology [18,24,25], which allows for discriminating contiguous vegetation cover types featuring different seasonality [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. 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), we 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 level. The implemented procedure resulted as being effective for mapping a highly fragmented coastal dune system. This is encouraging to take advantage of the earth observation through remote sensing technology in an open source perspective, even at the fine scale of highly fragmented sand dunes landscapes.

show abstract

Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Even the temporal NDVI profiles are an effective tool, which directly expresses the relationship between the remote-sensed phenology and the vegetation classified in the field, as similarly demonstrated in [18,57] for physiognomic types. The temporal NDVI curves of Figure 5 suggest that the NDVI time profiles can be a functional and diagnostic signature of the Mediterranean forests, not only at the physiognomic level but also at the plant association level.…”

Section: Mapping Performance and Methodological Considerationsmentioning

confidence: 97%

“…Considering the increasing availability of remotely sensed data with a high spatial and temporal resolution (e.g., Sentinel 2) [56], we believe that FPCA could promote the integration between classical phytosociological analyses and remotely sensed phenological data. For example, in addition to the methodology proposed here, we hypothesize that a preliminary unsupervised classification (e.g., k-means clustering) of the FPCA scores, which could identify distinctly homogeneous areas for phenological characteristics (called pheno-cluster in Bajocco et al, [57,58]) will contribute to define efficient phytosociological sampling strategies. Furthermore, FPCA scores and NDVI profiles could be functional vegetation attributes useful for validating new (syntaxonomic) vegetation classifications [59].…”

Section: Mapping Performance and Methodological Considerationsmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…The recent launch of the USA National Phenology Network (USA-NPN; https://www.usanpn.org/), the Pan European Phenology Project (PEP725; http://www. pep725.eu/), the Phenological Eyes Network in Japan (PEN; http://www.pheno-eye.org), the GLOBE phenology project (https://www.globe.gov/web/phenology-and-climate) confirm the current necessity of understanding phenological dynamics under a changing environment and at multiple geographical scales [4,5].…”

Section: Introductionmentioning

confidence: 87%

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

et al. 2019

View full text Add to dashboard Cite

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

show abstract

Remotely-sensed phenology of Italian forests: Going beyond the species

Cited by 34 publications

References 46 publications

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

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

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

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

Contact Info

Product

Resources

About