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Remote Sensing in Ecosystem Modelling

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 12674

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Dr. Ioannis Manakos

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Centre for Research and Technology Hellas, Information Technologies Institute, Hellas 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece
Interests: earth observation; geoinformation technologies; big data; time series analysis; uncertainty handling; biodiversity monitoring; food security
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Prof. Dr. Duccio Rocchini

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Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Interests: biodiversity estimate; computational ecology; ecological informatics; remote sensing; spatial ecology
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Prof. Dr. Giorgos Mountrakis

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Department of Environmental Resources Engineering, Intelligent Geocomputing Laboratory, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA
Interests: environmental monitoring; machine learning; image fusion; accuracy assessment; interdisciplinary land modeling
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Special Issue Information

Dear Colleagues,

Ecosystem models are fundamental for a deeper understanding of associated spatiotemporal dynamics. They also support the forecasting of ecological responses to future climate and land use changes, thus supporting relevant decision-making processes. Ecosystem modelling is challenging, given the complexity of natural ecosystems, since models need to consider several levels of environmental predictors and interplaying mechanistic processes.

Earth observation (EO) data and methods serve as a cost-efficient alternative to in-situ data collection at numerous spatial and temporal scales. EO data are now an essential competent in ecological modelling. For example, EO data are used to (i) provide variable estimation to implement ecological models; (ii) test, validate and verify the predictions of ecological models; and (iii) update or adjust process model predictions. These modelling and implementation challenges are investigated by several international projects and initiatives, including the ECOPOTENTIAL H2020 flagship project in Europe and the GEO Global Ecosystem Initiative. Motivated by the strong integration and new capabilities, this Special Issue is inviting manuscripts on the following topics:

- direct comparisons of EO with in-situ data;

- assessment of the added value of EO to ecosystem models;

- interoperability topics, for example spatial and temporal scale issues, derived from the incorporation of EO in ecosystem models;

- uncertainty propagation of EO-derived inputs in ecosystem models;

- benefits by the EO assimilation and side-effects in the designed processing chains;

- adjustments in ecosystem models to better integrate EO inputs;

- the new capacity being developed and explored by the installation and operation of the Data and Information Access Services (DIASs).

The team of Special Issue editors acknowledges the support of the Remote Sensing journal in promoting this discussion and will be at your disposal.

Dr. Ioannis Manakos
Prof. Duccio Rocchini
Prof. Giorgos Mountrakis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing
data interoperability
ecosystem modeling
uncertainty handling
cross-scale
co-design
processes standardization
open access

Published Papers (3 papers)

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16 pages, 8313 KiB

Open AccessArticle

Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires

by Saverio Vicario, Maria Adamo, Domingo Alcaraz-Segura and Cristina Tarantino

Remote Sens. 2020, 12(1), 83; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010083 - 24 Dec 2019

Cited by 3 | Viewed by 3438

Abstract

Vegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2. Full article

(This article belongs to the Special Issue Remote Sensing in Ecosystem Modelling)

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16 pages, 1964 KiB

Open AccessArticle

Hydrological Impacts of Large Fires and Future Climate: Modeling Approach Supported by Satellite Data

by Claudia Carvalho-Santos, Bruno Marcos, João Pedro Nunes, Adrián Regos, Elisa Palazzi, Silvia Terzago, António T. Monteiro and João Pradinho Honrado

Remote Sens. 2019, 11(23), 2832; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232832 - 28 Nov 2019

Cited by 13 | Viewed by 3918

Abstract

Fires have significant impacts on soil erosion and water supply that may be exacerbated by future climate. The aims of this study were: To simulate the effects of a large fire event in the SWAT (Soil and Water Assessment Tool) hydrological model previously calibrated to a medium-sized watershed in Portugal; and to predict the hydrological impacts of large fires and future climate on water supply and soil erosion. For this, post-fire recovery was parametrized in SWAT based on satellite information, namely, the fraction of vegetation cover (FVC) calculated from the normalized difference vegetation index (NDVI). The impact of future climate was based on four regional climate models under the stabilization (RCP 4.5) and high emission (RCP 8.5) scenarios, focusing on mid-century projections (2020–2049) compared to a historical period (1970–1999). Future large fire events (>3000 ha) were predicted from a multiple linear regression model, which uses the daily severity rating (DSR) fire weather index, precipitation anomaly, and burnt area in the previous three years; and subsequently simulated in SWAT under each climate model/scenario. Results suggest that time series of satellite indices are useful to inform SWAT about vegetation growth and post-fire recovery processes. Different land cover types require different time periods for returning to the pre-fire fraction of vegetation cover, ranging from 3 years for pines, eucalypts, and shrubs, to 6 years for sparsely vegetated low scrub. Future climate conditions are expected to include an increase in temperatures and a decrease in precipitation with marked uneven seasonal distribution, and this will likely trigger the growth of burnt area and an increased frequency of large fires, even considering differences across climate models. The future seasonal pattern of precipitation will have a strong influence on river discharge, with less water in the river during spring, summer, and autumn, but more discharge in winter, the latter being exacerbated under the large fire scenario. Overall, the decrease in water supply is more influenced by climate change, whereas soil erosion increase is more dependent on fire, although with a slight increase under climate change. These results emphasize the need for adaptation measures that target the combined hydrological consequences of future climate, fires, and post-fire vegetation dynamics. Full article

(This article belongs to the Special Issue Remote Sensing in Ecosystem Modelling)

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15 pages, 2003 KiB

Open AccessTechnical Note

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

by Arnon Karnieli, Noa Ohana-Levi, Micha Silver, Tarin Paz-Kagan, Natalya Panov, Dani Varghese, Nektarios Chrysoulakis and Antonello Provenzale

Remote Sens. 2019, 11(20), 2406; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202406 - 17 Oct 2019

Cited by 19 | Viewed by 3903

Abstract

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes. Full article

(This article belongs to the Special Issue Remote Sensing in Ecosystem Modelling)

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