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Assessment of Spatial and Temporal Patterns in Forest and Grassland...
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Assessment of Spatial and Temporal Patterns in Forest and Grassland Ecosystems Based on Spectral Metrics

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2116

Special Issue Editors

Dr. Laura Recuero

E-Mail Website
Guest Editor
Departamento de Economía Agraria, Estadística y Gestión de Empresas, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: time series analysis; remote sensing; GIS; vegetation dynamics; land use change; machine learning
Dr. Víctor Cicuéndez

E-Mail Website
Guest Editor
Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Interests: time series analysis; remote sensing; meteorology; forestry; crop monitoring
Dr. Margarita Huesca

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation–ITC, University of Twente, Hallenweg 8, 7522 NH Enschede, The Netherlands
Interests: forestry; biodiversity; ecosystem dynamics; environmental disturbances such as forest fires and droughts; vegetation monitoring; functional traits; time series analysis; change detection; spaciotemporal modeling; remote sensing; multispectral; hyperspectral; LiDAR and UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests and grasslands are two widely distributed terrestrial ecosystems in the world occupying approximately 30% and 24% of Earth’s land surface, respectively, playing a key role in regulating climate and maintaining biodiversity. The evaluation of their spatial and temporal patterns is crucial for understanding ecological processes and identifying changes driven by climate change effects or human activities.

Remote sensing techniques are an effective tool for monitoring terrestrial ecosystems. The spectral information acquired by the sensors can be summarized in spectral metrics providing highly useful information to evaluate the dynamics of forests and grasslands at different spatial and temporal scales.  The results can be helpful for decision making in natural resource management, biodiversity conservation, climate change mitigation and adaptation, and land-use planning.

This Special Issue intends to disseminate advanced research on forest and grassland monitoring based on spectral metrics. Articles may address, but are not limited to, the following topics:

  • Development of new spectral indices and/or spectral metrics to monitor forests and grasslands.
  • Mapping forest and grassland species diversity.
  • Identifying changes and trends in forest and grasslands driven by climate or human activities.
  • Assessment of biomass and phenology.
  • Modeling vegetation traits.

Dr. Laura Recuero
Dr. Víctor Cicuéndez
Dr. Margarita Huesca
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

  • hyperspectral and multispectral sensors (satellite, aerial and UAV)
  • vegetation biophysical parameters
  • forest and grassland functioning
  • changes and trends
  • modelling traits
  • machine learning algorithms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 4669 KiB  
Article
Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA)
by Yu Hong, Runfa Zhou, Jinfu Liu, Xiang Que, Bo Chen, Ke Chen, Zhongsheng He and Guanmin Huang
Remote Sens. 2025, 17(3), 549; https://doi.org/10.3390/rs17030549 - 6 Feb 2025
Viewed by 706
Abstract
Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion [...] Read more.
Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion models struggle with prolonged data gaps and heavy noise. This study proposes an optimized mangrove phenology extraction approach (OMPEA), which integrates Landsat and MODIS data with a denoising algorithm (e.g., Gap Filling and Savitzky–Golay filtering, GF–SG) and a spatiotemporal fusion model (e.g., Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF–SG algorithm filled data gaps from cloud cover and satellite transit gaps, providing high-quality input to ESTARFM and improving its accuracy of NDVI imagery reconstruction in mangrove phenology extraction. By conducting experiments on the GEE platform, OMPEA generates 1-day, 30 m NDVI imagery, from which phenological parameters (i.e., the start (SoS), end (EoS), length (LoS), and peak (PoS) of the growing season) are derived using the maximum separation (MS) method. Validation in four mangrove areas along the coastal China shows that OMPEA significantly improves the potential to capture mangrove phenology in the presence of incomplete data. The OMPEA significantly increased usable data, adding 7–33 Landsat images and 318–415 MODIS images per region. The generated NDVI series exhibits strong spatiotemporal consistency with original data (R2: 0.788–0.998, RMSE: 0.007–0.253) and revealed earlier SoS and longer LoS at lower latitudes. Cross-correlation analysis showed a 2–3 month lagged effects of temperature on mangroves’ growth, with precipitation having minimal impact. The proposed OMPEA improves the possibility of capturing mangrove phenology under non-continuous and low-resolution data, providing valuable insights for large-scale and long-term mangrove conservation and management. Full article
(This article belongs to the Special Issue Assessment of Spatial and Temporal Patterns in Forest and Grassland Ecosystems Based on Spectral Metrics)
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16 pages, 3324 KiB  
Article
Matching Vegetation Indices and Tree Vigor in Pyrenean Silver Fir Stands
by Juan Pablo Crespo-Antia, Antonio Gazol, Manuel Pizarro, Ester González de Andrés, Cristina Valeriano, Álvaro Rubio Cuadrado, Juan Carlos Linares and Jesús Julio Camarero
Remote Sens. 2024, 16(23), 4564; https://doi.org/10.3390/rs16234564 - 5 Dec 2024
Cited by 1 | Viewed by 922
Abstract
Forest health monitoring is crucial for sustainable management, especially with the challenges posed by climate warming. Remote sensing data provide vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), that are widely used in assessing forest health. [...] Read more.
Forest health monitoring is crucial for sustainable management, especially with the challenges posed by climate warming. Remote sensing data provide vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), that are widely used in assessing forest health. However, studies considering the validation of these data with field assessments of tree vigor are still scarce. To address this issue, we explored the relationships in declining (D) and non-declining (N) silver fir (Abies alba Mill.) stands from the Spanish Pyrenees between changes in canopy (a proxy of vigor), vegetation indices (NDVI, EVI) and climate variables. We compared trends in the NDVI and EVI for the period of 1984–2023 for D and N stands showing high and low crown defoliation levels, respectively. The EVI values allowed for the separation of stands according to their vigor earlier and more clearly than NDVI values, which did not show clear patterns throughout the time series. Significant negative correlations were found between the EVI and stand defoliation (r = −0.57) or mean radial growth (r = 0.81). Late-spring drought reduced the EVI. The EVI series reflected similar spatial patterns in terms of stand defoliation and tree growth, offering complementary information, along with the strengths of remote sensing with respect to its spatial and temporal coverage, for the early detection of forest dieback. This study also contributes to a better understanding of remote sensing indices, which is useful for forest health monitoring. Full article
(This article belongs to the Special Issue Assessment of Spatial and Temporal Patterns in Forest and Grassland Ecosystems Based on Spectral Metrics)
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