Special Issue "Forest Monitoring Systems and Assessments at Multiple Scales"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Prof. Dr. Maria José Vasconcelos
E-Mail Website
Guest Editor
Forest Research Centre, School of Agriculture, The University of Lisbon, Lisbon, Portugal
Interests: LULUCF; Earth observation; climate change; international policy; developing countries

Special Issue Information

Dear Colleagues,

Forests are among the world’s greatest assets. Their role in combating climate change and maintaining long-term environmental and social sustainability, from local to global scales, is well-established. Nevertheless, while monitoring for the wise management of forests is a major concern, obtaining adequate information and data over large extents and time periods can be expensive and challenging.

The attention given to the improvement of forest monitoring systems has sharply increased in recent years. For example, several organizations, also driven by the needs of international policies (e.g., REDD+), have promoted the development of tools for the collection, harmonization, and analysis of distributed forest observations (e.g., FAO FRA and Open-Foris). However, issues related to multi-source data integration, accessibility, and (re)usability, as well as intellectual and legal rights, still significantly hinder much-needed developments. Therefore, there is wide opportunity and need for the development of new data handling and analysis approaches that are capable of harnessing technological advances.

New measurement devices, such as proxy-remote sensors, and new data storage/processing capacities, such as online platforms, now allow the collection and manipulation of unprecedented volumes of forest observations and multi-temporal measurements. This data can result from a range of in-situ, to landscape, to regional or continental level studies, and can be focused on one or more knowledge domains and application realms. This multiplicity of sources, scales, and data types, as well as the pressing need for advancing knowledge supporting modelling and sustainability decisions, present relevant and exciting research challenges.  

We invite articles addressing advances in data and methods for Forest Monitoring Systems and Assessments at Multiple Scales. The focus is on the advancement of capacities for producing accurate and consistent multi-temporal and multi-scale forest information, be it in data collection, archiving, processing, management, and distribution; or in methodological developments for analysis and modelling. The main objective of this Special Issue is to foster access and promote the (re)usability of data sets and methods in science, thus adding value to information gathering and analysis efforts, promoting interchanges among scholar and entrepreneurial initiatives, and gaining cost-effectiveness in the advancement of knowledge for forest monitoring.

The list of subjects includes, but is not limited to:

Data and Methods for Forest Monitoring Systems

  • Data collection, including proxy-remote sensing and other devices (e.g., LiDAR, unmanned aerial vehicles and drones)
  • Earth observation systems and networks
  • Point cloud data
  • Multi-source and multi-temporal forest data integration methodologies and harmonization of forest measurements across scales
  • Data preprocessing, data analysis, data fusion techniques
  • Big Data processing and cloud computing
  • Data-driven techniques, including statistics and artificial intelligence methods
  • Data mining and machine learning methods
  • Parallel and distributed data analysis
  • Modelling and simulation
  • Image processing
  • Time series analysis
  • Crowd-sourcing
  • Social networks and social media (e.g., Twitter, Instagram)
  • Open source, open access, and open government data

Topics Relevant for Forest Monitoring Systems

  • Forest ecosystems characteristics, functioning, and health status at local and regional/biome levels
  • Forests in the stabilization of soils and watersheds
  • Forest restoration, forest conservation, and terrestrial habitats
  • Climate change, mitigation, and adaptation
  • Coastal ecosystems and habitats
  • Natural hazards and disasters related to forests, namely the monitoring of biomass burning and the prediction of forest fires
  • Biodiversity, farming, and forestry
  • Sustainable forest management and integrated landscape approaches
  • Socioeconomic models for improving forest uses, management, and benefits
  • Valuation of forest ecosystem services
  • Environment and forest ecology as contributions to the resilience of communities
  • Forests’ support of agriculture and human well-being

Prof. Dr. Maria José Vasconcelos
Guest Editor

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 papers will be 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. Data is an international peer-reviewed open access quarterly 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 1000 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.

Published Papers (3 papers)

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Open AccessArticle
Handling Data Gaps in Reported Field Measurements of Short Rotation Forestry
Data 2019, 4(4), 132; https://doi.org/10.3390/data4040132 - 25 Sep 2019
Abstract
Filling missing data in forest research is paramount for the analysis of primary data, forest statistics, land use strategies, as well as for the calibration/validation of forest growth models. Consequently, our main objective was to investigate several methods of filling missing data under [...] Read more.
Filling missing data in forest research is paramount for the analysis of primary data, forest statistics, land use strategies, as well as for the calibration/validation of forest growth models. Consequently, our main objective was to investigate several methods of filling missing data under a reduced sample size. From a complete dataset containing yearly first-rotation tree growth measurements over a period of eight years, we gradually retrieved two and then four years of measurements, hence operating on 72% and 43% of the original data. Secondly, 15 statistical models, five forest growth functions, and one biophysical, process-oriented, tree growth model were employed for filling these data gap representations accounting for 72% and 43% of the available data. Several models belonging to (i) regression analysis, (ii) statistical imputation, (iii) forest growth functions, and (iv) tree growth models were applied in order to retrieve information about the trees from existing yearly measurements. Subsequently, the findings of this study could lead to finding a handy tool for both researchers and practitioners dealing with incomplete datasets. Moreover, we underline the paramount demand for far-sighted, long-term research projects for the expansion and maintenance of a short rotation forestry (SRF) repository. Full article
(This article belongs to the Special Issue Forest Monitoring Systems and Assessments at Multiple Scales)
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Towards the Fulfillment of a Knowledge Gap: Wood Densities for Species of the Subtropical Atlantic Forest
Data 2019, 4(3), 104; https://doi.org/10.3390/data4030104 - 20 Jul 2019
Abstract
Wood density ( ρ ) is a trait involved in forest biomass estimates, forest ecology, prediction of stand stability, wood science, and engineering. Regardless of its importance, data on ρ are scarce for a substantial number of species of the vast Atlantic Forest [...] Read more.
Wood density ( ρ ) is a trait involved in forest biomass estimates, forest ecology, prediction of stand stability, wood science, and engineering. Regardless of its importance, data on ρ are scarce for a substantial number of species of the vast Atlantic Forest phytogeographic domain. Given that, the present paper describes a dataset composed of three data tables: (i) determinations of ρ (kg m−3) for 153 species growing in three forest types within the subtropical Atlantic Forest, based on wood samples collected throughout the state of Santa Catarina, southern Brazil; (ii) a list of 719 tree/shrub species observed by a state-level forest inventory and a ρ value assigned to each one of them based on local determinations and on a global database; (iii) the means and standard deviations of ρ for 477 permanent sample plots located in the subtropical Atlantic Forest, covering ∼95,000 km2. The mean ρ over the 153 sampled species is 538.6 kg m−3 (standard deviation = 120.5 kg m−3), and the mean ρ per sample plot, considering the three forest types, is 525.0 kg m−3 (standard error = 1.8 kg m−3). The described dataset has potential to underpin studies on forest biomass, forest ecology, alternative uses of timber resources, as well as to enlarge the coverage of global datasets. Full article
(This article belongs to the Special Issue Forest Monitoring Systems and Assessments at Multiple Scales)
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Stem-Maps of Forest Restoration Cuttings in Pinus ponderosa-Dominated Forests in the Interior West, USA
Data 2019, 4(2), 68; https://doi.org/10.3390/data4020068 - 14 May 2019
Cited by 1
Abstract
Stem-maps, maps of tree locations with optional associated measurements, are increasingly being used for ecological study in forest and plant sciences. Analyses of stem-map data have led to greater scientific understanding and improved forest management. However, availability of these data for reuse remains [...] Read more.
Stem-maps, maps of tree locations with optional associated measurements, are increasingly being used for ecological study in forest and plant sciences. Analyses of stem-map data have led to greater scientific understanding and improved forest management. However, availability of these data for reuse remains limited. We present a description of eight 4-ha stem-maps used in four prior research studies. These stem-maps contain locations and associated measurements of residual trees and stumps measured after forest restoration cuttings in Colorado, Arizona, and New Mexico. Data are published in two file formats to facilitate reuse. Full article
(This article belongs to the Special Issue Forest Monitoring Systems and Assessments at Multiple Scales)
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