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Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF)

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 11292

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Special Issue Editors

Dr. Ali Nadir Arslan

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Guest Editor

Space and Earth Observation Centre, Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Interests: remote sensing methods and applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Katarzyna Dabrowska-Zielinska

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Guest Editor

Remote Sensing Centre, Institute of Geodesy and Cartography, 02-679 Warsaw, Poland
Interests: remote sensing; impact of climate change on environment; energy exchange between surface and atmosphere; remote sensing applications on wetlands
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Dr. Jose Manuel Álvarez-Martínez

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Guest Editor

Environmental Hydraulics Institute “IH Cantabria”, University of Cantabria, ES-39011 Santander, Spain
Interests: copernicus; GIS, habitat mapping; land use and cover change; landscape ecology; remote sensing; scenarios; spatial modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The land-use, land-use change, and forestry (LULUCF) sector is increasingly becoming an important contributor to the success of climate policies that aim for carbon neutrality, such as those outlined by the Paris Agreement. It represents the only in the GHG inventory sector where carbon dioxide can be emitted but also removed from the atmosphere through carbon sequestration. Once all options for mitigation of greenhouse gases have technically and economically been exhausted in other economic sectors, the LULUCF sector can continue to compensate for the remaining greenhouse gas emissions into the atmosphere. This means there is an increasing demand for reliable data on emissions and removals in the LULUCF sector. In addition, there is an increasing demand for using explicit geospatial information in the calculation of LULUCF emissions and removals in national greenhouse gas inventories. Expanded LULUCF regulation and progress in remote sensing techniques and methodologies have stimulated the development of monitoring methods and the cooperation of many research institutions with stakeholders such as policy makers and national inventory compilers.

Remote sensing is on its way to becoming one of the most relevant data sources for LULUCF monitoring. Remote sensing systems from different platforms serve as an important method and tool for stakeholders and institutions responsible for LULUCF reporting. The open data policy and supporting programs (e.g., Copernicus) have evoked implementation of the increased use of remote sensing in LULUCF. On the other hand, there remain many gaps in the conceptual and harmonized implementation of remote sensing-based methods and data in the LULUCF reporting.

This Special Issue aims to gather relevant research studies that use remote sensing techniques and data in LULUCF monitoring and that apply these data in national greenhouse gas inventories. Authors are invited to submit papers that address new and state-of-the-art remote sensing methods and present novel and new approaches, in addition to general contributions that present applications in support of the improvement and quality control of national-level LULUCF emission inventories. Papers that deal with LULUCF reporting requirements and stakeholder needs in relation to the international and national implementation frameworks of LULUCF and that check the reliability of such data are especially welcome. Papers that deal with subnational- or global-level monitoring, reporting, and verification are welcome, provided that upscaling or downscaling methodologies in relation to national LULUCF emission inventories comprise a significant element of the presented study.

Dr. Ali Nadir Arslan
Prof. Dr. Katarzyna Dabrowska-Zielinska
Dr. Jose Manuel Álvarez-Martínez
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

land use, land-use change, and forestry (LULUCF)
GHG inventories
state-of-the-art LULUCF monitoring and reporting and perspectives
remote sensing in LULUCF monitoring and reporting and verification
remote sensing-based applications and databases for LULUCF stakeholders
national land-use/land-cover monitoring
classification and detection of changing land use/land cover
data fusion and combination for LULUCF
synergy of remote sensing and modeling techniques of carbon stocks
copernicus program

Published Papers (3 papers)

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Research

23 pages, 2450 KiB

Open AccessArticle

Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions

by Maria K. Tenkanen, Aki Tsuruta, Vilna Tyystjärvi, Markus Törmä, Iida Autio, Markus Haakana, Tarja Tuomainen, Antti Leppänen, Tiina Markkanen, Maarit Raivonen, Sini Niinistö, Ali Nadir Arslan and Tuula Aalto

Remote Sens. 2024, 16(1), 124; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010124 - 27 Dec 2023

Viewed by 722

Abstract

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector [...] Read more.

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), remain challenging. We estimated LULUCF-related CH

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emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH

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emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH

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emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH

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. Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH

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emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH

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emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF))

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20 pages, 16962 KiB

Open AccessArticle

Spatiotemporal Characteristics and Driving Factors of Land-Cover Change in the Heilongjiang (Amur) River Basin

by Shuzhen Jia and Yaping Yang

Remote Sens. 2023, 15(15), 3730; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15153730 - 26 Jul 2023

Cited by 2 | Viewed by 1135

Abstract

Monitoring land-use and land-cover change (LUCC) is extremely important in the sustainable development and management of terrestrial ecosystems. Taking the Heilongjiang (Amur) River Basin as the study area, this study aimed to identify the spatiotemporal characteristics of land cover at the entire basin and at the country levels of the three countries using the land-use change index, frequency statistics and land-cover change degree. The results showed that: (1) The LULC types were mainly forest land and grassland (accounting for nearly 83% in total) from 2001 to 2019. The main land-cover types in China, Russia and Mongolia were forest land, forest land and grassland, respectively. (2) The area of urban land, cropland and wetland increased significantly from 2001 to 2019, while the area of forest land and bare land decreased during this time. In general, the ecological environment has greatly improved over the last 19 years, although the relevant ecological restoration still needs to be further implemented. The findings can provide a scientific basis for ecological protection and sustainable development in the Heilongjiang (Amur) River Basin. The approaches here are also applicable to land-cover change research in other similar regions. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF))

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26 pages, 7734 KiB

Open AccessArticle

Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data—A Case Study of Czechia

by Jan Svoboda, Přemysl Štych, Josef Laštovička, Daniel Paluba and Natalia Kobliuk

Remote Sens. 2022, 14(5), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051189 - 28 Feb 2022

Cited by 25 | Viewed by 6851

Abstract

Land use, land-use change and forestry (LULUCF) is a greenhouse gas inventory sector that evaluates greenhouse gas changes in the atmosphere from land use and land-use change. This study focuses on the development of a Sentinel-2 data classification according to the LULUCF requirements on the cloud-based platform Google Earth Engine (GEE). The methods are tested in selected larger territorial regions (two Czech NUTS 2 units) using data collected in 2018. The Random Forest method was used for classification. In terms of classification accuracy, a combination of these parameters was tested: The Number of Trees (NT), the Variables per Split (VPS) and the Bag Fraction (BF). A total of 450 combinations of different parameters were tested. The highest accuracy classification with an overall accuracy = 89.1% and Cohen’s Kappa = 0.84 had the following combination: NT = 150, VPS = 3 and BF = 0.1. For classification purposes, a mosaic was created using the median method. The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution. Altitude values derived from SRTM and NDVI variance values were also included in the classification. These added bands were the most significant in terms of Gini importance. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF))

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