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Special Issue "Accelerating REDD+ Initiatives in Africa Using Remote Sensing"

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

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 3835

Special Issue Editors

Dr. Inge Jonckheere
E-Mail Website
Guest Editor
Forestry Department, Food and Agriculture Organization (FAO) of the United Nations (UN), 00153 Rome, Italy
Interests: remote sensing and earth observation; REDD+; climate change implications on forest carbon dynamics; forest degradation and monitoring
Special Issues, Collections and Topics in MDPI journals
Midhun (Mikey) Mohan
E-Mail Website
Guest Editor Assistant
Department of Geography, University of California, Berkeley, CA, USA
Interests: drones; LiDAR; satellite remote sensing; tropical forests; forest management and modeling; individual tree detection; forest carbon science; machine learning; biodiversity conservation
Dr. Rene Ngamabou Siwe
E-Mail Website
Guest Editor
U.S. Forest Service (USFS) Central Africa Program, Republic of Congo
Interests: Reducing Emissions from Deforestation and forest Degradation (REDD+) measurement; reporting and verification (MRV) system; Remote sensing; Forest monitoring; Greenhouse gas (GHG) emissions from land use, land-use change, and the forestry sector
Dr. Joseph Intsiful
E-Mail Website
Guest Editor
Green Climate Fund, Incheon, Korea
Interests: state-of-the-art climate research in Africa; land use land cover change; early warning systems; vegetation models for climate change impacts; high-performance computing and web technologies; climate finance; climate services
Ms. Aurélie C. Shapiro
E-Mail Website
Guest Editor Assistant
WWF Germany, Space+Science, Germany
Interests: REDD+; Earth observation for conservation; Low-cost methods for tracking forest degradation; Remote sensing for forest monitoring, forest biomass and disturbance; Big data processing; Tropical forest research; Remote sensing with drones; Data fusion approaches for mangrove mapping

Special Issue Information

Dear Colleagues,

Forest degradation and deforestation contributes to more than 12% of global greenhouse gas emissions and thereby aggravates the issue of climate change. To address this, United Nations Framework Convention on Climate Change (UNFCCC) developed an international framework called REDD+ (reducing emissions from deforestation and forest degradation, conservation of existing forest carbon stocks, sustainable forest management and enhancement of forest carbon stocks) which encourages developing countries to reduce carbon emissions from forested lands, especially from tropical forested areas, by offering financial incentives based on targets achieved.  Over the years, there has been a lot of developments to REDD+ – which include applications of remote sensing technology, in particular long term satellite imagery – however, questions still remain on whether REDD+ will bring transformational changes for combating climate change in the long run while upholding qualities of effectiveness, efficiency and equity.

Africa has over one-fifth of its area comprised of forests and houses one third of the world’s tropical forests, which underlines its importance in combating climate change through carbon sequestration. However, due to commercial logging, subsidence agriculture, collection of fuel wood, construction of dams, roads, etc. and cattle ranching, African forests are prone to extensive forest fragmentation, deforestation and degradation, which negatively affects their capability to absorb carbon. This further translates into the quality of life of local people and biodiversity as well. Therefore, the exigencies of a secure and sustainable economy demand probing into globally extrapolatable and easily accessible technologies such as remote sensing.

For addressing existing challenges and exploiting potential opportunities for accelerating REDD+ initiatives, advanced remote sensing strategies – such as Unmanned Aerial Vehicles, Multispectral, hyperspectral, LiDAR, GEDI, SAR, etc. – and sensor fusion approaches need to be further promoted. This special issue emphasizes on regional to national level case studies in Africa that apply Remote Sensing techniques for supporting REDD+ initiatives and we invite you to submit review articles as well as original articles that address one or more of the following topics:

  • Low-cost remote sensing technologies for monitoring forest growth, deforestation and degradation
  • Forest response to disturbances resulting from human activities (such as logging, urbanization), natural disasters (such as cyclones, forest fires) and climate change anomalies (such as reduced precipitation, increased droughts)
  • REDD+ performance evaluation studies (this can be remote sensing based research that intersects areas of environmental policy, biodiversity conservation, land use land cover change, climate finance, forest economics, political sciences, public administration, international relations, etc.)
  • Climate change mitigation strategies and carbon markets
  • Aboveground biomass estimation and spatial modeling
  • Feasibility of data fusion strategies (for example: combining SAR, GEDI, Landsat and/or UAVs) for improving forest mapping
  • Applications of machine learning and artificial intelligence techniques for forest resource assessment

Dr. Inge Jonckheere
Mr. Midhun (Mikey) Mohan
Dr. Rene Ngamabou Siwe
Dr. Joseph Intsiful
Ms. Aurélie C. Shapiro
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 2500 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

  • Forest Monitoring
  • Deforestation
  • Forest Degradation
  • Aboveground Biomass
  • Carbon Emission
  • UNFCCC REDD+
  • UN Sustainable Development Goals
  • Unmanned Aerial Vehicles UAVs
  • Light Detection and Ranging LiDAR
  • Multispectral Data
  • Satellite Remote Sensing
  • Data Fusion
  • Machine Learning

Published Papers (2 papers)

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Research

Article
Assessing the Predictive Power of Democratic Republic of Congo’s National Spaceborne Biomass Map over Independent Test Samples
Remote Sens. 2022, 14(16), 4126; https://doi.org/10.3390/rs14164126 - 22 Aug 2022
Viewed by 610
Abstract
Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the [...] Read more.
Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the accuracy of maps and of data to perform local validation. Here, we use the first national forest inventory (NFI) data of the Democratic Republic of Congo to perform an independent assessment of the country’s latest national spaceborne carbon stocks map. We compared plot-to-plot variations and areal estimates of forest aboveground biomass (AGB) derived from NFI data and from the map across jurisdictional and ecological domains. Across all plots, map predictions were nearly unbiased and captured c. 60% of the variation in NFI plots AGB. Map performance was not uniform along the AGB gradient, and saturated around c. 290 Mg ha−1, increasingly underestimating forest AGB above this threshold. Splitting NFI plots by land cover types, we found map predictions unbiased in the dominant terra firme Humid forest class, while plot-to-plot variations were poorly captured (R2 of c. 0.33, or c. 0.20 after excluding disturbed plots). In contrast, map predictions underestimated AGB by c. 33% in the small AGB woodland savanna class but captured a much greater share of plot-to-plot AGB variation (R2 of c. 0.41, or 0.58 after excluding disturbed plots). Areal estimates from the map and NFI data depicted a similar trend with a slightly smaller (but statistically indiscernible) mean AGB from the map across the entire study area (i.e., 252.7 vs. 280.6 Mg ha−1), owing to the underestimation of mean AGB in the woodland savanna domain (31.8 vs. 57.3 Mg ha−1), which was broadly consistent with the results obtained at the provincial level. This study provides insights and outlooks for country-wide AGB mapping efforts in the tropics and the computation of emission factors in Democratic Republic of Congo for carbon monitoring initiatives. Full article
(This article belongs to the Special Issue Accelerating REDD+ Initiatives in Africa Using Remote Sensing)
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Article
Mapping Canopy Cover in African Dry Forests from the Combined Use of Sentinel-1 and Sentinel-2 Data: Application to Tanzania for the Year 2018
Remote Sens. 2022, 14(6), 1522; https://doi.org/10.3390/rs14061522 - 21 Mar 2022
Cited by 1 | Viewed by 2059
Abstract
High-resolution Earth observation data is routinely used to monitor tropical forests. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. In this study, we demonstrate the potential of combining Sentinel-1 (S1) SAR and Sentinel-2 [...] Read more.
High-resolution Earth observation data is routinely used to monitor tropical forests. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. In this study, we demonstrate the potential of combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical sensors in order to map the tree cover in East Africa. The overall methodology consists of: (i) the generation of S1 and S2 layers, (ii) the collection of an expert-based training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows, together with different approaches to incorporating the spatial information to train the classifiers, are explored. Two types of maps were derived from these mapping approaches over Tanzania: (i) binary tree cover–no tree cover (TC/NTC) maps, and (ii) maps of the canopy cover classes. The overall accuracy of the maps is >95% for the TC/NTC maps and >85% for the forest types maps. Considering the neighboring pixels for training the classification improved the mapping of the areas that are covered by 1–10% tree cover. The study relied on open data and publicly available tools and can be integrated into national monitoring systems. Full article
(This article belongs to the Special Issue Accelerating REDD+ Initiatives in Africa Using Remote Sensing)
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