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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 33297

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Special Issue Editors

Dr. Frédéric Achard

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Guest Editor

European Commission Joint Research Centre, Italy
Interests: Earth observation techniques for the monitoring of tropical forests; implications of forest cover changes on the global carbon budget

Prof. Dr. Martin Herold

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Guest Editor

Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
Interests: large area land and forest monitoring; monitoring and reporting for UNFCCC and Sustainable Development Goals
Special Issues, Collections and Topics in MDPI journals

Dr. Christophe Sannier

E-Mail Website
Guest Editor

SIRS (Systèmes d’Information à Référence Spatiale), France
Interests: large area land and forest monitoring; change detection methods; forest cover area and area change estimates; EO based methods for characterising forest types

Dr. Sarah Carter

E-Mail Website
Guest Editor

Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
Interests: REDD+; Sustainable Development Goals; Tropical Forests and Land Use Change; Earth Observations

Special Issue Information

Dear Colleagues,

Requirements for REDD+ related forest monitoring are evolving and consolidating in terms of the types of information required, monitoring frequency, accuracy and transparency. While several performance-based REDD+ frameworks are moving forward, different methods, tools and frameworks for monitoring and reporting are maturing for use by national and other REDD+ stakeholders.
This Special Issue will focus on national to local case studies, covering tropical humid and dry forest domains, which focus on different monitoring targets (area change, forest degradation, carbon stocks, burned area, forest types and biodiversity), using novel methods for the analysis of satellite data, and showcasing how they evolve from research to operational use in country contexts. The use of open methods and free data (such as Copernicus data) is preferred and should be explored. In addition, general contributions that discuss reporting requirements and needs related to international, national and local implementation frameworks are welcome. Early warning forest monitoring systems, monitoring of deforestation drivers, and other concepts that feed into policymaking and the assessment of policy outcomes can also be included.

Dr. Frédéric Achard
Prof. Dr. Martin Herold
Dr. Christophe Sannier
Dr. Sarah Carter
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

UNFCCC REDD+
Tropical forests
National forest monitoring
Deforestation
Forest degradation
Forest regrowths
Sentinel
Landsat
Activity data
Emission factors

Published Papers (7 papers)

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Research

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22 pages, 12299 KiB

Open AccessArticle

Estimating Forest Stand Height in Savannakhet, Lao PDR Using InSAR and Backscatter Methods with L-Band SAR Data

by Helen Blue Parache, Timothy Mayer, Kelsey E. Herndon, Africa Ixmucane Flores-Anderson, Yang Lei, Quyen Nguyen, Thannarot Kunlamai and Robert Griffin

Remote Sens. 2021, 13(22), 4516; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224516 - 10 Nov 2021

Cited by 2 | Viewed by 2596

Abstract

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R² of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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8 pages, 1833 KiB

Open AccessCommunication

Toward a More Representative Monitoring of Land-Use and Land-Cover Dynamics: The Use of a Sample-Based Assessment through Augmented Visual Interpretation Using Open Foris Collect Earth

by Danae Maniatis, Daniel Dionisio, Laura Guarnieri, Giulio Marchi, Danilo Mollicone, Carmen Morales and Alfonso Sanchez-Paus Díaz

Remote Sens. 2021, 13(21), 4197; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214197 - 20 Oct 2021

Cited by 5 | Viewed by 2476

Abstract

High-quality data for REDD+ monitoring, measurement, and reporting are critical for the continued success of REDD+ implementation and Results-Based Payments. Collect Earth is a free, user-friendly, and open-source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). The tool allows countries to undertake land monitoring easily and rapidly through a sample-based approach and generate Activity Data (data on the magnitude of human activity resulting in emissions or removals during a given period of time) through augmented visual interpretation with low costs. Under the Paris Agreement, countries will have to update the greenhouse gas inventories that they report to the United Nations Framework Convention on Climate Change every two years through the Biennial Update Reports. One of the important benefits of using sample-based approaches such as the one proposed by Collect Earth is the possibility to achieve a detailed classification of the land-use sub-categories with high accuracy of the estimates for land-use changes occurring since 2000. However, most guidance documents developed for capacity building in developing countries for REDD+ reporting only advocate developing land-cover and land-cover change maps using remote sensing. As several countries already use Collect Earth and the sample-based methodology to report on REDD+, this commentary advocates for a more representative approach and a methodological debate on the potential of sample-based approaches using remote sensing, and when possible combined with ground truthing, to estimate Activity Data for REDD+ and countries’ greenhouse gas inventories for the Agriculture, Forestry and Other Land Use sector in general. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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29 pages, 9419 KiB

Open AccessArticle

Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar

by Gizelle Cota, Vasit Sagan, Maitiniyazi Maimaitijiang and Karen Freeman

Remote Sens. 2021, 13(17), 3495; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173495 - 03 Sep 2021

Cited by 3 | Viewed by 3095

Abstract

Documenting the impacts of climate change and human activities on tropical rainforests is imperative for protecting tropical biodiversity and for better implementation of REDD+ and UN Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning (ML) have provided improved mapping of fine-scale changes in the tropics. However, approaches so far focused on feature extraction or the extensive tuning of ML parameters, hindering the potential of ML in forest conservation mapping by not using textural information, which is found to be powerful for many applications. Additionally, the contribution of shortwave infrared (SWIR) bands in forest cover mapping is unknown. The objectives were to develop end-to-end mapping of the tropical forest using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and to evaluate human impact on the environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. FCNN (U-Net) using spatial/textural information was implemented and compared with feature-fed pixel-based methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). Results show that the FCNN model outperformed other models with an accuracy of 90.9%, while SVM, RF, and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. When SWIR bands were excluded from the input data, FCNN provided superior performance over other methods with a 1.87% decrease in accuracy, while the accuracies of other models—SVM, RF, and DNN—decreased by 5.42%, 3.18%, and 8.55%, respectively. Spatial–temporal analysis showed a 0.7% increase in Evergreen Forest within the BNR and a 32% increase in tree cover within residential areas likely due to forest regeneration and conservation efforts. Other effects of conservation efforts are also discussed. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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16 pages, 3356 KiB

Open AccessEditor’s ChoiceArticle

Tropical Forest Monitoring: Challenges and Recent Progress in Research

by Jennifer Murrins Misiukas, Sarah Carter and Martin Herold

Remote Sens. 2021, 13(12), 2252; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122252 - 09 Jun 2021

Cited by 5 | Viewed by 7907

Abstract

Forest monitoring is the recurrent measurement of forest parameters to identify changes over time. There is currently a rising demand for monitoring, as well as growing capacities for it. This study identifies recent research on tropical forest monitoring using a systematic literature review. The research explores whether the location of these studies is in the countries where monitoring is most needed. Three characteristics, biophysical conditions, anthropogenic influences, and forest monitoring capacities were used to identify the need for tropical forest monitoring advances. This provided an understanding as to where research should be targeted in the future. The findings revealed that research appears to be concentrated in countries with strong forest monitoring capabilities that face challenges due to biophysical and anthropogenic influences (e.g., logistically difficult ground sampling and rapid pace of forest change, respectively). Consequently, future research could be targeted in countries with lower capacities and higher needs, in order to improve forest monitoring and conservation. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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20 pages, 4144 KiB

Open AccessArticle

An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting

by Nikolaos Galiatsatos, Daniel N.M. Donoghue, Pete Watt, Pradeepa Bholanath, Jeffrey Pickering, Matthew C. Hansen and Abu R.J. Mahmood

Remote Sens. 2020, 12(11), 1790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111790 - 02 Jun 2020

Cited by 36 | Viewed by 6899

Abstract

Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001–2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R² = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R² = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001–2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer’s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer’s Accuracy. A year-by-year analysis of change from 2001–2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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16 pages, 2298 KiB

Open AccessArticle

Drivers of Forest Loss in a Megadiverse Hotspot on the Pacific Coast of Colombia

by Jesús A. Anaya, Víctor H. Gutiérrez-Vélez, Ana M. Pacheco-Pascagaza, Sebastián Palomino-Ángel, Natasha Han and Heiko Balzter

Remote Sens. 2020, 12(8), 1235; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081235 - 13 Apr 2020

Cited by 17 | Viewed by 4619

Abstract

Tropical forests are disappearing at unprecedented rates, but the drivers behind this transformation are not always clear. This limits the decision-making processes and the effectiveness of forest management policies. In this paper, we address the extent and drivers of deforestation of the Choco biodiversity hotspot, which has not received much scientific attention despite its high levels of plant diversity and endemism. The climate is characterized by persistent cloud cover which is a challenge for land cover mapping from optical satellite imagery. By using Google Earth Engine to select pixels with minimal cloud content and applying a random forest classifier to Landsat and Sentinel data, we produced a wall-to-wall land cover map, enabling a diagnosis of the status and drivers of forest loss in the region. Analyses of these new maps together with information from illicit crops and alluvial mining uncovered the pressure over intact forests. According to Global Forest Change (GFC) data, 2324 km² were deforested in this area from 2001 to 2018, reaching a maximum in 2016 and 2017. We found that 68% of the area is covered by broadleaf forests (67,473 km²) and 15% by shrublands (14,483 km²), the latter with enormous potential to promote restoration projects. This paper provides a new insight into the conservation of this exceptional forest with a discussion of the drivers of forest loss, where illicit crops and alluvial mining were found to be responsible for 60% of forest loss. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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13 pages, 2650 KiB

Open AccessTechnical Note

Capacity Development for Use of Remote Sensing for REDD+ MRV Using Online and Offline Activities: Impacts and Lessons Learned

by Sarah Carter, Martin Herold, Inge G. C. Jonckheere, Andres B. Espejo, Carly Green and Sylvia Wilson

Remote Sens. 2021, 13(11), 2172; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112172 - 01 Jun 2021

Cited by 3 | Viewed by 3340

Abstract

Four workshops and a webinar series were organized, with the aim of building capacity in countries to use Earth Observation Remote Sensing data to monitor forest cover changes and measure emissions reductions for REDD+ results-based payments. Webinars and workshops covered a variety of relevant tools and methods. The initiative was collaboratively organised by a number of Global Forest Observations Initiative (GFOI) partner institutions with funding from the World Bank’s Forest Carbon Partnership Facility (FCPF). The collaborative approach with multiple partners proved to be efficient and was able to reach a large audience, particularly in the case of the webinars. However, the impact in terms of use of tools and training of others after the events was higher for the workshops. In addition, engagement with experts was higher from workshop participants. In terms of efficiency, webinars are significantly cheaper to organize. A hybrid approach might be considered for future initiatives; and, this study of the effectiveness of both in-person and online capacity building can guide the development of future initiatives, something that is particularly pertinent in a COVID-19 era. Full article

(This article belongs to the Special Issue National REDD+ Monitoring and Reporting)

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