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Forest Degradation Monitoring

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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 (11 September 2020) | Viewed by 36298

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Special Issue Editors

Dr. Alexander Hernandez

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Guest Editor

Researcher, Utah State University, Logan, UT, USA
Interests: land-cover change; ecosystem services; spatiotemporal ecological modeling; hydrology; climate change; remote sensing

Dr. Rene Zamora-Cristales

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Guest Editor

World Resources Institute, the USA
Interests: spatial economics; climate change; optimization; GIS programming; forest planning
Special Issues, Collections and Topics in MDPI journals

Mr. Abner Jimenez

E-Mail Website
Guest Editor

REDD-CCAD/GIZ Program, Germany
Interests: forest monitoring; landscape restoration monitoring; ecosystem services; MRV; REDD+; remote sensing; spatial analytics

Special Issue Information

Dear Colleagues,

Mapping and monitoring forest degradation across the world’s biomes is an exciting field of remote sensing science and technology that aims at providing scientists, policymakers, and stakeholders with the pertinent information to understand the role of degradation in more complex global processes. Monitoring of forest degradation can also help to improve the decision-making process for better managing the world’s forests. Well-established and emerging remote-sensing-based monitoring techniques are contributing to unify, advance, and clarify the terminology around the concept of forest degradation, which is still subject to debate in the scientific arena. This debate focuses heavily on the limitations of remote sensing to adapt to the forest degradation definitions widely adopted in international forums. With the now widespread availability of long-term time series of satellite imagery and historical aerial photography, in conjunction with longstanding field observations and recently-acquired UAV measurements, there is unparalleled potential to develop monitoring applications of forest degradation in the tropics as well as temperate zones.

This is such an exciting moment for forest degradation scientists, and we would like to invite you to submit review articles as well as original articles of your most recent scientific findings covering one or more of the following topics:

Towards understanding different angles (i.e., some people focus on the economic value of the forest, while others focus on forest structure and species diversity) of forest degradation of the concept of forest degradation;
What metrics (directly derived from spectral information as well as those related to landscape fragmentation) are available for measuring and monitoring forest degradation, and what remote-sensing platforms are capable of providing one or several of those metrics;
The advantages of merging coarse, moderate, and fine spatial and temporal resolution imagery to map forest degradation;
Identification of cost-effective technologies to monitor forest degradation by country;
Contrasting the advantages and disadvantages of using widely-used long-term sampling platforms such as TimeSync, Collect Earth, etc. compared to more traditional wall-to-wall mapping;
Regional efforts that are testing the effectiveness of one or more remote-sensing-based forest degradation monitoring techniques that allow training models that can be used in more than one country;
The feasibility of using machine learning statistical techniques in mapping forest degradation;
Adaptive spatial monitoring frameworks to measure different degrees of forest degradation;
The feasibility of the use of artificial intelligence and optimization to recognize physical attributes of degradation in forests;
The relative effect of forest degradation on ecosystems services’ performance and sustainability;
Using LIDAR and UAV to map forest degradation based on vegetation’s structural parameters.

Dr. Alexander Hernandez
Dr. Rene Zamora-Cristales
Mr. Abner Jimenez
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

Forest degradation
Remote sensing
Species diversity
Landscape fragmentation
Forest economics
Machine learning
Spatial monitoring
Ecosystem services
Sentinel
Landsat
MODIS
Landscape change
Forest degradation metrics
Unmanned aerial vehicles UAV
Time series
Forest hydrology

Published Papers (7 papers)

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Research

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23 pages, 6198 KiB

Open AccessArticle

Integration of Geospatial Tools and Multi-source Geospatial Data to Evaluate the Tropical Forest Cover Change in Central America and Its Methodological Replicability in Brazil and the DRC

by Abner Jiménez, Alexander J. Hernández and Víctor M Rodríguez-Espinosa

Remote Sens. 2020, 12(17), 2705; https://doi.org/10.3390/rs12172705 - 21 Aug 2020

Cited by 2 | Viewed by 3291

Abstract

Satellite monitoring of forests plays a relevant role in the agendas of tropical countries, mainly in the framework of international negotiations to implement a mechanism that ensures a reduction in global CO₂ emissions from deforestation. An efficient way to approach this monitoring is to avoid duplication of efforts, generating products in a regional context that are subsequently adopted at the national level. In this effort, you should take advantage of the different data sources available by integrating geospatial tools and satellite image classification algorithms. In this research, a methodological framework was developed to generate cost-efficient national maps of forest cover and its dynamics for the countries of Central America, and its scalability and replicability was explored in the Democratic Republic of the Congo (DRC) and the State of Pará in Brazil. The maps were generated from Landsat images from the years 2000, 2012, and 2017. New geoprocessing elements have been incorporated into the digital classification procedures for satellite images, such as the automated extraction of training samples from secondary sources, the use of official national reference maps that respond to nationally adopted forest definitions, and automation of post-classification adjustments incorporating expert criteria. The applied regional approach offers advantages in terms of reducing costs and time, as well as improving the consistency and coherence of reports at different territorial levels (regional and national), reducing duplication of efforts and optimizing technical and financial resources. In Central America, the percentage of forest area decreased from 44% in 2000 to 38% in 2017. Average deforestation in the 2000–2012 period was 197,443 ha/year and that of 2012–2017 was 332,243 ha/year. Average deforestation for the complete period 2000–2017 was 264,843 ha/year. The tropical forests in both the State of Pará, Brazil, and the DRC have decreased over time. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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29 pages, 7817 KiB

Open AccessArticle

Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data

by Efraín Duarte, Juan A. Barrera, Francis Dube, Fabio Casco, Alexander J. Hernández and Erick Zagal

Remote Sens. 2020, 12(16), 2531; https://doi.org/10.3390/rs12162531 - 6 Aug 2020

Cited by 5 | Viewed by 5413

Abstract

Current estimates of CO₂ emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R² of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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23 pages, 6940 KiB

Open AccessArticle

A Machine Learning Approach to Detecting Pine Wilt Disease Using Airborne Spectral Imagery

by Marian-Daniel Iordache, Vasco Mantas, Elsa Baltazar, Klaas Pauly and Nicolas Lewyckyj

Remote Sens. 2020, 12(14), 2280; https://doi.org/10.3390/rs12142280 - 15 Jul 2020

Cited by 63 | Viewed by 5150

Abstract

Pine Wilt Disease is one of the most destructive pests affecting coniferous forests. After being infected by the harmful Bursaphelenchus xylophilus nematode, most trees die within one year. The complex spreading pattern of the disease and the tedious hard labor process of diagnosis involving field wood sampling followed by laboratory analysis call for alternative methods to detect and manage the infected areas. Remote sensing comes naturally into play owing to the possibility of covering relatively large areas and the ability to discriminate healthy from sick trees based on spectral characteristics. This paper presents the development of machine learning classification algorithms for the detection of Pine Wilt Disease in Pinus pinaster, performed in the framework of the European Commission’s Horizon 2020 project “Operational Forest Monitoring using Copernicus and UAV Hyperspectral Data” (FOCUS) in two provinces of central Portugal. Five flight campaigns have been carried out in two consecutive years in order to capture a multitemporal variation of disease distribution. Classification algorithms based on a Random Forest approach were separately designed for the acquired very-high-resolution multispectral and hyperspectral data, respectively. Both algorithms achieved overall accuracies higher than 0.91 in test data. Furthermore, our study shows that the early detection of decaying trees is feasible, even before symptoms are visible in the field. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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24 pages, 4342 KiB

Open AccessArticle

Monitoring Woody Cover Dynamics in Tropical Dry Forest Ecosystems Using Sentinel-2 Satellite Imagery

by Johanna Van Passel, Wanda De Keersmaecker and Ben Somers

Remote Sens. 2020, 12(8), 1276; https://doi.org/10.3390/rs12081276 - 17 Apr 2020

Cited by 16 | Viewed by 3770

Abstract

Dry forests in Sub-Saharan Africa are of critical importance for the livelihood of the local population given their strong dependence on forest products. Yet these forests are threatened due to rapid population growth and predicted changes in rainfall patterns. As such, large-scale woody cover monitoring of tropical dry forests is urgently required. Although promising, remote sensing-based estimation of woody cover in tropical dry forest ecosystems is challenging due to the heterogeneous woody and herbaceous vegetation structure and the large intra-annual variability in the vegetation due to the seasonal rainfall. To test the capability of Sentinel-2 satellite imagery for producing accurate woody cover estimations, two contrasting study sites in Ethiopia and Tanzania were used. The estimation accuracy of a linear regression model using the Normalised Difference Vegetation Index (NDVI), a Partial Least Squares Regression (PLSR), and a Random Forest regression model using both single-date and multi-temporal Sentinel-2 images were compared. Additionally, the robustness and site transferability of these methods were tested. Overall, the multi-temporal PLSR model achieved the most accurate and transferable estimations (R² = 0.70, RMSE = 4.12%). This model was then used to monitor the potential increase in woody coverage within several reforestation projects in the Degua Tembien district. In six of these projects, a significant increase in woody cover could be measured since the start of the project, which could be linked to their initial vegetation, location and shape. It can be concluded that a PLSR model combined with Sentinel-2 satellite imagery is capable of monitoring woody cover in these tropical dry forest regions, which can be used in support of reforestation efforts. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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17 pages, 14656 KiB

Open AccessArticle

Investigating the Impact of Land Parcelization on Forest Composition and Structure in Southeastern Ohio Using Multi-Source Remotely Sensed Data

by Xiaolin Zhu and Desheng Liu

Remote Sens. 2019, 11(19), 2195; https://doi.org/10.3390/rs11192195 - 20 Sep 2019

Cited by 3 | Viewed by 2973

Abstract

Forestland parcelization (i.e., a process by which large parcels of forestland ownership are divided into many small parcels) presents an increasing challenge to sustainable forest development in the United States. In Southeastern Ohio, forests also experienced intensive forestland parcelization, where the majority of forest owners own parcels smaller than 10 acres currently. To better understand the impact of forestland parcelization on forest development, this study employed multi-source remotely sensed data and land ownership data in Hocking County, Ohio to examine the relationship between forestland parcel size and forest attributes, including forest composition and structure. Our results show that private forestland parcels are generally smaller than public forestland (the average parcel sizes are 21.5 vs. 275.0 acres). Compared with private lands, public lands have higher values in all forest attributes, including forest coverage, abundance of oak-dominant stands, canopy height and aboveground biomass. A further investigation focusing on private forestland reveals that smaller parcels tend to have smaller forest coverage, less greenness, lower height and aboveground biomass, indicating that forests in smaller parcels may experience more human disturbances than larger parcels. The results also show that logarithmic models can well quantify the non-linear relationship between forest attributes and parcel size in the study area. Our study suggests that forestland parcelization indeed has negative effects on forest development, so it is very important to take appropriate measures to protect forests in small ownership parcels. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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18 pages, 5630 KiB

Open AccessArticle

Development of an Operational Algorithm for Automated Deforestation Mapping via the Bayesian Integration of Long-Term Optical and Microwave Satellite Data

by Hiroki Mizuochi, Masato Hayashi and Takeo Tadono

Remote Sens. 2019, 11(17), 2038; https://doi.org/10.3390/rs11172038 - 29 Aug 2019

Cited by 7 | Viewed by 3714

Abstract

The frequent fine-scale monitoring of deforestation using satellite sensors is important for the sustainable management of forests. Traditional optical satellite sensors suffer from cloud interruption, particularly in tropical regions, and recent active microwave sensors (i.e., synthetic aperture radar) demonstrate the difficulty in data interpretation owing to their inherent sensor noise and complicated backscatter features of forests. Although the sensor integration of optical and microwave sensors is of compelling research interest, particularly in the conduct of deforestation monitoring, this topic has not been widely studied. In this paper, we introduce an operational algorithm for automated deforestation mapping using long-term optical and L-band SAR data, including a simple time-series analysis of Landsat stacks and a multilayered neural network with Advanced Spaceborne Thermal Emission and Reflection Radiometer and Phased Array-type L-band Synthetic Aperture Radar-2, followed by sensor integration based on the Bayesian Updating of Land-Cover. We applied the algorithm over a deciduous tropical forest in Cambodia in 2003–2018 for validation, and the algorithm demonstrated better accuracy than existing approaches, which only depend on optical data or SAR data. Owing to the cloud penetration ability of SAR, observation gaps of optical data under cloudy conditions were filled, resulting in a prompter detection of deforestation even in the tropical rainy season. We also investigated the effect of posterior probability constraints in the Bayesian approach. The land-cover maps (forest/deforestation) created by the well-tuned Bayesian approach achieved 94.0% ± 4.5%, 80.0% ± 10.1%, and 96.4% ± 1.9% for the user’s accuracy, producer’s accuracy, and overall accuracy, respectively. In the future, small-scale commission errors in the resultant maps should be improved by using more sophisticated machine-learning approaches and considering the reforestation effects in the algorithm. The application of the algorithm to other landscapes with other sensor combinations is also desirable. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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Review

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24 pages, 1897 KiB

Open AccessReview

How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review

by Chloé Dupuis, Philippe Lejeune, Adrien Michez and Adeline Fayolle

Remote Sens. 2020, 12(7), 1087; https://doi.org/10.3390/rs12071087 - 28 Mar 2020

Cited by 37 | Viewed by 10140

Abstract

In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth’s species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. Geographical repartition, spatial extent and temporal evolution were analyzed. Indicators of compositional, structural and regeneration criteria were noted as well as remote sensing indices and metrics used. Tropical moist forest degradation is not extensively studied especially in the Congo basin and in southeast Asia. Forest structure (i.e., canopy gaps, fragmentation and biomass) is the most widely and easily measured criteria with remote sensing, while composition and regeneration are more difficult to characterize. Mixing LiDAR/Radar and optical data shows good potential as well as very high-resolution satellite data. The awaited GEDI and BIOMASS satellites data will fill the actual gap to a large extent and provide accurate structural information. LiDAR and unmanned aerial vehicles (UAVs) form a good bridge between field and satellite data. While the performance of the LiDAR is no longer to be demonstrated, particular attention should be brought to the UAV that shows great potential and could be more easily used by local communities and stakeholders. Full article

(This article belongs to the Special Issue Forest Degradation Monitoring)

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