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Applications of Satellite Data for Forest Monitoring and Management

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 14330

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Special Issue Editor

Prof. Dr. Svein Solberg

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Guest Editor

NIBIO (Norwegian Institute of Bioeconomy Research), Ås, Norway
Interests: drought stress; storm damage; Norway spruce; forest disturbance; forest health; deforestation; forest; monitoring; remote sensing

Special Issue Information

Dear Colleagues,

Forests are under increasing pressure and increasing demand. First, there is increasing climatic stress and damage to forests following climate change, for example due to drought stress. Secondly, pests and diseases are spreading to new continents, where trees are lacking specific resistance to them, for example the fungal diseases causing Dutch elm disease and ash dieback. Finally, forest degradation and deforestation continues at an alarming rate in the tropics. At the same time, satellite technologies continues to develop, providing novel tools for monitoring disturbance. Monitoring is an essential input for forest management actions to prevent or reduce further development of disturbance. In addition, foresters apparently spend less and less time outdoors, and need data on their computers for decision making.

This Special Issue of Forests is focused on satellite applications, both promising and operational methods. We welcome studies that include disturbance control and monitoring, identifying forest areas in need for silvicultural treatment, height growth monitoring and site index estimation.

Prof. Dr. Svein Solberg
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 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. Forests is an international peer-reviewed open access monthly 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 2600 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 disturbance
forest health
deforestation
forest
monitoring
remote sensing
satellite

Published Papers (4 papers)

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Research

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16 pages, 5295 KiB

Open AccessArticle

Multi-Model Approaches to the Spatialization of Tree Vitality Surveys: Constructing a National Tree Vitality Map

by Yuyoung Choi, Hye In Chung, Chul-Hee Lim, Jun-Hee Lee, Won Il Choi and Seong Woo Jeon

Forests 2021, 12(8), 1009; https://0-doi-org.brum.beds.ac.uk/10.3390/f12081009 - 29 Jul 2021

Cited by 3 | Viewed by 2098

Abstract

It is essential to maintain the health of forests so that they are protected against a diverse range of stressors and show improved resilience. An area-based forest health map is required for efficient forest management on a national scale however, most national forest inventories are based on in-situ observations. This study examined methodologies to establish an area-based map on tree vitality grade using field survey data, particularly that containing information on several trees at one point. The forest health monitoring dataset of the Republic of Korea was used in combination with 37 satellite-based environmental predictors. Four methods were considered: Multinomial logistic regression (MLR), random forest classification (RF), indicator kriging (IK), and multi-model ensemble (MME) approaches using species distribution models. The MLR and RF produced biased results, whereby almost all regions were classified as first grade; the spatialization results of these methods were considered inappropriate for forest management. The maps produced using the IK and MME methods improved the distinctions between the distributions of five grades compared to the previous two methodologies however, the MME method produced better results, reliably reflecting topographical and climatic characteristics. Comparisons with the vegetation condition index and bioclimate vulnerability index also emphasized the usefulness of the MME. This study is particularly relevant to the national forest managers who struggle to find the most effective forest monitoring and management strategies. Suggestions to improve spatialization of field survey data are further discussed. Full article

(This article belongs to the Special Issue Applications of Satellite Data for Forest Monitoring and Management)

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15 pages, 4117 KiB

Open AccessArticle

A Novel Multi-Scale Attention PFE-UNet for Forest Image Segmentation

by Boyang Zhang, Hongbo Mu, Mingyu Gao, Haiming Ni, Jianfeng Chen, Hong Yang and Dawei Qi

Forests 2021, 12(7), 937; https://0-doi-org.brum.beds.ac.uk/10.3390/f12070937 - 16 Jul 2021

Cited by 6 | Viewed by 2110

Abstract

The precise segmentation of forest areas is essential for monitoring tasks related to forest exploration, extraction, and statistics. However, the effective and accurate segmentation of forest images will be affected by factors such as blurring and discontinuity of forest boundaries. Therefore, a Pyramid Feature Extraction-UNet network (PFE-UNet) based on traditional UNet is proposed to be applied to end-to-end forest image segmentation. Among them, the Pyramid Feature Extraction module (PFE) is introduced in the network transition layer, which obtains multi-scale forest image information through different receptive fields. The spatial attention module (SA) and the channel-wise attention module (CA) are applied to low-level feature maps and PFE feature maps, respectively, to highlight specific segmentation task features while fusing context information and suppressing irrelevant regions. The standard convolution block is replaced by a novel depthwise separable convolutional unit (DSC Unit), which not only reduces the computational cost but also prevents overfitting. This paper presents an extensive evaluation with the DeepGlobe dataset and a comparative analysis with several state-of-the-art networks. The experimental results show that the PFE-UNet network obtains an accuracy of 94.23% in handling the real-time forest image segmentation, which is significantly higher than other advanced networks. This means that the proposed PFE-UNet also provides a valuable reference for the precise segmentation of forest images. Full article

(This article belongs to the Special Issue Applications of Satellite Data for Forest Monitoring and Management)

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15 pages, 8518 KiB

Open AccessArticle

Mapping and Monitoring the Canopy Cover and Greenness of Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines) in Central-Eastern Tennessee Using Multi-Temporal Landsat Satellite Data

by Clement Akumu, Raphael Smith and Solomon Haile

Forests 2021, 12(4), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/f12040499 - 16 Apr 2021

Cited by 1 | Viewed by 1851

Abstract

Southern yellow pines such as loblolly, Virginia and shortleaf pines constitute forest products and contribute significantly to the economy of the United States (U.S.). However, little is understood about the temporal change in canopy cover and greenness of southern yellow pines, especially in Tennessee where they are used for timber and pulpwood. This study aims to map and monitor the canopy cover and greenness of southern yellow pines i.e., loblolly (Pinus taeda), shortleaf (Pinus echinata), and Virginia (Pinus Virginiana) pines in the years 1988, 1999 and 2016 in central-eastern Tennessee. Landsat time-series satellite data acquired in December 1988, November 1999 and February 2016 were used to map and monitor the canopy cover and greenness of loblolly, shortleaf and Virginia pines. The classification and mapping of the canopy cover of southern yellow pines were performed using a machine-learning random forest classification algorithm. Normalized Difference Vegetation Index (NDVI) was used to monitor the temporal variation in canopy greenness. In total, the canopy cover of southern yellow pines decreased by about 35% between December 1988 and February 2016. This information could be used by foresters and forest managers to support forest inventory and management. Full article

(This article belongs to the Special Issue Applications of Satellite Data for Forest Monitoring and Management)

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Review

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31 pages, 4573 KiB

Open AccessReview

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

by Adeel Ahmad, Sajid Rashid Ahmad, Hammad Gilani, Aqil Tariq, Na Zhao, Rana Waqar Aslam and Faisal Mumtaz

Forests 2021, 12(9), 1211; https://0-doi-org.brum.beds.ac.uk/10.3390/f12091211 - 06 Sep 2021

Cited by 31 | Viewed by 7060

Abstract

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers’ citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively. Full article

(This article belongs to the Special Issue Applications of Satellite Data for Forest Monitoring and Management)

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