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Special Issue "Mapping and Monitoring Forest Cover"

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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 November 2020) | Viewed by 25291

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Special Issue Editor

Prof. Dr. Russell G. Congalton

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Guest Editor

Department of Natural Resources & the Environment, University of New Hampshire, 56 College Road, 114 James Hall, Durham, NH 03824, USA
Interests: remote sensing; geospatial analysis; spatial data uncertainty; validation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests cover over 30 percent of our planet and are very important for a variety of reasons including carbon storage, air quality, the products they provide, recreation, as habitats for many species, and so many others. Our ability to map and monitor our forests has never been more critical. Remote sensing and geospatial analysis provide many cutting-edge techniques for studying the forest. New developments in collection platforms such as unmanned aerial systems (UAS) have provided previously unavailable local imagery. New developments in sensors such as lidar, hyperspectral, and higher spatial resolution multispectral imagery offers unlimited opportunities. Freely available imagery (Landsat, Sentinel, and others) provides a multi-temporal analysis that was not previously possible.

This Special Issue is dedicated to advances in mapping and monitoring forests. Papers should include aspects of forest structure, type, and health for both local and global scales. Methods and applications of forest cover change are also very relevant. Techniques that show efficiencies gained for mapping and monitoring the forest from remote sensing and geospatial analysis are also welcome. Finally, the use of cloud computing has dramatically increased our ability to process imagery and conduct analysis. Papers demonstrating novel applications of these techniques applied to forests would be appropriate for this Special Issue.

Prof. Russell G. Congalton
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

Geospatial analysis of forests
Forest cover
Forest structure
Forest health
Forest change

Published Papers (7 papers)

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Editorial

Jump to: Research, Review

2 pages, 614 KiB

Open AccessEditorial

Mapping and Monitoring Forest Cover

Russell G. Congalton

Forests 2021, 12(9), 1184; https://doi.org/10.3390/f12091184 - 01 Sep 2021

Cited by 1 | Viewed by 1294

Abstract

Our Earth consists of approximately 70 percent water and 30 percent land [...] Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

Research

Jump to: Editorial, Review

17 pages, 3139 KiB

Open AccessArticle

Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

and

Christopher Robin Bryant

Forests 2021, 12(1), 76; https://doi.org/10.3390/f12010076 - 11 Jan 2021

Cited by 9 | Viewed by 2471

Abstract

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R²) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall. Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

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19 pages, 8195 KiB

Open AccessArticle

Monitoring Mangrove Forest Degradation and Regeneration: Landsat Time Series Analysis of Moisture and Vegetation Indices at Rabigh Lagoon, Red Sea

Mohammed Othman Aljahdali

Sana Munawar

and

Waseem Razzaq Khan

Forests 2021, 12(1), 52; https://doi.org/10.3390/f12010052 - 01 Jan 2021

Cited by 34 | Viewed by 5025

Abstract

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends. Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

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15 pages, 4520 KiB

Open AccessArticle

Monitoring Carbon Stock and Land-Use Change in 5000-Year-Old Juniper Forest Stand of Ziarat, Balochistan, through a Synergistic Approach

Hamayoon Jallat

Muhammad Fahim Khokhar

and

Forests 2021, 12(1), 51; https://doi.org/10.3390/f12010051 - 01 Jan 2021

Cited by 7 | Viewed by 4021

Abstract

The Juniper forest reserve of Ziarat is one of the biggest Juniperus forests in the world. This study assessed the land-use changes and carbon stock of Ziarat. Different types of carbon pools were quantified in terms of storage in the study area in tons/ha i.e., above ground, soil, shrubs and litter. The Juniper species of this forest is putatively called Juniperus excelsa Beiberstein. To estimate above-ground biomass, different allometric equations were applied. Average above ground carbon stock of the forest was estimated as 8.34 ton/ha, 7.79 ton/ha and 8.4 ton/ha using each equation. Average carbon stock in soil, shrubs and litter was calculated as 24.35 ton/ha, 0.05 ton/ha and 1.52 ton/ha, respectively. Based on our results, soil carbon stock in the Juniper forest of Ziarat came out to be higher than the living biomass. Furthermore, the spatio-temporal classified maps for Ziarat showed that forest area has significantly decreased, while agricultural and barren lands increased from 1988 to 2018. This was supported by the fact that estimated carbon stock also showed a decreasing pattern between the evaluation periods of 1988 to 2018. Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. The results corroborate the assumption that under a business as usual scenario, it is highly likely that the Juniperus forest will severely decline. Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

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20 pages, 8168 KiB

Open AccessArticle

Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data

Selina Ganz

Petra Adler

and

Gerald Kändler

Forests 2020, 11(12), 1322; https://doi.org/10.3390/f11121322 - 12 Dec 2020

Cited by 19 | Viewed by 3355

Abstract

Research Highlights: This study developed the first remote sensing-based forest cover map of Baden-Württemberg, Germany, in a very high level of detail. Background and Objectives: As available global or pan-European forest maps have a low level of detail and the forest definition is not considered, administrative data are often oversimplified or out of date. Consequently, there is an important need for spatio-temporally explicit forest maps. The main objective of the present study was to generate a forest cover map of Baden-Württemberg, taking the German forest definition into account. Furthermore, we compared the results to NFI data; incongruences were categorized and quantified. Materials and Methods: We used a multisensory approach involving both aerial images and Sentinel-2 data. The applied methods are almost completely automated and therefore suitable for area-wide forest mapping. Results: According to our results, approximately 37.12% of the state is covered by forest, which agrees very well with the results of the NFI report (37.26% ± 0.44%). We showed that the forest cover map could be derived by aerial images and Sentinel-2 data including various data acquisition conditions and settings. Comparisons between the forest cover map and 34,429 NFI plots resulted in a spatial agreement of 95.21% overall. We identified four reasons for incongruences: (a) edge effects at forest borders (2.08%), (b) different forest definitions since NFI does not specify minimum tree height (2.04%), (c) land cover does not match land use (0.66%) and (d) errors in the forest cover layer (0.01%). Conclusions: The introduced approach is a valuable technique for mapping forest cover in a high level of detail. The developed forest cover map is frequently updated and thus can be used for monitoring purposes and for assisting a wide range of forest science, biodiversity or climate change-related studies. Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

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15 pages, 3822 KiB

Open AccessArticle

A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

Jianyu Gu

Heather Grybas

and

Russell G. Congalton

Forests 2020, 11(6), 605; https://doi.org/10.3390/f11060605 - 26 May 2020

Cited by 18 | Viewed by 3272

Abstract

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation. Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

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Review

Jump to: Editorial, Research

41 pages, 2645 KiB

Open AccessReview

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

and

Forests 2020, 11(12), 1364; https://doi.org/10.3390/f11121364 - 19 Dec 2020

Cited by 43 | Viewed by 4988

Abstract

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. Full article

(This article belongs to the Special Issue Mapping and Monitoring Forest Cover)

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