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Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management

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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 October 2021) | Viewed by 24741

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Special Issue Editors

Dr. Giovanni Santopuoli

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Guest Editor

Department of Agricultural, Environmental and Food Sciences, University of Molise, 86100 Campobasso, Italy
Interests: forest biodiversity; forest inventory; criteria and indicators for sustainable forest management; forestry; LiDAR

Dr. Ivan Sačkov

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Guest Editor

Department of Forest Policy, Economics and Forest Management, National Forest Centre, Forest Research Institute, T. G. Masaryka 22, Zvolen, Slovakia
Interests: forest management and planning; forest inventory; remote sensing; LiDAR; software engineering; applied statistics

Prof. Dr. Bruno Lasserre

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Guest Editor

Department of Biosciences and Territory, University of Molise, Via Francesco De Sanctis, 1, 86100 Campobasso, CB, Italy
Interests: forestry; wood technology; forest utilization; sustainable forest management; remote sensing; LiDAR
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Special Issue Information

Dear Colleagues,

Balancing the conservation of forest biodiversity and the delivery of other forest ecosystem services is mandatory for reaching the sustainable forest management aims, and represents the main pillar of the newest integrate forest management approach. Forest biological diversity is a broad term that refers to all life forms found within forested areas and the ecological roles they perform. To retrieve accurate, timely and cost-effective information on forest biodiversity is strongly necessary to assess the state of forest ecosystems and to support forest management and planning.

Over the years, remote sensing techniques have been increasingly contributing to monitoring and assessing forest biodiversity-related characteristics and functions. The evolution of remote sensing tools and software allows the refinement of existing approaches and the development of innovative new ones for a better evaluation of the biodiversity of the forest ecosystems. The diverse spectral, spatial and temporal information acquired with different sensor types and platforms contributes to assess vary aspects of forest biodiversity at different detail and scale.

Considering that the interest and studies for observing and investigating on the tree-related microhabitat is increased in the last decade, as a tool for supporting integrate forest management approach, this forthcoming Special Issue on “Remote sensing to support biodiversity assessment and sustainable forest management” calls for original research papers with focus on the development of new or improvement of existing methodological approaches for assessing forest biodiversity through remote sensing. Papers focused to assess forest structure, to develop useful forest biodiversity indicators, as well as concerned to the tree-related microhabitat, are welcomed.

Dr. Giovanni Santopuoli
Dr. Ivan Sačkov
Prof. Dr. Bruno Lasserre
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 structure
Forest biodiversity
Microhabitat
LiDAR
Integrative forest management
Ecosystem Services trade-offs
Remote sensing

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Published Papers (6 papers)

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Research

25 pages, 6607 KiB

Open AccessArticle

Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest

by Cesar Alvites, Giovanni Santopuoli, Markus Hollaus, Norbert Pfeifer, Mauro Maesano, Federico Valerio Moresi, Marco Marchetti and Bruno Lasserre

Remote Sens. 2021, 13(21), 4265; https://doi.org/10.3390/rs13214265 - 23 Oct 2021

Cited by 7 | Viewed by 3103

Abstract

Timber assortments are some of the most important goods provided by forests worldwide. To quantify the amount and type of timber assortment is strongly important for socio-economic purposes, but also for accurate assessment of the carbon stored in the forest ecosystems, regardless of their main function. Terrestrial laser scanning (TLS) became a promising tool for timber assortment assessment compared to the traditional surveys, allowing reconstructing the tree architecture directly and rapidly. This study aims to introduce an approach for timber assortment assessment using TLS data in a mixed and multi-layered Mediterranean forest. It consists of five steps: (1) pre-processing, (2) timber-leaf discrimination, (3) stem detection, (4) stem reconstruction, and (5) timber assortment assessment. We assume that stem form drives the stem reconstruction, and therefore, it influences the timber assortment assessment. Results reveal that the timber-leaf discrimination accuracy is 0.98 through the Random Forests algorithm. The overall detection rate for all trees is 84.4%, and all trees with a diameter at breast height larger than 0.30 m are correctly identified. Results highlight that the main factors hindering stem reconstruction are the presence of defects outside the trunk, trees poorly covered by points, and the stem form. We expect that the proposed approach is a starting point for valorising the timber resources from unmanaged/managed forests, e.g., abandoned forests. Further studies to calibrate its performance under different forest stand conditions are furtherly required. Full article

(This article belongs to the Special Issue Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management)

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18 pages, 8002 KiB

Open AccessArticle

Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

by Jingyi Wang, Huaqiang Du, Xuejian Li, Fangjie Mao, Meng Zhang, Enbin Liu, Jiayi Ji and Fangfang Kang

Remote Sens. 2021, 13(15), 2962; https://doi.org/10.3390/rs13152962 - 28 Jul 2021

Cited by 23 | Viewed by 3784

Abstract

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R²) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error. Full article

(This article belongs to the Special Issue Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management)

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14 pages, 6570 KiB

Open AccessFeature PaperArticle

Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

by Loredana Oreti, Diego Giuliarelli, Antonio Tomao and Anna Barbati

Remote Sens. 2021, 13(13), 2508; https://doi.org/10.3390/rs13132508 - 26 Jun 2021

Cited by 20 | Viewed by 3971

Abstract

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m². This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales. Full article

(This article belongs to the Special Issue Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management)

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23 pages, 4919 KiB

Open AccessArticle

Woody Above-Ground Biomass Estimation on Abandoned Agriculture Land Using Sentinel-1 and Sentinel-2 Data

by Tomáš Bucha, Juraj Papčo, Ivan Sačkov, Jozef Pajtík, Maroš Sedliak, Ivan Barka and Ján Feranec

Remote Sens. 2021, 13(13), 2488; https://doi.org/10.3390/rs13132488 - 25 Jun 2021

Cited by 10 | Viewed by 3247

Abstract

Abandoned agricultural land (AAL) is a European problem and phenomenon when agricultural land is gradually overgrown with shrubs and forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove and validate the concept of the satellite-based estimation of woody above-ground biomass (AGB) on AAL in the Western Carpathian region. The analysis is based on Sentinel-1 and -2 satellite data, supported by field research and airborne laser scanning. An improved AGB estimate was achieved using radar and optical multi-temporal data and polarimetric coherence by creating integrated predictive models by multiple regression. Abandonment is represented by two basic AAL classes identified according to overgrowth by shrub formations (AAL1) and tree formations (AAL2). First, an allometric model for AAL1 estimation was derived based on empirical material obtained from blackthorn stands. AAL2 biomass was quantified by different procedures related to (1) mature trees, (2) stumps and (3) young trees. Then, three satellite-based predictive mathematical models for AGB were developed. The best model reached R² = 0.84 and RMSE = 41.2 t·ha⁻¹ (35.1%), parametrized for an AGB range of 4 to 350 t·ha⁻¹. In addition to 3214 hectares of forest land, we identified 992 hectares of shrub–tree formations on AAL with significantly lower wood AGB than on forest land and with simple shrub composition. Full article

(This article belongs to the Special Issue Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management)

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21 pages, 3333 KiB

Open AccessArticle

Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest

by Rafael Walter Albuquerque, Manuel Eduardo Ferreira, Søren Ingvor Olsen, Julio Ricardo Caetano Tymus, Cintia Palheta Balieiro, Hendrik Mansur, Ciro José Ribeiro Moura, João Vitor Silva Costa, Maurício Ruiz Castello Branco and Carlos Henrique Grohmann

Remote Sens. 2021, 13(12), 2401; https://doi.org/10.3390/rs13122401 - 19 Jun 2021

Cited by 7 | Viewed by 4669

Abstract

Traditional forest restoration (FR) monitoring methods employ spreadsheets and photos taken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolution and georeferenced view of the entire area of interest, this technology has high potential to improve the traditional FR monitoring methods. This study evaluates how low-cost RPA data may contribute to FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of Tree Density, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The point cloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters. The orthomosaic was used for a Random Forest classification that considered trees and grasses as a single land cover class. The Grass Infestation parameter was mapped by the difference between this land cover class (which considered trees and grasses) and the Vegetation Cover results (obtained by the point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameters presented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by the Error Percentage considering the traditional fieldwork and the RPA results. The Error Percentage was equal to 0.13 and was considered accurate because it estimated a 13% shorter height for trees that averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accurately measured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accurately measuring the structural parameters, this study reinforced the challenge of measuring the Biodiversity parameter via remote sensing because the classification of tree species was not possible. After all, the Brazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectral responses in the visible spectrum and similar geometric forms. Therefore, until improved automatic classification methods become available for tree species, traditional fieldwork remains necessary for a complete FR monitoring diagnostic. Full article

(This article belongs to the Special Issue Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management)

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20 pages, 12317 KiB

Open AccessArticle

Remote Sensing of Tropical Rainforest Biomass Changes in Hainan Island, China from 2003 to 2018

by Meizhi Lin, Qingping Ling, Huiqing Pei, Yanni Song, Zixuan Qiu, Cai Wang, Tiedong Liu and Wenfeng Gong

Remote Sens. 2021, 13(9), 1696; https://doi.org/10.3390/rs13091696 - 27 Apr 2021

Cited by 14 | Viewed by 4418

Abstract

The largest area of tropical rainforests in China is on Hainan Island, and it is an important part of the world’s tropical rainforests. The structure of the tropical rainforests in Hainan is complex, the biomass density is high, and conducting ground surveys is difficult, costly, and time-consuming. Remote sensing is a good monitoring method for biomass estimation. However, the saturation phenomenon of such data from different satellite sensors results in low forest biomass estimation accuracy in tropical rainforests with high biomass density. Based on environmental information, the biomass of permanent sample plots, and forest age, this study established a tropical rainforest database for Hainan. Forest age and 14 types of environmental information, combined with an enhanced vegetation index (EVI), were introduced to establish a tropical rainforest biomass estimation model for remote sensing that can overcome the saturation phenomenon present when using remote sensing data. The fitting determination coefficient

R^{2}

of the model was 0.694. The remote sensing estimate of relative bias was 2.29%, and the relative root mean square error was 35.41%. The tropical rainforest biomass in Hainan Island is mainly distributed in the central mountainous and southern areas. The tropical rainforests in the northern and coastal areas have been severely damaged by tourism and real estate development. Particularly in low-altitude areas, large areas of tropical rainforest have been replaced by economic forests. Furthermore, the tropical rainforest areas in some cities and counties have decreased, affecting the increase in tropical rainforest biomass. On Hainan Island, there were few tropical rainforests in areas with high rainfall. Therefore, afforestation in these areas could maximize the ecological benefits of tropical rainforests. To further strengthen the protection, there is an urgent need to establish a feasible, reliable, and effective tropical rainforest loss assessment system using quantitative scientific methodologies. Full article

(This article belongs to the Special Issue Remote Sensing to Support Forest Biodiversity Assessment and Sustainable Forest Management)

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