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Forests
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Study of Forest Landscape Development Based on Geospatial Technologies
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Study of Forest Landscape Development Based on Geospatial Technologies

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: 31 July 2024 | Viewed by 2939

Special Issue Editors

Prof. Dr. Leiguang Wang

E-Mail Website
Guest Editor
Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650233, China
Interests: remote sensing; vegetation mapping; machine learning; forest monitoring
Prof. Dr. Guanglong Ou

E-Mail Website
Guest Editor
College of Forestry, Southwest Forestry University, Kunming 650223, China
Interests: forest ecology; remote sensing; biomass modelling; forest management
Dr. Yihang Zhang

E-Mail Website
Guest Editor
Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
Interests: remote sensing; forest disturbance monitoring; forest cover/change mapping; multi-source data fusion; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Forests play a vital role in biodiversity conservation, climate regulation, and the provision of ecosystem services. Geospatial technologies, including remote sensing, GIS, and related tools, have revolutionized our understanding of forest landscapes. This Special Issue aims to bring together researchers and practitioners to explore the latest advancements in geospatial technologies and their applications in assessing, monitoring, and managing forest landscapes.

Potential topics include, but are not limited to:

  1. Prediction and analysis of forest landscape dynamics using remote sensing data;
  2. Spatial modeling of forest landscape changes and patterns;
  3. Monitoring and assessment of forest ecosystem health and resilience;
  4. Integration of geospatial technologies for forest management and conservation;
  5. Geospatial approaches for mapping and inventorying forest resources;
  6. Application of machine learning and artificial intelligence in forest landscape analysis;
  7. Geospatial tools for detecting and predicting forest disturbances and assessing their impacts;
  8. Spatial analysis of forest fragmentation and connectivity;

Geospatial techniques for studying the effects of climate change on forest landscapes.

Prof. Dr. Leiguang Wang
Prof. Dr. Guanglong Ou
Dr. Yihang Zhang
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. 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 technologies
  • remote sensing
  • machine learning
  • forests
  • forest management
  • forest disturbance
  • forest dynamics
  • forest inventory

Published Papers (3 papers)

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Research

20 pages, 7779 KiB  
Article
Ecological Risk Assessment of Forest Landscapes in Lushan National Nature Reserve in Jiangxi Province, China
by Jinfeng Rao, Xunzhi Ouyang, Ping Pan, Cheng Huang, Jianfeng Li and Qinglong Ye
Forests 2024, 15(3), 484; https://doi.org/10.3390/f15030484 - 05 Mar 2024
Viewed by 724
Abstract
It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National [...] Read more.
It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National Nature Reserve as its study object, this study performed grid processing for the nature reserve and classified forest landscape types using the Forest Resource Inventory Database in 2019. A landscape ecological index model was constructed to evaluate the ecological risk. Global and local Moran index values were used to reveal the autocorrelations for ecological risk. The geodetector method was used to comprehensively analyze the effects of natural and human factors on ecological risk. The results showed that, in general, the ecological risk level of the nature reserve was relatively low, as the proportion of the lowest-, lower-, and medium-risk areas to the total forestry land area accounted for 91.03%. The ecological risk ranking of each functional zone, from high to low, was in the order of the experimental zone, the buffer zone, and the core zone. The ecological risk levels of different forest landscape types were closely related to their area, spatial distribution, and succession stage, as well as human factors, such as the proximity to roads and settlements, etc. The forest landscape with the highest ecological risk was the Cunninghamia lanceolata (Lamb.) Hook. forest, and the forest landscape with the lowest ecological risk was other forestry land. Ecological risk had a positive spatial correlation and tended to be aggregated in space, demonstrating coupling with the proximity to roads and settlements. The ecological risk was affected by both human and natural factors, among which human factors played a dominant role. The proximity to roads and settlements, the relative humidity, and the temperature were the main driving factors. The interaction of pairwise factors had a stronger influence than that of single factors. Therefore, controlling the intensity of human activities and enhancing the coordination between humans and nature are beneficial for alleviating the ecological risks in the forest landscapes of nature reserves. Full article
(This article belongs to the Special Issue Study of Forest Landscape Development Based on Geospatial Technologies)
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17 pages, 5881 KiB  
Article
A Compatible Estimation Method for Biomass Factors Based on Allometric Relationship: A Case Study on Pinus densata Natural Forest in Yunnan Province of Southwest China
by Wenfang Li, Hui Xu, Yong Wu, Xiaoli Zhang, Chunxiao Liu, Chi Lu, Zhibo Yu and Guanglong Ou
Forests 2024, 15(1), 26; https://doi.org/10.3390/f15010026 - 22 Dec 2023
Viewed by 822
Abstract
Using various biomass factors, such as biomass expansion factor (BEF) and biomass conversion and expansion factor (BCEF), yields different results for estimating forest biomass. Therefore, ensuring compatibility between total biomass and its components when employing different biomass factors is crucial for developing a [...] Read more.
Using various biomass factors, such as biomass expansion factor (BEF) and biomass conversion and expansion factor (BCEF), yields different results for estimating forest biomass. Therefore, ensuring compatibility between total biomass and its components when employing different biomass factors is crucial for developing a set of rapid and efficient models for large-scale biomass calculation. In this study, allometric equations were utilized to construct independent models and the proportional values (root-to-shoot ratio (Rra), crown-to-stem ratio (Rcs), bark-to-wood ratio (Rbw), foliage-to-bark ratio (Rfb), and wood biomass-to-wood volume (ρ)) by using the mean height (Hm) and the mean diameter at breast height (Dg) of 98 Pinus densata plots in Shangri-La, Yunnan province, China. The compatible methods were applied to reveal the compatibility between the total biomass and each component’s biomass. The results showed the following: (1) Both the independent model and compatible model had a higher accuracy. The values were greater than 0.7 overall, but the foliage biomass accuracy was only 0.2. The total biomass and the component biomass showed compatibility. (2) The accuracy of BEF and BCEF exceeded 0.87 and the total error was less than 0.1 for most components. (3) The mean BEF (1.6) was greater than that of the Intergovernmental Panel on Climate Change (IPCC) (M = 1.3), and the mean BCEF was smaller than that of the IPCC; the values were 0.6 and 0.7, respectively. The range of BEF (1.4–2.1) and BCEF (0.44–0.89) were all within the range of the IPCC (1.15–3.2, 0.4–1.0). This study provides a more convenient and accurate method for calculating conversion coefficients (BEF and BCEF), especially when only Rcs data is available. Full article
(This article belongs to the Special Issue Study of Forest Landscape Development Based on Geospatial Technologies)
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17 pages, 5495 KiB  
Article
Forest Aboveground Biomass Estimation in Subtropical Mountain Areas Based on Improved Water Cloud Model and PolSAR Decomposition Using L-Band PolSAR Data
by Haibo Zhang, Changcheng Wang, Jianjun Zhu, Haiqiang Fu, Wentao Han and Hongqun Xie
Forests 2023, 14(12), 2303; https://doi.org/10.3390/f14122303 - 24 Nov 2023
Cited by 1 | Viewed by 795
Abstract
Forest aboveground biomass (AGB) retrieval using synthetic aperture radar (SAR) backscatter has received extensive attention. The water cloud model (WCM), because of its simplicity and physical significance, has been one of the most commonly used models for estimating forest AGB using SAR backscatter. [...] Read more.
Forest aboveground biomass (AGB) retrieval using synthetic aperture radar (SAR) backscatter has received extensive attention. The water cloud model (WCM), because of its simplicity and physical significance, has been one of the most commonly used models for estimating forest AGB using SAR backscatter. Nevertheless, forest AGB estimation using the WCM is usually based on simplified assumptions and empirical fitting, leading to results that tend to overestimate or underestimate. Moreover, the physical connection between the model and the polarimetric synthetic aperture radar (PolSAR) is not established, which leads to the limitation of the inversion scale. In this paper, based on the fully polarimetric SAR data from the Advanced Land Observing Satellite-2 (ALOS-2) Phased Array-type L-band Synthetic Aperture Radar (PALSAR-2), the relative contributions of the three major scattering mechanisms were first analyzed in a hilly area of southern China. On this basis, the traditional WCM was extended by considering the secondary scattering mechanism. Then, to establish the direct relationship between the vegetation scattering mechanism and forest AGB, a new relationship equation between the PolSAR decomposition model and the improved water cloud model (I-WCM) was constructed without the help of external data. Finally, a nonlinear iterative method was used to estimate the forest AGB. The results show that volume scattering is the dominant mechanism, accounting for more than 60%. Double-bounce scattering accounts for the smallest fraction, but still about 10%, which means that the contribution of the double-bounce scattering component is not negligible in forested areas because of the strong penetration capability of the long-wave SAR. The modified method provides a correlation coefficient R2 of 0.665 and a root mean square error (RMSE) of 21.902, which is an improvement of 36.42% compared to the traditional fitting method. Moreover, it enables the extraction of forest parameters at the pix scale using PolSAR data without the need for low-resolution external data and is thus helpful for high-resolution mapping of forest AGB. Full article
(This article belongs to the Special Issue Study of Forest Landscape Development Based on Geospatial Technologies)
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