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Forests
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Remote Sensing of Forest Biomass and Carbon Dynamics Using Multiple...
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Remote Sensing of Forest Biomass and Carbon Dynamics Using Multiple Sources and 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: 25 December 2024 | Viewed by 4781

Special Issue Editors

Dr. Qian Zhang

E-Mail Website
Guest Editor
School of Geometics Science and Technology, Nanjing Tech University, Nanjing 211816, China
Interests: quantitative remote sensing; carbon cycle; plant photosynthesis; aboveground biomass; spectral observation
Dr. Weiliang Fan

E-Mail Website
Guest Editor
School of Environmental and Resources Science, Zhejiang A&F University, Hangzhou 311300, China
Interests: quantitative remote sensing; canopy radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Yang

E-Mail Website
Guest Editor
Max Planck Institute for Biogeochemistry, D-07745 Jena, Germany
Interests: carbon-water coupling; forest dynamics; climate extreme events; process modeling; big data analysis

Special Issue Information

Dear Colleagues,

Forests absorb carbon dioxide in the atmosphere through photosynthesis and are characterized by a large carbon sink, low cost, and high ecological value-added. Forests are the largest source of carbon storage in the terrestrial ecosystem. Accurate estimations of forest biomass/carbon stocks and monitoring carbon dynamics are essential for modeling the global carbon cycle, quantifying carbon fluxes, and accomplishing carbon neutrality targets. In recent years, numerous remote sensing data (e.g., multispectral, hyperspectral, LiDAR, and SAR) with various platforms (e.g., satellite, airborne, unmanned aerial vehicle, and ground-based) and advanced artificial intelligence (e.g., machine learning, deep learning, and transfer learning) have been established and provided us with powerful tools to accurately estimate forest biomass/carbon stock and to monitor carbon dynamic.

For this Special Issue, we invite scientists actively applying remote sensing and related technology to assess forest biomass and monitor carbon dynamics in their research to submit their papers. Well-prepared, unpublished submissions that address one or more of the following topics (or related topics) are welcome:

  • The advantages of remote sensing in forest biomass estimation and carbon dynamics monitoring;
  • The estimation of forest biomass using remote sensing across scales;
  • The monitoring and modeling of the dynamics of forest biomass/carbon;
  • Deep learning or innovative artificial intelligence algorithms for forest biomass estimation;
  • The estimation of biophysical, biochemical, and physiological properties that are significant for forest biomass;
  • The impact of climate change on the carbon dynamics of forests;
  • The response of forest carbon dynamics to extremes (e.g., heavy precipitation, drought, heat, wildfire, insects) and its legacy effects;
  • The impact of forest mortality on carbon dynamics;
  • Forest growth modeling based on remote sensing techniques;
  • The combination of in situ observation and remote sensing data across scales;
  • Uncertainties and error analysis for the estimation of forest biomass.

Dr. Qian Zhang
Dr. Weiliang Fan
Dr. Hui Yang
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

  • multi-source remote sensing
  • different remote sensing platforms
  • multiple modeling methods
  • artificial intelligence
  • biomass/carbon stock
  • carbon dynamic

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

23 pages, 14850 KiB  
Article
Influence of Terrain on MODIS and GLASS Leaf Area Index (LAI) Products in Qinling Mountains Forests
by Jiaman Zheng, Mengyuan Wang, Mingyue Liang, Yuyang Gao, Mou Leong Tan, Mengyun Liu and Xiaoping Wang
Forests 2024, 15(11), 1871; https://doi.org/10.3390/f15111871 - 25 Oct 2024
Viewed by 577
Abstract
Leaf Area Index (LAI), as a pivotal parameter in characterizing the structural properties of vegetation ecosystems, holds significant importance in assessing the carbon sink function. Given the availability of multiple long-term LAI products, validating these LAI products with consideration of topographic factors is [...] Read more.
Leaf Area Index (LAI), as a pivotal parameter in characterizing the structural properties of vegetation ecosystems, holds significant importance in assessing the carbon sink function. Given the availability of multiple long-term LAI products, validating these LAI products with consideration of topographic factors is a prerequisite for enhancing the quality of LAI products in mountainous areas. Therefore, this study aims to evaluate the performance of MODIS LAI and GLASS LAI products from 2001 to 2021 by comparing and validating them with ground-measured LAI data, focusing on the spatio-temporal and topographic aspects in the Qinling Mountains. The results show that the GLASS LAI product is a better choice for estimating LAI in the Qinling Mountains. The GLASS LAI product has better completeness and generally higher values compared to the MODIS LAI product. The time-series curve of the GLASS LAI product is more continuous and smoother than the MODIS LAI product. Both products, however, face challenges in quantifying LAI values of evergreen vegetation during winter. The MODIS and GLASS LAI products exhibit differences between sunny and shady slopes, with mean LAI values peaking on sunny slopes and reaching their lowest on shady slopes. When the slope ranges from 0 to 10°, the mean values of GLASS LAI product show a higher increasing trend compared to the MODIS LAI product. At elevations between 1450 and 2450 m, the mean LAI values of the GLASS LAI product are higher than the MODIS LAI product, primarily in the southern Qinling Mountains. Compared to ground-measured LAI data, the GLASS LAI product (R² = 0.33, RMSE = 1.62, MAE = 0.61) shows a stronger correlation and higher accuracy than the MODIS LAI product (R² = 0.24, RMSE = 1.61, MAE = 0.68). Full article
(This article belongs to the Special Issue Remote Sensing of Forest Biomass and Carbon Dynamics Using Multiple Sources and Technologies)
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22 pages, 5800 KiB  
Article
Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
by Yujia Chen, Shunxue Zhang, Junshan Guo and Yao Shen
Forests 2024, 15(6), 913; https://doi.org/10.3390/f15060913 - 24 May 2024
Cited by 1 | Viewed by 1095
Abstract
Gross primary productivity (GPP), representing organic carbon fixation through photosynthesis, is crucial for developing science-based strategies for sustainable development. Given that the tropical region harbors nearly half of all species, it plays a pivotal role in safeguarding the global environment against climate change [...] Read more.
Gross primary productivity (GPP), representing organic carbon fixation through photosynthesis, is crucial for developing science-based strategies for sustainable development. Given that the tropical region harbors nearly half of all species, it plays a pivotal role in safeguarding the global environment against climate change and preserving global biodiversity. Thus, investigating changes in vegetation productivity within this region holds substantial practical importance for estimating global vegetation productivity. In this study, we employed an enhanced P model to estimate vegetation GPP in the tropical region from 2001 to 2020, based on which we quantified the spatiotemporal changes and associated mechanisms. The results reveal that the annual mean GPP in the tropical region ranged from 2603.9 to 2757.1 g·cm−2 a−1, demonstrating an overall apparent increasing trend. Inland areas were mainly influenced by precipitation, while coastal areas were primarily influenced by temperature. Land cover changes, especially conversion to cropland, significantly influence GPP, with deciduous—evergreen forest transitions causing notable decreases. Climate change emerges as the dominant factor affecting GPP, as indicated by the contribution rate analysis. This research interprets the spatiotemporal pattern and mechanisms of GPP in the tropics, offering valuable insights for sustainable ecosystem management. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Biomass and Carbon Dynamics Using Multiple Sources and Technologies)
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18 pages, 7237 KiB  
Article
Influence of BRDF Models and Solar Zenith Angles on Forest Above-Ground Biomass Derived from MODIS Multi-Angular Indices
by Lei Cui, Jiaying Zhang, Yiqun Dai, Rui Xie, Zhongzheng Zhu, Mei Sun, Xiaoning Zhang, Long He, Hu Zhang, Yadong Dong and Kaiguang Zhao
Forests 2024, 15(3), 541; https://doi.org/10.3390/f15030541 - 15 Mar 2024
Viewed by 1152
Abstract
Multi-angular remote sensing observation contains crucial information on forest structure parameters. Here, our goal is to examine the ability of multi-angular indices, which are constructed by the typical-angular reflectances in red and NIR bands from MODIS observations, for the retrieval of forest biomass [...] Read more.
Multi-angular remote sensing observation contains crucial information on forest structure parameters. Here, our goal is to examine the ability of multi-angular indices, which are constructed by the typical-angular reflectances in red and NIR bands from MODIS observations, for the retrieval of forest biomass based on the field-measured above-ground biomass (AGB) data. Specifically, we employed the updated version of the MCD43A1 BRDF parameter product as an input for BRDF models to reconstruct the MODIS typical-angular reflectances. Furthermore, we evaluated the effects of different configurations of BRDF models and solar zenith angles (SZA) on forest AGB estimation using our developed multi-angular indices. The semivariogram analysis strategy combined with Landsat ground-surface reflectance data was employed to determine the MODIS pixel heterogeneity; the survey data from field sites of homogeneous pixels was used in our analysis and validation. The results show that our developed multi-angular indices based on a hot-revised BRDF model, under a SZA of 45°, when combined with forest cover information, can account for up to 72% of the variation forest AGB, with an RMSE = 45 Mg/ha. We also found that different kernels for the BRDF models influenced the weight parameters of the biomass inversion equation but did not significantly affect the estimated AGB. In conclusion, our method can enable the better usage of MODIS multi-angular observations for forest AGB estimation. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Biomass and Carbon Dynamics Using Multiple Sources and Technologies)
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18 pages, 10899 KiB  
Article
Aerial Biomass Estimation in the Cerrado Biome Using Canopy Height Data
by Carlos Augusto Zangrando Toneli, Fernando Paiva Scardua, Rosana de Carvalho Cristo Martins, Eraldo Aparecido Trondoli Matricardi, Andressa Ribeiro and Antonio Carlos Ferraz Filho
Forests 2024, 15(3), 507; https://doi.org/10.3390/f15030507 - 8 Mar 2024
Cited by 1 | Viewed by 1395
Abstract
Adaptations to climate change rely on understanding the dynamics of plant biomass stocks on the planet. The high levels of deforestation in Cerrado have transformed this biome into the second-largest Brazilian source of carbon emissions. The objective of this study was to develop [...] Read more.
Adaptations to climate change rely on understanding the dynamics of plant biomass stocks on the planet. The high levels of deforestation in Cerrado have transformed this biome into the second-largest Brazilian source of carbon emissions. The objective of this study was to develop a method to accurately estimate aboveground and total biomass values among shrublands, savannas, and forests located in the Cerrado biome using an allometric equation adjusted from canopy height obtained through optical and laser sensors. The results show similarity between the estimates employed by our method and the data found in the literature review for different phytophysiognomies in the Cerrado biome. Shrubland formations showed higher biomass estimation uncertainties due to the discontinuity of isolated trees and the lower canopy height when compared to more clustered tree canopies in savannas and taller canopies in forests. Aboveground biomass estimates are related to expansion factors, and specific maps were developed for each compartment by root, litter, and necromass. The sum of these compartments is presented in the aboveground and below forest biomass map. This study presents, for the first time, the mapping of total biomass in 10 m pixels of all regions of the Cerrado biome. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Biomass and Carbon Dynamics Using Multiple Sources and Technologies)
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