Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Biomass Remote Sensing in Forest Landscapes II

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 11635

Share This Special Issue

Special Issue Editors

Dr. Caixia Liu

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: vegetation height; carbon storage; above biomass; land cover change

Dr. Xiaoyi Wang

E-Mail Website
Guest Editor

Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: forest cover change; above biomass; climate change; treeline extraction

Dr. Qin Ma

E-Mail Website
Guest Editor

School of Geography, Nanjing Normal University, Nanjing 210023，China
Interests: forest aboveground biomass; tree height; LiDAR; forest age; wildfire; drought; disturbances.
Special Issues, Collections and Topics in MDPI journals

Dr. Evgenii I. Ponomarev

E-Mail Website
Guest Editor

V. N. Sukachev Institute of Forest, Siberia, Russia
Interests: wildfire; remote sensing; fire monitoring

Dr. Oleg Antropov

E-Mail Website
Guest Editor

VTT Technical Research Centre of Finland, Espoo, Finland
Interests: forest; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests play an important role in mitigating climate change. The quantification of forest aboveground biomass is useful in forest carbon cycle studies and climate change mitigation actions. Therefore, it is particularly important to obtain accurate spatiotemporal distribution information. With the growth of biomass estimation from sample plot research to regional application, the increase in spatial scale creates challenges in obtaining macroscopic data and parameters. The remote sensing community has made several efforts to address the large-scale challenge of mapping forest carbon stores potential.

This Special Issue aims to collect outstanding contributions exploring the use of remote sensing methods to quantify forest and woodland biomass, carbon stocks, and the relationship of these factors with climate change. This is the second edition; for previous information, please see: https://www.mdpi.com/journal/remotesensing/special_issues/f-biomass_RS.

We encourage better calibration and validation of biomass in forests by integrating ground-based and various satellite and Earth observation data. Contributions to various sensors and detections of airborne, unmanned, spaceborne, and other vehicles or combinations thereof are welcome.

Dr. Caixia Liu
Dr. Xiaoyi Wang
Dr. Qin Ma
Dr. Evgenii I. Ponomarev
Dr. Oleg Antropov
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

above-ground biomass (AGB)
carbon cycle
biomass
remote sensing
drone
lidar
laser scanning

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 25518 KiB

Open AccessArticle

Evaluating Agreement Between Global Satellite Data Products for Forest Monitoring in Madagascar

by Oladimeji Mudele, Marissa L. Childs, Jayden Personnat and Christopher D. Golden

Remote Sens. 2025, 17(9), 1482; https://doi.org/10.3390/rs17091482 - 22 Apr 2025

Viewed by 358

Abstract

Producing high-quality local land cover data can be cost-prohibitive, leaving gaps in reliable estimates of forest cover and loss for environmental policy and planning. Remote sensing data (RSD) offer accessible, globally consistent layers for forest mapping. However, being able to produce reliable RSD-based land cover products with high local fidelity requires ground truth data, which are scarce and cost-intensive to obtain in settings like Madagascar. Global land cover datasets that rely on models trained mostly in well-studied regions claim to alleviate the problem of label scarcity. However, studies have shown that these products often fail to fulfill this promise. Given downstream studies focused on Madagascar still rely on these global land cover products, in this study we compared seven global RSD products measuring forest extent and change in Madagascar to explore levels of similarity across different forest ecoregions over multiple years. We also conducted temporal correlation analysis by checking the correlation between forest area from the different products. We found that agreement levels among the different data products varied by forest type and region, with higher disagreement levels in drier forest ecosystems (dry and spiny forests) than in more humid ones (moist forests and mangroves). For instance, if high agreement is defined as a pixel being classified as a forest by all or all but one product in a year, the average percentage of high-agreement pixels between 2016 and 2020 is just about 8% in the spiny forest and 16% in the dry forest region. These findings underscore the limitations of global RSD products and the importance of localized data for accurate forest monitoring, building justification for efforts to develop a local forest cover product for Madagascar. Our temporal similarity analysis indicates that, although pixel-level maps may show low agreement, temporal aggregates tend to be highly correlated in most cases. We synthesized these results with existing applications of global RSDs in Madagascar to propose practical recommendations for end-users of these products in Madagascar. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Figure 1

32 pages, 9739 KiB

Open AccessArticle

Estimating Spatiotemporal Dynamics of Carbon Storage in Roinia pseudoacacia Plantations in the Caijiachuan Watershed Using Sample Plots and Uncrewed Aerial Vehicle-Borne Laser Scanning Data

by Yawei Hu, Ruoxiu Sun, Miaomiao He, Jiongchang Zhao, Yang Li, Shengze Huang and Jianjun Zhang

Remote Sens. 2025, 17(8), 1365; https://doi.org/10.3390/rs17081365 - 11 Apr 2025

Viewed by 234

Abstract

Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology has emerged as a promising tool for rapidly acquiring three-dimensional spatial information on AGB and vegetation carbon storage. This study evaluates the applicability and accuracy of UAV-LiDAR technology in estimating the spatiotemporal dynamics of AGB and vegetation carbon storage in Robinia pseudoacacia (R. pseudoacacia) plantations in the gully regions of the Loess Plateau, China. At the sample plot scale, optimal parameters for individual tree segmentation (ITS) based on the canopy height model (CHM) were determined, and segmentation accuracy was validated. The results showed root mean square error (RMSE) values of 13.17 trees (25.16%) for tree count, 0.40 m (3.57%) for average tree height (AH), and 320.88 kg (16.94%) for AGB. The regression model, which links sample plot AGB with AH and tree count, generated AGB estimates that closely matched the observed AGB values. At the watershed scale, ULS data were used to estimate the AGB and vegetation carbon storage of R. pseudoacacia plantations in the Caijiachuan watershed. The analysis revealed a total of 68,992 trees, with a total carbon storage of 2890.34 Mg and a carbon density of 62.46 Mg ha⁻¹. Low-density forest areas (<1500 trees ha⁻¹) dominated the landscape, accounting for 94.38% of the tree count, 82.62% of the area, and 92.46% of the carbon storage. Analysis of tree-ring data revealed significant variation in the onset of growth decline across different density classes of plantations aged 0–30 years, with higher-density stands exhibiting delayed growth decline compared to lower-density stands. Compared to traditional methods based on diameter at breast height (DBH), carbon storage assessments demonstrated superior accuracy and scientific validity. This study underscores the feasibility and potential of ULS technology for AGB and carbon storage estimation in regions with complex terrain, such as the Loess Plateau. It highlights the importance of accounting for topographic factors to enhance estimation accuracy. The findings provide valuable data support for density management and high-quality development of R. pseudoacacia plantations in the Caijiachuan watershed and present an efficient approach for precise forest carbon sink accounting. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Figure 1

25 pages, 17434 KiB

Open AccessArticle

Using UAV RGB Images for Assessing Tree Species Diversity in Elevation Gradient of Zao Mountains

by Thi Cam Nhung Tran, Maximo Larry Lopez Caceres, Sergi Garcia i Riera, Marco Conciatori, Yoshiki Kuwabara, Ching-Ying Tsou and Yago Diez

Remote Sens. 2024, 16(20), 3831; https://doi.org/10.3390/rs16203831 - 15 Oct 2024

Viewed by 1419

Abstract

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Figure 1

18 pages, 5628 KiB

Open AccessArticle

Noise Analysis for Unbiased Tree Diameter Estimation from Personal Laser Scanning Data

by Karel Kuželka and Peter Surový

Remote Sens. 2024, 16(7), 1261; https://doi.org/10.3390/rs16071261 - 2 Apr 2024

Cited by 2 | Viewed by 1460

Abstract

Personal laser scanning devices employing Simultaneous Localization and Mapping (SLAM) technology have rightfully gained traction in various applications, including forest mensuration and inventories. This study focuses the inherent stochastic noise in SLAM data. An analysis of noise distribution is performed in GeoSLAM ZEB Horizon for point clouds of trees of two species, Norway spruce and European beech, to mitigate bias in diameter estimates. The method involved evaluating residuals of individual 3D points concerning the real tree surface model based on TLS data. The results show that the noise is not symmetrical regarding the real surface, showing significant negative difference, and moreover, the difference from zero mean significantly differs between species, with an average of −0.40 cm for spruce and −0.44 cm for beech. Furthermore, the residuals show significant dependence on the return distance between the scanner and the target and the incidence angle. An experimental comparison of RANSAC circle fitting outcomes under various configurations showed unbiased diameter estimates with extending the inlier tolerance to 5 cm with 2.5 cm asymmetry. By showing the nonvalidity of the assumption of zero mean in diameter estimation methods, the results contribute to fill a gap in the methodology of data processing with the widely utilized instrument. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Figure 1

21 pages, 9711 KiB

Open AccessArticle

Integrating Remote Sensing Data and CNN-LSTM-Attention Techniques for Improved Forest Stock Volume Estimation: A Comprehensive Analysis of Baishanzu Forest Park, China

by Bo Wang, Yao Chen, Zhijun Yan and Weiwei Liu

Remote Sens. 2024, 16(2), 324; https://doi.org/10.3390/rs16020324 - 12 Jan 2024

Cited by 5 | Viewed by 2757

Abstract

Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China’s Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R² = 0.8463, rMSE = 26.73 m³/ha, MAE = 16.47 m³/ha). Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Graphical abstract

20 pages, 20899 KiB

Open AccessArticle

Phenological Changes and Their Influencing Factors under the Joint Action of Water and Temperature in Northeast Asia

by Jia Wang, Suxin Meng, Weihong Zhu and Zhen Xu

Remote Sens. 2023, 15(22), 5298; https://doi.org/10.3390/rs15225298 - 9 Nov 2023

Cited by 2 | Viewed by 1604

Abstract

Phenology is an important indicator for how plants will respond to environmental changes and is closely related to biomass production. Due to global warming and the emergence of intermittent warming, vegetation in northeast Asia is undergoing drastic changes. Understanding vegetation phenology and its response to climate change is of great significance to understanding the changes in the sustainable development of ecosystems. Based on Global Inventory Modelling and Mapping Studies (GIMMS), normalized difference vegetation index (NDVI)3g data, and the mean value of phenological results extracted by five methods, combined with climatic data, this study analyzed the temporal changes in phenology and the responses to climatic factors of five vegetation types of broad-leaved, needle-leaf, mixed forests, grassland, and cultivated land in northeast Asia over 33 years (1982–2014). The results showed that, during the intermittent warming period (1999–2014), the start of the growing season (SOS) advancement (Julian days) trend of all vegetation types decreased. During 1982–2014, the average temperature sensitivity of the SOS was 1.5 d/°C. The correlation between the SOS and the pre-season temperature is significant in northeast Asia, while the correlation between the EOS and the pre-season precipitation is greater than that between temperature and radiation. The impact of radiation changes on the SOS is relatively small. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Graphical abstract

17 pages, 7640 KiB

Open AccessArticle

Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images

by Shaojia Ge, Oleg Antropov, Tuomas Häme, Ronald E. McRoberts and Jukka Miettinen

Remote Sens. 2023, 15(21), 5152; https://doi.org/10.3390/rs15215152 - 27 Oct 2023

Cited by 7 | Viewed by 2777

Abstract

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a “model transfer” (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of

2.70

m and R² of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)

► Show Figures

Journal Menu

Journal Browser

Biomass Remote Sensing in Forest Landscapes II

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI