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Remote Sensing
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Biomass Remote Sensing in Forest Landscapes II
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Biomass Remote Sensing in Forest Landscapes II

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: 31 July 2024 | Viewed by 3469

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

Published Papers (4 papers)

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Research

19 pages, 5628 KiB  
Article
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 - 02 Apr 2024
Viewed by 467
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 [...] Read more.
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)
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21 pages, 9711 KiB  
Article
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
Viewed by 753
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 [...] Read more.
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)
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20 pages, 20899 KiB  
Article
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 - 09 Nov 2023
Cited by 1 | Viewed by 792
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 [...] Read more.
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)
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Graphical abstract

17 pages, 7640 KiB  
Article
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 2 | Viewed by 1025
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 [...] Read more.
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)
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