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Forest Biomass and Carbon Observation with Remote Sensing
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Forest Biomass and Carbon Observation with Remote Sensing

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 December 2020) | Viewed by 57643

Special Issue Editors

Dr. Dmitry Schepaschenko

E-Mail Website
Guest Editor
Agriculture, Forestry, and Ecosystem Services (AFE) Research Group, Biodiversity and Natural Resources (BNR) Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, 2361 Laxenburg, Austria
Interests: boreal forests; soil carbon; biomass; land use land cover mapping; biomass remote sensing; forest growth
Special Issues, Collections and Topics in MDPI journals
Dr. Martin Thurner

E-Mail Website
Guest Editor
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
Interests: vegetation carbon cycle; remote sensing of forest biomass; model–data integration; biomass allometry; boreal and temperate forests
Dr. Maurizio Santoro

E-Mail Website
Guest Editor
Gamma Remote Sensing, Bern, Switzerland
Interests: radar remote sensing; thematic mapping; estimation of forest biomass
Prof. Dr. Heiko Balzter

E-Mail Website
Guest Editor
NERC National Centre for Earth Observation, Leicester Institute for Space and Earth Observation, School of Geography, Geology and Environment, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: landscape and climate research; land surface modelling; terrestrial remote sensing; synthetic aperture radar (SAR); light detection and ranging (LIDAR); forest monitoring, carbon cycle and climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Neha Joshi

E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), laxenburg, Austria
Interests: Synthetic Aperture Radar (SAR); mapping forest disturbances; time-series analysis and change detection; tropical forests

Special Issue Information

Dear Colleagues,

Forest biomass and carbon monitoring is high on the agenda of environmental research and policy due to the importance of forest carbon dynamics with regard to climate change mitigation, biodiversity preservation, and timber and bioenergy production. Multidisciplinary and multisensor remote sensing approaches are clearly needed to obtain a synoptic view of forest biomass, given the complexity of forest ecosystems, diversity of ecological and socioeconomic conditions, high dynamics of land use, and the limited accessibility of field information and reference data.

This Special Issue aims at gathering contributions exploring remote sensing approaches to quantify woody biomass and carbon stocks in forests and woodlands. We encourage applications tackling issues of integrating ground and satellite data for calibration and validation of remote sensing-based biomass observations. Contributions dealing with various types of sensors (active and passive) and carriers (terrestrial, airborne, unmanned aerial vehicles, space-borne) or combinations are welcome. Applications of data collected by new instruments (GEDI, ICESat-2) or within the framework of recent biomass mapping approaches (e.g., GlobBiomass, Biomass CCI) and their integration with state-of-the-art research to track biomass variation in space and time are very welcome. The key role of biomass remote sensing in forest and vegetation modeling, biodiversity, and forest management assessment is going to be the focus of this issue as well.

Dr. Dmitry Schepaschenko
Dr. Martin Thurner
Dr. Maurizio Santoro
Prof. Heiko Balzter
Dr. Neha Joshi
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)
  • Synergy of remote sensing and in-situ data
  • Calibration and validation (cal/val)
  • Monitoring, reporting, and verification (MRV) systems
  • Model–data integration
  • Vegetation carbon cycle

Published Papers (16 papers)

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12 pages, 20051 KiB  
Article
Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions
by Jonas Bohlin, Jörgen Wallerman and Johan E. S. Fransson
Remote Sens. 2021, 13(4), 720; https://doi.org/10.3390/rs13040720 - 16 Feb 2021
Cited by 1 | Viewed by 1834
Abstract
With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud [...] Read more.
With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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20 pages, 14065 KiB  
Article
High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China
by Bangqian Chen, Ting Yun, Jun Ma, Weili Kou, Hailiang Li, Chuan Yang, Xiangming Xiao, Xian Zhang, Rui Sun, Guishui Xie and Zhixiang Wu
Remote Sens. 2020, 12(23), 3853; https://doi.org/10.3390/rs12233853 - 24 Nov 2020
Cited by 15 | Viewed by 3674 | Correction
Abstract
Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of [...] Read more.
Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of difficulties in acquiring the Diameter at Breast Height (DBH) through remote sensing and the problem of biomass saturation. The stand age, which is closely related to the forest biomass, was proposed for biomass estimation in this study. A stand age map at an annual scale for Hainan Island, which is the second largest natural rubber production base in China, was generated using all Landsat and Sentinel-2 (LS2) data (1987–2017). Scatter plots and the correlation coefficient method were used to explore the relationship (e.g., biomass saturation) between rubber biomass and different LS2-based variables. Subsequently, a regression model fitted with the stand age (R2 = 0.96) and a Random Forest (RF) model parameterizing with LS2-based variables and/or the stand age were respectively employed to estimate rubber biomass for Hainan Island. The results show that rubber biomass was saturated around 65 Mg/ha with all LS2-based variables. The regression model estimated biomass accurately (R2 = 0.79 and Root Mean Square Error (RMSE) = 14.00 Mg/ha) and eliminated the saturation problem significantly. In addition to LS2-based variables, adding a stand age parameter to the RF models was found to significantly improve the prediction accuracy (R2 = 0.82–0.96 and RMSE = 4.08–10.59 Mg/ha, modeling using samples of different biomass sizes). However, all RF models overestimated the biomass of young plantations and underestimated the biomass of old plantations. A hybrid model integrating the optimal results of RF and regression models reduced estimation bias and generated the best performance (R2 = 0.83 and RMSE = 12.48 Mg/ha). The total rubber biomass of Hainan Island in 2017 was about 5.40 × 107 Mg. The northward and westward expansions after 2000 had great impact on the biomass distribution, leading to a higher biomass density for the inland coastal strip from south to northeast and a lower biomass density in the northern and western regions. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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16 pages, 4604 KiB  
Article
Aboveground Biomass Changes in Tropical Montane Forest of Northern Borneo Estimated Using Spaceborne and Airborne Digital Elevation Data
by Ho Yan Loh, Daniel James, Keiko Ioki, Wilson Vun Chiong Wong, Satoshi Tsuyuki and Mui-How Phua
Remote Sens. 2020, 12(22), 3677; https://doi.org/10.3390/rs12223677 - 10 Nov 2020
Cited by 4 | Viewed by 2547
Abstract
Monitoring anthropogenic disturbances on aboveground biomass (AGB) of tropical montane forests is crucial, but challenging, due to a lack of historical AGB information. We examined the use of spaceborne (Shuttle Radar Topographic Mission Digital Elevation Model (SRTM) digital surface model (DSM)) and airborne [...] Read more.
Monitoring anthropogenic disturbances on aboveground biomass (AGB) of tropical montane forests is crucial, but challenging, due to a lack of historical AGB information. We examined the use of spaceborne (Shuttle Radar Topographic Mission Digital Elevation Model (SRTM) digital surface model (DSM)) and airborne (Light Detection and Ranging (LiDAR)) digital elevation data to estimate tropical montane forest AGB changes in northern Borneo between 2000 and 2012. LiDAR canopy height model (CHM) mean values were used to calibrate SRTM CHM in different pixel resolutions (1, 5, 10, and 30 m). Regression analyses between field AGB of 2012 and LiDAR CHM means at different resolutions identified the LiDAR CHM mean at 1 m resolution as the best model (modeling efficiency = 0.798; relative root mean square error = 25.81%). Using the multitemporal AGB maps, the overall mean AGB decrease was estimated at 390.50 Mg/ha, but AGB removal up to 673.30 Mg/ha was estimated in the managed forests due to timber extraction. Over the 12 years, the AGB accumulated at a rate of 10.44 Mg/ha/yr, which was attributed to natural regeneration. The annual rate in the village area was 8.31 Mg/ha/yr, which was almost 20% lower than in the managed forests (10.21 Mg/ha/yr). This study identified forestry land use, especially commercial logging, as the main driver for the AGB changes in the montane forest. As SRTM DSM data are freely available, this approach can be used to estimate baseline historical AGB information for monitoring forest AGB changes in other tropical regions. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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16 pages, 9532 KiB  
Article
Assessment of Forest Biomass Estimation from Dry and Wet SAR Acquisitions Collected during the 2019 UAVSAR AM-PM Campaign in Southeastern United States
by Unmesh Khati, Marco Lavalle, Gustavo H. X. Shiroma, Victoria Meyer and Bruce Chapman
Remote Sens. 2020, 12(20), 3397; https://doi.org/10.3390/rs12203397 - 16 Oct 2020
Cited by 10 | Viewed by 2648
Abstract
Forest above-ground biomass (AGB) estimation from SAR backscatter is affected by varying imaging and environmental conditions. This paper quantifies and compares the performance of forest biomass estimation from L-band SAR backscatter measured selectively under dry and wet conditions during the 2019 AM-PM NASA [...] Read more.
Forest above-ground biomass (AGB) estimation from SAR backscatter is affected by varying imaging and environmental conditions. This paper quantifies and compares the performance of forest biomass estimation from L-band SAR backscatter measured selectively under dry and wet conditions during the 2019 AM-PM NASA airborne campaign. Seven Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images acquired between June and October 2019 over a temperate deciduous forest in Southeastern United States with varying moisture and precipitation conditions are examined in conjunction with LIDAR and field measurements. Biomass is estimated by fitting a 3-parameter modified Water Cloud Model (WCM) to radiometric terrain corrected SAR backscatter. Our experiment is designed to quantify the biomass estimation errors when biomass models are calibrated and validated on varying acquisition conditions (dry or wet). Multi-temporal estimation strategies are also evaluated and compared with single-acquisition estimation approaches. As an outcome, the experiment shows that the WCM model calibrated and validated on single acquisitions adapts to different soil moisture conditions with RMSD up to 18.7 Mg/ha. The AGB estimation performance, however, decreases with RMSD upwards of 30 Mg/ha when the model is cross-validated on moisture and precipitation conditions different than the calibration conditions. Results confirm that calibrating the model over the multi-temporal data using averaged backscatter or weighted combinations of individual AGB estimates, improves the biomass estimation accuracy up to about 20% at L-band. This study helps design biomass cal/val procedures and biomass estimation algorithms for dense time-series to be collected by low-frequency radar missions such as NASA-ISRO SAR (NISAR) and BIOMASS. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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25 pages, 17452 KiB  
Article
Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data
by Xiandie Jiang, Guiying Li, Dengsheng Lu, Emilio Moran and Mateus Batistella
Remote Sens. 2020, 12(20), 3330; https://doi.org/10.3390/rs12203330 - 14 Oct 2020
Cited by 7 | Viewed by 2897
Abstract
Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS [...] Read more.
Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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24 pages, 3967 KiB  
Article
Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin
by Pierre Migolet and Kalifa Goïta
Remote Sens. 2020, 12(18), 2926; https://doi.org/10.3390/rs12182926 - 09 Sep 2020
Cited by 4 | Viewed by 2932
Abstract
The present study developed methods using remote sensing for estimation of total dry aboveground biomass (AGB) of oil palm in the Congo Basin. To achieve this, stem diameters at breast height (DBH, 1.3 m) and stem heights were measured in an oil palm [...] Read more.
The present study developed methods using remote sensing for estimation of total dry aboveground biomass (AGB) of oil palm in the Congo Basin. To achieve this, stem diameters at breast height (DBH, 1.3 m) and stem heights were measured in an oil palm plantation located in Gabon (Congo Basin, Central Africa). These measurements were used to determine AGB in situ. The remote sensing approach that was used to estimate AGB was textural ordination (FOTO) based upon Fourier transforms that were applied, respectively, to PlanetScope and FORMOSAT-2 satellite images taken from the area. The FOTO method is based on the combined use of two-dimensional (2D) Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). In the context of the present study, it was used to characterize the variation in canopy structure and to estimate the aboveground biomass of mature oil palms. Two types of equations linking FOTO indices to in situ biomass were developed: multiple linear regressions (MLR); and multivariate adaptive spline regressions (MARS). All best models developed yielded significant results, regardless of whether they were derived from PlanetScope or from FORMOSAT-2 images. Coefficients of determination (R2) varied between 0.80 and 0.92 (p ≤ 0.0005); and relative root mean-square-errors (%RMSE) were less than 10.12% in all cases. The best model was obtained using MARS approach with FOTO indices from FORMOSAT-2 (%RMSE = 6.09%). Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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21 pages, 2384 KiB  
Article
Tracking Rates of Forest Disturbance and Associated Carbon Loss in Areas of Illegal Amber Mining in Ukraine Using Landsat Time Series
by Viktor Myroniuk, Andrii Bilous, Yevhenii Khan, Andrii Terentiev, Pavlo Kravets, Sergii Kovalevskyi and Linda See
Remote Sens. 2020, 12(14), 2235; https://doi.org/10.3390/rs12142235 - 12 Jul 2020
Cited by 16 | Viewed by 5074
Abstract
Mapping forest disturbance is crucial for many applications related to decision-making for sustainable forest management. This study identified the effect of illegal amber mining on forest change and accumulated carbon stock across a study area of 8125.5 ha in northern Ukraine. Our method [...] Read more.
Mapping forest disturbance is crucial for many applications related to decision-making for sustainable forest management. This study identified the effect of illegal amber mining on forest change and accumulated carbon stock across a study area of 8125.5 ha in northern Ukraine. Our method relies on the Google Earth Engine (GEE) implementation of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm of Landsat time-series (LTS) to derive yearly maps of forest disturbance and recovery in areas affected by amber extraction operations. We used virtual reality (VR) 360 interactive panoramic images taken from the sites to attribute four levels of forest disturbance associated with the delta normalized burn ratio (dNBR) and then calculated the carbon loss. We revealed that illegal amber extraction in Ukraine has been occurring since the middle of the 1990s, yielding 3260 ha of total disturbed area up to 2019. This study indicated that the area of forest disturbance increased dramatically during 2013–2014, and illegal amber operations persist. As a result, regrowth processes were mapped on only 375 ha of total disturbed area. The results were integrated into the Forest Stewardship Council® (FSC®) quality management system in the region to categorize Forest Management Units (FMUs) conforming to different disturbance rates and taking actions related to their certification status. Moreover, carbon loss evaluation allows the responsible forest management systems to be streamlined and to endorse ecosystem service assessment. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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17 pages, 3062 KiB  
Article
Carbon Dynamics in a Human-Modified Tropical Forest: A Case Study Using Multi-Temporal LiDAR Data
by Yhasmin Mendes de Moura, Heiko Balzter, Lênio S. Galvão, Ricardo Dalagnol, Fernando Espírito-Santo, Erone G. Santos, Mariano Garcia, Polyanna da Conceição Bispo, Raimundo C. Oliveira and Yosio E. Shimabukuro
Remote Sens. 2020, 12(3), 430; https://doi.org/10.3390/rs12030430 - 29 Jan 2020
Cited by 14 | Viewed by 5111
Abstract
Tropical forests hold significant amounts of carbon and play a critical role on Earth´s climate system. To date, carbon dynamics over tropical forests have been poorly assessed, especially over vast areas of the tropics that have been affected by some type of disturbance [...] Read more.
Tropical forests hold significant amounts of carbon and play a critical role on Earth´s climate system. To date, carbon dynamics over tropical forests have been poorly assessed, especially over vast areas of the tropics that have been affected by some type of disturbance (e.g., selective logging, understory fires, and fragmentation). Understanding the multi-temporal dynamics of carbon stocks over human-modified tropical forests (HMTF) is crucial to close the carbon cycle balance in the tropics. Here, we used multi-temporal and high-spatial resolution airborne LiDAR data to quantify rates of carbon dynamics over a large patch of HMTF in eastern Amazon, Brazil. We described a robust approach to monitor changes in aboveground forest carbon stocks between 2012 and 2018. Our results showed that this particular HMTF lost 0.57 m·yr−1 in mean forest canopy height and 1.38 Mg·C·ha−1·yr−1 of forest carbon between 2012 and 2018. LiDAR-based estimates of Aboveground Carbon Density (ACD) showed progressive loss through the years, from 77.9 Mg·C·ha−1 in 2012 to 53.1 Mg·C·ha−1 in 2018, thus a decrease of 31.8%. Rates of carbon stock changes were negative for all time intervals analyzed, yielding average annual carbon loss rates of −1.34 Mg·C·ha−1·yr−1. This suggests that this HMTF is acting more as a source of carbon than a sink, having great negative implications for carbon emission scenarios in tropical forests. Although more studies of forest dynamics in HMTFs are necessary to reduce the current remaining uncertainties in the carbon cycle, our results highlight the persistent effects of carbon losses for the study area. HMTFs are likely to expand across the Amazon in the near future. The resultant carbon source conditions, directly associated with disturbances, may be essential when considering climate projections and carbon accounting methods. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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19 pages, 19575 KiB  
Article
A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest
by Fernando Carvajal-Ramírez, João Manuel Pereira Ramalho Serrano, Francisco Agüera-Vega and Patricio Martínez-Carricondo
Remote Sens. 2019, 11(21), 2579; https://doi.org/10.3390/rs11212579 - 03 Nov 2019
Cited by 9 | Viewed by 3457
Abstract
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used [...] Read more.
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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19 pages, 6464 KiB  
Article
Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data
by Qiang Wang and Wenge Ni-Meister
Remote Sens. 2019, 11(21), 2566; https://doi.org/10.3390/rs11212566 - 01 Nov 2019
Cited by 13 | Viewed by 3516
Abstract
Both vegetation multi-angular and LiDAR (light detection and ranging) remote sensing data are indirectly and directly linked with 3D vegetation structure parameters, such as the tree height and vegetation gap fraction, which are critical elements in above-ground biomass and light profiles for photosynthesis [...] Read more.
Both vegetation multi-angular and LiDAR (light detection and ranging) remote sensing data are indirectly and directly linked with 3D vegetation structure parameters, such as the tree height and vegetation gap fraction, which are critical elements in above-ground biomass and light profiles for photosynthesis estimation. LiDAR, particularly LiDAR using waveform data, provides accurate estimates of these structural parameters but suffers from not enough spatial samplings. Structural parameters retrieved from multiangular imaging data through the inversion of physical models have larger uncertainties. This study searches for an analytical approach to fuse LiDAR and multiangular data. We explore the relationships between vegetation structure parameters derived from airborne vegetation LiDAR data and multiangular data and present a new potential angle vegetation index to retrieve the tree height and gap fraction using multi-angular data in Howland Forest, Maine. The BRDF (bidirectional reflectance distribution factor) index named NDMM (normalized difference between the maximum and minimum reflectance) linearly increases with the tree height and decreases with the gap fraction. In addition, these relationships are also dependent on the wavelength, tree species, and stand density. The NDMM index performs better in conifer (R = 0.451 for tree height and R = 0.472 for the gap fraction using the near infrared band) than in deciduous and mixed forests. It is superior in sparse (R = 0.569 for tree height and R = 0.604 for the gap fraction using the near infrared band) compared to dense forest. Moreover, the NDMM index is more strongly related to tree height and the gap fraction at the near infrared band than at the three visible bands. This study sheds light on the possibility of using multiangular data to map vegetation’s structural parameters in larger regions for carbon cycle studies through the fusion of LiDAR and multiangular remote sensing data. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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18 pages, 8442 KiB  
Article
Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
by Dailiang Peng, Helin Zhang, Liangyun Liu, Wenjiang Huang, Alfredo R. Huete, Xiaoyang Zhang, Fumin Wang, Le Yu, Qiaoyun Xie, Cheng Wang, Shezhou Luo, Cunjun Li and Bing Zhang
Remote Sens. 2019, 11(19), 2270; https://doi.org/10.3390/rs11192270 - 28 Sep 2019
Cited by 20 | Viewed by 4917
Abstract
Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over [...] Read more.
Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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20 pages, 3758 KiB  
Article
Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China
by Yuanyuan Fu, Hong S. He, Todd J. Hawbaker, Paul D. Henne, Zhiliang Zhu and David R. Larsen
Remote Sens. 2019, 11(17), 2005; https://doi.org/10.3390/rs11172005 - 25 Aug 2019
Cited by 20 | Viewed by 3852
Abstract
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level [...] Read more.
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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38 pages, 5047 KiB  
Review
Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales
by Sawaid Abbas, Man Sing Wong, Jin Wu, Naeem Shahzad and Syed Muhammad Irteza
Remote Sens. 2020, 12(20), 3351; https://doi.org/10.3390/rs12203351 - 14 Oct 2020
Cited by 32 | Viewed by 6924
Abstract
Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide [...] Read more.
Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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Other

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2 pages, 415 KiB  
Correction
Correction: Chen et al. High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China. Remote Sens. 2020, 12, 3853
by Bangqian Chen, Ting Yun, Jun Ma, Weili Kou, Hailiang Li, Chuan Yang, Xiangming Xiao, Xian Zhang, Rui Sun, Guishui Xie and Zhixiang Wu
Remote Sens. 2022, 14(19), 5044; https://doi.org/10.3390/rs14195044 - 10 Oct 2022
Viewed by 879
Abstract
In the original article [...] Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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1 pages, 142 KiB  
Erratum
Erratum: Wang, Q.; Ni-Meister, W. Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data (Short Title: Vegetation Structure from Lidar and Multiangular Data). Remote Sens. 2019, 11, 2566
by Qiang Wang and Wenge Ni-Meister
Remote Sens. 2021, 13(1), 17; https://doi.org/10.3390/rs13010017 - 22 Dec 2020
Viewed by 1296
Abstract
There is an error in the title of one of our publications [...] Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
13 pages, 4553 KiB  
Technical Note
A Comprehensive Forest Biomass Dataset for the USA Allows Customized Validation of Remotely Sensed Biomass Estimates
by James Menlove and Sean P. Healey
Remote Sens. 2020, 12(24), 4141; https://doi.org/10.3390/rs12244141 - 18 Dec 2020
Cited by 15 | Viewed by 3448
Abstract
There are several new and imminent space-based sensors intended to support mapping of forest structure and biomass. These instruments, along with advancing cloud-based mapping platforms, will soon contribute to a proliferation of biomass maps. One means of differentiating the quality of different maps [...] Read more.
There are several new and imminent space-based sensors intended to support mapping of forest structure and biomass. These instruments, along with advancing cloud-based mapping platforms, will soon contribute to a proliferation of biomass maps. One means of differentiating the quality of different maps and estimation strategies will be comparison of results against independent field-based estimates at various scales. The Forest Inventory and Analysis Program of the US Forest Service (FIA) maintains a designed sample of uniformly measured field plots across the conterminous United States. This paper reports production of a map of statistical estimates of mean biomass, created at approximately the finest scale (64,000-hectare hexagons) allowed by FIA’s sample density. This map may be useful for assessing the accuracy of future remotely sensed biomass estimates. Equally important, fine-scale mapping of FIA estimates highlights several ways in which field- and remote sensing-based methods must be aligned to ensure comparability. For example, the biomass in standing dead trees, which may or may not be included in biomass estimates, represents a source of potential discrepancy that FIA shows to be particularly important in the Western US. Likewise, alternative allometric equations (which link measurable tree dimensions such as diameter to difficult-to-measure variables like biomass) strongly impact biomass estimates in ways that can vary over short distances. Potential mismatch in the conditions counted as forests also varies greatly over space. Field-to-map comparisons will ideally minimize these sources of uncertainty by adopting common allometry, carbon pools, and forest definitions. Our national hexagon-level benchmark estimates, provided in Supplementary Files, therefore addresses multiple pools and allometric approaches independently, while providing explicit forest area and uncertainty information. This range of information is intended to allow scientists to minimize potential discrepancies in support of unambiguous validation. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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