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Biomass Remote Sensing in Forest Landscapes

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2018) | Viewed by 101498

Special Issue Editors

Dr. Pierre Couteron
E-Mail Website
Guest Editor
IRD, UMR AMAP, F-34000 Montpellier, France
Interests: optical remote-sensing; spatial statistics; modelling; tropical vegetation
Dr. Nicolas Barbier
E-Mail Website
Guest Editor
IRD, UMR AMAP, F-34000 Montpellier, France
Interests: optical remote-sensing; tropical vegetation structure and dynamics
Prof. Dr. Martin Herold
E-Mail Website
Guest Editor
Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
Interests: large area land and forest monitoring; monitoring and reporting for UNFCCC and Sustainable Development Goals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the follow up of decisions made at the Paris COP21, forest biomass and carbon are, more than ever, in the focus of decision makers, notably in the tropics and subtropics, due to their role in the global carbon cycle. Acknowledging this role, the REDD+ (reducing emissions from deforestation and forest degradation in developing countries) international framework, for instance, aims at introducing avoided emissions from tropical forests in the global carbon market. To answer the need for transparent Monitoring, Reporting and Verification (MRV) systems, the remote sensing community aims to provide operational tools allowing quantitative biomass monitoring over extensive forested landscapes and the effects of forest changes on carbon stocks. Multidisciplinary and multi-sensor approaches are clearly needed, given the complexity of the biological objects studied, diversity of ecological and socio-economical conditions of the concerned countries, the difficulty of atmospheric conditions, and the limited accessibility of field information and reference data.

This Special Issue aims at gathering contributions exploring cutting edge remote sensing approaches, either empirical or model-based, to quantify woody biomass and carbon stocks in forests and woodlands. We encourage applications also tackling issues of integrating ground and satellite data to better 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 thereof, are welcome. Modelling is viewed here in a broad sense, including climate and vegetation models, radiative transfer models as well as statistical error propagation studies considering all steps from the ground assessment of biomass to large scale predictions of biomass variation in space and time.

Dr. Pierre  Couteron
Dr. Nicolas  Barbier
Prof. Dr. Martin  Herold
Guest Editors

Manuscript Submission Information

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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

  • Monitoring woody vegetation Above-Ground Biomass (AGB)°
  • Multi-scale, multi-signal data fusion
  • Non-destructive field biomass assessment (e.g., T-lidar)
  • Upscaling and error propagation
  • Benchmarking biomass predictions methods via forest models
  • REDD+ implementation
  • Monitoring, Reporting and Verification (MRV) systems

Published Papers (14 papers)

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Research

23 pages, 8421 KiB  
Article
Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches
Remote Sens. 2018, 10(5), 731; https://doi.org/10.3390/rs10050731 - 09 May 2018
Cited by 16 | Viewed by 5165
Abstract
Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (ΔH) and the actual changes in aboveground biomass (ΔAGB). Different approaches can be used to accomplish this task: direct approaches link ΔH directly to ΔAGB, while [...] Read more.
Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (ΔH) and the actual changes in aboveground biomass (ΔAGB). Different approaches can be used to accomplish this task: direct approaches link ΔH directly to ΔAGB, while indirect approaches are based on deriving AGB stock estimates for two points in time and calculating the difference. In some studies, direct approaches led to more accurate estimations, while, in others, indirect approaches led to more accurate estimations. It is unknown how each approach performs under different conditions and over the full range of possible changes. Here, we used a forest model (FORMIND) to generate a large dataset (>28,000 ha) of natural and disturbed forest stands over time. Remote sensing of forest height was simulated on these stands to derive canopy height models for each time step. Three approaches for estimating ΔAGB were compared: (i) the direct approach; (ii) the indirect approach and (iii) an enhanced direct approach (dir+tex), using ΔH in combination with canopy texture. Total prediction accuracies of the three approaches measured as root mean squared errors (RMSE) were RMSEdirect = 18.7 t ha−1, RMSEindirect = 12.6 t ha−1 and RMSEdir+tex = 12.4 t ha−1. Further analyses revealed height-dependent biases in the ΔAGB estimates of the direct approach, which did not occur with the other approaches. Finally, the three approaches were applied on radar-derived (TanDEM-X) canopy height changes on Barro Colorado Island (Panama). The study demonstrates the potential of forest modeling for improving the interpretation of changes observed in remote sensing data and for comparing different methodologies. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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26 pages, 26470 KiB  
Article
Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data
Remote Sens. 2018, 10(5), 660; https://doi.org/10.3390/rs10050660 - 24 Apr 2018
Cited by 28 | Viewed by 8828
Abstract
A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF) is still complicated, due to fast-changing climate and topographic conditions, which modifies the forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly detectable by [...] Read more.
A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF) is still complicated, due to fast-changing climate and topographic conditions, which modifies the forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly detectable by small field plots, especially in natural TMF due to the high tree diversity and the inaccessibility of remote areas. Therefore, the present approach used remotely sensed data from a Light Detection and Ranging (LiDAR) sensor in combination with field measurements to estimate AGB accurately for a catchment in the Andes of south-eastern Ecuador. From the LiDAR data, information about horizontal and vertical structure of the TMF could be derived and the vegetation at tree level classified, differentiated between the prevailing forest types (ravine forest, ridge forest and Elfin Forest). Furthermore, topographical variables (Topographic Position Index, TPI; Morphometric Protection Index, MPI) were calculated by means of the high-resolution LiDAR data to analyse the AGB distribution within the catchment. The field measurements included different tree parameters of the species present in the plots, which were used to determine the local mean Wood Density (WD) as well as the specific height-diameter relationship to calculate AGB, applying regional scale modelling at tree level. The results confirmed that field plot measurements alone cannot capture completely the forest structure in TMF but in combination with high resolution LiDAR data, applying a classification at tree level, the AGB amount (Mg ha−1) and its distribution in the entire catchment could be estimated adequately (model accuracy at tree level: R2 > 0.91). It was found that the AGB distribution is strongly related to ridges and depressions (TPI) and to the protection of the site (MPI), because high AGB was also detected at higher elevations (up to 196.6 Mg ha−1, above 2700 m), if the site is situated in depressions (ravine forest) and protected by the surrounding terrain. In general, highest AGB is stored in the protected ravine TMF parts, also at higher elevations, which could only be detected by means of the remote sensed data in high resolution, because most of these areas are inaccessible. Other vegetation units, present in the study catchment (pasture and subpáramo) do not contain large AGB stocks, which underlines the importance of intact natural forest stands. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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21 pages, 4183 KiB  
Article
Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data
Remote Sens. 2018, 10(4), 637; https://doi.org/10.3390/rs10040637 - 20 Apr 2018
Cited by 23 | Viewed by 6185
Abstract
Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of [...] Read more.
Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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22 pages, 2205 KiB  
Article
Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region
Remote Sens. 2018, 10(4), 627; https://doi.org/10.3390/rs10040627 - 18 Apr 2018
Cited by 126 | Viewed by 7334
Abstract
Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic [...] Read more.
Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, and their combinations) and modeling algorithms (e.g., artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), and linear regression (LR)) for AGB estimation in a subtropical region under non-stratification and stratification of forest types. The results show the following: (1) Landsat TM imagery provides more accurate AGB estimates (root mean squared error (RMSE) values in 27.7–29.3 Mg/ha) than ALOS PALSAR (RMSE values in 30.3–33.7 Mg/ha). The combination of TM and PALSAR data has similar performance for ANN and SVR, worse performance for RF and KNN, and slightly improved performance for LR. (2) Overestimation for small AGB values and underestimation for large AGB values are major problems when using the optical (e.g., Landsat) or radar (e.g., ALOS PALSAR) data. (3) LR is still an important tool for AGB modeling, especially for the AGB range of 40–120 Mg/ha. Machine learning algorithms have limited effects on improving AGB estimation overall, but ANN can improve AGB modeling when AGB values are greater than 120 Mg/ha. (4) Forest type and AGB range are important factors that influence AGB modeling performance. (5) Stratification based on forest types improved AGB estimation, especially when AGB was greater than 160 Mg/ha, using the LR approach. This research provides new insight for remote sensing-based AGB modeling for the subtropical forest ecosystem through a comprehensive analysis of different source data, modeling algorithms, and forest types. It is critical to develop an optimal AGB modeling procedure, including the collection of a sufficient number of sample plots, extraction of suitable variables and modeling algorithms, and evaluation of the AGB estimates. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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23 pages, 547 KiB  
Article
Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations
Remote Sens. 2018, 10(4), 608; https://doi.org/10.3390/rs10040608 - 14 Apr 2018
Cited by 63 | Viewed by 6447
Abstract
Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; [...] Read more.
Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; and, (ii) single-image or multi-polarized retrieval schemes. The use of multi-temporal or multi-frequency data improved the biomass estimates when compared to single-image retrieval. Low frequency SAR backscatter contributed the most to the biomass estimates. Single-pass InSAR height was reported to be a more reliable predictor of biomass, overcoming the loss of sensitivity of SAR backscatter and coherence in high biomass forest. A variety of empirical and semi-empirical regression models relating biomass to the SAR observables were proposed. Semi-empirical models were mostly used for large-scale mapping because of the simple formulation and the robustness of the model parameters estimates to forest structure and environmental conditions. Non-parametric models were appraised for their capability to ingest multiple observations and perform accurate retrievals having a large number of training samples available. Some studies argued that estimating compartment biomass (in stems, branches, foliage) with different types of SAR observations would lead to an improved estimate of total biomass. Although promising, scientific evidence for such an assumption is still weak. The increased availability of free and open SAR observations from currently orbiting and forthcoming spaceborne SAR missions will foster studies on forest biomass retrieval. Approaches attempting to maximize the information content on biomass of individual data streams shall be pursued. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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18 pages, 18192 KiB  
Article
Biomass Growth from Multi-Temporal TanDEM-X Interferometric Synthetic Aperture Radar Observations of a Boreal Forest Site
Remote Sens. 2018, 10(4), 603; https://doi.org/10.3390/rs10040603 - 12 Apr 2018
Cited by 11 | Viewed by 3880
Abstract
Forest growth estimation is important in forest research and forest management, but complex to analyze in diverse forest stands. Twelve summertime TanDEM-X acquisitions from the boreal test site, Krycklan, in Sweden, with a known digital terrain model, DTM, have been used to study [...] Read more.
Forest growth estimation is important in forest research and forest management, but complex to analyze in diverse forest stands. Twelve summertime TanDEM-X acquisitions from the boreal test site, Krycklan, in Sweden, with a known digital terrain model, DTM, have been used to study phase height and aboveground biomass change over 3.2 years based on the Interferometric Water Cloud Model, IWCM. The maximum phase height rate was determined to 0.29 m/yr, while the mean phase height rate was 0.16 m/yr. The corresponding maximum growth rate of the aboveground dry biomass, AGB, was 4.0 Mg/ha/yr with a mean rate of 1.9 Mg/ha/yr for 27 stands, varying from 23 to 183 Mg/ha. The highest relative AGB growth was found for young stands and high growth rates up to an age of 150 years. Growth rate differences relative a simplified model assuming AGB to be proportional to the phase height were studied, and the possibility to avoid a DTM was discussed. Effects of tree species, thinning, and clear cutting were evaluated. Verifications using in situ data from 2008 and a different in situ dataset combined with airborne laser scanning data from 2015 have been discussed. It was concluded that the use of multi-temporal TanDEM-X interferometric synthetic aperture radar observations with AGB estimates of each individual observation can be an important method to derive growth rates in boreal forests. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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18 pages, 18978 KiB  
Article
Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data
Remote Sens. 2018, 10(4), 601; https://doi.org/10.3390/rs10040601 - 12 Apr 2018
Cited by 83 | Viewed by 10460
Abstract
Accurate assessment of above-ground biomass (AGB) is important for the sustainable management of forests, especially buffer zone (areas within the protected area, where restrictions are placed upon resource use and special measure are undertaken to intensify the conservation value of protected area) areas [...] Read more.
Accurate assessment of above-ground biomass (AGB) is important for the sustainable management of forests, especially buffer zone (areas within the protected area, where restrictions are placed upon resource use and special measure are undertaken to intensify the conservation value of protected area) areas with a high dependence on forest products. This study presents a new AGB estimation method and demonstrates the potential of medium-resolution Sentinel-2 Multi-Spectral Instrument (MSI) data application as an alternative to hyperspectral data in inaccessible regions. Sentinel-2 performance was evaluated for a buffer zone community forest in Parsa National Park, Nepal, using field-based AGB as a dependent variable, as well as spectral band values and spectral-derived vegetation indices as independent variables in the Random Forest (RF) algorithm. The 10-fold cross-validation was used to evaluate model effectiveness. The effect of the input variable number on AGB prediction was also investigated. The model using all extracted spectral information plus all derived spectral vegetation indices provided better AGB estimates (R2 = 0.81 and RMSE = 25.57 t ha−1). Incorporating the optimal subset of key variables did not improve model variance but reduced the error slightly. This result is explained by the technically-advanced nature of Sentinel-2, which includes fine spatial resolution (10, 20 m) and strategically-positioned bands (red-edge), conducted in flat topography with an advanced machine learning algorithm. However, assessing its transferability to other forest types with varying altitude would enable future performance and interpretability assessments of Sentinel-2. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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19 pages, 10310 KiB  
Article
Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China
Remote Sens. 2018, 10(4), 532; https://doi.org/10.3390/rs10040532 - 30 Mar 2018
Cited by 53 | Viewed by 5410
Abstract
Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources [...] Read more.
Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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21 pages, 6690 KiB  
Article
Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data
Remote Sens. 2018, 10(3), 438; https://doi.org/10.3390/rs10030438 - 11 Mar 2018
Cited by 21 | Viewed by 5821
Abstract
Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes [...] Read more.
Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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21 pages, 8281 KiB  
Article
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
Remote Sens. 2018, 10(2), 172; https://doi.org/10.3390/rs10020172 - 25 Jan 2018
Cited by 181 | Viewed by 13331
Abstract
The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the [...] Read more.
The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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1983 KiB  
Article
Relationships of S-Band Radar Backscatter and Forest Aboveground Biomass in Different Forest Types
Remote Sens. 2017, 9(11), 1116; https://doi.org/10.3390/rs9111116 - 02 Nov 2017
Cited by 27 | Viewed by 5641
Abstract
Synthetic Aperture Radar (SAR) signals respond to the interactions of microwaves with vegetation canopy scatterers that collectively characterise forest structure. The sensitivity of S-band (7.5–15 cm) backscatter to the different forest types (broadleaved, needleleaved) with varying aboveground biomass (AGB) across temperate (mixed, needleleaved) [...] Read more.
Synthetic Aperture Radar (SAR) signals respond to the interactions of microwaves with vegetation canopy scatterers that collectively characterise forest structure. The sensitivity of S-band (7.5–15 cm) backscatter to the different forest types (broadleaved, needleleaved) with varying aboveground biomass (AGB) across temperate (mixed, needleleaved) and tropical (broadleaved, woody savanna, secondary) forests is less well understood. In this study, Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model simulations showed strong volume scattering returns from S-band SAR for broadleaved canopies caused by ground/trunk interactions. A general relationship between AirSAR S-band measurements and MIMICS-I simulated radar backscatter with forest AGB up to nearly 100 t/ha in broadleaved forest in the UK was found. Simulated S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest biomass with a saturation level close to 100 t/ha and errors between 37 t/ha and 44 t/ha for HV and VV polarisations for tropical ecosystems. In the near future, satellite SAR-derived forest biomass from P-band BIOMASS mission and L-band ALOS-2 PALSAR-2 in combination with S-band UK NovaSAR-S and the joint NASA-ISRO NISAR sensors will provide better quantification of large-scale forest AGB at varying sensitivity levels across primary and secondary forests and woody savannas. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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6203 KiB  
Article
Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest
Remote Sens. 2017, 9(10), 1068; https://doi.org/10.3390/rs9101068 - 23 Oct 2017
Cited by 49 | Viewed by 8583
Abstract
Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar [...] Read more.
Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data
Remote Sens. 2017, 9(10), 984; https://doi.org/10.3390/rs9100984 - 22 Sep 2017
Cited by 10 | Viewed by 5531
Abstract
The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in [...] Read more.
The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of R M S E = 24.1 t·ha−1; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya
Remote Sens. 2017, 9(8), 827; https://doi.org/10.3390/rs9080827 - 11 Aug 2017
Cited by 21 | Viewed by 7239
Abstract
Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were [...] Read more.
Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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