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Special Issue "Digital Forest Resource Monitoring and Uncertainty Analysis"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Guangxing Wang

Southern Illinois University, Department of Geography and Environmental Resources
Website | E-Mail
Phone: 618-453-6017
Interests: remote sensing, GIS, spatial statistics, natural and environmental resources, sampling design; environmental quality assessment; forest and city vegetation carbon sequestration, desertification trend monitoring, quality assessment and spatial uncertainty analysis
Guest Editor
Prof. Dr. Erkki Tomppo

The Finnish Forest Research Institute, PO Box 18 (Jokiniemenkuja 1), FI-01301 VANTAA, Finland
Website | E-Mail
Interests: National forest inventory; forest inventory designs and methods; remote sensing
Guest Editor
Prof. Dengsheng Lu

School of Geographic Sciences, Fujian Normal University, No.8 Shangsan Road, Cangshan District, Fuzhou, Fujian, China 350007
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Interests: Remote sensing, urban and environment interaction, forest carbon modeling
Guest Editor
Prof. Dr. Huaiqing Zhang

Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Website | E-Mail
Interests: Forest inventory; wetlands; virtual forest; remote sensing
Guest Editor
Prof. Dr. Qi Chen

Department of Geography, University of Hawaiˈi at Mānoa, 2424 Maile Way, Honolulu, HI 96822, USA
Website | E-Mail
Interests: LiDAR remote sensing of vegetation; statistical learning; mathematical models; geospatial analysis

Special Issue Information

Dear Colleagues,

Currently, global warming is of major concern. To mitigate this effect, it is essential to provide policy makers with accurate information on the carbon cycle. As a significant carbon sink of terrestrial ecosystems, forests play a critical role in reducing carbon concentration in the atmosphere and in the mitigation of global warming. However, one great challenge in estimation of forest resources, and their carbon sequestration and dynamics is how to quantify its spatial distributions at various scales, including global, national, regional, and local levels. The estimates of forest resources and carbon stocks are also associated with large uncertainties and improving the quality of the products has become very important and urgent. Digital forest resource monitoring and uncertainty analysis provide the potential for searching for appropriate solutions to these challenges. Furthermore, new remote sensing technologies and their integrations with national forest sample plot data and growth models will offer powerful tools for developing solutions.

This Special Issue, "Digital Forest Resource Monitoring and Uncertainty Analysis”, will call for papers that demonstrate the original research that can overcome current significant gaps in the generation and quality assessment of digital forest resources and carbon products and provide quality control/assurance mechanisms to support decision-making regarding forest resource management and carbon simulation and thus mitigation of the greenhouse effect. Review articles are also welcome. It is expected that the papers will focus on the applications of remote sensing technologies to forest resource inventory and monitoring, and forest biomass/carbon modeling, and that the topics will include:

1)      Optimal sampling strategy and designs for forest resource inventory and monitoring;

2)      New methods and algorithms for forest resource inventory and monitoring, and forest biomass/carbon modeling;

3)      New remote sensing technologies for forest resource inventory and monitoring, and forest biomass/carbon modeling;

4)      Integration of multi-sensor data for forest resource inventory and monitoring, and forest biomass/carbon modeling;

5)      Accuracy assessment and uncertainty analysis of forest resource and biomass/carbon products.

Prof. Guangxing Wang
Prof. Erkki Tomppo
Prof. Dengsheng Lu
Prof. Huaiqing Zhang
Prof. Qi Chen
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 papers will be 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 1800 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.

Published Papers (16 papers)

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Research

Open AccessArticle Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection
Remote Sens. 2017, 9(3), 287; https://doi.org/10.3390/rs9030287
Received: 31 August 2016 / Revised: 10 February 2017 / Accepted: 12 March 2017 / Published: 18 March 2017
Cited by 5 | PDF Full-text (7306 KB) | HTML Full-text | XML Full-text
Abstract
Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate [...] Read more.
Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions
Remote Sens. 2017, 9(3), 205; https://doi.org/10.3390/rs9030205
Received: 8 December 2016 / Revised: 22 February 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
Cited by 8 | PDF Full-text (2556 KB) | HTML Full-text | XML Full-text
Abstract
Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface [...] Read more.
Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface and height models (CSMs and CHMs, respectively) over different forest types. The primary aim of this study is to investigate the applicability of image-based CHMs at different spatial resolutions (1 m, 2 m, 5 m) for use in stand-level forest inventory, with a special focus on estimation of stand-level merchantable volume of even-aged pedunculate oak (Quercus robur L.) forests. CHMs are generated by subtracting digital terrain models (DTMs), derived from the national digital terrain database, from corresponding digital surface models (DSMs), derived by the process of image matching of digital aerial images. Two types of stand-level volume regression models are developed for each CHM resolution. The first model is based solely on stand-level CHM metrics, whereas in the second model, easily obtainable variables from forest management databases are included in addition to CHM metrics. The estimation accuracies of the stand volume estimates based on stand-level metrics (relative root mean square error RMSE% = 12.53%–13.28%) are similar or slightly higher than those obtained from previous studies in which stand volume estimates were based on plot-level metrics. The inclusion of stand age as an independent variable in addition to CHM metrics improves the accuracy of the stand volume estimates. Improvements are notable for young and middle-aged stands, and negligible for mature and old stands. Results show that CHMs at the three different resolutions are capable of providing reasonably accurate volume estimates at the stand level. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods
Remote Sens. 2017, 9(3), 241; https://doi.org/10.3390/rs9030241
Received: 18 October 2016 / Revised: 22 February 2017 / Accepted: 1 March 2017 / Published: 5 March 2017
Cited by 5 | PDF Full-text (6048 KB) | HTML Full-text | XML Full-text
Abstract
The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest [...] Read more.
The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon
Remote Sens. 2017, 9(2), 154; https://doi.org/10.3390/rs9020154
Received: 2 September 2016 / Accepted: 4 February 2017 / Published: 14 February 2017
Cited by 3 | PDF Full-text (25808 KB) | HTML Full-text | XML Full-text
Abstract
Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based [...] Read more.
Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme—or LAMP—for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data
Remote Sens. 2017, 9(2), 148; https://doi.org/10.3390/rs9020148
Received: 30 August 2016 / Accepted: 3 February 2017 / Published: 10 February 2017
Cited by 2 | PDF Full-text (15901 KB) | HTML Full-text | XML Full-text
Abstract
Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on [...] Read more.
Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift—a non-parametric probability density-based clustering technique—to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees (“recall”) and 92 percent of the identified trees are correct (“precision”), showing good potential for use in the area of forest inventory. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods
Remote Sens. 2017, 9(1), 77; https://doi.org/10.3390/rs9010077
Received: 1 October 2016 / Revised: 5 December 2016 / Accepted: 9 January 2017 / Published: 15 January 2017
Cited by 4 | PDF Full-text (33934 KB) | HTML Full-text | XML Full-text
Abstract
Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work [...] Read more.
Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accuracy higher than 0.98 resulted in change mappings with Overall Accuracy around 0.83). Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms
Remote Sens. 2017, 9(1), 68; https://doi.org/10.3390/rs9010068
Received: 31 August 2016 / Revised: 13 December 2016 / Accepted: 1 January 2017 / Published: 12 January 2017
Cited by 6 | PDF Full-text (7958 KB) | HTML Full-text | XML Full-text
Abstract
The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging [...] Read more.
The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997–2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large-scale projects. In exceptional cases when data quality and quantity were adequate, we recommend the pre-processing approaches, noise reduction algorithms and breakpoint detection algorithms procedures that can enhance results. Finally, we include recommendations for achieving a faster and more accurate calibration of complex functions applied to remote sensing using genetic algorithms. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations
Remote Sens. 2017, 9(1), 47; https://doi.org/10.3390/rs9010047
Received: 1 September 2016 / Revised: 20 December 2016 / Accepted: 28 December 2016 / Published: 7 January 2017
Cited by 8 | PDF Full-text (2868 KB) | HTML Full-text | XML Full-text
Abstract
Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a [...] Read more.
Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests
Remote Sens. 2016, 8(12), 974; https://doi.org/10.3390/rs8120974
Received: 28 August 2016 / Revised: 16 October 2016 / Accepted: 18 November 2016 / Published: 28 November 2016
Cited by 10 | PDF Full-text (6158 KB) | HTML Full-text | XML Full-text
Abstract
Terrestrial laser scanning (TLS) is a promising technique for plot-wise acquisition of geometric attributes of forests. However, there still exists a need for TLS applications in mountain forests where tree stems’ growing directions are not vertical. This paper presents a novel method to [...] Read more.
Terrestrial laser scanning (TLS) is a promising technique for plot-wise acquisition of geometric attributes of forests. However, there still exists a need for TLS applications in mountain forests where tree stems’ growing directions are not vertical. This paper presents a novel method to model tree stems precisely in an alpine landslide-affected forest using TLS. Tree stems are automatically detected by a two-layer projection method. Stems are modeled by fitting a series of cylinders based on a 2D-3D random sample consensus (RANSAC)-based approach. Diameter at breast height (DBH) was manually measured in the field, and stem curves were measured from the point cloud as reference data. The results showed that all trees in the test area can be detected. The root mean square error (RMSE) of estimated DBH was 1.80 cm (5.5%). Stem curves were automatically generated and compared with reference data, as well as stem volumes. The results imply that the proposed method is able to map and model the stem curve precisely in complex forest conditions. The resulting stem parameters can be employed in single tree biomass estimation, tree growth quantification and other forest-related studies. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR
Remote Sens. 2016, 8(11), 942; https://doi.org/10.3390/rs8110942
Received: 14 July 2016 / Revised: 3 November 2016 / Accepted: 7 November 2016 / Published: 11 November 2016
Cited by 12 | PDF Full-text (7939 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this [...] Read more.
Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data
Remote Sens. 2016, 8(10), 820; https://doi.org/10.3390/rs8100820
Received: 2 July 2016 / Accepted: 28 September 2016 / Published: 5 October 2016
Cited by 4 | PDF Full-text (20875 KB) | HTML Full-text | XML Full-text
Abstract
On the basis of the Gaussian vertical backscatter (GVB) model, this paper proposes a new method for extracting pine forest height and forest underlying digital elevation model (FUDEM) from multi-baseline (MB) P-band polarimetric-interferometric radar (PolInSAR) data. Considering the linear ground-to-volume relationship, the GVB [...] Read more.
On the basis of the Gaussian vertical backscatter (GVB) model, this paper proposes a new method for extracting pine forest height and forest underlying digital elevation model (FUDEM) from multi-baseline (MB) P-band polarimetric-interferometric radar (PolInSAR) data. Considering the linear ground-to-volume relationship, the GVB is linked to the interferometric coherences of different polarizations. Subsequently, an inversion algorithm, weighted complex least squares adjustment (WCLSA), is formulated, including the mathematical model, the stochastic model and the parameter estimation method. The WCLSA method can take full advantage of the redundant observations, adjust the contributions of different observations and avoid null ground-to-volume ratio (GVR) assumption. The simulated experiment demonstrates that the WCLSA method is feasible to estimate the pure ground and volume scattering contributions. Finally, the WCLSA method is applied to E-SAR P-band data acquired over Krycklan Catchment covered with mixed pine forest. It is shown that the FUDEM highly agrees with those derived by LiDAR, with a root mean square error (RMSE) of 3.45 m, improved by 23.0% in comparison to the three-stage method. The difference between the extracted forest height and LiDAR forest height is assessed with a RMSE of 1.45 m, improved by 37.5% and 26.0%, respectively, for model and inversion aspects in comparison to three-stage inversion based on random volume over ground (RVoG) model. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data
Remote Sens. 2016, 8(9), 717; https://doi.org/10.3390/rs8090717
Received: 27 May 2016 / Revised: 18 August 2016 / Accepted: 26 August 2016 / Published: 31 August 2016
Cited by 5 | PDF Full-text (4448 KB) | HTML Full-text | XML Full-text
Abstract
Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the [...] Read more.
Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the stem diameter from TLS data. The methodology of stem diameter measurement by diameter tape was adopted. The stem cross-section at a given height along the stem was determined. Stem points for stem diameter retrieval were extracted according to the stem cross-section. Convex hull points of the extracted stem points were calculated in a projection plane. Then, a closed smooth curve was interpolated onto the convex hull points to simulate the path of the diameter tape, and stem diameter was calculated based on the length of the simulated path. The stems of different tree species with different properties were selected to verify the presented method. Compared with the field-measured diameter, the RMSE of the method was 0.0909 cm, which satisfies the accuracy requirement for forest inventory. This study provided a method for determining the stem cross-section and an efficient and precise curve fitting method for deriving stem diameter from TLS data. The importance of the stem cross-section and convex hull points in stem diameter retrieval was demonstrated. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design
Remote Sens. 2016, 8(7), 571; https://doi.org/10.3390/rs8070571
Received: 27 March 2016 / Revised: 25 June 2016 / Accepted: 30 June 2016 / Published: 6 July 2016
Cited by 1 | PDF Full-text (8492 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Combining sample plot and image data has been widely used to map forest carbon density at local, regional, national and global scales. When mapping is conducted using multiple spatial resolution images at different scales, field observations have to be collected at the corresponding [...] Read more.
Combining sample plot and image data has been widely used to map forest carbon density at local, regional, national and global scales. When mapping is conducted using multiple spatial resolution images at different scales, field observations have to be collected at the corresponding resolutions to match image values in pixel sizes. Given a study area, however, to save time and cost, field observations are often collected from sample plots having a fixed size. This will lead to inconsistency of spatial resolutions between sample plots and image pixels and impede the mapping and product quality assessment. In this study, a methodological framework was proposed to conduct mapping and accuracy assessment of forest carbon density at four spatial resolutions by combining remotely sensed data and reference values of sample plots from a systematical, nested and clustering sampling design. This design led to one field observation dataset at a 30 m spatial resolution sample plot level and three other reference datasets by averaging the observations from three, five and seven sample plots within each of 250 m and 500 m sub-blocks and 1000 m blocks, respectively. The datasets matched the pixel values of a Landsat 8 image and three MODIS products. A sequential Gaussian co-simulation (SGCS) and a sequential Gaussian block co-simulation (SGBCS), an upscaling algorithm, were employed to map forest carbon density at the spatial resolutions. This methodology was tested for mapping forest carbon density in Huang-Feng-Qiao forest farm of You County in Eastern Hunan of China. The results showed that: First, all of the means of predicted forest carbon density values at four spatial resolutions fell in the confidence intervals of the reference data at a significance level of 0.05. Second, the systematical, nested and clustering sampling design provided the potential to obtain spatial information of forest carbon density at multiple spatial resolutions. Third, the relative root mean square error (RMSE) of predicted values at the plot level was much greater than those at the sub-block and block levels. Moreover, the accuracies of the up-scaled estimates were much higher than those from previous studies. In addition, at the same spatial resolution, SGCSWA (scaling up the SGCS and Landsat derived 30 m resolution map using a window average (WA)) resulted in smallest relative RMSEs of up-scaled predictions, followed by combinations of Landsat images and SGBCS. The accuracies from both methods were significantly greater than those from the combinations of MODIS images and SGCS. Overall, this study implied that the combinations of Landsat 8 images and SGCSWA or SGBCS with the systematical, nested and clustering sampling design provided the potential to formulate a methodological framework to map forest carbon density and conduct accuracy assessment at multiple spatial resolutions. However, this methodology needs to be further refined and examined in other forest landscapes. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation
Remote Sens. 2016, 8(6), 469; https://doi.org/10.3390/rs8060469
Received: 3 March 2016 / Revised: 18 May 2016 / Accepted: 30 May 2016 / Published: 2 June 2016
Cited by 30 | PDF Full-text (3695 KB) | HTML Full-text | XML Full-text
Abstract
The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. [...] Read more.
The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80–120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry
Remote Sens. 2016, 8(2), 123; https://doi.org/10.3390/rs8020123
Received: 8 September 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
Cited by 15 | PDF Full-text (1574 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining [...] Read more.
Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining computer vision and photogrammetry may offer some of the coverage benefits of ALS and the improved accuracy of TLS; MVS combines computer vision research and automatic analysis of digital images from common commercial digital cameras with various algorithms to reconstruct three-dimensional (3D) objects with realistic shape and appearance. Despite the relative accuracy (relative geometrical distortion) of the reconstructions available in the processing software, the absolute accuracy is uncertain and difficult to evaluate. We evaluated the data collected by a common digital camera through the processing software (Agisoft PhotoScan ©) for photogrammetry by comparing those by direct measurement of the 3D magnetic motion tracker. Our analyses indicated that the error is mostly concentrated in the portions of the tree where visibility is lower, i.e., the bottom and upper parts of the stem. For each reference point from the digitizer we determined how many cameras could view this point. With a greater number of cameras we found increasing accuracy of the measured object space point positions (as expected), with a significant positive change in the trend beyond five cameras; when more than five cameras could view this point, the accuracy began to increase more abruptly, but eight cameras or more provided no increases in accuracy. This method allows for the retrieval of larger datasets from the measurements, which could improve the accuracy of estimates of 3D structure of trees at potentially reduced costs. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
Remote Sens. 2015, 7(12), 17272-17290; https://doi.org/10.3390/rs71215875
Received: 22 September 2015 / Revised: 1 December 2015 / Accepted: 10 December 2015 / Published: 18 December 2015
Cited by 1 | PDF Full-text (4666 KB) | HTML Full-text | XML Full-text
Abstract
Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at [...] Read more.
Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at spatial scales much finer than the eddy-covariance footprint. Scaling these contributions to the stand level requires a consideration of the heterogeneity in the canopy radiation field. This paper presents a stochastic ray tracing approach to predict the probabilities of light absorption from over a thousand hemispherical directions by thousands of individual scene elements. Once a look-up table of absorption probabilities is computed, dynamic illumination conditions can be simulated in a computationally realistic time, from which stand-level gross primary productivity can be obtained by integrating photosynthetic assimilation over the scene. We demonstrate the method by inverting a leaf-level photosynthesis model with eddy-covariance and meteorological data. Optimized leaf photosynthesis parameters and canopy structure were able to explain 75% of variation in eddy-covariance gross primary productivity estimates, and commonly used parameters, including photosynthetic capacity and quantum yield, fell within reported ranges. Remaining challenges are discussed including the need to address the distribution of radiation within shoots and needles. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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