Remote Sensing

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147 KiB

Open AccessEditorial

Laser Scanning in Forests

by Juha Hyyppä, Markus Holopainen and Håkan Olsson

Remote Sens. 2012, 4(10), 2919-2922; https://doi.org/10.3390/rs4102919 - 25 Sep 2012

Cited by 44 | Viewed by 9586

Abstract

The introduction of Airborne Laser Scanning (ALS) to forests has been revolutionary during the last decade. This development was facilitated by combining earlier ranging lidar discoveries [1–5], with experience obtained from full-waveform ranging radar [6,7] to new airborne laser scanning systems which had [...] Read more.

The introduction of Airborne Laser Scanning (ALS) to forests has been revolutionary during the last decade. This development was facilitated by combining earlier ranging lidar discoveries [1–5], with experience obtained from full-waveform ranging radar [6,7] to new airborne laser scanning systems which had components such as a GNSS receiver (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and a scanning mechanism. Since the first commercial ALS in 1994, new ALS-based forest inventory approaches have been reported feasible for operational activities [8–12]. ALS is currently operationally applied for stand level forest inventories, for example, in Nordic countries. In Finland alone, the adoption of ALS for forest data collection has led to an annual savings of around 20 M€/year, and the work is mainly done by companies instead of governmental organizations. In spite of the long implementation times and there being a limited tradition of making changes in the forest sector, laser scanning was commercially and operationally applied after about only one decade of research. When analyzing high-ranked journal papers from ISI Web of Science, the topic of laser scanning of forests has been the driving force for the whole laser scanning research society over the last decade. Thus, the topic “laser scanning in forests” has provided a significant industrial, societal and scientific impact. [...] Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

Research

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716 KiB

Open AccessArticle

Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

by Claudia Hilbert and Christiane Schmullius

Remote Sens. 2012, 4(8), 2210-2235; https://doi.org/10.3390/rs4082210 - 26 Jul 2012

Cited by 100 | Viewed by 9996

Abstract

This study explores ICESat/GLAS waveform data in Thuringian Forest, a low mountain range located in central Germany. Lidar remote sensing has been proven to directly derive tree height as a key variable of forest structure. The GLAS signal is, however, very sensitive to [...] Read more.

This study explores ICESat/GLAS waveform data in Thuringian Forest, a low mountain range located in central Germany. Lidar remote sensing has been proven to directly derive tree height as a key variable of forest structure. The GLAS signal is, however, very sensitive to surface topography because of the large footprint size. This study therefore focuses on forests in a mountainous area to assess the potential of GLAS data to derive terrain elevation and tree height. The work enhances the empirical knowledge about the interaction between GLAS waveform and landscape structure regarding a special temperate forest site with a complex terrain. An algorithm to retrieve tree height directly from GLA01 waveform data is proposed and compared to an approach using GLA14 Gaussian parameters. The results revealed that GLAS height estimates were accurate for areas with a slope up to 10° whereas waveforms of areas above 15° were problematic. Slopes between 10–15° have been found to be a critical crossover. Further, different waveform shape types and landscape structure classes were developed as a new possibility to explore the waveform in its whole structure. Based on the detailed analysis of some waveform examples, it could be demonstrated that the waveform shape can be regarded as a product of the complex interaction between surface and canopy structure. Consequently, there is a great variety of waveform shapes which in turn considerably hampers GLAS tree height extraction in areas with steep slopes and complex forest conditions. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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1147 KiB

Open AccessArticle

Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

by Alicia Peduzzi, Randolph H. Wynne, Valerie A. Thomas, Ross F. Nelson, James J. Reis and Mark Sanford

Remote Sens. 2012, 4(6), 1758-1780; https://doi.org/10.3390/rs4061758 - 13 Jun 2012

Cited by 16 | Viewed by 9437

Abstract

The objective of this study was to determine whether leaf area index (LAI) in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone, or both in combination. In [...] Read more.

The objective of this study was to determine whether leaf area index (LAI) in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone, or both in combination. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood; stand age ranging from 12-164 years; mean height ranging from 0.4 to 41.2 m) in the Appomattox-Buckingham State Forest, Virginia, USA. Lidar distributional metrics were calculated for all returns and for ten one meter deep crown density slices (a new metric), five above and five below the mode of the vegetation returns for each plot. GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes for each plot. Lidar metrics alone explained 69% of the variability in LAI, while GeoSAR metrics alone explained 52%. However, combining the lidar and GeoSAR metrics increased the R² to 0.77 with a CV-RMSE of 0.42. This study indicates the clear potential for X-band backscatter and interferometric height (both now available from spaceborne sensors), when combined with small-footprint lidar data, to improve LAI estimation in temperate mixed forests. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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1530 KiB

Open AccessArticle

Application of Semi-Automated Filter to Improve Waveform Lidar Sub-Canopy Elevation Model

by Geoffrey A. Fricker, Sassan S. Saatchi, Victoria Meyer, Thomas W. Gillespie and Yongwei Sheng

Remote Sens. 2012, 4(6), 1494-1518; https://doi.org/10.3390/rs4061494 - 25 May 2012

Cited by 7 | Viewed by 9542

Abstract

Modeling sub-canopy elevation is an important step in the processing of waveform lidar data to measure three dimensional forest structure. Here, we present a methodology based on high resolution discrete-return lidar (DRL) to correct the ground elevation derived from large-footprint Laser Vegetation Imaging [...] Read more.

Modeling sub-canopy elevation is an important step in the processing of waveform lidar data to measure three dimensional forest structure. Here, we present a methodology based on high resolution discrete-return lidar (DRL) to correct the ground elevation derived from large-footprint Laser Vegetation Imaging Sensor (LVIS) and to improve measurement of forest structure. We use data acquired over Barro Colorado Island, Panama by LVIS large-footprint lidar (LFL) in 1998 and DRL in 2009. The study found an average vertical difference of 28.7 cm between 98,040 LVIS last-return points and the discrete-return lidar ground surface across the island. The majority (82.3%) of all LVIS points matched discrete return elevations to 2 m or less. Using a multi-step process, the LVIS last-return data is filtered using an iterative approach, expanding window filter to identify outlier points which are not part of the ground surface, as well as applying vertical corrections based on terrain slope within the individual LVIS footprints. The results of the experiment demonstrate that LFL ground surfaces can be effectively filtered using methods adapted from discrete-return lidar point filtering, reducing the average vertical error by 15 cm and reducing the variance in LVIS last-return data by 70 cm. The filters also reduced the largest vertical estimations caused by sensor saturation in the upper reaches of the forest canopy by 14.35 m, which improve forest canopy structure measurement by increasing accuracy in the sub-canopy digital elevation model. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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321 KiB

Open AccessArticle

Advances in Forest Inventory Using Airborne Laser Scanning

by Juha Hyyppä, Xiaowei Yu, Hannu Hyyppä, Mikko Vastaranta, Markus Holopainen, Antero Kukko, Harri Kaartinen, Anttoni Jaakkola, Matti Vaaja, Jarkko Koskinen and Petteri Alho

Remote Sens. 2012, 4(5), 1190-1207; https://doi.org/10.3390/rs4051190 - 3 May 2012

Cited by 158 | Viewed by 13839

Abstract

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the [...] Read more.

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m². With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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565 KiB

Open AccessArticle

Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest

by Eva Lindberg and Markus Hollaus

Remote Sens. 2012, 4(4), 1004-1023; https://doi.org/10.3390/rs4041004 - 13 Apr 2012

Cited by 58 | Viewed by 9906

Abstract

This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with five different regression models: [...] Read more.

This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with five different regression models: one model based on height and density metrics from the ALS data derived from the whole field plot, two models based on similar combinations derived from 0.5 m raster cells, and two models based on canopy volumes from the ALS data. The best result was achieved with a model based on height and density metrics derived from 0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a model based on height and density metrics derived from the whole field plot (RMSE 41.9%). The stem number and the basal area were estimated with: (i) area-based regression models using height and density metrics from the ALS data; and (ii) single tree-based information derived from local maxima in a normalized digital surface model (nDSM) mean filtered with different conditions. The estimates from the regression model were more accurate (RMSE 52.7% for stem number and 21.5% for basal area) than those derived from the nDSM (RMSE 63.4%–91.9% and 57.0%–175.5%, respectively). The accuracy of the estimates from the nDSM varied depending on the filter size and the conditions of the applied filter. This suggests that conditional filtering is useful but sensitive to the conditions. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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1285 KiB

Open AccessArticle

An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

by Harri Kaartinen, Juha Hyyppä, Xiaowei Yu, Mikko Vastaranta, Hannu Hyyppä, Antero Kukko, Markus Holopainen, Christian Heipke, Manuela Hirschmugl, Felix Morsdorf, Erik Næsset, Juho Pitkänen, Sorin Popescu, Svein Solberg, Bernd Michael Wolf and Jee-Cheng Wu

Remote Sens. 2012, 4(4), 950-974; https://doi.org/10.3390/rs4040950 - 30 Mar 2012

Cited by 404 | Viewed by 26347

Abstract

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In [...] Read more.

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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1060 KiB

Open AccessArticle

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

by Paul Treitz, Kevin Lim, Murray Woods, Doug Pitt, Dave Nesbitt and Dave Etheridge

Remote Sens. 2012, 4(4), 830-848; https://doi.org/10.3390/rs4040830 - 27 Mar 2012

Cited by 120 | Viewed by 12447

Abstract

Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop [...] Read more.

Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m⁻² were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m⁻². Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R²(adj) = 0.75–0.95); (2) top height (R²(adj) = 0.74–0.98); (3) quadratic mean diameter (R²(adj) = 0.55–0.85); (4) basal area (R²(adj) = 0.22–0.93); (5) gross total volume (R²(adj) = 0.42–0.94); (6) gross merchantable volume (R²(adj) = 0.35–0.93); (7) total aboveground biomass (R²(adj) = 0.23–0.93); and (8) stem density (R²(adj) = 0.17–0.86). Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m⁻² is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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8630 KiB

Open AccessArticle

Forest Delineation Based on Airborne LIDAR Data

by Lothar Eysn, Markus Hollaus, Klemens Schadauer and Norbert Pfeifer

Remote Sens. 2012, 4(3), 762-783; https://doi.org/10.3390/rs4030762 - 20 Mar 2012

Cited by 64 | Viewed by 15631

Abstract

The delineation of forested areas is a critical task, because the resulting maps are a fundamental input for a broad field of applications and users. Different national and international forest definitions are available for manual or automatic delineation, but unfortunately most definitions lack [...] Read more.

The delineation of forested areas is a critical task, because the resulting maps are a fundamental input for a broad field of applications and users. Different national and international forest definitions are available for manual or automatic delineation, but unfortunately most definitions lack precise geometrical descriptions for the different criteria. A mandatory criterion in forest definitions is the criterion of crown coverage (CC), which defines the proportion of the forest floor covered by the vertical projection of the tree crowns. For loosely stocked areas, this criterion is especially critical, because the size and shape of the reference area for calculating CC is not clearly defined in most definitions. Thus current forest delineations differ and tend to be non-comparable because of different settings for checking the criterion of CC in the delineation process. This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laser scanning (ALS) data with a clearly defined method for calculating CC. The new approach, the ‘tree triples’ method, is based on defining CC as a relation between the sum of the crown areas of three neighboring trees and the area of their convex hull. The approach is applied and analyzed for two study areas in Tyrol, Austria. The selected areas show a loosely stocked forest at the upper timberline and a fragmented forest on the hillside. The fully automatic method presented for delineating forested areas from ALS data shows promising results with an overall accuracy of 96%, and provides a beneficial tool for operational applications. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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4061 KiB

Open AccessArticle

Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape

by Thomas Adams, Peter Beets and Christopher Parrish

Remote Sens. 2012, 4(3), 682-702; https://doi.org/10.3390/rs4030682 - 12 Mar 2012

Cited by 23 | Viewed by 9751

Abstract

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability [...] Read more.

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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Open AccessArticle

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

by Andrew Wallace, Caroline Nichol and Iain Woodhouse

Remote Sens. 2012, 4(2), 509-531; https://doi.org/10.3390/rs4020509 - 17 Feb 2012

Cited by 63 | Viewed by 10194

Abstract

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to [...] Read more.

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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Open AccessArticle

Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar

by Rupesh Shrestha and Randolph H. Wynne

Remote Sens. 2012, 4(2), 484-508; https://doi.org/10.3390/rs4020484 - 15 Feb 2012

Cited by 60 | Viewed by 12755

Abstract

Quantification of biophysical parameters of urban trees is important for urban planning, and for assessing carbon sequestration and ecosystem services. Airborne lidar has been used extensively in recent years to estimate biophysical parameters of trees in forested ecosystems. However, similar studies are largely [...] Read more.

Quantification of biophysical parameters of urban trees is important for urban planning, and for assessing carbon sequestration and ecosystem services. Airborne lidar has been used extensively in recent years to estimate biophysical parameters of trees in forested ecosystems. However, similar studies are largely lacking for individual trees in urban landscapes. Prediction models to estimate biophysical parameters such as height, crown area, diameter at breast height, and biomass for over two thousand individual trees were developed using best subsets multiple linear regression for a study area in central Oklahoma, USA using point cloud distributional metrics from an Optech ALTM 2050 lidar system. A high level of accuracy was attained for estimating individual tree height (R² = 0.89), dbh (R² = 0.82), crown diameter (R² = 0.90), and biomass (R² = 0.67) using lidar-based metrics for pooled data of all tree species. More variance was explained in species-specific estimates of biomass (R² = 0.68 for Juniperus virginiana to 0.84 for Ulmus parviflora) than in estimates from broadleaf deciduous (R² = 0.63) and coniferous (R² = 0.45) taxonomic groups—or the data set analysed as a whole (R² = 0.67). The metric crown area performed particularly well for most of the species-specific biomass equations, which suggests that tree crowns should be delineated accurately, whether manually or using automatic individual tree detection algorithms, to obtain a good estimation of biomass using lidar-based metrics. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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Open AccessArticle

Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar

by Nicholas R. Vaughn, L. Monika Moskal and Eric C. Turnblom

Remote Sens. 2012, 4(2), 377-403; https://doi.org/10.3390/rs4020377 - 2 Feb 2012

Cited by 87 | Viewed by 13085

Abstract

Species information is a key component of any forest inventory. However, when performing forest inventory from aerial scanning Lidar data, species classification can be very difficult. We investigated changes in classification accuracy while identifying five individual tree species (Douglas-fir, western redcedar, bigleaf maple, [...] Read more.

Species information is a key component of any forest inventory. However, when performing forest inventory from aerial scanning Lidar data, species classification can be very difficult. We investigated changes in classification accuracy while identifying five individual tree species (Douglas-fir, western redcedar, bigleaf maple, red alder, and black cottonwood) in the Pacific Northwest United States using two data sets: discrete point Lidar data alone and discrete point data in combination with waveform Lidar data. Waveform information included variables which summarize the frequency domain representation of all waveforms crossing individual trees. Discrete point data alone provided 79.2 percent overall accuracy (kappa = 0.74) for all 5 species and up to 97.8 percent (kappa = 0.96) when comparing individual pairs of these 5 species. Incorporating waveform information improved the overall accuracy to 85.4 percent (kappa = 0.817) for five species, and in several two-species comparisons. Improvements were most notable in comparing the two conifer species and in comparing two of the three hardwood species. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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Open AccessArticle

ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia

by Uwe Ballhorn, Juilson Jubanski and Florian Siegert

Remote Sens. 2011, 3(9), 1957-1982; https://doi.org/10.3390/rs3091957 - 2 Sep 2011

Cited by 45 | Viewed by 12233

Abstract

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. [...] Read more.

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R² = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R² = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R² = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R² = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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Open AccessArticle

Deriving Fuel Mass by Size Class in Douglas-fir (Pseudotsuga menziesii) Using Terrestrial Laser Scanning

by Carl Seielstad, Crystal Stonesifer, Eric Rowell and Lloyd Queen

Remote Sens. 2011, 3(8), 1691-1709; https://doi.org/10.3390/rs3081691 - 16 Aug 2011

Cited by 28 | Viewed by 8717

Abstract

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree [...] Read more.

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r² = 0.898; SE 22.7%) and branchwood (r² = 0.937; SE 28.9%), as well as for total mass (r² = 0.940; SE 25.5%). Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d². Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing. Full article

(This article belongs to the Special Issue Laser Scanning in Forests)

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