Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data

Maltamo, Matti; Packalén, Petteri; Yu, Xiaowei; Eerikäinen, Kalle; Hyyppä, Juha; Pitkänen, Juho

doi:10.1016/j.foreco.2005.05.034

Cited by 166 publications

(106 citation statements)

References 22 publications

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“…The features mainly described the properties of the trees in the dominant canopy such as the total stem volume, basal area, and mean diameter. Direct measures of the understory are difficult to obtain due to transmission losses occurring in the upper canopy [84] (see also [50]) and no correlations between the textural features quantifying the top of the canopy and the understory were observed here. Although a slight correlation between the textural features and CV(G) computed from the large plots was observed, a similar correlation was obtained when the textural features were computed from a smaller window.…”

Section: Discussionmentioning

confidence: 74%

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Niemi

2016

Remote Sensing

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Abstract:Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs) for both supervised (linear regression) and unsupervised (k-Means clustering) analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m 2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R 2 ) of up to 0.63-0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data.

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Section: Discussionmentioning

confidence: 74%

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Niemi

2016

Remote Sensing

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“…Efficiency savings have been possible through the recent proliferation of airborne lidar (laser scanning), where it has shown that it is possible to acquire information on three-dimensions of a forested canopy (x,y and z) over much larger areas than possible through ground survey (Maltamo et al, 2005;Goetz et al, 2007). Airborne laser scanning (ALS) is a rapidly growing technology and many technical improvements have featured in recent years.…”

Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

114

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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“…Returns from understorey vegetation can be expected to reduce the canopy height percentiles, especially when the overstorey pines have thin transparent crowns that allow greater penetration of LiDAR pulses to the understorey. Additional research may help resolve issues with understorey [41]. The use of (P95-P30) in regression models appeared to reduce the impact of understorey vegetation, possibly reducing bias expected to arise from this source.…”

Section: Discussionmentioning

confidence: 99%

Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

Beets

Reutebuch

Kimberley

et al. 2011

Forests

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Abstract:Relationships between discrete-return light detection and ranging (LiDAR) data and radiata pine leaf area index (LAI), stem volume, above ground carbon, and carbon sequestration were developed using 10 plots with directly measured biomass and leaf area data, and 36 plots with modelled carbon data. The plots included a range of genetic types established on north-and south-facing aspects. Modelled carbon was highly correlated with directly measured crown, stem, and above ground biomass data, with r = 0.92, 0.97 and 0.98, respectively. LiDAR canopy percentile height (P30) and cover, based on all returns above 0.5 m, explained 81, 88, and 93% of the variation in directly measured crown, stem, and above ground live carbon and 75, 89 and 88% of the modelled carbon, respectively. LAI (all surfaces) ranged between 8.8-19.1 in the 10 plots measured at age 9 years. The difference in canopy percentile heights (P95-P30) and cover based on first returns explained 80% of the variation in total LAI. Periodic mean annual increments in stem volume, above ground live carbon, and total carbon between ages 9 and 13 years were significantly related to (P95-P30), with regression models explaining 56, 58, and 55%, respectively, of the variation in growth rate per plot. When plot aspect and genetic type were included with (P95-P30), the R 2 of the regression models for stem volume, OPEN ACCESSForests 2011, 2 638 above ground live carbon, and total carbon increment increased to 90, 88, and 88%, respectively, which indicates that LiDAR regression equations for estimating stock changes can be substantially improved by incorporating supplementary site and crop data.

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Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data

Cited by 166 publications

References 22 publications

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

An overview of recent remote sensing and GIS based research in ecological informatics

Leaf Area Index, Biomass Carbon and Growth Rate of Radiata Pine Genetic Types and Relationships with LiDAR

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