Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia

Temesgen, Hailemariam; LeMay, Valerie; Froese, K.L; Marshall, Peter

doi:10.1016/s0378-1127(02)00321-3

Cited by 64 publications

(56 citation statements)

References 11 publications

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“…Nearest neighbour techniques can be used to predict categorical and continuous variables and univariate or multivariate response variables [10]. This ability to predict multivariate response variables makes nearest neighbour imputation particularly promising for the prediction of probability density functions, particularly for complex stands with multiple species and a variety of tree sizes [12]. The size class distributions for these stands tend to be multimodal and not easily represented by parametric functions [10].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

Penner¹,

Woods

Pitt

2015

Forests

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Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area-and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m 2 ).

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Section: Introductionmentioning

confidence: 99%

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

Penner¹,

Woods

Pitt

2015

Forests

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“…ground data) to aerial data (Temesgen et al, 2003), satellite imagery (Eskelson et al, 2009a), and light detection and ranging (LiDAR) data (Hudak et al, 2008;Goerndt et al, 2010). Different analyses have ranked the methods and data sources differently in different forest types.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

Gagliasso¹,

Hummel²,

Temesgen³

2014

OJF

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Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. The goal of this study was to compare various imputation methods to predict forest biomass and basal area, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. We examined the predictive performance of linear regression, geographic weighted regression (GWR), gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest. To test the different methods, a combination of ground inventory plots, light detection and ranging (LiDAR) data, satellite imagery, and climate data was analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k = 5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k = 3), followed by the GWR model, and the random forest imputation. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k = 5), followed by k-nn (k = 3), and the random forest method. For both metrics, the GNN method was the least accurate based on the ranking of RMSE and bias.

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“…For more details, see text. (Moeur & Stage 1995, Temesgen et al 2003, aerial images together with, or independently of, data derived from laser scanning (Naesset 1997, 2007, Means et al 1999, Holmström et al 2001, Muinonen et al 2001, Holmgren 2004, Eskelson et al 2008. More recently, similar research in Lithuania reached the conclusion that summary statistics only, such as the mean volume of growing stock per hectare for some areas, could be applicable operationally if the k-NN predictions were done at a forest compartment level .…”

Section: Resultsmentioning

confidence: 99%

Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data

Jonikavičius¹,

Mozgeris²

2013

iForest

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© iForest -Biogeosciences and Forestry IntroductionThe assessment of wind-caused forest damage includes some specifics that distinguish it from other tasks of detection and measuring of forest changes, at least in the context of Lithuanian forestry conditions. Fast and accurate determination of affected areas of forest and the severity of the damage are essential to enable forest managers to take immediate action on sanitary felling, timber processing and reforestation. Damage is usually geographically concentrated, some areas being difficult to access and measure in the field; however, all forests in Lithuania are precisely mapped and attributed by stand-wise forest inventories .This paper introduces a method of assessing the forest damage caused by a wind storm on August 8, 2010, in Lithuania. The introduced method may also be useful for solving other remote sensing based change detection tasks. The solution is based on integrating methodological approaches originating from two areas of satellite image usage related to the forest inventory: (i) forest change detection, and (ii) the nearest neighbor technique.The process of analyzing image data from two or more periods for the purpose of mapping cover change is commonly referred to as change detection (McRoberts et al. 2010). The principles of satellite image-based forest change detection were first discussed in detail more than a decade ago (Häme 1991, Olsson 1994, Varjo 1997 and are still a subject of current research and interest (Kennedy et al. 2009, McRoberts et al. 2010. Forests can be considered to grow at a constant rate of change, divided into long-term, short-term and rapid change (Häme 1991). Long-term change is usually connected to the growth of the stand, and even that can be monitored using state-of-the-art remote sensing techniques such as aerial imaging and laser scanning (Hyyppa et al. 2008). Satellite remote sensing can be considered to be a practical technique for long-term change observations, in particular because data with image characteristics suitable for monitoring forest changes are available for most of the Earth's surface for the past 40 years (e.g., the Landsat program -Wulder et al. 2008, McRoberts et al. 2010). Short-term or seasonal changes, usually related to reforestation, defoliation etc. can be monitored by satellite image-based remote sensing (Vogelmann et al. 2009, McRoberts & Walters 2012. The main challenge for foresters, however, is the detection and measuring of rapid changes such as tree felling, wind damage and so on. Satellite image-based remote sensing has become an essential data source for assessing rapid or abrupt changes, being low cost, easily and readily accessible, yet it still provides adequate information

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Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia

Cited by 64 publications

References 11 publications

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data

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