The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Eskelson, Bianca N.I.; Temesgen, Hailemariam; LeMay, Valerie; Barrett, Tara M.; Crookston, Nicholas L.; Hudak, Andrew T.

doi:10.1080/02827580902870490

Cited by 141 publications

(96 citation statements)

References 51 publications

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“…We used nearest neighbour methods [11] to impute the size class distributions for the target prediction units. The distance measure to identify nearest neighbours was calculated using randomForest and the predictions are referred to as randomForest Nearest Neighbour (RFNN).…”

Section: Non Parametric or Randomforest Nearest Neighbour Predictionmentioning

confidence: 99%

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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: Non Parametric or Randomforest Nearest Neighbour Predictionmentioning

confidence: 99%

“…Imputation is used to associate expensive but sparse data with inexpensive and spatially comprehensive data [11]. The response variable is measured on a subset of the prediction units in the population (the reference data set), and auxiliary or predictor variables are available for the entire population.…”

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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“…Model-based imputation methods use other variables in the dataset to impute missing data, but they substantially alter the univariate trait distributions and the covariance structure of the dataset (Gelman and Hill, 2007). Approaches such as k nearest neighbour (kNN) or machine-learning methods (Stekhoven and Bühlmann, 2012) may be more appropriate to impute multivariate datasets, preserving their covariance structure (Eskelson et al, 2009;Penone et al, 2014). In a multiple imputation framework, m imputed datasets are obtained through simulation and may be jointly analysed to provide parameter estimates that take into account the uncertainty introduced by the imputations themselves (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…While forest inventories have adopted statistical imputation methods for some time, as for example the kNN methods (Eskelson et al, 2009, and references therein), imputation methods have only recently started to be used in traitbased ecology (Baraloto et al, 2010;Pyšek et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

Poyatos

Sus

Badiella³

et al. 2018

Biogeosciences

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Abstract. The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km 2 ). We simulated gaps at different missingness levels (10-80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits.No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in the IEFC incomplete dataset (5495 plots) and quantify imputation uncertainty. Resulting spatial patterns of the studied traits in Catalan forests were broadly similar when using species means, regression kriging or the best-performing MICE application, but some important discrepancies were observed at the local level. Our results highlight the need to assess imputation quality beyond just imputation accuracy and show that including environmental information in statistical imputation approaches yields more plausible imputations in spatially explicit plant trait datasets.

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“…More sophisticated statistical modeling and machine-learning techniques have been developed in statistics and computer sciences, and tested and applied to tackle missing data problems in many fields such as industrial engineering (Lakshminarayan et al, 1999) and forestry (Eskelson et al, 2009) etc. Waddell (2009 suggests that k-nearest neighbors and support vector machines may be two promising techniques for imputing missing land use data, particularly parcel-level data.…”

Section: Introductionmentioning

confidence: 99%

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Wang

Kim²

2014

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There is an urgent need for improved models that address the interdependencies between land use and transportation, and considerable new work is underway to develop such models in Oregon and elsewhere. These models and planning practices to integrate land use into the process, however, require the integration of massive amounts of land use data that is messy and incomplete. There have been considerable advances in the treatment of such data problems in other domains, drawing on data mining and machine-learning techniques to address issues in various domains. To date, however, little systematic effort has applied these technological advances to the problem domain of land use and transportation data. Experience suggests that as much as 70% of the total effort in developing integrated land use and transportation models is directly or indirectly associated with data development, integration and cleaning, yet there is remarkably little systematic research focused on the development of reusable methods and tools to support this problem domain.In coordination with an ongoing project of similar theme funded by University of California Transportation Center (UCTC), this project will focus on bringing interdisciplinary methods to develop land use datasets for integrated land use and transportation planning and modeling, with special attention on preserving temporal dimension of the data and monitoring data quality through indicators. Utilizing statistics and machinelearning techniques, we will develop reusable tools that will create a harmonized and coherent land use database from various public and private sources. These tools will be made available to the public and can be used by cities, counties, metropolitan planning agencies, state agencies, universities or anyone else needing to develop a usable database for use in integrated planning and modeling. All of the resulting data, with the exception of proprietary or confidential input data where there is no viable alternative, would be public and reusable for planning and research.17.

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The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Cited by 141 publications

References 51 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

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

Continuous Data Integration for Land Use and Transportation Planning and Modeling

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