Forest inventory of small areas combining the calibration estimator and a spatial model

Lappi, Juha

doi:10.1139/cjfr-31-9-1551

Cited by 15 publications

(35 citation statements)

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“…However, the MSN estimates still produced negligibly low-biased predictions, though one may note that there is no reason for such estimates to be (approximately) unbiased. This follows Lappi (2001) who stated that off-site plots basically cannot be used if one insists on obtaining completely unbiased estimates for an area. In terms of SEI, RF models showed lower SEI values than those from MSN.…”

Section: Discussionmentioning

confidence: 97%

“…Provided that the inventoried stands are within the span of stands for which forest attributes are of interest (i.e. comparable structure and variation), off-site samples from the inventoried stands can be used for prediction of target variables in target stands of interest (Lappi, 2001, Nothdurft et al 2009). Those off-site samples can facilitate regionalization of response variables using spatial models e.g.…”

Section: Introductionmentioning

confidence: 99%

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Generalized Spatial Models of Forest Structure Using Airborne Multispectral and Laser Scanner Data

Latifi

Koch

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Forest structure was modelled to explore the nonparametric approaches for inventory of small forests. Models were built using information from airborne optical and LiDAR data across two adjacent forest sites using the remote sensing data collected by a similar instrument. Off-site samples from the inventoried reference stands were combined with drawn samples from the target area for modelling responses. The applied combinations included two imputation methods, three sampling designs, and two predictor subset sizes, where a Genetic Algorithm (GA) was used to prune the predictors. Diagnostic tools including Root Mean Square Error, bias and Standard Error of Imputation were employed to evaluate the results. Results showed that Random Forests produced more accurate results than Most Similar Neighbour inference, yet was slightly more biased than MSN. Appending systematically stratified samples from the target dataset yielded more accurate results, yet it was most influential up to a low-to-medium intensity. The use of pruned predictors resulted in reduced bias. By bootstrapping the RMSE, the majority of the simulations lied within the confidence intervals. The applied methods show positive potential towards producing spatial models of forest structure.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Generalized Spatial Models of Forest Structure Using Airborne Multispectral and Laser Scanner Data

Latifi

Koch

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Franco-Lopez et al 2001;Katila and Tomppo 2001;Reese et al 2003;Tomppo et al 2008) and also the Mahalanobis distance (Tokola et al 1996;Nilsson 1997;Fazakas et al 1999;Muinonen et al 2001) as well as similarity measure based on canonical correlation (Mouer and Stage 1995;Muinonen et al 2001) have been used. In small area estimation, sample plot weights can be interpreted as the area of similar forest as the plot represents in the total inventory area (Kilkki and Päivinen 1987;Tomppo 1996;Lappi 2001). Inventory area refers to the subregional, small area, such as a municipality, for which the forest statistics are calculated.…”

Section: Estimation Of Forest Attributesmentioning

confidence: 99%

“…Inventory area refers to the subregional, small area, such as a municipality, for which the forest statistics are calculated. Area interpretation is possible if the weights are positive and the same for all target variables (Tomppo 1996;Lappi 2001). However, the chosen nearest neighbours may not add up to unbiased or statistically optimal estimates for the total area (Lappi 2001).…”

Section: Estimation Of Forest Attributesmentioning

confidence: 99%

Multi-source forest inventory data for forest production and utilization analyses at different levels

Haakana¹

2017

Diss. For.

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National forest inventory (NFI) data are commonly used in national and regional scenario analyses on forest production and utilization possibilities. There is an increased demand for similar analyses at the sub-regional level, and further, to incorporate spatially explicit data into the analyses. However, the fairly sparse network of NFI sample plots allows analyses only for large areas. The present dissertation explored whether satellite imagery, NFI sample plot data and the k nearest neighbour estimation method can be employed in generating spatial forest data for scenario analyses at the local level. The method was first applied in the area of two villages in Eastern Finland to quantify the effects of administrative land use and technical land-form constraints on timber production. Secondly, the impacts of three alternative regional felling strategies on suitable habitat for the Siberian flying squirrel (Pteromys volans) were assessed.As a scenario analysis tool, the Finnish forestry dynamics model MELA was used. Management units for simulations of forest development and management activities were delineated by means of image segmentation and digital maps on restriction areas, and new weights for NFI sample plots, that is, the representativeness in these units, were estimated by means of satellite image data. The performance of different segmentation methods and different spectral features in the estimation were examined. Image segments corresponding to forest stands enabled the use of patch-and landscape-level models in the prediction of suitable habitat.Satellite image-based estimation of new NFI sample plot weights was found to be a feasible method for generating forest data for scenario analyses in areas smaller than is possible with the plot data only, for example, for municipalities. Satellite imagery with large geographic coverage and continuous NFI field measurements provide cost-efficient data sources for versatile impact and scenario analyses at the local level.

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“…The data obtained in this manner are quite adequate for the needs of forest policy concerning the whole country, but they are of little use for the definition of regional, and especially local policy. The estimation of the forest damage degree in a forest district, national park or reserve, covering a relatively small area, but representing a high biological value, should be based on small-area methods which provide an adequate accuracy and can be utilised on a larger scale (LAPPI 2001).…”

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confidence: 99%

A statistical small-area method of estimation of spatial distribution of the tree damage degree

Podlaski¹

2003

J. For. Sci.

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ABSTRACT:The objectives of this study are to demonstrate a small-area method of estimation of the spatial distribution of the tree damage degree, and to initially verify it during the assessment of fir (Abies alba Mill.) and beech (Fagus sylvatica L.) vitality in the Świętokrzyski National Park. In order to estimate the degree of tree damage the classification based on the degree of defoliation, and assumed by the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests -ICP, was used. The tree damage degree is presented in P 3 fields of the SINUS System of Information on Natural Environment. To estimate the spatial distribution of the tree damage degree an unrestricted simple random sampling scheme was used. During the initial verification of the presented method the total error as well as errors of the upper and lower intervals were analysed in two forest sections of the Świętokrzyski National Park, i.e. on the total area of 1,997.18 ha. The maximum total errors in individual P 3 fields of the SINUS system for fir and beech amounted to 31.3%. The average errors (total, of upper and lower intervals) were lower than 25% for both tree species. The method presented in this paper can become a valuable complement to existing large-area methods of the tree damage degree estimation since it allows to determine the tree vitality in a whole forest district or national park, as well as in individual compartments or even subcompartments.

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Forest inventory of small areas combining the calibration estimator and a spatial model

Cited by 15 publications

References 0 publications

Generalized Spatial Models of Forest Structure Using Airborne Multispectral and Laser Scanner Data

Generalized Spatial Models of Forest Structure Using Airborne Multispectral and Laser Scanner Data

Multi-source forest inventory data for forest production and utilization analyses at different levels

A statistical small-area method of estimation of spatial distribution of the tree damage degree

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