On the combination of multitemporal satellite and field data for forest inventories

Peng, Suping

doi:10.14214/aff.7647

Cited by 3 publications

(6 citation statements)

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“…Unsupervised classification was used for classifying the sample plots into four development classes. In this method, all the plots (75 permanent and 488 temporary sample plots) were first stratified into homogeneous strata with K-mean clustering and an Euclidean distance classifier (Peng 1987). The field information of 75 permanent plots for four development classes was studied within each of the strata.…”

Section: Methodsmentioning

confidence: 99%

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The use of digitized aerial photographs and local operation for classification of stand development classes

Wang

Poso²,

Waite³

et al. 1998

Silva Fenn.

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The increasing capacity of modern computers has created the opportunity to routinely process the very large data sets derived by digitizing aerial photographs. The very fine resolution of such data sets makes them better suited than satellite imagery for some applications; however, there may be problems in implementation resulting from variation in radial distortion and illumination across an aerial photograph. We investigated the feasibility of using local operators (e.g., non-overlapping moving window means and standard deviations) as auxiliary data for generating stand development classes via three steps: (i) derive 6 local operators intended to represent texture for a 16 by 16 m window corresponding to a forest inventory sampling unit, (ii) apply a calibration process (e.g., accounting for location relative to a photo's principal point and solar position) to these local operators, and (iii) apply the calibrated local operators to classify the forest for stand development. Results indicate that calibrated local operators significantly improve the classification compared to what is possible using uncalibrated local operators and satellite images.

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Section: Methodsmentioning

confidence: 99%

“…The unsupervised classification depended on several factors including the number of initial strata, classifiers, and auxiliary data sets used (Peng 1987, Wang 1996. In this study, the number of initial strata varied from 10 to 60 with an interval of 5.…”

Section: Methodsmentioning

confidence: 99%

The use of digitized aerial photographs and local operation for classification of stand development classes

Wang

Poso²,

Waite³

et al. 1998

Silva Fenn.

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“…These methods result in homogeneous polygons or strata of pixels and, therefore, smoothing of estimates and the disappearance of spatial heterogeneity. This shortcoming can be improved by a regression method (Peng, 1987) and a k-nearest neighbors method (Tomppo, 1996). However, the regression can lead to illogical or extreme estimates, while it is not clear whether k-nearest neighbors can lead to unbiased population estimates.…”

Section: Mapping Accuracy and Uncertainty Assessmentmentioning

confidence: 99%

Error and Uncertainty Analysis for Ecological Modeling and Simulation

Gertner¹

2001

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“…The application of two phase sampling for combining remote sensing and field measured sample plot information has a history of almost 30 years in Finnish forest inventory (Poso and Kujala 1971, 1978, Mattila 1985, Poso et al 1987, Kilkki and Päivinen 1987, Peng 1987, Tomppo 1993, Wang 1996. First, a large number of first phase sampling units in the inventory area are demarcated in a coordinate system, usually by systematic sampling with a square grid.…”

Section: Introductionmentioning

confidence: 99%

“…Tomppo (1993) applied the method with five nearest neighbors. Peng (1987) has applied the regression procedure. In this method, a regression model is constructed in which the auxiliary data of field plots are used as independent variables and the ground truth of the respective plots as dependent variables.…”

Section: Introductionmentioning

confidence: 99%

Weighting alternative estimates when using multi-source auxiliary data for forest inventory

Poso¹,

Wang²,

Tuominen³

1999

Silva Fenn.

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Tuominen, S. 1999. Weighting alternative estimates when using multisource auxiliary data for forest inventory. Silva Fennica 33(1): 41-50.Five auxiliary data sources (Landsat TM, IRS-IC, digitized aerial photographs, visual photo-interpretation and old forest compartment information) applying three study areas and three estimators, two-phase sampling with stratification, the k nearest neighbors and regression estimator, were examined. Auxiliary data were given for a high number of sample plots, which are here called first phase sample plots. The plots were distributed using a systematic grid over the study areas. Some of the plots were then measured in the field for the necessary ground truth. Each auxiliary data source in combination with field sample information was applied to produce a specific estimator for five forest stand characteristics: mean diameter, mean height, age, basal area, and volume of the growing stock. When five auxiliary data sources were used, each stand characteristic and each first phase sample plot were supplied with five alternative estimates with three alternative estimators. Mean square errors were then calculated for each alternative estimator using the cross validation method. The final estimates were produced by weighting alternative estimates inversely according to the mean square errors related to the corresponding estimator. The result was better than the final estimate of any of the single estimators. The improvement over the best single estimate, as measured in mean square error, was 16.9 % on average for all five forest stand characteristics. The improvement was fairly equal for all five forest stand characteristics. Only minor differences among the accuracies of the three alternative estimators were recorded.

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On the combination of multitemporal satellite and field data for forest inventories

Cited by 3 publications

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The use of digitized aerial photographs and local operation for classification of stand development classes

The use of digitized aerial photographs and local operation for classification of stand development classes

Error and Uncertainty Analysis for Ecological Modeling and Simulation

Weighting alternative estimates when using multi-source auxiliary data for forest inventory

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