Forest valuation using a two phase inventory method and satellite data

Thuresson, Tomas

doi:10.1080/02827589509382869

Cited by 3 publications

(3 citation statements)

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“…Häme (1991) uses geo-chemical data along with satellite images to estimate site conditions. Thuresson (1995) tries to improve economic decision variables derived from existing subjectively collected stand data by using Landsat TM data, but finds that the image does not contain additional information. However, if no ground information is used TM data explains much of the variance.…”

Section: Combination Estimates From Image and Additional Data Setsmentioning

confidence: 99%

“…Although the forest cover type classification is somewhat improved, the authors admit that the complexity of the algorithm makes the approach infeasible and favour traditional air photo interpretation for such mapping. * Thuresson (1995) tested whether Landsat TM data can be used to improve present net value (PNV) estimations at stand level in a two-phase inventory where subjective ground inventory data of "normal" precision (RMSE about 15-20%) (Stahl 1992) are also available. The satellite data did not contribute in any major way.…”

Section: Estimating Forest Variables For Forest Management Planning Pmentioning

confidence: 99%

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Satellite remote sensing for forestry planning—A review

Holmgren

Thuresson

1998

Scandinavian Journal of Forest Research

150

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Satellite remote sensing for forestry planning-a review. This review attempts, from a critical perspective, to put 25 years of satellite remote sensing research into the context of forestry planning. It is limited to temperate and boreal regions and to remote sensing in the visible and near infra-red spectra. It is structured as follows: firstly, the forestry planning problem is defined and information requirements are identified; secondly, the attempts to use satellite remote sensing as an information source for forestry planning are reviewed, divided into the topics inventory and monitoring; and, finally, the methods for extracting forest information from satellite images are re-examined, and their inherent limitations and methodological weaknesses are discussed.It is concluded that satellite images seldom contain enough information to support the decision process in applied forestry. Although regional level applications may be useful, few successful and reliable applications for local forest inventory, planning or damage monitoring have evolved. Stratification for multi-stage sampling and monitoring of clear-cuts are areas in which satellite images have been shown to be feasible. However, it is doubtful whether the costs involved can be justified for such uses alone. A further conclusion is that many studies have adopted simplistic views of the information needs in the forestry planning process. These studies do not relate the analysis to management decisions, but instead assume that plain and approximate mapping of the forest has a great value. Present developments in the discipline involve complex modelling of reflectance, taking into consideration internal shading, topography and other features, yet without dramatic improvements compared with earlier studies. Furthermore, satellite remote sensing and digital image analysis are no longer technologically spectacular, facts which may affect future research in the discipline. It is, perhaps, time to draw the conclusion that current satellite sensors are not in general suitable for forestry planning, since (a) they contain little relevant information, and (b) for forest management planning purposes there are often more efficient ways of collecting the information required.

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Section: Combination Estimates From Image and Additional Data Setsmentioning

confidence: 99%

Section: Estimating Forest Variables For Forest Management Planning Pmentioning

confidence: 99%

Satellite remote sensing for forestry planning—A review

Holmgren

Thuresson

1998

Scandinavian Journal of Forest Research

150

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“…RS images are the main source of auxiliary data for forest inventories, but other data have also been utilized, such as data from previous stand inventories or digital map data of land use, soil or topography (e.g. Hutchinson 1982, Poso et al 1987, Bolstad & Lillesand 1992, Tomppo 1992, Tomppo 1993, Thuresson 1995, Tokola & Heikkilä 1997. Additionally, information on the geophysical properties of the terrain and maps of climatological zones have been studied as auxiliary data (e.g.…”

Section: Two-phase Sampling In Forest Inventorymentioning

confidence: 99%

Estimation of local forest attributes by utilizing two-phase sampling and auxiliary data

Tuominen¹

2007

Diss. For.

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This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V).Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

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