Clear-cut Detection in Boreal Forest Aided by Remote Sensing

Saksa, Timo; Uuttera, Janne; Kolström, Taneli; Lehikoinen, Mikko; Pekkarinen, Anssi; Sarvi, Vesa

doi:10.1080/02827580310016881

Cited by 34 publications

(25 citation statements)

References 14 publications

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“…Saksa et al 2003, Haapanen and Pekkarinen 2000, Heikkonen and Varjo 2005, but also some improvement was achieved. The considerable changes were classified with high accuracy and, with the best data sets, without error.…”

Section: Discussionmentioning

confidence: 99%

“…Landsat TM images) have been successfully applied in detecting considerable changes like clear cuttings, removals of hold-over trees, soil preparations or drastic damage, while the detection of moderate changes like thinnings, preparatory cuttings or slight damage, has been difficult (e.g. Häme 1991, Varjo 1996, Holmgren and Thuresson 1998, Wilson and Sader 2002, Saksa et al 2003, Heikkonen and Varjo 2004. The reason for this is that typical thinnings, where about 20-40% of basal area is removed, cause only subtle change in reflectance (Häme 1991, Olsson 1994.…”

Section: Introductionmentioning

confidence: 99%

“…In both studies, good results using automatic change detection were achieved; but compared to the context of this study, the timeintervals were much wider and the changes more radical. In the study of Saksa et al (2003), clear cuttings were detected by using three approaches: pixel-by-pixel differencing and segmentation, pixel block-level differencing and thresholding, and presegmentation and unsupervised classification. All the methods were found to be suitable for operational use.…”

Section: Introductionmentioning

confidence: 99%

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Change detection in boreal forests using bi-temporal aerial photographs

Hyvönen¹,

Anttila²

2006

Silva Fenn.

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Increased need for timely forest information is leading to continuous updating of stand databases. In continuous updating, stand attributes are estimated in the field after an operation and stored in databases. To find the changes caused by operations and forest damage, a semi-automatic method based on bi-temporal aerial photographs was developed. The test data were classified into three classes: No-change (952 stands), Moderate-change (163 stands) and Considerable-change (44 stands). The aerial photographs were acquired in years 2001 and 2004 with almost the same image specifications. Altogether 110 features at stand level were extracted and used in change detection analysis. The test data were classified with stepwise discriminant analysis. The overall accuracy of classification varied between 75.3 and 84.7%. The considerable changes were found without error, whereas the Moderate-change and No-change classes were often confused. However, 84.2% of thinned stands were classified correctly. The best accuracy in classification was obtained by using the histogram and textural features extracted from the original, uncorrected images. Radiometric correction did not improve the accuracy of classification. Soil type, characteristics of the growing stock and the location of a stand in an image were found to affect the change detection. Before the method can be applied operationally, issues related to, e.g., confusion between No-change and Moderate-change must be solved.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Change detection in boreal forests using bi-temporal aerial photographs

Hyvönen¹,

Anttila²

2006

Silva Fenn.

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“…The presence of small-scale structural variation, small size of regeneration areas and the lack of multitemporal data lead to the conclusion that the resolution of traditional (e.g. Landsat) imagery is not sufficient for reliable regeneration monitoring in Finland (Häme et al 1998, Saksa et al 2003, Pesonen et al 2007. Instead of applying the new very-high-resolution (< 1 m) satellite images it may well be more cost-efficient to use digital aerial images, which have become readily available standard products offering extensive temporal and spatial coverage.…”

Section: Discussion Of Results and Conclusionmentioning

confidence: 99%

Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR – an exploratory analysis

Korpela¹,

Tuomola²,

Tokola³

et al. 2008

Silva Fenn.

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The potential for combined use of airborne discrete-return LiDAR and digital imagery in the classification and measurement of common seedling stand vegetation was examined in southern Finland (61°50´N, 24°20´E). Classification was based on spectral and textural image features in addition to geometric and radiometric features of the LiDAR. The accuracy of leaf-on, LiDAR-based terrain elevation models was tested as well as the accuracy of LiDAR in the measurement of vegetation heights. LiDAR-based canopy height and the range-normalized intensity of the LiDAR were strong explanatory variables in vegetation classification. Interspecies variation was observed in the height measurement accuracy of LiDAR for different tree, shrub and low vegetation canopies. Elevation models derived with 1−15 pulses per m 2 showed an inherent noise of app. 15−25 cm, which restricts the use of LiDAR in regeneration assessment of very young stands. The spatial pattern of the competing vegetation was reproduced in classification-based raster surfaces, which could be useful in deriving meaningful treatment proposals.

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“…Singh 1989, Häme 1991, Olsson 1994, Varjo 1996, Hyppänen 1999, Haapanen and Pekkarinen 2000, Saksa et al 2003, Heikkonen and Varjo 2004. The detection of radical changes, such as clear cuts (in Finnish conditions), has been reliable.…”

Section: Methods Based On Remote Sensingmentioning

confidence: 99%

The updating of forest resource data for management planning for privately owned forests in Finland

Hyvönen¹

2007

Diss. For.

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In Finland, the forest resource data for management planning is traditionally collected by periodic standwise field inventories, which are usually carried-out over different regions during 10-15 year intervals. The data is used for the compilation of management plans for forest estates. Between inventories the plan and the original stand data can be used, e.g., in the operational planning of timber sales and silvicultural treatments. In operational planning, up-to-date stand data are needed. Therefore, interest in the cost-efficient methods for the updating has increased in recent years.The aim of this thesis was to test potential methods for the updating of forest resource data for management planning in private forestry in Finland. The main criterion in the selection of the studied methods was that they can be applied to the current planning system of Regional Forest Centres, with moderate modifications. Three methods were tested: a) an automatic interpretation of remote sensing material utilising nonparametric estimation at stand-and at segment-levels, b) follow-up of changed stands and c) change detection at stand-level.The estimations based on the use of medium resolution satellite images at the standlevel and high resolution aerial photographs at the segment-level did not achieve the defined accuracy level of stand attributes and treatment proposals. Estimates based on these methods can be utilised for obtaining approximate information on the stand attributes. The estimation method at the segment-level was considered to be worth of further research.The follow-up of changed stands proved to be a potential method for updating forest data. The information for 75% of the management operations was found from the data bases of the Regional Forestry Centre and the Forest Management Association. An updated forest management plan was composed based on new inventory data on changed stands and computationally updated data on unchanged stands. The reliability of the updated plan was equal to that composed based only on new inventory data. The tested change detection with bi-temporal aerial photographs should play a part in the updating procedure. The overall accuracy of change detection was 84.7% at best, and 84.2% of the thinned stands were found.According to the results of this thesis, the follow-up of changes combined with change detection with remote sensing is a promising method for the updating of forest resource data in privately owned forests.

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Clear-cut Detection in Boreal Forest Aided by Remote Sensing

Cited by 34 publications

References 14 publications

Change detection in boreal forests using bi-temporal aerial photographs

Change detection in boreal forests using bi-temporal aerial photographs

Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR – an exploratory analysis

The updating of forest resource data for management planning for privately owned forests in Finland

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