Articles containing the keyword 'satellite image'

The paper presents a method based on two phase sampling and applicable to forest inventories. The first phase estimates are obtained from satellite imagery and, if required, from extra material such as maps. Second phase estimates are measured in the field. The method is flexible and also applicable to compartmentwise forest inventories. The experiments were based on six study areas with 439 relascope plots. The correlation coefficients between first and second stage estimates varied largely according to the study area.

The PDF includes a summary in Finnish.

• Poso, E-mail: sp@mm.unknown
• Häme, E-mail: th@mm.unknown
• Paananen, E-mail: rp@mm.unknown

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each other, and several multitemporal images covering different geographical locations. The radiometrically calibrated different images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field.

The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

• Varjo, E-mail: jv@mm.unknown

Two operative forest site class estimation methods utilizing satellite images have been developed for forest income taxation purposes. For this, two pixelwise classification methods and two post-processing methods for estimating forest site fertility are compared using different input data. The pixelwise methods are discriminant analysis, based on generalized squared distances, and logistic regression analysis. The results of pixelwise classifications are improved either with mode filtering within forest stands or assuming a Markov random field type dependence between pixels. The stand delineation is obtained by using ordinary segmentation techniques. Optionally, known stand boundaries given by the interpreter can be applied. The spectral values of images are corrected using a digital elevation model of the terrain. Some textural features are preliminary tested in classification. All methods are justified by using independent test data.

A test of the practical methods was carried out and a cost-benefit analysis computed. The estimated cost saving in site quality classification varies from 14% to 35% depending on the distribution of the site classes of the area. This means a saving of about 2.0–4.5 million FMK per year in site fertility classification for income taxation purposes. The cost savings would rise even to 60% if that version of the method were chosen where field checking is totally omitted. The classification accuracy at the forest holding level would still be similar to that of traditional method.

The PDF includes a summary in Finnish.

• Tomppo, E-mail: et@mm.unknown

Spectral characteristics of rapid changes in forest and the spectral separability of change categories were studied through the analysis of satellite scanner images. A computational model of the spectral reflectance of a Scots pine (Pinus sylvestris L.) stand as a function of time was constructed and compared with empirical data. The study area, centred at 61°51’ N, 24°22’ E, was located in boreal forest in Southern Finland. Ground truth data consisted of forest stands and sample plots. Spectral data comprised multitemporal Landsat Thematic Mapper and Spot images as well as spectroradiometer measurements. The separability of the changes was tested with statistical tests and classifications. The separability varied according to the change category. A scheme for fully automated change monitoring system was presented.

The PDF includes a summary in Finnish.

• Häme, E-mail: th@mm.unknown

Modern forestry, which mainly consists of clear-cutting, is one of the most important factors influencing today’s boreal forests. In Sweden, the breaking point for modern forestry is generally considered to be around 1950. Recently, our common knowledge of the implementation of clear-cutting in Sweden has increased, and new research indicates that clear-cutting systems were already applied before the 1950s. In this case study, we used aerial photographs from the 1940s to analyze the extent of contemporaneous clear-cuts and even-aged young forests in an area in northern Sweden. Our results show that almost 40% of the study area had already been clear-cut by the end of the 1940s, but also that clear-cutting had been applied to 10% of the forest land in the early 1900s. This implies that the historical development of forestry in northern Sweden is more complex than previously thought, and that certain proportions of the forest land were already second-generation forests in the 1950s. Our results have implications for the use of concepts such as “continuity forest”, suggesting that this concept should employ a time frame of at least 100 years.

• Lundmark, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, S-901 83 Umeå, Sweden https://orcid.org/0000-0001-8402-7152 E-mail: hanna.lundmark@slu.se
• Östlund, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, S-901 83 Umeå, Sweden https://orcid.org/0000-0002-7902-3672 E-mail: lars.ostlund@slu.se
• Josefsson, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, S-901 83 Umeå, Sweden https://orcid.org/0000-0002-8734-5778 E-mail: torbjorn.josefsson@slu.se

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.

• Kukkonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mikko.kukkonen@uef.fi
• Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
• Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi

Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric k-nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m² pixel size and units of 240 × 240 m², 1200 × 1200 m² and 12  000 × 12  000 m² aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12  000 × 12  000 m². The multiple testing corrections decreased the amount of significant p-values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.

• Katila, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland; https://orcid.org/0000-0001-6946-5736 E-mail: matti.katila@luke.fi
• Rajala, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.rajala@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi

• McRoberts, North Central Research Station, USDA Forest Service, 1992 Folwell Avenue, Saint Paul, Minnesota, USA 5510 E-mail: rmcroberts@fs.fed.us
• Wendt, Region 9, USDA Forest Service, 626 East Wisconsin Avenue, Milwaukee, Wisconsin 53202, USA E-mail: dgw@nn.us
• Liknes, North Central Research Station, USDA Forest Service, 1992 Folwell Avenue, Saint Paul, Minnesota, USA 5510 E-mail: gcl@nn.us

• Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: sakari.tuominen@metla.fi
• Haakana, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mh@nn.fi

• Tomppo, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: erkki.tomppo@metla.fi
• Korhonen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: ktk@nn.fi
• Heikkinen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: jh@nn.fi
• Yli-Kojola, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: hyk@nn.fi

• Kauhanen, Finnish Forest Research Institute, Kolari Research Station, Muoniontie 21 A, FIN-95900 Kolari, Finland E-mail: heikki.kauhanen@metla.fi

• Päivinen, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: risto.paivinen@efi.fi
• Anttila, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: pa@nn.fi