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Articles containing the keyword 'spatial autocorrelation'

Category : Research article

article id 7743, category Research article

Sakari Tuominen, Timo Pitkänen, Andras Balazs, Annika Kangas. (2017). Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging. Silva Fennica vol. 51 no. 4 article id 7743. https://doi.org/10.14214/sf.7743

Keywords: forest inventory; remote sensing; spatial autocorrelation; spatial distribution; aerial imagery; stereo-photogrammetry

Highlights: 3D aerial imaging provides a feasible method for estimating forest variables in the form of thematic maps in large area inventories; Photogrammetric 3D data based on aerial imagery that was originally acquired for orthomosaic production was tested in estimating stand variables; Photogrammetric 3D data highly improved the accuracy of forest estimates compared to traditional 2D remote sensing imagery.

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Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

Tuominen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: timo.p.pitkanen@luke.fi
Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: Annika.Kangas@luke.fi

article id 907, category Research article

Chunyu Zhang, Yazhou Zhao, Xiuhai Zhao, Klaus von Gadow. (2012). Species-habitat associations in a northern temperate forest in China. Silva Fennica vol. 46 no. 4 article id 907. https://doi.org/10.14214/sf.907

Keywords: species richness; spatial autocorrelation; dispersal limitations; indicator species; topographic differentiation

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This contribution identifies species-habitat associations in a temperate forest in north-eastern China, based on the assumption that habitats are spatially autocorrelated and species are spatially aggregated due to limited seed dispersal. The empirical observations were obtained in a large permanent experimental area covering 660 x 320 m. The experimental area was subdivided into four habitat types using multivariate regression tree (MRT) analysis. According to an indicator species analysis, 38 of the 47 studied species were found to be significant indicators of the MRT habitat types. The relationships between species richness and topographic variables were found to be scale-dependent, while the great majority of the species shows distinct habitat-dependence. There are 188 potential species-habitat associations, and 114 of these were significantly positive or negative based on habitat randomization. We identified 139 significant associations using a species randomization. A habitat is not a closed system it may be both, either a sink or a source. Therefore, additional to the randomization, the Poisson Cluster Model (PCM) was applied. PCM considers the spatial autocorrelation of species and habitats, and thus appears to be more realistic than the traditional randomization processes. It identified only 37 associations that were significant. In conclusion, the deviation from the random process, i.e. the high degree of species spatial mingling may be explained by persistent immigration across habitats.

Zhang, Key Laboratory for Forest Resources & Ecosystem Processes of Beijing, Beijing Forestry University, Beijing 100083, China E-mail: zcy_0520@163.com
Zhao, Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China E-mail: yz@nn.cn
Zhao, Key Laboratory for Forest Resources & Ecosystem Processes of Beijing, Beijing Forestry University, Beijing 100083, China E-mail: xz@nn.cn
von Gadow, Faculty of Forestry and Forest Ecology, Georg-August-University Göttingen, Büsgenweg 5, D-37077 Göttingen, Germany E-mail: KGadow@gwdg.de

article id 262, category Research article

Julian C. Fox, Huiquan Bi, Peter K. Ades. (2008). Modelling spatial dependence in an irregular natural forest. Silva Fennica vol. 42 no. 1 article id 262. https://doi.org/10.14214/sf.262

Keywords: correlogram; Eucalypt; growth modelling; moving average autoregression; Moran’s I; spatial autocorrelation

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The spatial dependence present in a natural stand of Eucalyptus pilularis (Smith) dominated mixed species forest was characterised and modelled. Two wildfires imposed a significant spatial dependence on the post disturbance stand. It was hypothesised that spatial variation in the intensity of the wildfires generated the observed structures. The influence of patch formation, micro-site variability and competitive influences were also noted in the residuals of a distance-dependent individual-tree growth model. A methodology capable of modelling these complicated patterns of observed dependence was sought, and candidates included the spatial interaction, direct specification and Papadakis methods. The spatial interaction method with a moving average autoregression was identified as the most appropriate method for explicitly modelling spatial dependence. Both the direct specification and Papadakis methods failed to capture the influence of competition. This study highlights the possibility that stand disturbances such as natural and artificial fires, insect and fungal attacks, and wind and snow damage are capable of imposing powerful spatial dependencies on the post disturbance stand. These dependencies need to be considered if individual tree growth models are to provide valid predictions in disturbed stands.

Fox, School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia E-mail: jcfox@unimelb.edu.au
Bi, Forest Resources Research, New South Wales Department of Primary Industries, PO Box 100, Beecroft, NSW 2119 Australia E-mail: hb@nn.au
Ades, School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia E-mail: pka@nn.au

article id 557, category Research article

Tuomo Wallenius, Timo Kuuluvainen, Raimo Heikkilä, Tapio Lindholm. (2002). Spatial tree age structure and fire history in two old-growth forests in eastern Fennoscandia. Silva Fennica vol. 36 no. 1 article id 557. https://doi.org/10.14214/sf.557

Keywords: Pinus sylvestris; Picea abies; disturbance dynamics; spatial autocorrelation; spatial pattern

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Two near natural old-growth forests, one dominated by Picea abies and the other by Pinus sylvestris, were studied for their fire history, and spatial patterns of trees and tree ages. The spatial tree age structure and the disturbance history of the forests were examined by drawing age class maps based on mapped and aged trees and by dating fires based on fire scars, and by using spatial analyses at tree scale. The tree age structures of the Picea and Pinus dominated forests were different, mainly due to differences in fire history and sensitivity of the dominant tree species to fire. Fire histories and tree age structures of both sites have probably been affected by human in the ancient past. However, in the Picea dominated site, the fires had been severe, killing most of the trees, whereas in the Pinus dominated site the severity of fires had been more variable, leaving some Pinus and even Picea trees alive. In the Pinus dominated site, the tree age distribution was multimodal, consisting of two Pinus cohorts, which were established after fires and a later Picea regeneration. The Picea dominated site was composed of four patches of different disturbance history. In the oldest patch, the tree age distribution was unimodal, with no distinct cohorts, while a single cohort that regenerated after severe fire disturbances dominated the three other patches. In both sites the overall spatial patterns of living and dead trees were random and the proportion of spatially autocorrelated variance of tree age was low. This means that trees of different age grew more or less mixed in the forest without forming spatially distinct regeneration patches, even in the oldest patch of Picea dominated Liimatanvaara, well over 200 years after a fire. The results show that detail knowledge of disturbance history is essential for understanding the development of tree age structures and their spatial patterns.

Wallenius, Department of Ecology and Systematics, University of Helsinki, P.O. Box 65, FIN-00014, Helsinki, Finland E-mail: tuomo.wallenius@helsinki.fi
Kuuluvainen, Department of Forest Ecology, University of Helsinki, P.O. Box 27, FIN-00014, Helsinki, Finland E-mail: tk@nn.fi
Heikkilä, Research Centre of Friendship Park, Tönölä, FIN-88900 Kuhmo, Finland E-mail: rh@nn.fi
Lindholm, Finnish Environment Institute, Nature and Land Use Division, P.O. Box 140, FIN-00251 Helsinki, Finland E-mail: tl@nn.fi

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