Articles containing the keyword 'spatial distribution'

The simulation model consists of a method to generate theoretical Norway spruce (Picea abies (L.) H. Karst.) stands, and a spatial growth model to predict the growth of these stands. The stand generation procedure first predicts the tree diameters from a few stand characteristics and from tree locations. Tree age and height are predicted using spatial models. Spatial growth models were made for both diameter growth and basal area growth. Past growth was used as a predictor in one pair of models and omitted in another pair. The stand generation method and the growth models were utilized in studying the effect of tree arrangement and thinning method on the growth of a Norway spruce stand.

The PDF includes an abstract in Finnish.

• Pukkala, E-mail: tp@mm.unknown
• Kolström, E-mail: tk@mm.unknown

The single tree growth models presented in this study were based on about 4,000 trees measured in 50 even-aged Scots pine (Pinus sylvestris L.) sample plots with varying density, spatial pattern of trees and stand age. Predictors that used information about tree locations decreased the relative standard error of estimate by 10 percentage points (15%), if past growth was not used as a predictor, and about 15 percentage points (30%) when past growth was one of the predictors. When ranked according to the degree of determination, the best growth models were obtained for the basal area increment, the next best for relative growth, and the poorest for diameter increment. The past growth decreased the relative standard error of estimate by 15–20 percentage points, but did not make the spatial predictors unnecessary. The degree of determination of the spatial basal area growth model was almost 80% if the past growth was unknown and almost 90% if the past growth was known. Variables that described the amount of removed competition did not improve the growth models.

The PDF includes an abstract in Finnish.

• Pukkala, E-mail: tp@mm.unknown

The study presents two methods of predicting tree dimensions in a Scots pine (Pinus sylvestris L.) stand if only the location of trees is known. The first method predicts the tree diameter from the spatial location of neighbours. In the second method the diameter distribution of a subarea is estimated from the local stand density. This distribution is then sampled to obtain diameters. In both methods the tree height is predicted with a spatial model on the basis of diameters and locations of trees. The main purpose of the presented models is to generate realistic stands for simulation studies.

The PDF includes an abstract in Finnish.

• Pukkala, E-mail: tp@mm.unknown

The effect of grouping on 5-year old volume increment was studied by a simulation technique using spatial growth models estimated in Scots pine (Pinus sylvestris L.) stands in the phase of the first commercial thinning. A total of 24 model stands were regenerated by applying 12 spatial processes for two different diameter distributions. In addition to model stands, 6 different thinnings were simulated in two real stands. The clustering of trees was described with Fisher’s grouping index and by estimating the relative interception of diffuse radiation. In model stands with constant diameter distribution the correlation between the grouping index and volume increment ranged from -0.81 to -0.91. The correlation between volume increment and interception was 0.81–0.83 with one diameter distribution and 0.70 if both distributions were combined. In one thinned stand the correlation between the growth estimate and grouping index varied between -0.33 and 0.76. The correlation between interception and growth was about 0.30 in one stand and 0.72 if both stands were combined. Small irregularities do not decrease the volume production of a young Scots pine stand, but if the clustering is considerable or there are reasonably wide harvest strips, growth will be reduced by 10–20%.

The PDF includes a summary in Finnish.

• Pukkala, E-mail: tp@mm.unknown

Thinning models are generally based on the density of the stand measured by the average basal area per hectare, for instance. These models are handicapped by the uneven structure of the stands. In uneven stands the averages are inadequate indicators for the need and amount of thinnings.

Small relascope plots were tested in the measurement of the spatial distribution of trees and in the determination of the need and amount of thinnings. The thinning quantity was determined as the difference between the actual distribution of the relascope plots into basal area classes and the ideal distribution after thinning. Sequential sampling was used in the derivation of the decision equations. A respective BASIC-program for a programmable pocket calculator is given.

The PDF includes a summary in English.

• Kilkki, E-mail: pk@mm.unknown
• Pohjola, E-mail: tp@mm.unknown
• Pohtila, E-mail: ep@mm.unknown

Studies of the spatial patterns of dominant plant species may provide significant insights into processes and mechanisms that maintain stand stability. This study was performed in a permanent 1 ha plot in evergreen and deciduous broad-leaved mixed forests on Tianmu Mountain. Based on two surveys (1996 and 2012), the dynamics of the spatial distribution pattern of the dominant population (Cyclobalanopsis myrsinifolia (Blume) Oersted) and the intra- and interspecific relationships between C. myrsinifolia and other dominant species populations were analyzed using Ripley’s K(r) function. We identified the importance value of a species in a community, which is the sum of the relative density, relative frequency, and relative dominance. The drivers of spatial distribution variation and the maintenance mechanisms of the forest were discussed. The results showed that the importance value of C. myrsinifolia within the community decreased over the past 16 years. The C. myrsinifolia population exhibited a significantly aggregated distribution within a spatial scale of 0–25 m in 1996 whereas it changed to a random distribution at scales larger than 5.5 m in 2012. From 1996 to 2012, the spatial distribution patterns between C. myrsinifolia and Cyclocarya paliurus (Batal.) Iljinsk. and between C. myrsinifolia and Cunninghamia lanceolata (Lamb.) Hook did not change significantly. In 1996, C. myrsinifolia and Daphniphyllum macropodum Miq. were positively associated at the scale of 0–25 m; this relationship was strongly significant at the scale of 6–10 m. However, there was no association between the populations of two species in terms of the spatial distribution at the scale of 0–25 m in 2012. Our findings indicate that the drivers of variation in the spatial distribution of the C. myrsinifolia population were intra- and interspecific mutual relationships as well the seed-spreading mechanism of this species.

• Yang, Zhejiang Forest Resources Monitoring Center, Hangzhou 310020, China E-mail: 20080095@zafu.edu.cn
• Yi, School of Forestry and Biotechnology, Zhejiang A & F University, Lin’an 311300, China E-mail: yilita@zafu.edu.cn
• Ye, School of Forestry and Biotechnology, Zhejiang A & F University, Lin’an 311300, China E-mail: 542243187@qq.com
• Wu, School of Forestry and Biotechnology, Zhejiang A & F University, Lin’an 311300, China E-mail: 326585523@qq.com
• Liu, School of Forestry and Biotechnology, Zhejiang A & F University, Lin’an 311300, China E-mail: mhliu@zafu.edu.cn

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

• Tanskanen, Department of Forest Ecology, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: niina.tanskanen@helsinki.fi
• Ilvesniemi, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: hi@nn.fi

• Kokkila, University of Helsinki, Department of Forest Ecology, P.O. Box 27, FIN-00014 Helsingin yliopisto, Finland E-mail: tero.kokkila@helsinki.fi
• Mäkelä, University of Helsinki, Department of Forest Ecology, P.O. Box 27, FIN-00014 Helsingin yliopisto, Finland E-mail: am@nn.fi
• Nikinmaa, University of Helsinki, Department of Forest Ecology, P.O. Box 27, FIN-00014 Helsingin yliopisto, Finland E-mail: en@nn.fi