A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data

2012 ◽  
Vol 271 ◽  
pp. 182-198 ◽  
Author(s):  
B. Tyler Wilson ◽  
Andrew J. Lister ◽  
Rachel I. Riemann
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Nearest Neighbor ◽  
Raster Data ◽  
Nearest Neighbor Imputation ◽  
Moderate Resolution ◽  
Imputation Approach

scholarly journals Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

2010 ◽  
Vol 40 (2) ◽  
pp. 184-199 ◽  
Author(s):  
Michael J. Falkowski ◽  
Andrew T. Hudak ◽  
Nicholas L. Crookston ◽  
Paul E. Gessler ◽  
Edward H. Uebler ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Nearest Neighbor ◽  
Forest Growth ◽  
Tree Level ◽  
Lidar Data ◽  
Inventory Data ◽  
K Nearest Neighbor ◽  
Forest Inventory Data ◽  
Forest Growth Model ◽  
Nearest Neighbor Imputation

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and species) across a 12 100 ha study area in northeastern Oregon, USA. The primary objective was to provide spatially explicit data to parameterize the Forest Vegetation Simulator, a tree-level forest growth model. The final imputation model utilized LiDAR-derived height measurements and topographic variables to spatially predict tree-level forest inventory data. When compared with an independent data set, the accuracy of forest inventory metrics was high; the root mean square difference of imputed basal area and stem volume estimates were 5 m2·ha–1 and 16 m3·ha–1, respectively. However, the error of imputed forest inventory metrics incorporating small trees (e.g., quadratic mean diameter, tree density) was considerably higher. Forest Vegetation Simulator growth projections based upon imputed forest inventory data follow trends similar to growth projections based upon independent inventory data. This study represents a significant improvement in our capabilities to predict detailed, tree-level forest inventory data across large areas, which could ultimately lead to more informed forest management practices and policies.

Contribution to the automation of the calculations involving the forest inventory with the aid of an office computer

1970 ◽  
Vol 20 ◽  
Author(s):  
R. Goossens
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Basal Area ◽  
Annual Increment ◽  
Mixed Stands ◽  
Mean Values ◽  
Standing Trees ◽  
General Program ◽  
Magnetic Card ◽  
The Right

Contribution to the automation of the calculations involving  the forest inventory with the aid of an office computer - In this contribution an attempt was made to perform the  calculations involving the forest inventory by means of an office computer  Olivetti P203.     The general program (flowchart 1), identical for all tree species except  for the values of the different parameters, occupies the tracks A and B of a  magnetic card used with this computer. For each tree species one magnetic  card is required, while some supplementary cards are used for the  subroutines. The first subroutine (flowchart 1) enables us to preserve  temporarily the subtotals between two tree species (mixed stands) and so  called special or stand cards (SC). After the last tree species the totals  per ha are calculated and printed on the former, the average trees occuring  on the line below. Appendix 1 gives an example of a similar form resulting  from calculations involving a sampling in a mixed stand consisting of Oak  (code 11), Red oak (code 12), Japanese larch (code 24) and Beech (code 13).  On this form we find from the left to the right: the diameter class (m), the  number of trees per ha, the basal area (m2/ha), the current annual increment  of the basal area (m2/year/ha), current annual volume increment (m3/year/ha),  the volume (m3/ha) and the money value of the standing trees (Bfr/ha). On the  line before the last, the totals of the quantities mentioned above and of all  the tree species together are to be found. The last line gives a survey of  the average values dg, g, ig, ig, v and w.     Besides this form each stand or plot has a so-called 'stand card SC' on  wich the totals cited above as well as the area of the stand or the plot and  its code are stored. Similar 'stand card' may replace in many cases  completely the classical index cards; moreover they have the advantage that  the data can be entered directly into the computer so that further  calculations, classifications or tabling can be carried out by means of an  appropriate program or subroutine. The subroutine 2 (flowchart 2) illustrates  the use of similar cards for a series of stands or eventually a complete  forest, the real values of the different quantities above are calculated and  tabled (taking into account the area). At the same time the general totals  and the general mean values per ha, as well as the average trees are  calculated and printed. Appendix 2 represents a form resulting from such  calculations by means of subroutine 2.

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

Spatially consistent nearest neighbor imputation of forest stand data

2009 ◽  
Vol 113 (3) ◽  
pp. 546-553 ◽  
Author(s):  
Andreas Barth ◽  
Jörgen Wallerman ◽  
Göran Ståhl
Keyword(s):  
Nearest Neighbor ◽  
Forest Stand ◽  
Nearest Neighbor Imputation

scholarly journals Significant differences and curvilinearity in the self-thinning relationships of 11 temperate tree species assessed from forest inventory data

2011 ◽  
Vol 69 (2) ◽  
pp. 195-205 ◽  
Author(s):  
Marie Charru ◽  
Ingrid Seynave ◽  
François Morneau ◽  
Michaël Rivoire ◽  
Jean-Daniel Bontemps
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
The Self ◽  
Inventory Data ◽  
Forest Inventory Data

scholarly journals INVENTÁRIO FLORESTAL PARA ADEQUAÇÃO AMBIENTAL DA FAZENDA SANTA RITA, MUNICÍPIO DE SANTARÉM, ESTADO DO PARÁ

FLORESTA ◽  
2010 ◽  
Vol 40 (3) ◽  
Author(s):  
João Ricardo Vasconcellos Gama ◽  
Josiel Carneiro Pinheiro
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Species Abundance ◽  
Sample Plot ◽  
Forest Fragment ◽  
Conservation Areas ◽  
Environmental Suitability ◽  
Number Of Species ◽  
Level Ii ◽  
Level I

O presente trabalho foi desenvolvido objetivando inventariar um fragmento florestal e indicar espécies arbóreas para a recuperação das áreas de reserva legal e preservação permanente da Fazenda Santa Rita, localizada na zona rural do município de Santarém, estado do Pará. Foram lançadas sistematicamente 18 parcelas de 20 x 50 m (1000 m²), totalizando uma área amostral de 18.000 m². Cada parcela foi dividida em dois níveis de inclusão: Nível I de inclusão, subparcela de 20 m x 25 m e mensuração de todas as árvores com DAP ≥ 10 cm e Nível II de inclusão, em parcelas de 20 x 50 m e medição de todas as árvores com DAP ³ 45 cm. Identificou-se 70 espécies e 33 famílias botânicas. As famílias que apresentaram maior riqueza de espécies foram: Lecythidaceae e Mimosaceae (7), Caesalpiniaceae e Sapotaceae (5) e Lauraceae (4). Estas famílias contribuíram com 40% das espécies amostradas, confirmando-se que poucas famílias botânicas representam grande parte da riqueza de espécies arbóreas em florestas de terra-firme.Palavras-chave: Inventário florestal; adequação ambiental; Amazônia. AbstractForest inventory for environmental adjustment in the Santa Rita farm, located in the County of Santarém, State of Pará, Brazil. This study was carried out aiming to identify a forest fragment and indicate tree species for recovering legal reserve and permanent conservation areas of the Santa Rita Farm. Eighteen sample plots, measuring 20 x50m each, were systematically distributed, totaling a sample area of 18.000 m². Each sample plot was divided into two levels. Level I: sub-plots of 20 x 25m, where all trees with DBH ≥ 10 cm were measured; Level II: plots of 20 x 50 m, where all trees with DAP ³ 45 cm were measured. A total of 70 tree species and 33 botanical families were identified. Families that presented higher number of species were: Lecythidaceae and Mimosaceae (7), Caesalpiniaceae and Sapotaceae (5) and Lauraceae (4). These families contributed with 40% of the species, confirming that few families represent a large part of the of tree species abundance in the Amazon high-land forests.Keywords: Forest inventory; environmental suitability; Amazon. 

Economic losses in carbon forestry due to errors in inventory data

2020 ◽  
Author(s):  
Roope Ruotsalainen ◽  
Timo Pukkala ◽  
Annika Kangas ◽  
Mari Myllymäki ◽  
Petteri Packalen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Net Present Value ◽  
Nearest Neighbor ◽  
Carbon Price ◽  
Wood Products ◽  
Total Carbon ◽  
Economic Losses ◽  
Inventory Data ◽  
Forest Inventory Data

Forestry can help to mitigate climate change by storing carbon in trees, forest soils and wood products. Forest owners can be subsidized if forestry removes carbon from the atmosphere and taxed if forestry produces emissions. Errors in forest inventory data can lead to losses in net present value (NPV) if management prescriptions are selected based on erroneous data but not on correct data. This study assesses the effect of inventory errors on economic losses in forest management when the objective is to maximize the total NPV of timber production and carbon payments. Errors similar as in airborne laser scanning based forest inventory were simulated in stand attributes with a vine copula approach and nearest neighbor method. Carbon payments were based on the total carbon balance of forestry (incl. trees, soil and wood-based products) and calculations were carried out for 30 years using carbon prices of € 0, 50, 75, 100, 125 and 150 t-1. The results revealed that increasing the carbon price and decreasing the level of errors led to decreased losses in NPV. The inclusion of carbon payments for the maximization of the NPV decreased the effect of errors on the losses, which suggests that the value of collecting more accurate forest inventory data may decrease when the carbon price increases.

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