8 research outputs found
Stand dynamics and carbon stock in a sal (Shorea robusta C.F. Gaertn) dominated forest in Southern Nepal
There is a lack of knowledge about the stand dynamics and carbon storage in forests dominated by Shorea robusta C.F. Gaertn (fam. Dipterocarpaceae) in southern Nepal, especially in forests managed by local communities. Since S. robusta is a major tree species and REDD+ is of growing importance in Nepal, it is necessary to know about these characteristics. This study aimed to explore these stand characteristics, by analysing field data from a 700-hectare big community forest. The data was collected at four occasions, 2005, 2010, 2013, 2015 and consisted of 68 randomly positioned and 500 mÂČ big plots, where measurements of diameter at breast height for more than 4000 trees were done, including 10 % of which also had their height measured. Height models were developed through regression analyses for all major species and existing volume equations were used to calculate the volume for all trees. Biomass was estimated by using existing algometric functions. Carbon stock was assumed to be 52 wt.% of the biomass. An additional analysis was done on single tree development of S. robusta. The standing volume was found to have increased from 99 to 161 mÂłhaâ»Âč over the years studied. Thus, the periodic annual increment (PAI) was 5.6 mÂłhaâ»Âč1yearâ»Âč. In 2015, S. robusta accounted for 74 % of the volume and a PAI of 4.1 mÂłhaâ»Âčyearâ»Âč. Recruitment showed a sudden decrease from 43 to 18 trees haâ»Âčyearâ»Âč. The above ground carbon stock of trees increased from 48 tonnes haâ»Âč to 80 tonnes haâ»Âč resulting in a PAI of 2.9 tonnes haâ»Âčyearâ»Âč. Standing volume, carbon stock, volume PAI and carbon stock PAI were at the highest in the diameter range between 10 and 30 cm. The strongest correlations with single tree growth of S. robusta were tree volume, tree basal area PAI and current and previous tree basal area. As expected, S. robusta dominated many of the processes in the forest. The carbon stock estimation showed that this type of S. robusta dominated forest under management of a local community, could be a carbon sink to count with.Det saknas kunskap rörande bestĂ„ndsdynamiken och kollagringen i skogar dominerade av Shorea robusta C.F. Gaertn (fam. Dipterocarpaceae) i södra Nepal, sĂ€rskilt i skogar skötta av lokala invĂ„nare. Eftersom S. robusta Ă€r ett viktigt trĂ€dslag och REDD+ Ă€r pĂ„ uppgĂ„ng i Nepal Ă€r det viktigt att veta mer om dessa egenskaper. Den hĂ€r studiens mĂ„l var undersöka dessa egenskaper genom att analysera fĂ€ltdata frĂ„n en 700 hektar stor byskog. Datat samlades in vid fyra tillfĂ€llen, 2005, 2010, 2013, 2015 och bestod av 68 slumpmĂ€ssigt utlagda och 500 mÂČ stora provytor, med diametermĂ€tningar för över 4000 trĂ€d, varav 10 % Ă€ven fĂ„tt sina höjder mĂ€tta. Höjdmodeller togs fram genom regressionsanalys för de vanligaste trĂ€darterna och redan existerande volymfunktioner anvĂ€ndes för att berĂ€kna volymen för alla trĂ€d. Biomassa uppskattades genom anvĂ€ndning av redan existerande allometriska funktioner. Kollagret antogs utgöra 52 viktprocent av biomassan. En analys gjordes ocksĂ„ pĂ„ enskild trĂ€dtillvĂ€xt hos S. robusta. Den stĂ„ende volymen ökade frĂ„n 99 till 161 mÂłhaâ»Âč under den studerade perioden, vilket motsvarar en periodisk Ă„rlig tillvĂ€xt (PAI) pĂ„ 5,6 mÂłhaâ»ÂčĂ„râ»Âč. 2015 hĂ€rrörde 74 % av den stĂ„ende volymen och 4,1 mÂłhaâ»ÂčĂ„râ»Âč av den Ă„rliga tillvĂ€xten frĂ„n S. robusta. InvĂ€xt minskade kraftigt frĂ„n 43 till 18 trĂ€d haâ»ÂčĂ„râ»Âč. TrĂ€dens kollager ovan jord ökade frĂ„n 48 ton haâ»Âč till 80 ton haâ»Âč, vilket motsvarade en tillvĂ€xt pĂ„ 2,9 ton haâ»ÂčĂ„râ»Âč. StĂ„ende volym, kollagret, volym- och koltillvĂ€xt var som störst i diameterspannet 10 till 30 cm. De starkaste korrelationer med enskilt trĂ€dtillvĂ€xt för S. robusta uppvisades av trĂ€dvolym, trĂ€dgrundytetillvĂ€xt och nuvarande och tidigare trĂ€dgrundyta. Som vĂ€ntat dominerade S. robusta mĂ„nga av processerna i skogen. Kollageruppskattningen visade att denna typ av S. robusta-dominerad skog skött genom byskogsbruk kan vara en kolsĂ€nka att rĂ€kna med
Profitability of thinnings in stands with varied degree of heterogeneity and need of thinning : an analysis made with Heureka PlanWise
Detta kandidatarbete genomfördes i samarbete med Bergvik Skog AB som önskade undersöka nĂ€r skötselĂ„tgĂ€rden gallring ska utföras i bestĂ„nd med olika grader av heterogenitet. Arbetet syftade dĂ€rför till att klargöra hur stor andel av ett bestĂ„nd som ska vara i behov av gallring för att gallringen ska bli som mest lönsam. Arbetet genomfördes genom att med inventerings-data frĂ„n Bergvik Skog dela upp företagets skogsinnehav i norra VĂ€rmland i tre hetero-genitetsklasser. De tre klasserna byggde pĂ„ den relativa standardavvikelsen för grundytevĂ€gd medelhöjd mellan provytor inom samma bestĂ„nd. För klasserna simulerades skogsbruk enligt Bergvik Skogs riktlinjer under 100 Ă„r i programmet Heureka PlanVis. Simuleringarna re-sulterade i nettonuvĂ€rden för den ekonomiska avkastningen beroende pĂ„ vilken andel av arealen som krĂ€vdes vara gallringsmogen innan gallring simulerades. Resultaten visade att de mest homogena bestĂ„nden bör gallras nĂ€r 86,5 % av bestĂ„ndet har gallringsbehov för att upp-nĂ„ maximal avkastning. Motsvarande siffra för heterogena bestĂ„nd var 89 %. För bestĂ„nd som Ă€r intermediĂ€ra krĂ€vdes dock enbart 69 % av arealen.The work underlying this bachelor thesis was completed in cooperation with Bergvik Skog AB, who requested an examination of when thinnings should be performed in stands with different degrees of heterogeneity. This thesis, therefore, aimed to clarify how big proportion of a stand that should be in need of thinning in order to obtain maximum profit after thinning is performed. The analysis was undertaken by using Bergvik Skogâs own inventory data to divide the forest stands in northern VĂ€rmland in three heterogeneity classes. The classes were based on the relative standard deviation of the Loreyâs mean height between plots, in the same stand. For each class, forest management according to Bergvik Skogâs principles, was simul-ated with the programme Heureka PlanWise, for a period of 100 years. The simulations re-sulted in different net present values which depended on the proportion of the stand area in need of thinning required before thinning was simulated. The results showed that the most homogeneous stands should be thinned when 86,5% of each standâs area require thinning to achieve maximum profit. The corresponding figure for heterogeneous stands was 89%. How-ever, only 69 % of the area was required for stands that were intermediate
Handling uncertainties in forest information: the hierarchical forest planning process and its use of information at large forest companies
This qualitative study aimed to map what information is used in the forest planning process at large forest-owning companies, how it is used, its level of uncertainty and currently employed strategies to handle forest information uncertainty. An additional aim was to assess the status of the paradigm of the forest planning hierarchy in forestry. We used data from semi-structured interviews with representatives of six large forest-owning companies in Sweden, representing 30 per cent of the productive forest land in the country. Our results show that the forest planning process is a hierarchical system of decisions where the information used in the different planning stages is of varying quality and that the traditional hierarchical planning paradigm still plays a vital role in forestry. The most central source of information in the whole forest-planning process is the forest stand database (forest inventory). This includes uncertain information from various sources, including subjective field measurements and aerial image interpretation. However, the use of remote sensing estimates to feed the databases is increasing, which will probably improve the overall quality. Another important finding is that forest companies tend not to use decision support systems or optimization models to solve planning problems outside the scope of strategic planning; thus, most planning is done manually, e.g. in a geographic information system (GIS) environment. Apart from the hierarchical division of the planning process itself, we identified six main strategies that the companies use to control information uncertainty, namely locking the future by making a decision, utilizing a surplus of available harvests, updating information before a decision is made, replanning when the plan is found to be infeasible, planning by looking back and ignoring the uncertainty, either intentionally or unintentionally. The results from this study increase our understanding of contemporary forest-planning practices and will be helpful in the development of decision support systems and methods for information collection
OsÀkra data i hierarkisk skoglig planering: KartlÀggning av datakvalité och osÀkerhetsstrategier
Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time â And how this affects applications
Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics, and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking. However, wall-to-wall information typically relies on model-based prediction, and several features of model-based prediction should be understood before extensively relying on this type of information. One such feature is that model-based predictors can be considered both unbiased and biased at the same time, which has important implications in several areas of application. In this discussion paper, we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed (or other) auxiliary data. From this point of view, model-based predictors are typically unbiased. Secondly, we show that for specific domains, identified based on their true values, the same model-based predictors can be considered biased, and sometimes severely so. We suggest distinguishing between conventional model-bias, defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted, and design-bias of model-based estimators, defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted. We show that model-based estimators (or predictors) are typically design-biased, and that there is a trend in the design-bias from overestimating small true values to underestimating large true values. Further, we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend. We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data
Strategisk skoglig planering â jĂ€mförelse mellan stratabaserade och areabaserade ansatser
I den strategiska skogliga planeringen utarbetas lÄngsiktiga strategier för nyttjandet av skogsresursen, vilket kan inkludera hur avverkningspotentialen ska nyttjas över tid, val av föryngringstrÀdslag, samt inriktningen för miljö- och naturvÄrd. Med andra ord sÄ lÀgger strategisk planering grunden till ett hÄllbart brukande av skogen. Vid mÄnga av de större skogsbolagen i Sverige tillÀmpas sedan Ärtionden en stickprovsvis, stratabaserad ansats för den strategiska planeringen. Ett alternativ till den stratabaserade ansatsen Àr den areabaserade ansatsen, dvs. att i planeringsmodellen anvÀnda sig av information frÄn alla avdelningar. Den areabaserade ansatsen har mÄnga fördelar, t.ex. att planeringen blir rumsligt explicit vilket möjliggör beaktande av en mÀngd aspekter kopplade till lÄngsiktig hÄllbarhet. Trots detta anvÀnds i dagslÀget den areabaserade ansatsen endast i begrÀnsad omfattning. I denna rapport beskriver vi strategisk planering med en strata- respektive areabaserad ansats. Vi gÄr ocksÄ igenom respektive ansats fördelar och utmaningar. UtgÄngspunkten i rapporten Àr skoglig planering för större skogsinnehav (tiotusentals ha till miljontals ha) med stöd av beslutsstödssystem som inkluderar optimeringsmetoder för att formulera och lösa det strategiska planeringsproblemet
Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time â and how this affects applications
Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics, and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decision-making. However, wall-to-wall information typically relies on model-based prediction, and several features of model-based prediction should be understood before extensively relying on this type of information. One such feature is that model-based predictors can be considered both unbiased and biased at the same time, which has important implications in several areas of application. In this discussion paper, we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed (or other) auxiliary data. From this point of view, model-based predictors are typically unbiased. Secondly, we show that for specific domains, identified based on their true values, the same model-based predictors can be considered biased, and sometimes severely so. We suggest distinguishing between conventional model-bias, defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted, and design-bias of model-based estimators, defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted. We show that model-based estimators (or predictors) are typically design-biased, and that there is a trend in the design-bias from overestimating small true values to underestimating large true values. Further, we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend. We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data