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 m² 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 m³ha⁻¹ over the years studied. Thus, the periodic annual increment (PAI) was 5.6 m³ha⁻¹1year⁻¹. In 2015, S. robusta accounted for 74 % of the volume and a PAI of 4.1 m³ha⁻¹year⁻¹. Recruitment showed a sudden decrease from 43 to 18 trees ha⁻¹year⁻¹. The above ground carbon stock of trees increased from 48 tonnes ha⁻¹ to 80 tonnes ha⁻¹ resulting in a PAI of 2.9 tonnes ha⁻¹year⁻¹. 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 beståndsdynamiken och kollagringen i skogar dominerade av Shorea robusta C.F. Gaertn (fam. Dipterocarpaceae) i södra Nepal, särskilt i skogar skötta av lokala invånare. Eftersom S. robusta är ett viktigt trädslag och REDD+ är på uppgång i Nepal är det viktigt att veta mer om dessa egenskaper. Den här studiens mål var undersöka dessa egenskaper genom att analysera fältdata från en 700 hektar stor byskog. Datat samlades in vid fyra tillfällen, 2005, 2010, 2013, 2015 och bestod av 68 slumpmässigt utlagda och 500 m² stora provytor, med diametermätningar för över 4000 träd, varav 10 % även fått sina höjder mätta. Höjdmodeller togs fram genom regressionsanalys för de vanligaste trädarterna och redan existerande volymfunktioner användes för att beräkna volymen för alla träd. Biomassa uppskattades genom användning av redan existerande allometriska funktioner. Kollagret antogs utgöra 52 viktprocent av biomassan. En analys gjordes också på enskild trädtillväxt hos S. robusta. Den stående volymen ökade från 99 till 161 m³ha⁻¹ under den studerade perioden, vilket motsvarar en periodisk årlig tillväxt (PAI) på 5,6 m³ha⁻¹år⁻¹. 2015 härrörde 74 % av den stående volymen och 4,1 m³ha⁻¹år⁻¹ av den årliga tillväxten från S. robusta. Inväxt minskade kraftigt från 43 till 18 träd ha⁻¹år⁻¹. Trädens kollager ovan jord ökade från 48 ton ha⁻¹ till 80 ton ha⁻¹, vilket motsvarade en tillväxt på 2,9 ton ha⁻¹år⁻¹. Stående volym, kollagret, volym- och koltillväxt var som störst i diameterspannet 10 till 30 cm. De starkaste korrelationer med enskilt trädtillväxt för S. robusta uppvisades av trädvolym, trädgrundytetillväxt och nuvarande och tidigare trädgrundyta. Som väntat dominerade S. robusta många av processerna i skogen. Kollageruppskattningen visade att denna typ av S. robusta-dominerad skog skött genom byskogsbruk kan vara en kolsänka att räkna 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 när skötselåtgärden gallring ska utföras i bestånd med olika grader av heterogenitet. Arbetet syftade därför till att klargöra hur stor andel av ett bestånd som ska vara i behov av gallring för att gallringen ska bli som mest lönsam. Arbetet genomfördes genom att med inventerings-data från Bergvik Skog dela upp företagets skogsinnehav i norra Värmland i tre hetero-genitetsklasser. De tre klasserna byggde på den relativa standardavvikelsen för grundytevägd medelhöjd mellan provytor inom samma bestånd. För klasserna simulerades skogsbruk enligt Bergvik Skogs riktlinjer under 100 år i programmet Heureka PlanVis. Simuleringarna re-sulterade i nettonuvärden för den ekonomiska avkastningen beroende på vilken andel av arealen som krävdes vara gallringsmogen innan gallring simulerades. Resultaten visade att de mest homogena bestånden bör gallras när 86,5 % av beståndet har gallringsbehov för att upp-nå maximal avkastning. Motsvarande siffra för heterogena bestånd var 89 %. För bestånd som är intermediära krävdes 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 Skog’s own inventory data to divide the forest stands in northern Värmland in three heterogeneity classes. The classes were based on the relative standard deviation of the Lorey’s mean height between plots, in the same stand. For each class, forest management according to Bergvik Skog’s 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 stand’s 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

Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – 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 – jämförelse mellan stratabaserade och areabaserade ansatser

I den strategiska skogliga planeringen utarbetas långsiktiga strategier för nyttjandet av skogsresursen, vilket kan inkludera hur avverkningspotentialen ska nyttjas över tid, val av föryngringsträdslag, samt inriktningen för miljö- och naturvård. Med andra ord så lägger strategisk planering grunden till ett hållbart brukande av skogen. Vid många av de större skogsbolagen i Sverige tillämpas sedan årtionden en stickprovsvis, stratabaserad ansats för den strategiska planeringen. Ett alternativ till den stratabaserade ansatsen är den areabaserade ansatsen, dvs. att i planeringsmodellen använda sig av information från alla avdelningar. Den areabaserade ansatsen har många fördelar, t.ex. att planeringen blir rumsligt explicit vilket möjliggör beaktande av en mängd aspekter kopplade till långsiktig hållbarhet. Trots detta används i dagsläget den areabaserade ansatsen endast i begränsad omfattning. I denna rapport beskriver vi strategisk planering med en strata- respektive areabaserad ansats. Vi går också igenom respektive ansats fördelar och utmaningar. Utgångspunkten i rapporten är 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 – 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