Data assimilation of forest variables predicted from remote sensing data

Forest information for management planning is today gathered through a combination of field inventories and remote sensing, but the available flow of remote sensing data over time is not yet utilized for continuously improving predictions of forest variables. In the thesis, the utility of data assimilation, in particular the Extended Kalman filter, for forest variable prediction is investigated. This is an iterative algorithm, where data are repeatedly merged and forecasted. The test site was a forest estate in southern Sweden (Lat. 58°N Long. 13°E). Data assimilation of remote sensing predictions of canopy surface models from digital aerial photogrammetry in paper I and predictions based on interferometric synthetic aperture radar in paper II provided a marginally improved accuracy. This gain was, however, far from the theoretical potential of data assimilation. The reason for this was suggested to be correlation of errors of subsequent predictions across time, i.e. residuals from different predictions over a certain forest area had a similar size and sign. In paper III these error correlations were quantified, and an example of the importance of considering them was given. In paper IV, it was shown that classical calibration could be applied to counteract regression toward the mean, and thus reduce the error correlations. In paper V, it was shown that data assimilation applied to a time series of data from various remote sensing sensors could be used to, over time, improve initial predictions based on aerial laser scanning data. It was also shown how the combination of classical calibration and a suggested modified version of the extended Kalman filter, that accounted for error correlations, contributed to these promising results

Green bonds a part of municipal work towards a sustainable urban development? : a commparative study between tree cities reasons behind the usage of green bonds

Hållbarhetsarbetet är en central del av den kommunala verksamheten. Krav på hållbarhet är stort och kommuner behöver bemöta framtida hot till följd av klimatförändringar. Flera kommuner i Sverige använder gröna obligationer som en ekonomisk innovation i arbetet med hållbarhet. Gröna obligationer skiljer sig från övriga obligationer genom att investeraren kan följa utgifterna och vilka miljö- och klimatrelaterade effekter det bidrar till. Hur Malmö, Göteborg och Stockholm resonerar kring användningen av gröna obligationer undersöks i denna uppsats. Syftet är att undersöka huruvida gröna obligationer bidrar till hållbara stadsutvecklingsprojekt i staden. Städernas hållbarhetsarbete undersöks primärt i relation till de globala hållbarhetsmålen. I synnerhet är mål elva, hållbara städer och samhällen, intressant. Följande huvudfrågeställning ämnar uppsatsen besvara: Hur använder och resonerar Sveriges tre största städer kring gröna obligationer för att uppnå hållbar stadsutveckling? Uppsatsen utgår från en kvalitativ metod, analys och informationsinsamling består av intervjuer med anställda inom kommuner och verksamheter relaterat till gröna obligationer. Utöver intervjuerna har även en litteraturstudie av dokument relaterat till kommunernas gröna obligationer genomförts. Uppsatsen har undersökt och jämfört tre storstäder och deras resonemang kring gröna obligationer. Studien har därför en komparativ forskningsdesign. Kommunernas gröna obligationer centreras kring ekonomisk tillväxt snarare än hållbarhetsarbete. Gröna obligationer har en stark inverkan på samverkan inom den offentliga verksamheten, men även samarbetet mellan offentliga verksamheter och finansmarknaden. Gröna obligationer bidrar enligt Göteborg och Malmö till att motverka stuprörstänk kring hållbarhetsarbete och bidrar till samverkan. Både Malmö och Göteborg ser gröna obligationer som en katalysator för gröna projekt med bestämda hållbarhetskrav. En skillnad mellan Malmö och Göteborg har varit deras val av extern granskare. Stockholm har dock valt att inte ge ut gröna obligationer. Stockholm stad motiverar sitt beslut baserat på onödiga kostnader och brist på belägg att det leder till accelererat hållbarhetsarbete. Huruvida Göteborg och Malmö fungerar som en barriär, möjliggörare, eller banar väg för hållbarhetsarbete med gröna obligationer beror på vilket perspektiv man väljer.Demands for sustainable innovations are high and Swedish municipalities need to address future threats posed by climate change. Green bonds constitute a relatively new asset class and work as a financial instrument that brings positive environmental benefits. How Malmö, Gothenburg and Stockholm take advantage of green bonds is examined in this essay. The aim of this essay is to investigate how green bonds contribute to sustainable urban development projects in Swedish municipalities. The cities’ work towards sustainability is examined primarily in relation to the global sustainability goals, and in particular, goal eleven, “Make cities and human settlements inclusive, safe, resilient and sustainable”. The essay intends to answer the question: How do Sweden's three largest cities reason and use green bonds to achieve sustainable urban development? A qualitative method is used throughout the thesis and consists of interviews with different municipality employees. The essay examines and comments on three different cities and their view on green bonds. Therefore, the study has a comparative research design. In addition to interviews, we also conduct a literature study of documents related to municipalities’ green bonds. Green bonds are mainly an instrument for economic growth rather than environmentally friendly benefits. Green bonds have a strong impact on partnership within the public sector and between public sector activities and the financial market. Both Malmö and Gothenburg identify green bonds as a catalyst for green projects with specific sustainability requirements. One difference between Malmö and Gothenburg is, among other things, their choice of external partners. Stockholm has chosen not to issue green bonds. The City of Stockholm justifies the decision based on unnecessary costs and a lack of evidence that it will accelerate the work towards a more resilient city. Whether Gothenburg and Malmö act as a barrier, enabler, or pave the way for a resilient city with the help of green bonds depends on the viewpoint. According to Gothenburg and Malmö, green bonds can bring different parts of the organisation together and unite them in the work towards sustainability

Klassning av fjällbjörkskog enligt FAO:s definition av skogsmark med hjälp av flygburen laserskanning

Sweden’s forestry legislation was updated in 2010 and a new definition of forest land was introduced. This definition was adapted to the one used by the Food and Agriculture Organization of the United Nations (FAO) for international statistics on the state of the world's forests. It is in short based on the lands ability to grow forest that reaches 5 meters, 10 % canopy closure and has a continuous distribution, according to FAO at least 0.5 hectares. A country-wide laser scanning is now carried out for the production of a new national elevation model; the laser data also provides information on forest height and density. The mountain birch forest makes up much of the border with other land types, and to map the distribution of forest land here would be of interest. A distribution map could provide information such as how much forest land that lies within protected areas. In this study, laser data was used to classify the forest in the Abisko area, using reference data from sample plots. From the point cloud obtained from the laser scanning, different types of metrics were calculated and used to classify the forest. Classification results were evaluated by cross-validation, suggesting an overall accuracy of 92% and a kappa coefficient of 0.85. Despite the high accuracy, there were problems associated with a somewhat small sample of ground reference data. In order to separate forest that meets the requirements of forest land from forest which is high but not dense enough, more reference data would be needed. Steep and stony terrain also caused some problems, where the edges and rocks in some areas were mistaken for vegetation. The methods and problems that emerged from this study can be important experiences for potential future mapping of the forest land in the Swedish mountains

Garnlavshabitat i Vilhelmina kommun

Intensivt skogsbruk har medfört stora förändringar i den boreala skogens struktur och sammansättning. Epifytiska gammelskogslavar så som garnlav, Alectoria sarmentosa, tillhör de arter som drabbats hårt av korta omloppstider och ökad fragmentering av skogen då de kräver gamla träd som substrat samt är känsliga för förändringar i mikroklimat. Vi har med en GIS-analys gjort en modellering över hur stor andel lämpligt habitat för garnlav som finns i Vilhelmina kommun samt hur det är fördelat i landskapet. Modellen baserades på en regressionsfunktion med data från Riksskogstaxeringens inventeringar av hänglavar som grund. En utsökning med kNN-data (satellitdata över Sveriges skogsmark) som bas gjordes och en karta över var i landskapet det förelåg hög sannolikhet att påträffa bra habitat för garnlavar producerades. Resultatet visade att 28,4 % av skogsmarken i Vilhelmina kommun med relativt högt skattad sannolikhet (över 0,35) innehåller bra habitat. Detta är en större andel än medelvärdet 14 % för Sverige, vilket kan förklaras av bl.a. lägre intensitet av skogsbruk och den förhållandevis höga granandelen i kommunen. Med anledning av garnlavens svårigheter att sprida och etablera sig till nya platser är områdesskydd av de områden som idag håller välmående garnlavspopulationer därför, enligt vår mening, den enskilt viktigaste åtgärden för att säkra garnlavens framtid i Vilhelmina kommun.Intensive forestry has brought major changes in forest structure and composition of boreal forests. Epiphytic old-forest lichens, such as Witch’s hair Alectoria sarmentosa, belongs to those species affected by short rotation periods and increasing fragmentation of the forest as they require old trees as a substrate and are sensitive to changes in microclimate. We have used a GIS-analysis to model the content of suitable habitat for Witch’s hair in Vilhelmina municipality and how it is distributed in the landscape. The model was based on a regression function with data from the Swedish national forest inventories of pendulous lichens. A search was made with kNN-data (satellite data of the Swedish forests) as a foundation and a map of the probability to find good habitat for Witch’s hair were produced. The results showed that 28,4 % of the forests in Vilhelmina municipality with a relatively high estimated probability (over 0,35) contains good habitat. This is a higher proportion than the average 14 % for Sweden, which can be explained partly by the historical use of the forest and the relative high percentage of spruce in the municipality. As a result of the species’ difficulties to spread and establish in new places, protection of areas that today have thriving Witch’s hair populations is in our opinion, the single most important measure to ensure the future of Witch’s hair in Vilhelmina municipality

Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58 degrees 27 ' N, 13 degrees 39 ' E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE)

Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics

Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sensed (RS) data have become available for estimating forest characteristics. A problem in forest inventory applications based on RS data is that errors from subsequent predictions tend to be strongly correlated, which limits the efficiency of DA. One reason for such a correlation is that model-based predictions, using techniques such as parametric or non-parametric regression, are normally biased conditional on the actual ground conditions, although they are unbiased conditional on the RS predictor variables. A typical case is that predictions are shifted towards the mean, i.e., small true values are overestimated, and large true values are underestimated. In this study, we evaluated if the classical calibration of RS-based predictions could remove this type of bias and improve DA results. Through a simulation study, we mimicked growing stock volume predictions from two different sensors: one from a metric strongly correlated with growing stock volume, mimicking airborne laser scanning, and one from a metric slightly less correlated with growing stock volume, mimicking data obtained from 3D digital photogrammetry. Consistent with previous findings, in areas such as chemistry, we found that classical calibration made the predictions approximately unbiased. Further, in most cases, calibration improved the DA results, evaluated in terms of the root mean square error of predicted volumes, evaluated at the end of a series of ten RS-based predictions

Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures

Data assimilation

The purpose of this report is to describe a data assimilation prototype program(Appendix A) developed to estimate forest stand data. The program was developed and tested on data col-lected on the forest estate Remningstorp in southern Sweden. Data assimilation can be used to sequentially combine remote sensing based estimates of forest variables with predictions from growth models. The assimilation routine implemented was the extended Kalman Filter. The program supports two different ways to assimilate the forest data: (1) pixel-wise and (2)stand-wise. In the pixel-wise way, raster cells are used as assimilation unit and can beaggregated to a stand for evaluation. In the stand-wise way, the whole stand is assimilatedas one unit. The two methods has pros and cons. The pixel-wise way is simple to use as nostand-delineation is needed, but might be subject to boundary effects and noise due to geo-metric errors. Using the developed code, it has been shown in three case studies that thecombination of time series of remote sensing data and forest growth functions provides bet-ter estimates of forest variables than only using forecasting, or only using the latest remotesensing data. This opens up for a new way to keep forest stand registers up to date