Forecasting of ALS data using TanDEM-X

För att ha möjlighet att sköta skogen på ett hållbart sätt krävs att vi har tillgång till tillförlitligt data om det skogliga tillståndet. Fjärranalys är och kommer vara en allt viktigare teknik för att tillägna sig denna information på ett kostnadseffektivt sätt och med önskad kvalitet. Satellitburen radar har visat sig ha potential för insamling av information om det skogliga tillståndet. Satellitparet TanDEM-X och TerraSAR-X levererar InSAR (Interferometric synthetic aperture radar) data med möjlighet till beräkning av en tredje dimension och potential för goda skattningar, med hög temporal upplösning. I detta arbete presenteras en metod för att väga samman en tidsserie av radarbilder tagna med TanDEM-X konstellationen och utifrån bilderna skriva fram skattningar utförda med en laserskanning från år 2010. Genom att nyttja flera radarbilder förväntas skattningsresultatet förbättras, ett antagande som testades genom att längden av tidsserien med radarbilder varierades. Studien utfördes på försöksfastigheten Remningstorp i Västra Götaland och som referensdata användes cirkulära ytor med en radie av 10 meter och 40 meter, inventerade år 2010 och 2014. Om 14 radarbilder tagna under perioden 2011 till 2014 används tillsammans med laserskanningen utförd år 2010, skattas den grundytevägda höjden med ett RMSE på 5,9% och volymen med ett RMSE på 18,2%, för skattningar på beståndsnivå år 2014. Skattningarna gynnades av en längre tidsserie bilder. Framskrivningsmetodiken som är beskriven i denna rapport visar god potential för framskrivning av skogliga skattningar, men behöver utvecklas ytterligare före den kan rekommenderas för praktisk tillämpning inom skogsinventering.To be able to manage the forest in a sustainable manner, we need to have access to reliable data of the forest condition. Remote sensing is and will be an important technique to obtain this information in a cost effective way and with the required quality. Satellite-borne radar has shown to have potential for collecting this information. The Satellite mission TanDEM-X and TerraSAR-X delivers InSAR (Interferometric synthetic aperture radar) data with potential for three dimensional calculations and good estimates, with high temporal resolution. This work presents a method for updating forest parameters from a time series of radar images acquired with the TanDEM-X constellation and a laser scanning from 2010. By using a longer time series of radar images the estimation quality is expected to be improved, an assumption that was tested by changing the length of the time series of radar images. The test site in this study was Remningstorp in southern Sweden and as reference data circular plots with a radius of 10 meter and 40 meter, inventoried in 2010 and 2014, were used. When 14 radar images acquired during the period 2011 to 2014 are used in combination with a laser scanning from 2010, the estimation quality for Lorey´s mean height hade a RMSE of 5.9% and for volume a RMSE of 18.2%, for estimation on stand level in 2014. The estimation quality improved when a longer time-series of radar images were used. The method described in this article shows good potential for forecasting forest variables, but need further development before it can be recommended for practical use in forest inventory

Forest Variable Estimation Using a High Altitude Single Photon Lidar System

As part of the digitalization of the forest planning process, 3D remote sensing data is an important data source. However, the demand for more detailed information with high temporal resolution and yet still being cost efficient is a challenging combination for the systems used today. A new lidar technology based on single photon counting has the possibility to meet these needs. The aim of this paper is to evaluate the new single photon lidar sensor Leica SPL100 for area-based forest variable estimations. In this study, it was found that data from the new system, operated from 3800 m above ground level, could be used for raster cell estimates with similar or slightly better accuracy than a linear system, with similar point density, operated from 400 m above ground level. The new single photon counting lidar sensor shows great potential to meet the need for efficient collection of detailed information, due to high altitude, flight speed and pulse repetition rate. Further research is needed to improve the method for extraction of information and to investigate the limitations and drawbacks with the technology. The authors emphasize solar noise filtering in forest environments and the effect of different atmospheric conditions, as interesting subjects for further research

The effect of sample plot positioning errors for the estimation of forest variables with airborne laser scanning

Regeringen har givit Skogsstyrelsen i uppdrag att ta fram bättre data om det svenska skogarna baserat på laserdata. Den här studien behandlar betydelsen av referensytors positionering och effekt på skattning av skogliga variabler. Riksskogstaxeringens (RT) provytor är positionerade med GPS, men under 2013 har ett antal av dessa ytor givits en förbättrad koordinat med DGPS. GPS och DGPS koordinatangivelserna har kopplats samman med motsvarande yta i laserdata från den nya nationella höjdmodellen. Med statistik från laserdata har regressionsmodeller använts för att skatta skogliga variabler genom att utnyttja RTs provytor som referensytor. Medelfelet för skattningarna beräknades på regionnivå och fördelat på höjdklasser. Differensen mellan skattningarnas medelfel, baserade på skattningar med GPS och DGPS koordinaterna, beräknades. Den förbättrade koordinatangivelsen resulterade i förbättrade skattningar av de skogliga variablerna i det flesta fall. Förbättringen på regions nivå är i storleksordningen 0,06 till 0,83 procentenheter för höjdskattningen, för grundytan -0,74 till 1,72 procentenheter, för diametern 0,38 till 1,25 procentenheter, för volymen -0,78 till 2,56 procentenheter och för biomassan -0,67 till 1,77 procentenheter. Ett begränsat provyteunderlag i studien kan ha påverkat resultatet. Den viktigaste slutsatsen är att de förbättrade koordinaterna har förbättrat skattningarna i de flesta fall, om än ytterst lite. Med stöd av tidigare studier anser vi resultatet förväntat.The government commissioned the Swedish Forest Agency to produce improved wall-to-wall data of the Swedish forests based on laser data. The objective of this study was to examine the effects on the estimations of forest variables due to the quality of the positioning of the reference sample plots. The National Forest Inventory (NFI) sample plots are positioned with GPS. In 2013, a number of these plots coordinates were improved with DGPS measurements. The GPS and DGPS coordinates were linked together to the corresponding plots of the laser data from the new National Elevation Model. With statistics from laser data regression models has been used to estimate forest variables by exploiting NFIs plots as reference surfaces. The mean error for the estimates were calculated at the regional level and divided into height classes. The difference between the estimations mean error, based on the old and the new coordinates were calculated. The DGPS coordinates resulted in improved estimates of forest variables in the majority of cases. The improvement in percentage at the regional level is in the range of 0.06 to 0.83 for height estimation, the basal area -0.74 to 1.72, the mean diameter of 0.38 to 1.25, for volume -0.78 to 2.56 and biomass -0.67 to 1.77. A limited number of plots in Region 1 and 5 have influenced the results. The main conclusion is that the better-quality coordinates improves estimates in most cases. On the basis of previous studies, we consider the results expected

Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data

Accurate and up-to-date data about growing stock volume are essential for forest management planning. Airborne Laser Scanning (ALS) is known for producing accurate wall-to-wall predictions but the data are at present collected at long time intervals. Digital Photogrammetry (DP) is cheaper and often more frequently available but known to be less accurate. This study investigates the potential of using contemporary DP data together with older ALS data and compares this with the case when only old ALS data are trained with recent field data. Combining ALS data from 2010 to 2011 with DP data from 2015, both trained with National Forest Inventory (NFI) field plot data from 2015, improved predictions of growing stock volume. Validation using data from 100 stands inventoried in 2015 gave an RMSE of 24.3% utilizing both old ALS data and recent DP data, 26.0% for old ALS only and 24.9% for recent DP only. If information about management actions were assumed available, combining old ALS and recent DP gave RMSE of 23.0%, only ALS 23.3% and only DP 23.8%

Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km(2)large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m x18 m map units was found to range between 9 and 447 Mg center dot ha(-1). The corresponding root mean square errors ranged between 10 and 162 Mg center dot ha(-1). For the entire study region, the mean aboveground biomass was 55 Mg center dot ha(-1)and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study

Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

Abstract Background The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha −1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha −1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha −1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study