Estimating site index from short term TanDEM-X canopy height models

The tree height growth from three vegetation seasons was fitted to height growth curves in order to estimate the site index, which is a variable related to forest site productivity. The tree height growth was evaluated for four different cases, in which remote sensing data from TanDEM-X and airborne laser scanning were used. The used method requires a digital terrain model and knowledge about the tree species. Furthermore, the remote sensing data were calibrated using Lorey'smean height heights or airborne laser scanning data. It was found that four annual acquisitions of calibrated TanDEM-X data covering three vegetation seasons could be used for estimating the site index on 27 0.5-ha field plots with 4.4-m (12.1%) RMSE. The site index could in a similarmanner be estimated from only two airborne laser scanning acquisitions, before and after four vegetation seasons, with 2.3-m (6.3%) RMSE

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

Analysis of seasonal variations for estimation of forest variables with InSAR technology

Skogen är viktig på många sätt, eftersom skog är en resurs både som råvara, energikälla och kolsänka. För kontroll av tillväxt, avgång och planering av skog och skogsskötsel har man historiskt genomfört inventering med manuella metoder genom fältpersonal, vilket både är kostsamt och endast ger en bedömning om skogens tillstånd just i de ögonblick skogen inventeras. De senaste tio åren har metoder baserade på fjärranalys implementerats på många sätt, för bättre och effektivare inventering. Skattningar av skogliga variabler med laserdata har uppvisat hög noggrannhet med god kvalité, men det är ett dilemma att skanningen är kostsam och att utförd skanning snabbt blir oanvändbar när skogen förändras. Satellitburna sensorer som genererar tredimensionella data över skogen har potentialen att ge tillräckligt bra skattningskvalitét, inte minst för att skriva fram befintliga skattningarna, dessa data kan även kombineras med andra skattningstekniker och metoder. Fördelen med satellitburna sensorer är att de kontinuerligt återkommer över samma område på kort tid. I denna studie har data från satellitkonstellationen TanDEM-X använts. Interferometrisk Synthetic Aperture Radar (InSAR) är en radarteknik som satelliterna i TanDEM-X möjliggör. Studier som använt sig av InSAR teknik uppvisar mycket goda skattningsresultat för både höjd och biomassa (Persson & Fransson, 2014a). I flera tidigare studier diskuteras det om säsongs- och vädervariationer eventuellt kan påverka kvalitén på InSAR data. Denna studies syfte har varit att analysera faktorer som kan tänkas påverka InSAR data för skogliga skattningar. Med belägg från andra studier (Solberg m.fl, 2015) kan det i denna studie konstateras att temperatur påverkar skattningar av skogliga variabler med InSAR.The forest is important in many ways because it is a resource as raw material, energy and a carbon sink. For monitoring of growth, mortality and forest management activities, historically forest inventory has been done by field staff, which is costly and only provides an assessment of forest condition at the time of inventory. The last ten years, methods based on remote sensing have been implemented in many ways, for better and more efficient inventory. Estimates of forest variables with airborne laser scanning data have provided high accuracy with good quality, but it´s a problem that scanning is costly and that the data quickly become useless when the forest is changing. Studies show that satellite-borne sensor techniques can provide good quality forest estimations, or can be combined with other estimation techniques and methods. The advantage of satellite-borne sensors is that they return to the same area over short time periods. In this study, data from the satellite constellation TanDEM-X is used. Interferometric Synthetic Aperture Radar (InSAR) is a radar technique that is possible due to the configuration of the TanDEM-X satellites. Studies that use the InSAR technique exhibit very good estimation results of both height and biomass (Persson & Fransson, 2014a). In several previous studies it is discussed whether there are seasonal and weather variations that affect the quality of the InSAR data. The purpose of this study was to analyze the factors that may affect InSAR data for estimating forest variables. This study, together with evidence from previous studies (Solberg m.fl, 2015) provides support for the conclusion that temperature affects the estimates of forest variables when using InSAR

Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery

Peer reviewe

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.</p

Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects of European spruce forests. A crucial measure in pest control is the removal of infested trees before the beetles leave the bark, which generally happens before the end of June. However, stressed tree crowns do not show any significant color changes in the visible spectrum at this early-stage of infestation, making early detection difficult. In order to detect the related forest stress at an early stage, we investigated the differences in radar and spectral signals of healthy and stressed trees. How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season, which covered the period before attacks (April), early-stage infestation ('green-attacks', May to July), and middle to late-stage infestation (August to October). The results show that spectral differences already existed at the beginning of the vegetation season, before the attacks. The spectral separability between the healthy and infested samples did not change significantly during the 'green-attack' stage. The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones. These stress-induced spectral changes could be more efficient indicators of early infestations than the 'green-attack' symptoms.In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019. The red and SWIR bands from Sentinel-2 showed the highest separability of healthy and stressed samples. The backscatter from Sentinel-1 and additional bands from Sentinel-2 contributed only slightly in the Random Forest classification models. We therefore propose the Normalized Distance Red & SWIR (NDRS) index as a new index based on our observations and the linear relationship between the red and SWIR bands. This index identified stressed forest with accuracies from 0.80 to 0.88 before the attacks, from 0.80 to 0.82 in the early-stage infestation, and from 0.81 to 0.91 in middle- and late-stage infestations. These accuracies are higher than those attained by established vegetation indices aimed at 'green-attack' detection, such as the Normalized Difference Water Index, Ratio Drought Index, and Disease Stress Water Index. By using the proposed method, we highlight the potential of using NDRS with Sentinel-2 images to estimate forest vulnerability to European spruce bark beetle attacks early in the vegetation season

Quantify and account for field reference errors in forest remote sensing studies

Field inventoried data are often used as references (ground truth) in forest remote sensing studies. However, the reference values are affected by various kinds of errors, which tend to make the reported accuracies of the remote sensing-based predictions worse than they are. The more accurate the remote sensing techniques are becoming, the more pronounced this problem will be. This paper addresses the impact of uncertainties in field reference data due to measurement errors, model errors, and position errors when evaluating the accuracy of biomass predictions from airborne laser scanning at plot level. We present novel theoretical analysis methods that take the interactions of the error sources into account. Further, an error characterization model (ECM) is used to describe the error structure of the remote sensing-based predictions, and we show how the parameters of the ECM can be adjusted when field references contain errors. We also show how root mean square error (RMSE) estimates can be adjusted. Based on data from Scandinavian forests, we conclude that the field reference errors have an impact on the remote sensing-based predictions. By accounting for these errors the RMSE of the remote sensing-based predictions was reduced by 6-18%. The most influential sources of error in the field references were found to be the residual errors of the allometric biomass model and the field plot position errors. Together, these two sources accounted for 97% of the variance while measurement errors and biomass model parameter uncertainties were negligible in our study