Updating forest maps through data assimilation using remotely sensed data and regression methods designed to avoid bias trends

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.

Lichen is a major forage resource for reindeer and may constitute up to 80% of a reindeer's winter diet. The reindeer grazing area in Sweden covers almost half of the country, with reindeer using mountainous areas in the summer and forested areas in the winter. Knowledge about the spatial distribution of ground lichens is important for both practical and decision-making purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of this study was to use lichen information collected in the Swedish National Forest Inventory (NFI) as training data to classify optical satellite images into ground lichen cover classes. The study site was located within the reindeer husbandry area in northern Sweden and consisted of the common area between two contiguous Satellite Pour l'Observation de la Terre (SPOT)-5 scenes and one Landsat-7 Enhanced Thematic Mapper Plus (ETM+) scene. Three classification methods were tested: Mahalanobis distance, maximum likelihood and spectral mixture analysis. Post-classification calibration was applied using a membership probability threshold in order to match the NFI-measured proportions of lichen coverage classes. The classification results were assessed using an independently collected field dataset (229 validation areas). The results demonstrated high classification accuracy of SPOT imagery for the classification of lichen-abundant and lichen-poor areas when using the Mahalanobis distance classifier (overall accuracy 84.3%, kappa = 0.68). The highest classification accuracy for Landsat was achieved using a maximum likelihood classification (overall accuracy 76.8%, kappa = 0.53). These results provided an initial indication of the utility of NFI data as training data in the process of mapping lichen classes over large areas.

Abstract:Data assimilation (DA) has been applied for several decades in areas such as meteorology and robotics, to predict the state of systems that evolve over time, by integrating model-based forecasting with repeated observations. Recently, DA has gained attention in forest inventory applications. For instance, study by Nyström et al. (2015) not only demonstrated the theoretical potential of employing dense time series of remotely sensed (RS) data but also identified several obstacles that must be overcome before the methodology can be practically adopted. Within the SmartForest project, we are further exploring the usefulness of DA techniques for forest inventory and mapping of forest attributes.Recent studies have shown that DA has a potential to maintain the accuracy of plot and stand level information, obtained from accurate but expensive surveys, such as airborne laser scanning (ALS), by making use of inexpensive optical satellite data and DA throughout several subsequent years. However, with ever-increasing amounts of RS data, it is important to evaluate not only how to make assessments and growth updates through DA, but also how to best utilize huge amounts of RS data from within single years. For example, the European Space Agency’s Sentinel-2 satellites currently provide new data across boreal forests every second week.In a study initiated within the Norwegian SmartForest programme, we evaluate whether building separate models for each RS dataset and applying composite estimation or merging all data into a single model through principles of partial least squares regression and random forest non-parametric regression, yields the best results in terms of prediction accuracy.Our investigation was conducted within the Våler municipality of Norway and focused on growing stock volume as our primary target variable. The RS data were acquired in 2022 and included ALS point clouds, digital aerial photogrammetric point clouds, and Sentinel-2 spectral data. Alongside comparing prediction accuracies, we conducted a qualitative assessment to discern the practical advantages and disadvantage of each method in integrating them into a multi-temporal data DA system.Reference:Nyström, M., Lindgren, N., Wallerman, J., Grafström, A., Muszta, A., Nyström, K., Bohlin, J., Willén, E., Fransson, J.E., Ehlers, S. and Olsson, H., 2015. Data assimilation in forest inventory: first empirical results. Forests, 6(12), pp.4540-4557.

Despite the abundance of available studies on optical and microwave methods devoted to investigating agricultural crop conditions, there is a lack of research that explores the integration between microwave and optical data and the link between photosynthetic activity, measured by PRI (photochemical reflectance index), and vegetation water content, detected by radar sensors. In particular, there is a lack of vision that links these measures to better understand how plants react and adapt to possible water stress conditions. Most of the existing research tends to treat optical and microwave information separately, without investigating how the integration of these techniques can provide a more complete and accurate understanding of the research topic, corroborated by ground data. In this paper, an integrated approach using microwave and optical satellite data, respectively acquired by Sentinel-1 (S-1) and Sentinel-2 (S-2), was presented for monitoring vegetation status. Experimental data and electromagnetic models have been combined to relate backscattering from S-1 and optical indices from S-2 to plant conditions, which were evaluated by measuring PRI, plant water content (PWC), and soil water content. Field data were collected in two sorghum fields close to Florence in Tuscany (Central Italy) during the summers of 2022 and 2023. The results show significant correlations between microwave and optical data with respect to field measurements, highlighting the potential of remote sensing techniques for agricultural monitoring and management, also in response to climate change. Determination coefficients of R2 = 0.51 between PRI and PWC, where PWC is retrieved by S-1, and R2 = 0.73 between PSRI (plant senescence reflectance index) and PRI were obtained.

The area of the research is a part of the Landscape Park and belongs to agri–environment ecosystem of South-West Poland. The study on vegetation growth conditions has been carried out applying optical data from TERRA/ASTER, TERRA/MODIS, and ENVISAT/MERIS as well as microwave data from ENVISAT/ASAR. In situ data collected at the time of satellite observations were as follows: soil moisture, leaf area index, biomass, vegetation moisture, crop height, types of crop and its actual vegetation phenological stage. The supervised maximum likelihood classification method was applied to multispectral optical and multitemporal microwave satellite data to distinguish different crop type. From optical satellite data different vegetation and soil moisture indices have been calculated on the basis of surface reflectance and surface temperature supported with meteorological data. Assessment of heat fluxes applying surface temperature calculated from TERRA/ASTER and TERRA/MODIS images was very important for water balance assessment. From microwave satellite data, registered in various polarisations and incidence angles, the backscattering coefficients have been derived and related to soil moisture. It was presented that the classification obtained from ENVISAT ASAR VV HH IS4 and IS6 and assessment of soil moisture yield into good results close to these obtained from optical data, what is very important in regard to often cloud cover. It was very important to get evapotranspiration from optical data. The results should be implemented into management of agri–environment ecosystem. ENVISAT images have been obtained from ESA for CAT-1 1427 project, TERRA images for the national project 4T12E02630.

In this paper, the accuracy of forest stem volume estimation using a combination of optical SPOT-5 satellite and TopEye laser scanner data is investigated, at stand level. It is anticipated that the accuracy will be improved for the combined stem volume estimate compared to using SPOT-5 data only. The test site is located in the south of Sweden and consists mainly of coniferous forest. The stem volume for the selected stands was in the range of 30-620 m/sup 3/ ha/sup -1/ with an average stem volume of 288 m/sup 3/ ha/sup -1/ and an average size of 2.9 ha. Regression analysis has been used to develop stem volume functions for each sensor and for the combination. In the combined stem volume function the horizontal forest structure is captured by the optical satellite data whereas the vertical structure is represented by the laser derived tree height data. The accuracy in terms of relative root mean square error was 30.8% of the average stem volume for SPOT-5 and 15.7% for the combination. Thus, compared to using only SPOT-5 data the improvement was found to be 49% The result implies that the combination of multi-spectral optical satellite and laser derived tree height data can be used for standwise stem volume estimation in forestry applications.

Synthetic aperture radar and optical satellite data for estimating the biomass of corn

In this study, we assess the potential of combining forest tree height derived from interferometric SAR data with satellite optical data for improving accuracy of forest stem volume mapping. Study site was located near the Hyytiala forestry station in central Finland, with terrain representative of the boreal coniferous forest. As a primary interferometric data, several data takes of TanDEM-X data are used. Satellite optical data were represented by Landsat 8 image. The ground reference data were information on stand level from forest management plans. Firstly, forest tree height is estimated from TanDEM-X interfer ometric SAR coherency. Further, retrieved tree heights are combined with optical data for predicting forest stem volume using linear regression framework. Results of regression analysis performed demonstrate considerable improvement in terms of obtained accuracy figures of the combined stem volume estimation (RMSE = 34%, R2=0.57) compared with use of optical satellite data only (RMSE=40%, R2=0.43).

Growing stock volume estimation from L-band ALOS PALSAR polarimetric coherence in Siberian forest

Digital oblique aerial camera (hereinafter called “oblique cameras”) is an assembly of medium format digital cameras capable of shooting digital aerial photographs in five directions i.e. nadir view and oblique views (forward and backward, left and right views) simultaneously and it is used for shooting digital aerial photographs efficiently for generating 3D models in a wide area. <br><br> For aerial photogrammetry of public survey in Japan, it is required to use large format cameras, like DMC and UltraCam series, to ensure aerial photogrammetric accuracy. <br><br> Although oblique cameras are intended to generate 3D models, digital aerial photographs in 5 directions taken with them should not be limited to 3D model production but they may also be allowed for digital mapping and photomaps of required public survey accuracy in Japan. <br><br> In order to verify the potency of using oblique cameras for aerial photogrammetry (simultaneous adjustment, digital mapping and photomaps), (1) a viewer was developed to interpret digital aerial photographs taken with oblique cameras, (2) digital aerial photographs were shot with an oblique camera owned by us, a Penta DigiCAM of IGI mbH, and (3) accuracy of 3D measurements was verified.

Abstract. Digital oblique aerial camera (hereinafter called “oblique cameras”) is an assembly of medium format digital cameras capable of shooting digital aerial photographs in five directions i.e. nadir view and oblique views (forward and backward, left and right views) simultaneously and it is used for shooting digital aerial photographs efficiently for generating 3D models in a wide area. For aerial photogrammetry of public survey in Japan, it is required to use large format cameras, like DMC and UltraCam series, to ensure aerial photogrammetric accuracy. Although oblique cameras are intended to generate 3D models, digital aerial photographs in 5 directions taken with them should not be limited to 3D model production but they may also be allowed for digital mapping and photomaps of required public survey accuracy in Japan. In order to verify the potency of using oblique cameras for aerial photogrammetry (simultaneous adjustment, digital mapping and photomaps), (1) a viewer was developed to interpret digital aerial photographs taken with oblique cameras, (2) digital aerial photographs were shot with an oblique camera owned by us, a Penta DigiCAM of IGI mbH, and (3) accuracy of 3D measurements was verified.

The effects of nitrogen deposition and site quality on amino acid concentrations in needles of Piceaabies (L.) Karst. and Pinussylvestris L. trees were studied in two areas that represent different levels of nitrogen deposition: one area in southern Sweden and one area in northern Sweden receive, respectively, approximately 20–30 and 3 kg•ha−1•year−1 of nitrogen. On each area three site quality classes were chosen for each tree species. The site classes were chosen to represent poor, medium, and good sites typical for each area. Free amino acids in the needles were analyzed as 9-fluorenylmethyl formate derivatives by high-performance liquid chromatography. The total nitrogen was determined with a CHN elemental analyzer, and other mineral nutrients were determined with an inductively coupled plasma analyzer. Arginine, glutamic acid, glutamine, γ-aminobutyric acid, and aspartic acid were the quantitatively dominating amino acids in the needles of both species from all sites in both northern and southern Sweden. These amino acids represented 50–80% of the total concentration of free amino acids in the needles. The concentration of arginine in the needles of both spruce and pine increased with decreasing site index and showed high variations between individual trees. For both species, the highest concentrations of arginine were found in the southern area, which had the highest deposition of nitrogen. Concentrations of glutamic acid, glutamine, and γ-aminobutyric acid in the needles of both species showed significant differences between some of the sites on both areas, but these differences showed no general pattern that correlated with the site indexes. In relation to nitrogen, low concentrations of phosphorus and potassium were found in needles from the poorest spruce sites in both areas compared with corresponding values for the good spruce sites. The results are discussed in relation to nitrogen deposition and mineral nutrient imbalance.

As soil moisture and vegetation water content both affect the emissivity from the land surface, each of them can be derived from satellite-based passive microwave measurements. In this study, we use soil moisture retrievals from the 36 km SMAP L2 product and X-band vegetation optical depth (VOD) from AMSR2 LPRM version 6. VOD is a proxy for vegetation water content, linked to the leaf area index (LAI). We developed a machine learning-based observation operator to map LAI to VOD.We assimilate the SMAP and AMSR2 products into the Noah-MP land surface model (LSM) with dynamic vegetation. This is done by means of a one-dimensional Ensemble Kalman Filter (EnKF) within the NASA Land Information System (LIS). SMAP soil moisture retrievals update soil moisture in each of the four soil layers of the LSM, while AMSR2 VOD retrievals update the LAI. A cumulative distribution function (CDF) matching approach rescales the soil moisture retrievals to the model climatology. Model LAI is mapped to VOD by means of the above-mentioned observation operator. The resulting data assimilation (DA) system produces consistent estimates of all land surface variables on a quarter-degree regular grid over the European continent from 1 April 2015 through 31 March 2022.This joint SMAP and AMSR2 DA system is validated by assessing a number of geophysical variables. The surface and root-zone soil moisture estimates are evaluated using in situ observations from the ISMN. Gross primary production (GPP) and evapotranspiration are evaluated using FLUXNET data. Estimates for LAI are compared with optical satellite data from MODIS. The results are compared with open loop (model-only), and SMAP- and AMSR2-only DA experiments.SMAP-only DA primarily improves soil moisture estimates, while AMSR2-only DA mainly improves estimates of GPP and ET. Preliminary results indicate that the joint DA has the potential to combine the improvements of both individual assimilation systems.

Introduction: Stoma complications are common and interfere with many aspects of everyday life. Stoma problems are usually managed by a specialised stoma nurse, a service not present in the rural areas of South Lapland in Sweden. The aim of this study was to describe how stoma patients in rural areas experience living with a stoma. Methods: A qualitative descriptive study with semi-structured interviews were conducted with 17 stoma patients living in rural municipalities and who received a part of their care at the local cottage hospital. Qualitative content analysis was employed. Results: Initially, the stoma was experienced as very depressing. Participants had difficulties in properly managing the dressing. Over time they learned how to properly care for their stoma, making their life easier. Both satisfaction and dissatisfaction with the healthcare were experienced. Those who were dissatisfied expressed a lack of competence in dealing with stoma-related problems. Conclusions: Living with a stoma in a rural area in northern Sweden is experienced as a learning process and acceptance of the stoma’s existence is important. This study emphasises the need for increased knowledge of stoma-related problems in rural primary healthcare in order to help patients cope with everyday life.

Long-term studies on transfer of 137Cs from soil to vegetation and to grazing lambs in a mountain area in Northern Sweden