In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forests in Europe, and detecting infested trees is crucial for damage control and informative decision-making regarding management. This study explores efficient methods of detecting infestations using multispectral drone images, focusing on how using different pixels from the crown segments influences the detection rates. Tree crowns were first segmented using marker-controlled watershed segmentation, and then two pixel-selection strategies were tested, including selecting the pixels closer to the tree tops, and selecting the bright pixels with values higher than certain percentiles of the entire crown segments. Two datasets were used from the same area, including 2021 with an epidemic outbreak and 2023 with an endemic outbreak, to present the potential differences caused by attack intensity. The results showed that, in the early stages (1 – 9 weeks of infestation), using the centermost pixels or the brightest pixels in the tree crowns had higher detectability than using all pixels. Red-edge-based VIs were more sensitive than red-green-based VIs. In the middle stage (10 – 16 weeks of infestation), using pixels from the entire tree crown, including tree tops and the low branches, showed higher detectability than using fewer pixels. For the late stages (after 19 weeks of infestation), using only the center pixel was sufficient, and there were minor differences between different VIs. The results were supported by observations from two datasets from different years, although variations in the detectability between different years and stands were also observed.

The synthetic aperture radar (SAR) has long been used from satellites for forest monitoring at global level. The boreal forests in Sweden are well described wall-to-wall from airborne laser scanning and airborne photography. Hence, in Sweden, SAR can often only provide a limited added value, due to the low resolution compared to other sensors, despite its all-weather acquisition capabilities. By accounting for interfering effects that currently degrade the useful information in SAR images, it can be extremely valuable for both vegetation mapping and belowground mapping (e.g., soil conditions and tree roots). In the current work, we present the configuration of the first drone-based SAR experiment that allows us to image the forest in 3D with very high spatial resolution. We have started the analyses by using tomography to derive reflectivity for the roots of single trees, and comparing these with reference root biomass. The linear relationship indicates a potential for using SAR to derive forest variables that were yet neglected or little researched. Moreover, extensive additional remote sensing data have been collected from both airborne and spaceborne platforms, and reference data for both the vegetation and soil have been inventoried in-situ using complementary measurements and sensors. Hence, this unique experimental setup enables many unprecedented analyses about SAR applied to boreal forests.

This paper describes time-series backscatter measurements using BorealScat-2, which is a multi-polarization, multi-frequency tomographic radar. It is installed on a 50-m tower at a boreal forest site in northern Sweden. We describe the system and show measurements related to forest water dynamics during summer conditions. The measurements include data at P-, UHF- and L-band. Results show periodic diurnal variations for all frequency bands but with different phases and amplitudes depending on frequency band and polarization. The results indicate the potential of estimating forest water dynamics from radar data.

Detecting forest insect damage before the visible discoloration (green attacks) using remote sensing data is challenging, but important for damage control. In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forest in Europe, and some studies have been conducted on the early detection of infestations and forest vulnerabilities before attacks. This study assessed the detectability of the green attacks using multispectral drone images and examined the possibility of detecting vulnerable trees before attacks. The study used multispectral drone images covering 24 plots from 6 forest stands in southern Sweden, acquired in May (before attacks), June (green attack), August (green and yellow attack), and October 2021 (red attack). Drone images of individual-tree crowns were segmented and vegetation indices (VIs) were calculated for every single tree. Trees with the same duration of infestation were grouped for the analysis. Random Forest Classification (RF) and linear discriminant analysis (LDA) were used to build and compare models using all bands, sensitive bands, all VIs, and single VIs, respectively. Results were also compared between different ways of dividing training and testing data. When randomly dividing 90% and 10% trees for training and testing, the models could classify vulnerable trees before attacks with low accuracy. However, when training on trees in five stands, no model could predict infestations in the remaining test stand. Similarly, the models could not identify trees infested for fewer than five weeks. We conclude that the detectability of vulnerable trees before attacks and attacked trees with fewer than five weeks of infestation is very low. We noticed a considerable overfitting when using RF with more variables compared to using LDA with single VIs.

Pairs of fully polarimetric ALOS-2 PALSAR-2 L-band SAR images were used to model biomass on backscatter change over seven growth seasons in a hemi-boreal forest. The biomass change was related to backscatter change via consecutive field surveys of 263 field plots with a 10 m radius. To correct for differences in backscatter not related to biomass abundance, a HV-VV polarization ratio based correction, previously used on airborne L-band data, was applied to the data. The uncertainty of obtained predictions (lowest model mean RMSE 65.1 t/ha, lowest model mean bias 7.1 t/ha) was almost identical whether model fitting and prediction used data from the same scene pair, or different scene pairs. This could possibly attest to the feasibility of the backscatter correction for PALSAR-2 data, but no large backscatter offsets were observed for uncorrected data, and significant variance in predictions, due to the inherent noise in the data and the comparatively small area of evaluation plots, inhibit the analysis.

Remote sensing (RS) techniques have been used for mapping forest variables, such as stem volume (important for forest management activities associated with timber production), over large areas which can be updated more frequently than with field inventory (FI) data. In this study, wall-to-wall TanDEM-X synthetic aperture radar images were used as auxiliary RS data for model-based prediction of stand-level volumes for two models, trained using volumes computed from FI (A) and airborne laser scanning estimations (B), respectively. The models were validated with harvester data available for independent stands. It was observed that the performance of model B was slightly better compared to model A based on adjusted R2and root mean squared error values. Therefore, it can be concluded that a completely RS based approach for prediction and mapping of stand volumes would be as promising as a method based on FI data along with being cost- and labour-efficient.

In this study, we compared the classification accuracy at plot-level of three tree species in a boreal forest using a deep learning (DL) network applied to very high resolution (VHR) ALS data acquired with ~593 points/m2, or a linear discriminant analysis (LDA) method applied to dual-wavelength (DW) ALS data, acquired with ~80 points/m2.The methods were applied to the single trees, which were aggregated to the plot-level (10 m radius) where the majority class was used for assessing the accuracy. The overall best results were obtained with LDA, relying on DW data, with an overall accuracy (OA) of 82.1% (n=28 plots), while for the DL method using VHR data, the best OA=75.0% (n=28 plots). We acknowledge that the point density of DW data (used for LDA) is already relatively high, and the benefit of using additional spectral information is therefore higher in our study than the value of increasing the point density and identify the tree species from geometric properties (DL approach).We conclude that both dense mono-wavelength ALS data and DW ALS data contain enough information that tree species can be classified at the singletree level in this boreal forest test site in Sweden. With the point densities in this study, the classifications were more accurate with DW data, while the DL approach may be improved with a refined segmentation and pre-processing approach.

Detecting forest insect damage before the visible discoloration (green attacks) using remote sensing data is challenging, but important for damage control. In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forest in Europe. However, it is still debatable how early the infestations can be detected with remote sensing data. Some studies showed a spectral difference between healthy and green-attacked spruce trees at the plot level, while others showed that spectral differences existed before attacks. Therefore, a hypothesis is proposed that no spectral difference can be identified between green-attacked forests compared to healthy forests if the differences do not exist before the attacks. In this study, we tested this hypothesis using Sentinel-2 and WorldView-3 SWIR images on 24 healthy plots and 24 plots with mild, moderate, and severe attacks. In the results, the severely attacked plots did not show significant spectral differences in the Sentinel-2 images until August, and the sensitivity was found in the blue, red, red-edge, and SWIR band. Only the red band showed a significant difference between the healthy and moderately attacked plots in August, and only the blue, red, and SWIR band showed significant differences in September, October, and November. No significant differences were observed in the WorldView-3 images at the plot or individual tree level. We accepted the hypothesis that green attacks do not show spectral differences with the healthy forests when the differences do not exist before the attacks. We concluded that the SWIR bands were sensitive to attacks in the Sentinel-2 images with 10 m resolution, but not in the WorldView-3 images with 3.7 m resolution. Further studies are needed to explore the methodology of using WorldView-3 SWIR images for the early detection of forest infestation.

This study investigated the potential of utilizing time series of TanDEM-X phase heights, corrected for penetration depth, to detect silvicultural treatments in hemi-boreal forest. In total, 34 field plots with 40 m radius were used in conjunction with detailed forest management records to construct a reliable data set of treatments. The study area is situated in Remningstorp, a forest test site in southern Sweden. In the analysis, the temporal mean corrected phase heights were compared before and after a silvicultural treatment in order to quantify the effects of thinnings and clear-cuts on the phase height. As expected clear-cuts were highly distinguishable, but thinnings, while exhibiting a negative change in phase height on average, were not individually distinct from all untreated plots. Moreover, the results regarding the utility of applying penetration depth correction for the task were inconclusive. Overall, the results look very promising for using time series of phase height from TanDEM-X to map thinnings and clear-cuts, especially when several observations are available before and after the silvicultural treatment.

This study investigated the potential of using polarimetric synthetic aperture radar (PolSAR) data acquired at C- and X-band to estimate forest aboveground biomass (biomass) at a test site in southern Sweden. The SAR data were acquired with RADARSAT-2 and TerraSAR-X in September 2015, and the biomass estimations were cross-validated with 48 field inventoried plots of 40 m radius (0.5 ha), covering dominantly coniferous hemi-boreal forest. The quad-pol SAR data (HH, HV, VH, VV) were decomposed using the Yamaguchi model and the mean modelled scatter components for the plots were extracted and used as predictors in linear regression models to estimate the biomass. The model performances were quantified using the root mean square error (RMSE) and adjusted coefficient of determination, R2adj. The leave-one-out cross-validated RMSEs were 58.1 (37.1%) and 60.6 (38.6%) tons/ha for C-and X-band, respectively, and the R2adjwere 0.52 and 0.47, respectively. The regression model for C-band data used the volume and helix scattering components in the Yamaguchi decomposition as predictors, while the corresponding X-band model used the double bounce and surface components. The differences were likely due to the different penetrations at C-and X-band. We noticed a tendency to saturation at about 300 tons/ha in the X-band predictions, while no such tendency was noticed for the C-band model. We conclude that the Yamaguchi polarimetric decomposition technique is a useful approach when estimating biomass in a hemi-boreal forest. The strengths are not comparable to approaches where height information are available (e.g., single-pass interferometry from TanDEM-X), but this polarimetric approach based on single satellite images showed sensitivity to the entire biomass range (0 - 400 tons/ha) in our dataset. The potential of using quad-pol SAR data is, therefore, important to investigate further.

When modeling forest variables from InSAR phase height data, phase noise is a nuisance that can be mitigated by spatial averaging. In this empirical study, based on data from a hemi-boreal forest, the relation between field measured forest variables and TanDEM-X phase height and its dependence on the size of the field plots was investigated. Also, the impact of extending the region of phase height extraction beyond the actual plot size was investigated in order to determine the usefulness of this approach when small plot sizes cause significant noise in the estimation of phase height. For fixed field plot sizes (7 m and 10 m radii), the maximum correlation between Lorey's height and phase height occurred for phase height extraction regions multiple times larger in area than the field plots, which were located in homogenous stands.

In this study, Synthetic Aperture Radar (SAR) backscatter data from the Swedish airborne CARABAS-II and LORA systems were used to estimate stem density. The analysis were performed at the test site Remingstorp, located in the south of Sweden, consisting mainly of coniferous in hemi-boreal forests. In total, ten 80 m × 80 m forested areas, where all trees were measured in situ, with stem densities in the range of 278-552 stems and stem volumes in the range of 70-550 m3ha-1were analysed. SAR data from CARABAS-II and LORA were acquired in 2006 with nine unique flight headings. Local maxima of the backscatter were used to estimate stem density. The results were compared with in situ data and the accuracy in terms of root mean square error (RMSE) for CARABAS-II and LORA were found to be 81 stems (20.9%) and 82 stems (20.2%), respectively, in the best cases. The accuracy assessment of the stem density were performed after averaging images from each SAR system. This was done in order to suppress noise to a greater extent compared to using single images. The results show a potential to estimate stem density with low-freqency SAR data for the benefit of forest management.

Due to the ongoing global warming, European spruce bark beetles has become a serious threat to the spruce forests in Europe and caused serious environmental and economic issues. This study proposes a new vegetation index, Normalized Projected Red & SWIR (NPRS), for detection of spruce bark beetle attacks. 29 healthy and 24 bark beetle attacked plots in southern Sweden were used for evaluating the classification accuracy using NPRS at early-, intermediate- and late-stage attacks. The obtained kappa coefficients were 0.73, 0.80 and 0.88, respectively. It was concluded that the NPRS is a feasible method for continuous bark beetle mapping over large areas.

In this study, stem volume measured by the Swedish National Forest Inventory were modelled using the k nearest neighbor (kNN) algorithm, with k=1, 3, or 5 neighbors. As independent variables, the combination of two satellite sensors were used: the active radar sensor TanDEM-X and the passive optical sensor Sentinel-2. The results indicate that stem volume per species can be predicted relatively accurately, mainly due to the inclusion of Sentinel-2 data, while the total stem volume is largely predicted well due to inclusion of the TanDEM-X phase height. The prediction of total stem volume was, however, not significantly improved with the additional spectral information from Sentinel-2 about the tree species. The kNN method is somewhat limited in the highest range of volumes, since no extrapolation is supported. Thus, it is important to have a reference dataset representing the entire range of the population for a successful application. The main advantage of combining the two data sources is the convenient procedure of obtaining both the tree species classification and volumes (divided per species) in a single method. It is concluded, that when sufficient reference data are available, the kNN approach with a combination of radar and optical data provides additional information about the stem volumes (in terms of tree species), but without improving the prediction of the total stem volume accuracy.

This study applies the two-level model to predict stem volume (VOL), presented as wall-to-wall rasters. The SAR data were acquired with the TanDEM-X system and 518 scenes covered the entire Sweden. For comparison, a multiple linear regression model is also evaluated. Compared to earlier studies, the model parameters are fitted separately for each satellite scene. The prediction accuracy at the stand-level is evaluated using field inventoried reference stands within one scene, located in Northern Sweden and provided by a Swedish forest company. The results from the two models were similar, with an RMSE of 34.8 m3/ha and 32.9 m3/ha at the stand-level, respectively, and the corresponding biases were 14.3 m3/ha and 12.1 m3/ha. The error is significantly lower, compared to a previous study (52-65 m3/ha) where a universal multiple linear regression model was used for all scenes. It can be concluded, that using model parameters fitted at the local scene appears to improve the prediction performance in terms of RMSE, but no significant difference could be determined between predictions based on the two- level model or multiple linear regression, evaluated in this study.

Three-year forest above-ground biomass change were measured using L- and P-band Synthetic Aperture Radar (SAR) backscatter. The SAR data were collected in the airborne BioSAR 2007 and BioSAR 2010 campaigns over the hemiboreal Remningstorp test site in southern Sweden. Regression models for biomass were developed using reference biomass maps created using airborne laser scanning data and field measurements. The results from regression analysis show that using HV backscatter (or VH) in a model with above-ground biomass and backscatter change on either natural logarithmic or square root, and decibel scale, respectively, explained most of the variation in the biomass change, both for L- and P-band. In the case of L-band, the two best cases showed R2values of 66%, when comparing two SAR images acquired 2007 and 2010. For P-band using the same models, the best cases showed R2values of 62%. In summary, the results look promising using L- and P-band backscattering for mapping biomass change.

In this paper, three approaches to forest change modeling with the two-level model (TLM) are compared by fitting the TLM to 12 VV-polarized TanDEM-X acquisitions over a hemi-boreal test site in southern Sweden. It is observed that the best inversion results are obtained when rapid forest change (e.g., harvesting) is modeled as change in canopy density, while growth is modeled as change in forest height.

A first evaluation of ALOS-2 PALSAR-2 data for forest stem volume estimation has been performed at a coniferous dominated test site in southern Sweden. Both the Fine Beam Dual (FBD) polarization and the Quad-polarimetric mode were investigated. Forest plots with stem volume reaching up to a maximum of about 620 m3ha−1(corresponding to 370 tons ha−1) were analyzed by relating backscatter intensity to field data using an exponential model derived from the Water Cloud Model. The estimation accuracy of stem volume at plot level (0.5 ha) was calculated in terms of Root Mean Square Error (RMSE). For the best case investigated an RMSE of 39.8% was obtained using one of the FBD HV-polarized images. The corresponding RMSE for the FBD HH-polarized images was 43.9%. In the Quad-polarimetric mode the lowest RMSE at HV- and HH-polarization was found to be 43.1% and 66.1%, respectively.

Interferometric TanDEM-X data from 2011 and 2014 were used to create biomass maps over the Swedish test site Remningstorp. These maps were used to compute the biomass change for four classes; pre-commercial thinning, thinning, clear-cutting, and untouched forest. Field inventory and ALS data from the corresponding years were used as reference data. The biomass change was compared on 12 subjectively chosen plots with 40 m radius for each class. It was found, that pre-commercial thinning was difficult to detect, as the biomass loss was less than the biomass growth during the four vegetation seasons investigated. Thinning could be detected from the biomass change being about zero or slightly negative, while clear-cut plots were obvious to notice, with the biomass withdrawal being several hundreds of tons ha-1. The untouched plots had a biomass growth of about 4 to 6 tons ha-1year-1. It was concluded, that annual TanDEM-X images can be used to detect also smaller silviculture activities such as thinning, but further research with shorter time periods would be desired.

In this paper, forest change detection and forest height estimation are studied using two-level model (TLM) inversion of multi-temporal TanDEM-X (TDM) data. Parameter Ah, describing the distance between ground and vegetation levels, is kept constant for all acquisitions, whereas parameter ¡jl, the area-weighted backscatter ratio, changes with acquisition. Two multi-temporal sets of TDM data, acquired over the hemi-boreal test site Remningstorp, situated in southern Sweden, are studied: one consisting of 12 acquisitions made in the summers of 2011, 2012, 2013, and 2014 with heights-of-ambiguity (HOAs) between 32 m and 63 m, and one consisting of 33 acquisitions made between August 2013 and August 2014 with HOAs between 38 m and 195 m. The first dataset is used to show that commercial thinnings and clear-cuts can be detected by studying the canopy density estimate r/o = 1/(1 + /x). The second dataset is used to show that seasonal change can be observed in r/o for deciduous plots, but not for coniferous plots. Moreover, it is shown that 1.3Ah is a good estimate of the basal area-weighted (Lorey's) height, with a correlation coefficient equal to 0.98 and a root-mean-square error of 0.9 m.

Three-dimensional (3D) data about forest captured by airborne laser scanning (ALS) have revolutionized forest management planning. Accurate, updated large-scale maps of forest variables produced with low costs today support greatly improved decisions about silvicultural treatments compared to the past practice based on field surveyed data only. These maps usually lack important information about forest age and site productivity, as this cannot be accurately assessed from the available ALS data. In Sweden, ALS has recently been performed nation-wide, except the mountainous area, to produce a new and accurate digital terrain model (DTM). This DTM enables extremely cost-efficient extraction of 3D data about the forest from other sources than ALS, such as automatic stereo-matching of aerial images as well as from single-pass spaceborne interferometric synthetic aperture radar (InSAR). In contrast to ALS, these data sources can provide low-cost time-series of 3D data. Aerial images of Sweden are often available in archives back to approximately 1960, and the TanDEM-X SAR system has the potential to provide new data every second week over large areas. These data have a potentially high value for forest management planning, since they may provide missing and highly important information - forest site productivity, Site Index (SI) and forest age. This pilot study explores a least-squares minimization approach to estimate forest age and SI from time series of 3D data produced by 1) image matching of DMC aerial images, and 2) TanDEM-X SAR data.

A two-level model (TLM) is introduced and investigated for the estimation of forest height and canopy density from a single ground-corrected InSAR complex correlation coefficient. The TLM models forest as two scattering levels, namely, ground and vegetation, separated by a distance Δh and with area-weighted backscatter ratio μ. The model is evaluated using eight VV-polarized bistatic-interferometric TanDEM-X image pairs acquired in the summers of 2011, 2012, and 2013 over the managed hemi-boreal test site Remningstorp, which is situated in southern Sweden. Ground phase is removed using a highresolution digital terrain model. Inverted TLM parameters for thirty-two 0.5-ha plots of four different types (regular plots, sparse plots, seed trees, and clear-cuts) are studied against reference lidar data. It is concluded that the level distance Δh can be used as an estimate of the 50th percentile forest height estimated from lidar (for regular plots: r > 0.95 and root-mean-square difference (σ)0.59 and σ ≈ 10%, or 0.07).

A model for aboveground biomass estimation from single-pass interferometric synthetic aperture radar (InSAR) data is presented. Forest height and canopy density estimates Δh and η0, respectively, obtained from two-level model (TLM) inversion, are used as biomass predictors. Eighteen bistatic VV-polarized TanDEM-X (TDM) acquisitions are used, made over two Swedish test sites in the summers of 2011, 2012, and 2013 (nominal incidence angle: 41°; height-of-ambiguity: 32-63 m) . Remningstorp features a hemiboreal forest in southern Sweden, with flat topography and where 32 circular plots have been sampled between 2010 and 2011 (area: 0.5 ha; biomass: 42-242 t/ha; height: 14-32 m) . Krycklan features a boreal forest in northern Sweden, 720-km north-northeast from Remningstorp, with significant topography and where 31 stands have been sampled in 2008 (area: 2.4-26.3 ha; biomass: 23-183 t/ha; height: 7-21 m) . A high-resolution digital terrain model has been used as ground reference during InSAR processing. For the aforementioned plots and stands and if the same acquisition is used for model training and validation, the new model explains 65%-89% of the observed variance, with root-mean-square error (RMSE) of 12%-19% (median: 15%) . By fixing two of the three model parameters, accurate biomass estimation can also be done when different acquisitions or different test sites are used for model training and validation, with RMSE of 12%-56% (median: 17%) . Compared with a simple scaling model computing biomass from the phase center elevation above ground, the proposed model shows significantly better performance in Remningstorp, as it accounts for the large canopy density variations caused by active management. In Krycklan, the two models show similar performance.

Presents corrections to the paper, "Estimation of forest biomass from two-level model inversion of single-pass InSAR data" (Soja, M.J., et al.,Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 5083–5099, Sep. 2015).

A new model for aboveground biomass estimation from forest height and canopy density estimates obtained from the inversion of a two-level model (TLM) is presented and studied using data from the hemi-boreal test site Remningstorp, situated in southern Sweden. Three bistatic-interferometric TanDEM-X acquisitions from the summers of 2011, 2012, and 2013 and with heights-of-ambiguity (HOAs) 49 m, 32 m, and 63 m, respectively, are used. An external, high-resolution digital terrain model (DTM) is used as ground reference during interferogram flattening. Model parameters are estimated for each acquisition separately, and the model is evaluated on all three acquisitions, to examine both its explanatory and predictive values. Residual root-mean-square errors (RMSEs) are 14%-19% and the model explains 67%-84% of the variance in the data. Prediction RMSE is 20% for the two images with the highest HOA, but much higher for the third image.

In this study, dense hemi-boreal forest biomass and height estimation was investigated based on optical satellite data and a high quality Digital Elevation Model (DEM) from airborne laser scanning. This analysis was carried out on data collected 2008-2010 over the test site Remningstorp in southern Sweden. The optical sensors SPOT-5 HRS and ASTER were tested to process a Digital Surface Model (DSM), i.e. the vegetation height above mean sea level, that is used together with the DEM (derived from laser data) to calculate a Canopy Height Model (CHM) as the difference between the former ones. By modeling biomass and height using regression analysis on spectral data from SPOT-5 HRG and height metrics from the CHM an improved Root Mean Squared Error (RMSE) and adjusted R2is expected, compared to using the single data sources alone. The best results showed a relative RMSE for standwise prediction of mean biomass and height of 30.3% and 23.3%, respectively. Adding CHM data to a spectral based (HRG) prediction model improved the mapping accuracy roughly 3%. In conclusion, the estimation accuracy did not improve significantly by adding height metrics to spectral data.

The use of hierarchical structures for providing excellent wireless connections for customers has attained growing attention. Models to investigate the QoS requirements rather quickly become complicated and hard to analyze. In the paper a first rough analytical approach for the answer is given. As the users devote more and more time to be connected through wireless connections, and at the same time becomes more and more mobile the traditional systems does not work. Our model is based on a hierarchical structure where different, already existing, wireless systems interact to maintain coverage for moving customers outdoors as well as indoors. The QoS requirements are based on the type of service used and the mobility pattern. The system should be able to configure the connection to make the optimal choice among those networks present at the location of the customer. Further, the mobile terminals must be able to handle the different networks. In a general case, systems with broader coverage and usually lower bandwidth will act as backup systems for higher quality systems. Our analytical tool shows the benefits and drawbacks of such a system. The transition duration times between usage of different systems are analyzed together with their variance and squared coefficient of variation. Each handover is a source for failure. By changing the parameter setting it is possible to get at better understanding of how to configure hierarchical wireless systems for best performance. With the presented formulas this could be achieved in a fast and thorough way.