Re-engineering strategies for legacy software systems

Re-engineering can be described as a process for updating an existing system in order to meet new requirements. Restructuring and refactoring are activities that can be performed as a part of the re-engineering process. Supporting new requirements like migrating to new frameworks, new environments and architectural styles is essential for preservation of quality attributes like maintainability and evolvability. Many larger legacy systems slowly deteriorate over time in quality and adding new functionality becomes increasingly difficult and costly as technical debt accumulates. To modernize a legacy system and improve the cost effectiveness of implementing new features a re-engineering process is often needed. The alternative is to develop a completely new system but this can often lead to loss of years of accumulated functionality and be too expensive. Re-engineering strategies can be specialized and solve specific needs like cloud migration or be more generic in nature supporting several kinds of needs. Different approaches are suitable for different kinds of source and target systems. The choice of a re-engineering strategy is also influenced by organisational and business factors. The re-engineering of a highly tailored legacy system in a small organisation is different from re-engineering a scalable system in a large organisation. Generic and flexible solutions are well suited for especially smaller organisations with complex systems. The re-engineering strategy Renaissance was applied in a case study at Roima Intelligence Oy in order to find out if such a strategy is realistically usable, useful and valuable for a smaller organization. The results show that a re-engineering strategy is possible to be used with low overhead in order to prioritize different parts of the system and determining a suitable modernization plan. Renaissance was also shown to add value especially in the form of deeper understanding of the system and a structured way to evaluate different options for modernization. This is achieved through assessing the system from different views taking into account especially business and technical aspects. A lesson learned about Renaissance is that determining an optimal scope for the system assessment is challenging. The results are applicable for other organisations dealing with complex legacy systems with constrained resources. Limitations of the study are that the number of different kinds of re-engineering strategies discussed is small and more suitable strategies than Renaissance could be discovered with a systematic mapping study. The amount of experts participating in the process itself as well as the evaluation was also low, introducing some uncertainty to the validity of the results. Further research is needed in order to determine how specialized and generic re-engineering strategies compare in terms of needed resources and added value

Effects of wood ash, green residues and N-free fertiliser on naturally regenerated birch and field vegetation in a young Norway spruce stand in SW Sweden

Treatments added to young conifer stands aiming to compensate for the loss of nutrients and alkalinity associated with whole-tree harvesting for bioenergy purposes have the potential to affect the growth of competitors to the conifers. Three different nutrient compensation treatments were applied to a young Picea abies (L.) Karst. stand in south-west Sweden, 2 or 3 years following final felling. The treatments were; fine fraction of harvest residues (15 Mg dw ha(-1)); granulated wood ash (4.1 Mg dw ha(-1)); nitrogen-free vitality fertiliser (twice 1.5 Mg ha(-1)); untreated control. Root biomass and total biomass of graminoids (mainly Deschampsia flexuosa (L.) Trin) were significantly greater in the wood ash and vitality fertiliser treatments than in the residues and control treatments. The aboveground and coarse root biomass of naturally regenerated birch (Betula spp.) and the aboveground biomass of dwarf shrubs (mainly Calluna vulgaris (L.) Hull.) and bottom layer were not affected by the treatments. Calcium and magnesium concentrations in the aboveground biomass of graminoids and phosphorus concentration in the biomass of bottom layer were significantly the highest in the vitality fertiliser treatment. Thus, nutrient compensation with vitality fertiliser or granulated wood ash may increase competition from graminoids in the establishment phase

Estimation of Aboveground Biomass in Alpine Forests: A Semi-Empirical Approach Considering Canopy Transparency Derived from Airborne LiDAR Data

In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can bemeasured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R2 (R2 = 0.70 to R2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation

Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar.

Hydraulic resistance of riparian forests is an unknown but important term in flood conveyance modeling. Lidar has proven to be a very important new data source to physically characterize floodplain vegetation. This research outlines a recent campaign that aims to retrieve vegetation fluid resistance terms from airborne laser scanning to parameterize trunk roughness. Information on crown characteristics and vegetation spacing can be extracted for individual trees to aid in the determining of trunk stem morphology. Airborne lidar data were used to explore the potential to characterize some of the prominent tree morphometric properties from natural and planted riparian poplar zones such as tree position, tree height, trunk location, and tree spacing. Allometric equations of tree characteristics extrapolated from ground measurements were used to infer below-canopy morphometric variables. Results are presented from six riparian-forested zones on the Garonne and Allier rivers in southern and central France. The tree detection and crown segmentation (TDCS) method identified individual trees with 85% accuracy, and the TreeVaW method detected trees with 83% accuracy. Tree heights were overall estimated at both river locations with an RMSE error of around 19% for both methods, but crown diameter at the six sites produced large deviations from ground-measured values of above 40% for both methods. Total height-derived trunk diameters using the TDCS method produced the closest roughness coefficient values to the ground-derived roughness coefficients. The stem roughness values produced from this method fell within guideline values

Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images

Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables

The quantification of forest ecosystems is important for a variety of purposes, including the assessment of wildlife habitat, nutrient cycles, timber yield and fire propagation. This research assesses the estimation of forest structure, composition and deadwood variables from small-footprint airborne lidar data, both discrete return (DR) and full waveform (FW), acquired under leaf-on and leaf-off conditions. The field site, in the New Forest, UK, includes managed plantation and ancient, semi-natural, coniferous and deciduous woodland. Point clouds were rendered from the FW data through Gaussian decomposition. An area-based regression approach (using Akaike Information Criterion analysis) was employed, separately for the DR and FW data, to model 23 field-measured forest variables. A combination of plot-level height, intensity/amplitude and echo-width variables (the latter for FW lidar only) generated from both leaf-on and leaf-off point cloud data were utilised, together with individual tree crown (ITC) metrics from rasterised leaf-on height data. Statistically significant predictive models (p<0.05) were generated for all 23 forest metrics using both the DR and FW lidar datasets, with R2 values for the best fit models in the range R2=0.43-0.94 for the DR data and R2=0.28-0.97 for the FW data (with normalised RMSE values being 18%-66% and 16%-48% respectively). For all but two forest metrics the difference between the NRMSE of the best performing DR and FW models was ≤7%, and there was an even split (11:12) as to which lidar dataset (DR or FW) generated the best model per forest metric. Overall, the DR data performed better at modelling structure variables, whilst the FW data performed better at modelling composition and deadwood variables. Neither showed a clear advantage at modelling variables from a particular vegetation layer (canopy, shrub or ground). Height, intensity/amplitude, and ITC-derived crown variables were shown to be important inputs across the best performing models (DR or FW), but the additional echo-width variables available from FW point data were relatively unimportant. Of perhaps greater significance to the choice between lidar data type (i.e. DR or FW) in determining the predictive power of the best performing models was the selection of leaf-on and/or leaf-off data. Of the 23 best models, 10 contained both leaf-on and leaf-off lidar variables, whilst 11 contained only leaf-on and two only leaf-off data. We therefore conclude that although FW lidar has greater vertical profile information than DR lidar, the greater complimentary information about the entire forest canopy profile that is available from both leaf-on and leaf-off data is of more benefit to forest inventory, in general, than the selection between DR or FW lidar