Area-based Approach Research Articles

Large-area inventory of species composition using airborne laser scanning and hyperspectral data

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m collected over 350 km of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.22Picea abiesPinus sylvestris

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively.

UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.

Fresh street: the development and feasibility of a place-based, subsidy for fresh fruit and vegetables.

BackgroundMany UK communities experience food insecurity, and consume diets high in energy-dense, nutrient poor, processed foods and low in fruit and vegetables (FV). We explored a novel area-based approach to promote FV consumption and healthy eating in one such community.MethodsWe developed a weekly subsidy scheme for fresh FV with key local stakeholders in an area of socioeconomic deprivation in Northern England. The scheme (Fresh Street) offered five £1 vouchers to every household, regardless of income or household type. Vouchers were redeemable with local suppliers of fresh FV (not supermarkets). The feasibility of the scheme was assessed in four streets using rapid ethnographic assessment and voucher redemption information.ResultsLocal councillors and public health teams were supportive of the scheme. Most eligible households joined the scheme (n = 80/97, 83%), and 89.3% (17 849/19 982) of vouchers issued were redeemed. Householders reported that the scheme made them think about what they were eating, and prompted them to buy and eat more FV.ConclusionsThis feasibility study reported high levels of acceptance for a place-based, household-level weekly FV subsidy scheme. Further research is required to evaluate the effectiveness of this approach to creating healthy diets, eating behaviours and food systems.

Forests, peoples, and governments: Persistent land-use conflict in Northern Thailand

Land-use conflict in Northern Thailand has led to large-scale deforestation. This article suggests two reasons why this conflict has not been resolved despite the many legal and institutional approaches taken by Thai governments over the decades. First, conflicting directions embedded within the national policymaking level caused uncertainty for policy implementors at ministerial levels. Second, policy-drivers at the local level interacted with the specific socioeconomic context of upland residents in a way to make land-use conflict persistent. Contradictory messages by top policymakers, combined with the national ministries’ focus on purely functional tasks, diminished the importance of a local area-based approach necessary for land-use conflict resolution. Additionally, vested interests favoring agricultural expansion into the forests have been more diverse and influential than those favoring forest conservation; the former having tools at hand to incentivize smallholders to encroach into forested areas. Further driving agricultural expansion was that, in a management vacuum, local private sector actors acted as the de facto policy coordinators for the fragmented government local operations; however, on the forest conservation front, there was no coordinating body. This imbalanced situation has proved fertile soil for conflict.

The Use of Remotely Sensed Data and Polish NFI Plots for Prediction of Growing Stock Volume Using Different Predictive Methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.

Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock

Abstract In this study, for five sites around the world, we look at the effects of different model types and variable selection approaches on forest yield modelling performances in an area-based approach (ABA). We compared ordinary least squares regression (OLS), k-nearest neighbours (kNN) and random forest (RF). Our objective was to test if there are systematic differences in accuracy between OLS, kNN and RF in ABA predictions of growing stock volume. The analyses are based on a 5-fold cross-validation at five study sites: an eucalyptus plantation, a temperate forest and three different boreal forests. Two completely independent validation datasets were also available for two of the boreal sites. For the kNN, we evaluated multiple measures of distance including Euclidean, Mahalanobis, most similar neighbour (MSN) and an RF-based distance metric. The variable selection approaches we examined included a heuristic approach (for OLS, kNN and RF), exhaustive search among all combinations (OLS only) and all variables together (RF only). Performances varied by model type and variable selection approaches among sites. OLS and RF had similar accuracies and were more efficient than any of the kNN variants. Variable selection did not affect RF performance. Heuristic and exhaustive variable selection performed similarly for OLS. kNN fared the poorest amongst model types, and kNN with RF distance was prone to overfitting when compared with a validation dataset. Additional caution is therefore required when building kNN models for volume prediction though ABA, being preferable instead to opt for models based on OLS with some variable selection, or RF with all variables together.

Comparing features of single and multi-photon lidar in boreal forests

The emerging single-photon laser scanning has made technological breakthrough in the collection of airborne laser scanning data. In principle, single-photon systems require only one detected photon for successful ranging. Further, the point density on the ground can be 10–100 times higher for single-photon lidar data than that obtained with multi-photon systems at the same flight altitude. This has great potential to reduce operation costs. Single-photon lidar technology is assumed to be the best for data acquisition when high point densities are required over very large areas, or when improvements in measurement rates can significantly reduce data acquisition costs, such as in nationwide laser scanning programmes, where the whole country is repeatedly covered with data every 5–10 years. This study investigates single-photon lidar and conventional multi-photon laser scanning data for their potential in characterizing ground and forest attributes. Performance is evaluated in a boreal forest by a comparative analysis, where single-photon lidar measurements with SPL100 (Leica/Hexagon) from two flight heights (1900 m and 3800 m) are compared with data from the Optech Titan (400 m) multi-photon airborne laser scanning (ALS) under summer conditions (i.e. leaves on). We found that SPL100 from both altitudes provides forest attribute estimates with comparable accuracy to that of Optech Titan from 400 m using an area-based method. This demonstrates that point density and flight altitude do not have significant impact on forest attribute estimation using the area-based approach. As a result, SPL100 is a cost-efficient alternative to a conventional laser scanner for forest inventories at large scale. There are systematic differences in behavior of the data sets due to differences in ranging sensitivity, beam size, and point density. We observed a higher proportion of ground returns in the SPL100 (3800 m) than in SPL100 (1900 m) data. Both SPL100 data in general produced a higher proportion of ground returns than Titan single channel did in structurally more homogeneous and one layer stands while higher proportion of ground returns from Titan than from SPL100 data in multi-layer stands. Forest structure and flight altitude has a notable impact on the distribution of points and further characteristics of the vertical structures. The pulse of Titan sensor penetrated deeper into the canopy than SPL100.

SCAN ANGLE IMPACT ON LIDAR-DERIVED METRICS USED IN ABA MODELS FOR PREDICTION OF FOREST STAND CHARACTERISTICS: A GRID BASED ANALYSIS

Abstract. Lidar scan angle can affect estimation of lidar-derived forest metrics used in area-based approaches (ABAs). As commonly used first-order metrics and various user-developed metrics are computed in the form of a grid or a raster, their response to various scan angles needs to be investigated similarly. The objective of this study was to highlight the impact of scan angles on 11 metrics (9 height-based and 2 other commonly used metrics) at the level of the grid-cell. The study area was divided into a grid of cell size 30 m. In every grid-cell, the flight lines that sampled at least 90% area of the grid-cell were identified. The flight lines and the corresponding point clouds were then classified based on their mean scan angle into four classes 0°–10°, 10°–20°, 20°–30° and 30°–40°. Metrics were computed for one flight line per class for each grid-cell. This resulted in a maximum of four values for a metric in every grid-cell. Comparing these values revealed the evolving nature of the metrics with the scan angle. For the comparison we used a paired t-test and simple linear regression. We observed that most of the metrics were systematically under-estimated with increasing scan angle. Gap-fraction, rumple index were affected more than standard deviation of height while the maximum height was relatively stable. Among the height percentiles, the higher percentiles were relatively more stable compared to the lower percentiles. Scan angles can indeed have an impact on the estimation of lidar derived metrics. Although, many of the metrics studied showed statistically significant differences in their computation for different scan angles, their impact on the accuracies of ABA models needs to be studied further by accounting for the differences as shown in this study.

Model-Based Estimation of Forest Inventory Attributes Using Lidar: A Comparison of the Area-Based and Semi-Individual Tree Crown Approaches

The use of individual tree detection methods to support forest management inventories has been a research topic for over two decades, but a formal assessment of these methods to produce stand-level and region-level predictions of forest attributes and measures of error is lacking. We employed model-based estimation methods in conjunction with the semi-individual tree crown approach (s-ITC) to produce predictions and measures of error for tree volume (VOL), basal area (BA), stem density (DEN), and quadratic mean diameter (QMD) at the scale of forest stands and the entire study region. We compared the s-ITC approach against the area-based approach (ABA) for predictions of region-level and stand-level attributes via model-based root mean squared errors (RMSEs). The study was conducted at the Panther Creek watershed in Oregon, USA using a set of 78 field plots and aerial lidar information. For region-level attributes, s-ITC RMSEs demonstrated changes between −31% and 17% relative to ABA models. At the stand level, median s-ITC RMSEs generally increased, with changes between −29% and 414% relative to ABA models, but demonstrated important reductions in stands where segmentation provided large increases in sample size and was less prone to extrapolation than ABA models. The ABA demonstrated smaller RMSEs in stands without sampled population units for all variables. Our findings motivate further research into niche applications where s-ITC models may consistently outperform ABA models.

Households’ Evacuation Decisions in Response to the 2011 Flood in Thailand

This study aims at clarifying households’ responses to the flood in Thailand. The result of this study helps fill the gap in literature about the factor affecting a household’s decision to evacuate in response to the flood, as such a decision varies with the type of natural disaster. The result of the study confirms that more vulnerable people are less likely to evacuate. However, they are more likely to evacuate, if at least one of their household members has reduced mobility. People in flood-prone areas exhibited moral hazards. Furthermore, people with relatively secured employment statuses are more likely to stay in the flood-prone area, to minimize their losses from the flood. If households with management-level employees received real-time and accurate updates about the flood, the decision to evacuate would be freely decided by such households, which can minimize their losses. Similarly, real-time and accurate data about potential damages and losses can reduce moral hazards. Thus, it is necessary for national and local governments to understand area-specific characteristics of people and linkages between societal vulnerability and economic resilience. The study’s implications highlight the importance of developing disaster management strategies in an integrated area-based approach.

Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the Betula and Salix genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R2 = 0.888; validation R2 = 0.774) nearly as well as the top multivariate linear regression models (calibration R2 = 0.896; validation R2 > 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.

Closing the gap between simulation and measured energy use in home archetypes

Recent climate action plans set ambitious targets for large-scale home energy retrofit. These retrofit targets also recommend switching from oil boilers to electrical heat pumps. One area-based approach to retrofit is neighbourhood scale. Ideally with local community participation, neighbourhood retrofit improves a group of structurally similar homes, of the same archetype. Defined home archetypes already exist for 20 European countries thanks to previous research. That same research attempted steady-state calculations of space heating energy using the ISO 13790 Seasonal Method. Those calculations diverged from measured energy use by a large gap. Taking example home archetypes from Ireland, this study narrowed this energy-use gap by incorporating realistic parameters and schedules. Physical white-box models were constructed in DesignBuilder, and simulated by EnergyPlus with local weather data. The simulations combined realistic heating schedules, internal gains, infiltration and ventilation; converging simulation energy use with typical measurements and mostly achieved model calibration indices. Therefore, the resultant energy-use data was capable of tuning future grey-box models to investigate home energy use across ranges of heating patterns and heating setpoints. The aims of this study are threefold. First, devise a repeatable method to produce heat energy-use values by simulating white-box models of home archetypes, that enable tuning of future grey-box models. Second, specify the heating variables that result in acceptable energy-use gaps between simulation of home archetype models and their typical measurements. Third, test if the retrofits of three Irish home archetypes under a typical occupant schedule, achieve national climate action targets.

Tropical tree size-frequency distributions from airborne lidar.

In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size-frequency distributions over an old-growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot- (97.4%; 3% bias) and tree-height-classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar-derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot-level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot- and tree-height-classes level (-5.2% and 2.1%). Our ITC method provides a powerful tool for tree- to landscape-level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area-based approaches that require local calibration using a large number of inventory plots.

Forest Inventory and Diversity Attribute Modelling Using Structural and Intensity Metrics from Multi-Spectral Airborne Laser Scanning Data

Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.

When does geography matter most? Age-specific geographical effects in the patterning of, and relationship between, mental wellbeing and mental illness.

Mental illness and mental wellbeing are related but distinct constructs. Despite this, geographical enquiry often references the two as interchangeable indicators of mental health and assumes the relationship between the two is consistent across different geographical scales. Furthermore, the importance of geography in such research is commonly assumed to be static for all age groups, despite the large body of evidence demonstrating contextual effects in age-specific populations. We leverage simultaneous measurement of a mental illness and mental wellbeing metric from Understanding Society, a UK population-based survey, and employ bivariate, cross-classified multilevel modelling to characterise the relationship between geographical context and mental health. Results provide strong evidence for contextual effects for both responses before and after covariate adjustment, with weaker evidence for area-classification and PSU-level contextual effects for the GHQ-12 after covariate adjustment. Results support a two-continua model of mental health at the individual level, but indicates that consensual benefit may be achieved across both dimensions by intervening at household and regional levels. There is also some evidence of a greater contextual effects for mental wellbeing than for mental illness. Results highlight the potential of the household as a target for intervention design for consensual benefit across both constructs. Results also suggest the increased importance of geographical context for older respondents across both responses. This research supports an area-based approach to improving both mental illness and mental wellbeing in older populations.

Applying geospatial technology in quantifying spatiotemporal shoreline dynamics along Marina El-Alamein Resort, Egypt.

Human interventions along the northwestern Egyptian Mediterranean coast recently interrupted the stability of Marina El-Alamein shoreline and resulted in spit evolution. Considering Egypt's vision 2030 for developing the northwestern Egyptian coast, continuous up-to-date monitoring programs became essential to ensure sustainability. This study, for the first time, aimed at coupling geospatial technology with Digital Shoreline Analysis System (DSAS) tool in monitoring, analyzing, and quantifying the impacts of anthropogenic activities on the spatiotemporal shoreline dynamics over the last 3 decades (1987-2017) at Marina El-Alamein resort, Egypt. In addition, the study carried out a quantitative geometrical temporal analysis for the newly formed spit from 2015 to 2020. The study used transect- and area-based approaches in estimating the shoreline changes for long- and short-term changes. The former approach computed the change in both the shoreline displacement and the rate of shoreline change, whereas the latter quantified the magnitude of spatial changes in the total land area. Results of the current study revealed that during 1987-2017, Marina El-Alamein shoreline experienced very high accretion, with an average rate of 2.8 ± 4.73m/year (end-point rate) and 2.52 ± 4.10m/year (linear regression rate). Quantitatively, Jetties #1 and #3 trapped sand on their western sides for a maximum distance of 706.31m and 406.5m, respectively. On the other hand, the eastern side of the resort experienced erosion with a maximum distance of 92.78m. Regarding changes in the total area, Marina El-Alamein's coast gained 1.130km2 (0.038km2/year) land and lost about 0.1115km2 (0.004km2/year) of its total area throughout the last 3 decades. During 2015, the continuous progressive accretion along the western side of J#1 resulted in the evolution of sand spit east of the jetty. Results of the temporal analysis showed that the spit's length was about 0.236km during May 2015 and reached 1.44km in April 2020 with an increment of 510.5% in length. In addition, the spit's total surface area increased by 33.606km2 in 5years (6.7212km2/year). Both the length and the area of the evolving spit grow annually with 102.11% and 108%, respectively. If this progressive accretion along the evolving spit continued, the lagoon's first inlet would probably suffer sedimentation that could cause its closure and deteriorate the lagoon's water quality. The study recommends carrying out an environmental impact assessment study for the newly formed spit to lessen its negative impact on the opposite tidal inlet and the lagoon's water quality.

Energy poverty in Madrid: Data exploitation at the city and district level

This paper presents the evaluation and quantification of energy poverty in Madrid, at the city and district level. The incidence of energy poverty is assessed by means of the High Share of Energy Expenditure in Income indicator combined with the monetary poverty perspective, enabling to identify different energy-poor household profiles. The study links the city and district statistical levels. The analysis of the different household groups at the city level allows identifying several determining factors linked to energy poverty in Madrid. Based on these determining factors, this research explores district statistics by following an area-based approach and assesses its energy poverty distribution. The High Energy Requirements index is then built on these determining factors to help to pinpoint the urban areas where energy-poor households are more prone to be located. The results show that 22.7% of households are at risk of energy poverty and suggest a need for effectively incorporating households’ income levels and a monetary approach into energy poverty assessments. Finally, based on the two-scale (city and district) analysis conducted, this paper concludes with energy retrofitting policy guidelines, distinguishing between the different levels of funding, and the efficiency measures to be implemented according to each particular energy poverty condition.

Area-based approaches are losing the essence of local targeting: LEADER/CLLD in the Czech Republic

ABSTRACT Since its accession to the EU, the Czech Republic has gradually strengthened its support for area-based approaches in local development. In the light of the increasing financial support and the growing number of local action groups (LAGs), optimism seems to be appropriate that the elements of local governance have the potential to be implemented in the Czech Republic now more than ever before. Community-Led Local Development (CLLD) in the programming period of 2014–2020, however, throws away the advantage of having the local focus, one of the essential elements of integrated territorial development tools. We back up this claim with an analysis of the implementation process of the CLLD method at the national level, the evaluation of the strategic and implementation parts of the 43 strategies of CLLD LAGs, and in-depth interviews with 23 LAG managers. Our results show that the offer only partially meets the identified local needs of LAGs. The final strategy of CLLD has been created in the background of the pragmatic approach of actors in the territory influenced by the defined range of topics. The potential for the strengthening of local governance is limited, while a hidden top-down effect is applied.

Virtual tube storage scheme for supporting mobile sink groups in wireless sensor networks

The research on mobile sink groups such as platoons in battlefields and firefights in disaster areas have been actively studied as a challenged and interesting issue in wireless sensor networks. For supporting mobile sink groups, the formation of effective data storage is one of the important design factors for energy-efficient and reliable data delivery to them by considering their mobility. Various data storage schemes have been proposed for mobile sink groups and they are usually categorized into two approaches: a point-based storage one and an area-based storage one. The point-based storage approach is very efficient when a mobile sink group has a small number of member sinks or it has a large-scale group region. On the other hand, the area-based storage approach only when a mobile sink group has a large number of member sink and it has a small-scale group region. In other words, these two approaches are suitable only for special cases of mobile sinks groups. Thus, this paper proposes a Virtual Tube Storage (VTS) scheme for efficiently supporting general cases of mobile sink groups in wireless sensor networks. We first present design principles of data storage and data dissemination. Then, we describe the method to efficiently construct a virtual tube storage based on the design principle of data storage. Next, we describe the process to disseminate data from a source node to a mobile sink group via the virtual tube storage based on the design principle of data dissemination. Simulation results verify that the proposed scheme achieves better performance than the existing point-based storage and area-based storage schemes in terms of energy-efficiency and reliable data dissemination.