Sparse Tree Research Articles

Структура древостоев и особенности накопления ими фитомассы на остепненных склонах массива гор Крака (Южный Урал)

Структура древостоев и особенности накопления ими фитомассы на остепненных склонах массива гор Крака (Южный Урал)

Optimal Substring Equality Queries with Applications to Sparse Text Indexing

We consider the problem of encoding a string of length n from an integer alphabet of size σ so access , substring equality , and Longest Common Extension (LCE) queries can be answered efficiently. We describe a new space-optimal data structure supporting logarithmic-time queries. Access and substring equality query times can furthermore be improved to the optimal O (1) if O (log n ) additional precomputed words are allowed in the total space. Additionally, we provide in-place algorithms for converting between the string and our data structure. Using this new string representation, we obtain the first in-place subquadratic algorithms for several string-processing problems in the restore model: The input string is rewritable and must be restored before the computation terminates. In particular, we describe the first in-place subquadratic Monte Carlo solutions to the sparse suffix sorting, sparse LCP array construction, and suffix selection problems. With the sole exception of suffix selection, our algorithms are also the first running in sublinear time for small enough sets of input suffixes. Combining these solutions, we obtain the first sublinear-time Monte Carlo algorithm for building the sparse suffix tree in compact space. We also show how to build a correct version of our data structure using small working space. This leads to the first Las Vegas in-place algorithm computing the full LCP array in O ( n log n ) time w.h.p. and to the first Las Vegas in-place algorithms solving the sparse suffix sorting and sparse LCP array construction problems in O ( n 1.5 √ log σ) time w.h.p.

Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches

BackgroundSocial-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis. A barrier to incorporating the full data is a lack of solid recommendations for variable selection, with researchers often hand-selecting a few variables. Thus, we evaluated the ability of empirical machine learning approaches to identify social-environmental factors having a true association with a health outcome.MethodsWe compared several popular machine learning methods, including penalized regressions (e.g. lasso, elastic net), and tree ensemble methods. Via simulation, we assessed the methods’ ability to identify census variables truly associated with binary and continuous outcomes while minimizing false positive results (10 true associations, 1000 total variables). We applied the most promising method to the full census data (p = 14,663 variables) linked to prostate cancer registry data (n = 76,186 cases) to identify social-environmental factors associated with advanced prostate cancer.ResultsIn simulations, we found that elastic net identified many true-positive variables, while lasso provided good control of false positives. Using a combined measure of accuracy, hierarchical clustering based on Spearman’s correlation with sparse group lasso regression performed the best overall. Bayesian Adaptive Regression Trees outperformed other tree ensemble methods, but not the sparse group lasso. In the full dataset, the sparse group lasso successfully identified a subset of variables, three of which replicated earlier findings.ConclusionsThis analysis demonstrated the potential of empirical machine learning approaches to identify a small subset of census variables having a true association with the outcome, and that replicate across empiric methods. Sparse clustered regression models performed best, as they identified many true positive variables while controlling false positive discoveries.

Trees in the Upper Treeline Ecotone in the Polar Urals: Centuries-Old Change and Spatial Patterns

Woody vegetation at the upper limit of its growth is a sensitive indicator of climate change. The aim of this study is to provide an analysis of the centuries-old spatiotemporal dynamics of larch trees at the upper limit of their growth (mountain massif Rai-Iz, Polar Urals, Russia). We used a ground-based method of mapping the remnants of trees that grew in the study area and died during the Little Ice Age. Aerial photographs from the 1960s and high-spatial-resolution satellite images from 2015 were used as data sources to define the locations of trees. Maps of the forest–tundra phytocoenochoras (areas of the terrain that are relatively homogeneous for one or more components of vegetation and/or other indicators) were created using a modified method of boundary detection between forest parcels with different stand densities. The proposed method of boundary detection between the main types of phytocoenochoras allowed us to identify a 15% total increase in areas of closed and open forest and areas with sparse tree growth, as well as a decrease in areas of tundra with single trees over these last decades. Using our spatiotemporal analysis of forest–tundra demographics over the last 50 years, we found that the number of trees in the ecotone had doubled. However, modern trees have not yet reached the areas occupied by trees in the past.

Quantifying the Estimation and Abundance of Plant Diversity of Shigar Valley, Gilgit -Baltistan, Pakistan

The studies were carried out from July 2017 to March 2018 in Shigar valley different Union Councils. The area lies between 7444 feet to 11694 feet from Above sea level in the Alpine zone including, Niali Nallah, Laxar Nallah, Nallah, Markuja union, Marapi union, Chorkah union, Gulapur. The study sites were randomly selected based on (1) herbs or shrubs land on the field periphery, (2) open grassland and arable land associated with sparse vegetation on rocks, stones, (3) forest land and open lands associated with sparse tree vegetation catchment of river and (4) forest land and arable land associated with dunes and rocky terrain. The quadrate method was used to record of vegetation from the selected study sites. A total of 59 plants species both medicinally and economically important were recorded at four study sites of CKNP and revealed that 30 herbs followed by 14 trees, 11 kinds of grass, and 4 shrubs respectively the dominant tree species recorded from all habitat types were Juniper sexcelsa, Elaeagnus ambulate, Morus alba, Salix Wilhelmina and Populus nigra. The most common herbs recorded were Artimisa brevifolia, Tanacetum, Echinops echinatus, Capparis sponsia, Ephedra intermedia, Peganum harmala, Daucus carota, Medicago sativa, Typha lotifuliya and Astragalus rhizanthus. The dominant shrubs were Rosa webbina, Hippophae rhamnoides, Sophora Molis and Myricaria germanica. The grasses recorded in the study area were Poa Alpina, Setaria Viridis, Hetropogon contortus, Cynodon dactylon, Taraxacum oritinlis, Trifolium repens and Cascuta reflexa. These plants are also used by local communities for fuelwood and timber. This study will be beneficial for locals and governments for the protection and conservation of this indigenous flora as well as fauna in the future.

Сорокопуди роду Lanius у Закарпатській області України

The publication presents status and distribution areas of Lanius collurio, L. minor and L. excubitor on the territory of the Zakarpattia region of Ukraine. By this time the status of L. excubitor and L. minor in Zakarpattia region not been determined. The publication presents a detailed literature review of the abundance, nature of the stay and distribution of these species of shrikes in the Zakarpattia region during the century. The analysis presents own data collected during the studies of the territory of the Zakarpattia lowland and the foothills of the inner edge of the Vyhorlat-Hutynskyi Range of the Ukrainian Carpathians for 1993–2000 and 2010–2018. As a result of the collected material, it was found that L. collurio is an ordinary and abundant species of any biotopes within the region, except for forest, wetland (water areas and wetlands) and settlements. L. minor and L. excubitor are rare nesting species for Zakarpattia, but within the limits of their habitats in the region they are usual. Both species are spread, mainly in the territory of lowland Zakarpattia. They also live in the oak and flood-plain forests of foothills Vygorlat-Hutynskyi Range. L. excubitor in Zakarpattia region is also wintering. This species flies into the flood-plain forests of mountainous areas of the region in winter. Both species choose for themselves the same or similar natural or transformed moist biotopes: 1) floodplain shrubs; 2) shrubs, poplar stands stands and edges of nemoral forests near water bodies – rivers, canals, reservoirs; 3) agrocenoses permeated with meliorative channels, with sparse fruit trees. The strategy for the conservation of these species should consist in preserving the biotopes that are important for their nesting and habitats: floodplain nemoral forests and shrubs within the RLP “Prytysiansky”, lower reaches of Latoritsa, Borzhava and Uzh rivers, as well as biotopes of melioration systems - Batarska and Berehivska - in the valley of the Tysa river. To do this, it is necessary to establish new nature-protected areas and units of the Emerald Network of Ukraine and to develop appropriate management plans for those protected areas, where these species are protected.

The Age of the Patom Crater: Dendrochronological and Biogeochemical Investigations

According to data of dendrochronological investigations of larch trees that had grown on the Patom crater (Irkutsk oblast, Bodaibinskii district) and near its base, the initial period of the formation of the crater can be dated back to the late 15th‒early 16th centuries. In 1841‒1842, the trees under investigation recorded in annual rings a catastrophic event which caused a disturbance to their root systems, damage to tree stems, and the formation of compression wood. It is obvious that the event is associated with the time of formation of the late stage ring swell on which sparse larch trees are 93‒101 years of age, and on the central mound emerging upon completion of the formation of the Patom crater the age of the oldest tree is 7 years. Thus dendrochronological investigations confirm a long-lasting and multievent formation of the Patoma crater. In some periods, the deposition cone was evolving with a different intensity, sometime showing an explosive character. X-ray fluorescent analysis revealed a double Sr enrichment of larch wood at the time of significant activity of crater formation (1852‒1859) when plutonic fluids from the igneous source were able to enter the upper horizons of the Earth’s crust at the time of magma intrusion into water-containing rocks. The fluids were enriched with CO2 and transported significant amounts of Sr, as a result of which the 87Sr/86Sr ratios in carbonate and terrigenous rocks assumed higher values in sandstone and schists on the early stage ring swell of the Patoma crater. The findings, coupled with geological data, suggest that the Patom crater emerged as a result of a phreatic explosion that occurred either at the time of magma intrusion into water-containing rocks or as a consequence of the fault and decompression of heated water-containing rocks.

Patterns and trends in lake concentrations of dissolved organic carbon in a landscape recovering from environmental degradation and widespread acidification

Concentrations of dissolved organic carbon (DOC) have increased in lakes throughout North America and Europe over the last three decades. Recovery from acid deposition and climate change have both been postulated as the primary mechanisms for the increase in DOC. To provide a clearer insight into the mechanisms responsible for increasing DOC we evaluated changes in lake and peat porewater chemistry collected in an area of approximately 33,000 km2 surrounding Sudbury, Ontario, a region undergoing dramatic recovery from acidic deposition. DOC concentrations varied considerably among the 44 lakes and over time (samples annually from 1981 to 2018), but the Sens Slope value showed a strong increase in lake DOC concentration over time, at 0.05 mg/ L y−1 (p < 0.001) that was related to increasing pH [0.03 units y−1, p < 0.001] and decreasing lake SO4 concentration [−0.24 mg/ L y−1; p < 0.001], but showed no relationship with temperature or precipitation. Similar strong relationships between DOC and pH (positive) and SO4 (negative) were observed in 18 peatlands sampled in the region. In a spatial analysis of 82 lakes sampled in 2018, concentrations of DOC in lakes were highest in flatter catchments with a greater wetland area, suggesting that wetlands are a major source of DOC in lakes. Optical properties of DOC obtained from extracts of wetland and upland soils at 6 catchments could be distinguished, primarily due to upland litter extracts having distinct optical properties from mineral soils or wetland soils. Optical properties of DOC in lakes however were inconsistent with those measured in soil extracts indicating that they are not useful for distinguishing DOC sources in these lakes. A predictive model was developed to explain DOC trends within Sudbury lakes using a stepwise linear regression combined with hierarchical partitioning to confirm the most influential processes on DOC. Almost 50% of the variability in DOC change in the 44 lakes was explained by the magnitude in lake pH change, catchment size and catchment sparse tree cover showing that recovery from acidic deposition is overwhelmingly responsible for increasing DOC in Sudbury lakes.

Geostatistical modeling and conservation implications for an endemic Ipomoea species in the Eastern Brazilian Amazon

The Canga ecosystem, in the Eastern Brazilian Amazon, is a biodiversity and endemism hotspot as well a potential resource for iron mining, raising conflicts between conservation and economic development. Here, we collected data from 60 randomly selected points in the Canga ecosystem in the Carajás National Forest, Brazil, to analyze the spatial patterns of Ipomoea cavalcantei occurrence in different phytophysiognomies. Through ordinary kriging and a GLM model, we identified that this endemic species occupies a very restricted range within the Canga ecosystem, specifically in areas of Open Rupestrian Fields and Shrub Rupestrian Fields where the iron crust restricts the soil depth to a few centimeters and the vegetation is mainly herbaceous or savannah shrub vegetation, with low sparse tree canopies. These conditions increased the passage of sunlight, revealing the importance of light conditions for the species. However, because the species occurs in suitable mining sites, there is continuing conflict between environmental and economic decisions. Spatial data showed that mining has increased in Carajás National Forest over the last 32 years, reducing the potential locations for I. cavalcantei to less than 50 % of its original range. Mining will potentially occupy the entire species range over the next three decades. Restoration outside its original habitat is difficult in the short term, because there is no knowledge of the species’ capacity for growth, survival, and replication. Conservation initiatives for the Canga ecosystem should prioritize strategies that reduce regional dependence on mining and provide alternative economic activities, such as sustainable exploitation of bio-products and ecological tourism.

Acceleration of MLFMA Simulations Using Trimmed Tree Structures

We present a novel approach to accelerate the electromagnetic simulations by the multilevel fast multipole algorithm (MLFMA). The strategy is based on a progressive elimination of the electromagnetic interactions, resulting in trimmed tree structures, during iterative solutions. To perform such eliminations systematically, artificial neural network (ANN) models are constructed and trained to estimate the errors in the updated surface current coefficients. These column eliminations are supported by straightforward row eliminations, leading to increasingly sparse tree structures and matrix equations as iterations continue. We show that the proposed implementation, namely, trimmed MLFMA (T-MLFMA), leads to significantly accelerated electromagnetic simulations of the large-scale objects, while the accuracy is still much better than the high-frequency techniques. T-MLFMA can be seen as an exemplar of the implementations, where machine learning is successfully integrated into an electromagnetic solver for enhanced simulations.

A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps.

Two recent global maps of tree restoration potential have identified vast regions where tree cover could be increased, ranging from 0.9 to 2.3 billion hectares. Both maps, however, emphasized dryland regions, with arid biomes making up 36%-42% of potential restoration area. Dryland biomes have repeatedly been recognized as inappropriate regions for expanding tree cover due to the risks of biodiversity loss, water overconsumption, and fire, so maps that highlight these regions for restoration must sustain careful scrutiny. Here, I show that both recent attempts to map restoration potential in arid regions have been hindered by underlying errors in the global tree cover maps they used. Systematic underestimates of existing sparse tree cover led directly to large overestimates of the potential for tree recovery in drylands. The Atlas of Forest Landscape Restoration Opportunities (Laestadius et al., Unasylva, 2011, 62, 47) overestimated tree restoration potential across a third of arid biomes by between 7% and 20% (55-166 million hectares [Mha]). Similarly, Bastin, Finegold, Garcia, Mollicone, et al. (Science, 2019, 365, 76) overestimated tree restoration potential across all arid biomes by 33%-45% (316-440Mha). These inaccuracies limit the utility of this research for policy decisions in drylands and overstate the potential for tree planting to address climate change. Given this long-standing but underappreciated challenge in mapping global tree cover, I propose various ways forward that keep this lesson in mind. To better monitor and restore tree cover, I call for re-interpretation and correction of existing global maps, and for a new focus on quantifying sparse tree cover in drylands and other systems.

Vegetation and Microtheriofauna Dynamics during the Late Glaciation and Holocene in the Yugyd-Va National Park

The results of studies of the Late Pleistocene and Holocene spore–pollen spectra and the microtheriofauna of loose deposits of four cave localities located in the Shchuger and Kozhym river basins within the territory of the Yugyd-Va National Park on the western slope of the Subpolar Urals are discussed. Assessment of the age of sediments from the karst cavities was based on the data of AMS radiocarbon dating of mammalian bones. The speleogenic formations of the localities studied contain layers assigned to the final stage of the degradation of the Polar Ural (Late Valdai) glaciation (Middle to Late Dryas) and to the Preboreal, Boreal, and Subatlantic periods of the Holocene. Cryoxerophilous tundra species predominated (88.3%) in the fossil microtheriofauna assemblage identified for the Allerod. The Middle Dryas and Allerod, the cold and warm periods of the Late Glacial, respectively, were reflected by the spore–pollen spectra. The microtheriofauna on the western slope of the Subpolar Urals remained quite cryophilous by the end of the Late Dryas. The broad distribution of yernik thickets and goosefoot–gramineous–mixed herb assemblages during that time has been established. The vegetation of the western foothills of the Subpolar Urals consisted of grass–shrub tundra with sparse spruce trees during the Late Preboreal cooling period. The microtheriofauna community also had a tundra-like composition. The presence of dark coniferous taiga forests in the south of the Subpolar Urals and sparse spruce–birch forests with pine and dwarf birch in the undergrowth in the north of the area during the Boreal period has been established. The rodent fauna acquired a forest-like character by that time. Northern taiga dark coniferous forests of spruce with a significant admixture of pine developed on the western slope of the Subpolar Urals by the middle of the Subatlantic period, and the content of tundra elements in the stands increased by the end of this period. The microtheriofauna of the studied region had a typical forest appearance characteristic of all Late Holocene taiga communities in northeastern Europe.

Flood-pulse disturbances as a threat for long-living Amazonian trees.

The long-lived tree species Eschweilera tenuifolia (O. Berg) Miers is characteristic of oligotrophic Amazonian black-water floodplain forests (igapó), seasonally inundated up to 10months per year, often forming monodominant stands. We investigated E.tenuifolia' growth and mortality patterns in undisturbed (Jaú National Park - JNP) and disturbed igapós (Uatumã Sustainable Development Reserve - USDR, downstream of the Balbina hydroelectric dam). We analysed age-diameter relationships, basal area increment (BAI) through 5-cm diameter classes, growth changes and growth ratios preceding death, BAI clustering, BAI ratio, and dated the individual year of death (14 C). Growth and mortality patterns were then related to climatic or anthropogenic disturbances. Results were similar for both populations for estimated maximum ages (JNP, 466yr; USDR, 498yr, except for one USDR tree with an estimated age of 820yr) and slightly different for mean diameter increment (JNP: 2.04mm; USDR: 2.28mm). Living trees from JNP showed altered growth post-1975 and sparse tree mortality occurred at various times, possibly induced by extreme hydroclimatic events. In contrast with the JNP, abrupt growth changes and massive mortality occurred in the USDR after the dam construction began (1983). Even more than 30yr after dam construction, flood-pulse alteration continues to affect both growth and mortality of E.tenuifolia. Besides its vulnerability to anthropogenic disturbances, this species is also susceptible to long-lasting dry and wet periods induced by climatic events, the combination of both processes may cause its local and regional extinction.

Globally Optimal Dense and Sparse Spanning Trees, and their Applications

Finding spanning trees under various constraints is a classic problem with applications in many fields. Recently, a novel notion of dense ( sparse ) tree, and in particular spanning tree (DST and SST respectively), is introduced as the structure that have a large (small) number of subtrees, or small (large) sum of distances between vertices. We show that finding DST and SST reduces to solving the discrete optimization problems. New and efficient approaches to find such spanning trees is achieved by imposing certain conditions on the vertex degrees which are then used to define an objective function that is minimized over all spanning trees of the graph under consideration. Solving this minimization problem exactly may be prohibitively time consuming for large graphs. Hence, we propose to use genetic algorithm (GA) which is one of well known metaheuristics methods to solve DST and SST approximately. As far as we are aware this is the first time GA has been used in this context.We also demonstrate on a number of applications that GA approach is well suited for these types of problems both in computational efficiency and accuracy of the approximate solution. Furthermore, we improve the efficiency of the proposed method by using Kruskal s algorithm in combination with GA. The application of our methods to several practical large graphs and networks is presented. Computational results show that they perform faster than previously proposed heuristic methods and produce more accurate solutions. Furthermore, the new feature of the proposed approach is that it can be applied recursively to sub-trees or spanning trees with additional constraints in order to further investigate the graphical properties of the graph and/or network. The application of this methodology on the gene network of a cancer cell led to isolating key genes in a network that were not obvious from previous studies.

Influence of the Land Use Type on the Wild Plant Diversity.

Studies on the impact of agricultural practices on plant diversity provide important information for policy makers and the conservation of the environment. The aim of the present work was to evaluate wild plant diversity across the agroecosystems in two contrasting regions of Bulgaria; Pazardzhik-Plovdiv (representing agroecosystems in the lowlands) and Western Stara Planina (the Balkan Mountains, representing agroecosystems in the foothills of the mountains). This study conducted a two-year assessment of plant diversity in different types of agricultural and forest ecosystems, representing more than 30 land use types. Plant diversity, measured by species number, was affected by the land use type only in Pazardzhik-Plovdiv region. More pronounced was the effect of the groups of land use types on the diversity, measured by the mean species number per scoring plot. Climatic conditions, measured by 19 bioclimatic variables, were the most important factor affecting plant species diversity. Six bioclimatic variables had a significant effect on the plant diversity, and the effect was more pronounced when the analysis considered pooled data of the two regions. The highest plant diversity was found on grazing land with sparse tree cover, while the lowest one was in the land use types representing annual crops or fallow. The study also established a database on weed species, relevant to agriculture. A number of common weeds were found in the Pazardzhik-Plovdiv region, while the most frequent species in the Western Stara Planina region were indigenous ones. Overall, the natural flora of Western Stara Planina was more conserved; eleven orchid species with conservation significance were found in the pastures and meadows in that region. The present study is the first attempt in Bulgaria to characterize the plant diversity across diverse agroecosystems representing many different land use types and environmental conditions. The results can contribute to nature conservation, biodiversity, and the sustainable use of plant resources.

General, stress relief and perceived safety preferences for green spaces in the historic city of Padua (Italy)

Public urban green spaces are crucial for citizens’ wellbeing and are an important part of daily life in cities. To maximize their benefits to quality of life a thorough knowledge of citizens’ preferences and preference heterogeneity is crucial in the planning and design of urban green spaces. This study investigated visitors’ perception of typical green spaces, with a focus on vegetation structure and the presence of typical historic city walls, as well as preferences within the context of perceived stress and safety. We conducted this study in the historic city of Padua in north-eastern Italy. In 2017, face-to-face interviews of citizens were held and choice sets, based on modified images of different green space scenarios, were used to test users’ preferences connected to both stress relief and safety perception. The study highlighted that general, stress relief and safety perception related preferences of the respondents depend on different site characteristics. Respondents preferred a complex but not too wild scenario with sparse trees and aesthetically appealing features such as colourful flowers. Historic walls had a negative effect on general preferences. While general preferences were very similar to stress relief preferences, preferences within the context of safety differed for some attributes. It seems that the vegetation structure and the presence of features linked to human recreational uses are important factors in planning and designing urban green spaces. Management and planning should take into consideration what users demand from green spaces as this will influence their suitable design.

Electrophoresis-Based Adaptive Manipulation of Nanowires in Fluid Suspension

Automated, highly precise online manipulation of nanoscale and microscale objects is essential to achieve scalable nanomanufacturing. However, nanoparticles and microparticles exhibit as yet unpredictable and, therefore, uncontrolled variations in their structures or compositions that can limit their functions and properties. In this article, we present an electric field-based adaptive manipulation scheme to precisely control the motion of individual nanowires, online estimate their unknown mobilities, and rapidly plan the desired trajectories with the estimated mobilities. The input saturation is considered in the adaptive controller, and the closed-loop system is proved to be asymptotically stable. The proposed motion planners (MPs), ${{\tt Bi}}$ - ${{\tt iSST}}$ s, are built on and extended by the stable sparse rapidly exploring random tree ( ${{\tt SST}}$ )-based kinodynamic motion-planning algorithms. Two sparse trees are maintained to increase the probability of finding a solution. The ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithms use the workspace information, heuristics, and optimization to effectively guide the search process. When compared with the state-of-the-art algorithms, a ${{\tt Bi}}$ - ${{\tt iSST}}$ variant, the ${{\tt opt}}$ - ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithm, quickly updates feasible solutions with the online estimated mobilities of multiple nanowires and converges to a near-optimal, minimum-time solution to increase the efficiency of simultaneous manipulation of the nanowires. Therefore, without complex characterization of each nanowire’s mobility, the nanowires can be steered simultaneously and efficiently to achieve precisely controlled positions without collisions. Simulation and experimental results confirm that the proposed integrated adaptive manipulation scheme precisely, independently, and simultaneously manipulates the motion of multiple nanowires.

Forest restoration scenarios produce synergies for agricultural production in southern Ethiopia

Understanding the factors influencing agricultural productivity is vital to food security, especially for food insecure smallholder farmers. Within agricultural landscape mosaics, the arrangement of tree cover such as forests, trees, and hedgerows can positively or negatively impact agricultural productivity in fields. In such settings, forest restoration has the potential to impact food security goals in addition to the typical restoration goals of reversing land degradation and supporting carbon storage. Here, we compare and contrast forest restoration scenarios in order to explore whether there are ideal amounts and configurations of forests that optimize benefits for food production. To do so, we explored forest restoration scenarios in an agricultural-forest landscape in southern Ethiopia where previous research showed increased wheat productivity in sections of fields adjacent to forests. We used hypothetical historically-relevant and plausible future scenarios to examine the landscape-level implications of forest restoration on total wheat production. To accomplish this, we first characterized contemporary forest cover as well as long-term trends using remote sensing. Then, trends in forests and hedgerows were used to create a series of hypothetical yet realistic scenarios to explore trade-offs among total forest cover, its configuration, and total wheat production. From 1967–2013, analysis of historical aerial photography and Landsat imagery showed a general trend of reforestation (an 8% increase in forest cover). While the abundance of primary forests remained constant from 1986–2013, mixed vegetation expanded from 16 % to occupy 26 % of the landscape, generally in the form of small forest fragments with sparse tree cover. Our hypothetical reforestation and hedgerow planting scenarios found that field productivity increases near forest edges resulted in non-linear food production benefits with hedgerow expansion. Benefits to total landscape-level wheat production were almost twice as great when transitioning from low to medium tree cover than when transitioning from medium-high to high tree cover. Furthermore, the benefits of hedgerows on agricultural production were asymptotic; productivity gains eventually levelled off with increasing hedgerows. Our results highlight potential benefits of a landscape approach for enhancing smallholder agricultural productivity in southern Ethiopia and beyond. We argue that considering a landscape perspective can help support food security goals particularly within the context of climate change and should play a more prominent role in forest conservation and restoration.

Direct estimation of leaf area index of tropical forests using LiDAR point cloud

Light Detection and Ranging (LiDAR) remote sensing is a promising method for accurate estimation of various forest structural parameters. A ground based LiDAR scanner, terrestrial laser scanner (TLS) has been used for the reference field measurements for the estimation of structural parameters such as tree height, DBH across large area forests sites. Leaf Area Index (LAI) is important forest structural parameter which has recently been estimated from TLS point cloud by geometrical/statistical modeling with tree height and DBH as proxy variables. In this work we present a method for direct estimation of LAI of tropical forests from TLS point cloud proposing a new algorithmic approach named as Point Spatial Density (PSD) algorithm. As an intermediate processing, the proposed method involves filtering and 3D reconstruction of individual trees using super voxel and min-cut segmentation approaches. Reconstructed trees are then extracted and geometrically modelled for estimating the LAI by the proposed PSD algorithm. The proposed PSD algorithm establishes a relationship between TLS point density, point spacing, and height of trees. Based on the number of points and the point spacing, the value of LAI found has been observed to be varying in sparse and dense canopy tree stands. Validation of the results with reference LAI measurements obtained using a LAI meter (LiCOR LAI Meter 2200) indicates a consistent correlation (R2 = 0.96) between the estimated and reference LAI measurements. The results suggest that the LAI in dense heterogeneous forests could be accurately estimated by the proposed PSD algorithm based approach.

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.