Site- and species-specific responses of forest growth to climate across the European continent

Aim To evaluate the climate sensitivity of model-based forest productivity estimates using a continental-scale tree-ring network. Location Europe and North Africa (30–70° N, 10° W–40° E). Methods We compiled close to 1000 annually resolved records of radial tree growth for all major European tree species and quantified changes in growth as a function of historical climatic variation. Sites were grouped using a neural network clustering technique to isolate spatiotemporal and species-specific climate response patterns. The resulting empirical climate sensitivities were compared with the sensitivities of net primary production (NPP) estimates derived from the ORCHIDEE-FM and LPJ-wsl dynamic global vegetation models (DGVMs). Results We found coherent biogeographic patterns in climate response that depend upon (1) phylogenetic controls and (2) ambient environmental conditions delineated by latitudinal/elevational location. Temperature controls dominate forest productivity in high-elevation and high-latitude areas whereas moisture sensitive sites are widespread at low elevation in central and southern Europe. DGVM simulations broadly reproduce the empirical patterns, but show less temperature sensitivity in the boreal zone and stronger precipitation sensitivity towards the mid-latitudes. Main conclusions Large-scale forest productivity is driven by monthly to seasonal climate controls, but our results emphasize species-specific growth patterns under comparable environmental conditions. Furthermore, we demonstrate that carry-over effects from the previous growing season can significantly influence tree growth, particularly in areas with harsh climatic conditions – an element not considered in most current-state DGVMs. Model–data discrepancies suggest that the simulated climate sensitivity of NPP will need refinement before carbon-cycle climate feedbacks can be accurately quantified.

Secondary succession and dipterocarp recruitment in Bornean rain forest after logging

To understand succession in dipterocarp rain forest after logging, the structure, species composition and dynamics of primary (PF) and secondary (SF) forest at Danum were compared. In 10 replicate 0.16-ha plots per forest type trees >= 10 cm gbh (3.2 cm dbh) were measured in 1995 and 2001. The SF had been logged in 1988, which allowed successional change to be recorded at 8 and 13 years. In 2001, saplings (1.0-3.1 cm dbh) were measured in nested quadrats. The forest types were similar in mean radiation at 2 m height, and in density, basal area and species number of all trees. Among small (10 <= 31.4) and large ( >= 31.4 cm gbh) trees, in both 1995 and 2001, there were 10- and 3-fold more dipterocarps in SF than PF respectively; and averaging over the two dates, there were correspondingly ca. 10- and 18-fold more pioneers. Mortality was ca. 60% higher in SF than PF, largely due to a seven-fold difference for pioneers: for dipterocarps there was little difference. Recruitment was similar in PF and SE Stem growth rates were 37% higher in SF than PF for all trees, although dipterocarps showed the opposite trend. Among saplings, dipterocarps dominated SF with a 10-fold higher density than in PF. For dipterocarps, the light (LH) and medium-heavy (MHH) canopy hardwoods, and the shade-tolerant, smaller-stature other (OTH) species (e.g. Hopea and Vatica) were in the ratios ca. 40:15:45 in SF and 85: < 1:15 in PF. LHs had higher mortality than OTHs in SE In PF ca. 80% of the saplings were LH: in SF ca. 70% were OTH. The predominance of OTHs in SF is explained by the logging of primary rain forest which was in a likely late stage of recovery from natural disturbance, plus the continuing shaded conditions in the understorey promoted by dense pioneer vegetation. At 13 years after logging succession appeared to be inhibited: LHs were being suppressed but MHHs and OTHs persisted. Succession in lowland dipterocarp, rain forests may therefore depend on the successional state of the primary forest when it is logged. A review of logged versus unlogged studies in Borneo highlights the need for more detailed ecological comparisons.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Do relocated villages experience more forest cover change? Resettlements, shifting cultivation and forests in the Lao PDR

This study explores the relationships between forest cover change and the village resettlement and land planning policies implemented in Laos, which have led to the relocation of remote and dispersed populations into clustered villages with easier access to state services and market facilities. We used the Global Forest Cover Change (2000–2012) and the most recent Lao Agricultural Census (2011) datasets to assess forest cover change in resettled and non-resettled villages throughout the country. We also reviewed a set of six case studies and performed an original case study in two villages of Luang Prabang province with 55 households, inquiring about relocation, land losses and intensification options. Our results show that resettled villages have greater baseline forest cover and total forest loss than most villages in Laos but not significant forest loss relative to that baseline. Resettled villages are consistently associated with forested areas, minority groups, and intermediate accessibility. The case studies highlight that resettlement coupled with land use planning does not necessarily lead to the abandonment of shifting cultivation or affect forest loss but lead to a re-spatialization of land use. This includes clustering of forest clearings, which might lead to fallow shortening and land degradation while limited intensification options exist in the resettled villages. This study provides a contribution to studying relationships between migration, forest cover change, livelihood strategies, land governance and agricultural practices in tropical forest environments.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.