Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests

Canopy cover is an important structural trait that is frequently used in forest inventories to assess sustainability as well as many other important aspects of forest stands. Remote sensing data is more effective and suitable for canopy cover estimating than traditional field measurements such as sample plots, especially at broad scales. Measurement and mapping this attribute in fine-scale is a difficult task. Aerial imagery using unmanned aerial vehicle (UAV) has been recognized as an excellent tool to estimate canopy attributes. In this study, we compared the potential of using digital hemispherical photography (DHP), digital cover photography (DCP), UAV RGB data, and canopy height model (CHM) for estimation of canopy cover of mix broad-leaved forest on seven different stands. The canopy cover was measured from two digital canopy photographic methods, including DHP (as the reference method) and DCP. The stand orthophotos were segmented using a multi-resolution image segmentation method. Afterward, the classification in two classes of the canopy cover and the non-canopy cover was conducted using minimum distance classification to estimate canopy cover. The CHM layer was generated based on the SfM algorithm and utilized in the canopy cover estimation in each stand as auxiliary data. The results showed a slight improvement when we used the CHM as auxiliary data. Overall, the results showed that the efficiency of the ground digital canopy photographic methods (zenith view) in multi-storied and dense forests is the lowest. In return, our method for digital aerial canopy photography (object-based canopy segmentation and classification) is simple, quick, efficient, and cost-effective.

ABILITY OF LANDSAT-8 OLI DERIVED TEXTURE METRICS IN ESTIMATING ABOVEGROUND CARBON STOCKS OF COPPICE OAK FORESTS

The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics were able to explain about half of the variation in aboveground carbon stocks. These results demonstrated that Landsat 8 derived texture metrics can be applied for mapping aboveground carbon stocks of coppice Oak Forests in large areas.

A combination of physical activity and computerized brain training improves verbal memory and increases cerebral glucose metabolism in the elderly

Physical exercise interventions and cognitive training programs have individually been reported to improve cognition in the healthy elderly population; however, the clinical significance of using a combined approach is currently lacking. This study evaluated whether physical activity (PA), computerized cognitive training and/or a combination of both could improve cognition. In this nonrandomized study, 224 healthy community-dwelling older adults (60-85 years) were assigned to 16 weeks home-based PA (n=64), computerized cognitive stimulation (n=62), a combination of both (combined, n=51) or a control group (n=47). Cognition was assessed using the Rey Auditory Verbal Learning Test, Controlled Oral Word Association Test and the CogState computerized battery at baseline, 8 and 16 weeks post intervention. Physical fitness assessments were performed at all time points. A subset (total n=45) of participants underwent [(18)F] fluorodeoxyglucose positron emission tomography scans at 16 weeks (post-intervention). One hundred and ninety-one participants completed the study and the data of 172 participants were included in the final analysis. Compared with the control group, the combined group showed improved verbal episodic memory and significantly higher brain glucose metabolism in the left sensorimotor cortex after controlling for age, sex, premorbid IQ, apolipoprotein E (APOE) status and history of head injury. The higher cerebral glucose metabolism in this brain region was positively associated with improved verbal memory seen in the combined group only. Our study provides evidence that a specific combination of physical and mental exercises for 16 weeks can improve cognition and increase cerebral glucose metabolism in cognitively intact healthy older adults.

Intraspecific sequence variation in the mitochondrial genome of rainbow trout (Oncorhynchus mykiss)

We examined the sequence of a 2214 base pair HindIII fragment from the mitochondrial genome of six rainbow trout. The fragment encodes four proteins and two tRNAs. Sequences for two fish from a single locality were identical. Those from separate localities differed by from 1 to 7 nucleotide substitutions. Of 13 variable sites, 12 were synonymous and 1 led to a conservative amino acid substitution. Transitions accounted for 12 of the 13 variants. In contrast to interspecific comparisons (Thomas and Beckenbach. 1989. J. Mol. Evol. 29: 233-245), the intraspecific divergence estimates based on sequence are less than those estimated from restriction fragment analysis, suggesting a complex, dynamic process for the accumulation of variation in the mitochondrial genome.