Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r 2?= 0.61, df = 1, F?= 14.3, p?= 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r 2?= 0.72, df = 1, F?= 23.09, p?= 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices

Multiscale Soil Investigations: Physical Concepts And Mathematical Techniques

Soil variability has often been considered to be composed of “functional” (explained) variations plus random fl uctuations or noise. However, the distinction between these two components is scale dependent because increasing the scale of observation almost always reveals structure in the noise (Burrough, 1983). Soils can be seen as the result of spatial variation operating over several scales, indicating that factors infl uencing spatial variability differ with scale. Th is observation points to variability as a key soil attribute that should be studied

Implications of differing input data sources and approaches upon forest carbon stock estimation

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42{\%} of stands; however, 77{\%} of stands were within +/-1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p < 0.001); differences for Douglas-fir-dominated stands (representing 85{\%} of all stands) were not significant (p = 0.288). Overall, the relationship between the two biomass estimates was strong (R(2) = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping

Analysis of Implementation the Evaluation of Guidance and Counseling Program at Senior High Schools of Singkawang

Focus of this study are (1) describe and analyze the implementation of the guidance and counseling program, (2) find some factors inhibiting the implementation of the guidance and counseling program. This study uses qualitative methods; using interview data collecting technique, tested its validity through triangulation. Subjects in this study are all teachers of guidance and counseling in the Senior High School of Singkawang as many as 10 people as well as principals and supervisors as the informants with the total of 11 people. Results (1) the implementation of evaluation of guidance and counseling program by the teachers still has many weaknesses on each phase of the evaluation, such as not understanding the evaluation models of the guidance and counseling program, how to apply them, and monitoring process that is not done in deeply and in detail, (2) Some factors inhibiting the implementation of the evaluation of guidance and counseling program are lack of knowledge and understanding of the evaluation of guidance and counseling program in the schools, lack of interest in developing professional competencies, and lack of guidance to the teachers in implementing the guidance and counseling evaluation program

Assessing spectral measures of post-harvest forest recovery with field plot data

Information regarding the nature and rate of forest recovery is required to inform forest management, monitoring, and reporting activities. Delayed establishment or return of forests has implications to harvest rotations and carbon uptake, among others, creating a need for spatially-explicit, large-area, characterizations of forest recovery. Landsat time series (LTS) has been demonstrated as a means to quantitatively relate forest recovery, noting that there are gaps in our understanding of the linkage between spectral measures of forest recovery and manifestations of forest structure and composition. Field plots provide a means to better understand the linkage between forest characteristics and spectral recovery indices. As such, from a large set of existing field plots, we considered the conditions present for the year in which the co-located pixel was considered spectrally recovered using the Years to Recovery (Y2R) metric. Y2R is a long-term metric of spectral recovery that indicates the number of years required for a pixel to return to 80% of its pre-disturbance Normalized Burn Ratio value. Absolute and relative metrics of recovery at 5 years post-disturbance were also considered. We used these three spectral recovery metrics to predict the stand development class assigned by the field crew for 284 seedling plots with an overall accuracy of 73.59%, with advanced seedling stands more accurately discriminated (omission error, OE = 15.74%) than young seedling stands (OE = 49.84%). We then used field-measured attributes (e.g. height, stem density, dominant species) from the seedling plots to classify the plots into three spectral recovery groups, which were defined using the Y2R metric: spectral recovery in (1) 1–5 years, (2) 6–10 years, or (3) 11–15 years. Overall accuracy for spectral recovery groups was 61.06%. Recovery groups 1 and 3 were discriminated with greater accuracy (producer’s and user’s accuracies > 66%) than recovery group 2 ( 66%) than recovery group 2 ( 66%) than recovery group 2 (<50%). The top field-measured predictors of spectral recovery were mean height, dominant species, and percentage of stems in the plot that were deciduous. Variability in stand establishment and condition make it challenging to accurately discriminate among recovery rates within 10 years post-harvest. Our results indicate that the long-term metric Y2R relates to forest structure and composition attributes measured in the field and that spectral development post-disturbance corresponds with expectations of structural development, particularly height, for different species, site types, and deciduous abundance. These results confirm the utility of spectral recovery measures derived from LTS data to augment landscape-level assessments of post-disturbance recovery.Peer reviewe

Lidar sampling for large-area forest characterization: A review

The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved.The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, we present the case for lidar sampling as a means to enable timely and robust large-area characterizations. We briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. We also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation. © 2012

Integration of LIDAR, optical remotely sensed and ancillary data for forest monitoring and Grizzly bear habitat characterizationIntegração de LIDAR, sensores remotos óticos e dados auxiliares para o monitoramento fl orestal e caracterização do habitat dos ursos Grizzly

Forest management and reporting information needs are becoming increasingly complex in Canada. Inclusion of timber and non-timber considerations for both management and reporting has resulted in opportunities for integration of data from differing sources to provide the desired information. Canada’s forested land-base is over 400 million hectares in size and fulfills important ecological and economic functions. In this communication we describe how remotely sensed data and other available spatial data layers capture different forest characteristics and conditions, and how these varying data sources may be combined to provide otherwise unavailable information. For instance, light detection and ranging (LIDAR) confers information regarding vertical forest structure; high spatial resolution imagery captures (in detail) the horizontal distribution and arrangement of vegetation and vegetation conditions; and, moderate spatial resolution imagery provides consistent wide-area depictions of forest conditions. Furthermore, coarse spatial resolution imagery, with a high temporal density, can be blended with data of a higher spatial resolution to generate moderate spatial resolution data with a high temporal density. These remotely sensed data sources, when combined with existing spatial data layers such as forest inventory and digital terrain models, provide useful information that may be used to address, through modeling, questions regarding forest condition, structure, and change. In this communication, we discuss the importance of data integration and ultimately, information generation, in the context of Grizzly bear habitat characterization. Grizzly bear habitat in western Canada is currently undergoing pressure from a combination of anthropogenic activities and a widespread outbreak of mountain pine beetle, resulting in a variety of information needs, including: detailed depictions of horizontal and vertical vegetation structure over large areas to support bark beetle susceptibility mapping and habitat modeling; moderate spatial resolution data to capture changes in infestation conditions over time to support change detection and wall-to-wall mapping; and, coarse spatial resolution data to provide increased temporal detail enabling capture of within-year alterations to Grizzly habitat.Resumo As necessidades do gerenciamento de florestas e do relato de informações estão ficando cada vez mais complexas no Canadá. A inclusão de considerações sobre madeira e não-madeira, tanto para o gerenciamento como para o relato de disponibilidade de recursos florestais, resultou em oportunidades para a integração de dados de diferentes fontes para a obtenção da informação desejada. As terras florestadas de uso potencial no Canadá têm um tamanho acima de 400 milhões de hectares e possui importantes funções ecológicas e econômicas. Nesta comunicação descrevemos como dados de sensoriamento remoto e outros dados espaciais disponíveis detectam as diferentes condições e características da floresta e como estas fontes de dados diversos podem ser combinadas, fornecendo informações que estariam indisponíveis de outra forma. Por exemplo, LIDAR (acrônimo de light detection and ranging) fornece informações sobre a estrutura vertical de florestas; imagens de alta resolução espacial detectam detalhadamente a distribuição horizontal e o arranjo da vegetação e as suas condições; enquanto imagens de resolução espacial moderada fornecem uma consistente visão das condições florestais em extensas áreas. Além disso, imagens com resolução espacial grosseira, com elevada densidade temporal, pode ser combinada com dados de resolução espacial mais fi na para gerar dados com uma resolução espacial moderada, porém com alta densidade temporal. Estas fontes de dados de sensoriamento remoto, quando combinadas com camadas de dados espaciais, tais como inventários florestais e modelos digitais de terreno fornecem informações úteis que podem ser usadas para, através de modelagem, analisar questões referentes a condição florestal, estrutura e mudanças. Nesta comunicação discutimos a importância da integração de dados e finalmente a geração de informação no contexto da caracterização do habitat dos ursos Grizzly. O habitat deste urso no oeste canadense está atualmente sendo pressionado devido a uma combinação de atividades humanas e por uma infestação ampla do besouro do pinheiro (pine beetle), tornando necessária uma série de informações, incluindo: detecção da estrutura horizontal e vertical da estrutura da vegetação para mapear as áreas de susceptibilidade deste inseto e para modelar o seu habitat; dados de resolução espacial moderada para capturar as mudanças das condições de infestação ao longo do tempo, para suportar a detecção de mudanças e mapeamento detalhado; dados de resolução espacial grosseira para fornecer um aumento de detalhe temporal, para detectar as alterações inter-anuais do habitat do Grizzly