Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions

The recent development of small form-factor (<6 kg), full range (400–2500 nm) pushbroom hyperspectral imaging systems (HSI) for unmanned aerial vehicles (UAV) poses a new range of opportunities for passive remote sensing applications. The flexible deployment of these UAV-HSI systems have the potential to expand the data acquisition window to acceptable (though non-ideal) atmospheric conditions. This is an important consideration for time-sensitive applications (e.g. phenology) in areas with persistent cloud cover. Since the majority of UAV studies have focused on applications with ideal illumination conditions (e.g. minimal or non-cloud cover), little is known to what extent UAV-HSI data are affected by changes in illumination conditions due to variable cloud cover. In this study, we acquired UAV pushbroom HSI (400–2500 nm) over three consecutive days with various illumination conditions (i.e. cloud cover), which were complemented with downwelling irradiance data to characterize illumination conditions and in-situ and laboratory reference panel measurements across a range of reflectivity (i.e. 2%, 10%, 18% and 50%) used to evaluate reflectance products. Using these data we address four fundamental aspects for UAV-HSI acquired under various conditions ranging from high (624.6 ± 16.63 W·m2) to low (2.5 ± 0.9 W·m2) direct irradiance: atmospheric compensation, signal-to-noise ratio (SNR), spectral vegetation indices and endmembers extraction. For instance, two atmospheric compensation methods were applied, a radiative transfer model suitable for high direct irradiance, and an Empirical Line Model (ELM) for diffuse irradiance conditions. SNR results for two distinctive vegetation classes (i.e. tree canopy vs herbaceous vegetation) reveal wavelength dependent attenuation by cloud cover, with higher SNR under high direct irradiance for canopy vegetation. Spectral vegetation index (SVIs) results revealed high variability and index dependent effects. For example, NDVI had significant differences (p < 0.05) across illumination conditions, while NDWI appeared insensitive at the canopy level. Finally, often neglected diffuse illumination conditions may be beneficial for revealing spectral features in vegetation that are obscured by the predominantly non-Lambertian reflectance encountered under high direct illumination. To our knowledge, our study is the first to use a full range pushbroom UAV sensor (400–2500 nm) for assessing illumination effects on the aforementioned variables. Our findings pave the way for understanding the advantages and limitations of ultra-high spatial resolution full range high fidelity UAV-HSI for ecological and other applications

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

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

Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime

Understanding canopy radiation regimes is critical to successfully modeling vegetation growth and function. For instance, the vertical distribution of photosynthetically active radiation (PAR) affects vegetation growth, informative upon carbon and energy cycling. Availing upon advances in information capture and computing power, geometrically explicit modeling of forest structure becomes increasingly possible. A primary challenge however is acquiring the forest mensuration data required to parameterize these models and the related automation of modeling forest structure. In this research, to address these issues we employ a novel and automated approach that capitalizes upon the rich information afforded by ground-based laser scanning technology. The method is implemented in two steps: in the first step, geometric explicit models of canopy structure are created from the ground-based laser scanning data. These geometric explicit models are used to simulate the vertical range to first hit. In the second step, we derive canopy gap probability from full waveform laser scanning data which have been used in a number of studies for characterization of radiation transmission (Jupp et al., 2009; Yang et al., 2010) and do not require any geometric explicit modeling. The radiative consistency of the geometric explicit models from step 1 is validated against the gap probabilities of step 2. The results show a strong relationship between the radiative transmission properties of the geometric models and canopy gap probabilities at plot level (R = 0.91 to 0.97), while the geometric models suggest the additional benefit to serve as a bridge in scaling between shoot level and canopy level radiation.Keywords: Laser scanning, Explicit geometric, Ray tracing, Canopy structure, Modeling, Photosynthetically active radiatio

Remote sensing of transpiration and heat fluxes using multi-angle observations

Surface energy balance is a major determinant of land surface temperature and the Earth's climate. To date, there is no approach that can produce effective, physically consistent, global and multi-decadal energy–water flux data over land. Net radiation (R[subscript n]) can be quantified regionally using satellite retrievals of surface reflectance and thermal emittance with errors b10%. However, consistent, useful retrieval of latent heat flux (λE) from remote sensing is not yet possible. In theory, λE could be inferred as a residual of R[subscript n], ground heat (G) and sensible heat (H) fluxes (R[subscript n]–H–G). However, large uncertainties in remote sensing of both H and G result in low accuracies for λE. Where vegetation is the dominant surface cover, λE is largely driven by transpiration of intercellular water through leaf stomata during the photosynthetic uptake of carbon. In these areas, satellite retrievals of photosynthesis (GPP) could be used to quantify transpiration rates through stomatal conductance. Here, we demonstrate how remote sensing of GPP could be applied to obtain λE from passive optical measurements of vegetation leaf reflectance related to the photosynthetic rate independent of knowledge of H, R[subscript n] and G. We validate the algorithm using five structurally and physiologically diverse eddy flux sites in western and central Canada. Results show that transpiration and H were accurately predicted from optical data and highly significant relationships were found between the energy budget obtained from eddy flux measurements and remote sensing (0.64 ≤ r² ≤ 0.85). We conclude that spaceborne estimates of GPP could significantly improve not only estimates of the carbon balance but also the energy balance over land.This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.elsevier.com/Keywords: Multi-angle remote sensing AMSPEC, Ball–Berry relationship, Transpiration, GPP, Stomatal conductanc