Occupancy

About: Occupancy is a research topic. Over the lifetime, 2757 publications have been published within this topic receiving 68288 citations.

Standardized Assessment of Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in Sight

Abstract: Extinction rates in the Anthropocene are three orders of magnitude higher than background and disproportionately occur in the tropics, home of half the world’s species. Despite global efforts to combat tropical species extinctions, lack of high-quality, objective information on tropical biodiversity has hampered quantitative evaluation of conservation strategies. In particular, the scarcity of population-level monitoring in tropical forests has stymied assessment of biodiversity outcomes, such as the status and trends of animal populations in protected areas. Here, we evaluate occupancy trends for 511 populations of terrestrial mammals and birds, representing 244 species from 15 tropical forest protected areas on three continents. For the first time to our knowledge, we use annual surveys from tropical forests worldwide that employ a standardized camera trapping protocol, and we compute data analytics that correct for imperfect detection. We found that occupancy declined in 22%, increased in 17%, and exhibited no change in 22% of populations during the last 3–8 years, while 39% of populations were detected too infrequently to assess occupancy changes. Despite extensive variability in occupancy trends, these 15 tropical protected areas have not exhibited systematic declines in biodiversity (i.e., occupancy, richness, or evenness) at the community level. Our results differ from reports of widespread biodiversity declines based on aggregated secondary data and expert opinion and suggest less extreme deterioration in tropical forest protected areas. We simultaneously fill an important conservation data gap and demonstrate the value of large-scale monitoring infrastructure and powerful analytics, which can be scaled to incorporate additional sites, ecosystems, and monitoring methods. In an era of catastrophic biodiversity loss, robust indicators produced from standardized monitoring infrastructure are critical to accurately assess population outcomes and identify conservation strategies that can avert biodiversity collapse.

Occupancy-based zoning climate control system and method

Abstract: A control system for managing a heating, ventilating and air conditioning (HVAC) system based on occupancy of an area is provided. The occupancy may be determined by anticipated programming based on time of day zoning, and/or by actual sensed occupancy. In the later, the control system includes an occupancy sensor that communicates with a programmable thermostat. The occupancy sensor is disposed in the area and senses a state of occupancy of the area. The programmable thermostat instructs the HVAC system to adjust the temperature of the area within the structure based on the state of occupancy of that particular area to enhance occupant comfort and energy efficiency. The thermostat may also include programming modes or scripts that may be run to adjust operational control when abnormal occupancy conditions are sensed. Controllable dampers may also be used by the thermostat to achieve micro zoning control of the HVAC system.

Occupancy detection through an extensive environmental sensor network in an open-plan office building

Abstract: Contemporary office buildings commonly experience changes in occupancy patterns and needs due to changes in business practice and personal churns. Hence, it is important to understand and accurately capture the information of such trends for applications in building design and subsequent building operations. Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security, and occupancy profiles are widely used in building simulations. However, the ability to discern the actual number of people in a space is often beyond the scope of current sensing techniques. This paper presents a study to develop algorithms for occupancy number detection based on the analysis of environmental data captured from existing sensors and ambient sensing networks. Both wireless and wired sensor networks are deployed in the Robert L. Preger Intelligent Workplace (IW) at Carnegie Mellon University, comprising six different types of sensors. An average of 80% accuracy on the occupancy number detection was achieved by Hidden Markov Models during testing periods. The findings also offer encouraging possibilities for incorporating the algorithms into building management systems for optimizing energy use while maintaining occupant comfort.

Improved Occupancy Grids for Map Building

Abstract: Occupancy grids are a probabilistic method for fusing multiple sensor readings into surface maps of the environment. Although the underlying theory has been understood for many years, the intricacies of applying it to realtime sensor interpretation have been neglected. This paper analyzes how refined sensor models (including specularity models) and assumptions about independence are crucial issues for occupancy grid interpretation. Using this analysis, the MURIEL method for occupancy grid update is developed. Experiments show how it can dramatically improve the fidelity of occupancy grid map-making in specular and realtime environments.