About CAMFER

CAMFER is dedicated to providing innovative, state-of-the-art monitoring of environment using geospatial technologies.  CAMFER research and outreach staff conduct studies in wetland monitoring and modeling, atmospheric emissions, forest biometrics, and watershed modeling.

Objectives

Advance resource information technology -- Develop theory and practical methods for creating accurate resource attribute maps and for lowering the cost of making and verifying the accuracy of these maps. Improve reliability of information supporting forest and environmental resource decision making by integrating ground inventory, remotely sensed data, and simulation technologies. Develop methods (e.g., scientific visualization) to effectively communicate this improved information to policy makers, decision makers, and their constituencies.

Improve effective application of information technology -- Through case studies and applied projects, improve capabilities to assess and project the condition and dynamics of forest and environmental systems under specified land use and development policies.

Monitoring of ecosystem change -- Develop effective and efficient methods of monitoring change in resource condition, structure, process and spatial arrangement by technology development and long-term research studies. These studies will establish the temporal data records needed to test and validate proposed methodologies.

Landscape level ecosystem processes -- Integrate assessment and measurement technologies with basic climatic, biological, and ecological process studies to quantify the dynamic responses of large ecosystems to natural and human intervention.

Transfer technology -- Through research, seminars, workshops, and formal classes, educate university students and resource professionals in the new discipline of resource information sciences.

Create a productive and intellectually exciting working environment -- Through modern facilities and an intellectual atmosphere, establish an environment where faculty, students, and visiting scholars can creatively interact, flourish and contribute to solving complex, interdisciplinary problems.

Projects

• Methodologies for integrated spatial assessment of wildlife habitat under alternative forest management policies over large multi-owner landscapes.
• Stochastic Ecology: spatial analysis to quantify the variance and expected value of future forest ecosystem outputs and structure under a given land use policy arising from fire, disease, and demographic disturbance.
• Construction and field validation of a spatial habitat relationship model for a 500,000 acre deer winter range based on remote sensing and GIS technology.
• Development of field sampling and videographic techniques for conducting map accuracy assessment of forest and wildlife habitat classes.
• Forest Ecosystem Classification: integrated analysis of spatial data from multiple sources based on evidential theory and artificial neural network techniques for forest ecosystem classification.
• Forest Scene Understanding: interpreting and understanding forest scenes by applying pattern recognition techniques to images derived from close range, aerial photography, airborne and satellite-borne remote sensing.
• Analysis and modeling of biogeochemical cycling and trace gas fluxes at regional to global scales.
• Modeling and validating the response of tree species to the fine-scale spatial patterning of resources in a second-growth, neotropical forest.
• Environmental ecometrics in soils, rangeland and forest ecosystems using advanced digital photography, spectroradiometry, remote sensing and field measurement techniques.
• Spatial Modeling in Economics: using GIS to account for spatial differences in hydrology when modeling optimal regulatory policy to correct for sea water intrusion into a coastal aquifer in Southern California.
• Monitoring land use conversion and assessing its effects on the biodiversity of California's northwest oak woodlands.
• Assessing the consequences of biodiversity for watershed function in oak woodland drainages.
• Change detection in California's Oak Woodlands.