Autonomous Field-Deployable Wildland Fire Sensors

An Autonomous Fire Detector (AFD) is a miniature electronic package combining position location capability [using the Global Positioning System (GPS)], communications (packet or voice-synthesized radio), and fire detection capability (thermal, gas, smoke detector) into an inexpensive, deployable package. The AFD can report fire-related parameters, like temperature, carbon monoxide concentration, or smoke levels via a radio link to firefighters located on the ground. These systems are designed to be inserted into the fire by spotter planes at a fire site or positioned by firefighters already on the ground. AFDs can also be used as early warning devices near critical assets in the urban–wildland interface. AFDs can now be made with commercial off-the-shelf components. Using modern micro-electronics, an AFD can operate for the duration of even the longest fire (weeks) using a simple dry battery pack, and can be designed to have a transmitting range of up to several kilometers with current low power radio communication technology. A receiver to capture the data stream from the AFD can be made as light, inexpensive and portable as the AFD itself. Inexpensive portable repeaters can be used to extend the range of the AFD and to coordinate many probes into an autonomous fire monitoring network

Building capacity in remote sensing for conservation: present and future challenges

Remote sensing (RS) has made significant contributions to conservation and ecology; however, direct use of RS-based information for conservation decision making is currently very limited. In this paper, we discuss the reasons and challenges associated with using RS technology by conservationists and suggest how training in RS for conservationists can be improved. We present the results from a survey organized by the Conservation Remote Sensing Network to understand the RS expertise and training needs of various categories of professionals involved in conservation research and implementation. The results of the survey highlight the main gaps and priorities in the current RS data and technology among conservation practitioners from academia, institutions, NGOs and industry. We suggest training to be focused around conservation questions that can be addressed using RS-derived information rather than training pure RS methods which are beyond the interest of conservation practitioners. We highlight the importance of developing essential biodiversity variables (EBVs) and how this can be achieved by increasing the RS capacity of the conservation community. Moreover, we suggest that open-source software is adopted more widely in the training modules to facilitate access to RS data and products in developing countries, and that online platforms providing mapping tools should also be more widely distributed. We believe that improved RS capacity among conservation scientists will be essential to improve conservation efforts on the ground and will make the conservation community a key player in the definition of future RS-based products that serve conservation and ecological needs

Soil organic matter and litter chemistry response to experimental N deposition in northern temperate deciduous forest ecosystems

The effects of atmospheric nitrogen (N) deposition on organic matter decomposition vary with the biochemical characteristics of plant litter. At the ecosystem-scale, net effects are difficult to predict because various soil organic matter (SOM) fractions may respond differentially. We investigated the relationship between SOM chemistry and microbial activity in three northern deciduous forest ecosystems that have been subjected to experimental N addition for 2 years. Extractable dissolved organic carbon (DOC), DOC aromaticity, C : N ratio, and functional group distribution, measured by Fourier transform infrared spectra (FTIR), were analyzed for litter and SOM. The largest biochemical changes were found in the sugar maple–basswood (SMBW) and black oak–white oak (BOWO) ecosystems. SMBW litter from the N addition treatment had less aromaticity, higher C : N ratios, and lower saturated carbon, lower carbonyl carbon, and higher carboxylates than controls; BOWO litter showed opposite trends, except for carbonyl and carboxylate contents. Litter from the sugar maple–red oak (SMRO) ecosystem had a lower C : N ratio, but no change in DOC aromaticity. For SOM, the C : N ratio increased with N addition in SMBW and SMRO ecosystems, but decreased in BOWO; N addition did not affect the aromaticity of DOC extracted from mineral soil. All ecosystems showed increases in extractable DOC from both litter and soil in response to N treatment. The biochemical changes are consistent with the divergent microbial responses observed in these systems. Extracellular oxidative enzyme activity has declined in the BOWO and SMRO ecosystems while activity in the SMBW ecosystem, particularly in the litter horizon, has increased. In all systems, enzyme activities associated with the hydrolysis and oxidation of polysaccharides have increased. At the ecosystem scale, the biochemical characteristics of the dominant litter appear to modulate the effects of N deposition on organic matter dynamics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72320/1/j.1365-2486.2005.01001.x.pd

Adaptive optical sensing in an object tracking DDDAS

The generalized optical remote sensing tracking problem for an object moving in a dynamic urban environment is complex. Two emerging capabilities that can help solve this problem are adaptive multimodal sensing and modeling with data assimilation. Adaptive multimodal sensing describes sensor hardware systems that can be rapidly reconfigured to collect the appropriate data as needed. Imaging of a moving target implies some ability to forecast where to image next so as to keep the object in the scene. Forecasts require models and to help solve this prediction problem, data assimilation techniques can be applied to update executing models with sensor data and thereby dynamically minimize forecast errors. The direct combination of these two capabilities is powerful but does not answer the questions of how or when to change the imaging modality. The Dynamic Data-Driven Applications Systems (DDDAS) paradigm is well-suited for solving this problem, where sensing must be adaptive to a complex changing environment and where the prediction of object movement and its interaction with the environment will enhance the ability of the sensing system to stay focused on the object of interest. Here we described our work on the creation of a modeling system for optical tracking in complex environments, with a focus on integrating an adaptive imaging sensor within the system framework