A multi-sensor network for the protection of cultural heritage

The paper presents a novel automatic early warning system to remotely monitor areas of archaeological and cultural interest from the risk of fire. Since these areas have been treasured and tended for very long periods of time, they are usually surrounded by old and valuable vegetation or situated close to forest regions, which exposes them to an increased risk of fire. The proposed system takes advantage of recent advances in multi-sensor surveillance technologies, using optical and infrared cameras, wireless sensor networks capable of monitoring different modalities (e.g. temperature and humidity) as well as local weather stations on the deployment site. The signals collected from these sensors are transmitted to a monitoring centre, which employs intelligent computer vision and pattern recognition algorithms as well as data fusion techniques to automatically analyze sensor information. The system is capable of generating automatic warning signals for local authorities whenever a dangerous situation arises, as well as estimating the propagation of the fire based on the fuel model of the area and other important parameters such as wind speed, slope, and aspect of the ground surface. © 2011 EURASIP

Toward impact-based monitoring of drought and its cascading hazards

Growth in satellite observations and modelling capabilities has transformed drought monitoring, offering near-real-time information. However, current monitoring efforts focus on hazards rather than impacts, and are further disconnected from drought-related compound or cascading hazards such as heatwaves, wildfires, floods and debris flows. In this Perspective, we advocate for impact-based drought monitoring and integration with broader drought-related hazards. Impact-based monitoring will go beyond top-down hazard information, linking drought to physical or societal impacts such as crop yield, food availability, energy generation or unemployment. This approach, specifically forecasts of drought event impacts, would accordingly benefit multiple stakeholders involved in drought planning, and risk and response management, with clear benefits for food and water security. Yet adoption and implementation is hindered by the absence of consistent drought impact data, limited information on local factors affecting water availability (including water demand, transfer and withdrawal), and impact assessment models being disconnected from drought monitoring tools. Implementation of impact-based drought monitoring thus requires the use of newly available remote sensors, the availability of large volumes of standardized data across drought-related fields, and the adoption of artificial intelligence to extract and synthesize physical and societal drought impacts.</p

Wildfire Smoke Detection with Cross Contrast Patch Embedding

The Transformer-based deep networks have increasingly shown significant advantages over CNNs. Some existing work has applied it in the field of wildfire recognition or detection. However, we observed that the vanilla Transformer is not friendly for extracting smoke features. Because low-level information such as color, transparency and texture is very important for smoke recognition, and transformer pays more attention to the semantic relevance between middle- or high-level features, and is not sensitive to the subtle changes of low-level features along the space. To solve this problem, we propose the Cross Contrast Patch Embedding(CCPE) module based on the Swin Transformer, which uses the multi-scales spatial frequency contrast information in both vertical and horizontal directions to improve the discrimination of the network on the underlying details. The fuzzy boundary of smoke makes the positive and negative label assignment for instances in a dilemma, which is another challenge for wildfires detection. To solve this problem, a Separable Negative Sampling Mechanism(SNSM) is proposed. By using two different negative instance sampling strategies on positive images and negative images respectively, the problem of supervision signal confusion caused by label diversity in the process of network training is alleviated. This paper also releases the RealFire Test, the largest real wildfire test set so far, to evaluate the proposed method and promote future research. It contains 50,535 images from 3,649 video clips. The proposed method has been extensively tested and evaluated on RealFire Test dataset, and has a significant performance improvement compared with the baseline detection models

Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia