Presented is a scheme of an embedded video remote monitoring system based on TMS320DM642. Using DM642 as the data processing core, the remote monitoring system is composed of video acquisition module, video processing...Presented is a scheme of an embedded video remote monitoring system based on TMS320DM642. Using DM642 as the data processing core, the remote monitoring system is composed of video acquisition module, video processing module and communication module, which gives an implementation of class/mini driver module in DSP/BIOS integrated developing environment and also a common task module in application layer is achieved. The system realizes the entire functions of the analog video signal acquisition, H.264 video coding and Internet transmission. It provides the general connection for the future development and has good flexibility and extendibility. The system uses a modular design and overall development of programming methods to improve the efficiency of system development.展开更多
Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and t...Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and the main aspects of monitoring forest diseases and pests by remote sensing are described, and the application prospect of this technology is forecasted.展开更多
Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and thei...Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.展开更多
This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi In...This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi Inthanon National Park. To mitigate the devastating impacts of these wildfires, effective monitoring and management strategies are necessary. Remote sensing technology provides a promising approach for mapping burnt areas and understanding fire regimes at a regional scale. The primary focus of this research is to employ the MODIS Aqua/Terra satellite system for obtaining historical remote sensing data on hotspots. The advantages of remote sensing include accurate identification and mapping of burnt areas, regular monitoring, rapid data acquisition, and historical data analysis. The MODIS sensor, specifically designed for fire monitoring, offers enhanced fire detection and diagnosis, multiple channels for qualitative and quantitative analysis, and precision positioning capabilities. The research results presented in the analysis contribute to the understanding of fire incidents and hotspot occurrences within the four national parks studied. This paper suggests the optimization of early detection of forest and land fires through the utilization of Artificial Intelligence (AI), presenting it as a recommendation for future endeavors. The research emphasizes the significance of implementing efficient policies and management strategies to effectively tackle the challenges associated with fires in these ecologically significant areas.展开更多
Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentu...Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentuated in recent years by signifi cant human pressure and eff ects of climate change;hence,the health of the stands needs to be monitored.In this study,the Google Engine Earth platform was leveraged to extract the normalized diff erence vegetation index(NDVI)and soil-adjusted vegetation index,from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco.Our results highlight the importance of interannual variations in NDVI in forest monitoring;the variations had a signifi cantly high relationship(p<0.001)with dieback severity.NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coeffi cients of 0.49 and−0.67,highlighting its ability to predict phenotypic changes in forest species.Monthly interannual variation in NDVI between 2016 and 2017 seemed to confi rm fi eld observations of cork oak dieback in 2018,with the largest decreases in NDVI(up to−38%)in December in the most-aff ected plots.Analysis of the infl uence of ecological factors on dieback highlighted the role of substrate as a driver of dieback,with the most severely aff ected plots characterized by granite-granodiorite substrates.展开更多
基金National Natural Science Foundation of China(40274047)
文摘Presented is a scheme of an embedded video remote monitoring system based on TMS320DM642. Using DM642 as the data processing core, the remote monitoring system is composed of video acquisition module, video processing module and communication module, which gives an implementation of class/mini driver module in DSP/BIOS integrated developing environment and also a common task module in application layer is achieved. The system realizes the entire functions of the analog video signal acquisition, H.264 video coding and Internet transmission. It provides the general connection for the future development and has good flexibility and extendibility. The system uses a modular design and overall development of programming methods to improve the efficiency of system development.
基金Supported by National Natural Science Foundation of China " Multiagent Simulation and Spatial Prediction of Forest Invasive Alien Species and Diffusion"(30871964)Ministry of Education,New Century Excellent Talents Support Project " Ecological Response Mechanism and Prediction of Spatial Pattern Dynamics of Forest Vegetation"(NCET06-0122)Ministry of Education Innovation Team " Early Warning of Major Forest Pest Disasters and Ecological Control Technology " (IRT0607)~~
文摘Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and the main aspects of monitoring forest diseases and pests by remote sensing are described, and the application prospect of this technology is forecasted.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.1/172/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.
文摘This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi Inthanon National Park. To mitigate the devastating impacts of these wildfires, effective monitoring and management strategies are necessary. Remote sensing technology provides a promising approach for mapping burnt areas and understanding fire regimes at a regional scale. The primary focus of this research is to employ the MODIS Aqua/Terra satellite system for obtaining historical remote sensing data on hotspots. The advantages of remote sensing include accurate identification and mapping of burnt areas, regular monitoring, rapid data acquisition, and historical data analysis. The MODIS sensor, specifically designed for fire monitoring, offers enhanced fire detection and diagnosis, multiple channels for qualitative and quantitative analysis, and precision positioning capabilities. The research results presented in the analysis contribute to the understanding of fire incidents and hotspot occurrences within the four national parks studied. This paper suggests the optimization of early detection of forest and land fires through the utilization of Artificial Intelligence (AI), presenting it as a recommendation for future endeavors. The research emphasizes the significance of implementing efficient policies and management strategies to effectively tackle the challenges associated with fires in these ecologically significant areas.
文摘Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentuated in recent years by signifi cant human pressure and eff ects of climate change;hence,the health of the stands needs to be monitored.In this study,the Google Engine Earth platform was leveraged to extract the normalized diff erence vegetation index(NDVI)and soil-adjusted vegetation index,from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco.Our results highlight the importance of interannual variations in NDVI in forest monitoring;the variations had a signifi cantly high relationship(p<0.001)with dieback severity.NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coeffi cients of 0.49 and−0.67,highlighting its ability to predict phenotypic changes in forest species.Monthly interannual variation in NDVI between 2016 and 2017 seemed to confi rm fi eld observations of cork oak dieback in 2018,with the largest decreases in NDVI(up to−38%)in December in the most-aff ected plots.Analysis of the infl uence of ecological factors on dieback highlighted the role of substrate as a driver of dieback,with the most severely aff ected plots characterized by granite-granodiorite substrates.
