Evaluation Of Urban New Thermal Infrared Remote Sensor And Preliminary Estimation Of Building Carbon Emission

With the acceleration of urbanization and industrialization process,China's greenhouse gas emissions are rising gradually,and China's building energy consumption also presents a continuous growth trend.China proposed that the energy consumption per unit of GDP in 2020 should be 25% lower than that in 2015.Building energy efficiency has received great attentation,and reasonable estimate of building energy consumption has also become an important component in achieving carbon reduction targets.Conventional satellite thermal infrared remote sensing can obtain thermal infrared images of large-scale urban areas at low cost,but the acquisition frequency is affected by the satellite revisit cycle and the weather condition.The traditional satellite thermal infrared remote sensing can acquire the thermal infrared images of a wide range of urban areas at low cost,but the acquisition frequency is affected by the satellite re-visit period and the weather.Unmanned aerial vehicle(UAV),as a new type of photogrammetry,has the advantages of small size,light weight,easy operation and low cost.Since its flexible operation and small influence on weather and site,and it can quickly acquire high resolution images,UAV has been widely used in remote sensing.Taking Guangzhou old city as an example,this paper evaluated the difference between Landsat TIRS and CBERS-04 IRS in the retrieval of urban surface cover temperature,especially in the built-up area.Furthermore,multi-rotor multi-angle UAV was taken as the flight platform.The area of 1.3 square kilometers in Zengcheng District of Guangzhou City was taken as the research area.The high-resolution remote sensing images of the research area were acquired by UAV system.The process and results of UAV image mosaic by Pix4 Dmapper and Agisoft PhotoScan were compared and analyzed.The results showed that the images generated by the Pix4 Dmapper are smoother and the splicing effect is better.Based on this result graph,the multi-scale segmentation of the mosaic image was completed by eCognition,and the high-precision classification of the image was accomplished by the object-oriented method,and the classification accuracy is 100%,which completed the effective extraction of feature information.At the same time,based on the tilt photography data of the UAV,three-dimensional reconstruction and building singulation were realized by the Smaert3 D software platform.Based on the above-mentioned land cover,surface temperature and building geometry data,the building energy consumption was modeled and estimated by eQUEST.Finally,the spatial and temporal distribution of individual buildings' carbon was obtained.Studies have shown that the annual carbon emissions of low-rise residential building cells are less than 100 kgC,while those of high-rise commercial residential buildings are greater than 1000 KgC.According to the method proposed in this paper,it is a fast and low-cost estimation method to estimate the carbon emissions of buildings by using the information obtained by remote sensing of UAV.