Thermal imaging for pests detecting-a review

Thermal remote sensing technology (thermography) is a non-destructive technique used to determine thermal properties of any objects of interest. The principle of thermal remote sensing is the invisible radiation patterns of objects converted into visible images and these images are called thermal images. These images can be acquired using portable, handheld or thermal sensors that are coupled with optical systems mounted on an airplane or satellite. This technology has grown into an important technology that is applied directly or indirectly in many applications such as civil engineering and industrial maintenance, etc. The potential use of thermal remote sensing in agriculture includes nursery and greenhouse monitoring, irrigation scheduling, plant disease detection, estimating fruit yield, evaluating the maturity of fruits and bruise detection in fruits and vegetables. However, in recent years, the usage of thermal imaging is gaining popularity in pest detection due to the reductions in the cost of the equipment and simple operating procedure. The purpose of this paper is two parts, the first part discusses about thermal remote sensing system while the second part epitomize various studies conducted on the potential application of thermal imaging system in pest detection

Thermal remote sensing of sea surface temperature

Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed

A Bayesian Estimator for Linear Calibration Error Effects in Thermal Remote Sensing

The Bayesian Land Surface Temperature estimator previously developed has been extended to include the effects of imperfectly known gain and offset calibration errors. It is possible to treat both gain and offset as nuisance parameters and, by integrating over an uninformative range for their magnitudes, eliminate the dependence of surface temperature and emissivity estimates upon the exact calibration error.Comment: 3 page

An airborne thermal remote sensing calibration technique

A calibration technique for airborne thermal remote sensing systems without the requirement for ground truth or multiple altitude measure ments is developed and evaluated. This technique is based on vertical and offset look angles and in effect corrects for image degradation due to atmospheric effects . The results were evaluated by comparison to a multiple altitude regression technique using previously generated imagery, and by statistical methods, including an error analysis. Assuming that the multiple altitude regression technique was exact, the error on apparent temperature produced by this angular technique was 0.32C

Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data

<p>Abstract</p> <p>Background</p> <p>Extreme heat events are the number one cause of weather-related fatalities in the United States. The current system of alert for extreme heat events does not take into account intra-urban spatial variation in risk. The purpose of this study is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature derived from thermal remote sensing data.</p> <p>Results</p> <p>Comparison of logistic regression models indicates that supplementing known sociodemographic risk factors with remote sensing estimates of land surface temperature improves the delineation of intra-urban variations in risk from extreme heat events.</p> <p>Conclusion</p> <p>Thermal remote sensing data can be utilized to improve understanding of intra-urban variations in risk from extreme heat. The refinement of current risk assessment systems could increase the likelihood of survival during extreme heat events and assist emergency personnel in the delivery of vital resources during such disasters.</p

The Improvement of Land Cover Classification by Thermal Remote Sensing

Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE) using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR) bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images

Characterizing soil stiffness using thermal remote sensing and machine learning

Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms

The Improvement of Land Cover Classification by Thermal Remote Sensing

Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE) using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR) bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images