Most of the landslide and flood prediction models require the Digital elevation Model (DEM) as the input. DEM is the digital representation of topographic and manmade features located on the surface of the earth. DEM also needed in hydrologic analyses, resource management and environmental assessment and including interferometry synthetic aperture radar (InSAR) applications. The increased use of DEM in these applications especially in the tropical and developing countries has led to a greater need for high resolution and low cost digital elevation data. One of the recent remote sensing methods for land deformation monitoring also require DEM for determining the exact location of the land movement. With the advancement of the small drone (ultra-light Unmanned Aerial Vehicle) technology, it is practical to use small drone which employing aerial photogrammetry to produce high resolution DEM. This paper discusses an application of a DJI Phantom vision 2 + quadcopter (drone) with a digital camera to produce high resolution DEM for land deformation monitoring using interferometry synthetic aperture radar (InSAR). An inexpensive method to produce centimeter resolution DEMs has been developed with the help of Ground Control Points (GCPs). Evaluations of the accuracy of the generated DEM have been carried out to verify the effectiveness of the proposed method.
<span>Crop management including the efficient use of nitrogen (N) fertilizer is important to ensure crop productivity. Human error in judging the leaf greenness when using the leaf color chart (LCC) to estimate the rice plant N nutrient status has encouraged numerous researchers to implement a machine-learning algorithm but experienced some issues in calibration and lighting. The datasets are created at 6.00-7.00AM (consistent lighting) and including light intensity, so each dataset contains RGB value and light intensity as inputs, and LCC value as a target. A system consists of a smartphone with an application that prevents user from taking an image if the light intensity is not in 2000-3500 lux, and a computer for preprocessing and classification purposes were developed. The preprocessing included cropping, splitting the rice leaf images, and calculating the average RGB values. A k-NN classifier is implemented and by using a cross-validation method is found k=5 gives the best accuracy of 97,22%. The in-site test of the system also works with an accuracy of 96.40%. </span>
This paper presents the design and development of a PC-based orientation sensing system using strapdown inertial measurement unit (SIMU). A 9-DOF (degree of freedom) SIMU, which consists of 3-DOF gyroscopes, 3-DOF accelerometers, and 3-DOF magnetometers, are used to estimate the orientation. Data fusion is performed to improve measurement accuracy. In this paper, the orientation measurements are compared and discussed using three different approaches, known as the conventional accelerometer/gyroscope approach, the Kalman filter based 9-DOF SIMU approach, and the gradient decent algorithm based 9-DOF SIMU approach. The derivation of each approach together with the design of the PC-based orientation sensing system is outlined in this paper.
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