Research on Photogrammetry and Remote Sensing Technology Based on Wavelet Analysis

At present, photogrammetry and remote sensing tech are undergoing the transformation of digitization, intelligence and informatization. Remote sensing and photographic info are continuously developed and utilized more fully, so that the scope and depth of its utilization can be further expanded and strengthened. The utilization of wavelet analysis in remote sensing photographic images has achieved remarkable results. Based on this, this paper first analyzes the concept and principle of wavelet analysis, then studies the connotation of photogrammetry and remote sensing tech, and finally gives the typical utilization of wavelet analysis in photogrammetry and remote sensing tech.

Systematic Review of t he Study of Flood Risks u sing Remote Sensing

Floods are one of the most devastating natural disasters that cause various losses by having an excess of rainfall in a short period of time, they cause a high flow in rivers, subsequently damaging crops and infrastructure. They also cause sedimentation of reservoirs and therefore limit the ability of existing dams to control floods. In other words, the purpose of assessing the risk of a flood is to identify the areas of a plan that are at risk of flooding based on the factors that are relevant to the risks of flooding. Therefore, it is important to create a flood map that is easy to read and quickly accessible. Maps provide a stronger and more direct impression of the spatial distribution of flood risk, like diagrams and verbal descriptions. On the other hand, the repeated taking of satellite images in periods of time of a few days makes it possible to know the evolution of the floods, helping the authorities to access the affected population, as well as to define safety areas. The current work aims to systematically evaluate the study of flood risk through remote sensing. A qualitative analysis was carried out through which 80 articles indexed between 2017 and 2021 were reviewed, distributed as follows: 49 articles are from Scopus, 10 from Ebsco and 21 from ScienceDirect; It is concluded that geographic information system together with remote sensing technology are the key tools for flood monitoring, as it is a very cost-effective way to reliably deliver the required data over a large area, as well as record data under extreme conditions to overcome the limitations of ground stations

Disaster Management: Tsunami and Remote Sensing Technology

Remote sensing technology has changed the way disasters like earthquakes and tsunamis are detected, monitored, and mapped in recent years. This paper summarizes the general theoretical study of Tsunami generation, propagation, and its inundation for deep, intermediate, and coastal waters. Tsunami is a Japanese word, which is made up of two words: “tsu” means harbor, and “nami” means waves. It means that Tsunami is the coastal gravity waves, which propagate close to the coastline. This analysis presents a novel method to explore the effects of tsunami waves on coastal areas. The methodology includes remote sensing nearness examinations and alteration identification strategies in remote sensing to outline a number of support routes along the coast and divide them into four homogenous sub-regions. The adjustments in the land spread are then measured in these sub-regions when the tidal wave occurs. The proposed paper gives a more solid and exact method than ordinary strategies to assess spatial examples of harmful territories through various land qualities along the coastline. The generative phase of tsunami development comprises the creation of an early disruption at the surface of the ocean due to the earthquake-generated distortion on the seafloor. Various comparative studies are also carried out using spatial technology to examine tsunami routes around the globe, taking into account the most recent tsunami occurrences.

CLASSIFICATION OF LANDS OF REMOTE SENSITIVE DATA BY NDVI METHOD IN SMART AGRICULTURE

A large share of the earth's surface is observed with remote sensing technology. Thanks to the data obtained from this process, information about the observed lands is obtained. In this study, NDVI (normalized difference), which is developed by applying mathematical operations on the reflection values of plants at different wavelengths from remote sensing technology and different application areas of this technology, electromagnetic rays, and spectral reflection values, and which is used as a method that provides a value expressing vegetation density. Vegetation index) method, NDVI value, and plant groups analyzed according to this value, sample MATLAB applications related to the NDVI method are mentioned. -Green-Blue) image of visible red and infrared regions, histogram graph showing the relationships between the intensities of values in NIR (near-infrared) and Red (visible Red) bands, NDVI image, and threshold function at the end. The NDVI image was obtained by using the direction (to detect areas that may have vegetation) is shown.

An Improved Backscattering Theoretical Model for Snow Area

Remote sensing has been studied for a long time to monitor the earth terrain. Remote sensing technology has been used globally in many different fields and one of the most popular area of study that uses remote sensing technology is snow monitoring. In previous researches, remote sensing has been modelled on snow area to study the scattering mechanisms of various scattering processes. In this paper, surface volume second order term that was dropped in previous study is derived, included and studied to observe the improvement in the surface volume backscattering coefficient. This new model is applied on snow layer above ground and the snow layer is modelled as a volume of ice particles as the Mie scatterers that are closely packed and bounded by irregular boundaries. Various parameters are used to investigate the improvement of adding the new term. Results show improvement in cross-polarized return, for all the range of parameters studied. Comparison is made with the field measurement result from U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) in 1990. Close agreement is shown between developed model and data field backscattering coefficient result.