Environmental Geographic Information System and Remote Sensing

  Prior to the analysis of remote sensing systems, they undergo a pre-processing stage . However, the radiation received by satellite sensors is subject to various phenomena that introduce radiometric and geometric distortions. As a result, two types of correction are required: atmospheric correction and geometric correction. During the atmospheric correction, the light detected by the satellite sensor differs from the light reflected by objects on the Earth's surface. The radiation reaching the sensor, known as reflectance , is influenced by the presence of the atmosphere. In contrast, the unaltered radiation is referred to as radiance . To accurately interpret the characteristics of the Earth's surface, it is necessary to eliminate the atmospheric influence and obtain the true reflectance values. Geometric correction is a crucial step in image acquisition to address distortions that can result in significant disparities between the actual Earth coordinates and the correspo

  Remote sensing systems can be categorized into two types: active and passive. Active remote sensing systems utilize their own source of illumination, emitting pulses of light and measuring the backscatter reflected to the sensor. On the other hand, passive remote sensing systems rely on the measurement of sunlight that is naturally emitted by the sun and reflected from the Earth's surface. When the sun is shining, passive sensors capture and measure this energy for analysis. There are three types of active remote sensing systems: radar, sonar, and lidar. Radar emits out radio waves and then detects the signals that bounce back. Various types of radar images include specular reflection, double-bound, and diffuse scattering. Sonar emits sound waves and measures their echoes in water. Lidar emits beams of light and measures their reflections. Diagram above shows the medium of transmission.  Remote sensing involves the use of radiation, which needs to interact with the atmospher

The Normalized Differentiation Vegetation Index (NDVI) is an index introduced in 1979 by Tucker to measure the health and density of vegetation. It compares the reflectance difference between Near Infra-Red (NIR) and Red bands . NDVI is commonly used to assess green biomass, leaf area index, and production patterns. The values of NDVI range from -1 to 1, with low values indicating barren areas, moderate values indicating sparse vegetation, and high values indicating dense vegetation. Table above shows the land cover classification of NDVI value NDVI can be obtained by calculating the ratio using satellite images. It is useful in land cover classification as it can differentiate different types of land cover based on the percentage of red light reflected back into space. The equation above uses the near infra-red values and red values. To obtain Landsat images, the USGS Earth Explorer platform is utilized. A dataset covering the period from October 1, 2022, to March 29, 2023, with a 30%

In the past, weather stations have been used to measure surface temperature, but limited station coverage has led to incomplete datasets. However, land surface temperature (LST) datasets derived from satellite measurements offer high-resolution and wide coverage, especially for studying urban heat island effects. Satellite technology, such as  Moderate Resolution Imaging Spectroradiometer ( MODIS), provides precise temporal and spatial resolution for LST estimation. The LST value can be extracted through pixel-based calculation. The value of each pixel will be calculated by using equation 1 (Wan, 2007):     𝑳𝒂𝒏𝒅 𝒔𝒖𝒓𝒇𝒂𝒄𝒆 𝒕𝒆𝒎𝒑𝒆𝒓𝒂𝒕𝒖𝒓𝒆 (𝒊𝒏 °𝑪) = (𝑝𝑖𝑥𝑒𝑙 𝑣𝑎𝑙𝑢𝑒 × 0.02) − 273.15 To obtain LST values, the study area (Cameron Highlands) is extracted from a shapefile, and monthly LST is derived by compositing four satellite images. The composite image's projection is adjusted to ensure proper alignment with the Earth's surface, as an incorrect coordina

  Remote Sensing is a technique to obtain information about the properties of an object without physically contacting it from a distance. In contrast to field-based measurement, remote sensing offer advantages in terms of time, cost, and also energy efficiency. It allows for the coverage of larger areas and is more effective compared to field-based measurement. Remote sensing involves several steps, including planning the mission and choosing the suitable sensors, receiving and recording the signal data, and the analyzing the resultant data. Satellites used in remote sensing do not directly measure the Earth's geophysical parameters. Instead, satellites will observe, capture and measure solar and terrestrial radiance (light) present in a vertical column of the atmosphere. These radiance measurements are then converted into geophysical parameters through the application of science-based algorithms (physics, assumptions and so on).  There are seven process components of remote sensin