GIS and Remote Sensing Lab Report

INTRODUCTION

In the field of remote sensing, band ratio plays a crucial role in atmospheric correction, allowing us to achieve continuity in reflectance measurements. Band ratio is employed to reduce topographic effects and enhance spectral differences between bands, thus producing images that highlight relative path intensity. This is achieved by dividing one spectral band by another. When differentiating between land cover types, it is essential to increase spectral differences in Band Ratio Images. Multispectral sensors offer numerous ratio combinations, with 36 unique ratio combinations available for nine Landsat 8 OLI reflective bands.

These ratio combinations can reveal subtle differences in reflection between surface materials, which are often challenging to detect in standard photographs.

In this lab report, we will explore how reflectance is utilized to calculate NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and CMRI (Combined Mangrove Recognition Index). NDVI is a quantitative indicator that combines visible and near-infrared electromagnetic spectrum bands to assess live green vegetation in remote sensing observations.

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NDWI, on the other hand, is used to delineate and monitor changes in water quality by utilizing the near-infrared (NIR) and green bands. While CMRI typically involves comparing multiple indices, in this report, we will focus on comparing NDVI with NDWI.

METHODOLOGY

1. Formulas:

   The following formulas were used in our analysis:

   • NDVI = ^{(B₅ - B₄)} / (B₅ + B₄)
   • NDWI = ^{(B₃ - B₅)} / (B₃ + B₅)
   • CMRI = NDVI - NDWI

2. NDVI Calculation:

   1. Open the software and select the NDVI calculation:
   2. Choose "Basic Tools" and then "Band Math".
   3. Insert the NDVI formula: ^{(B₅ - B₄)} / (B₅ + B₄).
   4. Click "OK" and select B₄ as "undefined B₄," representing reflectance B₄ converted from DN to reflectance.
   5. Select B₅ as "undefined B₅," representing reflectance B₅.
   6. Save the result as "NDVI" and click "Open."
   7. Click "OK" to load the NDVI band, and the image will appear.
   8. Hover the mouse over the image to obtain NDVI values.

3. NDWI Calculation:

   1. Open the software and select the NDWI calculation:
   2. Choose "Basic Tools" and then "Band Math".
   3. Insert the NDWI formula: ^{(B₃ - B₅)} / (B₃ + B₅).
   4. Click "OK" and select B₃ as "undefined B₃," representing reflectance B₃.
   5. Select B₅ as "undefined B₅," representing reflectance B₅.
   6. Save the result as "NDWI" and click "Open."
   7. Click "OK" to load the NDWI band, and the image will appear.
   8. Hover the mouse over the image to obtain NDWI values.

4. CMRI Calculation:

   1. Open the software and select the CMRI calculation:
   2. Choose "Basic Tools" and then "Band Math".
   3. Insert the CMRI formula: B₁ - B₂ (where B₁ represents NDVI and B₂ represents NDWI).
   4. Click "OK" and select NDVI as "undefined B₁."
   5. Select NDWI as "undefined B₂."
   6. Save the result as "CMRI" and click "Open."
   7. Click "OK" to load the CMRI band, and the image will appear.
   8. Hover the mouse over the image to obtain CMRI values.

RESULTS

DISCUSSION

In this section, we will discuss the three indices: NDVI, NDWI, and CMRI, focusing on the methodology and results obtained.

NDVI is calculated using the reflectance values from band 4 (representing red light) and band 5 (representing near-infrared, NIR). A positive NDVI value indicates the presence of green vegetation, with higher values corresponding to denser vegetation. Conversely, NDVI values close to 0 suggest non-vegetated or urban areas, while negative values may indicate water bodies. NDVI provides a reliable basis for assessing vegetation density and can be used to detect changes in vegetation over time.

NDWI, on the other hand, is used to identify and monitor water bodies. It is calculated using reflectance values from band 3 (representing green light) and band 5 (NIR). Positive NDWI values indicate water presence, while zero or negative values suggest the absence of water or the dominance of non-aqueous materials. NDWI is particularly effective in capturing water-related data and is less affected by atmospheric scattering compared to NDVI.

CMRI was developed to distinguish mangrove vegetation from non-mangrove vegetation by combining NDVI and NDWI outputs. Mangroves, which thrive in high-saline conditions, exhibit high water content in their leaves. This water content allows them to tolerate various soil salts and maintain turbidity at low water potential without increasing cell osmotic pressure. Due to these unique characteristics, mangroves display distinct NDWI values. CMRI values greater than 1 indicate the presence of mangroves, making it a valuable tool for mangrove recognition.

CONCLUSION

In this comprehensive lab report, we explored the application of band ratio techniques and remote sensing indices, specifically NDVI, NDWI, and CMRI. These indices play a pivotal role in extracting valuable information from remotely sensed data, aiding in the analysis of various environmental factors. The key findings and conclusions from our study are as follows:

1. NDVI: The Normalized Difference Vegetation Index (NDVI) provides a quantitative measure of vegetation density by comparing reflectance in the near-infrared and red spectral bands. Positive NDVI values indicate the presence of live green vegetation, while values close to 0 suggest non-vegetated or urban areas. Negative values often correspond to water bodies. NDVI is a valuable tool for monitoring changes in vegetation over time and assessing its relative density.
2. NDWI: The Normalized Difference Water Index (NDWI) is used to detect and monitor water bodies. It calculates the difference in reflectance between the green and near-infrared bands. Positive NDWI values indicate the presence of water, while zero or negative values indicate non-aqueous materials. NDWI is particularly useful for capturing water-related data and is less susceptible to atmospheric scattering effects.
3. CMRI: The Combined Mangrove Recognition Index (CMRI) is designed to distinguish mangrove vegetation from non-mangrove vegetation by combining NDVI and NDWI outputs. Mangroves, known for their ability to thrive in saline conditions, exhibit unique water content characteristics in their leaves, making them distinguishable through CMRI values greater than 1. CMRI is a valuable tool for the identification and monitoring of mangrove ecosystems.

Overall, these remote sensing indices provide valuable insights into environmental changes and are widely used in applications such as land cover classification, vegetation health assessment, and water quality monitoring. They enable researchers and environmental scientists to make informed decisions and contribute to our understanding of the Earth's ecosystems.