Mapping the Above ground biomass using EO data

Mapping Above Ground Biomass (AGB) with Earth Observation (EO) data involves utilizing advanced remote sensing techniques to assess and quantify the amount of biomass present in a given area.

🌳🛰️ The script developed here utilizes Earth Observation (EO) data, incorporating GEDI L4B, Sentinel-1, Sentinel-2, Copernicus elevation, and ESA World Cover datasets. Its purpose is to model the density of Above Ground Biomass (AGB) in a forested region of Portugal, specifically within the Municipality of Sines.

🌲💻 The script utilizes the power of the Random Forest (RF) classifier to predict AGB density, considering various factors like polarization data, slope, elevation, and cloud-filtered Sentinel II images.

🚀🌐 This model provides insights into the dynamics of forest structure and AGB, contributing to our understanding of ecological health and sustainability.

Vegetation status mapping

Mapping vegetation status via remote sensing indices has emerged as a vital tool in environmental monitoring, offering crucial insights and precautionary measures. This script was designed to map the vegetation status in the Municipality of Selmes (The script allows users to easily customize the study area by assigning the municipality name as a variable), a well known agricultural site in Portugal. The indices utilized in this mapping include,

  1. NDVI, EVI, and kNDVI for assessing vegetation greenness and vigor.
  2. Leaf Area Index (LAI) aids in estimating vegetated area yields.
  3. Canopy Chlorophyll Content Index (CCCI) assesses canopy nitrogen content.
  4. The Normalized Difference Moisture Index (NDMI) evaluates vegetation water/moisture content.
  5. Canopy Fire Potential Index estimates vegetation canopy susceptibility to fire under specific conditions.



Estimating the forest canopy loss using sentinel-1 C band SAR images

In this research, the main objective was to detect deforested areas in Wilpattu from RADAR remote sensing techniques by applying different change detection methods. Basically, complex coherence, correlation coefficient, image differencing and images rationing techniques were applied for two sentinel 1 SAR C band images before and after the event. The deforestation observed in the results is very limited and less significant within the boundaries of Wilpattu National Park, but significant loss of tree covering is observed outside the boundaries of the park.

Deforestation and its impact on land surface temperature: a case study in Wilpattu National Park in Sri Lanka

In this project we assessed the changes in land cover before and after deforestation as well as the changes in temperature driven by deforestation using remote sensing methods. We found that the area affected by deforestation from 2001 to 2018 was of 15.3 km2 (7% of the study area). Moreover, areas in which deforestation had mean increase in temperature of 1.3ºC (95% CI 0.6, 2).

Land Degradation Detection Using Remote Sensing and GIS for Hambantota District in Sri Lanka

“Increasing land degradation lead to the acceleration of the sensitivity of the land surface to wind erosion and then to formation of dust storms which has negatively serious impact on the environment and public health”
The study developed a computerized method for assessing the severity of land degradation using four vegetation indicators such as NDVI, NDWI, NDBI, and NDSDI. The findings of the study reveal that most of the divisional secretariat areas in the study area are at serious risk of land degradation and drought.

Semiautomatic Road Extraction from High Resolution Satellite Image using Thresholding and Morphological Operations.

This research experimentation is carried out in order to extract the urban roads from the high-resolution satellite image. The concept is primarily implemented using threshold-based image segmentation and morphological operations. The experimental results proved that proposed approach can be used in reliable way for automatic detection of roads from high resolution satellite image.

Evaluate the performance of pan-sharpening techniques to increase the spatial resolution of the satellite images

In this project, we used seven different panchromatic image sharpening methods to find the best pan-sharpening method to improve the spatial and spectral resolution of the Deimos2 optical sensor. The method evaluation consists of two analytical methods, visual analysis and quantitative analysis. Of the seven methods, the nearest-neighbor diffusion (NN diffusion) pan-sharpening method developed in ENVI 5.1 offers the best results over the other methods.

Remote sensing and GIS for assessment of Land use change and deforestation in Sri Lanka (review)

The forest is peculiar organism of unlimited kindness and benevolence that makes no demands for its sustenance and extends generously that produce of life activity; it affords protection to all beings, offering shade even to the axe man who destroy it.
~Gautama Buddha~

The purpose of this study was to investigate the use of remote sensing and GIS technologies developed to study deforestation, land-use change, and their impact in Sri Lanka, and to understand the methodologies behind those studies.