Overview

This project analyses the vegetation dynamics in the Dhaka Division of Bangladesh using NDVI derived from Landsat satellite imagery on Google Earth Engine.

Methodology

Time-series NDVI data from 2015 to 2025 was processed to assess seasonal and long-term vegetation change. Statistical trend analysis was applied to identify areas of vegetation gain, loss, and stability in response to climate variability and land-use pressures.

Tools Used

Layouts

Overview

Urban expansion and socioeconomic growth patterns across Bangladesh quantified using VIIRS Day/Night Band and DMSP-OLS nighttime light imagery.

Methodology

Multi-temporal analysis of nighttime light radiance values was used to detect urban sprawl, identify areas of development, and correlate light intensity with demographic and economic growth indicators.

Tools Used

Layouts

Overview

Applied PCA algorithms to multispectral satellite imagery to eliminate data redundancy and enhance the classification of land-use patterns, improving computational efficiency by focusing on high-variance principal components

Methodology

A methodology using ArcGIS and TerrSet involves preprocessing multiband rasters in ArcGIS, performing the Principal Components Analysis within TerrSet’s specialized statistical engine to extract significant variance, and returning to ArcGIS for final thematic mapping and spatial decision-making.

Tools Used

Layouts

Overview

Applied watershed analysis techniques on high-resolution DEM data to delineate drainage basins and hydrological flow patterns. This approach enhances the understanding of surface water dynamics, runoff distribution, and catchment characteristics for improved spatial decision-making.

Methodology

The workflow involves preprocessing the DEM in ArcGIS/ArcGIS Pro, including sink filling to remove depressions, followed by flow direction and flow accumulation modeling. Watershed boundaries are then delineated using pour points, enabling accurate extraction of drainage networks and basin extents for further hydrological analysis and mapping.

Tools Used

Layouts

Overview

Spatial suitability analysis integrating multiple environmental, social, and geographic datasets to identify optimal zones for a specific land-use purpose.

Methodology

GIS-based weighted overlay and multi-criteria evaluation (MCE) techniques applied, combining slope, land cover, proximity to infrastructure, and environmental sensitivity into a composite suitability map.

Tools Used

Layouts

Full Title

Modelling and Forecasting of Vegetation Dynamics in the Chittagong Hill Tracts using Remote Sensing (2000–2025)

Authors

Joti Shaha, Chandan Roy & UMong Ching Marma

Event

International Conference on Geosciences for Achieving Sustainable Development Goals (ICGSDG), 14–15 November 2025

Abstract

The Chittagong Hill Tracts (CHT), located in the southeastern part of Bangladesh, characterized by a humid subtropical climate with high monsoonal rainfall. It has experienced significant changes in vegetation cover during the recent decades due to anthropogenic activities and climatic influences. This study investigates vegetation dynamics in the CHT, during the last 25 years using Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 and Landsat-5 TM multi-temporal satellite imagery. NDVI is a widely used remote sensing based metric to measure vegetation health and density. Assessments in 2015, 2020, and 2025 were conducted using Sentinel-2 imagery, while Landsat 5 TM data was utilized for 2000, 2005, and 2010. Annual average NDVI composites were generated using satellite images with less than 5% cloud cover for each studied year in Google Earth Engine (GEE) platform. Based on NDVI thresholds, the lands were classified into two major categories, dense vegetation (Class-1) covered and sparse or non-vegetation (Class-2) covered lands. The high NDVI values 0.6 to 1 indicate the areas with the dense vegetation and the low NDVI values 0 to 0.5 indicate the areas with sparse or non-vegetation. Cellular Automata–Markov (CA–Markov) modeling was also applied to reveal the vegetation characteristics in the CHT in 2030. In order to identify changes in dense vegetation covered areas across the CHT, area under dense vegetation cover was calculated at 5-year intervals. The findings show significant variations in the vegetation cover over 2000 to 2025 in the CHT. Dense vegetation decreased rapidly from 26.83% to 16.35% during the years 2000 to 2005, indicating a major loss most likely to be associated with anthropogenic activities, including shifting cultivation, illegal hill logging, timber extraction, and growth of settlement. In 2010, it moderately recovered 23.32% and slightly increased to 26.05% by 2015. But after that, there was a sudden fall to 22.86% in 2020 and a further drop of 21.09% by 2025. Results generated by the CA–Markov model shows that area under dense vegetation is most likely to decline by 2.72% in 2030 compared to the dense vegetation-covered area in 2025. This trend of vegetation loss is alarming and expected to contribute to ecosystem services in the CHT.

Documents

Full Title

Assessing Flash Flood Vulnerability in the Chittagong Hill Tracts, Bangladesh using GIS and Multi-Platform Data

Authors

Joti Shaha, Chandan Roy, Shehan Tawsif & UMong Ching Marma

Event

International Conference on Geosciences for Achieving Sustainable Development Goals (ICGSDG), 14–15 November 2025

Abstract

Full Title

Identification of Vegetation Dynamics in Bangladesh Under a Changing Climatic Scenario

Authors

Joti Shaha, Chandan Roy & Arpita Adhikary

Event

Rajshahi University Research Society (RURS) · 22–23 February 2024 · Credential ID: 24035

Abstract

NDVI incorporates spectral difference between red (R) and near-infrared (NIR) wavelengths in satellite image. NDVI was used to measure vegetation health, while maximum and minimum temperature, precipitation, and relative humidity were analyzed as climatic variables. NDVI value decreases at extreme high and low temperatures, conversely higher precipitation and humidity generally contribute to positive changes in NDVI values. We aim to determine, how climate change dynamics contributes to changes in vegetation patterns. Even though human activities have significant impacts on vegetation change, it has not been considered in this study due to the lack of required field verification data related to anthropogenic pressure on vegetation pattern. Terra MODIS NDVI data of July 1 in 2000, 2012, and 2024 were sourced from The National Aeronautics and Space Administration (NASA) Earth data webpage to evaluate seasonal and yearly changes. The highest NDVI values indicate the areas with the highest vegetation health and the lowest NDVI values indicate the areas with the lowest vegetation health. NDVI provides a valuable overview of the spatial and temporal variation in vegetation health in Bangladesh. The analysis shows that the maximum and minimum value is 0.87 and -0.19 and overall mean is 0.435 of the month of July 2024 and the maximum and minimum value is 0.90 and -0.18 and overall mean is 0.499 of the month of July 2000. It indicates downward shift in NDVI values over the 25-year period. Due to low temperatures and humidity, as well as high precipitation, which raises the NDVI values, the vegetation patterns in the CHT, Sundarbans, and Sylhet regions were denser between 2000 and 2012. However, there was a significant shift in 2024, with low temperatures, copious precipitation, and high humidity, which resulted in low NDVI values. This result indicates a strong correlation between NDVI and climatic variables.

Documents

Full Title

Identification of Sea Surface Temperature Variability in the Bay of Bengal during the period of 1990 to 2024

Authors

Joti Shaha, Chandan Roy & Arpita Adhikary

Event

Rajshahi University Research Society (RURS) · 22–23 February 2024 · Credential ID: 24085

Documents

Topic

MICRO CANAL: A Way To Prevent Water Scarcity In Cropland

Event

World Environment Day Poster Presentation, 05 June 2024, University of Rajshahi

Description

Explored micro-canal design as a sustainable low-cost irrigation solution to address seasonal water scarcity in agricultural land, with spatial analysis of optimal routing using GIS.

Documents

Topic

Identification of Drainage Pattern of Bangladesh

Event

Hydrology and Fluvial Morphology based poster presentation, 04 August 2024, University of Rajshahi

Description

Bangladesh has one of the most complex and active water systems in the world, mainly driven by the Ganges-Brahmaputra-Meghna (GBM) river systems. The country's landscape creates a mostly branching and winding drainage system across its wide deltaic plains, while the eastern hilly areas show trellis and rectangular patterns. These river systems carry a lot of sediment and experience changes in water flow throughout the seasons, which causes regular shifting and intertwining of channels. This study uses Geospatial Analysis and Digital Elevation Models (DEM) to outline watershed boundaries and measure shape characteristics. This helps us understand the water behavior, flood risk, and environmental changes in the Bengal Basin.

Documents

Topic

Urban Green Space Dynamics in Dhaka City during the period of 2015 to 2025 using Landsat 8 NDVI on Google Earth Engine

Competition

SimplyGIS National Level Mapathon 2025

Methodology

Landsat 8 satellite imagery processed on GEE to compute NDVI values for Dhaka City across multiple years. Change detection analysis applied to quantify the spatial distribution and temporal change of urban green spaces.

Documents

Topic

AUTOMATED INFRASTRUCTURAL DAMAGE ASSESSMENT: Developing Real Time Maps of Infrastructure Damage

Competition

Disaster Hackathon 2.0, Forewarn Bangladesh Start Network, 2024

Description

Developed a system for automated detection and mapping of infrastructure damage using real-time satellite imagery and GIS tools, designed to support rapid disaster response in Bangladesh.

Documents

Event

OSMBD Mapathon with OpenStreetMap, OpenStreetMap Bangladesh, May 22, 2024

Description

Participated in a national-level mapathon contributing geospatial data to OpenStreetMap Bangladesh, supporting humanitarian and open mapping goals across the country.

Documents

Role

Member — YouthMappers at RU, Rajshahi University Chapter

Period

May 2022 – Present

Responsibilities

Volunteered for geospatial data input, organized community mapping events, and built partnerships with other YouthMappers chapters and OpenStreetMap communities.

Role

Volunteer and Speaker — State of the Map Asia, Bangladesh

Period

July 2024 – December 2024

Responsibilities

Participated in social welfare activities and geospatial community engagement at the State of the Map Asia conference in Bangladesh.

Role

Open Mapping Guru — Open Mapping Hub Asia Pacific

Period

October 2024 – Present

Description

Recognized as an Open Mapping Guru by the Asia Pacific Humanitarian OpenStreetMap Team, contributing to regional open mapping capacity building and community leadership.

Role

Program Executive — Space and Environment Research Center (SERC), Bangladesh

Period

June 2024 – February 2025

Project

Wetland Conservation Bangladesh — contributed to geospatial analysis, mapping, and environmental monitoring for wetland conservation initiatives across Bangladesh.