A5039776264- Remote-Sensing Image Classification
- Hydrology and Watershed Management Studies
- Flood Risk Assessment and Management
- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Land Use and Ecosystem Services
- Precipitation Measurement and Analysis
- Hydrological Forecasting Using AI
- Remote Sensing and Land Use
- Hydrology and Drought Analysis
Asian Disaster Preparedness Center
2020-2023
Waseda University
2019
Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient high quality methods mapping using Synthetic Aperture Radar (SAR). However, few explored effects SAR pre-processing steps used subsequent results as inputs into algorithms. This study leverages Google Earth Engine compare two unsupervised histogram-based...
Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety approaches are available for water, but deep learning not commonplace as they 'data hungry' require large amounts computational resources. However, with availability various satellite sensors rapid development cloud computing, scientific community is adapting modern approaches. The new integration cloud-based Google AI platform Earth Engine enables users to deploy calculations at...
Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of plantations would help formulating effective strategies to reduce burning. High resolution satellite imagery combined with deep-learning technologies can be map high precision. However, land cover mapping using data computationally intensive networks costly. In this study, we used Planet that has been made available public through Norway's International Climate Forest...
Satellite-based forest alert systems are an important tool for ecosystem monitoring, planning conservation, and increasing public awareness of cover change. Continuous monitoring in tropical regions, such as those experiencing pronounced monsoon seasons, can be complicated by spatially extensive persistent cloud cover. One solution is to use Synthetic Aperture Radar (SAR) imagery acquired the European Space Agency’s Sentinel-1A B satellites. The Sentinel 1A satellites acquire C-band radar...
Understanding land cover change dynamics and potential pathways of is critical importance for sustainable resource management, to promote food security resilience on a range spatial scales. Data scarcity key concern, however, with the availability free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed robust machine learning (random forest) approach utilizing EO Geographic Information System (GIS) which enables an innovative means our...
Land cover classification and change detection analysis based on remote sensing images using machine learning algorithm has become one of the important factors for environmental management urban planning. We select Yangon as study area because government faces many problems in planning sectors due to population growth sprawl. Therefore, proposed method aims perform land Random Forest (RF) classifier Google Earth Engine (GEE) post-classification between 1987 2017 with 5 years interval periods...