A5084441416- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Soil Geostatistics and Mapping
- Toxoplasma gondii Research Studies
- Remote Sensing and Land Use
- Travel-related health issues
- Fish Biology and Ecology Studies
- Livestock Farming and Management
- Fisheries and Aquaculture Studies
- Tropical and Extratropical Cyclones Research
- Coastal wetland ecosystem dynamics
- Flood Risk Assessment and Management
- Species Distribution and Climate Change
- Marine and Coastal Ecosystems
- Groundwater and Watershed Analysis
- Leaf Properties and Growth Measurement
- Parasitic Infections and Diagnostics
- Mosquito-borne diseases and control
- Remote-Sensing Image Classification
- Ocean Waves and Remote Sensing
- Rangeland Management and Livestock Ecology
- Fire effects on ecosystems
- Soil and Land Suitability Analysis
- Geochemistry and Geologic Mapping
Universitas Gadjah Mada
2020-2025
Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial spectral dimensionality data, methods have developed incorporated into the machine framework to ensure an efficient optimal computational process. This research aims assess accuracy estimating forest height AISA (airborne imaging spectrometer applications) hyperspectral bands (479 bands) airborne light...
Coastal regions are one of the most vulnerable areas to effects global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, socioeconomic importance coastal where populated cities located high. However, changes Indonesia relatively understudied. particular, detailed monitoring with remote sensing data lacking despite abundance datasets availability easily accessible cloud computing platforms such as Google Earth Engine that able...
The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration multi-sensor and multi-temporal analysis, which is useful the identification land-cover classes based on their temporal characteristics. Our study aims to employ patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Water (MNDWI), Built-up (NDBI), Landsat...
Crop intensity information describes the productivity and sustainability of agricultural land. This can be used to determine which lands should prioritized for intensification or protection. Time-series data from remote sensing derive crop information; however, this application is limited when using medium coarse resolution data. study aims use 3.7 m-PlanetScope™ Dove constellation data, provides daily observations, map land in Magelang District, Indonesia. Two-stage histogram matching,...
The availability of free Synthetic Aperture Radar (SAR) data Sentinel 1A/B, with the high temporal resolution, has provoked usage time-series backscatter values from SAR for mapping paddy field extent and crop phenology. However, over complex terrain areas is rarely conducted, effect shadows on accuracy classification not been addressed yet. This study attempted to identify using monthly median composites S1A in 2018 perform effort minimize misclassification by incorporating dem-derived...
Abstract Dual-polarized (VV and VH) Sentinel-1 Synthetic-Aperture Radar (SAR) Ground Range Detected (GRD) data are available in 9-m spatial resolution 12-day repeat orbit. A constellation of two satellites, Sentinel 1A 1B, capture these with ascending descending orbits, thus increasing the revisit time at equator to every six days. Those specifications allow creating dense cross-orbit time-series a relatively high resolution, beneficial for identifying land-covers land-uses unique temporal...
The identification of land cover and use is necessary to support the strategic management coastal areas.The utilization remote sensing technology such as synthetic aperture radar (SAR) data has been widely used for mapping distribution use.This application includes detection aquaculture ponds in areas due SAR's sensitivity surface water content.In addition, multitemporal Sentinel-1 helps distinguish between rice fields that possess a visually similar appearance during flooding stage.This...
Normalized Difference Vegetation Index (NDVI) data is the most commonly used vegetation proxy from remote sensing to model biophysical properties. The longest time-series of NDVI earlier era satellites available AVHRR GIMMS employing red and near-infrared bands in NOAA sensors 1981 2015 8-km spatial resolution monthly interval. This study aims evaluate compatibility newer such as MODIS Terra (MOD13C2), Proba-V Visible Infrared Imaging Radiometer Suite (VIIRS) when combined with data....
Paddy fields are complex land-use entities with various surface covers depending on the timing of planting stages. Therefore, best practice to map paddy using remote sensing has benefited from availability multi-temporal data which were used characterize phenology related fields. However, this may require more RS be obtained and processed. Other mapping methods by capitalizing spatial configuration, such as image segmentation in Object-Based Image Analysis (OBIA) object recognition Deep...