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Week - 1 |
Introduction to remote sensing; basic concepts, electromagnetic spectrum, sensors, and applications in forest studies. |
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Week - 2 |
Remote sensing data types; characteristics of optical, thermal, radar, and LiDAR data and their roles in forestry applications. |
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Week - 3 |
Data acquisition and workflow in forest remote sensing; integrated use of satellite, UAV, and ground-based data, sampling design, and planning of the analysis process. |
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Week - 4 |
Spectral behaviour of forests; vegetation reflectance properties, vegetation indices, and assessment of forest health. |
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Week - 5 |
Tree species classification; sampling strategies, feature selection, and introduction to classification approaches. |
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Week - 6 |
Introduction to machine learning; supervised classification, train-test separation, accuracy assessment, and model evaluation. |
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Week - 7 |
Use of radar data in forestry; Sentinel-1, forest structure, moisture, biomass, and cloud-independent observation capabilities. |
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Week - 8 |
Forest phenology and time series; monitoring seasonal changes, leaf emergence, and senescence using remote sensing. |
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Week - 9 |
Drought, fire, and stress monitoring; risk detection, damage assessment, and change analysis using remote sensing. |
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Week - 10 |
Climate change and forest resilience; evaluating ecosystem responses, vulnerability, and resilience through remote sensing. |
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Week - 11 |
Fieldwork: UAV-based data collection; flight planning, image acquisition, and practical application in forest areas. |
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Week - 12 |
Fieldwork: Terrestrial laser scanner use; measuring 3D forest structure, tree stems, and canopy characteristics. |
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Week - 13 |
Fieldwork: Phenological observation; recording leaf development, colour change, drying symptoms, and observation data. |
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Week - 14 |
Assessment of data collected during fieldwork; processing, comparison, and interpretation of UAV, terrestrial laser scanner, and phenological observation data. |
Eskisehir Technical University Info Package