Classification and Observed Seasonal Phenology of Broadleaf Deciduous Forests in a Tropical Region by Using Multitemporal Sentinel-1A and Landsat 8 Data

Broadleaf deciduous forests (BDFs) or dry dipterocarp play an important role in biodiversity conservation tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change assist management. Remotely sensed observations are often used derive the parameters corresponding seasonal vegetation dynamics. Data acquired from Sentinel-1A satellite holds a great potential improve type at medium-large scale. This article presents integrated object-based method by using Landsat 8 OLI data during different phenological periods (rainy seasons). The nondeciduous areas classified NDVI (normalized difference index) cloud-free composite images taken (from February April) rainy June October) seasons. Shorea siamensis Miq. (S. siamensis), obtusa Wall. ex Blume obtusa), Dipterocarpus tuberculatus Roxb. (D. tuberculatus) area based on correlation between BDFs Yok Don National Park backscatter values time-series imagery areas. One hundred five plots were selected field survey study area, consisting dominant species, tree height, canopy diameter. Thirty-nine training decide broadleaf proposed method, other sixty-six validation. Our approach changes multitemporal SAR implement BDF mapping with acceptable accuracy. overall accuracy is about 79%, kappa coefficient 0.7. Accurate can help authorities management future.