Abstract
Broadleaf deciduous forests (BDFs) or dry dipterocarp forests play an important role in biodiversity conservation in tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change to assist forest management. Remotely sensed observations are often used to derive the parameters corresponding to seasonal vegetation dynamics. Data acquired from the Sentinel-1A satellite holds a great potential to improve forest type classification at a medium-large scale. This article presents an integrated object-based classification method by using Sentinel-1A and Landsat 8 OLI data acquired during different phenological periods (rainy and dry seasons). The deciduous forest and nondeciduous forest areas are classified by using NDVI (normalized difference vegetation index) from Landsat 8 cloud-free composite images taken during dry (from February to April) and rainy (from June to October) seasons. Shorea siamensis Miq. (S. siamensis), Shorea obtusa Wall. ex Blume (S. obtusa), and Dipterocarpus tuberculatus Roxb. (D. tuberculatus) in the deciduous forest area are classified based on the correlation between phenology of BDFs in Yok Don National Park and backscatter values of time-series Sentinel-1A imagery in deciduous forest areas. One hundred and five plots were selected during the field survey in the study area, consisting of dominant deciduous species, tree height, and canopy diameter. Thirty-nine plots were used for training to decide the broadleaf deciduous forest areas of the classified BDFs by the proposed method, and the other sixty-six plots were used for validation. Our proposed approach used the changes of backscatter in multitemporal SAR images to implement BDF classification mapping with acceptable accuracy. The overall accuracy of classification is about 79%, with a kappa coefficient of 0.7. Accurate classification and mapping of the BDFs using the proposed method can help authorities implement forest management in the future.
Highlights
Dry dipterocarp forest (DDF) or broadleaf deciduous forest (BDF) is a typical forest of Southeast Asian countries dominated by humid tropical climate with a rainy and a severe dry season
Forest phenology observation and classification is commonly viewed as a vital diagnostic of climate variation, and it is the first-order control on biosphere–atmosphere interaction
Earth observation data have been instrumental in monitoring and mapping the forest phenology over the past decades
Summary
Dry dipterocarp forest (DDF) or broadleaf deciduous forest (BDF) is a typical forest of Southeast Asian countries dominated by humid tropical climate with a rainy and a severe dry season. In Vietnam, BDFs are dominated by Dipterocarpaceae with deciduous season in the dry season. BDFs have been gradually replaced by industrial trees that are expected to generate higher economic values [1]. It is suggested that environmental protection is crucial by controlling the access to forestry, limiting agricultural activities, and enhancing green industry to improve environmental facilities and restore ecological values [3]. Forest phenology is the first-order control on terrestrial carbon and energy budgets, while Earth observations are usually utilized to derive parameters of the seasonal vegetation dynamics in ecosystem models
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
