Abstract
Abstract. Radar data has been historically expensive and complex to process. However, in this milieu of cloud-computing platforms and open-source datasets, radar data analysis has become convenient and can now be performed for more exploratory researches. This study aims to perform multi-temporal analysis of radar backscatter to characterize dense and sparse forest from Sentinel-1 images. The area of study are reforested sites under the National Greening Program (NGP) of the Philippines. Ground data were collected: (1) in 2019, from a 1.35 ha -site in Brgy. Calula, Ipil, Zamboanga Sibugay, (2) in 2019, from a 1.10 ha- site in Brgy. Cabatuanan, Basay, Negros Oriental, and (3) from PhilLiDAR 2 – Project 3: FRExLS’ 2.4 ha -validated site in Ubay, Bohol. SAR intensity values were derived from Sentinel-1 from Google Earth Engine, which is a cloud-based platform with a repository of satellite images and functionalities for data extraction and processing. The temporal variation in C-band radar backscatter from 2014 to 2018 were analyzed. The results show, for the whole period of analysis, that: in VH polarization, dense forest samples backscatter range from −11 to −18 dB in VH and −2 to -13 dB in VV; sparse forest samples range from −12 to -21 dB in VH and −7 to −14 dB in VV; ground samples range from −12 to −24 dB in VH and −6 to −15 dB in VV; and water samples range from −21 to −30 dB in VH and −11 to −26 dB in VV. Forest backscatter are expected to saturate over time, especially in dense forests. These variations are due to differences in forest species, landscape, environmental and climatic drivers, and phenomenon or interventions on the site.
Highlights
Remote Sensing (RS) is both a technology and science, used to observe, measure, and map objects on Earth’s surface from a distance
In terms of trend, dense forests have higher backscatter response compared to sparse forests because of the scattering mechanisms they exhibit
Dense forests are mostly influenced by direct scattering, whereas sparse forests are mostly influenced by diffuse scattering
Summary
Remote Sensing (RS) is both a technology and science, used to observe, measure, and map objects on Earth’s surface from a distance. Through the years, it has been the focus of a lot of research and development works in terms of sensors, processing, simulation, and analysis according to CIFOR (2010) One of the key applications of RS is in the field of forestry. RS techniques such as forest classification and mapping contribute to forest management, inventory, and understanding. Et al, (2016) has shown that the wealth of remotely-sensed data complemented by advanced remote sensing techniques contribute towards accurately and efficiently determining forest information for strategic planning and management
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